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tags/V2.7.3^2
chenyukun 1 year ago
parent
f70cb7aac9
commit
7ceeee93b3
41 changed files with 2828 additions and 2114 deletions
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  1. +3
    -38
      .idea/deployment.xml
  2. +1
    -1
      .idea/misc.xml
  3. +150
    -64
      .idea/workspace.xml
  4. +7
    -5
      common/Constant.py
  5. +4
    -1
      concurrency/FeedbackThread.py
  6. +0
    -108
      concurrency/FileUpdateThread.py
  7. +157
    -0
      concurrency/FileUploadThread.py
  8. +3
    -3
      concurrency/HeartbeatThread.py
  9. +651
    -933
      concurrency/IntelligentRecognitionProcess.py
  10. +15
    -16
      concurrency/PullStreamThread.py
  11. +86
    -103
      concurrency/PullVideoStreamProcess.py
  12. +4
    -2
      dsp_application.yml
  13. +6
    -1
      dsp_master.py
  14. +2
    -3
      entity/FeedBack.py
  15. +22
    -9
      enums/BaiduSdkEnum.py
  16. +25
    -1
      enums/ExceptionEnum.py
  17. +27
    -13
      enums/ModelTypeEnum.py
  18. +5
    -2
      exception/CustomerException.py
  19. +234
    -261
      service/Dispatcher.py
  20. +0
    -0
      test/demo/__init__.py
  21. +223
    -0
      test/demo/demo1.py
  22. +189
    -0
      test/demo/demo2.py
  23. +0
    -5
      test/ffmpeg11/ffmpeg2.py
  24. +139
    -0
      test/validate/Test.py
  25. +0
    -0
      test/validate/__init__.py
  26. +34
    -0
      test/协程/协程笔记.py
  27. +44
    -0
      test/线程/Test.py
  28. +5
    -0
      test/路径/Test.py
  29. +0
    -0
      test/路径/__init__.py
  30. +7
    -0
      test/集合/test.py
  31. +4
    -24
      util/AliyunSdk.py
  32. +156
    -332
      util/Cv2Utils.py
  33. +0
    -1
      util/FileUtils.py
  34. +11
    -0
      util/GPUtils.py
  35. +116
    -33
      util/ImageUtils.py
  36. +59
    -12
      util/ImgBaiduSdk.py
  37. +2
    -2
      util/KafkaUtils.py
  38. +1
    -0
      util/LogUtils.py
  39. +352
    -78
      util/ModelUtils.py
  40. +80
    -57
      util/OcrBaiduSdk.py
  41. +4
    -6
      util/YmlUtils.py

+ 3
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.idea/deployment.xml View File

@@ -1,6 +1,6 @@
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+ 1
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+ 150
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<SUITE FILE_PATH="coverage/tuoheng_alg$ffmpeg13.coverage" NAME="ffmpeg13 覆盖结果" MODIFIED="1675394160900" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/ffmpeg11" />
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</component>
</project>

+ 7
- 5
common/Constant.py View File

@@ -1,11 +1,13 @@
# 配置文件名称
APPLICATION_CONFIG="dsp_application.yml"
APPLICATION_CONFIG = "dsp_application.yml"
# 编码格式
UTF_8="utf-8"

UTF_8 = "utf-8"

# 文件读模式
R='r'
R = 'r'

# 进度100%
success_progess="1.0000"
success_progess = "1.0000"

# 拉流每帧图片缩小宽度大小限制, 大于1400像素缩小一半, 小于1400像素不变
width = 1400

+ 4
- 1
concurrency/FeedbackThread.py View File

@@ -16,6 +16,9 @@ class FeedbackThread(Thread):
self.fbQueue = fbQueue
self.content = content

'''
阻塞获取反馈消息
'''
def getFeedback(self):
return self.fbQueue.get()

@@ -40,4 +43,4 @@ class FeedbackThread(Thread):
time.sleep(1)
except Exception as e:
logger.exception("问题反馈异常：{}, requestId:{}", e, feedback.get("request_id"))
logger.info("问题反馈进程执行完成")
logger.info("问题反馈线程执行完成")

+ 0
- 108
concurrency/FileUpdateThread.py View File

@@ -1,108 +0,0 @@
import asyncio
import time
from threading import Thread
from loguru import logger
import cv2
from util.AliyunSdk import AliyunOssSdk
from util import TimeUtils, ImageUtils
from entity import FeedBack
from enums.AnalysisStatusEnum import AnalysisStatus
import numpy as np
from PIL import Image


class FileUpdate(Thread):
def __init__(self, fbQueue, content, msg, imageQueue, mode_service):
super().__init__()
self.fbQueue = fbQueue
self.content = content
self.imageQueue = imageQueue
self.mode_service = mode_service
self.msg = msg

# # 获取下一个事件
def getImageQueue(self):
eBody = None
try:
eBody = self.imageQueue.get()
except Exception as e:
pass
return eBody

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


def build_image_name(base_dir, time_now, current_frame, last_frame, random_num, mode_type, requestId, image_type):
image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}-{base_dir}" \
"-{requestId}_{image_type}.jpg"
image_name = image_format.format(
base_dir=base_dir,
time_now=time_now,
current_frame=current_frame,
last_frame=last_frame,
random_num=random_num,
mode_type=mode_type,
requestId=requestId,
image_type=image_type)
return image_name


class ImageFileUpdate(FileUpdate):

def run(self):
logger.info("开始启动图片上传线程, requestId:{}", self.msg.get("request_id"))
aliyunOssSdk = AliyunOssSdk(self.content, logger, self.msg.get("request_id"))
aliyunOssSdk.get_oss_bucket()
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
while True:
try:
image_msg = self.getImageQueue()
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':
logger.info("触发文件上传停止指令！")
break
if image_dict is not None and len(image_dict) > 0:
# 图片帧数编码
or_result, or_image = cv2.imencode(".jpg", image_dict.get("or_frame"))
ai_result, ai_image = cv2.imencode(".jpg", image_dict.get("ai_frame"))
# 定义上传图片名称
random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
# 图片名称待后期修改
or_image_name = build_image_name(self.msg.get('results_base_dir'), time_now,
str(image_dict.get("current_frame")),
str(image_dict.get("last_frame")),
random_num,
image_dict.get("mode_service"),
self.msg.get('request_id'), "OR")
ai_image_name = build_image_name(self.msg.get('results_base_dir'), time_now,
str(image_dict.get("current_frame")),
str(image_dict.get("last_frame")),
random_num,
image_dict.get("mode_service"),
self.msg.get('request_id'), "AI")
task = loop.create_task(aliyunOssSdk.upload_file(or_image_name, or_image.tobytes()))
task1 = loop.create_task(aliyunOssSdk.upload_file(ai_image_name, ai_image.tobytes()))
loop.run_until_complete(asyncio.wait([task, task1]))
# 上传原图片
# aliyunOssSdk.upload_file(or_image_name, Image.fromarray(np.uint8(or_image)).tobytes())
# aliyunOssSdk.upload_file(ai_image_name, Image.fromarray(np.uint8(ai_image)).tobytes())
# 发送kafka消息
self.sendResult({"feedback": FeedBack.message_feedback(self.msg.get('request_id'),
AnalysisStatus.RUNNING.value,
self.mode_service, "", "",
image_dict.get("progress"),
or_image_name,
ai_image_name,
image_dict.get("model_type_code"),
image_dict.get("model_detection_code"),
TimeUtils.now_date_to_str())})
except Exception as e:
logger.exception("图片上传异常：{}, requestId:{}", e, self.msg.get("request_id"))
loop.close()
logger.info("结束图片上传线程, requestId:{}", self.msg.get("request_id"))

+ 157
- 0
concurrency/FileUploadThread.py View File

@@ -0,0 +1,157 @@
import asyncio
from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from loguru import logger
import cv2
from util.AliyunSdk import AliyunOssSdk
from util import TimeUtils, ImageUtils
from entity import FeedBack
from enums.AnalysisStatusEnum import AnalysisStatus


class FileUpload(Thread):
def __init__(self, fbQueue, content, msg, imageQueue, analyse_type):
super().__init__()
self.fbQueue = fbQueue
self.content = content
self.imageQueue = imageQueue
self.analyse_type = analyse_type
self.msg = msg
self.similarity = self.content["service"]["filter"]["similarity"]
self.picture_similarity = self.content["service"]["filter"]["picture_similarity"]
self.frame_score = float(self.content["service"]["frame_score"])
self.frame_step = int(self.content["service"]["filter"]["frame_step"])

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

def build_image_name(self, current_frame, last_frame, mode_type, image_type):
image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}-{base_dir}" \
"-{requestId}_{image_type}.jpg"
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=self.msg.get('results_base_dir'),
time_now=time_now,
current_frame=current_frame,
last_frame=last_frame,
random_num=random_num,
mode_type=mode_type,
requestId=self.msg.get('request_id'),
image_type=image_type)
return image_name


'''
图片上传线程
'''


class ImageFileUpload(FileUpload):

def handle_image(self, high_score_image, frame_all):
flag = True
if self.picture_similarity and len(high_score_image) > 0:
hash1 = ImageUtils.dHash(high_score_image.get("or_frame"))
hash2 = ImageUtils.dHash(frame_all.get("frame"))
dist = ImageUtils.Hamming_distance(hash1, hash2)
similarity = 1 - dist * 1.0 / 64
if similarity >= self.similarity:
flag = False
if len(high_score_image) > 0:
diff_frame_num = frame_all.get("cct_frame") - high_score_image.get("current_frame")
if diff_frame_num < self.frame_step:
flag = False
det_result = frame_all.get("det_xywh")
if flag and det_result is not None and len(det_result) > 0:
model_info = []
# 封装当前图片信息
num = 0
for c in list(det_result.keys()):
mode_code_info = {"code": c, "target": []}
det_xywh = det_result.get(c)
det_num = 0
if det_xywh is not None and len(det_xywh) > 0:
# [float(cls_c), xc,yc,w,h, float(conf_c),code]
for d in det_xywh:
score = d[5] # 得分
target = str(int(d[0])) # 检测目标
mode_code_info["target"].append(target)
if score < self.frame_score:
det_num += 1
model_info.append(mode_code_info)
if det_num != len(det_xywh):
num += 1
if num == 0:
return None
if len(model_info) > 0:
high_score_image["or_frame"] = frame_all.get("frame")
high_score_image["current_frame"] = frame_all.get("cct_frame")
image_result = {
"or_frame": frame_all.get("frame"),
"ai_frame": frame_all.get("ai_frame"),
"current_frame": frame_all.get("cct_frame"),
"last_frame": frame_all.get("cct_frame") + self.frame_step,
"progress": "",
"mode_service": self.analyse_type,
"model_info": model_info
}
return image_result
return None

def run(self):
logger.info("启动图片上传线程, requestId:{}", self.msg.get("request_id"))
# 初始化oss客户端
aliyunOssSdk = AliyunOssSdk(self.content, logger, self.msg.get("request_id"))
aliyunOssSdk.get_oss_bucket()
high_score_image = {}
with ThreadPoolExecutor(max_workers=2) as t:
try:
while True:
try:
# 获取队列中的消息
image_msg = self.imageQueue.get()
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_resut = self.handle_image(high_score_image, image_dict)
if image_resut is not None:
# 图片帧数编码
or_result, or_image = cv2.imencode(".jpg", image_resut.get("or_frame"))
ai_result, ai_image = cv2.imencode(".jpg", image_resut.get("ai_frame"))
# 图片名称待后期修改
or_image_name = self.build_image_name(str(image_resut.get("current_frame")),
str(image_resut.get("last_frame")),
image_resut.get("mode_service"),
"OR")
ai_image_name = self.build_image_name(str(image_resut.get("current_frame")),
str(image_resut.get("last_frame")),
image_resut.get("mode_service"),
"AI")
or_future = t.submit(aliyunOssSdk.sync_upload_file, or_image_name,
or_image.tobytes())
ai_future = t.submit(aliyunOssSdk.sync_upload_file, ai_image_name,
ai_image.tobytes())
or_future.result()
ai_future.result()
# 发送kafka消息
self.sendResult({"feedback": FeedBack.message_feedback(self.msg.get('request_id'),
AnalysisStatus.RUNNING.value,
self.analyse_type, "", "",
image_resut.get("progress"),
or_image_name,
ai_image_name,
image_resut.get(
"model_info"),
TimeUtils.now_date_to_str())})
except Exception as e:
logger.exception("图片上传异常：{}, requestId:{}", e, self.msg.get("request_id"))
finally:
high_score_image.clear()
t.shutdown(wait=True) # wait=True等待所有线程完成

logger.info("停止图片上传线程, requestId:{}", self.msg.get("request_id"))

+ 3
- 3
concurrency/HeartbeatThread.py View File

@@ -9,12 +9,12 @@ from entity.FeedBack import message_feedback


class Heartbeat(Thread):
def __init__(self, fbQueue, hbQueue, request_id, mode_service):
def __init__(self, fbQueue, hbQueue, request_id, analyse_type):
super().__init__()
self.fbQueue = fbQueue
self.hbQueue = hbQueue
self.request_id = request_id
self.mode_service = mode_service
self.analyse_type = analyse_type
self.progress = "0.0000"

def getHbQueue(self):
@@ -35,7 +35,7 @@ class Heartbeat(Thread):
def sendhbMessage(self, analysisStatus):
self.sendResult({"feedback": message_feedback(self.request_id,
analysisStatus,
self.mode_service,
self.analyse_type,
progress=self.progress,
analyse_time=TimeUtils.now_date_to_str())})


+ 651
- 933
concurrency/IntelligentRecognitionProcess.py
File diff suppressed because it is too large
View File


+ 15
- 16
concurrency/PullStreamThread.py View File

@@ -3,23 +3,24 @@ import time
from queue import Queue
from threading import Thread

import GPUtil
from loguru import logger

from entity.FeedBack import recording_feedback
from enums.ExceptionEnum import ExceptionType
from enums.RecordingStatusEnum import RecordingStatus
from exception.CustomerException import ServiceException
from util import GPUtils
from util.Cv2Utils import Cv2Util


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

@@ -44,14 +45,15 @@ class RecordingPullStreamThread(PullStreamThread):
cv2tool = None
try:
logger.info("录屏任务, 开启拉流, requestId:{}", self.msg.get("request_id"))
cv2tool = Cv2Util(self.msg.get('pull_url'), requestId=self.msg.get("request_id"), content=self.content, gpu_ids=self.gpu_ids)
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)
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()
cv2tool.recording_pull_p()
while True:
body = self.getCommand()
if body is not None and len(body) > 0:
@@ -60,17 +62,11 @@ class RecordingPullStreamThread(PullStreamThread):
self.sendPullQueue({"status": "2"})
break
if self.pullQueue.full():
time.sleep(0.05)
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"))
# 如果没有拉倒流, 每30秒发送一次提示消息，告诉用户拉流地址无法拉取视频流
if cv2_init_num % 60 == 0:
self.fbQueue.put({"recording": recording_feedback(self.msg.get("request_id"),
RecordingStatus.RECORDING_RUNNING.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[1])})
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,
@@ -87,16 +83,19 @@ class RecordingPullStreamThread(PullStreamThread):
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 init_pull_num % 60 == 0:
self.fbQueue.put({"recording": recording_feedback(self.msg.get("request_id"),
RecordingStatus.RECORDING_RUNNING.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[0],
ExceptionType.OR_VIDEO_ADDRESS_EXCEPTION.value[1])})
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"})
break
init_pull_num += 1
time.sleep(0.5)
cv2tool.recording_pull_p()

+ 86
- 103
concurrency/PullVideoStreamProcess.py View File

@@ -4,9 +4,8 @@ from multiprocessing import Process, Queue

from loguru import logger

from concurrency.FileUpdateThread import ImageFileUpdate
from concurrency.FileUploadThread import ImageFileUpload
from concurrency.HeartbeatThread import Heartbeat
from enums.AnalysisTypeEnum import AnalysisType
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util import LogUtils
@@ -14,7 +13,7 @@ from util.Cv2Utils import Cv2Util


class PullVideoStreamProcess(Process):
def __init__(self, msg, content, pullQueue, fbQueue, hbQueue, imageQueue):
def __init__(self, msg, content, pullQueue, fbQueue, hbQueue, imageQueue, analyse_type):
super().__init__()
self.command = Queue()
self.msg = msg
@@ -23,12 +22,11 @@ class PullVideoStreamProcess(Process):
self.fbQueue = fbQueue
self.hbQueue = hbQueue
self.imageQueue = imageQueue
self.step = int(self.content["service"]["frame_step"])
self.analyse_type = analyse_type
self.pull_stream_timeout = int(self.content["service"]["cv2_pull_stream_timeout"])
self.read_stream_timeout = int(self.content["service"]["cv2_read_stream_timeout"])
self.service_timeout = int(self.content["service"]["timeout"])


def getCommand(self):
eBody = None
try:
@@ -46,80 +44,87 @@ class PullVideoStreamProcess(Process):
def sendImageResult(self, result):
self.imageQueue.put(result)

def start_File_upload(self):
imageFileUpload = ImageFileUpload(self.fbQueue, self.content, self.msg, self.imageQueue, self.analyse_type)
imageFileUpload.setDaemon(True)
imageFileUpload.start()
return imageFileUpload

def start_heartbeat(self):
hb = Heartbeat(self.fbQueue, self.hbQueue, self.msg.get("request_id"), self.analyse_type)
hb.setDaemon(True)
hb.start()
return hb

def check(self, start_time, imageFileUpload, hb):
create_task_time = time.time() - start_time
if create_task_time > self.service_timeout:
logger.error("分析超时, 超时时间：{}, requestId: {}", create_task_time, self.msg.get("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 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, reuqestId:{}", self.msg.get("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!")
# 检测心跳线程是否正常运行
if 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, reuqestId:{}", self.msg.get("request_id"))
raise Exception("未检测到心跳线程活动，心跳线程可能出现异常!")


class OnlinePullVideoStreamProcess(PullVideoStreamProcess):

def run(self):
LogUtils.init_log(self.content)
cv2tool = None
imageFileUpdate = None
imageFileUpload = None
hb = None
try:
imageFileUpdate = ImageFileUpdate(self.fbQueue, self.content, self.msg, self.imageQueue, AnalysisType.ONLINE.value)
imageFileUpdate.setDaemon(True)
imageFileUpdate.start()
hb = Heartbeat(self.fbQueue, self.hbQueue, self.msg.get("request_id"), AnalysisType.ONLINE.value)
hb.setDaemon(True)
hb.start()
logger.info("开启视频拉流线程, requestId:{}", self.msg.get("request_id"))
# 加载日志框架
LogUtils.init_log(self.content)
logger.info("开启视频拉流进程, requestId:{}", self.msg.get("request_id"))
# 开启图片上传线程
imageFileUpload = self.start_File_upload()
# 开启心跳线程
hb = self.start_heartbeat()
# 初始化拉流工具类
cv2tool = Cv2Util(self.msg.get('pull_url'), requestId=self.msg.get("request_id"))
cv2_init_num = 1
init_pull_num = 1
start_time = time.time()
start_time_1 = time.time()
start_time_2 = time.time()
pull_stream_start_time = time.time()
pull_stream_read_start_time = time.time()
concurrent_frame = 1
stop_imageFile = False
stop_pull_stream_step = False
while True:
end_time = time.time()
create_task_time = int(end_time - start_time)
# 检测任务执行是否超时
if create_task_time > self.service_timeout:
logger.error("分析超时, 超时时间：{}, requestId: {}", create_task_time, self.msg.get("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 not imageFileUpdate.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, reuqestId:{}", self.msg.get("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!")
# 检测心跳线程是否正常运行
if 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, reuqestId:{}", self.msg.get("request_id"))
raise Exception("未检测到心跳线程活动，心跳线程可能出现异常!")
body = self.getCommand()
if body is not None and len(body) > 0:
if 'stop_pull_stream' == body.get("command"):
# 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
self.check(start_time, imageFileUpload, hb)
# 获取指令信息
command = self.getCommand()
if command is not None and len(command) > 0:
# 停止拉流
if 'stop_pull_stream' == command.get("command"):
self.sendPullQueue({"status": "9"}) # 9 停止拉流
stop_imageFile = True
stop_pull_stream_step = True
cv2tool.close()
continue
if 'stop_image' == body.get("command"):
time.sleep(5)
# 停止图片上传线程
if 'stop_image_hb' == command.get("command"):
self.sendImageResult({"command": "stop"})
self.hbQueue.put({"command": "stop"})
imageFileUpdate.join(60*3)
hb.join(60*3)
imageFileUpload.join(60 * 3)
hb.join(60 * 3)
logger.error("图片线程停止完成, reuqestId:{}", self.msg.get("request_id"))
break
if 'stop_ex' == body.get("command"):
time.sleep(5)
self.sendImageResult({"command": "stop"})
self.hbQueue.put({"command": "stop"})
imageFileUpdate.join(60*3)
hb.join(60*3)
# self.pullQueue.cancel_join_thread()
logger.error("拉流、图片线程停止完成, reuqestId:{}", self.msg.get("request_id"))
break
if stop_imageFile:
if stop_pull_stream_step:
time.sleep(1)
continue
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() or cv2tool.pull_p is None:
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_1
pull_stream_init_timeout = time.time() - pull_stream_start_time
if pull_stream_init_timeout > self.pull_stream_timeout:
logger.info("开始拉流超时, 超时时间：{}, requestId:{}", pull_stream_init_timeout,
self.msg.get("request_id"))
@@ -128,27 +133,25 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
cv2_init_num += 1
time.sleep(0.5)
cv2tool.get_video_info()
cv2tool.build_pull_p()
continue
start_time_1 = time.time()
pull_stream_start_time = time.time()
cv2_init_num = 1
frame = cv2tool.read()
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
pull_stream_read_timeout = time.time() - pull_stream_read_start_time
if pull_stream_read_timeout > self.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,
self.msg.get("request_id"))
self.sendPullQueue({"status": "3"}) # 3 超时
stop_imageFile = True
stop_pull_stream_step = True
cv2tool.close()
continue
init_pull_num += 1
time.sleep(0.1)
cv2tool.build_pull_p()
continue
init_pull_num = 1
start_time_2 = time.time()
pull_stream_read_start_time = time.time()
self.sendPullQueue({"status": "4",
"frame": frame,
"cct_frame": concurrent_frame,
@@ -166,86 +169,66 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
finally:
if cv2tool:
cv2tool.close()
if imageFileUpdate:
if imageFileUpload:
self.sendImageResult({"command": "stop"})
imageFileUpdate.join(60*3)
imageFileUpload.join(60 * 3)
if hb:
self.hbQueue.put({"command": "stop"})
hb.join(60*3)
hb.join(60 * 3)
logger.info("实时拉流线程结束, requestId: {}", self.msg.get("request_id"))


class OfflinePullVideoStreamProcess(PullVideoStreamProcess):

def run(self):
LogUtils.init_log(self.content)
cv2tool = None
imageFileUpdate = None
imageFileUpload = None
hb = None
try:
imageFileUpdate = ImageFileUpdate(self.fbQueue, self.content, self.msg, self.imageQueue, AnalysisType.OFFLINE.value)
imageFileUpdate.setDaemon(True)
imageFileUpdate.start()
hb = Heartbeat(self.fbQueue, self.hbQueue, self.msg.get("request_id"), AnalysisType.OFFLINE.value)
hb.setDaemon(True)
hb.start()
# 初始化日志
LogUtils.init_log(self.content)
# 开启图片上传线程
imageFileUpload = self.start_File_upload()
# 开启心跳线程
hb = self.start_heartbeat()
cv2tool = Cv2Util(pullUrl=self.msg.get('original_url'), requestId=self.msg.get("request_id"))
cv2_init_num = 1
start_time = time.time()
cv2tool.get_video_info()
concurrent_frame = 1
stop_imageFile = False
stop_pull_stream_step = False
while True:
end_time = time.time()
create_task_time = int(end_time - start_time)
if create_task_time > self.service_timeout:
logger.error("分析超时, 超时时间：{}, requestId: {}", create_task_time, self.msg.get("request_id"))
raise ServiceException(ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[0],
ExceptionType.ANALYSE_TIMEOUT_EXCEPTION.value[1])
if not imageFileUpdate.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, reuqestId:{}", self.msg.get("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!")
if 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, reuqestId:{}", self.msg.get("request_id"))
raise Exception("未检测到心跳线程活动，心跳线程可能出现异常!")
self.check(start_time, imageFileUpload, hb)
body = self.getCommand()
if body is not None and len(body) > 0:
if 'stop_pull_stream' == body.get("command"):
self.sendPullQueue({"status": "9"}) # 9 停止拉流
stop_imageFile = True
stop_pull_stream_step = True
cv2tool.close()
continue
if 'stop_image' == body.get("command"):
if 'stop_image_hb' == body.get("command"):
self.sendImageResult({"command": "stop"})
self.hbQueue.put({"command": "stop"})
imageFileUpdate.join(60*3)
hb.join(60*3)
imageFileUpload.join(60 * 3)
hb.join(60 * 3)
logger.info("图片线程停止完成, reuqestId:{}", self.msg.get("request_id"))
break
if 'stop_ex' == body.get("command"):
self.sendImageResult({"command": "stop"})
self.hbQueue.put({"command": "stop"})
imageFileUpdate.join(60*3)
hb.join(60*3)
# self.pullQueue.cancel_join_thread()
logger.info("图片线程停止完成, reuqestId:{}", self.msg.get("request_id"))
break
if stop_imageFile:
if stop_pull_stream_step:
time.sleep(1)
continue
if self.pullQueue.full():
time.sleep(0.1)
continue
if cv2tool.checkconfig() or cv2tool.pull_p is None:
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"))
if cv2_init_num > 3:
logger.info("视频信息获取失败, 重试: {}次, requestId: {}", cv2_init_num, self.msg.get("request_id"))
logger.info("视频信息获取失败, 重试: {}次, requestId: {}", cv2_init_num,
self.msg.get("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)
cv2tool.get_video_info()
cv2tool.build_pull_p()
continue
frame = cv2tool.read()
if frame is None:
@@ -255,13 +238,13 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
if concurrent_frame < cv2tool.all_frames - 100:
logger.info("离线拉流异常结束：requestId: {}", self.msg.get("request_id"))
self.sendPullQueue({"status": "3"})
stop_imageFile = True
stop_pull_stream_step = True
cv2tool.close()
continue
logger.info("离线拉流线程结束, requestId: {}", self.msg.get("request_id"))
self.sendPullQueue({"status": "2"})
cv2tool.close()
stop_imageFile = True
stop_pull_stream_step = True
continue
self.sendPullQueue({"status": "4",
"frame": frame,
@@ -280,10 +263,10 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
finally:
if cv2tool is not None:
cv2tool.close()
if imageFileUpdate:
if imageFileUpload:
self.sendImageResult({"command": "stop"})
imageFileUpdate.join(60*3)
imageFileUpload.join(60 * 3)
if hb:
self.hbQueue.put({"command": "stop"})
hb.join(60*3)
hb.join(60 * 3)
logger.info("离线拉流线程结束, requestId: {}", self.msg.get("request_id"))

+ 4
- 2
dsp_application.yml View File

@@ -126,13 +126,13 @@ aliyun:
prod:
CateId: 1000468340
service:
frame_step: 300 # 多少帧数步长之间获取一次分析图片
frame_score: 0.4 # 获取最低得分以上的图片
filter:
# 识别相似度是否开启
picture_similarity: True
# 相似度阀值
similarity: 0.65
frame_step: 80
timeout: 21600 # 一次识别任务超时时间，单位秒，默认6个小时
cv2_pull_stream_timeout: 3600 # 直播开始视频未推流超时时间
cv2_read_stream_timeout: 1800 # 直播读流中超时时间
@@ -153,7 +153,9 @@ baidu:
APP_ID: 31096755
API_KEY: CiWrt4iyxOly36n3kR7utiAG
SECRET_KEY: K7y6V3XTGdyXvgtCNCwTGUEooxxDuX9v

# 模型相关配置
model:
limit: 5 # 模型组合个数限制
# 日志设置
log:
# 是否开启文件输出 True：开启 False：关闭

+ 6
- 1
dsp_master.py View File

@@ -1,4 +1,7 @@
# -*- coding: utf-8 -*-
import os
import sys

from service import Dispatcher
# import torch

@@ -7,5 +10,7 @@ from service import Dispatcher
'''
if __name__ == '__main__':
print("(♥◠‿◠)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
base_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
# torch.multiprocessing.set_start_method('spawn')
Dispatcher.DispatcherService().start_service()
Dispatcher.DispatcherService(base_dir).start_service()

+ 2
- 3
entity/FeedBack.py View File

@@ -1,5 +1,5 @@
def message_feedback(requestId, status, type, error_code="", error_msg="", progress="", original_url="", sign_url="",
model_type_code="", model_detection_code='', analyse_time="", analyse_results=""):
model_info=[], analyse_time="", analyse_results=""):
taskfb = {}
results = []
result_msg = {}
@@ -12,8 +12,7 @@ def message_feedback(requestId, status, type, error_code="", error_msg="", progr
result_msg["original_url"] = original_url
result_msg["sign_url"] = sign_url
result_msg["analyse_results"] = analyse_results
result_msg["model_type_code"] = model_type_code
result_msg["model_detection_code"] = model_detection_code
result_msg["model_info"] = model_info
result_msg["analyse_time"] = analyse_time
results.append(result_msg)
taskfb["results"] = results

+ 22
- 9
enums/BaiduSdkEnum.py View File

@@ -1,6 +1,7 @@
from enum import Enum, unique

'''
ocr官方文档: https://ai.baidu.com/ai-doc/OCR/zkibizyhz
官方文档: https://ai.baidu.com/ai-doc/VEHICLE/rk3inf9tj
参数1: 异常编号
参数2: 异常英文描述
@@ -28,25 +29,25 @@ class BaiduSdkErrorEnum(Enum):

GET_SERVICE_TOKEN_FAILED = (13, "Get service token failed", "获取token失败", 0, 2)

IAM_CERTIFICATION_FAILED = (14, "IAM Certification failed", "IAM 鉴权失败", 0, 2)
IAM_CERTIFICATION_FAILED = (14, "IAM Certification failed", "IAM 鉴权失败", 0, 1)

APP_NOT_EXSITS_OR_CREATE_FAILED = (15, "app not exsits or create failed", "应用不存在或者创建失败", 0, 0)

API_DAILY_REQUEST_LIMIT_REACHED = (17, "Open api daily request limit reached", "每天请求量超限额!", 1, 5)
API_DAILY_REQUEST_LIMIT_REACHED = (17, "Open api daily request limit reached", "每天请求量超限额!", 1, 2)

API_QPS_REQUEST_LIMIT_REACHED = (18, "Open api qps request limit reached", "QPS超限额!", 1, 5)
API_QPS_REQUEST_LIMIT_REACHED = (18, "Open api qps request limit reached", "QPS超限额!", 1, 10)

API_TOTAL_REQUEST_LIMIT_REACHED = (19, "Open api total request limit reached", "请求总量超限额!", 1, 2)

INVALID_TOKEN = (100, "Invalid parameter", "无效的access_token参数，token拉取失败", 0, 2)
INVALID_TOKEN = (100, "Invalid parameter", "无效的access_token参数，token拉取失败", 0, 1)

ACCESS_TOKEN_INVALID_OR_NO_LONGER_VALID = (110, "Access token invalid or no longer valid", "access_token无效，token有效期为30天", 0, 2)
ACCESS_TOKEN_INVALID_OR_NO_LONGER_VALID = (110, "Access token invalid or no longer valid", "access_token无效，token有效期为30天", 0, 1)

ACCESS_TOKEN_EXPIRED = (111, "Access token expired", "access token过期，token有效期为30天", 0, 2)
ACCESS_TOKEN_EXPIRED = (111, "Access token expired", "access token过期，token有效期为30天", 0, 1)

INTERNAL_ERROR = (282000, "internal error", "服务器内部错误", 0, 1)

INVALID_PARAM = (216100, "invalid param", "请求中包含非法参数!", 0, 0)
INVALID_PARAM = (216100, "invalid param", "请求中包含非法参数!", 0, 1)

NOT_ENOUGH_PARAM = (216101, "not enough param", "缺少必须的参数!", 0, 0)

@@ -64,9 +65,9 @@ class BaiduSdkErrorEnum(Enum):

IMAGE_SIZE_BASE_ERROR = (216203, "image size error", "上传的图片编码有误", 1, 0)

RECOGNIZE_ERROR = (216630, "recognize error", "识别错误", 2, 3)
RECOGNIZE_ERROR = (216630, "recognize error", "识别错误", 2, 2)

DETECT_ERROR = (216634, "detect error", "检测错误", 2, 3)
DETECT_ERROR = (216634, "detect error", "检测错误", 2, 2)

MISSING_PARAMETERS = (282003, "missing parameters: {参数名}", "请求参数缺失", 0, 0)

@@ -82,6 +83,14 @@ class BaiduSdkErrorEnum(Enum):

TARGET_RECOGNIZE_ERROR = (282103, "target recognize error", "图片目标识别错误!", 2, 1)

URLS_NOT_EXIT = (282110, "urls not exit", "URL参数不存在，请核对URL后再次提交!", 1, 0)

URL_FORMAT_ILLEGAL = (282111, "url format illegal", "URL格式非法!", 1, 0)

URL_DOWNLOAD_TIMEOUT = (282112, "url download timeout", "URL格式非法!", 1, 0)

URL_RESPONSE_INVALID = (282113, "url response invalid", "URL返回无效参数!", 1, 0)

URL_SIZE_ERROR = (282114, "url size error", "URL长度超过1024字节或为0!", 1, 0)

REQUEST_ID_NOT_EXIST = (282808, "request id: xxxxx not exist", "request id xxxxx 不存在", 0, 0)
@@ -153,6 +162,10 @@ BAIDUERRORDATA = {
BaiduSdkErrorEnum.SDK_READ_IMAGE_FILE_ERROR.value[0]: BaiduSdkErrorEnum.SDK_READ_IMAGE_FILE_ERROR,
BaiduSdkErrorEnum.SDK_CONNECTION_OR_READ_DATA_TIME_OUT.value[0]: BaiduSdkErrorEnum.SDK_CONNECTION_OR_READ_DATA_TIME_OUT,
BaiduSdkErrorEnum.SDK_UNSUPPORTED_IMAGE_FORMAT.value[0]: BaiduSdkErrorEnum.SDK_UNSUPPORTED_IMAGE_FORMAT,
BaiduSdkErrorEnum.URLS_NOT_EXIT.value[0]: BaiduSdkErrorEnum.URLS_NOT_EXIT,
BaiduSdkErrorEnum.URL_FORMAT_ILLEGAL.value[0]: BaiduSdkErrorEnum.URL_FORMAT_ILLEGAL,
BaiduSdkErrorEnum.URL_DOWNLOAD_TIMEOUT.value[0]: BaiduSdkErrorEnum.URL_DOWNLOAD_TIMEOUT,
BaiduSdkErrorEnum.URL_RESPONSE_INVALID.value[0]: BaiduSdkErrorEnum.URL_RESPONSE_INVALID
}

@unique

+ 25
- 1
enums/ExceptionEnum.py View File

@@ -27,7 +27,7 @@ class ExceptionType(Enum):

ILLEGAL_PARAMETER_FORMAT = ("SP010", "非法参数格式!")

PUSH_STREAMING_CHANNEL_IS_OCCUPIED = ("SP011", "推流通道被占用, 请稍后再试!")
PUSH_STREAMING_CHANNEL_IS_OCCUPIED = ("SP011", "推流通道可能被占用, 请稍后再试!")

VIDEO_RESOLUTION_EXCEPTION = ("SP012", "不支持该分辨率类型的视频，请切换分辨率再试!")

@@ -39,6 +39,30 @@ class ExceptionType(Enum):

OR_VIDEO_DO_NOT_EXEIST_EXCEPTION = ("SP016", "原视频不存在!")

MODEL_LOADING_EXCEPTION = ("SP017", "模型加载异常!")

MODEL_ANALYSE_EXCEPTION = ("SP018", "算法模型分析异常!")

AI_MODEL_CONFIG_EXCEPTION = ("SP019", "模型配置不能为空!")

AI_MODEL_GET_CONFIG_EXCEPTION = ("SP020", "获取模型配置异常, 请检查模型配置是否正确!")

MODEL_GROUP_LIMIT_EXCEPTION = ("SP021", "模型组合个数超过限制!")

MODEL_NOT_SUPPORT_VIDEO_EXCEPTION = ("SP022", "%s不支持视频识别!")

MODEL_NOT_SUPPORT_IMAGE_EXCEPTION = ("SP023", "%s不支持图片识别!")

THE_DETECTION_TARGET_CANNOT_BE_EMPTY = ("SP024", "检测目标不能为空!")

URL_ADDRESS_ACCESS_FAILED = ("SP025", "URL地址访问失败, 请检测URL地址是否正确!")

UNIVERSAL_TEXT_RECOGNITION_FAILED = ("SP026", "识别失败!")

COORDINATE_ACQUISITION_FAILED = ("SP027", "飞行坐标识别异常!")

SERVICE_COMMON_EXCEPTION = ("SP997", "公共服务异常!")

NO_GPU_RESOURCES = ("SP998", "暂无GPU资源可以使用，请稍后再试!")

SERVICE_INNER_EXCEPTION = ("SP999", "系统内部异常!")

+ 27
- 13
enums/ModelTypeEnum.py View File

@@ -1,32 +1,46 @@
from enum import Enum, unique

# 异常枚举
'''
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
5. 是否可以参与多个模型组合调用
0: 视频、图片模型组合都支持
1: 只支持视频模型之间的组合
2: 只支持图片模型之间的组合
'''

@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river')
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', 0)

FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', 0)

# TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'road')

FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2')
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', 0)

TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'road')
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, 2)

EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None)
PLATE_MODEL = ("5", "005", "车牌模型", None, 2)

PLATE_MODEL = ("5", "005", "车牌模型", None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', 0)

VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle')
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', 0)

PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian')
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', 0)

SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire')
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', 0)

ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer')
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', 0)

COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad')
SHIP_MODEL = ("11", "011", "船只模型", 'ship', 0)

SHIP_MODEL = ("11", "011", "船只模型", 'ship')
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, 2)

BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None)
# TRAFFICACCIDENT_MODEL = ("13", "013", "交通事故模型", 'trafficAccident', 0)

def checkCode(code):
for model in ModelType:

+ 5
- 2
exception/CustomerException.py View File

@@ -8,9 +8,12 @@ from loguru import logger


class ServiceException(Exception): # 继承异常类
def __init__(self, code, msg):
def __init__(self, code, msg, desc=None):
self.code = code
self.msg = msg
if desc is None:
self.msg = msg
else:
self.msg = msg % desc

def __str__(self):
logger.error("异常编码：{}, 异常描述：{}", self.code, self.msg)

+ 234
- 261
service/Dispatcher.py View File

@@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import time
import GPUtil

from cerberus import Validator
from concurrency.FeedbackThread import FeedbackThread
from entity.FeedBack import message_feedback, recording_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
@@ -24,127 +23,121 @@ from util import GPUtils
class DispatcherService:

# 初始化
def __init__(self):
# 获取DSP环境所需要的配置
self.content = YmlUtils.getConfigs()
# 初始化日志
def __init__(self, base_dir):
##################################### 初始化alg相关配置 #####################################
self.base_dir = base_dir
self.content = YmlUtils.getConfigs(base_dir)
self.content['base_dir'] = base_dir
self.feedbackThread = None

###################################### 初始化日志框架 ######################################
LogUtils.init_log(self.content)
# 检查视频保存地址，不存在创建文件夹,迁移初始化

#################################### 初始化视频保存文件夹 ####################################
FileUtils.create_dir_not_exist(self.content["video"]["file_path"])
# 记录当前正在执行的实时流分析任务
self.onlineProcesses = {}
# 记录当前正在执行的离线视频分析任务
self.offlineProcesses = {}
# 记录当前正在执行的图片分析任务
self.photoProcesses = {}
# 记录当前录屏任务
self.recordingProcesses = {}
# 算法反馈队列

###################################### 创建任务记录字典 ######################################
self.onlineProcesses = {} # 记录当前正在执行的实时流分析任务
self.offlineProcesses = {} # 记录当前正在执行的离线视频分析任务
self.photoProcesses = {} # 记录当前正在执行的图片分析任务
self.recordingProcesses = {} # 记录当前录屏任务
self.listeningProcesses = [self.onlineProcesses, self.offlineProcesses, self.photoProcesses,
self.recordingProcesses]

######################################## 反馈队列 ########################################
self.fbQueue = Queue()

###################################### 监听topic信息 ######################################
self.online_topic = self.content["kafka"]["topic"]["dsp-alg-online-tasks-topic"]
self.offline_topic = self.content["kafka"]["topic"]["dsp-alg-offline-tasks-topic"]
self.image_topic = self.content["kafka"]["topic"]["dsp-alg-image-tasks-topic"]
self.recording_task_topic = self.content["kafka"]["topic"]["dsp-recording-task-topic"]

self.topics = [self.online_topic, self.offline_topic, self.image_topic, self.recording_task_topic]

self.analysisType = {
self.online_topic: (AnalysisType.ONLINE.value, lambda x: self.online(x)),
self.offline_topic: (AnalysisType.OFFLINE.value, lambda x: self.offline(x)),
self.image_topic: (AnalysisType.IMAGE.value, lambda x: self.image(x)),
self.recording_task_topic: (AnalysisType.RECORDING.value, lambda x: self.recording(x))
self.online_topic: (AnalysisType.ONLINE.value,
lambda x: self.online(x),
lambda x, y, z, t: self.identify_method(x, y, z, t)),
self.offline_topic: (AnalysisType.OFFLINE.value,
lambda x: self.offline(x),
lambda x, y, z, t: self.identify_method(x, y, z, t)),
self.image_topic: (AnalysisType.IMAGE.value,
lambda x: self.image(x),
lambda x, y, z, t: self.identify_method(x, y, z, t)),
self.recording_task_topic: (AnalysisType.RECORDING.value,
lambda x: self.recording(x),
lambda x, y, z, t: self.recording_method(x, y, z, t))
}

# 服务调用启动方法
def start_service(self):
# 启动问题反馈线程
feedbackThread = self.start_feedback_thread()
# 初始化kafka监听者
customerKafkaConsumer = KafkaUtils.CustomerKafkaConsumer(self.content, topics=self.topics)
print("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】启动成功 ლ(´ڡ`ლ)ﾞ")
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
while True:
try:
# 检查任务进程运行情况，去除活动的任务
self.check_process_task()
# 校验问题反馈线程是否正常
if not feedbackThread.is_alive():
logger.error("======================问题反馈线程异常停止======================")
break
self.start_feedback_thread()
msg = customerKafkaConsumer.poll()
if msg is not None and len(msg) > 0:
for k, v in msg.items():
for m in v:
message = m.value
analysisType = self.analysisType.get(m.topic)[0]
# 录屏逻辑
print("aaaaaaaaaaaaaaaaaaaaaaaaaa", analysisType)
if analysisType == AnalysisType.RECORDING.value:
try:
customerKafkaConsumer.commit_offset(m)
logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, message.get("request_id"))
self.analysisType.get(m.topic)[1](message)
except ServiceException as s:
logger.exception("消息监听异常：{}, requestId: {}", s.msg, message.get("request_id"))
if analysisType is not None:
recording = {"recording": recording_feedback(message.get("request_id"),
RecordingStatus.RECORDING_FAILED.value[0],
error_code=s.code,
error_msg=s.msg)}
self.fbQueue.put(recording)
except Exception as e:
logger.exception("消息监听异常：{}, requestId: {}", e, message.get("request_id"))
recording = {
"recording": recording_feedback(message.get("request_id"),
RecordingStatus.RECORDING_FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])}
self.fbQueue.put(recording)
else:
try:
customerKafkaConsumer.commit_offset(m)
logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, message.get("request_id"))
self.analysisType.get(m.topic)[1](message)
except ServiceException as s:
logger.exception("消息监听异常：{}, requestId: {}", s.msg, message.get("request_id"))
feedback = {
"feedback": message_feedback(message.get("request_id"),
AnalysisStatus.FAILED.value,
analysisType,
s.code,
s.msg,
analyse_time=TimeUtils.now_date_to_str())}
self.fbQueue.put(feedback)
except Exception as e:
logger.exception("消息监听异常：{}, requestId: {}", e, message.get("request_id"))
feedback = {
"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())}
self.fbQueue.put(feedback)
self.analysisType.get(m.topic)[2](customerKafkaConsumer, m, message, analysisType)
else:
time.sleep(1)
except Exception as e:
logger.exception("主线程异常：", e)

def checkGPU(self, msgId):
gpu_ids = None
while True:
GPUtil.showUtilization()
gpu_ids = GPUtils.get_gpu_ids(self.content)
if gpu_ids is None or len(gpu_ids) == 0:
logger.warning("暂无可用GPU资源，5秒后重试, 可用gpu数: {}, msgId: {}", len(gpu_ids), msgId)
time.sleep(5)
continue
else:
break
return gpu_ids
def identify_method(self, customerKafkaConsumer, m, message, analysisType):
try:
customerKafkaConsumer.commit_offset(m)
logger.info(
"当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, message.get("request_id"))
self.analysisType.get(m.topic)[1](message)
except ServiceException as s:
logger.exception("消息监听异常：{}, requestId: {}", s.msg, 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())})
except Exception as e:
logger.exception("消息监听异常：{}, requestId: {}", e, 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())})

def recording_method(self, customerKafkaConsumer, m, message, analysisType):
try:
customerKafkaConsumer.commit_offset(m)
logger.info(
"当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, message.get("request_id"))
self.analysisType.get(m.topic)[1](message)
except ServiceException as s:
logger.exception("消息监听异常：{}, requestId: {}", s.msg, 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)})
except Exception as e:
logger.exception("消息监听异常：{}, requestId: {}", e, 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])})

# 开启实时进程
def startOnlineProcess(self, msg, gpu_ids):
@@ -152,7 +145,8 @@ class DispatcherService:
if self.onlineProcesses.get(msg.get("request_id")):
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
return
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids}
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
"analyse_type": AnalysisType.ONLINE.value}
# 创建在线识别进程并启动
oirp = OnlineIntelligentRecognitionProcess(cfg)
oirp.start()
@@ -169,18 +163,10 @@ class DispatcherService:

# 检查实时、离线进程任务运行情况，去除不活动的任务
def check_process_task(self):
for requestId in list(self.onlineProcesses.keys()):
if not self.onlineProcesses[requestId].is_alive():
del self.onlineProcesses[requestId]
for requestId in list(self.offlineProcesses.keys()):
if not self.offlineProcesses[requestId].is_alive():
del self.offlineProcesses[requestId]
for requestId in list(self.photoProcesses.keys()):
if not self.photoProcesses[requestId].is_alive():
del self.photoProcesses[requestId]
for requestId in list(self.recordingProcesses.keys()):
if not self.recordingProcesses[requestId].is_alive():
del self.recordingProcesses[requestId]
for process in self.listeningProcesses:
for requestId in list(process.keys()):
if not process[requestId].is_alive():
del process[requestId]

# 开启离线进程
def startOfflineProcess(self, msg, gpu_ids):
@@ -188,7 +174,8 @@ class DispatcherService:
if self.offlineProcesses.get(msg.get("request_id")):
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
return
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids}
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
"analyse_type": AnalysisType.OFFLINE.value}
# 创建在线识别进程并启动
ofirp = OfflineIntelligentRecognitionProcess(cfg)
ofirp.start()
@@ -205,128 +192,56 @@ class DispatcherService:
# 开启图片分析进程
def startImageProcess(self, msg, gpu_ids):
# 相同的requestId不在执行
if self.photoProcesses.get(msg.get("request_id")):
pp = self.photoProcesses.get(msg.get("request_id"))
if pp is not None:
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
return
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids}
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
"analyse_type": AnalysisType.IMAGE.value}
# 创建在线识别进程并启动
imagep = PhotosIntelligentRecognitionProcess(cfg)
imagep.start()
self.photoProcesses[msg.get("request_id")] = imagep

# 校验实时kafka消息
def check_online_msg(self, msg):
requestId = msg.get("request_id")
command = msg.get("command")
models = msg.get("models")
pull_url = msg.get("pull_url")
push_url = msg.get("push_url")
results_base_dir = msg.get("results_base_dir")
if command is None:
return False
if requestId is None:
return False
if command == "start" and models is None:
return False
if models is not None:
for model in models:
if model.get("code") is None:
return False
if model.get("categories") is None:
return False
if command == "start" and pull_url is None:
return False
if command == "start" and push_url is None:
return False
if command == "start" and results_base_dir is None:
return False
return True

# 校验实时kafka消息
def check_offline_msg(self, msg):
requestId = msg.get("request_id")
models = msg.get("models")
command = msg.get("command")
original_url = msg.get("original_url")
original_type = msg.get("original_type")
push_url = msg.get("push_url")
results_base_dir = msg.get("results_base_dir")
if command is None:
return False
if requestId is None:
return False
if command == 'start' and models is None:
return False
if models is not None:
for model in models:
if model.get("code") is None:
return False
if model.get("categories") is None:
return False
if command == 'start' and original_url is None:
return False
if command == 'start' and push_url is None:
return False
if command == 'start' and original_type is None:
return False
if command == 'start' and results_base_dir is None:
return False
return True

# 校验图片kafka消息
def check_image_msg(self, msg):
requestId = msg.get("request_id")
models = msg.get("models")
command = msg.get("command")
image_urls = msg.get("image_urls")
results_base_dir = msg.get("results_base_dir")
if command is None:
return False
if requestId is None:
return False
if command == 'start' and models is None:
return False
if models is not None:
for model in models:
if model.get("code") is None:
return False
if model.get("categories") is None:
return False
if command == 'start' and image_urls is None:
return False
if command == 'start' and results_base_dir is None:
return False
return True

'''
开启问题反馈线程
校验kafka消息
'''

def check_msg(self, msg):
try:
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") is not None and len(msg.get("request_id")) > 0:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0], v.errors)
return result
except ServiceException as s:
raise s
except Exception as e:
logger.exception("参数校验异常: {}", e)
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])

def start_feedback_thread(self):
feedbackThread = FeedbackThread(self.fbQueue, self.content)
feedbackThread.setDaemon(True)
feedbackThread.start()
return feedbackThread
if self.feedbackThread is None or not self.feedbackThread.is_alive():
self.feedbackThread = FeedbackThread(self.fbQueue, self.content)
self.feedbackThread.setDaemon(True)
self.feedbackThread.start()

def online(self, message):

check_result = self.check_online_msg(message)

'''
在线分析逻辑
'''

def online(self, message):
# 参数校验
check_result = self.check_msg(message)
if not check_result:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
return
if 'start' == message.get("command"):
logger.info("开始实时分析")
gpu_ids = GPUtils.get_gpu_ids(self.content)
if gpu_ids is None or len(gpu_ids) == 0 or (0 not in gpu_ids and str(0) not in gpu_ids):
feedback = {
"feedback": message_feedback(message.get("request_id"),
AnalysisStatus.FAILED.value,
AnalysisType.ONLINE.value,
ExceptionType.NO_GPU_RESOURCES.value[0],
ExceptionType.NO_GPU_RESOURCES.value[1],
analyse_time=TimeUtils.now_date_to_str())}
self.fbQueue.put(feedback)
return
gpu_ids = GPUtils.check_gpu_resource(self.content)
self.startOnlineProcess(message, gpu_ids)
elif 'stop' == message.get("command"):
self.stopOnlineProcess(message)
@@ -334,82 +249,46 @@ class DispatcherService:
pass

def offline(self, message):
check_result = self.check_offline_msg(message)
check_result = self.check_msg(message)
if not check_result:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
return
if 'start' == message.get("command"):
logger.info("开始离线分析")
gpu_ids = GPUtils.get_gpu_ids(self.content)
if gpu_ids is None or len(gpu_ids) == 0 or (0 not in gpu_ids and str(0) not in gpu_ids):
feedback = {
"feedback": message_feedback(message.get("request_id"),
AnalysisStatus.FAILED.value,
AnalysisType.OFFLINE.value,
ExceptionType.NO_GPU_RESOURCES.value[0],
ExceptionType.NO_GPU_RESOURCES.value[1],
analyse_time=TimeUtils.now_date_to_str())}
self.fbQueue.put(feedback)
return
gpu_ids = GPUtils.check_gpu_resource(self.content)
self.startOfflineProcess(message, gpu_ids)
time.sleep(3)
elif 'stop' == message.get("command"):
self.stopOfflineProcess(message)
else:
pass

def image(self, message):
check_result = self.check_image_msg(message)
check_result = self.check_msg(message)
if not check_result:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
return
if 'start' == message.get("command"):
logger.info("开始图片分析")
gpu_ids = GPUtils.get_gpu_ids(self.content)
if gpu_ids is None or len(gpu_ids) == 0:
raise ServiceException(ExceptionType.NO_GPU_RESOURCES.value[0],
ExceptionType.NO_GPU_RESOURCES.value[1])
gpu_ids = GPUtils.check_gpu_resource(self.content)
self.startImageProcess(message, gpu_ids)
# elif 'stop' == message.get("command"):
# self.stopImageProcess(message)
else:
pass

# 校验录屏参数
def check_recording_msg(self, msg):
requestId = msg.get("request_id")
command = msg.get("command")
pullUrl = msg.get("pull_url")
pushUrl = msg.get("push_url")
if not command or command not in ["start", "stop"]:
return False
if not requestId:
return False
if command == 'start' and not pullUrl:
return False
return True

def recording(self, message):
# 校验入参
check_result = self.check_recording_msg(message)
check_result = self.check_msg(message)
if not check_result:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
return
if 'start' == message.get("command"):
logger.info("开始实时分析")
gpu_ids = GPUtils.get_gpu_ids(self.content)
self.startRecordingProcess(message, gpu_ids)
logger.info("开始录屏")
self.startRecordingProcess(message)
elif 'stop' == message.get("command"):
self.stopRecordingProcess(message)
else:
pass

# 开启录屏进程
def startRecordingProcess(self, msg, gpu_ids):
def startRecordingProcess(self, msg):
if self.recordingProcesses.get(msg.get("request_id")):
logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
return
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids}
cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg,
"analyse_type": AnalysisType.RECORDING.value}
srp = ScreenRecordingProcess(cfg)
srp.start()
self.recordingProcesses[msg.get("request_id")] = srp
@@ -421,3 +300,97 @@ class DispatcherService:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg.get("request_id"))
return
rdp.sendEvent({'command': 'stop'})


# 校验schema规则定义
schema = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'allowed': ['start', 'stop']
},
'pull_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'push_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'original_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'original_type': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'image_urls': {
'type': 'list',
'required': False,
'schema': {
'type': 'string',
'empty': False,
'maxlength': 5000
}
},
'results_base_dir': {
'type': 'string',
'required': False,
'nullable': True,
'regex': r'^[a-zA-Z0-9]{0,36}$'
},
'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}$'
},
'categories': {
'type': 'list',
'required': True,
'dependencies': 'code',
'schema': {
'type': 'dict',
'required': True,
'schema': {
'id': {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
'config': {
'type': 'dict',
'required': False,
'dependencies': 'id',
}
}
}
}
}
}
}
}

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


+ 223
- 0
test/demo/demo1.py View File

@@ -0,0 +1,223 @@
import copy
import json
import os
import time
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Queue, Process

from loguru import logger
import subprocess as sp

import cv2
import numpy as np
from aip import AipImageClassify
import sys
from enums.BaiduSdkEnum import BAIDUERRORDATA, VehicleEnumVALUE
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType
from exception.CustomerException import ServiceException
from util.ModelUtils import Model


def get_recording_video_info(url):
try:
video_info = 'ffprobe -show_format -show_streams -of json %s' % url
p = sp.Popen(video_info, stdout=sp.PIPE, stderr=sp.PIPE, shell=True)
out, err = p.communicate(timeout=17)
if p.returncode != 0:
raise Exception("未获取视频信息！！！！！")
probe = json.loads(out.decode('utf-8'))
if probe is None or probe.get("streams") is None:
raise Exception("未获取视频信息！！！！！:")
video_stream = next((stream for stream in probe['streams'] if stream.get('codec_type') == 'video'), None)
if video_stream is None:
raise Exception("未获取视频信息！！！！！")
width = video_stream.get('width')
height = video_stream.get('height')
nb_frames = video_stream.get('nb_frames')
fps = video_stream.get('r_frame_rate')
up, down = str(fps).split('/')
fps = int(eval(up) / eval(down))
return (width, height, nb_frames, fps)
except Exception as e:
raise e

client = AipImageClassify(str(31096670), 'Dam3O4tgPRN3qh4OYE82dbg7', '1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa')
def vehicleDetect(client, iamge, options={}):
reply_num = 0
reply_value = None
while True:
try:
options["show"] = "true"
res_image = client.vehicleDetect(iamge,options)
error_code = res_image.get("error_code")
if error_code:
enum = BAIDUERRORDATA.get(error_code)
# 如果异常编码未知, 返回空值
if enum is None:
logger.error("百度云车辆检测异常！error_code:{}", error_code)
return None
# 重试指定次数后，还是异常，输出统一内部异常
if enum.value[3] == 0:
if reply_value is None:
reply_value = 10
logger.error("百度云车辆检测异常！error_code:{}, error_msg:{}, reply_num:{}", enum.value[0], enum.value[2], reply_num)
raise Exception()
# 重试指定次数后，还是异常，输出对应的异常
if enum.value[3] == 1:
if reply_value is None:
reply_value = 10
raise ServiceException(str(enum.value[0]), enum.value[2])
# 重试指定次数后，还是异常，输出空
if enum.value[3] == 2:
if reply_value is None:
reply_value = 10
if reply_num >= reply_value:
return None
return res_image
except ServiceException as s:
time.sleep(0.2)
reply_num += 1
if reply_num > reply_value:
logger.exception("车辆检测识别失败: {}", s.msg)
raise ServiceException(e.code, e.msg)
except Exception as e:
logger.exception("车辆检测失败: {}, 当前重试次数：{}", e, reply_num)
time.sleep(0.2)
reply_num += 1
if reply_num > reply_value:
logger.exception("车辆检测识别失败: {}", e)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

def mark(content, info, img, color):
score = info.get("probability")
if score is None:
score = info.get("location").get("score")
text = "%s: %.2f]" % (content, score)
text_xy = (info.get("location").get("left"), info.get("location").get("top") - 25)
img_lu = (info.get("location").get("left"), info.get("location").get("top"))
img_rd = (info.get("location").get("left") + info.get("location").get("width"),
info.get("location").get("top") + info.get("location").get("height"))
cv2.putText(img, text, text_xy, cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2, cv2.LINE_AA)
count = 1
if img.shape[1] > 1600:
count = 2
cv2.rectangle(img, img_lu, img_rd, color, count)
return img

def pull_stream(url, queue, nb_frames):
command = ['ffmpeg -re -y -i ' + url +' -f rawvideo -pix_fmt bgr24 -an -']
pull_p = sp.Popen(command, stdout=sp.PIPE, shell=True)
aa = 0
try:
while True:
if queue.qsize() == 200:
time.sleep(1)
continue
in_bytes = pull_p.stdout.read(width*height*3)
if in_bytes is not None and len(in_bytes) > 0:
img = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
queue.put({"status": "1", "img": img})
aa+=1
else:
if aa -10 > nb_frames:
queue.put({"status": "2"})
pull_p.terminate()
pull_p.wait()
break;
except Exception as e:
logger.exception("拉流异常: {}", e)
finally:
pull_p.terminate()
pull_p.wait()

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

def buildFrame(queue, senlin_mod, client, width, height, nb_frames, fps):
frames = []
status = None
for i in range(queue.qsize()):
frame_result = getQueue(queue)
if frame_result is None:
time.sleep(0.01)
continue
if frame_result.get("status") == '1':
frames.append((frame_result.get("img"), senlin_mod, client, width, height, nb_frames, fps))
else:
status = frame_result.get("status")
return frames, status

def process(frame):
try:
p_result, timeOut = frame[1].process(copy.deepcopy(frame[0]), frame[3])
or_result, or_image = cv2.imencode(".jpg", frame[0])
result = vehicleDetect(frame[2], or_image)
if result is not None:
vehicleInfo = result.get("vehicle_info")
if vehicleInfo is not None and len(vehicleInfo) > 0:
for i, info in enumerate(vehicleInfo):
value = VehicleEnumVALUE.get(info.get("type"))
if value is None:
logger.error("车辆识别出现未支持的目标类型！type:{}", info.get("type"))
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
p_result[1] = mark(value.value[1], info, p_result[1], (255, 0, 255))
frame_merge = np.hstack((frame[0], p_result[1]))
return frame_merge
except Exception as e:
logger.exception("模型分析异常: {}", e)
return None

queue = Queue(200)
url ='/home/th/tuo_heng/dev/11.mp4'
width, height, nb_frames, fps = get_recording_video_info(url)
my_process = Process(target = pull_stream, args=(url, queue, nb_frames))
#启动子进程
my_process.start()



current_path = os.path.abspath(os.path.dirname(__file__))
import GPUtil
senlin_mod = Model(str(GPUtil.getAvailable()[0]), [2,3,4], logger, "11111", ModelType.FOREST_FARM_MODEL)
or_video_file = cv2.VideoWriter("aaa.mp4", cv2.VideoWriter_fourcc(*'mp4v'), fps,
(int(width) * 2, int(height)))


with ThreadPoolExecutor(max_workers=3) as t:
task_frame = None
while True:
frames = []
status = None
if task_frame is not None:
frames, status = task_frame.result()
task_frame = t.submit(buildFrame, queue, senlin_mod, client, width, height, nb_frames, fps)
if len(frames) == 0 and status is None:
time.sleep(0.02)
continue
if frames is not None and len(frames) > 0:
for result in t.map(process, frames):
if result is not None:
or_video_file.write(result)
if status is None:
continue
if status.get("status") == "2":
t.shutdown(wait=False)
or_video_file.release()

t.shutdown(wait=False)
or_video_file.release()







+ 189
- 0
test/demo/demo2.py View File

@@ -0,0 +1,189 @@
import asyncio
import copy
import json
import os
import time
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Queue, Process

from loguru import logger
import subprocess as sp

import cv2
import numpy as np
from aip import AipImageClassify
import sys
from enums.BaiduSdkEnum import BAIDUERRORDATA, VehicleEnumVALUE
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType
from exception.CustomerException import ServiceException
from util.ModelUtils import Model


def get_recording_video_info(url):
try:
video_info = 'ffprobe -show_format -show_streams -of json %s' % url
p = sp.Popen(video_info, stdout=sp.PIPE, stderr=sp.PIPE, shell=True)
out, err = p.communicate(timeout=17)
if p.returncode != 0:
raise Exception("未获取视频信息！！！！！")
probe = json.loads(out.decode('utf-8'))
if probe is None or probe.get("streams") is None:
raise Exception("未获取视频信息！！！！！:")
video_stream = next((stream for stream in probe['streams'] if stream.get('codec_type') == 'video'), None)
if video_stream is None:
raise Exception("未获取视频信息！！！！！")
width = video_stream.get('width')
height = video_stream.get('height')
nb_frames = video_stream.get('nb_frames')
fps = video_stream.get('r_frame_rate')
up, down = str(fps).split('/')
fps = int(eval(up) / eval(down))
return (width, height, nb_frames, fps)
except Exception as e:
raise e

client = AipImageClassify(str(31096670), 'Dam3O4tgPRN3qh4OYE82dbg7', '1PGZ9LAXRR5zcT5MN9rHcW8kLBIS5DAa')
def vehicleDetect(client, iamge, options={}):
reply_num = 0
reply_value = None
while True:
try:
options["show"] = "true"
res_image = client.vehicleDetect(iamge,options)
error_code = res_image.get("error_code")
if error_code:
enum = BAIDUERRORDATA.get(error_code)
# 如果异常编码未知, 返回空值
if enum is None:
logger.error("百度云车辆检测异常！error_code:{}", error_code)
return None
# 重试指定次数后，还是异常，输出统一内部异常
if enum.value[3] == 0:
if reply_value is None:
reply_value = 10
logger.error("百度云车辆检测异常！error_code:{}, error_msg:{}, reply_num:{}", enum.value[0], enum.value[2], reply_num)
raise Exception()
# 重试指定次数后，还是异常，输出对应的异常
if enum.value[3] == 1:
if reply_value is None:
reply_value = 10
raise ServiceException(str(enum.value[0]), enum.value[2])
# 重试指定次数后，还是异常，输出空
if enum.value[3] == 2:
if reply_value is None:
reply_value = 10
if reply_num >= reply_value:
return None
return res_image
except ServiceException as s:
time.sleep(0.2)
reply_num += 1
if reply_num > reply_value:
logger.exception("车辆检测识别失败: {}", s.msg)
raise ServiceException(e.code, e.msg)
except Exception as e:
logger.exception("车辆检测失败: {}, 当前重试次数：{}", e, reply_num)
time.sleep(0.2)
reply_num += 1
if reply_num > reply_value:
logger.exception("车辆检测识别失败: {}", e)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

def mark(content, info, img, color):
score = info.get("probability")
if score is None:
score = info.get("location").get("score")
text = "%s: %.2f" % (content, score)
text_xy = (info.get("location").get("left"), info.get("location").get("top") - 25)
img_lu = (info.get("location").get("left"), info.get("location").get("top"))
img_rd = (info.get("location").get("left") + info.get("location").get("width"),
info.get("location").get("top") + info.get("location").get("height"))
cv2.putText(img, text, text_xy, cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2, cv2.LINE_AA)
count = 1
if img.shape[1] > 1600:
count = 2
cv2.rectangle(img, img_lu, img_rd, color, count)
return img

async def mode_handler(img, width):
return senlin_mod.process(copy.deepcopy(img), width)

async def modprocess(img, width):
p_result, timeOut = await mode_handler(img, width)
return p_result, timeOut


async def car_handler(img, width):
return car_mod.process(copy.deepcopy(img), width)

async def carprocess(img, width):
p_result, timeOut = await car_handler(img, width)
return p_result, timeOut


async def baidu_handler(img, client):
or_result, or_image = cv2.imencode(".jpg", img)
return vehicleDetect(client, or_image)

async def baiduprocess(img, client):
result = await baidu_handler(img, client)
return result



url ='/home/th/tuo_heng/dev/11.mp4'
width, height, nb_frames, fps = get_recording_video_info(url)

current_path = os.path.abspath(os.path.dirname(__file__))
import GPUtil
senlin_mod = Model(str(GPUtil.getAvailable()[0]), [2,3,4], logger, "11112", ModelType.FOREST_FARM_MODEL)
car_mod = Model(str(GPUtil.getAvailable()[0]), [0], logger, "11112", ModelType.VEHICLE_MODEL)
or_video_file = cv2.VideoWriter("aaa2.mp4", cv2.VideoWriter_fourcc(*'mp4v'), fps,
(int(width) * 2, int(height)))

command = ['ffmpeg -re -y -i ' + url +' -f rawvideo -pix_fmt bgr24 -an -']
pull_p = sp.Popen(command, stdout=sp.PIPE, shell=True)
num = 0
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
while True:
print(num, nb_frames)
in_bytes = pull_p.stdout.read(width*height*3)
if in_bytes is not None and len(in_bytes) > 0:
img = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
# r = loop.run_until_complete(asyncio.gather(modprocess(img, width), carprocess(img, width)))
p_result, timeOut = senlin_mod.process(copy.deepcopy(img), width)
p_result1, timeOut1 = car_mod.process(copy.deepcopy(p_result[1]), width)
# r = loop.run_until_complete(asyncio.gather(modprocess(img, width), baiduprocess(img, client)))
# p_result, timeOut = r[0]
# result = r[1]
# p_result, timeOut = senlin_mod.process(copy.deepcopy(img), width)

# if result is not None:
# vehicleInfo = result.get("vehicle_info")
# if vehicleInfo is not None and len(vehicleInfo) > 0:
# for i, info in enumerate(vehicleInfo):
# value = VehicleEnumVALUE.get(info.get("type"))
# if value is None:
# logger.error("车辆识别出现未支持的目标类型！type:{}", info.get("type"))
# raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
# ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
# p_result[1] = mark(value.value[1], info, p_result[1], (255, 0, 255))
frame_merge = np.hstack((img, p_result1[1]))
or_video_file.write(frame_merge)
num+=1
else:
if num -10 > nb_frames:
break;
finally:
or_video_file.release()
pull_p.terminate()
pull_p.wait()






+ 0
- 5
test/ffmpeg11/ffmpeg2.py View File

@@ -1,9 +1,6 @@
import copy
import subprocess as sp
from enum import Enum, unique
from PIL import Image
import time
import cv2

import sys
sys.path.extend(['..','../AIlib' ])
@@ -15,8 +12,6 @@ from models.experimental import attempt_load
from utils.torch_utils import select_device
from utilsK.queRiver import get_labelnames,get_label_arrays
import numpy as np
import torch
from utilsK.masterUtils import get_needed_objectsIndex




+ 139
- 0
test/validate/Test.py View File

@@ -0,0 +1,139 @@

import re
from cerberus import Validator
# pattern = re.compile('^[a-zA-Z0-9]{1,36}$') # 用于匹配至少一个数字
# m = pattern.match('111aaa3213213123123213123a222')
# print(m)
#
# schema = {
# 'name': {'type': 'string', 'required': True},
# 'age': {'type': 'integer', 'required': True, 'min': 18},
# 'email': {'type': 'string', 'required': True, 'regex': r'\w+@\w+\.\w+'}
# }
# v = Validator(schema)
# print(v.validate({ 'name': '11', 'age': 20, 'email': '764784960@qq.com'}))
# aa = str({ 'name': '11', 'age': 20, 'email': '764784960@qq.com'})
# print(isinstance(aa, dict))

# schema = {'name': {'type': 'string'}}
# v = Validator(schema)
# document = {'name1': 'john doe'}
# print(v.validate(document))
# print(v.validate(document, schema))

schema = {
'request_id': {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{1,36}$'
},
'command': {
'type': 'string',
'required': True,
'allowed': ['start', 'stop']
},
'pull_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'push_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'original_url': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'original_type': {
'type': 'string',
'required': False,
'nullable': True,
'maxlength': 255
},
'image_urls': {
'type': 'list',
'required': False,
'schema': {
'type': 'string',
'empty': False,
'maxlength': 5000
}
},
'results_base_dir': {
'type': 'string',
'required': False,
'nullable': True,
'regex': r'^[a-zA-Z0-9]{0,36}$'
},
'models': {
'type': 'list',
'required': False,
'schema': {
'type': 'dict',
'required': False,
'schema': {
'code': {
'type': 'string',
'required': True,
'empty': False,
'dependencies': 'categories',
'regex': r'^[a-zA-Z0-9]{1,255}$'
},
'categories': {
'type': 'list',
'required': True,
'dependencies': 'code',
'schema': {
'type': 'dict',
'required': True,
'schema': {
'id': {
'type': 'string',
'required': True,
'empty': False,
'regex': r'^[a-zA-Z0-9]{0,255}$'},
'config': {
'type': 'dict',
'required': False,
'dependencies': 'id',
}
}
}
}
}
}
}
}

v = Validator(schema, allow_unknown=True)
aa={
'request_id': "111",
'command': 'start',
'pull_url': None,
'push_url': None,
'original_url': '',
'original_type': '',
'image_urls': ['1','1'],
'results_base_dir': '111',
'models': [
# {
# 'code': '1',
# 'categories': [
# # {
# # 'id': '1',
# # 'config': {}
# # }
# ]
# }
]
}

print(v.validate(aa))
print(v.errors)

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


+ 34
- 0
test/协程/协程笔记.py View File

@@ -0,0 +1,34 @@
import asyncio
import time


async def sleep(delay):
time.sleep(delay)
print("1111")


async def say_after(delay, what):
await sleep(delay)
print(what)

async def main():
task1 = asyncio.create_task(say_after(1, 'hello'))
task2 = asyncio.create_task(say_after(2, 'world'))
# await task1
# await task2
await asyncio.gather(task1, task2)
# await say_after(1, 'hello')
# await say_after(2, 'world')


start = time.time()
loop = asyncio.new_event_loop()
print(loop)
asyncio.set_event_loop(loop)
task1 = loop.create_task(say_after(1, 'hello'))
task2 = loop.create_task(say_after(2, 'world'))
loop.run_until_complete(asyncio.wait([task1, task2]))
loop.close()
# asyncio.run(main())
print(time.time() - start)


+ 44
- 0
test/线程/Test.py View File

@@ -0,0 +1,44 @@
# -*- coding: utf-8 -*-
import threading
import time
from concurrent.futures import ThreadPoolExecutor


class Test(object):
def __init__(self):
# threading.Thread.__init__(self)
self._sName = "machao"

def process(self):
#args是关键字参数，需要加上名字，写成args=(self,)
th1 = threading.Thread(target=self.buildList, args=())
th1.start()
th1.join()

def buildList(self):
while True:
print("start")
print(self._sName)
self._sName = "1111111"
time.sleep(3)
def bb():
print("!1111111111")

def aa(t):
while True:
t.submit(bb)

# test = Test()
# test.process()
# print(3//2)
# with ThreadPoolExecutor(max_workers=10) as t:
# t.submit(aa, t)
# time.sleep(1000)
# codeArray=['']
# codeStr = ','.join(codeArray)
# print(codeStr)
aa={'aaaa': []}
aa["aaaa"].append("1111111")
aa["aaaa"].append("1111111")
aa["aaaa"].append("1111111")
print(aa)

+ 5
- 0
test/路径/Test.py View File

@@ -0,0 +1,5 @@
import os

print(os.getcwd())

print(os.path.relpath(__file__))

+ 0
- 0
test/路径/__init__.py View File


+ 7
- 0
test/集合/test.py View File

@@ -0,0 +1,7 @@
list1 = [1, 2, 3, 4]
list2 = [1,2,4]
if set(list2) == set(list1):
print("1111111")
else:
print("222222")


+ 4
- 24
util/AliyunSdk.py View File

@@ -22,7 +22,8 @@ class AliyunOssSdk():
if self.bucket is None:
self.logger.info("初始化oss桶, requestId:{}", self.requestId)
auth = oss2.Auth(self.content["aliyun"]["access_key"], self.content["aliyun"]["access_secret"])
self.bucket = oss2.Bucket(auth, self.content["aliyun"]["oss"]["endpoint"],
self.bucket = oss2.Bucket(auth,
self.content["aliyun"]["oss"]["endpoint"],
self.content["aliyun"]["oss"]["bucket"],
connect_timeout=self.content["aliyun"]["oss"]["connect_timeout"])

@@ -45,28 +46,6 @@ class AliyunOssSdk():
raise e


async def put_object(self, updatePath, fileByte):
self.bucket.put_object(updatePath, fileByte)

async def upload_file(self, updatePath, fileByte):
self.logger.info("开始上传文件到oss, requestId:{}", self.requestId)
self.get_oss_bucket()
MAX_RETRIES = 3
retry_count = 0
while True:
try:
await self.put_object(updatePath, fileByte)
self.logger.info("上传文件到oss成功! requestId:{}", self.requestId)
break
except Exception as e:
retry_count += 1
time.sleep(1)
self.logger.info("上传文件到oss失败, 重试次数：{}, requestId:{}", retry_count, self.requestId)
if retry_count > MAX_RETRIES:
self.logger.exception("上传文件到oss重试失败:{}, requestId:{}", e, self.requestId)
raise e


class ThAliyunVodSdk():

def __init__(self, content, logger, requestId):
@@ -107,7 +86,8 @@ class ThAliyunVodSdk():
ExceptionType.GET_VIDEO_URL_EXCEPTION.value[1])
diff_time = current_time - start
if diff_time > 60 * 60 * 2:
self.logger.exception("获取视频地址失败超时异常: {}，超时时间：{}, requestId: {}", e, diff_time, self.requestId)
self.logger.exception("获取视频地址失败超时异常: {}，超时时间：{}, requestId: {}", e, diff_time,
self.requestId)
raise ServiceException(ExceptionType.GET_VIDEO_URL_TIMEOUT_EXCEPTION.value[0],
ExceptionType.GET_VIDEO_URL_TIMEOUT_EXCEPTION.value[1])


+ 156
- 332
util/Cv2Utils.py View File

@@ -7,13 +7,16 @@ import subprocess as sp

import numpy as np
from loguru import logger

from common import Constant
from exception.CustomerException import ServiceException
from enums.ExceptionEnum import ExceptionType


class Cv2Util():

def __init__(self, pullUrl=None, pushUrl=None, orFilePath=None, aiFilePath=None, requestId=None, content=None, gpu_ids=None):
def __init__(self, pullUrl=None, pushUrl=None, orFilePath=None, aiFilePath=None, requestId=None, content=None,
gpu_ids=None):
self.pullUrl = pullUrl
self.pushUrl = pushUrl
self.orFilePath = orFilePath
@@ -25,18 +28,14 @@ class Cv2Util():
self.fps = None
self.width = None
self.height = None
self.wah = None
self.wh = None
self.h = None
self.hn = None
self.w = None
self.all_frames = None
self.bit_rate = None
self.pull_p = None
self.requestId = requestId
self.p_push_retry_num = 0
self.resize_status = False
self.current_frame = 0
self.isGpu = False
self.read_w_h = None
self.content = content
@@ -44,54 +43,16 @@ class Cv2Util():
self.isGpu = True

def getFrameConfig(self, fps, width, height):
if self.fps is None:
if self.fps is None or self.width != width or self.height != height:
self.fps = fps
self.width = width
self.height = height
if width > 1600:
self.wh = int(width * height * 3 // 4)
self.wah = '%sx%s' % (int(self.width / 2), int(self.height / 2))
self.h = int(self.height // 2)
self.w = int(self.width // 2)
self.hn = int(self.height // 2)
self.wn = int(self.width // 2)
w_f = self.wh != width * height * 3 / 4
h_f = self.h != self.height / 2
wd_f = self.w != self.width / 2
if w_f or h_f or wd_f:
self.resize_status = True
self.wh = int(width * height * 3)
self.wah = '%sx%s' % (int(self.width), int(self.height))
self.h = int(self.height)
self.w = int(self.width)
# self.wh = int(width * height * 3 // 8)
# self.wah = '%sx%s' % (int(self.width / 2), int(self.height / 2))
# self.h = int(self.height * 3 // 4)
# self.w = int(self.width // 2)
# self.hn = int(self.height // 2)
# self.wn = int(self.width // 2)
# w_f = self.wh != width * height * 3 / 8
# h_f = self.h != self.height * 3 / 4
# wd_f = self.w != self.width / 2
# if w_f or h_f or wd_f:
# self.resize_status = True
# self.wh = int(width * height * 3 // 2)
# self.wah = '%sx%s' % (int(self.width), int(self.height))
# self.h = int(self.height * 3 // 2)
# self.w = int(self.width)
if width > Constant.width:
self.h = int(self.height//2)
self.w = int(self.width//2)
else:
self.wh = int(width * height * 3)
self.wah = '%sx%s' % (int(self.width), int(self.height))
self.h = int(self.height)
self.w = int(self.width)
self.hn = int(self.height)
self.wn = int(self.width)
# self.wh = int(width * height * 3 // 2)
# self.wah = '%sx%s' % (int(self.width), int(self.height))
# self.h = int(self.height * 3 // 2)
# self.w = int(self.width)
# self.hn = int(self.height)
# self.wn = int(self.width)

def clear_video_info(self):
self.fps = None
@@ -104,7 +65,7 @@ class Cv2Util():

def get_video_info(self):
try:
if self.pullUrl is None:
if self.pullUrl is None or len(self.pullUrl) == 0:
logger.error("拉流地址不能为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.PULL_STREAM_URL_EXCEPTION.value[0],
ExceptionType.PULL_STREAM_URL_EXCEPTION.value[1])
@@ -128,61 +89,25 @@ class Cv2Util():
fps = video_stream.get('r_frame_rate')
# duration = video_stream.get('duration')
# bit_rate = video_stream.get('bit_rate')
self.width = int(width)
self.height = int(height)
if width > 1600:
self.wh = int(width * height * 3)
self.wah = '%sx%s' % (int(self.width / 2), int(self.height / 2))
self.h = int(self.height // 2)
self.w = int(self.width // 2)
self.hn = int(self.height // 2)
self.wn = int(self.width // 2)
w_f = self.wh != width * height * 3 / 4
h_f = self.h != self.height / 2
wd_f = self.w != self.width / 2
if w_f or h_f or wd_f:
self.resize_status = True
self.wh = int(width * height * 3)
self.wah = '%sx%s' % (int(self.width), int(self.height))
if width is not None and height is not None:
self.width = int(width)
self.height = int(height)
self.wh = self.width * self.height * 3
if width > Constant.width:
self.h = int(self.height//2)
self.w = int(self.width//2)
else:
self.h = int(self.height)
self.w = int(self.width)
# self.wh = int(width * height * 3 // 8)
# self.wah = '%sx%s' % (int(self.width / 2), int(self.height / 2))
# self.h = int(self.height * 3 // 4)
# self.w = int(self.width / 2)
# self.hn = int(self.height / 2)
# self.wn = int(self.width // 2)
# w_f = self.wh != width * height * 3 / 8
# h_f = self.h != self.height * 3 / 4
# wd_f = self.w != self.width / 2
# if w_f or h_f or wd_f:
# self.resize_status = True
# self.wh = int(width * height * 3 // 2)
# self.wah = '%sx%s' % (int(self.width), int(self.height))
# self.h = int(self.height * 3 // 2)
# self.w = int(self.width)
else:
self.wh = int(width * height * 3)
self.wah = '%sx%s' % (int(self.width), int(self.height))
self.h = int(self.height)
self.w = int(self.width)
self.hn = int(self.height)
self.wn = int(self.width)
# self.wh = int(width * height * 3 // 2)
# self.wah = '%sx%s' % (int(self.width), int(self.height))
# self.h = int(self.height * 3 // 2)
# self.w = int(self.width)
# self.hn = int(self.height)
# self.wn = int(self.width)
if nb_frames:
self.all_frames = int(nb_frames)
up, down = str(fps).split('/')
self.fps = int(eval(up) / eval(down))
# if duration:
# self.duration = float(video_stream['duration'])
# self.bit_rate = int(bit_rate) / 1000
logger.info("视频信息, width:{}|height:{}|fps:{}|all_frames:{}|bit_rate:{}, requestId:{}", self.width,
self.height, self.fps, self.all_frames, self.bit_rate, self.requestId)
if nb_frames:
self.all_frames = int(nb_frames)
up, down = str(fps).split('/')
self.fps = int(eval(up) / eval(down))
# if duration:
# self.duration = float(video_stream['duration'])
# self.bit_rate = int(bit_rate) / 1000
logger.info("视频信息, width:{}|height:{}|fps:{}|all_frames:{}|bit_rate:{}, requestId:{}", self.width,
self.height, self.fps, self.all_frames, self.bit_rate, self.requestId)
except ServiceException as s:
logger.error("获取视频信息异常: {}, requestId:{}", s.msg, self.requestId)
self.clear_video_info()
@@ -194,7 +119,6 @@ class Cv2Util():
'''
录屏任务获取视频信息
'''

def get_recording_video_info(self):
try:
video_info = 'ffprobe -show_format -show_streams -of json %s' % self.pullUrl
@@ -205,9 +129,6 @@ class Cv2Util():
probe = json.loads(out.decode('utf-8'))
if probe is None or probe.get("streams") is None:
raise Exception("未获取视频信息！！！！！requestId:" + self.requestId)
# 视频大小
# format = probe['format']
# size = int(format['size'])/1024/1024
video_stream = next((stream for stream in probe['streams'] if stream.get('codec_type') == 'video'), None)
if video_stream is None:
raise Exception("未获取视频信息！！！！！requestId:" + self.requestId)
@@ -215,28 +136,18 @@ class Cv2Util():
height = video_stream.get('height')
nb_frames = video_stream.get('nb_frames')
fps = video_stream.get('r_frame_rate')
# duration = video_stream.get('duration')
# bit_rate = video_stream.get('bit_rate')
if width and int(width) > 0:
self.width = int(width)
if height and int(height) > 0:
self.height = int(height)
if self.width and self.height:
if self.isGpu:
self.wh = int(width * height * 3)
self.read_w_h = ([self.height, self.width, 3])
# self.read_w_h = (self.height, self.width)
else:
self.wh = int(width * height * 3)
self.read_w_h = ([self.height, self.width, 3])
self.wh = int(width * height * 3)
self.read_w_h = ([self.height, self.width, 3])
if nb_frames and int(nb_frames) > 0:
self.all_frames = int(nb_frames)
if fps:
up, down = str(fps).split('/')
self.fps = int(eval(up) / eval(down))
# if duration:
# self.duration = float(video_stream['duration'])
# self.bit_rate = int(bit_rate) / 1000
logger.info("视频信息, width:{}|height:{}|fps:{}|all_frames:{}, requestId:{}", self.width,
self.height, self.fps, self.all_frames, self.requestId)
except ServiceException as s:
@@ -255,7 +166,6 @@ class Cv2Util():
'''
录屏拉取视频
'''

def recording_pull_p(self):
try:
# 如果视频信息不存在, 不初始化拉流
@@ -264,31 +174,18 @@ class Cv2Util():
# 如果已经初始化, 不再初始化
if self.pull_p:
return
# 有GPU资源使用GPU, 没有GPU资源使用CPU
if not self.isGpu:
command = ['ffmpeg -re', '-y'
# '-hide_banner',
]
if self.pullUrl.startswith('rtsp://'):
command.extend(['-rtsp_transport', 'tcp'])
command.extend(['-i', self.pullUrl,
# '-vf', f'scale=iw*{scale_percent//100}:ih*{scale_percent//100}',
'-f', 'rawvideo',
'-f'
'-an',
'-'])
else:
command = ['ffmpeg']
if self.pullUrl.startswith('rtsp://'):
command.extend(['-rtsp_transport', 'tcp'])
command.extend(['-re',
'-y',
'-hwaccel', 'cuda',
'-i', self.pullUrl,
'-f', 'rawvideo',
'-pix_fmt', 'bgr24',
'-an',
'-'])
command = ['ffmpeg -re', '-y'
# '-hide_banner',
]
if self.pullUrl.startswith('rtsp://'):
command.extend(['-rtsp_transport', 'tcp'])
if self.isGpu:
command.extend(['-hwaccel', 'cuda'])
command.extend(['-i', self.pullUrl,
'-f', 'rawvideo',
'-pix_fmt', 'bgr24',
'-an',
'-'])
self.pull_p = sp.Popen(command, stdout=sp.PIPE)
except ServiceException as s:
logger.exception("构建拉流管道异常: {}, requestId:{}", s.msg, self.requestId)
@@ -313,24 +210,31 @@ class Cv2Util():
def recording_read(self):
result = None
try:
self.recording_pull_p()
in_bytes = self.pull_p.stdout.read(self.wh)
if in_bytes is not None and len(in_bytes) > 0:
try:
img = np.frombuffer(in_bytes, np.uint8).reshape(self.read_w_h)
result = np.frombuffer(in_bytes, np.uint8).reshape(self.read_w_h)
except Exception as ei:
logger.exception("视频格式异常：{}, requestId:{}", ei, self.requestId)
raise ServiceException(ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[0],
ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[1])
result = img
# if self.isGpu:
# result = cv2.cvtColor(img, cv2.COLOR_YUV2BGR_NV12)
# else:
# result = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
self.current_frame += 1
except ServiceException as s:
if self.pull_p:
logger.info("重试, 关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
self.pull_p.terminate()
self.pull_p.wait()
self.pull_p = None
raise s
except Exception as e:
logger.exception("读流异常：{}, requestId:{}", e, self.requestId)
if self.pull_p:
logger.info("重试, 关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
self.pull_p.terminate()
self.pull_p.wait()
self.pull_p = None
return result

'''
@@ -339,57 +243,27 @@ class Cv2Util():

def build_pull_p(self):
try:
if self.wah is None:
if self.pull_p is not None:
return
if self.pull_p:
logger.info("重试, 关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
self.pull_p.terminate()
self.pull_p.wait()
# command = ['ffmpeg',
# # '-b:v', '3000k',
# '-i', self.pullUrl,
# '-f', 'rawvideo',
# '-vcodec', 'rawvideo',
# '-pix_fmt', 'bgr24',
# # '-s', "{}x{}".format(int(width), int(height)),
# '-an',
# '-']
# input_config = {'c:v': 'h264_cuvid', 'resize': self.wah}
# process = (
# ffmpeg
# .input(self.pullUrl, **input_config)
# .output('pipe:', format='rawvideo', r=str(self.fps)) # pix_fmt='bgr24'
# .overwrite_output()
# .global_args('-an')
# .run_async(pipe_stdout=True)
# )
command = ['ffmpeg']
if self.pullUrl.startswith("rtsp://"):
command.extend(['-rtsp_transport', 'tcp'])
command.extend(['-re',
'-y',
'-hwaccel', 'cuda',
'-resize', self.wah,
# '-resize', self.wah,
'-i', self.pullUrl,
'-f', 'rawvideo',
'-pix_fmt', 'bgr24',
'-an',
'-'])
self.pull_p = sp.Popen(command, stdout=sp.PIPE)
# self.pull_p = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE)
# self.pull_p = process
except ServiceException as s:
logger.exception("构建拉流管道异常: {}, requestId:{}", s.msg, self.requestId)
if self.pull_p:
logger.info("重试, 关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
self.pull_p.terminate()
self.pull_p.wait()
self.pull_p = None
raise s
except Exception as e:
logger.exception("构建拉流管道异常：{}, requestId:{}", e, self.requestId)
self.clear_video_info()
if self.pull_p:
logger.info("重试, 关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
@@ -405,11 +279,9 @@ class Cv2Util():
def read(self):
result = None
try:
# if self.pull_p is None:
# logger.error("拉流管道为空, requestId:{}", self.requestId)
# raise ServiceException(ExceptionType.PULL_PIPELINE_INIT_EXCEPTION.value[0],
# ExceptionType.PULL_PIPELINE_INIT_EXCEPTION.value[1])
# in_bytes = self.pull_p.stdout.read(self.wh)
self.build_pull_p()
if self.pull_p is None:
return result
in_bytes = self.pull_p.stdout.read(self.wh)
if in_bytes is not None and len(in_bytes) > 0:
try:
@@ -421,14 +293,18 @@ class Cv2Util():
ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[1])
# result = cv2.cvtColor(img, cv2.COLOR_YUV2BGR_NV12)
# result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
if self.resize_status:
if self.width > 1600:
result = cv2.resize(result, (int(self.width / 2), int(self.height / 2)),
interpolation=cv2.INTER_LINEAR)
self.current_frame += 1
if self.width > Constant.width:
result = cv2.resize(result, self.w, self.h, interpolation=cv2.INTER_LINEAR)
except ServiceException as s:
raise s
except Exception as e:
self.clear_video_info()
if self.pull_p:
logger.info("关闭拉流管道, requestId:{}", self.requestId)
self.pull_p.stdout.close()
self.pull_p.terminate()
self.pull_p.wait()
self.pull_p = None
logger.exception("读流异常：{}, requestId:{}", e, self.requestId)
return result

@@ -512,21 +388,10 @@ class Cv2Util():
# 构建 cv2
def build_p(self):
try:
if self.p:
logger.info("重试, 关闭管道, requestId:{}", self.requestId)
if self.p.stdin:
self.p.stdin.close()
self.p.terminate()
self.p.wait()
# self.p.communicate()
# self.p.kill()
if self.pushUrl is None:
if self.pushUrl is None or len(self.pushUrl) == 0:
logger.error("推流地址不能为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.PUSH_STREAM_URL_EXCEPTION.value[0],
ExceptionType.PUSH_STREAM_URL_EXCEPTION.value[1])
width = int(self.width)
if width <= 1600:
width = 2 * int(self.width)
command = ['ffmpeg',
# '-loglevel', 'debug',
'-y',
@@ -534,8 +399,7 @@ class Cv2Util():
'-vcodec', 'rawvideo',
'-pix_fmt', 'bgr24',
'-thread_queue_size', '1024',
# '-s', "{}x{}".format(self.width * 2, self.height),
'-s', "{}x{}".format(width, int(self.hn)),
'-s', "{}x{}".format(self.w * 2, self.h),
'-r', str(self.fps),
'-i', '-', # 指定输入文件
'-g', str(self.fps),
@@ -556,62 +420,34 @@ class Cv2Util():
"-an",
# '-flvflags', 'no_duration_filesize',
# '-preset', 'fast', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-preset', 'p6', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-tune', 'll',
'-preset', 'llhq', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-f', 'flv',
self.pushUrl]
# command = 'ffmpeg -loglevel debug -y -f rawvideo -vcodec rawvideo -pix_fmt bgr24' +\
# ' -s ' + "{}x{}".format(int(self.width), int(self.height/2))\
# + ' -i - ' + '-g ' + str(self.fps)+\
# ' -b:v 6000k -tune zerolatency -c:v libx264 -pix_fmt yuv420p -preset ultrafast'+\
# ' -f flv ' + self.pushUrl

# kwargs = {'format': 'rawvideo',
# # 'vcodec': 'rawvideo',
# 'pix_fmt': 'bgr24',
# 's': '{}x{}'.format(int(self.width), int(self.height/2))}
# out = {
# 'r': str(self.fps),
# 'g': str(self.fps),
# 'b:v': '5500k', # 恒定码率
# # 'maxrate': '15000k',
# # 'crf': '18',
# 'bufsize': '5500k',
# 'tune': 'zerolatency', # 加速编码速度
# 'c:v': 'libx264', # 指定视频编码器
# 'sc_threshold': '0',
# 'pix_fmt': 'yuv420p',
# # 'flvflags': 'no_duration_filesize',
# 'preset': 'medium', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
# # superfast, veryfast, faster, fast, medium, slow, slower, veryslow。
# 'format': 'flv'}
# 管道配置
# process2 = (
# ffmpeg
# .input('pipe:', **kwargs)
# .output(self.pushUrl, **out)
# .global_args('-y', '-an')
# .overwrite_output()
# .run_async(pipe_stdin=True)
# )
logger.info("fps:{}|height:{}|width:{}|requestId:{}", self.fps, self.height, self.width, self.requestId)
self.p = sp.Popen(command, stdin=sp.PIPE, shell=False)
# self.p = process2
except ServiceException as s:
if self.p:
if self.p.stdin:
self.p.stdin.close()
self.p.terminate()
self.p.wait()
logger.exception("构建p管道异常: {}, requestId:{}", s.msg, self.requestId)
raise s
except Exception as e:
if self.p:
if self.p.stdin:
self.p.stdin.close()
self.p.terminate()
self.p.wait()
logger.exception("初始化p管道异常：{}, requestId:{}", e, self.requestId)

async def push_stream_write(self, frame):
self.p.stdin.write(frame.tostring())

async def push_stream(self, frame):
if self.p is None:
self.build_p()
def push_stream(self, frame):
try:
await self.push_stream_write(frame)
return True
if self.p is None:
self.build_p()
self.p.stdin.write(frame.tostring())
except ServiceException as s:
raise s
except Exception as ex:
logger.exception("推流进管道异常：{}, requestId: {}", ex, self.requestId)
current_retry_num = 0
@@ -621,116 +457,104 @@ class Cv2Util():
self.p_push_retry_num += 1
current_retry_num += 1
if current_retry_num > 3 or self.p_push_retry_num > 600:
return False
raise ServiceException(ExceptionType.PUSH_STREAMING_CHANNEL_IS_OCCUPIED.value[0],
ExceptionType.PUSH_STREAMING_CHANNEL_IS_OCCUPIED.value[1])
self.build_p()
await self.push_stream_write(frame)
self.p.stdin.write(frame.tostring())
logger.info("构建p管道重试成功, 当前重试次数: {}, requestId: {}", current_retry_num,
self.requestId)
return True
except ServiceException as ss:
raise ss
except Exception as e:
logger.exception("构建p管道异常：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
current_retry_num, self.requestId)
return False

async def video_frame_write(self, or_frame, ai_frame):
if or_frame is not None:
self.or_video_file.write(or_frame)
if ai_frame is not None:
self.ai_video_file.write(ai_frame)
def build_or_write(self):
try:
if self.orFilePath is not None and self.or_video_file is None:
self.or_video_file = cv2.VideoWriter(self.orFilePath, cv2.VideoWriter_fourcc(*'mp4v'), self.fps,
(self.w, self.h))
if self.or_video_file is None:
logger.error("or_video_file为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
except ServiceException as s:
if self.or_video_file is not None:
self.or_video_file.release()
logger.exception("构建OR文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
raise s
except Exception as e:
if self.or_video_file is not None:
self.or_video_file.release()
logger.exception("构建OR文件写对象异常: {}, requestId:{}", e, self.requestId)
raise e

async def video_write(self, or_frame, ai_frame):
def build_ai_write(self):
try:
self.build_write()
if or_frame is not None and len(or_frame) > 0:
await self.video_frame_write(or_frame, None)
if ai_frame is not None and len(ai_frame) > 0:
await self.video_frame_write(None, ai_frame)
return True
if self.aiFilePath is not None and self.ai_video_file is None:
self.ai_video_file = cv2.VideoWriter(self.aiFilePath, cv2.VideoWriter_fourcc(*'mp4v'), self.fps,
(self.w * 2, self.h))
if self.ai_video_file is None:
logger.error("ai_video_file为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
except ServiceException as s:
if self.ai_video_file is not None:
self.ai_video_file.release()
logger.exception("构建AI文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
raise s
except Exception as e:
if self.ai_video_file is not None:
self.ai_video_file.release()
logger.exception("构建AI文件写对象异常: {}, requestId:{}", e, self.requestId)
raise e

def video_or_write(self, frame):
try:
if self.or_video_file is None:
self.build_or_write()
self.or_video_file.write(frame)
except ServiceException as s:
raise s
except Exception as ex:
ai_retry_num = 0
while True:
try:
ai_retry_num += 1
if ai_retry_num > 3:
logger.exception("重新写入离线分析后视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
return False
if or_frame is not None and len(or_frame) > 0:
await self.or_video_file.write(or_frame)
if ai_frame is not None and len(ai_frame) > 0:
await self.ai_video_file.write(ai_frame)
logger.info("重新写入离线分析后视频到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
logger.exception("重新写入原视频视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
self.or_video_file.write(frame)
logger.info("重新写入原视频视到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
self.requestId)
return True
except Exception as e:
logger.exception("重新写入离线分析后视频到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
logger.exception("重新写入原视频视到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
ai_retry_num, self.requestId)

def revording_video_write(self, or_frame):
def video_ai_write(self, frame):
try:
self.recording_build_write()
if or_frame is not None and len(or_frame) > 0:
self.or_video_file.write(or_frame)
if self.ai_video_file is None:
self.build_ai_write()
self.ai_video_file.write(frame)
except ServiceException as s:
raise s
except Exception as ex:
ai_retry_num = 0
while True:
try:
ai_retry_num += 1
if ai_retry_num > 3:
logger.exception("重新写入离线分析后视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
raise ServiceException(ExceptionType.WRITE_STREAM_EXCEPTION.value[0],
ExceptionType.WRITE_STREAM_EXCEPTION.value[1])
if or_frame is not None and len(or_frame) > 0:
self.or_video_file.write(or_frame)
logger.info("重新写入离线分析后视频到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
logger.exception("重新写入分析后的视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
self.ai_video_file.write(frame)
logger.info("重新写入分析后的视频到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
self.requestId)
break
except Exception as e:
logger.exception("重新写入离线分析后视频到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
logger.exception("重新写入分析后的视频到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
ai_retry_num, self.requestId)

def recording_build_write(self):
try:
if self.fps is None or self.width is None or self.height is None:
logger.error("fps、 width、 height为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if self.orFilePath is not None and self.or_video_file is None:
self.or_video_file = cv2.VideoWriter(self.orFilePath, cv2.VideoWriter_fourcc(*'mp4v'), self.fps,
(int(self.width), int(self.height)))
except ServiceException as s:
logger.exception("构建文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
raise s
except Exception as e:
logger.exception("构建文件写对象异常: {}, requestId:{}", e, self.requestId)
raise e

def build_write(self):
try:
if self.fps is None or self.width is None or self.height is None:
logger.error("fps、 width、 height为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if self.orFilePath is not None and self.or_video_file is None:
self.or_video_file = cv2.VideoWriter(self.orFilePath, cv2.VideoWriter_fourcc(*'mp4v'), self.fps,
(int(self.wn), int(self.hn)))
if self.or_video_file is None:
logger.error("or_video_file为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
if self.aiFilePath is not None and self.ai_video_file is None:
self.ai_video_file = cv2.VideoWriter(self.aiFilePath, cv2.VideoWriter_fourcc(*'mp4v'), self.fps,
(int(self.wn * 2), int(self.hn)))
if self.ai_video_file is None:
logger.error("ai_video_file为空, requestId:{}", self.requestId)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
except ServiceException as s:
logger.exception("构建文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
raise s
except Exception as e:
logger.exception("构建文件写对象异常: {}, requestId:{}", e, self.requestId)
raise e

def video_merge(self, frame1, frame2):
# frameLeft = cv2.resize(frame1, (int(self.width / 2), int(self.height / 2)), interpolation=cv2.INTER_LINEAR)
# frameRight = cv2.resize(frame2, (int(self.width / 2), int(self.height / 2)), interpolation=cv2.INTER_LINEAR)

+ 0
- 1
util/FileUtils.py View File

@@ -8,7 +8,6 @@ from loguru import logger


def create_dir_not_exist(path):
logger.info("检查文件夹是否存在: {}", path)
if not os.path.exists(path):
logger.info("开始创建文件夹: {}", path)
os.makedirs(path)

+ 11
- 0
util/GPUtils.py View File

@@ -1,5 +1,8 @@
import GPUtil

from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException


# order- 确定返回可用 GPU 设备 ID 的顺序。order应指定为以下字符串之一：
# 'first'- 按升序排列可用的 GPU 设备 ID（默认）
@@ -28,3 +31,11 @@ def get_gpu_ids(content):

def get_all_gpu_ids():
return GPUtil.getGPUs()


def check_gpu_resource(content):
gpu_ids = get_gpu_ids(content)
if gpu_ids is None or len(gpu_ids) == 0 or (0 not in gpu_ids and str(0) not in gpu_ids):
raise ServiceException(ExceptionType.NO_GPU_RESOURCES.value[0],
ExceptionType.NO_GPU_RESOURCES.value[1])
return gpu_ids

+ 116
- 33
util/ImageUtils.py View File

@@ -5,6 +5,10 @@ import cv2
import requests
from PIL import Image, ImageDraw, ImageFont
import numpy as np
from loguru import logger

from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException

'''
文字水印
@@ -134,33 +138,38 @@ class PictureWaterMark:
return cv2.cvtColor(np.asarray(b), cv2.COLOR_BGR2RGB)

def common_water_1(self, image, logo, alpha=1):
h, w = image.shape[0], image.shape[1]
# if w >= h:
rate = int(w * 0.1) / logo.shape[1]
# else:
# rate = int(h * 0.1) / logo.shape[0]
mask = cv2.resize(logo, None, fx=rate, fy=rate, interpolation=cv2.INTER_NEAREST)
mask_h, mask_w = mask.shape[0], mask.shape[1]
mask_channels = cv2.split(mask)
dst_channels = cv2.split(image)
# b, g, r, a = cv2.split(mask)
# 计算mask在图片的坐标
# if w >= h:
ul_points = (int(h * 0.95) - mask_h, int(w - h * 0.05 - mask_w))
dr_points = (int(h * 0.95), int(w - h * 0.05))
# else:
# ul_points = (int(h * 0.95) - mask_h, int(w - h * 0.05 - mask_w))
# dr_points = (int(h * 0.95), int(w - h * 0.05))
for i in range(3):
dst_channels[i][ul_points[0]: dr_points[0], ul_points[1]: dr_points[1]] = dst_channels[i][
ul_points[0]: dr_points[0],
ul_points[1]: dr_points[1]] * (
255.0 - mask_channels[
3] * alpha) / 255
dst_channels[i][ul_points[0]: dr_points[0], ul_points[1]: dr_points[1]] += np.array(
mask_channels[i] * (mask_channels[3] * alpha / 255), dtype=np.uint8)
dst_img = cv2.merge(dst_channels)
return dst_img
try:
h, w = image.shape[0], image.shape[1]
# if w >= h:
rate = int(w * 0.1) / logo.shape[1]
# else:
# rate = int(h * 0.1) / logo.shape[0]
mask = cv2.resize(logo, None, fx=rate, fy=rate, interpolation=cv2.INTER_NEAREST)
mask_h, mask_w = mask.shape[0], mask.shape[1]
mask_channels = cv2.split(mask)
dst_channels = cv2.split(image)
# b, g, r, a = cv2.split(mask)
# 计算mask在图片的坐标
# if w >= h:
ul_points = (int(h * 0.95) - mask_h, int(w - h * 0.05 - mask_w))
dr_points = (int(h * 0.95), int(w - h * 0.05))
# else:
# ul_points = (int(h * 0.95) - mask_h, int(w - h * 0.05 - mask_w))
# dr_points = (int(h * 0.95), int(w - h * 0.05))
for i in range(3):
dst_channels[i][ul_points[0]: dr_points[0], ul_points[1]: dr_points[1]] = dst_channels[i][
ul_points[0]: dr_points[0],
ul_points[1]: dr_points[
1]] * (
255.0 - mask_channels[
3] * alpha) / 255
dst_channels[i][ul_points[0]: dr_points[0], ul_points[1]: dr_points[1]] += np.array(
mask_channels[i] * (mask_channels[3] * alpha / 255), dtype=np.uint8)
dst_img = cv2.merge(dst_channels)
return dst_img
except Exception as e:
logger.exception("加水印异常", e)
return image


# 差值感知算法
@@ -191,16 +200,23 @@ def Hamming_distance(hash1, hash2):


def url2Array(url):
response = requests.get(url)
image = Image.open(BytesIO(response.content))
image1 = np.array(image)
img_bgr = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR)
return img_bgr
try:
response = requests.get(url)
image = Image.open(BytesIO(response.content))
image1 = np.array(image)
img_bgr = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR)
return img_bgr
except Exception as e:
logger.exception("url地址请求异常: {}", e)
raise ServiceException(ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1])


def url2Content(url):
response = requests.get(url)
return response.content


def url2Image(url):
response = requests.get(url)
image = Image.open(BytesIO(response.content))
@@ -214,9 +230,10 @@ def url2Byte(url):
response = requests.get(url)
return BytesIO(response.content)


def markRectangle(url, text, textCoordinate, imageLeftUpCoordinate, imageRightDownCoordinate, color):
img = url2Array(url)
#( 蓝, 绿, 红)
# ( 蓝, 绿, 红)
# 红色 (0, 0, 255)
# 洋红色 (255, 0, 255)
# 青色 (255, 255, 0)
@@ -229,6 +246,72 @@ def markRectangle(url, text, textCoordinate, imageLeftUpCoordinate, imageRightDo
cv2.rectangle(img, imageLeftUpCoordinate, imageRightDownCoordinate, color, 2)
return img


# def draw_painting_joint(img, xywh, score=0.5, color=None,
# font={'line_thickness': None, 'boxLine_thickness': None, 'fontSize': None}):
# imh, imw, imc = img.shape
# tl = font['line_thickness'] or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
# box_tl = font['boxLine_thickness'] or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # 根据图像的尺寸计算出适合用于绘制图像边框的线宽。
# c1, c2 = (int(xywh[0]), int(xywh[1])), (int(xywh[2]), int(xywh[3]))
# cv2.rectangle(img, c1, c2, color, thickness=box_tl, lineType=cv2.LINE_AA)
#
# label = ' %.2f' % (score)
# tf = max(tl, 1) # font thickness
# fontScale = font['fontSize'] or tl * 0.33
# t_size = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
# cv2.rectangle(img, (int(box[0]) + lw, int(box[1])), c2, color, -1, cv2.LINE_AA) # filled
# cv2.putText(img, label, (c1[0] + lw, c1[1] - (lh - t_size[1]) // 2), 0, fontScale, [225, 255, 255], thickness=tf,
# lineType=cv2.LINE_AA)
# # print('#####line224 fontScale:',fontScale,' thickness:',tf,' line_thickness:',font['line_thickness'],' boxLine thickness:',box_tl)
# return img


def img_pad(img, size, pad_value=[114, 114, 114]):
###填充成固定尺寸
H, W, _ = img.shape
r = max(H / size[0], W / size[1])
img_r = cv2.resize(img, (int(W / r), int(H / r)))
tb = size[0] - img_r.shape[0]
lr = size[1] - img_r.shape[1]
top = int(tb / 2)
bottom = tb - top
left = int(lr / 2)
right = lr - left
pad_image = cv2.copyMakeBorder(img_r, top, bottom, left, right, cv2.BORDER_CONSTANT, value=pad_value)
return pad_image, (top, left, r)


def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# 调整图像大小和填充图像，同时满足步幅多重约束
shape = img.shape[:2] # current shape [height, width] 当前形状 [高度、宽度]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)

# Scale ratio (new / old) 缩放比例（新/旧）
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # 仅缩减，不纵向扩展（为了更好的测试 mAP）
r = min(r, 1.0)

ratio = r, r # width, height ratios 宽度、高度比
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # 最小矩形
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios

dw /= 2 # divide padding into 2 sides
dh /= 2

if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, (dw, dh)

# if __name__ == '__main__':
# # img = cv2.imread("../test/a.jpg", -1)
# # fontcolor = 'yellow'

+ 59
- 12
util/ImgBaiduSdk.py View File

@@ -61,20 +61,15 @@ class AipImageClassifyClient:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except ServiceException as s:
time.sleep(1)
reply_num += 1
self.init_client()
if reply_num > reply_value:
logger.exception("车辆检测识别失败: {}, request_id: {}", s.msg, request_id)
raise ServiceException(e.code, e.msg)
except Exception as e:
logger.exception("车辆检测失败: {}, 当前重试次数：{}, request_id: {}", e, reply_num, request_id)
time.sleep(1)
reply_num += 1
self.init_client()
if reply_num > reply_value:
if isinstance(e, ServiceException):
raise ServiceException(e.code, e.msg)
logger.exception("车辆检测识别失败: {}, request_id: {}", e, request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
@@ -85,6 +80,58 @@ class AipImageClassifyClient:
# finally:
# self.lock.release()

'''
车辆检测
'''

def vehicleDetect(self, iamge, request_id, options={}):
self.init_client()
# try:
# self.lock.acquire()
reply_num = 0
reply_value = None
while True:
try:
options["show"] = "true"
res_image = self.__client.vehicleDetect(iamge, options)
error_code = res_image.get("error_code")
if error_code:
enum = BAIDUERRORDATA.get(error_code)
# 如果异常编码未知, 返回空值
if enum is None:
logger.error("百度云车辆检测异常！error_code:{}, request_id: {}", error_code, request_id)
return None
# 重试指定次数后，还是异常，输出统一内部异常
if enum.value[3] == 0:
if reply_value is None:
reply_value = enum.value[4]
logger.error("百度云车辆检测异常！error_code:{}, error_msg:{}, reply_num:{}, request_id: {}",
enum.value[0], enum.value[2], reply_num, request_id)
raise Exception()
# 重试指定次数后，还是异常，输出对应的异常
if enum.value[3] == 1:
if reply_value is None:
reply_value = enum.value[4]
raise ServiceException(str(enum.value[0]), enum.value[2])
# 重试指定次数后，还是异常，输出空
if enum.value[3] == 2:
if reply_value is None:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except Exception as e:
time.sleep(1)
reply_num += 1
self.init_client()
if reply_num > reply_value:
if isinstance(e, ServiceException):
raise ServiceException(e.code, e.msg)
logger.exception("车辆检测识别失败: {}, request_id: {}", e, request_id)
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])


class AipBodyAnalysisClient:

@@ -140,6 +187,7 @@ class AipBodyAnalysisClient:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except Exception as e:
time.sleep(0.5)
@@ -198,9 +246,8 @@ class AipBodyAnalysisClient:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except ServiceException as e:
raise ServiceException(e.code, e.msg)
except Exception as e:
time.sleep(0.5)
reply_num += 1
@@ -209,8 +256,8 @@ class AipBodyAnalysisClient:
if isinstance(e, ServiceException):
raise ServiceException(e.code, e.msg)
logger.exception("人流量统计失败: {}, request_id: {}", e, request_id)
raise ServiceException(ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[0],
ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[1])
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
# except Exception as ee:
# logger.exception("车辆检测加锁异常: {}, request_id: {}", ee, request_id)
# raise ServiceException(ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[0],

+ 2
- 2
util/KafkaUtils.py View File

@@ -57,9 +57,9 @@ class CustomerKafkaProducer():
raise e
break
except Exception as e:
logger.exception("kafka发送消息异常: {}, requestId:{}", e, message.get("request_id"))
self.customerProducer = None
retry_send_num += 1
logger.error("kafka发送消息异常, 开始重试, 当前重试次数：{} requestId:{}", retry_send_num, message.get("request_id"))
self.customerProducer = None
if retry_send_num > 3:
logger.exception("kafka发送消息重试失败: {}, requestId:{}", e, message.get("request_id"))
raise e

+ 1
- 0
util/LogUtils.py View File

@@ -6,6 +6,7 @@ from loguru import logger

# 初始化日志配置
def init_log(content):
# 判断日志文件是否存在，不存在创建
if not os.path.exists(content["log"]["base_path"]):
os.makedirs(content["log"]["base_path"])
# 移除日志设置

+ 352
- 78
util/ModelUtils.py View File

@@ -9,12 +9,13 @@ from util.GPUtils import get_all_gpu_ids
from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient

sys.path.extend(['..', '../AIlib2'])
from AI import AI_process, AI_process_forest, get_postProcess_para
from AI import AI_process, AI_process_forest, get_postProcess_para, AI_Seg_process, ocr_process
import cv2, os, time
from segutils.segmodel import SegModel
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
from ocrUtils.ocrUtils import CTCLabelConverter, AlignCollate
import numpy as np
import torch, glob
import tensorrt as trt
@@ -28,6 +29,8 @@ class Model:
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
self.logger = logger
self.requestId = requestId
self.modeType = modeType
self.allowedList = allowedList
self.trtFlag_det = True, # 检测模型是否采用TRT
@@ -52,7 +55,8 @@ class Model:
detweights = "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
segweights = '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
par = {
'device': device, # 显卡号,如果用TRT模型，只支持0（单显卡）
# 'device': 'cuda:%s' % device,
'device': str(device), # 显卡号,如果用TRT模型，只支持0（单显卡）
'labelnames': "../AIlib2/weights/conf/%s/labelnames.json" % modeType.value[3], # 检测类别对照表
'trtFlag_det': self.trtFlag_det, # 检测模型是否采用TRT
'trtFlag_seg': self.trtFlag_seg, # 分割模型是否采用TRT
@@ -68,17 +72,46 @@ class Model:
'Segweights': segweights, # 分割模型权重位置
'postFile': '../AIlib2/weights/conf/%s/para.json' % modeType.value[3] # 后处理参数文件
}
# 如果是河道模型
if ModelType.WATER_SURFACE_MODEL == modeType:
par['segRegionCnt'] = 1
par['slopeIndex'] = [5, 6, 7]
# 如果是交通模型
if ModelType.TRAFFIC_FARM_MODEL == modeType:
par['segRegionCnt'] = 2
par['slopeIndex'] = [5, 6, 7]
par['seg_nclass'] = 3
par['slopeIndex'] = []
par['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
}
par['postPar'] = {
'label_csv': '../AIlib2/weights/conf/%s/class_dict.csv' % modeType.value[3],
'speedRoadArea': 16000,
'vehicleArea': 200,
'speedRoadVehicleAngleMin': 15,
'speedRoadVehicleAngleMax': 75,
'vehicleLengthWidthThreshold': 4,
'vehicleSafeDistance': 7,
'vehicleLengthWidthRatio': 0.7,
'cls': 9
}
par['mode'] = 'highWay3.0'
else:
self.trtFlag_seg = False
par['trtFlag_seg'] = False
par['segRegionCnt'] = 0
par['slopeIndex'] = []
par['segPar'] = None
par['Segweights'] = None

self.mode = par['mode'] if 'mode' in par.keys() else 'others'
self.postPar = par['postPar'] if 'postPar' in par.keys() else None

self.device = select_device(par.get('device'))
self.names = get_labelnames(par.get('labelnames'))
self.half = self.device.type != 'cpu'
@@ -96,8 +129,7 @@ class Model:
Segweights = par.get('Segweights')
if Segweights is not None:
if self.trtFlag_seg:
log = trt.Logger(trt.Logger.ERROR)
with open(Segweights, "rb") as f, trt.Runtime(log) as runtime:
with open(Segweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
self.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#######')
else:
@@ -120,34 +152,183 @@ class Model:
self.label_arraylist = None
self.digitFont = None
except Exception as ee:
logger.exception("异常：{}, requestId:{}", ee, requestId)
raise ee
logger.exception("模型加载异常：{}, requestId:{}", ee, requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

'''
#输入参数
# im0s---原始图像列表
# model---检测模型，segmodel---分割模型（如若没有用到，则为None）
#输出：两个元素（列表，字符）构成的元组，[im0s[0],im0,det_xywh,iframe],strout
# [im0s[0],im0,det_xywh,iframe]中，
# im0s[0]--原始图像，im0--AI处理后的图像,iframe--帧号/暂时不需用到。
# det_xywh--检测结果，是一个列表。
# 其中每一个元素表示一个目标构成如：[float(cls_c), xc,yc,w,h, float(conf_c)]
# #cls_c--类别，如0,1,2,3; xc,yc,w,h--中心点坐标及宽；conf_c--得分, 取值范围在0-1之间
# #strout---统计AI处理个环节的时间
'''

def process(self, frame, width=1920):
if self.label_arraylist is None:
fontsize = int(width / 1920 * 40)
line_thickness = 1
boxLine_thickness = 1
waterLineWidth = 1
if width >= 960:
line_thickness = int(round(width / 1920 * 3) - 1)
boxLine_thickness = int(round(width / 1920 * 3))
waterLineWidth = int(round(width / 1920 * 3))
numFontSize = float(format(width / 1920 * 1.1, '.1f')) # 文字大小
self.digitFont = {'line_thickness': line_thickness,
'boxLine_thickness': boxLine_thickness,
'fontSize': numFontSize,
'waterLineColor': (0, 255, 255),
'waterLineWidth': waterLineWidth}
self.label_arraylist = get_label_arrays(self.names, self.rainbows, outfontsize=fontsize,
fontpath="../AIlib2/conf/platech.ttf")
if ModelType.WATER_SURFACE_MODEL == self.modeType or ModelType.TRAFFIC_FARM_MODEL == self.modeType:
return AI_process([frame], self.model, self.segmodel, self.names, self.label_arraylist,
self.rainbows, objectPar=self.objectPar, font=self.digitFont, segPar=self.segPar)
else:
return AI_process_forest([frame], self.model, self.segmodel, self.names, self.label_arraylist,
self.rainbows, self.half, self.device, self.conf_thres, self.iou_thres,
self.allowedList, font=self.digitFont, trtFlag_det=self.trtFlag_det)
try:
if self.label_arraylist is None:
fontsize = int(width / 1920 * 40)
line_thickness = 1
boxLine_thickness = 1
waterLineWidth = 1
if width >= 960:
line_thickness = int(round(width / 1920 * 3) - 1)
boxLine_thickness = int(round(width / 1920 * 3))
waterLineWidth = int(round(width / 1920 * 3))
numFontSize = float(format(width / 1920 * 1.1, '.1f')) # 文字大小
self.digitFont = {'line_thickness': line_thickness,
'boxLine_thickness': boxLine_thickness,
'fontSize': numFontSize,
'waterLineColor': (0, 255, 255),
'waterLineWidth': waterLineWidth}
self.label_arraylist = get_label_arrays(self.names, self.rainbows, outfontsize=fontsize,
fontpath="../AIlib2/conf/platech.ttf")
if ModelType.WATER_SURFACE_MODEL == self.modeType or ModelType.TRAFFIC_FARM_MODEL == self.modeType:
return AI_process([frame], self.model, self.segmodel, self.names, self.label_arraylist,
self.rainbows, objectPar=self.objectPar, font=self.digitFont, segPar=self.segPar,
mode=self.mode, postPar=self.postPar)
else:
return AI_process_forest([frame], self.model, self.segmodel, self.names, self.label_arraylist,
self.rainbows, self.half, self.device, self.conf_thres, self.iou_thres,
self.allowedList, font=self.digitFont, trtFlag_det=self.trtFlag_det)
except Exception as ee:
self.logger.exception("算法模型分析异常：{}, requestId:{}", ee, self.requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


# '''
# 交通事故模型
# '''
#
#
# class TrafficaccidentModel:
# def __init__(self, device, allowedList=None, logger=None, requestId=None, modeType=None, content=None):
# try:
# logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
# requestId)
# self.logger = logger
# self.requestId = requestId
# self.modeType = modeType
# self.allowedList = allowedList
# self.trtFlag_seg = True # 分割模型是否采用TRT
# if self.trtFlag_seg:
# gpu = get_all_gpu_ids()[int(device)]
# if '3090' in gpu.name:
# segweights = '../AIlib2/weights/%s/stdc_1440X810_fp16_3090.engine' % modeType.value[3]
# elif '2080' in gpu.name:
# segweights = '../AIlib2/weights/%s/stdc_1440X810_fp16_2080Ti.engine' % modeType.value[3]
# elif '4090' in gpu.name:
# segweights = '../AIlib2/weights/%s/stdc_1440X810_fp16_4090.engine' % modeType.value[3]
# elif 'A10' in gpu.name:
# segweights = '../AIlib2/weights/%s/stdc_1440X810_fp16_A10.engine' % modeType.value[3]
# else:
# raise Exception("未匹配到该GPU名称的模型, GPU: " + gpu.name)
# else:
# segweights = '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
# par = {
# # 'device': 'cuda:%s' % device,
# 'device': device, # 显卡号,如果用TRT模型，只支持0（单显卡）
# 'labelnames': "../AIlib2/weights/conf/%s/labelnames.json" % modeType.value[3], # 检测类别对照表
# 'trtFlag_seg': self.trtFlag_seg, # 分割模型是否采用TRT
# # 'seg_nclass': 2, # 分割模型类别数目，默认2类
# 'segPar': {
# 'modelSize': (1440, 810),
# 'mean': (0.485, 0.456, 0.406),
# 'std': (0.229, 0.224, 0.225),
# 'nclass': 3,
# 'predResize': False,
# 'numpy': False,
# 'RGB_convert_first': True
# }, # 分割模型预处理参数
# 'postPar': {
# 'label_csv': '../AIlib2/weights/conf/%s/class_dict.csv' % modeType.value[3],
# 'speedRoadArea': 5100,
# 'vehicleArea': 100,
# 'speedRoadVehicleAngleMin': 15,
# 'speedRoadVehicleAngleMax': 75,
# 'vehicleLengthWidthThreshold': 12,
# 'vehicleSafeDistance': 7
# },
# 'digitWordFont': {'line_thickness': 2, 'boxLine_thickness': 1, 'wordSize': 40, 'fontSize': 1.0},
# 'Segweights': segweights, # 分割模型权重位置
# 'postFile': '../AIlib2/weights/conf/%s/para.json' % modeType.value[3] # 后处理参数文件
# }
# self.device = select_device(par.get('device'))
# self.names = get_labelnames(par.get('labelnames'))
#
# # 加载分割模型
# seg_nclass = par['segPar']['nclass']
# self.segPar = par['segPar']
# self.postPar = par['postPar']
# Segweights = par.get('Segweights')
# if self.trtFlag_seg:
# log = trt.Logger(trt.Logger.ERROR)
# with open(Segweights, "rb") as f, trt.Runtime(log) as runtime:
# self.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#######')
# else:
# self.segmodel = SegModel(nclass=seg_nclass, weights=Segweights, device=self.device)
# print('############locad seg model pth success#######')
#
# self.digitWordFont = par["digitWordFont"]
# self.conf_thres, self.iou_thres, self.classes, self.rainbows = get_postProcess_para(par["postFile"])
# self.label_arraylist = None
# except Exception as ee:
# logger.exception("模型加载异常：{}, requestId:{}", ee, requestId)
# raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
# ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

# '''
# 输入参数
# im0s---原始图像列表
# segmodel---分割模型，segmodel---分割模型（如若没有用到，则为None）
# digitWordFont--显示字体，数字等参数
# trtFlag_seg--模型是否是TRT格式
# segPar--分割模型的参数
# postPar--后处理参数
# 输出
# seg_pred--返回语义分割的结果图（0,1,2...表示）
# img_draw--原图上带有矩形框的图
# segstr-----文本数据包括时间信息
# list1-----返回目标的坐标结果，每一个目标用[ cls, x0,y0,x1,y1,conf ]
# '''
#
# def process(self, frame, width=1920):
# try:
# if self.label_arraylist is None:
# line_thickness = 1
# boxLine_thickness = 1
# wordSize = 20
# if width >= 960:
# line_thickness = int(round(width / 1920 * 3) - 1)
# boxLine_thickness = int(round(width / 1920 * 3))
# wordSize = int(round(width / 1920 * 40))
# numFontSize = float(format(width / 1920 * 1.1, '.1f')) # 文字大小
# self.label_arraylist = get_label_arrays(self.names, self.rainbows, outfontsize=wordSize,
# fontpath="../AIlib2/conf/platech.ttf")
# self.digitWordFont['names'] = self.names
# self.digitWordFont['rainbows'] = self.rainbows
# self.digitWordFont['label_arraylist'] = self.label_arraylist
# # 'digitWordFont': { 'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0},
# self.digitWordFont['line_thickness'] = line_thickness
# self.digitWordFont['boxLine_thickness'] = boxLine_thickness
# self.digitWordFont['wordSize'] = wordSize
# self.digitWordFont['fontSize'] = numFontSize
# seg_pred, img_draw, segstr, list1 = AI_Seg_process([frame], self.segmodel, self.digitWordFont,
# self.trtFlag_seg,
# self.segPar, self.postPar)
# result = [seg_pred, img_draw, list1]
# return result, segstr
# except Exception as ee:
# self.logger.exception("算法模型分析异常：{}, requestId:{}", ee, self.requestId)
# raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
# ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


# # 河道模型
@@ -691,95 +872,188 @@ class Model:
class IMModel:

def __init__(self, device, allowedList=None, logger=None, requestId=None, modeType=None, content=None):
logger.info("########################加载{}########################, requestId:{}", modeType.value[2], requestId)
self.allowedList = allowedList
if ModelType.EPIDEMIC_PREVENTION_MODEL == modeType:
self.img_type = 'code' ## code,plate
if ModelType.PLATE_MODEL == modeType:
self.img_type = 'plate' ## code,plate
self.par = {
'code': {'weights': '../AIlib2/weights/conf/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
'plate': {'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
'conf_thres': 0.4,
'iou_thres': 0.45,
'device': 'cuda:%s' % device,
'plate_dilate': (0.5, 0.3)
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
self.logger = logger
self.requestId = requestId
self.allowedList = allowedList
if ModelType.EPIDEMIC_PREVENTION_MODEL == modeType:
self.img_type = 'code' ## code,plate
if ModelType.PLATE_MODEL == modeType:
self.img_type = 'plate' ## code,plate
self.par = {
'code': {'weights': '../AIlib2/weights/conf/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
'plate': {'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
'conf_thres': 0.4,
'iou_thres': 0.45,
'device': 'cuda:%s' % device,
'plate_dilate': (0.5, 0.3)
}
self.device = torch.device(self.par['device'])
self.model = torch.jit.load(self.par[self.img_type]['weights'])
self.device = torch.device(self.par['device'])
self.model = torch.jit.load(self.par[self.img_type]['weights'])
except Exception as ee:
logger.exception("模型加载异常：{}, requestId:{}", ee, requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

def process(self, frame):
img, padInfos = pre_process(frame, self.device) ##预处理
pred = self.model(img) ##模型推理
boxes = post_process(pred, padInfos, self.device, conf_thres=self.par['conf_thres'],
iou_thres=self.par['iou_thres'], nc=self.par[self.img_type]['nc']) # 后处理
dataBack = get_return_data(frame, boxes, modelType=self.img_type, plate_dilate=self.par['plate_dilate'])
return dataBack
try:
img, padInfos = pre_process(frame, self.device) ##预处理
pred = self.model(img) ##模型推理
boxes = post_process(pred, padInfos, self.device, conf_thres=self.par['conf_thres'],
iou_thres=self.par['iou_thres'], nc=self.par[self.img_type]['nc']) # 后处理
dataBack = get_return_data(frame, boxes, modelType=self.img_type, plate_dilate=self.par['plate_dilate'])
return dataBack
except Exception as ee:
self.logger.exception("算法模型分析异常：{}, requestId:{}", ee, self.requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


class OCR_Model:

def __init__(self, device=None, logger=None, requestId=None):
try:
logger.info("######################## 加载OCR模型 ########################, requestId:{}", requestId)
self.requestId = requestId
self.__logger = logger
self.trtFlag_det = True
if self.trtFlag_det:
gpu = get_all_gpu_ids()[int(device)]
if '3090' in gpu.name:
TRTfile = "../AIlib2/weights/ocr_en/english_g2_3090_fp16_448X32.engine"
elif '2080' in gpu.name:
TRTfile = "../AIlib2/weights/ocr_en/english_2080Ti_g2_h64_fp16.engine"
elif '4090' in gpu.name:
TRTfile = "../AIlib2/weights/ocr_en/english_g2_4090_fp16_448X32.engine"
elif 'A10' in gpu.name:
TRTfile = "../AIlib2/weights/ocr_en/english_g2_A10_fp16_448X32.engine"
else:
raise Exception("未匹配到该GPU名称的模型, GPU: " + gpu.name)
else:
TRTfile = "../AIlib2/weights/conf/ocr_en/english_g2.pth"
par = {
'TRTfile': TRTfile,
'device': 'cuda: %s' % device,
'dict_list': {'en': '../AIlib2/weights/conf/ocr_en/en.txt'},
'char_file': '../AIlib2/weights/conf/ocr_en/en_character.csv',
'imgH': 100,
'imgW': 400
}
TRTfile = par['TRTfile']
self.device = par['device']
dict_list = par['dict_list']
char_file = par['char_file']
imgH = par['imgH']
imgW = par['imgW']
logger = trt.Logger(trt.Logger.ERROR)
with open(TRTfile, "rb") as f, trt.Runtime(logger) as runtime:
self.engine = runtime.deserialize_cuda_engine(f.read()) # 输入trt本地文件，返回ICudaEngine对象
print('#####load TRT file:', TRTfile, 'success #####')
self.context = self.engine.create_execution_context()

with open(char_file, 'r') as fp:
characters = fp.readlines()[0].strip()
self.converter = CTCLabelConverter(characters, {}, dict_list)
self.AlignCollate_normal = AlignCollate(imgH=imgH, imgW=imgW, keep_ratio_with_pad=True)
except Exception as ee:
self.__logger.exception("模型加载异常：{}, requestId:{}", ee, requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

def process(self, frame):
try:
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
par = [gray_frame, self.engine, self.context, self.converter, self.AlignCollate_normal, self.device]
return ocr_process(par)
except Exception as ee:
self.__logger.exception("ocr坐标识别异常：{}, requestId:{}", ee, self.requestId)
raise ServiceException(ExceptionType.COORDINATE_ACQUISITION_FAILED.value[0],
ExceptionType.COORDINATE_ACQUISITION_FAILED.value[1])


# 百度AI图片识别模型
class BaiduAiImageModel:

def __init__(self, device=None, allowedList=None, logger=None, requestId=None, modeType=None, content=None):
logger.info("########################加载{}########################, requestId:{}", modeType.value[2], requestId)
self.allowedList = allowedList
self.__requestId = requestId
self.__logger = logger
self.__aipBodyAnalysisClient = AipBodyAnalysisClient(content)
self.__aipImageClassifyClient = AipImageClassifyClient(content)
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
self.logger = logger
self.requestId = requestId
self.allowedList = allowedList
self.__requestId = requestId
self.__logger = logger
self.__aipBodyAnalysisClient = AipBodyAnalysisClient(content)
self.__aipImageClassifyClient = AipImageClassifyClient(content)
except Exception as ee:
logger.exception("模型加载异常：{}, requestId:{}", ee, requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

def process(self, target, url):
baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
if baiduEnum is None:
raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
+ " target: " + target)
return baiduEnum.value[2](self.__aipImageClassifyClient, self.__aipBodyAnalysisClient, url, self.__requestId)
try:
baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
if baiduEnum is None:
raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
+ " target: " + target)
return baiduEnum.value[2](self.__aipImageClassifyClient, self.__aipBodyAnalysisClient, url,
self.__requestId)
except Exception as ee:
self.logger.exception("算法模型分析异常：{}, requestId:{}", ee, self.requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


MODEL_CONFIG = {
# 加载河道模型
ModelType.WATER_SURFACE_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.WATER_SURFACE_MODEL, t),
ModelType.WATER_SURFACE_MODEL.value[1]),
# 加载林场模型
ModelType.WATER_SURFACE_MODEL),
# 加载森林模型
ModelType.FOREST_FARM_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.FOREST_FARM_MODEL, t),
ModelType.FOREST_FARM_MODEL.value[1]),
ModelType.FOREST_FARM_MODEL),
# 加载交通模型
ModelType.TRAFFIC_FARM_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.TRAFFIC_FARM_MODEL, t),
ModelType.TRAFFIC_FARM_MODEL.value[1]),
ModelType.TRAFFIC_FARM_MODEL),
# 加载防疫模型
ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]: (
lambda x, y, z, r, t: IMModel(x, y, z, r, ModelType.EPIDEMIC_PREVENTION_MODEL, t),
ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]),
ModelType.EPIDEMIC_PREVENTION_MODEL),
# 加载车牌模型
ModelType.PLATE_MODEL.value[1]: (
lambda x, y, z, r, t: IMModel(x, y, z, r, ModelType.PLATE_MODEL, t), ModelType.PLATE_MODEL.value[1]),
lambda x, y, z, r, t: IMModel(x, y, z, r, ModelType.PLATE_MODEL, t), ModelType.PLATE_MODEL),
# 加载车辆模型
ModelType.VEHICLE_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.VEHICLE_MODEL, t), ModelType.VEHICLE_MODEL.value[1]),
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.VEHICLE_MODEL, t), ModelType.VEHICLE_MODEL),
# 加载行人模型
ModelType.PEDESTRIAN_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.PEDESTRIAN_MODEL, t),
ModelType.PEDESTRIAN_MODEL.value[1]),
ModelType.PEDESTRIAN_MODEL),
# 加载烟火模型
ModelType.SMOGFIRE_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.SMOGFIRE_MODEL, t), ModelType.SMOGFIRE_MODEL.value[1]),
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.SMOGFIRE_MODEL, t), ModelType.SMOGFIRE_MODEL),
# 加载钓鱼游泳模型
ModelType.ANGLERSWIMMER_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.ANGLERSWIMMER_MODEL, t),
ModelType.ANGLERSWIMMER_MODEL.value[1]),
ModelType.ANGLERSWIMMER_MODEL),
# 加载乡村模型
ModelType.COUNTRYROAD_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.COUNTRYROAD_MODEL, t),
ModelType.COUNTRYROAD_MODEL.value[1]),
ModelType.COUNTRYROAD_MODEL),
# 加载船只模型
ModelType.SHIP_MODEL.value[1]: (
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.SHIP_MODEL, t), ModelType.SHIP_MODEL.value[1]),
lambda x, y, z, r, t: Model(x, y, z, r, ModelType.SHIP_MODEL, t), ModelType.SHIP_MODEL),
# 百度AI图片识别模型
ModelType.BAIDU_MODEL.value[1]: (
lambda x, y, z, r, t: BaiduAiImageModel(x, y, z, r, ModelType.BAIDU_MODEL, t),
ModelType.BAIDU_MODEL.value[1])
}
ModelType.BAIDU_MODEL)
# # 交通事故模型
# ModelType.TRAFFICACCIDENT_MODEL.value[1]: (
# lambda x, y, z, r, t: TrafficaccidentModel(x, y, z, r, ModelType.TRAFFICACCIDENT_MODEL, t),
# ModelType.TRAFFICACCIDENT_MODEL)
}

+ 80
- 57
util/OcrBaiduSdk.py View File

@@ -1,10 +1,10 @@
import time
from threading import Lock

import cv2
from aip import AipOcr
from loguru import logger

from enums.BaiduSdkEnum import BAIDUERRORDATA
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException

@@ -13,11 +13,12 @@ class OcrBaiduSdk:

def __init__(self, content):
self.content = content
self.client = None
self.init_client()
# self.lock = Lock()

def init_client(self):
self.client = AipOcr(str(self.content["baidu"]["orc"]["APP_ID"]), self.content["baidu"]["orc"]["API_KEY"],
if self.client is None:
self.client = AipOcr(str(self.content["baidu"]["orc"]["APP_ID"]), self.content["baidu"]["orc"]["API_KEY"],
self.content["baidu"]["orc"]["SECRET_KEY"])

'''
@@ -33,41 +34,55 @@ class OcrBaiduSdk:
'''

def universal_text_recognition(self, image, request_id):
# try:
# self.lock.acquire()
reply_num = 1
reply_num = 0
reply_value = None
while True:
try:
or_result, or_image = cv2.imencode(".jpg", image)
res_image = self.client.basicGeneral(or_image.tobytes())
if res_image.get("error_code") == 216630 or res_image.get("error_msg") == 'recognize error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") == 282403 or res_image.get("error_msg") == 'target recognize error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") == 216202 or res_image.get("error_msg") == 'image size error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") is not None:
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
raise Exception("百度云调接口失败")
options = {
"language_type": "CHN_ENG",
"detect_direction": "true",
"detect_language": "true",
"probability": "true"
}
res_image = self.client.basicGeneral(or_image.tobytes(), options)
error_code = res_image.get("error_code")
if error_code:
enum = BAIDUERRORDATA.get(error_code)
# 如果异常编码未知, 返回空值
if enum is None:
logger.error("百度云人流量统计异常！error_code:{}, request_id: {}", error_code, request_id)
return None
# 重试指定次数后，还是异常，输出统一内部异常
if enum.value[3] == 0:
if reply_value is None:
reply_value = enum.value[4]
logger.error("百度云人流量统计异常！error_code:{}, error_msg:{}, reply_num:{}, request_id: {}",
enum.value[0], enum.value[2], reply_num, request_id)
raise Exception()
# 重试指定次数后，还是异常，输出对应的异常
if enum.value[3] == 1:
if reply_value is None:
reply_value = enum.value[4]
raise ServiceException(str(enum.value[0]), enum.value[2])
# 重试指定次数后，还是异常，输出空
if enum.value[3] == 2:
if reply_value is None:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except Exception as e:
logger.exception("通用文字识别失败: {}, 当前重试次数：{}, request_id: {}", e, reply_num, request_id)
time.sleep(1)
reply_num += 1
reply_num += 0.5
self.init_client()
if reply_num > 5:
if reply_num > reply_value:
if isinstance(e, ServiceException):
raise ServiceException(e.code, e.msg)
logger.exception("通用文字识别失败: {}, request_id: {}", e, request_id)
raise ServiceException(ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[0],
ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[1])
# except Exception as ee:
# logger.exception("通用文字识别加锁异常: {}, request_id: {}", ee, request_id)
# raise ServiceException(ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[0],
# ExceptionType.UNIVERSAL_TEXT_RECOGNITION_FAILED.value[1])
# finally:
# self.lock.release()
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])

'''
{
@@ -78,40 +93,48 @@ class OcrBaiduSdk:
}
}
'''

def license_plate_recognition(self, image, request_id):
# try:
# self.lock.acquire()
reply_num = 1
reply_num = 0
reply_value = None
while True:
try:
or_result, or_image = cv2.imencode(".jpg", image)
res_image = self.client.licensePlate(or_image.tobytes())
if res_image.get("error_code") == 282403 or res_image.get("error_msg") == 'target recognize error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") == 216630 or res_image.get("error_msg") == 'recognize error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") == 216202 or res_image.get("error_msg") == 'image size error':
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
return None
if res_image.get("error_code") is not None:
logger.error("百度云调接口失败: {}, 当前重试次数：{}, request_id: {}", res_image, reply_num, request_id)
raise Exception("百度云调接口失败")
res_image = self.client.licensePlate(or_image.tobytes(), {"multi_detect": "true"})
error_code = res_image.get("error_code")
if error_code:
enum = BAIDUERRORDATA.get(error_code)
# 如果异常编码未知, 返回空值
if enum is None:
logger.error("百度云人流量统计异常！error_code:{}, request_id: {}", error_code, request_id)
return None
# 重试指定次数后，还是异常，输出统一内部异常
if enum.value[3] == 0:
if reply_value is None:
reply_value = enum.value[4]
logger.error("百度云人流量统计异常！error_code:{}, error_msg:{}, reply_num:{}, request_id: {}",
enum.value[0], enum.value[2], reply_num, request_id)
raise Exception()
# 重试指定次数后，还是异常，输出对应的异常
if enum.value[3] == 1:
if reply_value is None:
reply_value = enum.value[4]
raise ServiceException(str(enum.value[0]), enum.value[2])
# 重试指定次数后，还是异常，输出空
if enum.value[3] == 2:
if reply_value is None:
reply_value = enum.value[4]
if reply_num >= reply_value:
return None
raise Exception()
return res_image
except Exception as e:
logger.exception("车牌识别失败: {}, 当前重试次数：{}, request_id: {}", e, reply_num, request_id)
time.sleep(1)
reply_num += 1
self.init_client()
if reply_num > 5:
if reply_num > reply_value:
if isinstance(e, ServiceException):
raise ServiceException(e.code, e.msg)
logger.exception("车牌识别失败: {}, request_id: {}", e, request_id)
raise ServiceException(ExceptionType.ABNORMAL_LICENSE_PLATE_RECOGNITION.value[0],
ExceptionType.ABNORMAL_LICENSE_PLATE_RECOGNITION.value[1])
# except Exception as ee:
# logger.exception("车牌识别加锁异常: {}, request_id: {}", ee, request_id)
# raise ServiceException(ExceptionType.ABNORMAL_LICENSE_PLATE_RECOGNITION.value[0],
# ExceptionType.ABNORMAL_LICENSE_PLATE_RECOGNITION.value[1])
# finally:
# self.lock.release()
raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1])


+ 4
- 6
util/YmlUtils.py View File

@@ -4,15 +4,13 @@ from common import Constant


# 从配置文件读取所有配置信息
def getConfigs():
print("开始读取配置文件，获取配置消息：", Constant.APPLICATION_CONFIG)
applicationConfigPath = os.path.abspath(Constant.APPLICATION_CONFIG)
def getConfigs(base_dir):
applicationConfigPath = os.path.join(base_dir, Constant.APPLICATION_CONFIG)
if not os.path.exists(applicationConfigPath):
raise Exception("未找到配置文件：{}".format(Constant.APPLICATION_CONFIG))
raise Exception("未找到配置文件：%s" % applicationConfigPath)
with open(applicationConfigPath, Constant.R, encoding=Constant.UTF_8) as f:
file_content = f.read()
content = yaml.load(file_content, yaml.FullLoader)
if not content:
raise Exception("配置项不能为空：{}".format(Constant.APPLICATION_CONFIG))
print("读取配置文件完成！")
raise Exception("配置项不能为空：%s" % applicationConfigPath)
return content

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