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tuoheng_alg_move_20240715
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Merge branch 'release' of gitadmin/tuoheng_alg into master

tags/V2.8.3
chenyukun 1 year ago
parent
d2ed06dd3f 7cd0e7ba0e
commit
87029b78bb
100 changed files with 8108 additions and 4061 deletions
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  28. +181
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  69. +10
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  70. +432
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  85. +40
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  86. +19
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  88. +32
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  89. +16
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  90. +25
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  91. +167
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  92. +10
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  93. +837
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  94. +122
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  95. +80
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  96. +17
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  97. +51
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  98. +0
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  99. +19
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  100. +0
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</project>

BIN
__pycache__/dsp_master.cpython-38.pyc View File


+ 420
- 22
common/Constant.py View File

@@ -1,6 +1,4 @@
# -*- coding: utf-8 -*-
# 配置文件名称
APPLICATION_CONFIG = "dsp_application.json"
# 编码格式
UTF_8 = "utf-8"

@@ -18,28 +16,428 @@ success_progess = "1.0000"
width = 1400

COLOR = (
[0, 0, 255],
[255, 0, 0],
[211, 0, 148],
[0, 127, 0],
[0, 69, 255],
[0, 255, 0],
[255, 0, 255],
[0, 0, 127],
[127, 0, 255],
[255, 129, 0],
[139, 139, 0],
[255, 255, 0],
[127, 255, 0],
[0, 127, 255],
[0, 255, 127],
[255, 127, 255],
[8, 101, 139],
[171, 130, 255],
[139, 112, 74],
[205, 205, 180])
[0, 0, 255],
[255, 0, 0],
[211, 0, 148],
[0, 127, 0],
[0, 69, 255],
[0, 255, 0],
[255, 0, 255],
[0, 0, 127],
[127, 0, 255],
[255, 129, 0],
[139, 139, 0],
[255, 255, 0],
[127, 255, 0],
[0, 127, 255],
[0, 255, 127],
[255, 127, 255],
[8, 101, 139],
[171, 130, 255],
[139, 112, 74],
[205, 205, 180])

ONLINE = "online"
OFFLINE = "offline"
PHOTO = "photo"
RECORDING = "recording"

ONLINE_START_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start"]
},
"pull_url": {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 255
},
"push_url": {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 255
},
"logo_url": {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
"models": {
'type': 'list',
'required': True,
'nullable': False,
'minlength': 1,
'maxlength': 3,
'schema': {
'type': 'dict',
'required': True,
'schema': {
"code": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "categories",
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
"is_video": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"is_image": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"categories": {
'type': 'list',
'required': True,
'dependencies': "code",
'schema': {
'type': 'dict',
'required': True,
'schema': {
"id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
"config": {
'type': 'dict',
'required': False,
'dependencies': "id",
}
}
}
}
}
}
}
}

ONLINE_STOP_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["stop"]
}
}

OFFLINE_START_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start"]
},
"push_url": {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 255
},
"pull_url": {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 255
},
"logo_url": {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
"models": {
'type': 'list',
'required': True,
'maxlength': 3,
'minlength': 1,
'schema': {
'type': 'dict',
'required': True,
'schema': {
"code": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "categories",
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
"is_video": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"is_image": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"categories": {
'type': 'list',
'required': True,
'dependencies': "code",
'schema': {
'type': 'dict',
'required': True,
'schema': {
"id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
"config": {
'type': 'dict',
'required': False,
'dependencies': "id",
}
}
}
}
}
}
}
}

OFFLINE_STOP_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["stop"]
}
}

IMAGE_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start"]
},
"logo_url": {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
"image_urls": {
'type': 'list',
'required': True,
'minlength': 1,
'schema': {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 5000
}
},
"models": {
'type': 'list',
'required': True,
'schema': {
'type': 'dict',
'required': True,
'schema': {
"code": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "categories",
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
"is_video": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"is_image": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "code",
'allowed': ["0", "1"]
},
"categories": {
'type': 'list',
'required': True,
'dependencies': "code",
'schema': {
'type': 'dict',
'required': True,
'schema': {
"id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
"config": {
'type': 'dict',
'required': False,
'dependencies': "id",
}
}
}
}
}
}
}
}

RECORDING_START_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start"]
},
"pull_url": {
'type': 'string',
'required': True,
'empty': False,
'maxlength': 255
},
"push_url": {
'type': 'string',
'required': False,
'empty': True,
'maxlength': 255
},
"logo_url": {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
}
}

RECORDING_STOP_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["stop"]
}
}

PULL2PUSH_START_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start"]
},
"video_urls": {
'type': 'list',
'required': True,
'nullable': False,
'schema': {
'type': 'dict',
'required': True,
'schema': {
"id": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "pull_url",
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
"pull_url": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "push_url",
'regex': r'^(https|http|rtsp|rtmp|artc|webrtc|ws)://\w.+$'
},
"push_url": {
'type': 'string',
'required': True,
'empty': False,
'dependencies': "id",
'regex': r'^(https|http|rtsp|rtmp|artc|webrtc|ws)://\w.+$'
}
}
}
}
}
PULL2PUSH_STOP_SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
"command": {
'type': 'string',
'required': True,
'allowed': ["start", "stop"]
},
"video_ids": {
'type': 'list',
'required': False,
'nullable': True,
'schema': {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,255}$'
}
}
}

+ 0
- 256
common/RequestSchema.py View File

@@ -1,256 +0,0 @@
# -*- coding: utf-8 -*-

model = {
'type': 'list',
'required': True,
'nullable': False,
'minlength': 1,
'maxlength': 3,
'schema': {
'type': 'dict',
'required': True,
'schema': {
'code': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'dependencies': 'categories',
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
'categories': {
'type': 'list',
'required': True,
'dependencies': 'code',
'minlength': 1,
'schema': {
'type': 'dict',
'required': True,
'schema': {
'id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,255}$'},
'config': {
'type': 'dict',
'required': False,
'dependencies': 'id',
}
}
}
}
}
}
}

# 在线参数校验
ONLINE_START_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
},
'pull_url': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'maxlength': 255
},
'push_url': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
},
'results_base_dir': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{0,36}$'
},
'models': model
}
# 在线停止参数校验
ONLINE_STOP_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
}
}

# 离线开始参数校验
OFFLINE_START_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
},
'original_url': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
},
'push_url': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
},
'original_type': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
},
'results_base_dir': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'models': model
}

# 离线停止参数校验
OFFLINE_STOP_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
}
}

# 图片开始参数校验
IMAGE_START_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
},
'image_urls': {
'type': 'list',
'required': True,
'minlength': 1,
'schema': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 5000
}
},
'results_base_dir': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'models': model
}

# 录屏参数校验
RECORDING_START_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
},
'pull_url': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
},
'push_url': {
'type': 'string',
'required': True,
'nullable': False,
'empty': False,
'maxlength': 255
}
}

# 录屏停止参数校验
RECORDING_STOP_SCHEMA = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'empty': False,
'nullable': False,
'allowed': ['start', 'stop']
}
}

+ 8
- 402
common/YmlConstant.py View File

@@ -1,404 +1,10 @@
# -*- coding: utf-8 -*-
BASE_DIR = 'base_dir'
GPU_CODES = ['3090', '2080', '4090', 'A10']
GPU_NAME = 'gpu_name'
GPU_2080 = '2080'
GPU_2080_Ti = '2080Ti'
KAFKA_ACKS = "acks"
KAFKA_RETRIES = "retries"
KAFKA_LINGER_MS = "linger_ms"
KAFKA_RETRY_BACKOFF_MS = "retry_backoff_ms"
KAFKA_MAX_IN_FLIGHT_REQUESTS = "max_in_flight_requests_per_connection"
KAFKA_CLIENT_ID = "client_id"
KAFKA_GROUP_ID = "group_id"
KAFKA_AUTO_OFFSET_RESET = "auto_offset_reset"
KAFKA_ENABLE_AUTO_COMMIT = "enable_auto_commit"
KAFKA_MAX_POLL_RECORDS = "max_poll_records"
REQUEST_IDREQUEST_ID = "request_id"
FEEDBACK = "feedback"
RECORDING = "recording"
FBQUEUE = "fbQueue"
CONTEXT = "context"
MODEL = 'model'
MSG = "msg"
GPU_IDS = "gpu_ids"
ANALYSE_TYPE = "analyse_type"
COMMAND = "command"
START = "start"
STOP = "stop"
SERVICE = "service"
FRAME_SCORE = "frame_score"
PULL_URL = "pull_url"
PUSH_URL = "push_url"
ORIGINAL_URL = "original_url"
ORIGINAL_TYPE = "original_type"
IMAGE_URLS = "image_urls"
RESULTS_BASE_DIR = "results_base_dir"
MODELS = "models"
CODE = 'code'
CATEGORIES = "categories"
ID = 'id'
CONFIG = "config"
VIDEO = "video"
FILE_PATH = "file_path"
KAFKA = "kafka"
TOPIC = "topic"
DSP_ALG_ONLINE_TASKS_TOPIC = "dsp-alg-online-tasks-topic"
DSP_ALG_OFFLINE_TASKS_TOPIC = "dsp-alg-offline-tasks-topic"
DSP_ALG_IMAGE_TASKS_TOPI = "dsp-alg-image-tasks-topic"
DSP_RECORDING_TASKS_TOPI = "dsp-recording-task-topic"
DSP_ALG_RESULTS_TOPIC = "dsp-alg-results-topic"
DSP_RECORDING_RESULTS_TOPIC = "dsp-recording-result-topic"
DSP = "dsp"
ACTIVE = "active"
PRODUCER = "producer"
CONSUMER = "consumer"
BOOTSTRAP_SERVERS = "bootstrap_servers"
ALIYUN = "aliyun"
ACCESS_KEY = "access_key"
ACCESS_SECRET = "access_secret"
OSS = "oss"
ENDPOINT = "endpoint"
BUCKET = "bucket"
CONNECT_TIMEOUT = "connect_timeout"
VOD = "vod"
ECSREGIONID = "ecsRegionId"
CATEID = "CateId"
GPU = "gpu"
ORDER = "order"
LIMIT = "limit"
MAXLOAD = "maxLoad"
MAXMEMORY = "maxMemory"
INCLUDENAN = "includeNan"
EXCLUDEID = "excludeID"
EXCLUDEUUID = "excludeUUID"
BAIDU = "baidu"
VEHICLE = "vehicle"
APP_ID = "APP_ID"
API_KEY = "API_KEY"
SECRET_KEY = "SECRET_KEY"
PERSON = "person"
ORC = "orc"
LOG = 'log'
IS_VIDEO = "is_video"
IS_IMAGE = "is_image"
# 校验schema规则定义
SCHEMA = {
"request_id": {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
COMMAND: {
'type': 'string',
'required': True,
'allowed': [START, STOP]
},
PULL_URL: {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
PUSH_URL: {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
ORIGINAL_URL: {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
ORIGINAL_TYPE: {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
"logo_url": {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
IMAGE_URLS: {
'type': 'list',
'required': False,
'schema': {
'type': 'string',
'empty': False,
'maxlength': 5000
}
},
RESULTS_BASE_DIR: {
'type': 'string',
'required': False,
'nullable': True,
'regex': r'^[a-zA-Z0-9]{0,36}$'
},
MODELS: {
'type': 'list',
'required': False,
'nullable': True,
'schema': {
'type': 'dict',
'required': True,
'schema': {
CODE: {
'type': 'string',
'required': True,
'empty': False,
'dependencies': CATEGORIES,
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
IS_VIDEO: {
'type': 'string',
'required': True,
'empty': False,
'dependencies': CODE,
'allowed': ["0", "1"]
},
IS_IMAGE: {
'type': 'string',
'required': True,
'empty': False,
'dependencies': CODE,
'allowed': ["0", "1"]
},
CATEGORIES: {
'type': 'list',
'required': True,
'dependencies': CODE,
'schema': {
'type': 'dict',
'required': True,
'schema': {
ID: {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
CONFIG: {
'type': 'dict',
'required': False,
'dependencies': ID,
}
}
}
}
}
}
}
}


def get_file_path(context):
return context[VIDEO][FILE_PATH]


def get_video_add_water(context):
return context[VIDEO]["video_add_water"]


def get_online_tasks_topic(context):
return context["kafka"]["topic"]["dsp-alg-online-tasks-topic"]


def get_offline_tasks_topic(context):
return context[KAFKA][TOPIC][DSP_ALG_OFFLINE_TASKS_TOPIC]


def get_image_tasks_topic(context):
return context[KAFKA][TOPIC][DSP_ALG_IMAGE_TASKS_TOPI]


def get_recording_tasks_topic(context):
return context[KAFKA][TOPIC][DSP_RECORDING_TASKS_TOPI]


def get_kafka_producer_config(context):
return context[KAFKA][context[DSP][ACTIVE]][PRODUCER]


def get_kafka_consumer_config(context):
return context[KAFKA][context[DSP][ACTIVE]][CONSUMER]


def get_kafka_bootstrap_servers(context):
return context[KAFKA][context[DSP][ACTIVE]][BOOTSTRAP_SERVERS]


def get_kafka_results_topic(context):
return context[KAFKA][TOPIC][DSP_ALG_RESULTS_TOPIC]


def get_kafka_recording_result_topic(context):
return context[KAFKA][TOPIC][DSP_RECORDING_RESULTS_TOPIC]


def get_aliyun_access_key(context):
return context[ALIYUN][ACCESS_KEY]


def get_aliyun_access_secret(context):
return context[ALIYUN][ACCESS_SECRET]


def get_aliyun_oss_endpoint(context):
return context[ALIYUN][OSS][ENDPOINT]


def get_aliyun_oss_bucket(context):
return context[ALIYUN][OSS][BUCKET]


def get_aliyun_oss_connect_timeout(context):
return context[ALIYUN][OSS][CONNECT_TIMEOUT]


def get_aliyun_vod_ecsRegionId(context):
return context[ALIYUN][VOD][ECSREGIONID]


def get_aliyun_vod_cateId(context):
return context[ALIYUN][VOD][context[DSP][ACTIVE]][CATEID]


def get_gpu_order(context):
return context[GPU][ORDER]


def get_gpu_limit(context):
return context[GPU][LIMIT]


def get_gpu_maxLoad(context):
return context[GPU][MAXLOAD]


def get_gpu_maxMemory(context):
return context[GPU][MAXMEMORY]


def get_gpu_includeNan(context):
return context[GPU][INCLUDENAN]


def get_gpu_excludeID(context):
return context[GPU][EXCLUDEID]


def get_gpu_excludeUUID(context):
return context[GPU][EXCLUDEUUID]


def get_baidu_vehicle_APP_ID(context):
return context[BAIDU][VEHICLE][APP_ID]


def get_baidu_vehicle_API_KEY(context):
return context[BAIDU][VEHICLE][API_KEY]


def get_baidu_vehicle_SECRET_KEY(context):
return context[BAIDU][VEHICLE][SECRET_KEY]


def get_baidu_person_APP_ID(context):
return context[BAIDU][PERSON][APP_ID]


def get_baidu_person_API_KEY(context):
return context[BAIDU][PERSON][API_KEY]


def get_baidu_person_SECRET_KEY(context):
return context[BAIDU][PERSON][SECRET_KEY]


def get_baidu_ocr_APP_ID(context):
return context[BAIDU][ORC][APP_ID]


def get_baidu_ocr_API_KEY(context):
return context[BAIDU][ORC][API_KEY]


def get_baidu_ocr_SECRET_KEY(context):
return context[BAIDU][ORC][SECRET_KEY]


def get_log_base_path(context):
return context[LOG]["base_path"]


def get_log_enable_file(context):
return context[LOG]["enable_file_log"]


def get_log_log_name(context):
return context[LOG]["log_name"]


def get_log_rotation(context):
return context[LOG]["rotation"]


def get_log_retention(context):
return context[LOG]["retention"]


def get_log_log_fmt(context):
return context[LOG]["log_fmt"]


def get_log_level(context):
return context[LOG]["level"]


def get_log_enqueue(context):
return context[LOG]["enqueue"]


def get_log_encoding(context):
return context[LOG]["encoding"]


def get_log_enable_stderr(context):
return context[LOG]["enable_stderr"]


CV2_PULL_STREAM_TIMEOUT = "cv2_pull_stream_timeout"
CV2_READ_STREAM_TIMEOUT = "cv2_read_stream_timeout"


def get_pull_stream_timeout(context):
return int(context[SERVICE][CV2_PULL_STREAM_TIMEOUT])


def get_read_stream_timeout(context):
return int(context[SERVICE][CV2_READ_STREAM_TIMEOUT])


def get_service_timeout(context):
return int(context[SERVICE]["timeout"])


FILTER = "filter"


def get_similarity(context):
return context[SERVICE][FILTER]["similarity"]


def get_picture_similarity(context):
return context[SERVICE][FILTER]["picture_similarity"]


def get_frame_step(context):
return int(context[SERVICE][FILTER]["frame_step"])

# 服务配置路径
service_yml_path = 'config/service/dsp_%s_service.yml'
# kafka配置路径
kafka_yml_path = 'config/kafka/dsp_%s_kafka.yml'
# 阿里云配置路径
aliyun_yml_path = "config/aliyun/dsp_%s_aliyun.yml"
# 百度配置路径
baidu_yml_path = 'config/baidu/dsp_%s_baidu.yml'

BIN
common/__pycache__/Constant.cpython-38.pyc View File


BIN
common/__pycache__/YmlConstant.cpython-38.pyc View File


BIN
common/__pycache__/__init__.cpython-38.pyc View File


+ 3
- 10
concurrency/CommonThread.py View File

@@ -4,24 +4,17 @@ from loguru import logger

class Common(Thread):

__slots__ = [
'__context',
'__func',
'__param1',
'__param2',
'__result',
]
__slots__ = ('__func', '__param1', '__param2', '__result')

def __init__(self, context, func, param1, param2):
def __init__(self, func, param1, param2):
super(Common, self).__init__()
self.__context = context
self.__func = func
self.__param1 = param1
self.__param2 = param2
self.__result = None

def get_result(self):
self.join(60 * 60 * 12)
self.join()
return self.__result

def run(self):

+ 14
- 9
concurrency/FeedbackThread.py View File

@@ -13,15 +13,12 @@ from util.KafkaUtils import CustomerKafkaProducer


class FeedbackThread(Thread):
__slots__ = [
'__fbQueue',
'__context'
]
__slots__ = ('__fbQueue', '__kafka_config')

def __init__(self, fbQueue, context):
def __init__(self, fbQueue, kafka_config):
super().__init__()
self.__fbQueue = fbQueue
self.__context = context
self.__kafka_config = kafka_config

'''
阻塞获取反馈消息
@@ -32,24 +29,30 @@ class FeedbackThread(Thread):

def run(self):
logger.info("启动问题反馈线程")
kafkaProducer = CustomerKafkaProducer(self.__context)
dsp_alg_results_topic = self.__context["kafka"]["topic"]["dsp-alg-results-topic"]
dsp_recording_result_topic = self.__context["kafka"]["topic"]["dsp-recording-result-topic"]
kafkaProducer = CustomerKafkaProducer(self.__kafka_config)
dsp_alg_results_topic = self.__kafka_config["topic"]["dsp-alg-results-topic"]
dsp_recording_result_topic = self.__kafka_config["topic"]["dsp-recording-result-topic"]
dsp_push_stream_result_topic = self.__kafka_config["topic"]["dsp-push-stream-result-topic"]
while True:
logger.info("问题反馈发送消息循环")
feedback = None
recording = None
pull_stream = None
try:
fb = self.getFeedback()
if fb is not None and len(fb) > 0:
feedback = fb.get("feedback")
recording = fb.get("recording")
pull_stream = fb.get("pull_stream")
if feedback is not None and len(feedback) > 0:
kafkaProducer.sender(dsp_alg_results_topic, feedback["request_id"], feedback, 1)
feedback = None
if recording is not None and len(recording) > 0:
kafkaProducer.sender(dsp_recording_result_topic, recording["request_id"], recording, 1)
recording = None
if pull_stream is not None and len(pull_stream) > 0:
kafkaProducer.sender(dsp_push_stream_result_topic, pull_stream["request_id"], pull_stream, 1)
pull_stream = None
else:
time.sleep(1)
except Exception:
@@ -57,6 +60,8 @@ class FeedbackThread(Thread):
logger.error("问题反馈异常：{}, requestId:{}", format_exc(), feedback.get("request_id"))
elif recording and recording.get("request_id"):
logger.error("问题反馈异常：{}, requestId:{}", format_exc(), recording.get("request_id"))
elif pull_stream and pull_stream.get("request_id"):
logger.error("问题反馈异常：{}, requestId:{}", format_exc(), pull_stream.get("request_id"))
else:
logger.error("问题反馈异常：{}", format_exc())
logger.info("问题反馈线程执行完成")

+ 225
- 151
concurrency/FileUploadThread.py View File

@@ -1,202 +1,276 @@
from concurrent.futures import ThreadPoolExecutor, as_completed
# -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from time import sleep
from time import sleep, time
from traceback import format_exc

from loguru import logger
import cv2

from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.AliyunSdk import AliyunOssSdk
from util import TimeUtils, ImageUtils
from entity import FeedBack
from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue, get_no_block_queue, clear_queue


class FileUpload(Thread):
__slots__ = ('_fbQueue', '_context', '_imageQueue', '_analyse_type', '_msg', '_base_dir')
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')

def __init__(self, fbQueue, context, msg, imageQueue, analyse_type, base_dir):
def __init__(self, *args):
super().__init__()
self._fbQueue = fbQueue
self._context = context
self._imageQueue = imageQueue
self._analyse_type = analyse_type
self._msg = msg
self._base_dir = base_dir


'''
图片上传线程
'''
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args


class ImageFileUpload(FileUpload):
__slots__ = ()

@staticmethod
def handle_image(frame_msg, frame_step):
# (high_score_image["code"], all_frames, draw_config["font_config"])
# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
det_xywh, frame, current_frame, all_frames, font_config = frame_msg
'''
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
模型编号：modeCode
检测目标：detectTargetCode
'''
model_info = []
# 更加模型编码解析数据
for code, det_list in det_xywh.items():
if len(det_list) > 0:
for cls, target_list in det_list.items():
if len(target_list) > 0:
aFrame = frame.copy()
for target in target_list:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config)
model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame})
if len(model_info) > 0:
image_result = {
"or_frame": frame,
"model_info": model_info,
"current_frame": current_frame,
"last_frame": current_frame + frame_step
}
return image_result
return None

def run(self):
requestId = self._msg.get("request_id")
logger.info("启动图片上传线程, requestId:{}", requestId)
# 初始化oss客户端
aliyunOssSdk = AliyunOssSdk(self._base_dir, requestId)
aliyunOssSdk.get_oss_bucket()
high_score_image = {}
similarity = self._context["service"]["filter"]["similarity"]
picture_similarity = bool(self._context["service"]["filter"]["picture_similarity"])
frame_step = int(self._context["service"]["filter"]["frame_step"])
image_queue = self._imageQueue
analyse_type = self._analyse_type
results_base_dir = self._msg.get("results_base_dir")
fb_queue = self._fbQueue
with ThreadPoolExecutor(max_workers=5) as t:
try:
msg, context = self._msg, self._context
service = context["service"]
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(service["timeout"])
frame_step = int(service["filter"]["frame_step"]) + 120
try:
with ThreadPoolExecutor(max_workers=2) as t:
# 初始化oss客户端
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("图片上线线程运行超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 获取队列中的消息
image_msg = image_queue.get(timeout=43200)
if image_msg is not None and len(image_msg) > 0:
image_dict = image_msg.get("image")
command = image_msg.get("command")
if command == "stop":
break
if image_dict is not None and len(image_dict) > 0:
image_result = handle_image(high_score_image, image_dict, picture_similarity,
similarity, frame_step, analyse_type)
del image_dict
if image_result:
# 图片帧数编码
image_msg = get_no_block_queue(image_queue)
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step)
if image_result is not None:
task = []
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
or_image_name = build_image_name(str(image_result.get("current_frame")),
str(image_result.get("last_frame")),
image_result.get("mode_service"),
"OR", "O", results_base_dir, requestId)
or_future = t.submit(aliyunOssSdk.sync_upload_file, or_image_name,
or_image.tobytes())
or_image = cv2.imencode(".jpg", image_result["or_frame"])[1]
or_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"OR", "0", "0", request_id)
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
task.append(or_future)
model_info_list = image_result.get("model_info")
model_info_list = image_result["model_info"]
msg_list = []
for model_info in model_info_list:
ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
ai_image_name = build_image_name(str(image_result.get("current_frame")),
str(image_result.get("last_frame")),
image_result.get("mode_service"),
"AI", model_info.get("detectTargetCode"),
results_base_dir, requestId)
ai_future = t.submit(aliyunOssSdk.sync_upload_file, ai_image_name,
ai_image = cv2.imencode(".jpg", model_info["aFrame"])[1]
ai_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"AI",
model_info["modelCode"],
model_info["detectTargetCode"],
request_id)
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(
{"feedback": FeedBack.message_feedback(requestId,
AnalysisStatus.RUNNING.value,
analyse_type, "", "",
image_result.get("progress"),
or_image_name,
ai_image_name,
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
TimeUtils.now_date_to_str())})
for thread_result in as_completed(task):
thread_result.result()
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
or_image_name,
ai_image_name,
model_info['modelCode'],
model_info['detectTargetCode']))
for tk in task:
tk.result()
for msg in msg_list:
sendResult(fb_queue, msg, requestId)
put_queue(fb_queue, msg, timeout=2, is_ex=False)
del task, msg_list
else:
sleep(1)
del image_msg
except Exception:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), requestId)
finally:
high_score_image.clear()
logger.info("停止图片上传线程, requestId:{}", requestId)


def sendResult(fbQueue, result, requestId):
try:
fbQueue.put(result, timeout=10)
except Exception:
logger.error("添加反馈到队列超时异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
logger.info("停止图片上传线程0, requestId:{}", request_id)
clear_queue(image_queue)
logger.info("停止图片上传线程1, requestId:{}", request_id)


def build_image_name(current_frame, last_frame, mode_type, image_type, target, results_base_dir, requestId):
image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
"-{target}-{requestId}_{image_type}.jpg"
def build_image_name(*args):
"""
{requestId}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
"-{modeCode}-{target}_{image_type}.jpg
"""
current_frame, last_frame, mode_type, image_type, modeCode, target, request_id = args
random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
image_name = image_format.format(
base_dir=results_base_dir,
time_now=time_now,
current_frame=current_frame,
last_frame=last_frame,
random_num=random_num,
mode_type=mode_type,
target=target,
requestId=requestId,
image_type=image_type)
return image_name


def handle_image(high_score_image, frame_all, picture_similarity, similarity, frame_step, analyse_type):
flag = True
if picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame_all[0][1])
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity_1 = 1 - dist * 1.0 / 64
if similarity_1 >= similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_all[0][2] - high_score_image.get("current_frame")
if diff_frame_num < frame_step:
flag = False
# if diff_frame_num >= frame_step:
# hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
# hash2 = ImageUtils.dHash(frame_all[0][1])
# dist = ImageUtils.Hamming_distance(hash1, hash2)
# similarity_1 = 1 - dist * 1.0 / 64
# if similarity_1 != 1:
# flag = True
det_result = frame_all[1]
model_info = []
if flag and det_result is not None and len(det_result) > 0:
'''
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
return "%s/%s_frame-%s-%s_type_%s-%s-%s-%s_%s.jpg" % (request_id, time_now, current_frame, last_frame,
random_num, mode_type, modeCode, target, image_type)


class ImageTypeImageFileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')

def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args

@staticmethod
def handle_image(det_xywh, copy_frame, font_config):
"""
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
}
模型编号：modeCode
检测目标：detectTargetCode
'''
模型编号：modeCode
检测目标：detectTargetCode
"""
model_info = []
# 更加模型编码解析数据
for modelCode in list(det_result.keys()):
# 模型编号下面的检测目标对象
det_info = det_result.get(modelCode)
for code, det_info in det_xywh.items():
if det_info is not None and len(det_info) > 0:
for detectTargetCode in list(det_info.keys()):
target_list = det_info.get(detectTargetCode)
for cls, target_list in det_info.items():
if target_list is not None and len(target_list) > 0:
# orFrame = loads(dumps(frame_all.get("frame")))
orFrame = frame_all[0][1].copy()
aiFrame = copy_frame.copy()
for target in target_list:
draw_painting_joint(target[1], orFrame, target[3], target[2], target[4], "leftTop")
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
model_info.append({
"modelCode": str(modelCode),
"detectTargetCode": str(detectTargetCode),
"frame": orFrame
"modelCode": str(code),
"detectTargetCode": str(cls),
"frame": aiFrame
})
if len(model_info) > 0:
high_score_image["or_frame"] = frame_all[0][1]
high_score_image["current_frame"] = frame_all[0][2]
image_result = {
"or_frame": frame_all[0][1],
"or_frame": copy_frame,
"model_info": model_info,
"current_frame": frame_all[0][2],
"last_frame": frame_all[0][2] + frame_step,
"progress": "",
"mode_service": analyse_type,
"current_frame": 0,
"last_frame": 0
}
return image_result
return None
return None

def run(self):
context, msg = self._context, self._msg
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片识别图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(context["service"]["timeout"])
with ThreadPoolExecutor(max_workers=2) as t:
try:
# 初始化oss客户端
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("拉流进程运行超时, requestId: {}", request_id)
break
# 获取队列中的消息
image_msg = image_queue.get()
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
task, msg_list = [], []
det_xywh, image_url, copy_frame, font_config, result = image_msg[1]
if det_xywh is None:
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id)
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
result.get("modelCode"),
result.get("type"),
analyse_results=result))
else:
image_result = self.handle_image(det_xywh, copy_frame, font_config)
if image_result:
# 图片帧数编码
if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
image_url = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
or_future = t.submit(aliyunOssSdk.put_object, image_url,
or_image.tobytes())
task.append(or_future)
model_info_list = image_result.get("model_info")
for model_info in model_info_list:
ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
ai_image_name = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"AI",
model_info.get("modelCode"),
model_info.get("detectTargetCode"),
request_id)
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
analyse_results=result))
for thread_result in task:
thread_result.result()
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
else:
sleep(1)
except Exception as e:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
clear_queue(image_queue)
logger.info("停止图片识别图片上传线程, requestId:{}", request_id)

+ 30
- 57
concurrency/HeartbeatThread.py View File

@@ -1,84 +1,57 @@
# -*- coding: utf-8 -*-
from threading import Thread
import time
from time import sleep, time
from traceback import format_exc

from loguru import logger

from common.Constant import init_progess
from common.YmlConstant import FEEDBACK
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue, clear_queue


class Heartbeat(Thread):
__slots__ = ('__fbQueue', '__hbQueue', '__request_id', '__analyse_type', '__progress')
__slots__ = ('__fb_queue', '__hb_queue', '__request_id', '__analyse_type', "_context")

def __init__(self, fbQueue, hbQueue, request_id, analyse_type):
def __init__(self, *args):
super().__init__()
self.__fbQueue = fbQueue
self.__hbQueue = hbQueue
self.__request_id = request_id
self.__analyse_type = analyse_type
self.__progress = init_progess

def getHbQueue(self):
eBody = None
try:
eBody = self.__hbQueue.get(block=False)
except Exception as e:
pass
return eBody

# 推送执行结果
def sendResult(self, result):
try:
self.__fbQueue.put(result, timeout=10)
except Exception:
logger.error("添加反馈到队列超时异常：{}, requestId:{}", format_exc(), self.__request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

def sendHbQueue(self, result):
try:
self.__hbQueue.put(result, timeout=10)
except Exception:
logger.error("添加心跳到队列超时异常：{}, requestId:{}", format_exc(), self.__request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

def sendhbMessage(self, analysisStatus):
self.sendResult({FEEDBACK: message_feedback(self.__request_id,
analysisStatus,
self.__analyse_type,
progress=self.__progress,
analyse_time=TimeUtils.now_date_to_str())})
self.__fb_queue, self.__hb_queue, self.__request_id, self.__analyse_type, self._context = args

def run(self):
request_id, hb_queue, progress = self.__request_id, self.__hb_queue, init_progess
analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
service_timeout = int(self._context["service"]["timeout"]) + 120
try:
logger.info("开始启动心跳线程！requestId:{}", self.__request_id)
logger.info("开始启动心跳线程！requestId:{}", request_id)
start_time = time()
hb_init_num = 0
start_time = time.time()
while True:
if time.time() - start_time > 43200:
logger.info("心跳线程运行超时！！！！requestId:{}", self.__request_id)
break
time.sleep(3)
hb_msg = self.getHbQueue()
if hb_msg is not None and len(hb_msg) > 0:
if time() - start_time > service_timeout:
logger.error("心跳运行超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
sleep(3)
hb_msg = get_no_block_queue(hb_queue)
if hb_msg is not None:
command = hb_msg.get("command")
hb_value = hb_msg.get("hb_value")
if 'stop' == command:
logger.info("开始终止心跳线程, requestId:{}", self.__request_id)
logger.info("开始终止心跳线程, requestId:{}", request_id)
break
if hb_value is not None:
self.__progress = hb_value
progress = hb_value
if hb_init_num % 30 == 0:
self.sendhbMessage(AnalysisStatus.RUNNING.value)
hb_init_num = 0
put_queue(fb_queue, message_feedback(request_id, AnalysisStatus.RUNNING.value, analyse_type,
progress=progress), timeout=3, is_ex=True)

hb_init_num += 3
del hb_msg
except Exception:
logger.error("心跳线程异常:{}, requestId:{}", format_exc(), self.__request_id)
logger.info("心跳线程停止完成！requestId:{}", self.__request_id)
logger.error("心跳线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
clear_queue(hb_queue)
logger.info("心跳线程停止完成！requestId:{}", request_id)

+ 1064
- 1238
concurrency/IntelligentRecognitionProcess.py
File diff suppressed because it is too large
View File


+ 1095
- 0
concurrency/IntelligentRecognitionProcess2.py
File diff suppressed because it is too large
View File


+ 153
- 0
concurrency/Pull2PushStreamProcess.py View File

@@ -0,0 +1,153 @@
# -*- coding: utf-8 -*-
import time
from traceback import format_exc

from multiprocessing import Process, Queue

from loguru import logger

from concurrency.Pull2PushStreamThread import PushSteamThread
from enums.StatusEnum import PushStreamStatus, ExecuteStatus
from util.LogUtils import init_log

from enums.ExceptionEnum import ExceptionType
from entity.FeedBack import pull_stream_feedback
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue


class PushStreamProcess(Process):
__slots__ = ('_fb_queue', 'event_queue', '_context', '_msg', '_analysisType')

def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._analysisType = args
self.event_queue = Queue()

def sendEvent(self, eBody):
try:
self.event_queue.put(eBody, timeout=2)
except Exception:
logger.error("添加事件到队列超时异常：{}, requestId:{}", format_exc(), self._msg["request_id"])
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

def run(self):
msg, context = self._msg, self._context
requestId, videoUrls = msg["request_id"], msg["video_urls"]
base_dir, env = context['base_dir'], context['env']
fb_queue = self._fb_queue
task, videoStatus = {}, {}
ex = None
try:
init_log(base_dir, env)
if videoUrls is None or len(videoUrls) == 0:
raise ServiceException(ExceptionType.PUSH_STREAM_URL_IS_NULL.value[0],
ExceptionType.PUSH_STREAM_URL_IS_NULL.value[1])
if len(videoUrls) > 5:
logger.error("推流数量超过限制, 当前推流数量: {}, requestId:{}", len(videoUrls), requestId)
raise ServiceException(ExceptionType.PULL_STREAM_NUM_LIMIT_EXCEPTION.value[0],
ExceptionType.PULL_STREAM_NUM_LIMIT_EXCEPTION.value[1])
videoInfo = [{"id": url["id"], "status": PushStreamStatus.WAITING.value[0]} for url in videoUrls if
url.get("id")]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.WAITING.value[0], "", "", videoInfo))
for videoUrl in videoUrls:
pushThread = PushSteamThread(videoUrl["pull_url"], videoUrl["push_url"], requestId, videoUrl["id"])
pushThread.start()
task[videoUrl["id"]] = pushThread
enable_time = time.time()
for video in videoInfo:
videoStatus[video.get("id")] = video.get("status")
count = 0
while True:
# 整个推流任务超时时间
if time.time() - enable_time > 43200:
logger.error("任务执行超时, requestId:{}", requestId)
for t in list(task.keys()):
if task[t].is_alive():
task[t].status = False
task[t].pushStreamUtil.close_push_stream_sp()
task[t].join(120)
videoStatus[t] = PushStreamStatus.TIMEOUT.value[0]
videoInfo_timeout = [{"id": k, "status": v} for k, v in videoStatus.items()]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1],
videoInfo_timeout))
break
# 接受停止指令
event_result = get_no_block_queue(self.event_queue)
if event_result is not None:
command = event_result.get("command")
videoIds = event_result.get("videoIds")
if "stop" == command:
# 如果videoIds是空停止所有任务
if videoIds is None or len(videoIds) == 0:
logger.info("停止所有执行的推流任务, requestId:{}", requestId)
for t in list(task.keys()):
if task[t].is_alive():
task[t].status = False
task[t].pushStreamUtil.close_push_stream_sp()
task[t].join(120)
videoStatus[t] = PushStreamStatus.SUCCESS.value[0]
videoInfo_sucess = [{"id": k, "status": v} for k, v in videoStatus.items()]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.SUCCESS.value[0], "", "",
videoInfo_sucess))
break
else:
logger.info("停止指定的推流任务, requestId:{}", requestId)
alive_thread = 0
for t in list(task.keys()):
if task[t].is_alive():
if t in videoIds:
task[t].status = False
task[t].pushStreamUtil.close_push_stream_sp()
task[t].join(120)
videoStatus[t] = PushStreamStatus.SUCCESS.value[0]
else:
alive_thread += 1
if alive_thread == 0:
videoInfo_sucess = [{"id": k, "status": v} for k, v in videoStatus.items()]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.SUCCESS.value[0], "",
"", videoInfo_sucess))
break
for t in list(task.keys()):
if task[t].status and not task[t].is_alive():
videoStatus[t] = PushStreamStatus.FAILED.value[0]
logger.error("检测到推流线程异常停止！videoId:{}, requestId:{}", t, requestId)
if task[t].ex:
raise task[t].ex
raise Exception("检测到推流线程异常停止！")
if task[t].is_alive():
videoStatus[t] = task[t].excute_status
if count % 10 == 0:
videoInfo_hb = [{"id": k, "status": v} for k, v in videoStatus.items()]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.RUNNING.value[0], "", "",
videoInfo_hb))
count = 0
count += 1
time.sleep(1)
except ServiceException as s:
ex = s.code, s.msg
logger.error("服务异常，异常编号:{}, 异常描述：{}, requestId: {}", s.code, s.msg, requestId)
except Exception:
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
logger.error("服务异常: {}, requestId: {},", format_exc(), requestId)
finally:
if ex:
errorCode, errorMsg = ex
for t in list(task.keys()):
if task[t].is_alive():
task[t].status = False
task[t].pushStreamUtil.close_push_stream_sp()
task[t].join(120)
videoStatus[t] = PushStreamStatus.FAILED.value[0]
videoInfo_ex = [{"id": k, "status": v} for k, v in videoStatus.items()]
put_queue(fb_queue, pull_stream_feedback(requestId, ExecuteStatus.FAILED.value[0], errorCode, errorMsg,
videoInfo_ex))
for t in list(task.keys()):
if task[t].is_alive():
task[t].status = False
task[t].pushStreamUtil.close_push_stream_sp()
task[t].join(120)
logger.info("推流任务完成, requestId: {}", requestId)

+ 55
- 0
concurrency/Pull2PushStreamThread.py View File

@@ -0,0 +1,55 @@
# -*- coding: utf-8 -*-
from threading import Thread
import time
from traceback import format_exc

from loguru import logger

from enums.StatusEnum import PushStreamStatus
from exception.CustomerException import ServiceException
from util.PushStreamUtils import PushStreamUtil


class PushSteamThread(Thread):
__slots__ = ("pushStreamUtil", "requestId", "videoId", "status", "ex")

def __init__(self, pullUrl, pushUrl, requestId, videoId):
super().__init__()
self.pushStreamUtil = PushStreamUtil(pullUrl, pushUrl, requestId)
self.requestId = requestId
self.videoId = videoId
self.status = True
self.excute_status = PushStreamStatus.WAITING.value[0]
self.ex = None

def run(self):
logger.info("开始启动推流线程, 视频id: {}, requestId:{}", self.videoId, self.requestId)
while True:
try:
self.pushStreamUtil.start_push_stream()
self.excute_status = PushStreamStatus.RUNNING.value[0]
out, err = self.pushStreamUtil.push_stream_sp.communicate()
# 异常断流
if self.status:
logger.warning("推流异常，请检测拉流地址和推流地址是否正常！")
if self.pushStreamUtil.push_stream_sp.returncode != 0:
logger.error("推流异常:{}, 视频id: {}, requestId:{}", err.decode(), self.videoId,
self.requestId)
self.excute_status = PushStreamStatus.RETRYING.value[0]
self.pushStreamUtil.close_push_stream_sp()
time.sleep(5)
# 手动断流
if not self.status:
self.pushStreamUtil.close_push_stream_sp()
break
except ServiceException as s:
logger.error("异常: {}, 视频id: {}, requestId:{}", s.msg, self.videoId, self.requestId)
self.pushStreamUtil.close_push_stream_sp()
self.ex = s
break
except Exception as e:
logger.error("异常：{}, 视频id: {}, requestId:{}", format_exc(), self.videoId, self.requestId)
self.pushStreamUtil.close_push_stream_sp()
self.ex = e
break
logger.info("结束推流线程, 视频id: {}, requestId:{}", self.videoId, self.requestId)

+ 155
- 95
concurrency/PullStreamThread.py View File

@@ -1,122 +1,182 @@
# -*- coding: utf-8 -*-
import time
from queue import Queue
from threading import Thread
from time import time, sleep
from traceback import format_exc

from loguru import logger

from enums.ExceptionEnum import ExceptionType
from enums.RecordingStatusEnum import RecordingStatus
from exception.CustomerException import ServiceException
from util import GPUtils
from util.Cv2Utils import Cv2Util
from util.Cv2Utils import check_video_stream, clear_pull_p, build_video_info2, pull_read_video_stream2
from util.QueUtil import put_queue, get_no_block_queue, clear_queue, put_queue_result


class PullStreamThread(Thread):
def __init__(self, msg, content, pullQueue, fbQueue):
super().__init__()
self.command = Queue()
self.msg = msg
self.content = content
self.pullQueue = pullQueue
self.fbQueue = fbQueue
self.recording_pull_stream_timeout = int(self.content["service"]["recording_pull_stream_timeout"])

def getCommand(self):
eBody = None
try:
eBody = self.command.get(block=False)
except Exception as e:
pass
return eBody
__slots__ = ('_command', '_pull_queue', '_hb_queue', '_fb_queue', '_msg', '_context')

def sendCommand(self, result):
self.command.put(result)
def __init__(self, *args):
super().__init__()
self._msg, self._context, self._pull_queue, self._hb_queue, self._fb_queue, self._frame_num = args
self._command = Queue()

def sendPullQueue(self, result):
self.pullQueue.put(result)
def sendEvent(self, result):
put_queue(self._command, result, timeout=10, is_ex=False)


class RecordingPullStreamThread(PullStreamThread):

def run(self):
cv2tool = None
msg, context, frame_num = self._msg, self._context, self._frame_num
request_id, pull_url = msg["request_id"], msg['pull_url']
service = context["service"]
pull_stream_timeout = int(service["recording_pull_stream_timeout"])
read_stream_timeout = int(service["cv2_read_stream_timeout"])
service_timeout = int(service["timeout"])
command_queue, pull_queue, fb_queue, hb_queue = self._command, self._pull_queue, self._fb_queue, self._hb_queue
width, height, width_height_3, all_frames, w, h = None, None, None, 0, None, None
read_start_time, pull_p, ex = None, None, None
frame_list, frame_index_list = [], []
stop_ex = True
pull_stream_start_time = time()
try:
logger.info("录屏任务, 开启拉流, requestId:{}", self.msg.get("request_id"))
gpu_ids = GPUtils.get_gpu_ids(self.content)
cv2tool = Cv2Util(self.msg.get('pull_url'), requestId=self.msg.get("request_id"), content=self.content,
gpu_ids=gpu_ids, log=logger)
cv2_init_num = 1
init_pull_num = 1
start_time = time.time()
start_time_2 = time.time()
concurrent_frame = 1
cv2tool.get_recording_video_info()
logger.info("录屏拉流线程开始启动, requestId: {}", request_id)
cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
start_time = time()
while True:
body = self.getCommand()
if body is not None and len(body) > 0:
if 'stop' == body.get("command"):
logger.info("录屏任务, 拉流线程停止中, reuqestId:{}", self.msg.get("request_id"))
self.sendPullQueue({"status": "2"})
# 检查任务是否超时
if time() - start_time > service_timeout:
logger.error("录屏拉流超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 最终停止拉流
event = get_no_block_queue(command_queue)
if event is not None:
# 当接收到停止指令，说明不会再处理视频帧了, 直接退出
if 'stop' == event.get("command"):
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
logger.info("录屏拉流线程开始停止, requestId: {}", request_id)
break
if self.pullQueue.full():
time.sleep(0.1)
continue
# 检测视频信息是否存在或拉流对象是否存在
if cv2tool.checkconfig():
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, self.msg.get("request_id"))
pull_stream_init_timeout = time.time() - start_time
if pull_stream_init_timeout > self.recording_pull_stream_timeout:
logger.info("录屏拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout,
self.msg.get("request_id"))
raise ServiceException(ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[1])
cv2_init_num += 1
time.sleep(0.5)
cv2tool.get_recording_video_info()
continue
start_time = time.time()
cv2_init_num = 1
frame = cv2tool.recording_read()
# 主进程异常，停止子线程
if 'stop_ex' == event.get("command"):
logger.info("录屏异常拉开始停止拉流线程, requestId: {}", request_id)
stop_ex = False
break
# 如果是离线拉流
if pull_url.startswith('http'):
if check_video_stream(width, height):
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
# 当是离线地址重试3次还是拉取不到视频流，关闭拉流管道，返回失败信息
if cv2_init_num > 3:
logger.info("离线拉流重试失败, 重试次数: {}, requestId: {}", cv2_init_num, request_id)
raise ServiceException(ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[1])
cv2_init_num += 1
width, height, width_height_3, all_frames, w, h = build_video_info2(pull_url, request_id)
if width is not None:
put_queue(hb_queue, {"status": RecordingStatus.RECORDING_RUNNING.value[0]}, timeout=2)
else:
if cv2_init_num < 2:
put_queue(hb_queue, {"status": RecordingStatus.RECORDING_RETRYING.value[0]}, timeout=2)
continue
# 当离线视频时, 队列满了, 等待1秒后再试
if pull_queue.full():
logger.info("pull拉流队列满了: {}, requestId: {}", pull_queue.qsize(), request_id)
sleep(1)
continue
# 如果是实时拉流
else:
if check_video_stream(width, height):
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
pull_stream_init_timeout = time() - pull_stream_start_time
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
if pull_stream_init_timeout > pull_stream_timeout:
logger.error("开始拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout, request_id)
raise ServiceException(ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[1])
cv2_init_num += 1
width, height, width_height_3, all_frames, w, h = build_video_info2(pull_url, request_id)
if width is not None:
put_queue(hb_queue, {"status": RecordingStatus.RECORDING_RUNNING.value[0]}, timeout=1)
else:
if cv2_init_num < 3:
put_queue(hb_queue, {"status": RecordingStatus.RECORDING_RETRYING.value[0]}, timeout=1)
sleep(1)
continue
pull_stream_start_time = time()
cv2_init_num = 1
frame, pull_p, width, height = pull_read_video_stream2(pull_p, pull_url, width, height,
width_height_3, w, h, request_id)
if frame is None:
logger.info("获取帧为空, 开始重试: {}次, requestId: {}", init_pull_num, self.msg.get("request_id"))
pull_stream_read_timeout = time.time() - start_time_2
if pull_stream_read_timeout > self.recording_pull_stream_timeout:
logger.info("拉流过程中断了重试超时, 超时时间: {}, requestId: {}", pull_stream_read_timeout,
self.msg.get("request_id"))
raise ServiceException(ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[1])
if cv2tool.all_frames is not None and len(cv2tool.all_frames) > 0:
if concurrent_frame < cv2tool.all_frames - 100:
logger.info("流异常结束：requestId: {}", self.msg.get("request_id"))
self.sendPullQueue({"status": "3"})
break
logger.info("拉流线程结束, requestId: {}", self.msg.get("request_id"))
self.sendPullQueue({"status": "2"})
if pull_url.startswith('http'):
clear_pull_p(pull_p, request_id)
logger.info("总帧数: {}, 当前帧数: {}, requestId: {}", all_frames, concurrent_frame, request_id)
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
if concurrent_frame < all_frames - 100:
logger.info("离线拉流异常结束：requestId: {}", request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
logger.info("离线拉流线程结束, requestId: {}", request_id)
break
init_pull_num += 1
time.sleep(0.5)
cv2tool.recording_pull_p()
continue
else:
logger.info("获取帧为空, 开始重试: {}次, requestId: {}", init_pull_num, request_id)
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
if read_start_time is None:
read_start_time = time()
pull_stream_read_timeout = time() - read_start_time
if pull_stream_read_timeout > read_stream_timeout:
logger.info("拉流过程中断了重试超时, 超时时间: {}, requestId: {}", pull_stream_read_timeout,
request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
init_pull_num += 1
continue
init_pull_num = 1
start_time_2 = time.time()
self.sendPullQueue({"status": "4",
"frame": frame,
"cct_frame": cv2tool.current_frame,
"width": cv2tool.width,
"height": cv2tool.height,
"fps": cv2tool.fps,
"all_frame": cv2tool.all_frames})
read_start_time = None
if pull_queue.full():
sleep(1)
logger.info("pull拉流队列满了:{}, requestId: {}", pull_queue.qsize(), request_id)
continue
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
concurrent_frame += 1
del frame
except ServiceException as s:
self.sendPullQueue({"status": "1", "error": {"code": s.code, "msg": s.msg}})
except Exception as e:
logger.exception("实时拉流异常: {}, requestId:{}", e, self.msg.get("request_id"))
self.sendPullQueue({"status": "1", "error": {"code": ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
"msg": ExceptionType.SERVICE_INNER_EXCEPTION.value[1]}})
ex = s.code, s.msg
except Exception:
logger.exception("实时拉流异常: {}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
if cv2tool:
cv2tool.close()
logger.info("录屏拉流线程结束, requestId: {}", self.msg.get("request_id"))



clear_pull_p(pull_p, request_id)
if stop_ex:
if ex:
error_code, error_msg = ex
result = put_queue_result(pull_queue, (1, error_code, error_msg), timeout=3)
else:
result = put_queue_result(pull_queue, (2,), timeout=3)
if result:
# 3分钟超时时间
cr_time = time()
while time() - cr_time < 180:
event = get_no_block_queue(command_queue)
if event is not None:
# 当接收到停止指令，说明不会再处理视频帧了, 直接退出
if 'stop' == event.get("command"):
logger.info("录屏拉流线程开始停止, requestId: {}", request_id)
break
sleep(1)
clear_queue(command_queue)
clear_queue(pull_queue)
clear_queue(hb_queue)
del frame_list, frame_index_list
logger.info("录屏拉流线程结束, requestId: {}", request_id)

+ 255
- 331
concurrency/PullVideoStreamProcess.py View File

@@ -1,8 +1,8 @@
# -*- coding: utf-8 -*-
import os
import time
from multiprocessing import Process, Queue
from os import getpid
from time import time, sleep
from traceback import format_exc

import psutil
@@ -10,397 +10,321 @@ from loguru import logger

from util.LogUtils import init_log
from concurrency.FileUploadThread import ImageFileUpload
from concurrency.HeartbeatThread import Heartbeat
from entity.FeedBack import message_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.Cv2Utils import check_video_stream, build_video_info, pull_read_video_stream, clear_pull_p
from util.TimeUtils import now_date_to_str
from util.QueUtil import get_no_block_queue, put_queue, clear_queue, put_queue_result


class PullVideoStreamProcess(Process):
__slots__ = ('_command', '_msg', '_context', '_pullQueue', '_fbQueue', '_hbQueue', '_imageQueue', '_analyse_type',
"_base_dir")
__slots__ = ("_command_queue", "_msg", "_context", "_fb_queue", "_pull_queue", "_image_queue", "_analyse_type",
"_frame_num")

def __init__(self, msg, context, pullQueue, fbQueue, hbQueue, imageQueue, analyse_type, base_dir):
def __init__(self, *args):
super().__init__()
self._command = Queue()
self._msg = msg
self._context = context
self._pullQueue = pullQueue
self._fbQueue = fbQueue
self._hbQueue = hbQueue
self._imageQueue = imageQueue
self._analyse_type = analyse_type
self._base_dir = base_dir

def sendCommand(self, result, enable_ex=True):
try:
self._command.put(result, timeout=10)
except Exception:
logger.error("添加队列超时异常：{}, requestId:{}", format_exc(), self._msg.get("request_id"))
if enable_ex:
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
def start_heartbeat(self, hb):
if hb is None:
hb = Heartbeat(self._fbQueue, self._hbQueue, self._msg.get("request_id"), self._analyse_type)
hb.setDaemon(True)
hb.start()
start_time = time.time()
retry_count = 0
while True:
if hb.is_alive():
return hb
retry_count += 1
if retry_count > 10:
logger.error("心跳线程异常重试失败！requestId:{}", self._msg.get("request_id"))
del hb
raise Exception("心跳线程启动失败")
if not hb.is_alive():
logger.warning("心跳线程异常等待中, requestId:{}", self._msg.get("request_id"))
if time.time() - start_time <= 3:
time.sleep(0.5)
continue
if time.time() - start_time > 3:
logger.warning("心跳线程异常重启中, requestId:{}", self._msg.get("request_id"))
hb = Heartbeat(self._fbQueue, self._hbQueue, self._msg.get("request_id"), self._analyse_type)
hb.setDaemon(True)
hb.start()
continue

def start_File_upload(self, imageFileUpload):
if imageFileUpload is None:
imageFileUpload = ImageFileUpload(self._fbQueue, self._context, self._msg, self._imageQueue,
self._analyse_type, self._base_dir)
imageFileUpload.setDaemon(True)
imageFileUpload.start()
start_time = time.time()
retry_count = 0
while True:
if imageFileUpload.is_alive():
return imageFileUpload
retry_count += 1
if retry_count > 10:
logger.error("图片上传线程异常重试失败！requestId:{}", self._msg.get("request_id"))
raise Exception("图片线程启动失败")
if not imageFileUpload.is_alive():
logger.warning("图片上传线程异常等待中, requestId:{}", self._msg.get("request_id"))
if time.time() - start_time <= 3:
time.sleep(0.5)
continue
if time.time() - start_time > 3:
logger.warning("图片上传线程异常重启中, requestId:{}", self._msg.get("request_id"))
imageFileUpload = ImageFileUpload(self._fbQueue, self._context, self._msg, self._imageQueue,
self._analyse_type, self._base_dir)
imageFileUpload.setDaemon(True)
imageFileUpload.start()
start_time = time.time()
continue
# 自带参数
self._command_queue = Queue()

# 传参
self._msg, self._context, self._fb_queue, self._pull_queue, self._image_queue, self._analyse_type, \
self._frame_num = args

def putQueue(queue, result, requestId, enable_ex=True):
try:
queue.put(result, timeout=10)
except Exception:
logger.error("添加队列超时异常：{}, requestId:{}", format_exc(), requestId)
if enable_ex:
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
def sendCommand(self, result):
put_queue(self._command_queue, result, timeout=2, is_ex=True)

@staticmethod
def start_File_upload(fb_queue, context, msg, image_queue, analyse_type):
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type)
image_thread.setDaemon(True)
image_thread.start()
return image_thread

def getNoBlockQueue(queue):
eBody = None
try:
eBody = queue.get(block=False)
except Exception:
pass
return eBody

def check(start_time, service_timeout, requestId, imageFileUpload, hb):
create_task_time = time.time() - start_time
if create_task_time > service_timeout:
logger.error("分析超时, 超时时间：{}, requestId: {}", create_task_time, requestId)
raise ServiceException(ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[0],
ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[1])
@staticmethod
def check(start_time, service_timeout, request_id, image_thread):
if time() - start_time > service_timeout:
logger.error("拉流进程运行超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 检测图片上传线程是否正常运行
if imageFileUpload is not None and not imageFileUpload.is_alive():
logger.error("未检测到图片上传线程活动，图片上传线程可能出现异常, requestId:{}", requestId)
raise Exception("未检测到图片上传线程活动，图片上传线程可能出现异常!")
# 检测心跳线程是否正常运行
if hb is not None and not hb.is_alive():
logger.error("未检测到心跳线程活动，心跳线程可能出现异常, requestId:{}", requestId)
raise Exception("未检测到心跳线程活动，心跳线程可能出现异常!")
if image_thread and not image_thread.is_alive():
logger.error("未检测到图片上传线程活动，图片上传线程可能出现异常, requestId:{}", request_id)
raise Exception("未检测到图片上传线程活动，图片上传线程可能出现异常!")


class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
__slots__ = ()

def run(self):
pull_p = None
imageFileUpload = None
hb = None
requestId = '1'
pull_queue = self._pullQueue
fb_queue = self._fbQueue
image_queue = self._imageQueue
hb_queue = self._hbQueue
# 避免循环调用性能影响, 优先赋值
context, msg, analyse_type, frame_num = self._context, self._msg, self._analyse_type, self._frame_num
base_dir, env, service = context['base_dir'], context['env'], context["service"]
request_id, pull_url = msg["request_id"], msg["pull_url"]
pull_stream_timeout, read_stream_timeout, service_timeout = int(service["cv2_pull_stream_timeout"]), \
int(service["cv2_read_stream_timeout"]), int(service["timeout"]) + 120
command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue
image_thread, ex = None, None
width, height, width_height_3, all_frames, w_2, h_2, pull_p = None, None, None, 0, None, None, None
frame_list, frame_index_list = [], []
ex_status = True
try:
base_dir = self._base_dir
# 加载日志框架
init_log(base_dir)
requestId = self._msg.get("request_id")
pull_url = self._msg.get("pull_url")

logger.info("开启视频拉流进程, requestId:{}", requestId)

pull_stream_timeout = int(self._context["service"]["cv2_pull_stream_timeout"])
read_stream_timeout = int(self._context["service"]["cv2_read_stream_timeout"])
service_timeout = int(self._context["service"]["timeout"])
command_queue = self._command

# 视频相关配置
width = None
height = None
width_height_3 = None
all_frames = 0
w_2 = None
h_2 = None

# 初始化日志
init_log(base_dir, env)
logger.info("开启启动实时视频拉流进程, requestId:{}", request_id)
# 开启图片上传线程
imageFileUpload = self.start_File_upload(imageFileUpload)
# 开启心跳线程
hb = self.start_heartbeat(hb)
# 初始化拉流工具类
cv2_init_num = 1
init_pull_num = 1
start_time = time.time()
pull_stream_start_time = time.time()
pull_stream_read_start_time = time.time()
kill_parent_process_timeout = time.time()
concurrent_frame = 1
stop_pull_stream_step = False
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
start_time, pull_stream_start_time, read_start_time, full_timeout = time(), None, None, None
while True:
# 检测任务执行是否超时、心跳线程是否正常、图片上传线程是否正常
check(start_time, service_timeout, requestId, imageFileUpload, hb)
# 获取指令信息
command = getNoBlockQueue(command_queue)
if command is not None:
if 'stop_pull_stream' == command.get("command"):
putQueue(pull_queue, ("9",), requestId) # 9 停止拉流
stop_pull_stream_step = True
clear_pull_p(pull_p, requestId)
continue
# 停止图片上传线程
if 'stop_image_hb' == command.get("command"):
putQueue(image_queue, {"command": "stop"}, requestId)
putQueue(hb_queue, {"command": "stop"}, requestId)
clear_pull_p(pull_p, requestId)
imageFileUpload.join(60 * 3)
hb.join(60 * 3)
logger.error("图片线程停止完成, requestId:{}", requestId)
# 检测任务执行是否超时、图片上传线程是否正常
self.check(start_time, service_timeout, request_id, image_thread)
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
break
if 'stop_ex' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
ex_status = False
break
if stop_pull_stream_step:
time.sleep(1)
continue
# 检测视频信息是否存在或拉流对象是否存在
check_vide_result = check_video_stream(width, height)
if check_vide_result:
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, requestId)
pull_stream_init_timeout = time.time() - pull_stream_start_time
if check_video_stream(width, height):
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
if pull_stream_start_time is None:
pull_stream_start_time = time()
pull_stream_init_timeout = time() - pull_stream_start_time
if pull_stream_init_timeout > pull_stream_timeout:
logger.info("开始拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout, requestId)
logger.info("开始拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout, request_id)
# 如果超时了, 将异常信息发送给主进程，如果队列满了，抛出异常
raise ServiceException(ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[1])
cv2_init_num += 1
time.sleep(1)
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, requestId)
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
if width is None:
sleep(1)
continue
pull_stream_start_time, cv2_init_num = None, 1
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
logger.error("拉流队列阻塞超时, 请检查父进程是否正常！requestId: {}", request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if psutil.Process(getpid()).ppid() == 1:
clear_pull_p(pull_p, request_id)
ex_status = False
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
image_thread.join(120)
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", request_id)
put_queue(self._fb_queue, message_feedback(request_id, AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1]), timeout=2)
break
del frame
continue
pull_stream_start_time = time.time()
cv2_init_num = 1
frame, pull_p, width, height, width_height_3 = pull_read_video_stream(pull_p, pull_url, width,
height, width_height_3, w_2, h_2,
requestId)
full_timeout = None
if frame is None:
logger.info("获取帧为空, 开始重试: {}次, requestId: {}", init_pull_num, requestId)
pull_stream_read_timeout = time.time() - pull_stream_read_start_time
clear_pull_p(pull_p, request_id)
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
logger.info("获取帧为空, 开始重试: {}次, requestId: {}", init_pull_num, request_id)
if read_start_time is None:
read_start_time = time()
pull_stream_read_timeout = time() - read_start_time
if pull_stream_read_timeout > read_stream_timeout:
logger.info("拉流过程中断了重试超时, 超时时间: {}, requestId: {}", pull_stream_read_timeout,
requestId)
putQueue(pull_queue, ("3",), requestId) # 3 超时
stop_pull_stream_step = True
clear_pull_p(pull_p, requestId)
continue
clear_pull_p(pull_p, requestId)
request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
init_pull_num += 1
continue
init_pull_num = 1
pull_stream_read_start_time = time.time()
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), requestId)
# 如果一直有视频流，队列一直是满的，应该是父进程挂了，直接等待60退出
if time.time() - kill_parent_process_timeout > 60:
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", requestId)
putQueue(fb_queue, {"feedback": message_feedback(requestId,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_CPU_RESOURCES.value[0],
ExceptionType.NO_CPU_RESOURCES.value[1],
analyse_time=now_date_to_str())},
requestId)
break
# logger.info("当前视频帧队列处理满队列状态, requestId: {}", requestId)
if psutil.Process(getpid()).ppid() == 1:
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", requestId)
putQueue(fb_queue, {"feedback": message_feedback(requestId,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_CPU_RESOURCES.value[0],
ExceptionType.NO_CPU_RESOURCES.value[1],
analyse_time=now_date_to_str())},
requestId)
break
continue
kill_parent_process_timeout = time.time()
putQueue(pull_queue, ("4", frame, concurrent_frame, w_2, h_2, all_frames), requestId)
init_pull_num, read_start_time = 1, None
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
frame_list, frame_index_list = [], []
concurrent_frame += 1
del frame
except ServiceException as s:
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", s.msg, pull_queue.qsize(), requestId)
putQueue(pull_queue, ("1", s.code, s.msg), requestId, enable_ex=False)
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", s.msg, pull_queue.qsize(), request_id)
ex = s.code, s.msg
except Exception:
logger.error("实时拉流异常: {}, requestId:{}", format_exc(), requestId)
putQueue(pull_queue, ("1", ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), requestId, enable_ex=False)
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", format_exc(), pull_queue.qsize(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
clear_pull_p(pull_p, requestId)
if imageFileUpload:
putQueue(image_queue, {"command": "stop"}, requestId, enable_ex=False)
imageFileUpload.join(60 * 3)
if hb:
putQueue(hb_queue, {"command": "stop"}, requestId, enable_ex=False)
hb.join(60 * 3)
logger.info("实时拉流线程结束, requestId: {}", requestId)
clear_pull_p(pull_p, request_id)
del frame_list, frame_index_list
if ex_status:
if ex:
code, msg = ex
r = put_queue_result(pull_queue, (1, code, msg), timeout=10)
else:
r = put_queue_result(pull_queue, (2,), timeout=10)
if r:
c_time = time()
while time() - c_time < 60:
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
break
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
logger.info("实时拉流线程结束, 图片队列: {}, 拉流队列: {}, 图片进程的状态: {} requestId: {}",
image_queue.qsize(), pull_queue.qsize(), image_thread.is_alive(), request_id)


class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
__slots__ = ()

def run(self):
pull_p = None
imageFileUpload = None
hb = None
requestId = '1'
pull_queue = self._pullQueue
fb_queue = self._fbQueue
image_queue = self._imageQueue
hb_queue = self._hbQueue
msg, context, frame_num, analyse_type = self._msg, self._context, self._frame_num, self._analyse_type
request_id, base_dir, env, pull_url = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"]
ex, service_timeout, full_timeout = None, int(context["service"]["timeout"]) + 120, None
command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue
image_thread, pull_p = None, None
width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
frame_list, frame_index_list = [], []
ex_status = True
try:
base_dir = self._base_dir
init_log(base_dir)
requestId = self._msg.get("request_id")
pull_url = self._msg.get("original_url")
logger.info("开启离线视频拉流进程, requestId:{}", requestId)

service_timeout = int(self._context["service"]["timeout"])
command_queue = self._command
# 初始化日志
init_log(base_dir, env)
logger.info("开启离线视频拉流进程, requestId:{}", request_id)

# 开启图片上传线程
imageFileUpload = self.start_File_upload(imageFileUpload)
# 开启心跳线程
hb = self.start_heartbeat(hb)
cv2_init_num = 1
start_time = time.time()
concurrent_frame = 1
stop_pull_stream_step = False
kill_parent_process_timeout = time.time()
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, requestId)
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)

# 初始化拉流工具类
cv2_init_num, concurrent_frame = 0, 1
start_time = time()
while True:
check(start_time, service_timeout, requestId, imageFileUpload, hb)
command = getNoBlockQueue(command_queue)
if command is not None and len(command) > 0:
if 'stop_pull_stream' == command.get("command"):
putQueue(pull_queue, ("9",), requestId) # 9 停止拉流
stop_pull_stream_step = True
clear_pull_p(pull_p, requestId)
continue
if 'stop_image_hb' == command.get("command"):
putQueue(image_queue, {"command": "stop"}, requestId)
putQueue(hb_queue, {"command": "stop"}, requestId)
clear_pull_p(pull_p, requestId)
imageFileUpload.join(60 * 3)
hb.join(60 * 3)
logger.error("图片线程停止完成, requestId:{}", requestId)
# 检测任务执行是否超时、图片上传线程是否正常
self.check(start_time, service_timeout, request_id, image_thread)
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止离线拉流进程, requestId:{}", request_id)
break
if stop_pull_stream_step:
time.sleep(1)
continue
if pull_queue.full():
logger.info("当前视频帧队列处理满队列状态, requestId: {}", requestId)
# 如果一直有视频流，队列一直是满的，应该是父进程挂了，直接等待60退出
if time.time() - kill_parent_process_timeout > 60:
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", requestId)
putQueue(fb_queue, {"feedback": message_feedback(requestId,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_CPU_RESOURCES.value[0],
ExceptionType.NO_CPU_RESOURCES.value[1],
analyse_time=now_date_to_str())},
requestId)
if 'stop_ex' == command_msg.get("command"):
logger.info("开始停止离线拉流进程, requestId:{}", request_id)
ex_status = False
break
if psutil.Process(getpid()).ppid() == 1:
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", requestId)
putQueue(fb_queue, {"feedback": message_feedback(requestId,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_CPU_RESOURCES.value[0],
ExceptionType.NO_CPU_RESOURCES.value[1],
analyse_time=now_date_to_str())},
requestId)
break
continue
kill_parent_process_timeout = time.time()
check_vide_result = check_video_stream(width, height)
if check_vide_result:
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, requestId)
# 检测视频信息是否存在或拉流对象是否存在
if check_video_stream(width, height):
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
if cv2_init_num > 3:
logger.info("视频信息获取失败, 重试: {}次, requestId: {}", cv2_init_num, requestId)
logger.info("离线拉流重试失败, 重试次数: {}, requestId: {}", cv2_init_num, request_id)
raise ServiceException(ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[1])
cv2_init_num += 1
time.sleep(1)
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, requestId)
sleep(1)
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
continue
frame, pull_p, width, height, width_height_3 = pull_read_video_stream(pull_p, pull_url, width,
height, width_height_3, w_2, h_2,
requestId)
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
logger.error("pull队列阻塞超时，请检测父进程是否正常！requestId: {}", request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if psutil.Process(getpid()).ppid() == 1:
clear_pull_p(pull_p, request_id)
ex_status = False
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
put_queue(image_queue, (2, "stop"), timeout=1)
image_thread.join(120)
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", request_id)
put_queue(self._fb_queue, message_feedback(request_id,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1]), timeout=2)
break
continue
full_timeout = None
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height,
width_height_3, w_2, h_2, request_id)
if frame is None:
logger.info("总帧数: {}, 当前帧数: {}, requestId: {}", all_frames, concurrent_frame, requestId)
logger.info("总帧数: {}, 当前帧数: {}, requestId: {}", all_frames, concurrent_frame, request_id)
clear_pull_p(pull_p, request_id)
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
# 允许100帧的误差
if concurrent_frame < all_frames - 100:
logger.info("离线拉流异常结束：requestId: {}", requestId)
putQueue(pull_queue, ("3",), requestId)
stop_pull_stream_step = True
continue
logger.info("离线拉流线程结束, requestId: {}", requestId)
putQueue(pull_queue, ("2",), requestId)
stop_pull_stream_step = True
continue
putQueue(pull_queue, ("4", frame, concurrent_frame, w_2, h_2, all_frames), requestId)
logger.info("离线拉流异常结束：requestId: {}", request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
logger.info("离线拉流线程结束, requestId: {}", request_id)
break
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
frame_list, frame_index_list = [], []
concurrent_frame += 1
del frame
except ServiceException as s:
logger.error("离线任务拉流出现异常:{}, requestId:{}", s.msg, requestId)
putQueue(pull_queue, ("1", s.code, s.msg), requestId, enable_ex=False)
logger.error("离线拉流异常: {}, 队列大小:{}, requestId:{}", s.msg, pull_queue.qsize(), request_id)
ex = s.code, s.msg
except Exception:
logger.error("离线拉流异常: {}, requestId:{}", format_exc(), requestId)
putQueue(pull_queue, ("1", ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), requestId, enable_ex=False)
logger.error("离线拉流异常: {}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
clear_pull_p(pull_p, requestId)
if imageFileUpload:
putQueue(image_queue, {"command": "stop"}, requestId, enable_ex=False)
imageFileUpload.join(60 * 3)
if hb:
putQueue(hb_queue, {"command": "stop"}, requestId, enable_ex=False)
hb.join(60 * 3)
logger.info("离线拉流线程结束, requestId: {}", requestId)
clear_pull_p(pull_p, request_id)
del frame_list, frame_index_list
if ex_status:
if ex:
code, msg = ex
r = put_queue_result(pull_queue, (1, code, msg), timeout=10)
else:
r = put_queue_result(pull_queue, (2,), timeout=10)
if r:
c_time = time()
while time() - c_time < 180:
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
break
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
logger.info("离线拉流线程结束, 图片队列: {}, 拉流队列: {}, 图片进程的状态: {} requestId: {}",
image_queue.qsize(), pull_queue.qsize(), image_thread.is_alive(), request_id)

+ 349
- 0
concurrency/PullVideoStreamProcess2.py View File

@@ -0,0 +1,349 @@
# -*- coding: utf-8 -*-
import os
from multiprocessing import Process, Queue
from os import getpid
from time import time, sleep
from traceback import format_exc

import psutil
from loguru import logger

from util.LogUtils import init_log
from concurrency.FileUploadThread import ImageFileUpload
from entity.FeedBack import message_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.Cv2Utils import check_video_stream, build_video_info, pull_read_video_stream, clear_pull_p
from util.QueUtil import get_no_block_queue, put_queue, clear_queue, put_queue_result


class PullVideoStreamProcess2(Process):
__slots__ = ("_command_queue", "_msg", "_context", "_fb_queue", "_pull_queue", "_image_queue", "_analyse_type",
"_frame_num")

def __init__(self, *args):
super().__init__()
# 自带参数
self._command_queue = Queue()

# 传参
self._msg, self._context, self._fb_queue, self._pull_queue, self._image_queue, self._analyse_type, \
self._frame_num = args

def sendCommand(self, result):
try:
self._command_queue.put(result, timeout=10)
except Exception:
logger.error("添加队列超时异常：{}, requestId:{}", format_exc(), self._msg.get("request_id"))
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

@staticmethod
def start_File_upload(*args):
fb_queue, context, msg, image_queue, analyse_type = args
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type)
image_thread.setDaemon(True)
image_thread.start()
return image_thread

@staticmethod
def check(start_time, service_timeout, request_id, image_thread):
if time() - start_time > service_timeout:
logger.error("分析超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[0],
ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[1])
# 检测图片上传线程是否正常运行
if image_thread is not None and not image_thread.is_alive():
logger.error("未检测到图片上传线程活动，图片上传线程可能出现异常, requestId:{}", request_id)
raise Exception("未检测到图片上传线程活动，图片上传线程可能出现异常!")


class OnlinePullVideoStreamProcess2(PullVideoStreamProcess2):
__slots__ = ()

def run(self):
# 避免循环调用性能影响, 优先赋值
context, msg, analyse_type = self._context, self._msg, self._analyse_type
request_id, base_dir, env = msg["request_id"], context['base_dir'], context['env']
pull_url, frame_num = msg["pull_url"], self._frame_num
pull_stream_timeout = int(context["service"]["cv2_pull_stream_timeout"])
read_stream_timeout = int(context["service"]["cv2_read_stream_timeout"])
service_timeout = int(context["service"]["timeout"])
command_queue, pull_queue, image_queue = self._command_queue, self._pull_queue, self._image_queue
fb_queue = self._fb_queue
image_thread, pull_p = None, None
width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
frame_list, frame_index_list = [], []
ex = None
ex_status = True
full_timeout = None
try:
# 初始化日志
init_log(base_dir, env)
logger.info("开启实时视频拉流进程, requestId:{}", request_id)

# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)

# 初始化拉流工具类
cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
start_time, pull_start_time, read_start_time = time(), None, None
while True:
# 检测任务执行是否超时、图片上传线程是否正常
self.check(start_time, service_timeout, request_id, image_thread)
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
break
if 'stop_ex' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
ex_status = False
break
# 检测视频信息是否存在或拉流对象是否存在
if check_video_stream(width, height):
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
if pull_start_time is None:
pull_start_time = time()
pull_stream_init_timeout = time() - pull_start_time
if pull_stream_init_timeout > pull_stream_timeout:
logger.info("开始拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout, request_id)
raise ServiceException(ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PULLSTREAM_TIMEOUT_EXCEPTION.value[1])
cv2_init_num += 1
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
if width is None:
sleep(1)
continue
pull_start_time, cv2_init_num = None, 1
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if frame is None:
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
logger.info("获取帧为空, 开始重试: {}次, requestId: {}", init_pull_num, request_id)
if read_start_time is None:
read_start_time = time()
pull_stream_read_timeout = time() - read_start_time
if pull_stream_read_timeout > read_stream_timeout:
logger.info("拉流过程中断了重试超时, 超时时间: {}, requestId: {}", pull_stream_read_timeout,
request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
init_pull_num += 1
continue
init_pull_num, read_start_time = 1, None
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
logger.error("拉流队列阻塞异常, requestId: {}", request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if psutil.Process(getpid()).ppid() == 1:
clear_pull_p(pull_p, request_id)
ex_status = False
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
image_thread.join(120)
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", request_id)
put_queue(self._fb_queue, message_feedback(request_id,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1]))
break
del frame
continue
full_timeout = None
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
frame_list, frame_index_list = [], []
concurrent_frame += 1
del frame
except ServiceException as s:
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", s.msg, pull_queue.qsize(), request_id)
ex = s.code, s.msg
except Exception:
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", format_exc(), pull_queue.qsize(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
clear_pull_p(pull_p, request_id)
del frame_list, frame_index_list
if ex_status:
if ex:
code, msg = ex
r = put_queue_result(pull_queue, (1, code, msg), timeout=10)
else:
r = put_queue_result(pull_queue, (2,), timeout=10)
if r:
c_time = time()
while time() - c_time < 180:
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止实时拉流进程, requestId:{}", request_id)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
break
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
logger.info("实时拉流线程结束, 图片队列: {}, 拉流队列: {}, 图片进程的状态: {} requestId: {}",
image_queue.qsize(), pull_queue.qsize(), image_thread.is_alive(), request_id)


class OfflinePullVideoStreamProcess2(PullVideoStreamProcess2):
__slots__ = ()

def run(self):
msg, context, frame_num, analyse_type = self._msg, self._context, self._frame_num, self._analyse_type
request_id, base_dir, env, pull_url = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"]
ex, service_timeout = None, int(context["service"]["timeout"])
command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue
image_thread, pull_p = None, None
width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
frame_list, frame_index_list = [], []
ex_status = True
full_timeout = None
try:
# 初始化日志
init_log(base_dir, env)
logger.info("开启离线视频拉流进程, requestId:{}", request_id)

# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)

# 初始化拉流工具类
cv2_init_num = 0
concurrent_frame = 1
start_time = time()
while True:
# 检测任务执行是否超时、图片上传线程是否正常
self.check(start_time, service_timeout, request_id, image_thread)
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止离线拉流进程, requestId:{}", request_id)
break
if 'stop_ex' == command_msg.get("command"):
logger.info("开始停止离线拉流进程, requestId:{}", request_id)
ex_status = False
break
# 检测视频信息是否存在或拉流对象是否存在
if check_video_stream(width, height):
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1)
frame_list, frame_index_list = [], []
logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, request_id)
if cv2_init_num > 3:
clear_pull_p(pull_p, request_id)
logger.info("离线拉流重试失败, 重试次数: {}, requestId: {}", cv2_init_num, request_id)
raise ServiceException(ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[1])
cv2_init_num += 1
sleep(1)
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
continue
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 300:
logger.error("拉流队列阻塞超时, 请检查父进程是否正常！requestId: {}", request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if psutil.Process(getpid()).ppid() == 1:
clear_pull_p(pull_p, request_id)
ex_status = False
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
image_thread.join(120)
logger.info("检测到父进程异常停止, 请检测服务器资源是否负载过高, requestId: {}", request_id)
put_queue(self._fb_queue, message_feedback(request_id,
AnalysisStatus.FAILED.value,
self._analyse_type,
ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1]))
break
continue
full_timeout = None
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if frame is None:
logger.info("总帧数: {}, 当前帧数: {}, requestId: {}", all_frames, concurrent_frame, request_id)
clear_pull_p(pull_p, request_id)
if len(frame_list) > 0:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=2, is_ex=False)
frame_list, frame_index_list = [], []
# 允许100帧的误差
if concurrent_frame < all_frames - 100:
logger.info("离线拉流异常结束：requestId: {}", request_id)
raise ServiceException(ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
logger.info("离线拉流线程结束, requestId: {}", request_id)
break
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
frame_list, frame_index_list = [], []
concurrent_frame += 1
del frame
except ServiceException as s:
logger.error("实时拉流异常: {}, 队列大小:{}, requestId:{}", s.msg, pull_queue.qsize(), request_id)
ex = s.code, s.msg
except Exception:
logger.error("实时拉流异常: {}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
clear_pull_p(pull_p, request_id)
del frame_list, frame_index_list
if ex_status:
if ex:
code, msg = ex
r = put_queue_result(pull_queue, (1, code, msg), timeout=10)
else:
r = put_queue_result(pull_queue, (2,), timeout=10)
if r:
c_time = time()
while time() - c_time < 180:
command_msg = get_no_block_queue(command_queue)
if command_msg is not None:
if 'stop' == command_msg.get("command"):
logger.info("开始停止离线拉流进程, requestId:{}", request_id)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
break
for q in [command_queue, pull_queue, image_queue]:
clear_queue(q)
if image_thread and image_thread.is_alive():
put_queue(image_queue, (2, "stop"), timeout=1)
logger.info("停止图片上传线程, requestId:{}", request_id)
image_thread.join(120)
logger.info("停止图片上传线程结束, requestId:{}", request_id)
logger.info("离线拉流线程结束, 图片队列: {}, 拉流队列: {}, 图片进程的状态: {} requestId: {}",
image_queue.qsize(), pull_queue.qsize(), image_thread.is_alive(), request_id)

+ 181
- 0
concurrency/PushStreamThread.py View File

@@ -0,0 +1,181 @@
# -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor
from os.path import join
from threading import Thread
from traceback import format_exc

import cv2
import numpy as np
from loguru import logger
from util.Cv2Utils import write_or_video, write_ai_video, push_video_stream, close_all_p, video_conjuncing
from util.ImageUtils import url2Array, add_water_pic
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue


class OnPushStreamThread(Thread):
__slots__ = ('_msg', '_push_queue', '_context', 'ex', '_logo', '_image_queue')

def __init__(self, *args):
super().__init__()
# 传参
self._msg, self._push_queue, self._image_queue, self._context = args
# 自带参数
self.ex = None
self._logo = None
if self._context["video"]["video_add_water"]:
self._logo = self._msg.get("logo_url")
if self._logo:
self._logo = url2Array(self._logo, enable_ex=False)
if not self._logo:
self._logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)

def run(self):
request_id, push_queue, image_queue = self._msg.get("request_id"), self._push_queue, self._image_queue
orFilePath, aiFilePath, logo = self._context.get("orFilePath"), self._context.get("aiFilePath"), self._logo
or_video_file, ai_video_file, push_p = None, None, None
push_url = self._msg.get("push_url")
try:
logger.info("开始启动推流线程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
while True:
push_parm = push_queue.get()
if push_parm is not None:
# [(1, 原视频帧, 分析视频帧)]
# # [视频帧、当前帧数、 总帧数、 [(问题数组、code、allowedList、label_arraylist、rainbows)]]
# res = (1, (pull_frame[1], pull_frame[2], pull_frame[3], []))
# [(2, 操作指令)]
if push_parm[0] == 1: # 视频帧操作
frame, current_frame, all_frames, ques_list = push_parm[1]
copy_frame = frame.copy()
det_xywh = {}
if len(ques_list) > 0:
for qs in ques_list:
det_xywh[qs[1]] = {}
detect_targets_code = int(qs[0][0])
score = qs[0][-1]
label_array = qs[3][detect_targets_code]
color = qs[4][detect_targets_code]
if not isinstance(qs[0][1], (list, tuple, np.ndarray)):
xc, yc, x2, y2 = int(qs[0][1]), int(qs[0][2]), int(qs[0][3]), int(qs[0][4])
box = [(xc, yc), (x2, yc), (x2, y2), (xc, y2)]
else:
box = qs[0][1]
draw_painting_joint(box, copy_frame, label_array, score, color, "leftTop")
cd = det_xywh[qs[1]].get(detect_targets_code)
if cd is None:
det_xywh[qs[1]][detect_targets_code] = [
[detect_targets_code, box, score, label_array, color]]
else:
det_xywh[qs[1]][detect_targets_code].append(
[detect_targets_code, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
or_write_status, request_id)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath, ai_video_file,
ai_write_status, request_id)
if len(det_xywh) > 0:
put_queue(image_queue, (1, (det_xywh, frame, current_frame, all_frames)))
push_p = push_video_stream(frame_merge, push_p, push_url, p_push_status, request_id)
ai_video_file = write_ai_video_result.result()
or_video_file = write_or_video_result.result()
if push_parm[0] == 2:
if 'stop' == push_parm[1]:
logger.info("停止推流线程, requestId: {}", request_id)
close_all_p(push_p, or_video_file, ai_video_file, request_id)
or_video_file, ai_video_file, push_p = None, None, None
break
except Exception as e:
logger.error("推流线程异常:{}, requestId:{}", format_exc(), request_id)
self.ex = e
finally:
close_all_p(push_p, or_video_file, ai_video_file, request_id)
logger.info("推流线程停止完成！requestId:{}", request_id)


class OffPushStreamThread(Thread):
__slots__ = ('_msg', '_push_queue', '_context', 'ex', '_logo', '_image_queue')

def __init__(self, *args):
super().__init__()
# 传参
self._msg, self._push_queue, self._image_queue, self._context = args
# 自带参数
self.ex = None
self._logo = None
if self._context["video"]["video_add_water"]:
self._logo = self._msg.get("logo_url")
if self._logo:
self._logo = url2Array(self._logo, enable_ex=False)
if not self._logo:
self._logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)

def run(self):
request_id, push_queue, image_queue = self._msg.get("request_id"), self._push_queue, self._image_queue
aiFilePath, logo = self._context.get("aiFilePath"), self._logo
ai_video_file, push_p = None, None
push_url = self._msg.get("push_url")
try:
logger.info("开始启动推流线程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=1) as t:
p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
while True:
push_parm = push_queue.get()
if push_parm is not None:
# [(1, 原视频帧, 分析视频帧)]
# # [视频帧、当前帧数、 总帧数、 [(问题数组、code、allowedList、label_arraylist、rainbows)]]
# res = (1, (pull_frame[1], pull_frame[2], pull_frame[3], []))
# [(2, 操作指令)]
if push_parm[0] == 1: # 视频帧操作
frame, current_frame, all_frames, ques_list = push_parm[1]
copy_frame = frame.copy()
det_xywh = {}
if len(ques_list) > 0:
for qs in ques_list:
det_xywh[qs[1]] = {}
detect_targets_code = int(qs[0][0])
score = qs[0][-1]
label_array = qs[3][detect_targets_code]
color = qs[4][detect_targets_code]
if not isinstance(qs[0][1], (list, tuple, np.ndarray)):
xc, yc, x2, y2 = int(qs[0][1]), int(qs[0][2]), int(qs[0][3]), int(qs[0][4])
box = [(xc, yc), (x2, yc), (x2, y2), (xc, y2)]
else:
box = qs[0][1]
draw_painting_joint(box, copy_frame, label_array, score, color, "leftTop")
cd = det_xywh[qs[1]].get(detect_targets_code)
if cd is None:
det_xywh[qs[1]][detect_targets_code] = [
[detect_targets_code, box, score, label_array, color]]
else:
det_xywh[qs[1]][detect_targets_code].append(
[detect_targets_code, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
frame_merge = video_conjuncing(frame, copy_frame)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath, ai_video_file,
ai_write_status, request_id)
if len(det_xywh) > 0:
put_queue(image_queue, (1, (det_xywh, frame, current_frame, all_frames)))
push_p = push_video_stream(frame_merge, push_p, push_url, p_push_status, request_id)
ai_video_file = write_ai_video_result.result()
if push_parm[0] == 2:
if 'stop' == push_parm[1]:
logger.info("停止推流线程, requestId: {}", request_id)
close_all_p(push_p, None, ai_video_file, request_id)
ai_video_file, push_p = None, None
break
except Exception as e:
logger.error("推流线程异常:{}, requestId:{}", format_exc(), request_id)
self.ex = e
finally:
close_all_p(push_p, None, ai_video_file, request_id)
logger.info("推流线程停止完成！requestId:{}", request_id)

+ 201
- 0
concurrency/PushStreamThread2.py View File

@@ -0,0 +1,201 @@
# -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED
from os.path import join
from threading import Thread
from traceback import format_exc

import cv2
import numpy as np
from loguru import logger
from util.Cv2Utils import write_or_video, write_ai_video, push_video_stream, close_all_p, video_conjuncing
from util.ImageUtils import url2Array, add_water_pic
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue


class OnPushStreamThread2(Thread):
__slots__ = ('_msg', '_push_queue', '_context', 'ex', '_logo', '_image_queue')

def __init__(self, *args):
super().__init__()
# 传参
self._msg, self._push_queue, self._image_queue, self._context = args
# 自带参数
self.ex = None
self._logo = None
if self._context["video"]["video_add_water"]:
self._logo = self._msg.get("logo_url")
if self._logo:
self._logo = url2Array(self._logo, enable_ex=False)
if not self._logo:
self._logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)

def run(self):
request_id, push_queue, image_queue = self._msg.get("request_id"), self._push_queue, self._image_queue
orFilePath, aiFilePath, logo = self._context.get("orFilePath"), self._context.get("aiFilePath"), self._logo
or_video_file, ai_video_file, push_p = None, None, None
push_url = self._msg.get("push_url")
try:
logger.info("开始启动推流线程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
with ThreadPoolExecutor(max_workers=5) as tt:
p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
while True:
push_r = push_queue.get()
if push_r is not None:
# [(1, 原视频帧, 分析视频帧)]
# [(code, retResults[2])]
# [(2, 操作指令)]
if push_r[0] == 1: # 视频帧操作
frame_list, frame_index_list, all_frames = push_r[1]
allowedList, rainbows, label_arrays, font_config = push_r[2]
for i, frame in enumerate(frame_list):
copy_frame = frame.copy()
det_xywh = {}
# 每帧可能存在多模型，多模型问题处理
thread_p = []
for det in push_r[3]:
code, retResults = det
det_xywh[code] = {}
# 如果识别到了检测目标
if len(retResults[i]) > 0:
for qs in retResults[i]:
detect_targets_code = int(qs[6])
if detect_targets_code not in allowedList:
logger.warning("当前检测目标不在检测目标中: {}, requestId: {}", detect_targets_code, request_id)
continue
score = qs[5]
label_array = label_arrays[detect_targets_code]
color = rainbows[detect_targets_code]
if not isinstance(qs[1], (list, tuple, np.ndarray)):
xc, yc, x2, y2 = int(qs[1]), int(qs[2]), int(qs[3]), int(qs[4])
box = [(xc, yc), (x2, yc), (x2, y2), (xc, y2)]
else:
box = qs[1]
# box, img, label_array, score=0.5, color=None, config=None
dp = tt.submit(draw_painting_joint, box, copy_frame, label_array, score,
color, font_config)
thread_p.append(dp)
cd = det_xywh[code].get(detect_targets_code)
if cd is None:
det_xywh[code][detect_targets_code] = [
[detect_targets_code, box, score, label_array, color]]
else:
det_xywh[code][detect_targets_code].append(
[detect_targets_code, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
completed_results = wait(thread_p, timeout=60, return_when=ALL_COMPLETED)
completed_futures = completed_results.done
for r in completed_futures:
if r.exception():
raise r.exception()
frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
or_write_status, request_id)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath, ai_video_file,
ai_write_status, request_id)
if len(det_xywh) > 0:
put_queue(image_queue, (1, (det_xywh, frame, frame_index_list[i], all_frames,
font_config)))
push_p = push_video_stream(frame_merge, push_p, push_url, p_push_status, request_id)
ai_video_file = write_ai_video_result.result()
or_video_file = write_or_video_result.result()
if push_r[0] == 2:
if 'stop' == push_r[1]:
logger.info("停止推流线程, requestId: {}", request_id)
close_all_p(push_p, or_video_file, ai_video_file, request_id)
or_video_file, ai_video_file, push_p = None, None, None
break
except Exception as e:
logger.error("推流线程异常:{}, requestId:{}", format_exc(), request_id)
self.ex = e
finally:
close_all_p(push_p, or_video_file, ai_video_file, request_id)
logger.info("推流线程停止完成！requestId:{}", request_id)


# class OffPushStreamThread(Thread):
# __slots__ = ('_msg', '_push_queue', '_context', 'ex', '_logo', '_image_queue')
#
# def __init__(self, *args):
# super().__init__()
# # 传参
# self._msg, self._push_queue, self._image_queue, self._context = args
# # 自带参数
# self.ex = None
# self._logo = None
# if self._context["video"]["video_add_water"]:
# self._logo = self._msg.get("logo_url")
# if self._logo:
# self._logo = url2Array(self._logo, enable_ex=False)
# if not self._logo:
# self._logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)
#
# def run(self):
# request_id, push_queue, image_queue = self._msg.get("request_id"), self._push_queue, self._image_queue
# aiFilePath, logo = self._context.get("aiFilePath"), self._logo
# ai_video_file, push_p = None, None
# push_url = self._msg.get("push_url")
# try:
# logger.info("开始启动推流线程！requestId:{}", request_id)
# with ThreadPoolExecutor(max_workers=1) as t:
# p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
# while True:
# push_parm = push_queue.get()
# if push_parm is not None:
# # [(1, 原视频帧, 分析视频帧)]
# # # [视频帧、当前帧数、 总帧数、 [(问题数组、code、allowedList、label_arraylist、rainbows)]]
# # res = (1, (pull_frame[1], pull_frame[2], pull_frame[3], []))
# # [(2, 操作指令)]
# if push_parm[0] == 1: # 视频帧操作
# frame, current_frame, all_frames, ques_list = push_parm[1]
# copy_frame = frame.copy()
# det_xywh = {}
# if len(ques_list) > 0:
# for qs in ques_list:
# det_xywh[qs[1]] = {}
# detect_targets_code = int(qs[0][0])
# score = qs[0][-1]
# label_array = qs[3][detect_targets_code]
# color = qs[4][detect_targets_code]
# if not isinstance(qs[0][1], (list, tuple, np.ndarray)):
# xc, yc, x2, y2 = int(qs[0][1]), int(qs[0][2]), int(qs[0][3]), int(qs[0][4])
# box = [(xc, yc), (x2, yc), (x2, y2), (xc, y2)]
# else:
# box = qs[0][1]
# draw_painting_joint(box, copy_frame, label_array, score, color, "leftTop")
# cd = det_xywh[qs[1]].get(detect_targets_code)
# if cd is None:
# det_xywh[qs[1]][detect_targets_code] = [
# [detect_targets_code, box, score, label_array, color]]
# else:
# det_xywh[qs[1]][detect_targets_code].append(
# [detect_targets_code, box, score, label_array, color])
# if logo:
# frame = add_water_pic(frame, logo, request_id)
# copy_frame = add_water_pic(copy_frame, logo, request_id)
# frame_merge = video_conjuncing(frame, copy_frame)
# # 写识别视频到本地
# write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath, ai_video_file,
# ai_write_status, request_id)
# if len(det_xywh) > 0:
# put_queue(image_queue, (1, (det_xywh, frame, current_frame, all_frames)))
# push_p = push_video_stream(frame_merge, push_p, push_url, p_push_status, request_id)
# ai_video_file = write_ai_video_result.result()
# if push_parm[0] == 2:
# if 'stop' == push_parm[1]:
# logger.info("停止推流线程, requestId: {}", request_id)
# close_all_p(push_p, None, ai_video_file, request_id)
# ai_video_file, push_p = None, None
# break
# except Exception as e:
# logger.error("推流线程异常:{}, requestId:{}", format_exc(), request_id)
# self.ex = e
# finally:
# close_all_p(push_p, None, ai_video_file, request_id)
# logger.info("推流线程停止完成！requestId:{}", request_id)

+ 362
- 0
concurrency/PushVideoStreamProcess.py View File

@@ -0,0 +1,362 @@
# -*- coding: utf-8 -*-

from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Process
from os import getpid

from os.path import join
from time import time, sleep
from traceback import format_exc

import cv2
import psutil

from loguru import logger

from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util import ImageUtils
from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push_video_stream, close_all_p
from util.ImageUtils import url2Array, add_water_pic
from util.LogUtils import init_log

from util.PlotsUtils import draw_painting_joint, xywh2xyxy2

from util.QueUtil import get_no_block_queue, put_queue, clear_queue


class PushStreamProcess(Process):
__slots__ = ("_msg", "_push_queue", "_image_queue", '_push_ex_queue', '_hb_queue', "_context")

def __init__(self, *args):
super().__init__()
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args

def build_logo_url(self):
logo = None
if self._context["video"]["video_add_water"]:
logo = self._msg.get("logo_url")
if logo:
logo = url2Array(logo, enable_ex=False)
if logo is None:
logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)
self._context["logo"] = logo

@staticmethod
def handle_image(det_xywh, det, frame_score, copy_frame, draw_config, code_list):
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
draw_painting_joint(box, copy_frame, label_array, score, color, font_config)
if det_xywh.get(code) is None:
det_xywh[code], code_list[code] = {}, {}
cd = det_xywh[code].get(cls)
if cd is None:
code_list[code][cls] = 1
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
code_list[code][cls] += 1
det_xywh[code][cls].append([cls, box, score, label_array, color])


class OnPushStreamProcess(PushStreamProcess):
__slots__ = ()

def run(self):
self.build_logo_url()
msg, context = self._msg, self._context
base_dir, env, orFilePath, aiFilePath, logo, service_timeout, frame_score = context["base_dir"], \
context['env'], context["orFilePath"], context["aiFilePath"], context["logo"], \
int(context["service"]["timeout"]) + 120, context["service"]["filter"]["frame_score"]
request_id, push_url = msg["request_id"], msg["push_url"]
push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
self._hb_queue
or_video_file, ai_video_file, push_p, ex = None, None, None, None
ex_status = True
high_score_image = {}
# 相似度, 默认值0.65
similarity = context["service"]["filter"]["similarity"]
# 图片相似度开关
picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
frame_step = int(context["service"]["filter"]["frame_step"])
try:
init_log(base_dir, env)
logger.info("开始实时启动推流进程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
start_time = time()
while True:
# 检测推流执行超时时间, 1.防止任务运行超时 2.主进程挂了,子进程运行超时
if time() - start_time > service_timeout:
logger.error("推流超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
if psutil.Process(getpid()).ppid() == 1:
logger.info("推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
ex_status = False
for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
clear_queue(q)
break
# 获取推流的视频帧
push_r = get_no_block_queue(push_queue)
if push_r is not None:
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
for i, frame in enumerate(frame_list):
# 复制帧用来画图
copy_frame = frame.copy()
det_xywh, thread_p = {}, []
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
for r in thread_p:
r.result()
frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
or_write_status, request_id)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
ai_video_file, ai_write_status, request_id)
push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status, request_id)
# 如果有问题, 走下面的逻辑
if len(det_xywh) > 0:
flag = True
if picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame)
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity_1 = 1 - dist * 1.0 / 64
if similarity_1 >= similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_index_list[i] - high_score_image.get("current_frame")
if diff_frame_num < frame_step:
flag = False
if flag:
high_score_image["or_frame"] = frame
high_score_image["current_frame"] = frame_index_list[i]
put_queue(image_queue, (1, [det_xywh, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
or_video_file = write_or_video_result.result(timeout=60)
# 接收停止指令
if push_r[0] == 2:
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
if 'stop_ex' == push_r[1]:
ex_status = False
logger.info("停止推流进程, requestId: {}", request_id)
break
del push_r
else:
sleep(1)
except ServiceException as s:
logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
ex = s.code, s.msg
except Exception:
logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
# 关闭推流管, 原视频写对象, 分析视频写对象
close_all_p(push_p, or_video_file, ai_video_file, request_id)
if ex:
code, msg = ex
put_queue(push_ex_queue, (1, code, msg), timeout=2)
else:
if ex_status:
# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
c_time = time()
while time() - c_time < 60:
if image_queue.qsize() == 0 or image_queue.empty():
break
sleep(2)
for q in [push_queue, image_queue, hb_queue]:
clear_queue(q)
logger.info("推流进程停止完成！图片队列大小: {}, requestId:{}", image_queue.qsize(), request_id)


class OffPushStreamProcess(PushStreamProcess):
__slots__ = ()

def run(self):
self.build_logo_url()
msg, context = self._msg, self._context
request_id = msg["request_id"]
base_dir, env = context["base_dir"], context['env']
push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
self._hb_queue
aiFilePath, logo = context["aiFilePath"], context["logo"]
ai_video_file, push_p, push_url = None, None, msg["push_url"]
service_timeout = int(context["service"]["timeout"]) + 120
frame_score = context["service"]["filter"]["frame_score"]
ex = None
ex_status = True
high_score_image = {}
# 相似度, 默认值0.65
similarity = context["service"]["filter"]["similarity"]
# 图片相似度开关
picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
frame_step = int(context["service"]["filter"]["frame_step"])
try:
init_log(base_dir, env)
logger.info("开始启动离线推流进程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
p_push_status, ai_write_status = [0, 0], [0, 0]
start_time = time()
while True:
# 检测推流执行超时时间
if time() - start_time > service_timeout:
logger.error("离线推流超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
if psutil.Process(getpid()).ppid() == 1:
logger.info("离线推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
ex_status = False
for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
clear_queue(q)
break
# 获取推流的视频帧
push_r = get_no_block_queue(push_queue)
if push_r is not None:
# [(1, ...] 视频帧操作
# [(2, 操作指令)] 指令操作
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每一帧图片
for i, frame in enumerate(frame_list):
if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
# 复制帧用来画图
copy_frame = frame.copy()
# 所有问题记录字典
det_xywh, thread_p = {}, []
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
for r in thread_p:
r.result()
frame_merge = video_conjuncing(frame, copy_frame)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
ai_video_file,
ai_write_status, request_id)
push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status, request_id)

if len(det_xywh) > 0:
flag = True
if picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame)
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity_1 = 1 - dist * 1.0 / 64
if similarity_1 >= similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_index_list[i] - high_score_image.get("current_frame")
if diff_frame_num < frame_step:
flag = False
if flag:
high_score_image["or_frame"] = frame
high_score_image["current_frame"] = frame_index_list[i]
put_queue(image_queue, (1, [det_xywh, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
# 接收停止指令
if push_r[0] == 2:
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
if 'stop_ex' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
ex_status = False
break
del push_r
else:
sleep(1)
except ServiceException as s:
logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
ex = s.code, s.msg
except Exception:
logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
# 关闭推流管, 分析视频写对象
close_all_p(push_p, None, ai_video_file, request_id)
if ex:
code, msg = ex
put_queue(push_ex_queue, (1, code, msg), timeout=2)
else:
if ex_status:
# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
c_time = time()
while time() - c_time < 60:
if image_queue.qsize() == 0 or image_queue.empty():
break
sleep(2)
for q in [push_queue, image_queue, hb_queue]:
clear_queue(q)
logger.info("推流进程停止完成！requestId:{}", request_id)

+ 342
- 0
concurrency/PushVideoStreamProcess2.py View File

@@ -0,0 +1,342 @@
# -*- coding: utf-8 -*-

from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Process
from os import getpid

from os.path import join
from time import time, sleep
from traceback import format_exc

import cv2
import psutil

from loguru import logger

from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util import ImageUtils
from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push_video_stream, close_all_p
from util.ImageUtils import url2Array, add_water_pic
from util.LogUtils import init_log

from util.PlotsUtils import draw_painting_joint, xywh2xyxy2

from util.QueUtil import get_no_block_queue, put_queue, clear_queue


class PushStreamProcess2(Process):
__slots__ = ("_msg", "_push_queue", "_image_queue", '_push_ex_queue', '_hb_queue', "_context")

def __init__(self, *args):
super().__init__()
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args

def build_logo_url(self):
logo = None
if self._context["video"]["video_add_water"]:
logo = self._msg.get("logo_url")
if logo:
logo = url2Array(logo, enable_ex=False)
if logo is None:
logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)
self._context["logo"] = logo


class OnPushStreamProcess2(PushStreamProcess2):
__slots__ = ()

def run(self):
msg, context = self._msg, self._context
self.build_logo_url()
request_id = msg["request_id"]
base_dir, env = context["base_dir"], context['env']
push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
self._hb_queue
orFilePath, aiFilePath, logo = context["orFilePath"], context["aiFilePath"], context["logo"]
or_video_file, ai_video_file, push_p, push_url = None, None, None, msg["push_url"]
service_timeout = int(context["service"]["timeout"]) + 120
frame_score = context["service"]["filter"]["frame_score"]
ex = None
ex_status = True
high_score_image = {}
# 相似度, 默认值0.65
similarity = context["service"]["filter"]["similarity"]
# 图片相似度开关
picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
frame_step = int(context["service"]["filter"]["frame_step"])
try:
init_log(base_dir, env)
logger.info("开始启动推流进程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=3) as t:
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
start_time = time()
while True:
# 检测推流执行超时时间
if time() - start_time > service_timeout:
logger.error("推流超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.PUSH_STREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PUSH_STREAM_TIMEOUT_EXCEPTION.value[1])
# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
if psutil.Process(getpid()).ppid() == 1:
ex_status = False
logger.info("推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
clear_queue(q)
break
# 获取推流的视频帧
push_r = get_no_block_queue(push_queue)
if push_r is not None:
# [(1, ...] 视频帧操作
# [(2, 操作指令)] 指令操作
if push_r[0] == 1:
# 如果是多模型push_objs数组可能包含[模型1识别数组, 模型2识别数组, 模型3识别数组]
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每一帧图片
for i, frame in enumerate(frame_list):
# 复制帧用来画图
copy_frame = frame.copy()
# 所有问题记录字典
det_xywh, thread_p = {}, []
# [模型1识别数组, 模型2识别数组, 模型3识别数组]
for s_det_list in push_objs:
code, det_result = s_det_list[0], s_det_list[1][i]
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
for r in thread_p:
r.result()
frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
or_write_status, request_id)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
ai_video_file, ai_write_status, request_id)
push_p_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status,
request_id)
if len(det_xywh) > 0:
flag = True
if picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame)
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity_1 = 1 - dist * 1.0 / 64
if similarity_1 >= similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_index_list[i] - high_score_image.get("current_frame")
if diff_frame_num < frame_step:
flag = False
if flag:
high_score_image["or_frame"] = frame
high_score_image["current_frame"] = frame_index_list[i]
put_queue(image_queue, (1, [det_xywh, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_p_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
or_video_file = write_or_video_result.result(timeout=60)
# 接收停止指令
if push_r[0] == 2:
if 'stop' == push_r[1]:
logger.info("停止推流线程, requestId: {}", request_id)
break
if 'stop_ex' == push_r[1]:
logger.info("停止推流线程, requestId: {}", request_id)
ex_status = False
break
del push_r
else:
sleep(1)
except ServiceException as s:
logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
ex = s.code, s.msg
except Exception:
logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
# 关闭推流管, 原视频写对象, 分析视频写对象
close_all_p(push_p, or_video_file, ai_video_file, request_id)
if ex:
code, msg = ex
put_queue(push_ex_queue, (1, code, msg), timeout=2)
else:
if ex_status:
# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
c_time = time()
while time() - c_time < 60:
if image_queue.qsize() == 0 or image_queue.empty():
break
sleep(2)
for q in [push_queue, image_queue, hb_queue]:
clear_queue(q)
logger.info("推流进程停止完成！requestId:{}", request_id)


class OffPushStreamProcess2(PushStreamProcess2):
__slots__ = ()

def run(self):
self.build_logo_url()
msg, context = self._msg, self._context
request_id = msg["request_id"]
base_dir, env = context["base_dir"], context['env']
push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
self._hb_queue
aiFilePath, logo = context["aiFilePath"], context["logo"]
ai_video_file, push_p, push_url = None, None, msg["push_url"]
service_timeout = int(context["service"]["timeout"]) + 120
frame_score = context["service"]["filter"]["frame_score"]
ex = None
ex_status = True
high_score_image = {}
# 相似度, 默认值0.65
similarity = context["service"]["filter"]["similarity"]
# 图片相似度开关
picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
frame_step = int(context["service"]["filter"]["frame_step"])
try:
init_log(base_dir, env)
logger.info("开始启动离线推流进程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
p_push_status, ai_write_status = [0, 0], [0, 0]
start_time = time()
while True:
# 检测推流执行超时时间
if time() - start_time > service_timeout:
logger.error("离线推流超时, requestId: {}", request_id)
raise ServiceException(ExceptionType.PUSH_STREAM_TIMEOUT_EXCEPTION.value[0],
ExceptionType.PUSH_STREAM_TIMEOUT_EXCEPTION.value[1])
# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
if psutil.Process(getpid()).ppid() == 1:
ex_status = False
logger.info("离线推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
clear_queue(q)
break
# 获取推流的视频帧
push_r = get_no_block_queue(push_queue)
if push_r is not None:
# [(1, ...] 视频帧操作
# [(2, 操作指令)] 指令操作
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每一帧图片
for i, frame in enumerate(frame_list):
if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
# 复制帧用来画图
copy_frame = frame.copy()
# 所有问题记录字典
det_xywh, thread_p = {}, []
for s_det_list in push_objs:
code, det_result = s_det_list[0], s_det_list[1][i]
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
for r in thread_p:
r.result()
frame_merge = video_conjuncing(frame, copy_frame)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
ai_video_file,
ai_write_status, request_id)
push_p_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status,
request_id)
if len(det_xywh) > 0:
flag = True
if picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame)
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity_1 = 1 - dist * 1.0 / 64
if similarity_1 >= similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_index_list[i] - high_score_image.get("current_frame")
if diff_frame_num < frame_step:
flag = False
if flag:
high_score_image["or_frame"] = frame
high_score_image["current_frame"] = frame_index_list[i]
put_queue(image_queue, (1, [det_xywh, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_p_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
# 接收停止指令
if push_r[0] == 2:
if 'stop' == push_r[1]:
logger.info("停止推流线程, requestId: {}", request_id)
break
if 'stop_ex' == push_r[1]:
logger.info("停止推流线程, requestId: {}", request_id)
ex_status = False
break
del push_r
else:
sleep(1)
except ServiceException as s:
logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
ex = s.code, s.msg
except Exception:
logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
finally:
# 关闭推流管, 分析视频写对象
close_all_p(push_p, None, ai_video_file, request_id)
if ex:
code, msg = ex
put_queue(push_ex_queue, (1, code, msg), timeout=2)
else:
if ex_status:
# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
c_time = time()
while time() - c_time < 60:
if image_queue.qsize() == 0 or image_queue.empty():
break
sleep(2)
for q in [push_queue, image_queue, hb_queue]:
clear_queue(q)
logger.info("推流进程停止完成！requestId:{}", request_id)

+ 33
- 34
concurrency/RecordingHeartbeatThread.py View File

@@ -1,51 +1,50 @@
# -*- coding: utf-8 -*-
from threading import Thread
import time
from traceback import format_exc

from loguru import logger

from entity.FeedBack import recording_feedback
from enums.RecordingStatusEnum import RecordingStatus
from util.QueUtil import get_no_block_queue, put_queue, clear_queue


class RecordingHeartbeat(Thread):
def __init__(self, fbQueue, hbQueue, request_id):
super().__init__()
self.fbQueue = fbQueue
self.hbQueue = hbQueue
self.request_id = request_id

def getHbQueue(self):
eBody = None
try:
eBody = self.hbQueue.get(block=False)
except Exception as e:
pass
return eBody
__slots__ = ('_fb_queue', '_hb_queue', '_request_id')

# 推送执行结果
def sendResult(self, result):
self.fbQueue.put(result)

def sendHbQueue(self, result):
self.hbQueue.put(result)

def sendhbMessage(self, statusl):
self.sendResult({"recording": recording_feedback(self.request_id, statusl)})
def __init__(self, fb_queue, hb_queue, request_id):
super().__init__()
self._fb_queue = fb_queue
self._hb_queue = hb_queue
self._request_id = request_id

def run(self):
logger.info("开始启动录屏心跳线程！requestId:{}", self.request_id)
hb_init_num = 0
while True:
try:
request_id = self._request_id
hb_queue, fb_queue = self._hb_queue, self._fb_queue
logger.info("开始启动录屏心跳线程！requestId:{}", request_id)
hb_init_num, progress = 0, '0.0000'
status = RecordingStatus.RECORDING_WAITING.value[0]
try:
while True:
time.sleep(3)
hb_msg = self.getHbQueue()
hb_msg = get_no_block_queue(hb_queue)
if hb_msg is not None and len(hb_msg) > 0:
command = hb_msg.get("command")
if 'stop' == command:
logger.info("开始终止心跳线程, requestId:{}", self.request_id)
command_que = hb_msg.get("command")
progress_que = hb_msg.get("progress")
status_que = hb_msg.get("status")
if progress_que is not None:
progress = progress_que
if status_que is not None:
status = status_que
if 'stop' == command_que:
logger.info("开始终止心跳线程, requestId:{}", request_id)
break
if hb_init_num % 30 == 0:
self.sendhbMessage(RecordingStatus.RECORDING_RUNNING.value[0])
put_queue(fb_queue, recording_feedback(request_id, status, progress=progress), timeout=5, is_ex=True)
hb_init_num += 3
except Exception as e:
logger.exception("心跳线程异常:{}, requestId:{}", e, self.request_id)
logger.info("心跳线程停止完成！requestId:{}", self.request_id)
except Exception:
logger.error("心跳线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
clear_queue(hb_queue)
logger.info("心跳线程停止完成！requestId:{}", request_id)

BIN
concurrency/__pycache__/CommonThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/FeedbackThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/FileUploadThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/HeartbeatThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/IntelligentRecognitionProcess.cpython-38.pyc View File


BIN
concurrency/__pycache__/PullStreamThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/PullVideoStreamProcess.cpython-38.pyc View File


BIN
concurrency/__pycache__/PushStreamThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/PushStreamThread2.cpython-38.pyc View File


BIN
concurrency/__pycache__/RecordingHeartbeatThread.cpython-38.pyc View File


BIN
concurrency/__pycache__/__init__.cpython-38.pyc View File


+ 10
- 0
config/aliyun/dsp_dev_aliyun.yml View File

@@ -0,0 +1,10 @@
access_key: "LTAI5tSJ62TLMUb4SZuf285A"
access_secret: "MWYynm30filZ7x0HqSHlU3pdLVNeI7"
oss:
endpoint: "http://oss-cn-shanghai.aliyuncs.com"
bucket: "ta-tech-image"
connect_timeout: 30
vod:
host_address: "https://vod.play.t-aaron.com/"
ecsRegionId: "cn-shanghai"
cateId: 1000468341

+ 10
- 0
config/aliyun/dsp_prod_aliyun.yml View File

@@ -0,0 +1,10 @@
access_key: "LTAI5tSJ62TLMUb4SZuf285A"
access_secret: "MWYynm30filZ7x0HqSHlU3pdLVNeI7"
oss:
endpoint: "http://oss-cn-shanghai.aliyuncs.com"
bucket: "ta-tech-image"
connect_timeout: 30
vod:
host_address: "https://vod.play.t-aaron.com/"
ecsRegionId: "cn-shanghai"
cateId: 1000468340

+ 11
- 0
config/aliyun/dsp_test_aliyun.yml View File

@@ -0,0 +1,11 @@
access_key: "LTAI5tSJ62TLMUb4SZuf285A"
access_secret: "MWYynm30filZ7x0HqSHlU3pdLVNeI7"
oss:
endpoint: "http://oss-cn-shanghai.aliyuncs.com"
bucket: "ta-tech-image"
connect_timeout: 30
vod:
host_address: "https://vod.play.t-aaron.com/"
ecsRegionId: "cn-shanghai"
cateId: 1000468338


+ 12
- 0
config/baidu/dsp_dev_baidu.yml View File

@@ -0,0 +1,12 @@
orc:
APP_ID: 28173504
API_KEY: "kqrFE7VuygIaFer7z6cRxzoi"
SECRET_KEY: "yp7xBokyl4TItyGhay7skAN1cMwfvEXf"
vehicle:
APP_ID: 31096670
API_KEY: "Dam3O4tgPRN3qh4OYE82dbg7"
SECRET_KEY: "1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa"
person:
APP_ID: 31096755
API_KEY: "CiWrt4iyxOly36n3kR7utiAG"
SECRET_KEY: "K7y6V3XTGdyXvgtCNCwTGUEooxxDuX9v"

+ 12
- 0
config/baidu/dsp_prod_baidu.yml View File

@@ -0,0 +1,12 @@
orc:
APP_ID: 28173504
API_KEY: "kqrFE7VuygIaFer7z6cRxzoi"
SECRET_KEY: "yp7xBokyl4TItyGhay7skAN1cMwfvEXf"
vehicle:
APP_ID: 31096670
API_KEY: "Dam3O4tgPRN3qh4OYE82dbg7"
SECRET_KEY: "1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa"
person:
APP_ID: 31096755
API_KEY: "CiWrt4iyxOly36n3kR7utiAG"
SECRET_KEY: "K7y6V3XTGdyXvgtCNCwTGUEooxxDuX9v"

+ 12
- 0
config/baidu/dsp_test_baidu.yml View File

@@ -0,0 +1,12 @@
orc:
APP_ID: 28173504
API_KEY: "kqrFE7VuygIaFer7z6cRxzoi"
SECRET_KEY: "yp7xBokyl4TItyGhay7skAN1cMwfvEXf"
vehicle:
APP_ID: 31096670
API_KEY: "Dam3O4tgPRN3qh4OYE82dbg7"
SECRET_KEY: "1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa"
person:
APP_ID: 31096755
API_KEY: "CiWrt4iyxOly36n3kR7utiAG"
SECRET_KEY: "K7y6V3XTGdyXvgtCNCwTGUEooxxDuX9v"

+ 0
- 22
config/dsp_aliyun.json View File

@@ -1,22 +0,0 @@
{
"access_key": "LTAI5tSJ62TLMUb4SZuf285A",
"access_secret": "MWYynm30filZ7x0HqSHlU3pdLVNeI7",
"oss": {
"endpoint": "http://oss-cn-shanghai.aliyuncs.com",
"bucket": "ta-tech-image",
"connect_timeout": 30
},
"vod": {
"host_address": "https://vod.play.t-aaron.com/",
"ecsRegionId": "cn-shanghai",
"dev": {
"CateId": 1000468341
},
"test": {
"CateId": 1000468338
},
"prod": {
"CateId": 1000468340
}
}
}

+ 0
- 116
config/dsp_application.json View File

@@ -1,116 +0,0 @@
{
"dsp": {
"active": "dev"
},
"kafka": {
"topic": {
"dsp-alg-online-tasks-topic": "dsp-alg-online-tasks",
"dsp-alg-offline-tasks-topic": "dsp-alg-offline-tasks",
"dsp-alg-image-tasks-topic": "dsp-alg-image-tasks",
"dsp-alg-results-topic": "dsp-alg-task-results",
"dsp-recording-task-topic": "dsp-recording-task",
"dsp-recording-result-topic": "dsp-recording-result"
},
"dev": {
"bootstrap_servers": ["192.168.11.13:9092"],
"dsp-alg-online-tasks": {
"partition": [0]
},
"dsp-alg-offline-tasks": {
"partition": [0]
},
"dsp-alg-task-results": {
"partition": [0]
},
"producer": {
"acks": -1,
"retries": 3,
"linger_ms": 50,
"retry_backoff_ms": 1000,
"max_in_flight_requests_per_connection": 5
},
"consumer": {
"client_id": "dsp_ai_server",
"group_id": "dsp-ai-dev",
"auto_offset_reset": "latest",
"enable_auto_commit": 0,
"max_poll_records": 1
}
},
"test": {
"bootstrap_servers": ["106.14.96.218:19092"],
"dsp-alg-online-tasks": {
"partition": [0]
},
"dsp-alg-offline-tasks": {
"partition": [0]
},
"dsp-alg-task-results": {
"partition": [0]
},
"producer": {
"acks": -1,
"retries": 3,
"linger_ms": 50,
"retry_backoff_ms": 1000,
"max_in_flight_requests_per_connection": 5
},
"consumer": {
"client_id": "dsp_ai_server",
"group_id": "dsp-ai-test",
"auto_offset_reset": "latest",
"enable_auto_commit": 0,
"max_poll_records": 1
}
},
"prod": {
"bootstrap_servers": ["101.132.127.1:19094"],
"dsp-alg-online-tasks": {
"partition": [0]
},
"dsp-alg-offline-tasks": {
"partition": [0]
},
"dsp-alg-task-results": {
"partition": [0]
},
"producer": {
"acks": -1,
"retries": 3,
"linger_ms": 50,
"retry_backoff_ms": 1000,
"max_in_flight_requests_per_connection": 5
},
"consumer": {
"client_id": "dsp_ai_server",
"group_id": "dsp-ai-prod",
"auto_offset_reset": "latest",
"enable_auto_commit": 0,
"max_poll_records": 1
}
}
},
"video": {
"file_path": "../dsp/video/",
"video_add_water": 0
},
"service": {
"frame_score": 0.4,
"filter": {
"picture_similarity": 1,
"similarity": 0.65,
"frame_step": 160
},
"timeout": 21600,
"cv2_pull_stream_timeout": 1000,
"cv2_read_stream_timeout": 1000,
"recording_pull_stream_timeout": 600
},
"model": {
"limit": 3
},
"task": {
"limit": 5
}
}


+ 0
- 17
config/dsp_baidu.json View File

@@ -1,17 +0,0 @@
{
"orc": {
"APP_ID": 28173504,
"API_KEY": "kqrFE7VuygIaFer7z6cRxzoi",
"SECRET_KEY": "yp7xBokyl4TItyGhay7skAN1cMwfvEXf"
},
"vehicle": {
"APP_ID": 31096670,
"API_KEY": "Dam3O4tgPRN3qh4OYE82dbg7",
"SECRET_KEY": "1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa"
},
"person": {
"APP_ID": 31096755,
"API_KEY": "CiWrt4iyxOly36n3kR7utiAG",
"SECRET_KEY": "K7y6V3XTGdyXvgtCNCwTGUEooxxDuX9v"
}
}

+ 0
- 12
config/dsp_logger.json View File

@@ -1,12 +0,0 @@
{
"enable_file_log": 1,
"enable_stderr": 1,
"base_path": "../dsp/logs",
"log_name": "dsp.log",
"log_fmt": "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}",
"level": "INFO",
"rotation": "00:00",
"retention": "7 days",
"encoding": "utf8"
}


+ 25
- 0
config/kafka/dsp_dev_kafka.yml View File

@@ -0,0 +1,25 @@
bootstrap_servers: ["192.168.11.13:9092"]
topic:
dsp-alg-online-tasks-topic: "dsp-alg-online-tasks"
dsp-alg-offline-tasks-topic: "dsp-alg-offline-tasks"
dsp-alg-image-tasks-topic: "dsp-alg-image-tasks"
dsp-alg-results-topic: "dsp-alg-task-results"
dsp-recording-task-topic: "dsp-recording-task"
dsp-recording-result-topic: "dsp-recording-result"
dsp-push-stream-task-topic: "dsp-push-stream-task"
dsp-push-stream-result-topic: "dsp-push-stream-result"
producer:
acks: -1
retries: 3
linger_ms: 50
retry_backoff_ms: 1000
max_in_flight_requests_per_connection: 5
consumer:
client_id: "dsp_ai_server"
group_id: "dsp-ai-dev"
auto_offset_reset: "latest"
enable_auto_commit: false
max_poll_records: 1




+ 22
- 0
config/kafka/dsp_prod_kafka.yml View File

@@ -0,0 +1,22 @@
bootstrap_servers: ["101.132.127.1:19094"]
topic:
dsp-alg-online-tasks-topic: "dsp-alg-online-tasks"
dsp-alg-offline-tasks-topic: "dsp-alg-offline-tasks"
dsp-alg-image-tasks-topic: "dsp-alg-image-tasks"
dsp-alg-results-topic: "dsp-alg-task-results"
dsp-recording-task-topic: "dsp-recording-task"
dsp-recording-result-topic: "dsp-recording-result"
dsp-push-stream-task-topic: "dsp-push-stream-task"
dsp-push-stream-result-topic: "dsp-push-stream-result"
producer:
acks: -1
retries: 3
linger_ms: 50
retry_backoff_ms: 1000
max_in_flight_requests_per_connection: 5
consumer:
client_id: "dsp_ai_server"
group_id: "dsp-ai-prod"
auto_offset_reset: "latest"
enable_auto_commit: false
max_poll_records: 1

+ 24
- 0
config/kafka/dsp_test_kafka.yml View File

@@ -0,0 +1,24 @@
bootstrap_servers: ["106.14.96.218:19092"]
topic:
dsp-alg-online-tasks-topic: "dsp-alg-online-tasks"
dsp-alg-offline-tasks-topic: "dsp-alg-offline-tasks"
dsp-alg-image-tasks-topic: "dsp-alg-image-tasks"
dsp-alg-results-topic: "dsp-alg-task-results"
dsp-recording-task-topic: "dsp-recording-task"
dsp-recording-result-topic: "dsp-recording-result"
dsp-push-stream-task-topic: "dsp-push-stream-task"
dsp-push-stream-result-topic: "dsp-push-stream-result"
producer:
acks: -1
retries: 3
linger_ms: 50
retry_backoff_ms: 1000
max_in_flight_requests_per_connection: 5
consumer:
client_id: "dsp_ai_server"
group_id: "dsp-ai-test"
auto_offset_reset: "latest"
enable_auto_commit: false
max_poll_records: 1



+ 10
- 0
config/logger/dsp_dev_logger.yml View File

@@ -0,0 +1,10 @@
enable_file_log: true
enable_stderr: true
base_path: "../dsp/logs"
log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "1 days"
encoding: "utf8"


+ 10
- 0
config/logger/dsp_prod_logger.yml View File

@@ -0,0 +1,10 @@
enable_file_log: true
enable_stderr: false
base_path: "../dsp/logs"
log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "7 days"
encoding: "utf8"


+ 10
- 0
config/logger/dsp_test_logger.yml View File

@@ -0,0 +1,10 @@
enable_file_log: true
enable_stderr: false
base_path: "../dsp/logs"
log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "3 days"
encoding: "utf8"


+ 30
- 0
config/service/dsp_dev_service.yml View File

@@ -0,0 +1,30 @@
video:
# 视频本地保存地址
file_path: "../dsp/video/"
# 是否添加水印
video_add_water: false
service:
filter:
# 图片得分多少分以上返回图片
frame_score: 0.4
# 图片相似度过滤
picture_similarity: true
similarity: 0.65
frame_step: 160
timeout: 21600
cv2_pull_stream_timeout: 1000
cv2_read_stream_timeout: 1000
recording_pull_stream_timeout: 600
model:
# 使用哪种识别方式
# 1 普通方式
# 2 模型追踪
model_type: 1
limit: 3
task:
# 任务限制5个
limit: 5
image:
limit: 20



+ 29
- 0
config/service/dsp_prod_service.yml View File

@@ -0,0 +1,29 @@
video:
# 视频本地保存地址
file_path: "../dsp/video/"
# 是否添加水印
video_add_water: false
service:
filter:
# 图片得分多少分以上返回图片
frame_score: 0.4
# 图片相似度过滤
picture_similarity: true
similarity: 0.65
frame_step: 160
timeout: 21600
cv2_pull_stream_timeout: 1000
cv2_read_stream_timeout: 1000
recording_pull_stream_timeout: 600
model:
# 使用哪种识别方式
# 1 普通方式
# 2 模型追踪
model_type: 1
limit: 3
task:
# 任务限制5个
limit: 5
image:
limit: 20


+ 29
- 0
config/service/dsp_test_service.yml View File

@@ -0,0 +1,29 @@
video:
# 视频本地保存地址
file_path: "../dsp/video/"
# 是否添加水印
video_add_water: false
service:
filter:
# 图片得分多少分以上返回图片
frame_score: 0.4
# 图片相似度过滤
picture_similarity: true
similarity: 0.65
frame_step: 160
timeout: 21600
cv2_pull_stream_timeout: 1000
cv2_read_stream_timeout: 1000
recording_pull_stream_timeout: 600
model:
# 使用哪种识别方式
# 1 普通方式
# 2 模型追踪
model_type: 1
limit: 3
task:
# 任务限制5个
limit: 5
image:
limit: 20


+ 7
- 5
dsp_master.py View File

@@ -14,11 +14,13 @@ from util.LogUtils import init_log
if __name__ == '__main__':
multiprocessing.set_start_method('spawn')
base_dir = dirname(realpath(__file__))
init_log(base_dir)
logger.info("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】开始启动 ლ(´ڡ`ლ)ﾞ")
# 获取主程序执行根路径
arg = argv
logger.info("脚本启动参数: {}", arg)
print("脚本启动参数: ", arg)
envs = ('dev', 'test', 'prod')
env = envs[0]
active = [env for env in envs if env in arg]
DispatcherService(base_dir, active)
if len(active) != 0:
env = active[0]
init_log(base_dir, env)
logger.info("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】开始启动 ლ(´ڡ`ლ)ﾞ")
DispatcherService(base_dir, env)

+ 51
- 28
entity/FeedBack.py View File

@@ -1,30 +1,53 @@
def message_feedback(requestId, status, type, error_code="", error_msg="", progress="", original_url="", sign_url="",
modelCode="", detectTargetCode="", analyse_time="", analyse_results=""):
taskfb = {}
results = []
result_msg = {}
taskfb["request_id"] = requestId
taskfb["status"] = status
taskfb["type"] = type
taskfb["error_code"] = error_code
taskfb["error_msg"] = error_msg
taskfb["progress"] = progress
result_msg["original_url"] = original_url
result_msg["sign_url"] = sign_url
result_msg["analyse_results"] = analyse_results
result_msg["model_code"] = modelCode
result_msg["detect_targets_code"] = detectTargetCode
result_msg["analyse_time"] = analyse_time
results.append(result_msg)
taskfb["results"] = results
return taskfb
from json import dumps

from util.TimeUtils import now_date_to_str

def recording_feedback(requestId, status, error_code="", error_msg="", recording_video_url=""):
rdfb = {}
rdfb["request_id"] = requestId
rdfb["status"] = status
rdfb["error_code"] = error_code
rdfb["error_msg"] = error_msg
rdfb["recording_video_url"] = recording_video_url
return rdfb

def message_feedback(requestId, status, analyse_type, error_code="", error_msg="", progress="", original_url="",
sign_url="", modelCode="", detectTargetCode="", analyse_results="", video_url="", ai_video_url=""):
if len(analyse_results) > 0:
analyse_results = dumps(analyse_results)
taskbar = {
"request_id": requestId,
"status": status,
"type": analyse_type,
"video_url": video_url,
"ai_video_url": ai_video_url,
"error_code": error_code,
"error_msg": error_msg,
"progress": progress,
"results": [
{
"original_url": original_url,
"sign_url": sign_url,
"analyse_results": analyse_results,
"model_code": modelCode,
"detect_targets_code": detectTargetCode,
"analyse_time": now_date_to_str()
}
]
}
return {"feedback": taskbar}


def recording_feedback(requestId, status, error_code="", error_msg="", progress="", video_url=""):
rdf = {
"request_id": requestId,
"status": status,
"error_code": error_code,
"error_msg": error_msg,
"progress": progress,
"video_url": video_url
}
return {"recording": rdf}


def pull_stream_feedback(requestId, status, error_code="", error_msg="", videoInfo=[]):
return {"pull_stream": {
"request_id": requestId,
"video_info_list": videoInfo,
"push_stream_status": status,
"error_code": error_code,
"error_msg": error_msg,
"current_time": now_date_to_str()
}}

+ 0
- 12
entity/TaskParam.py View File

@@ -1,12 +0,0 @@

class Param:

__slots__ = ('fbqueue', 'msg', 'analyse_type', 'base_dir', 'context', 'gpu_name')

def __init__(self, fbqueue, msg, analyse_type, base_dir, context, gpu_name):
self.fbqueue = fbqueue
self.msg = msg
self.analyse_type = analyse_type
self.base_dir = base_dir
self.context = context
self.gpu_name = gpu_name

BIN
entity/__pycache__/FeedBack.cpython-38.pyc View File


BIN
entity/__pycache__/__init__.cpython-38.pyc View File


+ 3
- 0
enums/AnalysisTypeEnum.py View File

@@ -16,6 +16,9 @@ class AnalysisType(Enum):
# 录屏
RECORDING = "9999"

# 转推流
PULLTOPUSH = "10000"





+ 10
- 2
enums/ExceptionEnum.py View File

@@ -63,9 +63,17 @@ class ExceptionType(Enum):

PUSH_STREAM_EXCEPTION = ("SP028", "推流异常!")

NOT_REQUESTID_TASK_EXCEPTION = ("SP993", "未查询到该任务，无法停止任务！")
MODEL_DUPLICATE_EXCEPTION = ("SP029", "存在重复模型配置!")

DETECTION_TARGET_NOT_SUPPORT = ("SP031", "存在不支持的检测目标!")

TASK_EXCUTE_TIMEOUT = ("SP032", "任务执行超时!")

GPU_EXCEPTION = ("SP994", "GPU出现异常！")
PUSH_STREAM_URL_IS_NULL = ("SP033", "拉流、推流地址不能为空!")

PULL_STREAM_NUM_LIMIT_EXCEPTION = ("SP034", "转推流数量超过限制!")

NOT_REQUESTID_TASK_EXCEPTION = ("SP993", "未查询到该任务，无法停止任务！")

NO_RESOURCES = ("SP995", "服务器暂无资源可以使用，请稍后30秒后再试!")


+ 432
- 18
enums/ModelTypeEnum.py View File

@@ -1,50 +1,452 @@
import sys
from enum import Enum, unique

from common.Constant import COLOR

sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess

'''
参数说明
1. 编号
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
5. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''


@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river')
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})

FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2')
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾"],
'trtFlag_det': True,
'trtFlag_seg': False,
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2')
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})

EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None)
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)

PLATE_MODEL = ("5", "005", "车牌模型", None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)

VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle')
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆"],
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian')
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["行人"],
'seg_nclass': 2,
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire')
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["烟雾", "火焰"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer')
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["钓鱼", "游泳"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad')
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["违法种植"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

SHIP_MODEL = ("11", "011", "船只模型", 'ship2')
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.18,
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
'decoder': None,
'test_flag': True,
"rainbows": COLOR,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'label_array': None,
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
})

BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None)
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)

CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency')
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["人"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})

RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2')
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})

CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement')
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
'seg_nclass': 4, # 分割模型类别数目，默认2类
'segRegionCnt': 2,
'trtFlag_det': True,
'trtFlag_seg': True,
'Detweights': "../AIlib2/weights/cityMangement2/yolov5_%s_fp16.engine" % gpuName,
'segPar': {
'depth_factor': 32,
'NUM_FEATURE_MAP_CHANNEL': 6,
'dmpr_thresh': 0.3,
'dmprimg_size': 640,
'mixFunction': {
'function': dmpr_yolo,
'pars': {'carCls': 0, 'illCls': 3}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': '../AIlib2/weights/cityMangement2/dmpr_%s.engine' % gpuName
})

DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning')
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360)
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})

NOPARKING_MODEL = ("18", "018", "城市违章模型", 'noParking')
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True, ###分割模型预处理参数
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {
'modelSize': (640, 360),
'roundness': 0.3,
'cls': 9,
'laneArea': 10,
'laneAngleCha': 5,
'RoadArea': 16000
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})

ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车", "T角点", "L角点", "违停"],
'trtFlag_seg': False,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'mixFunction': {
'function': illParking_postprocess,
'pars': {}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})

@staticmethod
def checkCode(code):
for model in ModelType:
if model.value[1] == code:
@@ -75,3 +477,15 @@ BAIDU_MODEL_TARGET_CONFIG = {
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}

EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}


# 模型分析方式
@unique
class ModelMethodTypeEnum(Enum):
# 方式一: 正常识别方式
NORMAL = 1

# 方式二: 追踪识别方式
TRACE = 2

+ 688
- 0
enums/ModelTypeEnum2.py View File

@@ -0,0 +1,688 @@
import sys
from enum import Enum, unique

from common.Constant import COLOR

sys.path.extend(['..', '../AIlib2'])
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo

'''
参数说明
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
'''


@unique
class ModelType2(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'detModelpara': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': {
'trtFlag_seg': True,
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225), 'numpy': False,
'RGB_convert_first': True, # 分割模型预处理参数
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'device': device,
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': device,
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "事故"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'trtFlag_seg': True,
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'RoadArea': 16000,
'modelSize': (640, 360),
'vehicleArea': 10,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
})

EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)

PLATE_MODEL = ("5", "005", "车牌模型", None, None)

VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 3
}
})

PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'labelnames': ["行人"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'labelnames': ["烟雾", "火焰"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'labelnames': ["钓鱼", "游泳"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
},
})

COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["违法种植"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'obbModelPar': {
'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"],
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.3,
'down_ratio': 4,
'num_classes': 15,
'dataset': 'dota',
'heads': {
'hm': None,
'wh': 10,
'reg': 2,
'cls_theta': 1
},
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'half': False,
'test_flag': True,
'decoder': None,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'device': "cuda:%s" % device,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'drawPar': {
"rainbows": COLOR,
'digitWordFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'wordSize': 40,
'fontSize': 1.0,
'label_location': 'leftTop'
}
},
'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"]
})

BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)

CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'labelnames': ["人"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'Segweights': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': {
'trtFlag_seg': True,
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225), 'numpy': False,
'RGB_convert_first': True, # 分割模型预处理参数
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})

CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/cityMangement2/yolov5_%s_fp16.engine" % gpuName,
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'trtFlag_seg': True,
'depth_factor': 32,
'NUM_FEATURE_MAP_CHANNEL': 6,
'dmpr_thresh': 0.3,
'dmprimg_size': 640,
'mixFunction': {
'function': dmpr_yolo,
'pars': {'carCls': 0, 'illCls': 3}
}
},
'Segweights': '../AIlib2/weights/cityMangement2/dmpr_%s.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 2
}
})

DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'trtFlag_seg': True,
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360),
}
}
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
})

NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'half': True,
'trtFlag_det': True,
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'trtFlag_seg': True,
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {'modelSize': (640, 360), 'roundness': 0.3, 'cls': 9, 'laneArea': 10, 'laneAngleCha': 5,
'RoadArea': 16000}
}
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 5, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 25, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
}
)

@staticmethod
def checkCode(code):
for model in ModelType2:
if model.value[1] == code:
return True
return False


'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''


@unique
class BaiduModelTarget2(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))

HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))

PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))


BAIDU_MODEL_TARGET_CONFIG2 = {
BaiduModelTarget2.VEHICLE_DETECTION.value[1]: BaiduModelTarget2.VEHICLE_DETECTION,
BaiduModelTarget2.HUMAN_DETECTION.value[1]: BaiduModelTarget2.HUMAN_DETECTION,
BaiduModelTarget2.PEOPLE_COUNTING.value[1]: BaiduModelTarget2.PEOPLE_COUNTING
}

EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}


# 模型分析方式
@unique
class ModelMethodTypeEnum2(Enum):
# 方式一: 正常识别方式
NORMAL = 1

# 方式二: 追踪识别方式
TRACE = 2

+ 6
- 4
enums/RecordingStatusEnum.py View File

@@ -7,10 +7,12 @@ class RecordingStatus(Enum):
RECORDING_WAITING = ("5", "待录制")

RECORDING_RUNNING = ("10", "录制中")
RECORDING_RETRYING = ("10", "重试中")

RECORDING_SUCCESS = ("15", "录制完成")
RECORDING_RUNNING = ("15", "录制中")

RECORDING_TIMEOUT = ("20", "录制超时")
RECORDING_SUCCESS = ("20", "录制完成")

RECORDING_FAILED = ("25", "录制失败")
RECORDING_TIMEOUT = ("25", "录制超时")

RECORDING_FAILED = ("30", "录制失败")

+ 33
- 0
enums/StatusEnum.py View File

@@ -0,0 +1,33 @@
from enum import Enum, unique


@unique
class PushStreamStatus(Enum):
WAITING = (5, "待推流")

RETRYING = (10, "重试中")

RUNNING = (15, "推流中")

STOPPING = (20, "停止中")

SUCCESS = (25, "完成")

TIMEOUT = (30, "超时")

FAILED = (35, "失败")


@unique
class ExecuteStatus(Enum):
WAITING = (5, "待执行")

RUNNING = (10, "执行中")

STOPPING = (15, "停止中")

SUCCESS = (20, "执行完成")

TIMEOUT = (25, "超时")

FAILED = (30, "失败")

BIN
enums/__pycache__/AnalysisStatusEnum.cpython-38.pyc View File


BIN
enums/__pycache__/AnalysisTypeEnum.cpython-38.pyc View File


BIN
enums/__pycache__/ExceptionEnum.cpython-38.pyc View File


BIN
enums/__pycache__/ModelTypeEnum.cpython-38.pyc View File


BIN
enums/__pycache__/RecordingStatusEnum.cpython-38.pyc View File


BIN
enums/__pycache__/__init__.cpython-38.pyc View File


BIN
exception/__pycache__/CustomerException.cpython-38.pyc View File


BIN
exception/__pycache__/__init__.cpython-38.pyc View File


+ 227
- 260
service/Dispatcher.py View File

@@ -1,29 +1,34 @@
# -*- coding: utf-8 -*-
import time
from os.path import join
from traceback import format_exc

from cerberus import Validator
from torch.cuda import is_available

from common.YmlConstant import SCHEMA
from common.Constant import ONLINE_START_SCHEMA, ONLINE_STOP_SCHEMA, OFFLINE_START_SCHEMA, OFFLINE_STOP_SCHEMA, \
IMAGE_SCHEMA, RECORDING_START_SCHEMA, RECORDING_STOP_SCHEMA, PULL2PUSH_START_SCHEMA, PULL2PUSH_STOP_SCHEMA
from common.YmlConstant import service_yml_path, kafka_yml_path
from concurrency.FeedbackThread import FeedbackThread
from entity.FeedBack import message_feedback, recording_feedback
from entity.TaskParam import Param
from concurrency.IntelligentRecognitionProcess2 import OnlineIntelligentRecognitionProcess2, \
OfflineIntelligentRecognitionProcess2, PhotosIntelligentRecognitionProcess2
from concurrency.Pull2PushStreamProcess import PushStreamProcess
from entity.FeedBack import message_feedback, recording_feedback, pull_stream_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
from enums.AnalysisTypeEnum import AnalysisType
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelMethodTypeEnum, ModelType
from enums.RecordingStatusEnum import RecordingStatus
from enums.StatusEnum import PushStreamStatus, ExecuteStatus
from exception.CustomerException import ServiceException
from util import TimeUtils
from loguru import logger
from multiprocessing import Queue
from concurrency.IntelligentRecognitionProcess import OnlineIntelligentRecognitionProcess, \
OfflineIntelligentRecognitionProcess, PhotosIntelligentRecognitionProcess, ScreenRecordingProcess
from util import GPUtils
from util.CpuUtils import check_cpu, print_cpu_ex_status
from util.CpuUtils import print_cpu_ex_status
from util.FileUtils import create_dir_not_exist
from util.GPUtils import get_first_gpu_name, print_gpu_ex_status
from util.GPUtils import get_first_gpu_name, print_gpu_ex_status, check_cude_is_available
from util.KafkaUtils import CustomerKafkaConsumer
from util.QueUtil import put_queue
from util.RWUtils import getConfigs

'''
@@ -32,44 +37,37 @@ from util.RWUtils import getConfigs


class DispatcherService:
__slots__ = (
'__base_dir',
'__context',
'__feedbackThread',
'__listeningProcesses',
'__fbQueue',
'__topics',
'__analysisType',
'__gpu_name',
'__resource_status'
)

"""
初始化
"""

def __init__(self, base_dir, active):
if not is_available():
raise Exception("cuda不在活动状态, 请检测显卡驱动是否正常！！！！")
self.__context = getConfigs(base_dir, 'config/dsp_application.json')
create_dir_not_exist(base_dir, self.__context["video"]["file_path"])
self.__base_dir = base_dir
if len(active) > 0:
self.__context["dsp"]["active"] = active[0]

self.__resource_status = False
self.__feedbackThread = None # 初始化反馈线程对象

__slots__ = ('__context', '__feedbackThread', '__listeningProcesses', '__fbQueue', '__topics', '__task_type',
'__kafka_config', '__recordingProcesses', '__pull2PushProcesses')

def __init__(self, base_dir, env):
# 检测cuda是否活动
check_cude_is_available()
# 获取全局上下文配置
self.__context = getConfigs(join(base_dir, service_yml_path % env))
# 创建任务执行, 视频保存路径
create_dir_not_exist(join(base_dir, self.__context["video"]["file_path"]))
# 将根路径和环境设置到上下文中
self.__context["base_dir"], self.__context["env"] = base_dir, env

# 问题反馈线程
self.__feedbackThread, self.__fbQueue = None, Queue()
# 实时、离线、图片任务进程字典
self.__listeningProcesses = {}
self.__fbQueue = Queue()

# 录屏任务进程字典
self.__recordingProcesses = {}
# 转推流任务进程字典
self.__pull2PushProcesses = {}
self.__kafka_config = getConfigs(join(base_dir, kafka_yml_path % env))
self.__topics = (
self.__context["kafka"]["topic"]["dsp-alg-online-tasks-topic"],
self.__context["kafka"]["topic"]["dsp-alg-offline-tasks-topic"],
self.__context["kafka"]["topic"]["dsp-alg-image-tasks-topic"],
self.__context["kafka"]["topic"]["dsp-recording-task-topic"]
self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"], # 实时监听topic
self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"], # 离线监听topic
self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"], # 图片监听topic
self.__kafka_config["topic"]["dsp-recording-task-topic"], # 录屏监听topic
self.__kafka_config["topic"]["dsp-push-stream-task-topic"] # 推流监听topic
)
self.__analysisType = {
# 对应topic的各个lambda表达式
self.__task_type = {
self.__topics[0]: (AnalysisType.ONLINE.value, lambda x, y: self.online(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[1]: (AnalysisType.OFFLINE.value, lambda x, y: self.offline(x, y),
@@ -77,242 +75,197 @@ class DispatcherService:
self.__topics[2]: (AnalysisType.IMAGE.value, lambda x, y: self.image(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[3]: (AnalysisType.RECORDING.value, lambda x, y: self.recording(x, y),
lambda x, y, z: self.recording_method(x, y, z))
lambda x, y, z: self.recording_method(x, y, z)),
self.__topics[4]: (AnalysisType.PULLTOPUSH.value, lambda x, y: self.pullStream(x, y),
lambda x, y, z: self.push_stream_method(x, y, z))
}

gpu_name_array = get_first_gpu_name()
gpu_codes = ('3090', '2080', '4090', 'A10')
gpu_array = [g for g in gpu_codes if g in gpu_name_array]
self.__gpu_name = '2080Ti'
gpu_array = [g for g in ('3090', '2080', '4090', 'A10') if g in gpu_name_array]
gpu_name = '2080Ti'
if len(gpu_array) > 0:
if gpu_array[0] != '2080':
self.__gpu_name = gpu_array[0]
gpu_name = gpu_array[0]
else:
raise Exception("GPU资源不在提供的模型所支持的范围内！请先提供对应的GPU模型！")
logger.info("当前服务环境为: {}, 服务器GPU使用型号: {}", self.__context["dsp"]["active"], self.__gpu_name)
logger.info("当前服务环境为: {}, 服务器GPU使用型号: {}", env, gpu_name)
self.__context["gpu_name"] = gpu_name
self.start_service()

# 服务调用启动方法
def start_service(self):
# 初始化kafka监听者
customerKafkaConsumer = CustomerKafkaConsumer(self.__context, topics=self.__topics)
customerKafkaConsumer = CustomerKafkaConsumer(self.__kafka_config, topics=self.__topics)
logger.info("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】启动成功 ლ(´ڡ`ლ)ﾞ")
# 循环消息处理
start_time = time.time()
# persistent_time = time.time()
# full_count = 0
while True:
try:
# 检查任务进程运行情况，去除活动的任务
# 检查任务进程运行情况，去除结束的任务
self.check_process_task()
start_time = self.check_service_resource(start_time)
# if len(self.__listeningProcesses) > 0:
# now = time.time()
# requestIds = list(self.__listeningProcesses.keys())
# requestId = requestIds[-1]
# task_process = self.__listeningProcesses.get(requestId)
# end_time = now - task_process.start_proccess_time
# if end_time > 80 and task_process.pullQueue.full() and time.time() - persistent_time < 10:
# full_count += 1
# if full_count > 4:
# logger.error("服务器资源限制, 暂无资源可以使用！ requestId:{}", requestId)
# task_process.sendEvent({"command": "stop_ex"})
# full_count = 0
# persistent_time = time.time()
# if end_time > 80 and task_process.pullQueue.full() and time.time() - persistent_time >= 10:
# full_count = 0
# persistent_time = time.time()
# 启动反馈线程
self.start_feedback_thread()
msg = customerKafkaConsumer.poll()
time.sleep(1)
if msg is not None and len(msg) > 0:
for k, v in msg.items():
for m in v:
message = m.value
customerKafkaConsumer.commit_offset(m)
requestId = self.getRequestId(message.get("request_id"))
requestId = message.get("request_id")
if requestId is None:
logger.error("请求参数格式错误, 请检查请求体格式是否正确！")
continue
customerKafkaConsumer.commit_offset(m, requestId)
logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, requestId)
topic_method = self.__analysisType.get(m.topic)
topic_method[2](m.topic, message, topic_method[0])

topic_method = self.__task_type[m.topic]
topic_method[2](topic_method[1], message, topic_method[0])
else:
print_gpu_ex_status()
print_cpu_ex_status(self.__context["base_dir"])
time.sleep(1)
except Exception:
logger.exception("主线程异常：{}", format_exc())
logger.error("主线程异常：{}", format_exc())

'''
考虑到requestId为空的场景
'''
@staticmethod
def getRequestId(request_id):
if not request_id:
return '1'
return request_id
def identify_method(self, topic, message, analysisType):
"""
实时、离线、图片识别逻辑
1. topic topic
2. 请求消息体
3. 分析类型：实时、离线、图片
"""
def identify_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
s.code, s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message
def push_stream_method(self, handle_method, message, analysisType):
try:
# 校验参数
check_result = self.check_msg(message)
if not check_result:
return
if not is_available():
raise ServiceException(ExceptionType.GPU_EXCEPTION.value[0],
ExceptionType.GPU_EXCEPTION.value[1])
self.__analysisType.get(topic)[1](message, analysisType)
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg,
self.getRequestId(message.get("request_id")))
if message.get("request_id"):
self.__fbQueue.put({
"feedback": message_feedback(message.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
s.code,
s.msg,
analyse_time=TimeUtils.now_date_to_str())}, timeout=10)
logger.error("消息监听异常：{}, requestId: {}", s.msg, message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message['request_id'], ExecuteStatus.FAILED.value[0],
s.code, s.msg, videoInfo), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(),
self.getRequestId(message.get("request_id")))
if message.get("request_id"):
self.__fbQueue.put({
"feedback": message_feedback(message.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1],
analyse_time=TimeUtils.now_date_to_str())}, timeout=10)

def recording_method(self, topic, message, analysisType):
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message.get("request_id"), ExecuteStatus.FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1], videoInfo),
timeout=1)
finally:
del message

def recording_method(self, handle_method, message, analysisType):
try:
# 校验参数
check_result = self.check_msg(message)
if not check_result:
return
self.__analysisType.get(topic)[1](message, analysisType)
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg,
self.getRequestId(message.get("request_id")))
if message.get("request_id"):
self.__fbQueue.put({
"recording": recording_feedback(message.get("request_id"),
RecordingStatus.RECORDING_FAILED.value[0],
error_code=s.code,
error_msg=s.msg)}, timeout=10)
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
error_code=s.code, error_msg=s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(),
self.getRequestId(message.get("request_id")))
if message.get("request_id"):
self.__fbQueue.put({
"recording": recording_feedback(message.get("request_id"),
RecordingStatus.RECORDING_FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])}, timeout=10)
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message

# 开启实时进程
def startOnlineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg.get("request_id")):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
param = Param(self.__fbQueue, msg, analysisType, self.__base_dir, self.__context, self.__gpu_name)
# 创建在线识别进程并启动
coir = OnlineIntelligentRecognitionProcess(param)
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
coir = OnlineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
coir = OnlineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
coir.start()
# 记录请求与进程映射
self.__listeningProcesses[msg.get("request_id")] = coir
self.__listeningProcesses[msg["request_id"]] = coir

# 结束实时进程
def stopOnlineProcess(self, msg, analysisType):
ps = self.__listeningProcesses.get(msg.get("request_id"))
def stopOnlineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg.get("request_id"))
putQueue(self.__fbQueue, {
"feedback": message_feedback(msg.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[0],
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[1],
analyse_time=TimeUtils.now_date_to_str())}, msg.get("request_id"))
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})

def check_service_resource(self, start_time, requestId=None):
if len(self.__listeningProcesses) > 0:
gpu_result = print_gpu_ex_status(requestId)
cpu_result = print_cpu_ex_status(self.__base_dir, requestId)
if gpu_result or cpu_result:
self.__resource_status = True
return time.time()
if not gpu_result and not cpu_result and time.time() - start_time > 30:
self.__resource_status = False
return time.time()
return start_time
@staticmethod
def check_process(listeningProcess):
for requestId in list(listeningProcess.keys()):
if not listeningProcess[requestId].is_alive():
del listeningProcess[requestId]

def check_process_task(self):
for requestId in list(self.__listeningProcesses.keys()):
if not self.__listeningProcesses[requestId].is_alive():
del self.__listeningProcesses[requestId]
self.check_process(self.__listeningProcesses)
self.check_process(self.__recordingProcesses)
self.check_process(self.__pull2PushProcesses)

# 开启离线进程
def startOfflineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg.get("request_id")):
logger.warning("离线重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("离线重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
param = Param(self.__fbQueue, msg, analysisType, self.__base_dir, self.__context, self.__gpu_name)
first = OfflineIntelligentRecognitionProcess(param)
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
first = OfflineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
first = OfflineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
first.start()
self.__listeningProcesses[msg.get("request_id")] = first
self.__listeningProcesses[msg["request_id"]] = first

# 结束离线进程
def stopOfflineProcess(self, msg, analysisType):
ps = self.__listeningProcesses.get(msg.get("request_id"))
def stopOfflineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg.get("request_id"))
putQueue(self.__fbQueue, {
"feedback": message_feedback(msg.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[0],
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[1],
analyse_time=TimeUtils.now_date_to_str())}, msg.get("request_id"))
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})

# 开启图片分析进程
def startImageProcess(self, msg, analysisType):
pp = self.__listeningProcesses.get(msg.get("request_id"))
pp = self.__listeningProcesses.get(msg["request_id"])
if pp is not None:
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
param = Param(self.__fbQueue, msg, analysisType, self.__base_dir, self.__context, self.__gpu_name)
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
imaged = PhotosIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
imaged = PhotosIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
# 创建在线识别进程并启动
imaged = PhotosIntelligentRecognitionProcess(param)
imaged.start()
self.__listeningProcesses[msg.get("request_id")] = imaged
self.__listeningProcesses[msg["request_id"]] = imaged

'''
校验kafka消息
'''

@staticmethod
def check_msg(msg):
def check_msg(msg, schema):
try:
v = Validator(SCHEMA, allow_unknown=True)
v = Validator(schema, allow_unknown=True)
result = v.validate(msg)
if not result:
logger.error("参数校验异常: {}", v.errors)
if msg.get("request_id"):
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0], v.errors)
return result
logger.error("参数校验异常: {}, requestId: {}", v.errors, msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
except ServiceException as s:
raise s
except Exception:
logger.error("参数校验异常: {}", format_exc())
logger.error("参数校验异常: {}, requestId: {}", format_exc(), msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

@@ -322,29 +275,16 @@ class DispatcherService:

def start_feedback_thread(self):
if self.__feedbackThread is None:
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__context)
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
start_time = time.time()
retry_count = 0
while True:
if self.__feedbackThread.is_alive():
break
retry_count += 1
if retry_count > 8:
self.__feedbackThread = None
logger.error("反馈线程异常重试失败！！！！！！")
break
if time.time() - start_time <= 3:
logger.error("反馈线程异常等待中")
time.sleep(1)
continue
logger.error("反馈线程异常重启中")
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__context)
time.sleep(1)
if self.__feedbackThread and not self.__feedbackThread.is_alive():
logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
start_time = time.time()
continue
time.sleep(1)

'''
在线分析逻辑
@@ -352,74 +292,101 @@ class DispatcherService:

def online(self, message, analysisType):
if "start" == message.get("command"):
if self.__resource_status or len(self.__listeningProcesses) >= int(self.__context["task"]["limit"]):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif "stop" == message.get("command"):
self.stopOnlineProcess(message, analysisType)
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
pass
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

def offline(self, message, analysisType):
if "start" == message.get("command"):
if self.__resource_status or len(self.__listeningProcesses) >= int(self.__context["task"]["limit"]):
self.check_msg(message, OFFLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOfflineProcess(message, analysisType)
elif "stop" == message.get("command"):
self.stopOfflineProcess(message, analysisType)
self.check_msg(message, OFFLINE_STOP_SCHEMA)
self.stopOfflineProcess(message)
else:
pass
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

def image(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, IMAGE_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["image"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startImageProcess(message, analysisType)
else:
pass
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

def recording(self, message, analysisType):
if "start" == message.get("command"):
logger.info("开始录屏")
check_cpu(self.__base_dir, message.get("request_id"))
GPUtils.check_gpu_resource(message.get("request_id"))
self.check_msg(message, RECORDING_START_SCHEMA)
if len(self.__recordingProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startRecordingProcess(message, analysisType)
elif "stop" == message.get("command"):
self.stopRecordingProcess(message, analysisType)
self.check_msg(message, RECORDING_STOP_SCHEMA)
self.stopRecordingProcess(message)
else:
pass
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

# 开启录屏进程
def startRecordingProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg.get("request_id")):
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
param = Param(self.__fbQueue, msg, analysisType, self.__base_dir, self.__context, self.__gpu_name)
srp = ScreenRecordingProcess(param)
srp = ScreenRecordingProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__listeningProcesses[msg.get("request_id")] = srp
self.__recordingProcesses[msg["request_id"]] = srp

# 结束录屏进程
def stopRecordingProcess(self, msg, analysisType):
rdp = self.__listeningProcesses.get(msg.get("request_id"))
def stopRecordingProcess(self, msg):
rdp = self.__recordingProcesses.get(msg["request_id"])
if rdp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg.get("request_id"))
putQueue(self.__fbQueue, {
"recording": message_feedback(msg.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[0],
ExceptionType.NOT_REQUESTID_TASK_EXCEPTION.value[1],
analyse_time=TimeUtils.now_date_to_str())}, msg.get("request_id"))
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
rdp.sendEvent({"command": "stop"})

def pullStream(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, PULL2PUSH_START_SCHEMA)
if len(self.__pull2PushProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])

def putQueue(queue, result, requestId, enable_ex=True):
try:
queue.put(result, timeout=10)
except Exception:
logger.error("添加队列超时异常：{}, requestId:{}", format_exc(), requestId)
if enable_ex:
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
self.startPushStreamProcess(message, analysisType)
elif "stop" == message.get("command"):
self.check_msg(message, PULL2PUSH_STOP_SCHEMA)
self.stopPushStreamProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

def startPushStreamProcess(self, msg, analysisType):
if self.__pull2PushProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
srp = PushStreamProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__pull2PushProcesses[msg["request_id"]] = srp

# 结束录屏进程
def stopPushStreamProcess(self, msg):
srp = self.__pull2PushProcesses.get(msg["request_id"])
if srp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
srp.sendEvent({"command": "stop", "videoIds": msg.get("video_ids", [])})

BIN
service/__pycache__/Dispatcher.cpython-38.pyc View File


BIN
service/__pycache__/__init__.cpython-38.pyc View File


+ 40
- 0
start.sh View File

@@ -0,0 +1,40 @@
#!/bin/bash
current_dir=$(cd "$(dirname "$0")"; pwd)
active=$(basename "$(dirname "$current_dir")")
conda_env="alg"
echo "当前程序所在目录: $current_dir, 当前程序启动环境: $active"
if [[ "a${active}" != "adev" && "a${active}" != "atest" && "a${active}" != "aprod" ]]; then
echo "###############################################################";
echo "启动失败, 当前环境只支持dev、test、prod";
echo "环境是根据程序所在目录自动匹配的, 请检测程序路径配置是否正确！";
echo "###############################################################";
exit 1
fi
cd $current_dir
pid=`ps x | grep "/home/th/anaconda3/envs/${conda_env}/bin/python3.8" | grep -v grep | awk '{print $1}'`
if [ -n "$pid" ]; then
echo "alg进程已存在, 进程id: $pid"
kill -9 ${pid};
echo "杀掉当前alg进程, 进程号：$pid"
sleep 1
pid_1=`ps x | grep "/home/th/anaconda3/envs/${conda_env}/bin/python3.8" | grep -v grep | awk '{print $1}'`
if [ -n "$pid_1" ]; then
echo "###############################################################";
echo "杀掉alg进程失败！"
echo "###############################################################";
exit 1
else
echo "杀掉alg进程成功！！"
fi
fi
nohup /home/th/anaconda3/envs/${conda_env}/bin/python3.8 dsp_master.py ${active} > /dev/null 2>&1 &
sleep 1
pid_end=`ps x | grep "/home/th/anaconda3/envs/${conda_env}/bin/python3.8" | grep -v grep | awk '{print $1}'`
if [ -n "$pid_end" ]; then
echo "alg启动成功, $pid_end"
else
echo "###############################################################";
echo "alg启动失败！!!!!!!!!!!!!!!!!!!!!!"
echo "###############################################################";
exit 1
fi

+ 19
- 0
stop.sh View File

@@ -0,0 +1,19 @@
#!/bin/bash
conda_env="alg"
pid=`ps x | grep "/home/th/anaconda3/envs/${conda_env}/bin/python3.8" | grep -v grep | awk '{print $1}'`
if [ -n "$pid" ]; then
kill -9 ${pid};
echo "杀掉当前alg进程, 进程号：$pid"
fi
sleep 1
pid_end=`ps x | grep "/home/th/anaconda3/envs/${conda_env}/bin/python3.8" | grep -v grep | awk '{print $1}'`
if [ -n "$pid_end" ]; then
echo "###############################################################";
echo "alg停止失败！！！！！, $pid_end"
echo "###############################################################";
exit 1
else
echo "###############################################################";
echo "alg停止成功！!!!!!!!!!!!!!!!!!!!!!"
echo "###############################################################";
fi

+ 15
- 0
test/demo/demo3.py View File

@@ -0,0 +1,15 @@
import multiprocessing as mp
import time
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from multiprocessing import Queue, shared_memory

import tensorrt as trt

# multiprocessing.set_start_method('spawn')
Detweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/yolov5_2080Ti_fp16.engine"
with open(Detweights, "rb") as f:
model = f.read()
Segweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/stdc_360X640_2080Ti_fp16.engine"
with open(Segweights, "rb") as f:
segmodel = f.read()
print(type(model), type(segmodel))

+ 32
- 0
test/demo/demo4.py View File

@@ -0,0 +1,32 @@
import multiprocessing as mp
import time
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import Queue, shared_memory

import tensorrt as trt

# multiprocessing.set_start_method('spawn')
Detweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/yolov5_2080Ti_fp16.engine"
start = time.time()
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
print(time.time() - start)
start1 = time.time()
Segweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/stdc_360X640_2080Ti_fp16.engine"
with open(Segweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
segmodel = runtime.deserialize_cuda_engine(f.read())
print(time.time() - start1)

def aa(buf):
print(id(buf[0]), id(buf[1]))
shm = shared_memory.SharedMemory(name='share', create=True, size=10000)
buf = shm.buf
buf = model
buf[1] = segmodel
print(id(model), id(segmodel))
p = mp.Process(target=aa, args=(buf,))
p1 = mp.Process(target=aa, args=(buf,))
p.start()
p1.start()
time.sleep(10)


+ 16
- 0
test/demo/demo5.py View File

@@ -0,0 +1,16 @@
import multiprocessing as mp


def aa1(aa2):
print("1111111111", id(aa2), aa2)


aa = [1, 2, 3, 4, 5, 6]
print(id(aa))
# num = mp.Array('i', aa)
p = mp.Process(target=aa1, args=(aa,))
p1 = mp.Process(target=aa1, args=(aa,))
p2 = mp.Process(target=aa1, args=(aa,))
p.start()
p1.start()
p2.start()

+ 25
- 0
test/demo/demo6.py View File

@@ -0,0 +1,25 @@
import multiprocessing
from multiprocessing import Process, Lock, Queue
import time
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend([r'D:\tuoheng\codenew\update\tuoheng_alg\test\demo\demo6.py'])
# Detweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/yolov5_2080Ti_fp16.engine"
# with open(Detweights, "rb") as f:
# model = f.read()
# Segweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/stdc_360X640_2080Ti_fp16.engine"
# with open(Segweights, "rb") as f:
# segmodel = f.read()
def add_one(aaa):
aaa.put("111111111")
aaa.cancel_join_thread()


if __name__ == '__main__':
aa = Queue()

p1 = Process(target=add_one, args=(aa,))
p1.start()
time.sleep(2)
print(aa.get())



+ 167
- 0
test/demo/demo7.py View File

@@ -0,0 +1,167 @@
import multiprocessing as mp
import sys
import time
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, wait, ALL_COMPLETED
from multiprocessing import Queue

import cv2
import tensorrt as trt
sys.path.extend(['/home/th/tuo_heng/dev/tuoheng_alg', '/home/th/tuo_heng/dev/tuoheng_alg/util'])
from util.PlotsUtils import get_label_arrays
from util.TorchUtils import select_device

sys.path.extend(['/home/th/tuo_heng/', '/home/th/tuo_heng/dev', '/home/th/tuo_heng/dev/AIlib2', '/home/th/tuo_heng/dev/AIlib2/segutils'])
from segutils.segmodel import SegModel
from models.experimental import attempt_load
from AI import AI_process
from utilsK.queRiver import riverDetSegMixProcess

COLOR = (
[0, 0, 255],
[255, 0, 0],
[211, 0, 148],
[0, 127, 0],
[0, 69, 255],
[0, 255, 0],
[255, 0, 255],
[0, 0, 127],
[127, 0, 255],
[255, 129, 0],
[139, 139, 0],
[255, 255, 0],
[127, 255, 0],
[0, 127, 255],
[0, 255, 127],
[255, 127, 255],
[8, 101, 139],
[171, 130, 255],
[139, 112, 74],
[205, 205, 180])
par = {
'device': '0',
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "/home/th/tuo_heng/dev/AIlib2/weights/river/yolov5_2080Ti_fp16.engine",
'Segweights': '/home/th/tuo_heng/dev/AIlib2/weights/river/stdc_360X640_2080Ti_fp16.engine'
}
mode, postPar, segPar = par.get('mode', 'others'), par.get('postPar'), par.get('segPar')
new_device = select_device(par.get('device'))
names = par['labelnames']
half = new_device.type != 'cpu'
Detweights = par['Detweights']
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
Segweights = par['Segweights']
if Segweights:
with open(Segweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
segmodel = runtime.deserialize_cuda_engine(f.read())
else:
segmodel = None
postFile = par['postFile']
rainbows = postFile["rainbows"]
objectPar = {
'half': half,
'device': new_device,
'conf_thres': postFile["conf_thres"],
'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
'iou_thres': postFile["iou_thres"],
'allowedList': [],
'segRegionCnt': par['segRegionCnt'],
'trtFlag_det': par['trtFlag_det'],
'trtFlag_seg': par['trtFlag_seg']
}
Detweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/yolov5_2080Ti_fp16.engine"
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
Segweights = "/home/th/tuo_heng/dev/AIlib2/weights/river2/stdc_360X640_2080Ti_fp16.engine"
with open(Segweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
segmodel = runtime.deserialize_cuda_engine(f.read())


allowedList = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]



def one_label(width, height, model_param):
names = model_param[4]
rainbows = model_param[6]
digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
"""
font_config, frame, model, segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar,
requestId
"""
model_param[5] = label_arraylist
model_param[8] = digitFont
model_param[0] = font_config


def get_label_arraylist(*args):
width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
line = int(width / 1920 * 3 - 1)
label = ' 0.95'
tf = max(line, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
fontsize = int(width / 1920 * 40)
numFontSize = float(format(width / 1920 * 1.1, '.1f'))
digitFont = {'line_thickness': line,
'boxLine_thickness': line,
'fontSize': numFontSize,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': line}
label_arraylist = get_label_arrays(names, rainbows, text_height, fontSize=fontsize,
fontPath="/home/th/tuo_heng/dev/AIlib2/conf/platech.ttf")
return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
image = cv2.imread("/home/th/tuo_heng/dev/ompv2fn94m_1687259193110.jpg")
start_time1 = time.time()
with ThreadPoolExecutor(max_workers=3) as t:
rs = []
for i in range(500):
rr = t.submit(AI_process, [image], model, segmodel, names, None, rainbows, objectPar=objectPar,
font={'line_thickness': 1,
'boxLine_thickness': 1,
'fontSize': 1.1,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 1}, segPar=segPar, mode=mode, postPar=postPar)
rs.append(rr)
for i in rs:
i.result()
print(time.time() - start_time1)

start_time = time.time()
for i in range(500):
AI_process([image], model, segmodel, names, None, rainbows, objectPar=objectPar,
font={'line_thickness': 1,
'boxLine_thickness': 1,
'fontSize': 1.1,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 1}, segPar=segPar, mode=mode, postPar=postPar)
print(time.time() - start_time)

+ 10
- 0
test/demo/demo8.py View File

@@ -0,0 +1,10 @@
import cv2
from PIL import ImageFont

label = ' 0.95'
# fontScale=fontScale, thickness=tf
text_width, text_height = cv2.getTextSize(label, 0, fontScale=1, thickness=1)[0]
print(text_height)
font = ImageFont.truetype("/home/th/tuo_heng/dev/AIlib2/conf/platech.ttf", 22, encoding='utf-8')
x, y, width, height = font.getbbox("植被")
print(x, y, width, height)

+ 837
- 21
test/ffmpeg11/Test.py View File

@@ -1,27 +1,843 @@
import asyncio
import aiohttp
import json
import sys, yaml
from easydict import EasyDict as edict
from concurrent.futures import ThreadPoolExecutor

sys.path.extend(['..','../AIlib2' ])

from AI import AI_process,AI_process_forest,get_postProcess_para,get_postProcess_para_dic,ocr_process,AI_det_track,AI_det_track_batch
import cv2,os,time
from segutils.segmodel import SegModel
from segutils.segmodel import SegModel
from segutils.trafficUtils import tracfficAccidentMixFunction
from models.experimental import attempt_load
from utils.torch_utils import select_device
from utilsK.queRiver import get_labelnames,get_label_arrays,save_problem_images,riverDetSegMixProcess
from ocrUtils.ocrUtils import CTCLabelConverter,AlignCollate
from trackUtils.sort import Sort,track_draw_boxAndTrace,track_draw_trace_boxes,moving_average_wang,drawBoxTraceSimplied
from trackUtils.sort_obb import OBB_Sort,obbTohbb,track_draw_all_boxes,track_draw_trace
from obbUtils.shipUtils import OBB_infer,OBB_tracker,draw_obb,OBB_tracker_batch
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from obbUtils.load_obb_model import load_model_decoder_OBB
import numpy as np
import torch,glob
import tensorrt as trt
from utilsK.masterUtils import get_needed_objectsIndex
from copy import deepcopy
from scipy import interpolate
from utilsK.drownUtils import mixDrowing_water_postprocess
#import warnings
#warnings.filterwarnings("error")

def view_bar(num, total,time1,prefix='prefix'):
rate = num / total
time_n=time.time()
rate_num = int(rate * 30)
rate_nums = np.round(rate * 100)
r = '\r %s %d / %d [%s%s] %.2f s'%(prefix,num,total, ">" * rate_num, " " * (30 - rate_num), time_n-time1 )
sys.stdout.write(r)
sys.stdout.flush()


'''
多线程
'''

def process_v1(frame):
#try:
print('demo.py beging to :',frame[8])
time00 = time.time()
H,W,C = frame[0][0].shape

p_result,timeOut = AI_process(frame[0],frame[1],frame[2],frame[3],frame[4],frame[5],objectPar=frame[6],font=frame[7],segPar=frame[9],mode=frame[10],postPar=frame[11])

time11 = time.time()
image_array = p_result[1]

cv2.imwrite(os.path.join('images/results/',frame[8] ) ,image_array)
bname = frame[8].split('.')[0]
if len(p_result)==5:
image_mask = p_result[4]
cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))

boxes=p_result[2]
with open( os.path.join('images/results/',bname+'.txt' ),'w' ) as fp:
for box in boxes:
box_str=[str(x) for x in box]
out_str=','.join(box_str)+'\n'
fp.write(out_str)
time22 = time.time()
print('%s,%d*%d,AI-process: %.1f,image save:%.1f , %s'%(frame[8],H,W, (time11 - time00) * 1000.0, (time22-time11)*1000.0,timeOut), boxes)
return 'success'
#except Exception as e:
# return 'failed:'+str(e)
def process_video(video,par0,mode='detSeg'):
cap=cv2.VideoCapture(video)
if not cap.isOpened():
print('#####error url:',video)
return False
bname=os.path.basename(video).split('.')[0]
fps = int(cap.get(cv2.CAP_PROP_FPS)+0.5)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH )+0.5)
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)+0.5)
framecnt=int(cap.get(7)+0.5)
save_path_AI = os.path.join(par0['outpth'],os.path.basename(video))
problem_image_dir= os.path.join( par0['outpth'], 'probleImages' )
os.makedirs(problem_image_dir,exist_ok=True)
vid_writer_AI = cv2.VideoWriter(save_path_AI, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width,height))
num=0
iframe=0;post_results=[];fpsample=30*10

imgarray_list = []; iframe_list = []

patch_cnt = par0['trackPar']['patchCnt']
##windowsize 对逐帧插值后的结果做平滑，windowsize为平滑的长度,没隔det_cnt帧做一次跟踪。
trackPar={'det_cnt':10,'windowsize':29 }


##track_det_result_update= np.empty((0,8)) ###每100帧跑出来的结果，放在track_det_result_update，只保留当前100帧里有的tracker Id.
while cap.isOpened():
ret, imgarray = cap.read() #读取摄像头画面
iframe +=1
if not ret:break
if mode=='detSeg':
p_result,timeOut = AI_process([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],objectPar=par0['objectPar'],font=par0['digitFont'],segPar=par0['segPar'])
elif mode == 'track':
#sampleCount=10
imgarray_list.append( imgarray )
iframe_list.append(iframe )
if iframe%patch_cnt==0:
time_patch0 = time.time()
retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
#print('###line111:',retResults[2])
###需要保存成一个二维list，每一个list是一帧检测结果。
###track_det_result 内容格式：x1, y1, x2, y2, conf, cls,iframe,trackId
time_patch2 = time.time()
frame_min = iframe_list[0];frame_max=iframe_list[-1]
for iiframe in range(frame_min,frame_max+1):
img_draw = imgarray_list[ iiframe- frame_min ]
img_draw = drawBoxTraceSimplied(retResults[1] ,iiframe, img_draw,rainbows=par0['drawPar']['rainbows'],boxFlag=True,traceFlag=True,names=par0['drawPar']['names'] )
ret = vid_writer_AI.write(img_draw)
view_bar(iiframe, framecnt,time.time(),prefix=os.path.basename(video))
imgarray_list=[];iframe_list=[]
elif mode =='obbTrack':
imgarray_list.append( imgarray )
iframe_list.append(iframe )
if iframe%patch_cnt==0:
time_patch0 = time.time()

track_det_results, timeInfos = OBB_tracker_batch(imgarray_list,iframe_list,par0['modelPar'],par0['obbModelPar'],par0['sort_tracker'],par0['trackPar'],segPar=None)
print( timeInfos )

#对结果画图
track_det_np = track_det_results[1]
frame_min = iframe_list[0];frame_max=iframe_list[-1]
for iiframe in range(frame_min,frame_max+1):
img_draw = imgarray_list[ iiframe- frame_min ]

if len( track_det_results[2][ iiframe- frame_min]) > 0:
img_draw = draw_obb( track_det_results[2][iiframe- frame_min ] ,img_draw,par0['drawPar'])
if True:
frameIdex=12;trackIdex=13;
boxes_oneFrame = track_det_np[ track_det_np[:,frameIdex]==iiframe ]

###在某一帧上，画上轨迹
track_ids = boxes_oneFrame[:,trackIdex].tolist()
boxes_before_oneFrame = track_det_np[ track_det_np[:,frameIdex]<=iiframe ]
for trackId in track_ids:
boxes_before_oneFrame_oneId = boxes_before_oneFrame[boxes_before_oneFrame[:,trackIdex]==trackId]
xcs = boxes_before_oneFrame_oneId[:,8]
ycs = boxes_before_oneFrame_oneId[:,9]
[cv2.line(img_draw, ( int(xcs[i]) , int(ycs[i]) ),
( int(xcs[i+1]),int(ycs[i+1]) ),(255,0,0), thickness=2)
for i,_ in enumerate(xcs) if i < len(xcs)-1 ]

ret = vid_writer_AI.write(img_draw)

#sys.exit(0)
#print('vide writer ret:',ret)
imgarray_list=[];iframe_list=[]

view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video))

else:
p_result,timeOut = AI_process_forest([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],par0['half'],par0['device'],par0['conf_thres'], par0['iou_thres'],par0['allowedList'],font=par0['digitFont'],trtFlag_det=par0['trtFlag_det'])


if mode not in [ 'track','obbTrack']:
image_array = p_result[1];num+=1
ret = vid_writer_AI.write(image_array)
view_bar(num, framecnt,time.time(),prefix=os.path.basename(video))
##每隔 fpsample帧处理一次，如果有问题就保存图片
if (iframe % fpsample == 0) and (len(post_results)>0) :
parImage=save_problem_images(post_results,iframe,par0['names'],streamName=bname,outImaDir=problem_image_dir,imageTxtFile=False)
post_results=[]

if len(p_result[2] )>0:
post_results.append(p_result)

vid_writer_AI.release();

def det_track_demo(business, videopaths):
'''
跟踪参数说明：
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100}
sort_max_age--跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
sort_min_hits--每隔目标连续出现的次数，超过这个次数才认为是一个目标。
sort_iou_thresh--检测最小的置信度。
det_cnt--每隔几次做一个跟踪和检测，默认10。
windowsize--轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。
patchCnt--每次送入图像的数量，不宜少于100帧。
'''

''' 以下是基于检测和分割的跟踪模型，分割用来修正检测的结果'''
####河道巡检的跟踪模型参数
if opt['business'] == 'river' or opt['business'] == 'river2' :
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
'gpuname':'2080Ti',###显卡名称
'max_workers':1, ###并行线程数
'half':True,
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出

'seg_nclass':2,###分割模型类别数目，默认2类
'segRegionCnt':0,###分割模型结果需要保留的等值线数目

'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,#分割模型预处理参数
'mixFunction':{'function':riverDetSegMixProcess,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}} #分割和检测混合处理的函数
},

'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},

'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json'%( opt['business'] ),###后处理参数文件
'txtFontSize':80,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
#'testImgPath':'images/videos/river',###测试图像的位置
'testImgPath':'images/tt',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置

async def get_video_info(url: str) -> dict:
async with aiohttp.ClientSession() as session:
params = {
'format': 'json',
'url': url
}
async with session.get('rtmp://192.168.10.101:19350/rlive/stream_107?sign=NQe66OXS') as resp:
if resp.status == 200:
text = await resp.text()
return json.loads(text)

return {}
if opt['business'] == 'highWay2':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
'half':True,
'gpuname':'3090',###显卡名称
'max_workers':1, ###并行线程数
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'seg_nclass':3,###分割模型类别数目，默认2类
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
'mixFunction':{'function':tracfficAccidentMixFunction,
'pars':{ 'RoadArea': 16000, 'vehicleArea': 10, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75,'radius': 50 , 'roundness': 1.0, 'cls': 9, 'vehicleFactor': 0.1,'cls':9, 'confThres':0.25,'roadIou':0.6,'vehicleFlag':False,'distanceFlag': False }
}
},

'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'mode':'highWay3.0',
'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
'txtFontSize':20,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':0.5,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
#'testImgPath':'images/trafficAccident/8.png',###测试图像的位置
'testImgPath':'images/noParking/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
if opt['business'] == 'noParking':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
'half':True,
'gpuname':'3090',###显卡名称
'max_workers':1, ###并行线程数
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'seg_nclass':4,###分割模型类别数目，默认2类
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
'mixFunction':{'function':mixNoParking_road_postprocess,
'pars':
#{ 'roundness': 0.3, 'cls': 9, 'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000, }

{'RoadArea': 16000, 'roadVehicleAngle': 15,'radius': 50, 'distanceFlag': False, 'vehicleFlag': False}
}
},



'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'mode':'highWay3.0',
'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
'txtFontSize':20,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
'testImgPath':'images/noParking/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
if opt['business'] == 'drowning':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
'half':True,
'gpuname':'3090',###显卡名称
'max_workers':1, ###并行线程数
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'seg_nclass':2,###分割模型类别数目，默认2类
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
'mixFunction':{'function':mixDrowing_water_postprocess,
'pars':{ }
}
},

'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
'txtFontSize':20,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
'testImgPath':'images/drowning/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']


''' 以下是基于检测的跟踪模型，只有检测没有分割 '''
if opt['business'] == 'forest2':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/forest2/labelnames.json", ###检测类别对照表
'gpuname':opt['gpu'],###显卡名称
'max_workers':1, ###并行线程数
'half':True,
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
#'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###分割模型类别数目，默认2类
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/forest/para.json',###后处理参数文件
'txtFontSize':80,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}

###车辆巡检参数
if opt['business'] == 'vehicle':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/vehicle/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###分割模型类别数目，默认2类
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/vehicle/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'images/videos/vehicle/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
###行人检测模型
if opt['business'] == 'pedestrian':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/pedestrian/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###分割模型类别数目，默认2类
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/pedestrian/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/pedestrian/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
if opt['business'] == 'smogfire':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/smogfire/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###没有分割模型，此处不用
'segRegionCnt':0,###没有分割模型，此处不用
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/smogfire/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/smogfire/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
###钓鱼游泳检测
if opt['business'] == 'AnglerSwimmer':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/AnglerSwimmer/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###没有分割模型，此处不用
'segRegionCnt':0,###没有分割模型，此处不用
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/AnglerSwimmer/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/AnglerSwimmer/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}
###航道应急，做落水人员检测, channelEmergency
if opt['business'] == 'channelEmergency':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/channelEmergency/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
#'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###没有分割模型，此处不用
'segRegionCnt':0,###没有分割模型，此处不用
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/channelEmergency/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/channelEmergency/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}

###乡村路违法种植
if opt['business'] == 'countryRoad':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/countryRoad/labelnames.json", ###检测类别对照表
'gpuname':'2080T',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###没有分割模型，此处不用
'segRegionCnt':0,###没有分割模型，此处不用
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/countryRoad/para.json',###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'../AIdemo2/images/countryRoad/',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}

###城管项目，检测城市垃圾和车辆
if opt['business'] == 'cityMangement':
par={
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
'gpuname':'2080Ti',###显卡名称
'half':True,
'max_workers':1, ###并行线程数
'trtFlag_det':True,###检测模型是否采用TRT
'trtFlag_seg':False,###分割模型是否采用TRT
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'seg_nclass':2,###没有分割模型，此处不用
'segRegionCnt':0,###没有分割模型，此处不用
'segPar':None,###分割模型预处理参数
'Segweights' : None,###分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business']),###后处理参数文件
'txtFontSize':40,###文本字符的大小
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
'testImgPath':'images/cityMangement',###测试图像的位置
'testOutPath':'images/results/',###输出测试图像位置
}



par['trtFlag_det']=True if par['Detweights'].endswith('.engine') else False
if par['Segweights']:
par['segPar']['trtFlag_seg']=True if par['Segweights'].endswith('.engine') else False

##使用森林，道路模型,business 控制['forest','road']
##预先设置的参数
gpuname=par['gpuname']#如果用trt就需要此参数，只能是"3090" "2080Ti"
device_=par['device'] ##选定模型，可选 cpu,'0','1'


device = select_device(device_)
half = device.type != 'cpu' # half precision only supported on CUDA
trtFlag_det=par['trtFlag_det'] ###是否采用TRT模型加速

##以下参数目前不可改
imageW=1080 ####道路模型
digitFont= par['digitFont']


####加载检测模型
if trtFlag_det:
Detweights=par['Detweights']
logger = trt.Logger(trt.Logger.ERROR)
with open(Detweights, "rb") as f, trt.Runtime(logger) as runtime:
model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
print('####load TRT model :%s'%(Detweights))
else:
Detweights=par['Detweights']
model = attempt_load(Detweights, map_location=device) # load FP32 model
if half: model.half()

####加载分割模型
seg_nclass = par['seg_nclass']
segPar=par['segPar']
if par['Segweights']:
if par['segPar']['trtFlag_seg']:
Segweights = par['Segweights']
logger = trt.Logger(trt.Logger.ERROR)
with open(Segweights, "rb") as f, trt.Runtime(logger) as runtime:
segmodel=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
print('############locad seg model trt success: ',Segweights)
else:
Segweights = par['Segweights']
segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
print('############locad seg model pth success:',Segweights)
else:
segmodel=None


trackPar=par['trackPar']
sort_tracker = Sort(max_age=trackPar['sort_max_age'],
min_hits=trackPar['sort_min_hits'],
iou_threshold=trackPar['sort_iou_thresh'])


labelnames = par['labelnames']
postFile= par['postFile']
print( Detweights,labelnames )
conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)

detPostPar = get_postProcess_para_dic(postFile)
conf_thres,iou_thres,classes,rainbows = detPostPar["conf_thres"],detPostPar["iou_thres"],detPostPar["classes"],detPostPar["rainbows"]
if 'ovlap_thres_crossCategory' in detPostPar.keys(): iou2nd=detPostPar['ovlap_thres_crossCategory']
else:iou2nd = None


####模型选择参数用如下：
mode_paras=par['detModelpara']

allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
#slopeIndex = par['slopeIndex']
##只加载检测模型，准备好显示字符

names=get_labelnames(labelnames)
#imageW=4915;###默认是1920，在森林巡检的高清图像中是4920
outfontsize=int(imageW/1920*40);###
label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")




##图像测试和视频
outpth = par['testOutPath']
impth = par['testImgPath']
imgpaths=[]###获取文件里所有的图像
videopaths=videopaths###获取文件里所有的视频
img_postfixs = ['.jpg','.JPG','.PNG','.png'];
vides_postfixs= ['.MP4','.mp4','.avi']
if os.path.isdir(impth):
for postfix in img_postfixs:
imgpaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
for postfix in ['.MP4','.mp4','.avi']:
videopaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
else:
postfix = os.path.splitext(impth)[-1]
if postfix in img_postfixs: imgpaths=[ impth ]
if postfix in vides_postfixs: videopaths = [impth ]




imgpaths.sort()


modelPar={ 'det_Model': model,'seg_Model':segmodel }

processPar={'half':par['half'],'device':device,'conf_thres':conf_thres,'iou_thres':iou_thres,'trtFlag_det':trtFlag_det,'iou2nd':iou2nd}
drawPar={'names':names,'label_arraylist':label_arraylist,'rainbows':rainbows,'font': par['digitFont'],'allowedList':allowedList}

for i in range(len(imgpaths)):
#for i in range(2):
#imgpath = os.path.join(impth, folders[i])
imgpath = imgpaths[i]
bname = os.path.basename(imgpath )
im0s=[cv2.imread(imgpath)]
time00 = time.time()
retResults,timeOut = AI_det_track_batch(im0s, [i] ,modelPar,processPar,sort_tracker ,trackPar,segPar)
#print('###line627:',retResults[2])
#retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
if len(retResults[1])>0:
retResults[0][0] = drawBoxTraceSimplied(retResults[1],i, retResults[0][0],rainbows=rainbows,boxFlag=True,traceFlag=False,names=drawPar['names'])
time11 = time.time()
image_array = retResults[0][0]
'''
返回值retResults[2] --list,其中每一个元素为一个list，表示每一帧的检测结果，每一个结果是由多个list构成，每个list表示一个框，格式为[ cls , x0 ,y0 ,x1 ,y1 ,conf,ifrmae,trackId ]
--etc. retResults[2][j][k]表示第j帧的第k个框。
'''
cv2.imwrite( os.path.join( outpth,bname ) ,image_array )

print('----image:%s, process:%s ( %s ),save:%s'%(bname,(time11-time00) * 1000, timeOut,(time.time() - time11) * 1000) )

##process video

print('##begin to process videos, total %d videos'%( len(videopaths)))
for i,video in enumerate(videopaths):
print('process video%d :%s '%(i,video))
par0={'modelPar':modelPar,'processPar':processPar,'drawPar':drawPar,'outpth':par['testOutPath'], 'sort_tracker':sort_tracker,'trackPar':trackPar,'segPar':segPar}
process_video(video,par0,mode='track')



def OCR_demo2(opt):
from ocrUtils2 import crnn_model
from ocrUtils2.ocrUtils import get_cfg,recognition_ocr,strLabelConverter

if opt['business'] == 'ocr2':
par={
'image_dir':'images/ocr_en',
'outtxt':'images/results',
'weights':'../AIlib2/weights/conf/ocr2/crnn_448X32.pth',

#'weights':'../weights/2080Ti/AIlib2/ocr2/crnn_2080Ti_fp16_448X32.engine',
'device':'cuda:0',
'cfg':'../AIlib2/weights/conf/ocr2/360CC_config.yaml',

'char_file':'../AIlib2/weights/conf/ocr2/chars.txt',
'imgH':32,
'imgW':448,
'workers':1


}
image_dir=par['image_dir']
outtxt=par['outtxt']
workers=par['workers']
weights= par['weights']
device=par['device']

char_file=par['char_file']
imgH=par['imgH']
imgW=par['imgW']
cfg = par['cfg']



config = get_cfg(cfg, char_file)


par['contextFlag']=False
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
if weights.endswith('.pth'):
model = crnn_model.get_crnn(config,weights=weights).to(device)
par['model_mode']='pth'
else:
logger = trt.Logger(trt.Logger.ERROR)
with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
model = runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
print('#####load TRT file:',weights,'success #####')
context = model.create_execution_context()
par['model_mode']='trt';par['contextFlag']=context

converter = strLabelConverter(config.DATASET.ALPHABETS)

img_urls=glob.glob('%s/*.jpg'%( image_dir ))
img_urls.extend( glob.glob('%s/*.png'%( image_dir )) )
cnt=len(img_urls)
print('%s has %d images'%(image_dir ,len(img_urls) ) )
# 准备数据
parList=[]
for i in range(cnt):
img_patch=cv2.imread( img_urls[i] , cv2.IMREAD_GRAYSCALE)
started = time.time()
img = cv2.imread(img_urls[i])
sim_pred = recognition_ocr(config, img, model, converter, device,par=par)
finished = time.time()
print('{0}: elapsed time: {1} prd:{2} '.format( os.path.basename( img_urls[i] ), finished - started, sim_pred ))





def OBB_track_demo(opt):
###倾斜框（OBB）的ship目标检测
'''
par={
'model_size':(608,608), #width,height
'K':100, #Maximum of objects'
'conf_thresh':0.18,##Confidence threshold, 0.1 for general evaluation
'device':"cuda:0",

'down_ratio':4,'num_classes':15,
#'weights':'../AIlib2/weights/conf/ship2/obb_608X608.engine',
'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),
'dataset':'dota',
'test_dir': '/mnt/thsw2/DSP2/videos/obbShips',
'outpth': 'images/results',
'half': False,
'mean':(0.5, 0.5, 0.5),
'std':(1, 1, 1),
'model_size':(608,608),##width,height
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
'decoder':None,
'test_flag':True,
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
'drawBox':True,#####是否画框
'digitWordFont': { 'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'},
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
}
'''
par={

'obbModelPar':{
'model_size':(608,608),'K':100,'conf_thresh':0.3, 'down_ratio':4,'num_classes':15,'dataset':'dota',
'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
'mean':(0.5, 0.5, 0.5),'std':(1, 1, 1), 'half': False,'decoder':None,
'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),

},
'outpth': 'images/results',
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'device':"cuda:0",

#'test_dir': '/mnt/thsw2/DSP2/videos/obbShips/DJI_20230208110806_0001_W_6M.MP4',
'test_dir':'/mnt/thsw2/DSP2/videos/obbShips/freighter2.mp4',

'test_flag':True,
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
'drawBox':True,#####是否画框
'drawPar': { 'digitWordFont' :{'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'}} ,
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
}
#par['model_size'],par['mean'],par['std'],par['half'],par['saveType'],par['heads'],par['labelnames'],par['decoder'],par['down_ratio'],par['drawBox']
#par['rainbows'],par['label_array'],par['digitWordFont']

obbModelPar = par['obbModelPar']
####加载模型
model,decoder2=load_model_decoder_OBB(obbModelPar)
obbModelPar['decoder']=decoder2

names=get_labelnames(par['labelnames']);obbModelPar['labelnames']=names

_,_,_,rainbows=get_postProcess_para(par['postFile']);par['drawPar']['rainbows']=rainbows


label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['drawPar']['digitWordFont']['wordSize'],fontpath="../AIlib2/conf/platech.ttf")
#par['label_array']=label_arraylist

trackPar=par['trackPar']
sort_tracker = OBB_Sort(max_age=trackPar['sort_max_age'],
min_hits=trackPar['sort_min_hits'],
iou_threshold=trackPar['sort_iou_thresh'])


##图像测试和视频
impth = par['test_dir']
img_urls=[]###获取文件里所有的图像
video_urls=[]###获取文件里所有的视频
img_postfixs = ['.jpg','.JPG','.PNG','.png'];
vides_postfixs= ['.MP4','.mp4','.avi']
if os.path.isdir(impth):
for postfix in img_postfixs:
img_urls.extend(glob.glob('%s/*%s'%(impth,postfix )) )
for postfix in ['.MP4','.mp4','.avi']:
video_urls.extend(glob.glob('%s/*%s'%(impth,postfix )) )
else:
postfix = os.path.splitext(impth)[-1]
if postfix in img_postfixs: img_urls=[ impth ]
if postfix in vides_postfixs: video_urls = [impth ]

parIn = {'obbModelPar':obbModelPar,'modelPar':{'obbmodel': model},'sort_tracker':sort_tracker,'outpth':par['outpth'],'trackPar':trackPar,'drawPar':par['drawPar']}
par['drawPar']['label_array']=label_arraylist
for img_url in img_urls:
#print(img_url)
ori_image=cv2.imread(img_url)

#ori_image_list,infos = OBB_infer(model,ori_image,obbModelPar)

ori_image_list,infos = OBB_tracker_batch([ori_image],[0],parIn['modelPar'],parIn['obbModelPar'],None,parIn['trackPar'],None)

ori_image_list[1] = draw_obb(ori_image_list[2] ,ori_image_list[1],par['drawPar'])

imgName = os.path.basename(img_url)
saveFile = os.path.join(par['outpth'], imgName)
ret=cv2.imwrite(saveFile, ori_image_list[1])
if not ret:
print(saveFile, ' not created ')
print( os.path.basename(img_url),':',infos,ori_image_list[2])

###处理视频

async def main():
urls = ['rtmp://192.168.10.101:19350/rlive/stream_107?sign=NQe66OXS'] # 一个RTMP流URL的列表
tasks = [get_video_info(url) for url in urls]
results = await asyncio.gather(*tasks)
for video_url in video_urls:
process_video(video_url, parIn ,mode='obbTrack')

for result in results:
print(result)
if __name__=="__main__":

if __name__ == '__main__':
asyncio.run(main())
#jkm_demo()
#businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2']
businessAll = ['river2']
videopaths = ['/home/th/tuo_heng/dev/DJI_20211229100908_0002_S.mp4']
for busi in businessAll:
print('-'*40,'beg to test:',busi,'-'*40)
opt={'gpu':'2080Ti','business':busi}
if busi in ['ship2']:
OBB_track_demo(opt)
else:
#if opt['business'] in ['river','highWay2','noParking','drowning','']:
det_track_demo(opt, videopaths)

+ 122
- 0
test/ffmpeg11/test1.py View File

@@ -0,0 +1,122 @@
import json
import time
import subprocess as sp
from concurrent.futures import ThreadPoolExecutor
from traceback import format_exc

import ffmpeg
import sys

import numpy as np

"""
获取视频基本信息
"""


def get_video_info(in_file):
try:
probe = ffmpeg.probe(
'https://vod.play.t-aaron.com/customerTrans/edc96ea2115a0723a003730956208134/55547af9-184f0827dae-0004-f90c-f2c-7ec68.mp4')
# format = probe['format']
# size = int(format['size'])/1024/1024
video_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None)
if video_stream is None:
print('No video stream found', file=sys.stderr)
return
width = int(video_stream['width'])
height = int(video_stream['height'])
num_frames = int(video_stream['nb_frames'])
up, down = str(video_stream['r_frame_rate']).split('/')
fps = eval(up) / eval(down)
print("fbs:", fps)
# duration = float(video_stream['duration'])
bit_rate = int(video_stream['bit_rate']) / 1000
print('width: {}'.format(width))
print('height: {}'.format(height))
# print('num_frames: {}'.format(num_frames))
print('bit_rate: {}k'.format(bit_rate))
# print('fps: {}'.format(fps))
# print('size: {}MB'.format(size))
# print('duration: {}'.format(duration))
return video_stream



except Exception as err:
if isinstance(err, ffmpeg._run.Error):
print(err.stderr.decode(encoding='utf-8'))
raise err

def aa(p1, in_bytes):
try:
p1.stdin.write(in_bytes)
except Exception:
print(format_exc())

if __name__ == '__main__':
file_path = 'rtsp://localhost:8554/live'
command = ['ffmpeg',
'-c:v', 'h264_cuvid',
'-i', file_path,
'-f', 'rawvideo',
'-pix_fmt', 'bgr24',
'-an',
'-']
p = sp.Popen(command, stdout=sp.PIPE)
command1 = ['ffmpeg',
'-y',
"-an",
'-f', 'rawvideo',
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-thread_queue_size', '1024',
'-s', "{}x{}".format(1280, 720),
'-i', '-', # 指定输入文件
'-r', str(25),
'-g', str(25),
'-maxrate', '6000k',
'-b:v', '4000k',
'-c:v', 'h264_nvenc', #
'-bufsize', '4000k',
'-pix_fmt', 'yuv420p',
'-preset', 'p6',
'-tune', 'll',
'-f', 'flv',
"rtmp://192.168.10.101:19350/rlive/stream_124?sign=YJ8aBPFp"]
# # 管道配置
p1 = sp.Popen(command1, stdin=sp.PIPE, shell=False)
command2 = ['ffmpeg',
'-y',
"-an",
'-f', 'rawvideo',
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-thread_queue_size', '1024',
'-s', "{}x{}".format(1280, 720),
'-i', '-', # 指定输入文件
'-r', str(25),
'-g', str(25),
'-maxrate', '6000k',
'-b:v', '4000k',
'-c:v', 'h264_nvenc', #
'-bufsize', '4000k',
'-pix_fmt', 'yuv420p',
'-preset', 'p6',
'-tune', 'll',
'-f', 'flv',
"rtmp://192.168.10.101:19350/rlive/stream_125?sign=uMdRHj9R"]
# # 管道配置
p2 = sp.Popen(command1, stdin=sp.PIPE, shell=False)
start1 = time.time()
num = 0
with ThreadPoolExecutor(max_workers=100) as t:
while True:
in_bytes = p.stdout.read(1280 * 720 * 3)
if in_bytes:
img = (np.frombuffer(in_bytes, np.uint8)).reshape((720, 1280, 3))
for i in range(1):
t.submit(aa, p1, in_bytes)
t.submit(aa, p2, in_bytes)
else:
break

+ 80
- 0
test/ffmpeg11/test222.py View File

@@ -0,0 +1,80 @@
from traceback import format_exc

import cv2
import numpy as np
from loguru import logger
import subprocess as sp
from common import Constant
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.Cv2Utils import push_video_stream, clear_pull_p

pull_p = None
push_p =None
p_push_status=[0,0]

def start_pull_p(pull_url, requestId):
try:
command = ['ffmpeg']
# if pull_url.startswith("rtsp://"):
# command.extend(['-timeout', '20000000', '-rtsp_transport', 'tcp'])
# if pull_url.startswith("http") or pull_url.startswith("rtmp"):
# command.extend(['-rw_timeout', '20000000'])
command.extend(['-re',
'-y',
'-an',
# '-hwaccel', 'cuda', cuvid
'-c:v', 'h264_cuvid',
# '-resize', self.wah,
'-i', pull_url,
'-f', 'rawvideo',
# '-pix_fmt', 'bgr24',
'-r', '25',
'-'])
return sp.Popen(command, stdout=sp.PIPE)
except ServiceException as s:
logger.error("构建拉流管道异常: {}, requestId:{}", s.msg, requestId)
raise s
except Exception as e:
logger.error("构建拉流管道异常：{}, requestId:{}", format_exc(), requestId)
raise e

def pull_read_video_stream(pull_p, pull_url, width, height, width_height_3, w_2, h_2, requestId):
result = None
try:
if pull_p is None:
pull_p = start_pull_p(pull_url, requestId)
in_bytes = pull_p.stdout.read(width_height_3)
if in_bytes is not None and len(in_bytes) > 0:
try:
# result = (np.frombuffer(in_bytes, np.uint8).reshape([height * 3 // 2, width, 3]))
# ValueError: cannot reshape array of size 3110400 into shape (1080,1920)
result = (np.frombuffer(in_bytes, np.uint8)).reshape((height, width))
result = cv2.cvtColor(result, cv2.COLOR_YUV2BGR_NV12)
# result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
if result.shape[1] > Constant.width:
result = cv2.resize(result, (result.shape[1] // 2, result.shape[0] // 2), interpolation=cv2.INTER_LINEAR)
except Exception:
logger.error("视频格式异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[0],
ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[1])
except ServiceException as s:
clear_pull_p(pull_p, requestId)
raise s
except Exception:
clear_pull_p(pull_p, requestId)
pull_p = None
width = None
height = None
width_height_3 = None
logger.error("读流异常：{}, requestId:{}", format_exc(), requestId)
return result, pull_p, width, height, width_height_3

while True:
frame, pull_p, width, height, width_height_3 = pull_read_video_stream(pull_p, 'rtmp://live.play.t-aaron.com/live/THSAr', 1920,
1080*3//2, 1920*1080*3//2, 960, 540,
'111')
if frame is not None:
push_p = push_video_stream(frame, push_p, 'rtmp://live.push.t-aaron.com/live/THSAs', p_push_status, '11')
clear_pull_p(pull_p, "11111")
close_all_p(push_p, None, None, "11111")

+ 17
- 13
test/str/test.py View File

@@ -7,16 +7,20 @@
# v["1"] = "5"
#
# print(aaa)
class base(object):
__slots__=('x')
var=8
def __init__(self):
pass

def aa(self):
print("aa")

b=base()
b.x=88
print(b.aa())

# class base(object):
# __slots__=('x')
# var=8
# def __init__(self):
# pass
#
# def aa(self):
# print("aa")
#
# b=base()
# b.x=88
# print(b.aa())
aa = [1,2,3]
a,b,c = aa
print(a)
print(b)
print(c)

+ 51
- 0
test/video.py View File

@@ -0,0 +1,51 @@
import time
from multiprocessing import Queue
from os import getpid

import cv2
import psutil
import requests
from moviepy.editor import VideoFileClip
from pymediainfo import MediaInfo

# aa = time.time()
# try:
# clip = VideoFileClip("rtmp://192.168.10.101:19350/rlive/stream_107?sign=NQe66OXS")
# print("分辨率:", clip.size)
# print("帧率:", clip.fps)
# print("持续时间:", clip.duration)
# except:
# pass
# print(time.time() - aa)
# aa = time.time()
# cap = cv2.VideoCapture("rtmp://192.168.10.101:19350/rlive/stream_123?sign=w6RNKsFF")
# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# total_frames = int(cap.get(7))
# fps = cap.get(cv2.CAP_PROP_FPS)
# duration = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000
# cap.release()
# print("分辨率:", (width, height))
# print("帧率:", fps)
# print("持续时间:", duration)
# print("总帧数:", total_frames)
# print(time.time() - aa)
# aa = Queue()
# for i in range(1000):
# aa.put("1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111"
# "11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111"
# "11111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111")
# aa.cancel_join_thread()
# aa.cancel_join_thread()
# aa.close()
# aa.cancel_join_thread()
# aa.cancel_join_thread()
# aa.close()
# aa.cancel_join_thread()
# aa.cancel_join_thread()
# aa.get()
# aa = time.time()
# psutil.Process(getpid()).ppid()
# print(time.time()-aa)



+ 0
- 0
test/webrtc/__init__.py View File


+ 19
- 0
test/webrtc/test.py View File

@@ -0,0 +1,19 @@
import cv2
import numpy as np
# 创建VideoCapture对象并打开WebRTC视频流
cap = cv2.VideoCapture('webrtc://221.230.150.241:10800/rtc/stream_14')
while True:
print(cap.isOpened())
# 读取视频流的一帧
ret, frame = cap.read()
if ret:
# 在这里进行帧处理
# 例如，可以对每一帧进行图像处理、对象检测等操作
# 显示处理后的帧
cv2.imshow('Frame', frame)
# 按下'q'键退出循环
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放VideoCapture对象和窗口
cap.release()
cv2.destroyAllWindows()

+ 0
- 0
test/字典/字典.py View File


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