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Author SHA1 Message Date
th 615d96c707 ini 2025-04-26 10:18:49 +08:00
102 changed files with 2358 additions and 3289 deletions
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8
README.md
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@ -1,8 +0,0 @@
1.2025.01.21把之前的tuoheng alg仓库代码重新开个仓库 (1)在config/service/dsp_test_service.yml里面添加参数，控制存储用的oss还是minio storage_source: 1 2.2025.02.06 (1)修改代码，把mqtt读取加入到系统中。config/service/dsp_test_service.yml，中添加mqtt_flag,决定是否启用。 (2)修改了minio情况下的，文件名命名方式。 3.2025.02.12 (1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。
4、2025.07.10 周树亮 - 增加人群计数，自研车牌模型，裸土覆盖3个场景
5、江朝庆 -- 0715
1）代码整理，删除冗余代码。
2）增加requirements.txt,方便部署
3) logs

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4
common/Constant.py
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@ -16,6 +16,7 @@ success_progess = "1.0000"
width = 1400
COLOR = (
[0, 0, 255],
[255, 0, 0],
[211, 0, 148],
[0, 127, 0],
@ -34,8 +35,7 @@ COLOR = (
[8, 101, 139],
[171, 130, 255],
[139, 112, 74],
[205, 205, 180],
[0, 0, 255],)
[205, 205, 180])
ONLINE = "online"
OFFLINE = "offline"

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207
concurrency/FileUploadThread.py
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@ -3,56 +3,36 @@ from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from time import sleep, time
from traceback import format_exc
import numpy as np
from loguru import logger
import cv2
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType
from exception.CustomerException import ServiceException
from util.AliyunSdk import AliyunOssSdk
from util.MinioSdk import MinioSdk
from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus
from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd,draw_transparent_red_polygon
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue, get_no_block_queue, clear_queue
import io
from util.LocationUtils import locate_byMqtt
class FileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg','_mqtt_list')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type,self._mqtt_list = args
self._storage_source = self._context['service']['storage_source']
self._algStatus = False # 默认关闭
# self._algStatus = True # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
# 0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
print("执行默认程序（defaultEnabled=True）")
self._algSwitch = True
# 这里放默认逻辑的代码
else:
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self._algSwitch = False
print("---line46 :FileUploadThread.py---", self._algSwitch)
# 如果任务是在线、离线处理，则用此类
self._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
#如果任务是在线、离线处理，则用此类
class ImageFileUpload(FileUpload):
__slots__ = ()
# @staticmethod
def handle_image(self, frame_msg, frame_step):
#@staticmethod
def handle_image(self,frame_msg, frame_step):
# (high_score_image["code"], all_frames, draw_config["font_config"])
# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
det_xywh, frame, current_frame, all_frames, font_config = frame_msg
@ -65,40 +45,26 @@ class ImageFileUpload(FileUpload):
模型编号：modeCode
检测目标：detectTargetCode
'''
aFrame = frame.copy()
igH, igW = aFrame.shape[0:2]
print('*'*100,' mqtt_list:',len(self._mqtt_list))
model_info = []
mqttPares= det_xywh['mqttPares']
border = None
gps = [None, None]
camParas = None
if mqttPares is not None:
if mqttPares[0] == 1:
border = mqttPares[1]
elif mqttPares[0] == 0:
camParas = mqttPares[1]
if border is not None:
aFrame = draw_transparent_red_polygon(aFrame, np.array(border, np.int32), alpha=0.25)
det_xywh.pop('mqttPares')
# 更加模型编码解析数据
for code, det_list in det_xywh.items():
if len(det_list) > 0:
for cls, target_list in det_list.items():
if len(target_list) > 0:
aFrame = frame.copy()
for target in target_list:
if camParas is not None:
gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
# 自研车牌模型判断
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
draw_name_ocr(target[1], aFrame, target[4])
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target[1], aFrame, target[4])
else:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
target[5],border)
model_info.append(
{"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5])
igH,igW = aFrame.shape[0:2]
if len(self._mqtt_list)>=1:
#camParas = self._mqtt_list[0]['data']
camParas = self._mqtt_list[0]
gps = locate_byMqtt(target[1],igW,igH,camParas,outFormat='wgs84')
else:
gps=[None,None]
model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame,'gps':gps})
if len(model_info) > 0:
image_result = {
"or_frame": frame,
@ -117,15 +83,13 @@ class ImageFileUpload(FileUpload):
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(service["timeout"])
frame_step = int(service["filter"]["frame_step"]) + 120
if msg['taskType'] == 0:
self._algStatus = False
else:
self._algStatus = True
if msg['taskType']==0: self._algStatus = False
else: self._algStatus = True
try:
with ThreadPoolExecutor(max_workers=2) as t:
# 初始化oss客户端
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
@ -139,16 +103,15 @@ class ImageFileUpload(FileUpload):
# 获取队列中的消息
image_msg = get_no_block_queue(image_queue)
if image_msg is not None:
if image_msg[0] == 2:
logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
logger.info("图片上传线程收到命令:{}, requestId: {}",image_msg[1] ,request_id)
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if 'algStart' == image_msg[1]: self._algStatus = True; logger.info(
"图片上传线程,执行算法开启命令, requestId:{}", request_id)
if 'algStop' == image_msg[1]: self._algStatus = False; logger.info(
"图片上传线程,执行算法关闭命令, requestId:{}", request_id)
if 'algStart' == image_msg[1]: self._algStatus = True; logger.info("图片上传线程,执行算法开启命令, requestId:{}", request_id)
if 'algStop' == image_msg[1]: self._algStatus = False; logger.info("图片上传线程,执行算法关闭命令, requestId:{}", request_id)
if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step)
if image_result is not None:
@ -158,8 +121,8 @@ class ImageFileUpload(FileUpload):
image_result["last_frame"],
analyse_type,
"OR", "0", "0", request_id)
if self._storage_source == 1:
or_future = t.submit(minioSdk.put_object, or_image, or_image_name)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
else:
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
task.append(or_future)
@ -174,38 +137,38 @@ class ImageFileUpload(FileUpload):
model_info["modelCode"],
model_info["detectTargetCode"],
request_id)
if self._storage_source == 1:
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image,
ai_image_name)
ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
ai_image.tobytes())
task.append(ai_future)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# or_image_name,
# ai_image_name,
# model_info['modelCode'],
# model_info['detectTargetCode']))
remote_image_list = []
remote_image_list=[]
for tk in task:
remote_image_list.append(tk.result())
remote_image_list.append( tk.result())
for ii, model_info in enumerate(model_info_list):
msg_list.append(message_feedback(request_id,
for ii,model_info in enumerate(model_info_list):
msg_list.append( message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
remote_image_list[0],
remote_image_list[ii + 1],
remote_image_list[ii+1],
model_info['modelCode'],
model_info['detectTargetCode'],
longitude=model_info['gps'][0],
latitude=model_info['gps'][1],
))
if (not self._algSwitch) or (self._algStatus and self._algSwitch):
) )
if (not self._algSwitch) or ( self._algStatus and self._algSwitch):
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
del task, msg_list
@ -230,9 +193,9 @@ def build_image_name(*args):
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
return "%s/%s_frame-%s-%s_type_%s-%s-%s-%s_%s.jpg" % (request_id, time_now, current_frame, last_frame,
random_num, mode_type, modeCode, target, image_type)
# 如果任务是图像处理，则用此类
#如果任务是图像处理，则用此类
class ImageTypeImageFileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
@ -240,7 +203,6 @@ class ImageTypeImageFileUpload(Thread):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
@staticmethod
def handle_image(det_xywh, copy_frame, font_config):
"""
@ -260,21 +222,12 @@ class ImageTypeImageFileUpload(Thread):
if target_list is not None and len(target_list) > 0:
aiFrame = copy_frame.copy()
for target in target_list:
# 自研车牌模型判断
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
draw_name_ocr(target, aiFrame, font_config[cls])
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or \
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target, aiFrame, font_config[cls])
else:
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
model_info.append({
"modelCode": str(code),
"detectTargetCode": str(cls),
"frame": aiFrame
})
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
model_info.append({
"modelCode": str(code),
"detectTargetCode": str(cls),
"frame": aiFrame
})
if len(model_info) > 0:
image_result = {
"or_frame": copy_frame,
@ -294,11 +247,11 @@ class ImageTypeImageFileUpload(Thread):
with ThreadPoolExecutor(max_workers=2) as t:
try:
# 初始化oss客户端
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
@ -319,15 +272,15 @@ class ImageTypeImageFileUpload(Thread):
if det_xywh is None:
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id)
if self._storage_source == 1:
ai_future = t.submit(minioSdk.put_object, copy_frame, ai_image_name)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future)
remote_names.append(ai_image_name)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# image_url,
@ -338,17 +291,17 @@ class ImageTypeImageFileUpload(Thread):
else:
image_result = self.handle_image(det_xywh, copy_frame, font_config)
if image_result:
# 图片帧数编码
if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
image_url_0 = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
if self._storage_source == 1:
or_future = t.submit(minioSdk.put_object, or_image, image_url_0)
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,image_url_0)
else:
or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
or_image.tobytes())
@ -364,14 +317,14 @@ class ImageTypeImageFileUpload(Thread):
model_info.get("modelCode"),
model_info.get("detectTargetCode"),
request_id)
if self._storage_source == 1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else:
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
remote_names.append(ai_image_name)
# msg_list.append(message_feedback(request_id,
#msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value,
# analyse_type, "", "", "",
# image_url,
@ -382,8 +335,9 @@ class ImageTypeImageFileUpload(Thread):
remote_url_list = []
for thread_result in task:
remote_url_list.append(thread_result.result())
# 以下代码是为了获取图像上传后，返回的全路径地址
#以下代码是为了获取图像上传后，返回的全路径地址
if det_xywh is None:
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
@ -396,12 +350,12 @@ class ImageTypeImageFileUpload(Thread):
else:
if image_result:
if image_url is None:
for ii in range(len(remote_names) - 1):
for ii in range(len(remote_names)-1):
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
remote_url_list[0],
remote_url_list[1 + ii],
remote_url_list[1+ii],
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
analyse_results=result))
@ -413,10 +367,13 @@ class ImageTypeImageFileUpload(Thread):
image_url,
remote_url_list[ii],
model_info_list[ii].get('modelCode'),
model_info_list[ii].get(
'detectTargetCode'),
model_info_list[ii].get('detectTargetCode'),
analyse_results=result))
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
else:

305
concurrency/FileUploadThread_20250106.py Normal file
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@ -0,0 +1,305 @@
# -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
import cv2
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.AliyunSdk import AliyunOssSdk
from util.MinioSdk import MinioSdk
from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus
from util.PlotsUtils import draw_painting_joint
from util.QueUtil import put_queue, get_no_block_queue, clear_queue
import io
class FileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
class ImageFileUpload(FileUpload):
__slots__ = ()
@staticmethod
def handle_image(frame_msg, frame_step):
# (high_score_image["code"], all_frames, draw_config["font_config"])
# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
det_xywh, frame, current_frame, all_frames, font_config = frame_msg
'''
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
模型编号：modeCode
检测目标：detectTargetCode
'''
model_info = []
# 更加模型编码解析数据
for code, det_list in det_xywh.items():
if len(det_list) > 0:
for cls, target_list in det_list.items():
if len(target_list) > 0:
aFrame = frame.copy()
for target in target_list:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5])
model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame})
if len(model_info) > 0:
image_result = {
"or_frame": frame,
"model_info": model_info,
"current_frame": current_frame,
"last_frame": current_frame + frame_step
}
return image_result
return None
def run(self):
msg, context = self._msg, self._context
service = context["service"]
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(service["timeout"])
frame_step = int(service["filter"]["frame_step"]) + 120
try:
with ThreadPoolExecutor(max_workers=2) as t:
# 初始化oss客户端
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("图片上传线程运行超时, requestId: {}", request_id)
break
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
# 获取队列中的消息
image_msg = get_no_block_queue(image_queue)
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step)
if image_result is not None:
task = []
or_image = cv2.imencode(".jpg", image_result["or_frame"])[1]
or_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"OR", "0", "0", request_id)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
else:
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
task.append(or_future)
model_info_list = image_result["model_info"]
msg_list = []
for model_info in model_info_list:
ai_image = cv2.imencode(".jpg", model_info["aFrame"])[1]
ai_image_name = build_image_name(image_result["current_frame"],
image_result["last_frame"],
analyse_type,
"AI",
model_info["modelCode"],
model_info["detectTargetCode"],
request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image,
ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
or_image_name,
ai_image_name,
model_info['modelCode'],
model_info['detectTargetCode']))
for tk in task:
tk.result()
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
del task, msg_list
else:
sleep(1)
del image_msg
except Exception:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
logger.info("停止图片上传线程0, requestId:{}", request_id)
clear_queue(image_queue)
logger.info("停止图片上传线程1, requestId:{}", request_id)
def build_image_name(*args):
"""
{requestId}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
"-{modeCode}-{target}_{image_type}.jpg
"""
current_frame, last_frame, mode_type, image_type, modeCode, target, request_id = args
random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
return "%s/%s_frame-%s-%s_type_%s-%s-%s-%s_%s.jpg" % (request_id, time_now, current_frame, last_frame,
random_num, mode_type, modeCode, target, image_type)
class ImageTypeImageFileUpload(Thread):
__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
def __init__(self, *args):
super().__init__()
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
self._storage_source = self._context['service']['storage_source']
@staticmethod
def handle_image(det_xywh, copy_frame, font_config):
"""
det_xywh:{
'code':{
1: [[detect_targets_code, box, score, label_array, color]]
}
}
模型编号：modeCode
检测目标：detectTargetCode
"""
model_info = []
# 更加模型编码解析数据
for code, det_info in det_xywh.items():
if det_info is not None and len(det_info) > 0:
for cls, target_list in det_info.items():
if target_list is not None and len(target_list) > 0:
aiFrame = copy_frame.copy()
for target in target_list:
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
model_info.append({
"modelCode": str(code),
"detectTargetCode": str(cls),
"frame": aiFrame
})
if len(model_info) > 0:
image_result = {
"or_frame": copy_frame,
"model_info": model_info,
"current_frame": 0,
"last_frame": 0
}
return image_result
return None
def run(self):
context, msg = self._context, self._msg
base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
logger.info("启动图片识别图片上传线程, requestId: {}", request_id)
image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
service_timeout = int(context["service"]["timeout"])
with ThreadPoolExecutor(max_workers=2) as t:
try:
# 初始化oss客户端
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time()
while True:
try:
if time() - start_time > service_timeout:
logger.error("图片上传进程运行超时, requestId: {}", request_id)
break
# 获取队列中的消息
image_msg = image_queue.get()
if image_msg is not None:
if image_msg[0] == 2:
if 'stop' == image_msg[1]:
logger.info("开始停止图片上传线程, requestId:{}", request_id)
break
if image_msg[0] == 1:
task, msg_list = [], []
det_xywh, image_url, copy_frame, font_config, result = image_msg[1]
if det_xywh is None:
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
result.get("modelCode"),
result.get("type"),
analyse_results=result))
else:
image_result = self.handle_image(det_xywh, copy_frame, font_config)
if image_result:
# 图片帧数编码
if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
image_url = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"OR", "0", "O", request_id)
if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,image_url)
else:
or_future = t.submit(aliyunOssSdk.put_object, image_url,
or_image.tobytes())
task.append(or_future)
model_info_list = image_result.get("model_info")
for model_info in model_info_list:
ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
ai_image_name = build_image_name(image_result.get("current_frame"),
image_result.get("last_frame"),
analyse_type,
"AI",
model_info.get("modelCode"),
model_info.get("detectTargetCode"),
request_id)
if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes())
task.append(ai_future)
msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value,
analyse_type, "", "", "",
image_url,
ai_image_name,
model_info.get('modelCode'),
model_info.get('detectTargetCode'),
analyse_results=result))
for thread_result in task:
thread_result.result()
for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False)
else:
sleep(1)
except Exception as e:
logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
finally:
clear_queue(image_queue)
logger.info("停止图片识别图片上传线程, requestId:{}", request_id)

255
concurrency/IntelligentRecognitionProcess.py
View File

@ -62,9 +62,8 @@ class IntelligentRecognitionProcess(Process):
# 发送waitting消息
put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
self._storage_source = self._context['service']['storage_source']
self._storage_source = self._context['service']['storage_source']
self._algStatus = False
def sendEvent(self, eBody):
put_queue(self.event_queue, eBody, timeout=2, is_ex=True)
@ -92,6 +91,9 @@ class IntelligentRecognitionProcess(Process):
hb_thread.start()
return hb_thread
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = ()
@ -111,16 +113,19 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
pullProcess.start()
return pullProcess
def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id)
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_or = Common(minioSdk.put_object, orFilePath, "or_online_%s.mp4" % request_id)
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
else:
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_or = Common(aliyunVodSdk.get_play_url, orFilePath, "or_online_%s" % request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_or.setDaemon(True)
upload_video_thread_ai.setDaemon(True)
upload_video_thread_or.start()
@ -128,7 +133,6 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
or_url = upload_video_thread_or.get_result()
ai_url = upload_video_thread_ai.get_result()
return or_url, ai_url
'''
@staticmethod
def upload_video(base_dir, env, request_id, orFilePath, aiFilePath):
@ -142,7 +146,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
or_url = upload_video_thread_or.get_result()
ai_url = upload_video_thread_ai.get_result()
return or_url, ai_url
'''
'''
@staticmethod
def ai_normal_dtection(model, frame, request_id):
@ -222,10 +226,10 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
ex = None
# 拉流进程、推流进程、心跳线程
pull_process, push_process, hb_thread = None, None, None
# 事件队列、拉流队列、心跳队列、反馈队列
event_queue, pull_queue, hb_queue, fb_queue = self.event_queue, self._pull_queue, self._hb_queue, self._fb_queue
# 推流队列、推流异常队列、图片队列
push_queue, push_ex_queue, image_queue = self._push_queue, self._push_ex_queue, self._image_queue
try:
@ -233,18 +237,19 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
init_log(base_dir, env)
# 打印启动日志
logger.info("开始启动实时分析进程！requestId: {}", request_id)
# 启动拉流进程(包含拉流线程, 图片上传线程,mqtt读取线程)
# 拉流进程初始化时间长, 先启动
pull_process = self.start_pull_stream(msg, context, fb_queue, pull_queue, image_queue, analyse_type, 25)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
# 启动心跳线程
hb_thread = self.start_heartbeat(fb_queue, hb_queue, request_id, analyse_type, context)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
# 加载算法模型
model_array = get_model(msg, context, analyse_type)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
# 启动推流进程
push_process = self.start_push_stream(msg, push_queue, image_queue, push_ex_queue, hb_queue, context)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
@ -268,7 +273,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
raise ServiceException(push_status[1], push_status[2])
# 获取停止指令
event_result = get_no_block_queue(event_queue)
if event_result:
cmdStr = event_result.get("command")
#接收到算法开启、或者关闭的命令
@ -276,7 +281,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
pull_process.sendCommand({"command": cmdStr})
# 接收到停止指令
if "stop" == cmdStr:
logger.info("实时任务开始停止, requestId: {}", request_id)
@ -296,44 +301,32 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1
for i, model in enumerate(model_array):
model_conf, code = model
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config["font_config"] = model_conf[4]
draw_config[code]["allowedList"] = 0
draw_config[code]["label_arrays"] = [None]
draw_config[code]["rainbows"] = model_conf[4]
else:
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config[code]["allowedList"] = model_conf[2]
draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["label_arrays"] = model_param['label_arraylist']
if "label_dict" in model_param:
draw_config[code]["label_dict"] = model_param['label_dict']
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config[code]["allowedList"] = model_conf[2]
draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["label_arrays"] = model_param['label_arraylist']
if "label_dict" in model_param:
draw_config[code]["label_dict"] = model_param['label_dict']
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
# 多线程并发处理, 经过测试两个线程最优
det_array = []
for i, [frame,_] in enumerate(frame_list):
for i, frame in enumerate(frame_list):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
frame_index_list[i], tt, request_id)
det_array.append(det_result)
push_objs = [det.result() for det in det_array]
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
put_queue(push_queue,
(1, (frame_list, frame_index_list, all_frames, draw_config, push_objs)),
timeout=2, is_ex=True)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
del det_array, push_objs
del frame_list, frame_index_list, all_frames
elif pull_result[0] == 1:
@ -444,23 +437,23 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = ()
def upload_video(self, base_dir, env, request_id, aiFilePath):
def upload_video(self,base_dir, env, request_id, aiFilePath):
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_ai.setDaemon(True)
upload_video_thread_ai.start()
ai_url = upload_video_thread_ai.get_result()
return ai_url
'''
@staticmethod
def upload_video(base_dir, env, request_id, aiFilePath):
@ -471,7 +464,6 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
ai_url = upload_video_thread_ai.get_result()
return ai_url
'''
@staticmethod
def ai_normal_dtection(model, frame, request_id):
model_conf, code = model
@ -610,7 +602,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
pull_process.sendCommand({"command": cmdStr})
pull_result = get_no_block_queue(pull_queue)
if pull_result is None:
sleep(1)
@ -624,34 +616,21 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1
for i, model in enumerate(model_array):
model_conf, code = model
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config["font_config"] = model_conf[4]
draw_config[code]["allowedList"] = 0
draw_config[code]["label_arrays"] = [None]
draw_config[code]["rainbows"] = model_conf[4]
else:
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config[code]["allowedList"] = model_conf[2]
draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["label_arrays"] = model_param['label_arraylist']
if "label_dict" in model_param:
draw_config[code]["label_dict"] = model_param['label_dict']
model_param = model_conf[1]
# (modeType, model_param, allowedList, names, rainbows)
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
model_conf)
if draw_config.get("font_config") is None:
draw_config["font_config"] = model_param['font_config']
if draw_config.get(code) is None:
draw_config[code] = {}
draw_config[code]["allowedList"] = model_conf[2]
draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["label_arrays"] = model_param['label_arraylist']
if "label_dict" in model_param:
draw_config[code]["label_dict"] = model_param['label_dict']
det_array = []
for i, [frame,_] in enumerate(frame_list):
for i, frame in enumerate(frame_list):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
frame_index_list[i], tt, request_id)
det_array.append(det_result)
@ -766,7 +745,7 @@ class PhotosIntelligentRecognitionProcess(Process):
put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
self.build_logo(self._msg, self._context)
self._storage_source = self._context['service']['storage_source']
self._storage_source = self._context['service']['storage_source']
@staticmethod
def build_logo(msg, context):
@ -943,62 +922,6 @@ class PhotosIntelligentRecognitionProcess(Process):
logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
raise e
# 自研究车牌模型
def carplate_rec(self, imageUrl, mod, image_queue, request_id):
try:
# model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
model_conf, code = mod
modeType, device, modelList, detpar, rainbows = model_conf
image = url2Array(imageUrl)
dets = {code: {}}
# param = [image, new_device, model, par, img_type, request_id]
# model_conf, frame, device, requestId
dataBack = MODEL_CONFIG[code][3]([[modeType, device, modelList, detpar], image, request_id])[0][2]
dets[code][0] = dataBack
if not dataBack:
logger.info("车牌识别为空")
# for ai_result in dataBack:
# label, box = ai_result
# color = rainbows
if len(dataBack) > 0:
put_queue(image_queue, (1, (dets, imageUrl, image, rainbows, "")), timeout=2, is_ex=False)
except ServiceException as s:
raise s
except Exception as e:
logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
raise e
#密集人群计数
def denscrowdcount_rec(self, imageUrl, mod, image_queue, request_id):
try:
# model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
model_conf, code = mod
modeType, device, model, postPar, rainbows = model_conf
image = url2Array(imageUrl)
dets = {code: {}}
# param = [image, new_device, model, par, img_type, request_id]
# model_conf, frame, device, requestId
dataBack = MODEL_CONFIG[code][3]([[modeType, device, model, postPar], image, request_id])[0][2]
dets[code][0] = dataBack
if not dataBack:
logger.info("当前页面无人")
# for ai_result in dataBack:
# label, box = ai_result
# color = rainbows
if len(dataBack) > 0:
put_queue(image_queue, (1, (dets, imageUrl, image, rainbows, '')), timeout=2, is_ex=False)
except ServiceException as s:
raise s
except Exception as e:
logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
raise e
'''
# 防疫模型
'''
@ -1013,26 +936,6 @@ class PhotosIntelligentRecognitionProcess(Process):
for r in obj_list:
r.result(60)
# 自研车牌识别模型：
def carpalteRec(self, imageUrls, model, image_queue, request_id):
with ThreadPoolExecutor(max_workers=2) as t:
obj_list = []
for imageUrl in imageUrls:
obj = t.submit(self.carplate_rec, imageUrl, model, image_queue, request_id)
obj_list.append(obj)
for r in obj_list:
r.result(60)
# 密集人群计数：CITY_DENSECROWDCOUNT_MODEL
def denscrowdcountRec(self, imageUrls, model, image_queue, request_id):
with ThreadPoolExecutor(max_workers=2) as t:
obj_list = []
for imageUrl in imageUrls:
obj = t.submit(self.denscrowdcount_rec, imageUrl, model, image_queue, request_id)
obj_list.append(obj)
for r in obj_list:
r.result(60)
def image_recognition(self, imageUrl, mod, image_queue, logo, request_id):
try:
model_conf, code = mod
@ -1054,8 +957,6 @@ class PhotosIntelligentRecognitionProcess(Process):
ai_result_list = p_result[2]
for ai_result in ai_result_list:
box, score, cls = xywh2xyxy2(ai_result)
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(ai_result[-1])
# 如果检测目标在识别任务中,继续处理
if cls in allowedList:
label_array = label_arraylist[cls]
@ -1213,8 +1114,7 @@ class PhotosIntelligentRecognitionProcess(Process):
image_thread.setDaemon(True)
image_thread.start()
return image_thread
def check_ImageUrl_Vaild(self, url, timeout=1):
def check_ImageUrl_Vaild(self,url,timeout=1):
try:
# 发送 HTTP 请求，尝试访问图片
response = requests.get(url, timeout=timeout) # 设置超时时间为 10 秒
@ -1225,7 +1125,7 @@ class PhotosIntelligentRecognitionProcess(Process):
except requests.exceptions.RequestException as e:
# 捕获请求过程中可能出现的异常（如网络问题、超时等）
return False,str(e)
def run(self):
fb_queue, msg, analyse_type, context = self._fb_queue, self._msg, self._analyse_type, self._context
request_id, logo, image_queue = msg["request_id"], context['logo'], self._image_queue
@ -1236,7 +1136,7 @@ class PhotosIntelligentRecognitionProcess(Process):
valFlag=True
for url in imageUrls:
valFlag,ret = self.check_ImageUrl_Vaild(url,timeout=1)
if not valFlag:
logger.error("图片分析异常: {}, requestId:{},url:{}",ret, request_id,url)
#print("AnalysisStatus.FAILED.value:{}，ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0]:{},ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]:{}".format(AnalysisStatus.FAILED.value,ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1] ) )
@ -1245,7 +1145,8 @@ class PhotosIntelligentRecognitionProcess(Process):
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2)
return
return
with ThreadPoolExecutor(max_workers=1) as t:
try:
@ -1267,15 +1168,6 @@ class PhotosIntelligentRecognitionProcess(Process):
elif model[1] == ModelType.PLATE_MODEL.value[1]:
result = t.submit(self.epidemicPrevention, imageUrls, model, base_dir, env, request_id)
task_list.append(result)
# 自研车牌模型
elif model[1] == ModelType.CITY_CARPLATE_MODEL.value[1]:
result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
task_list.append(result)
# 人群计数模型
elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] or \
model[1] == ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]:
result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
task_list.append(result)
else:
result = t.submit(self.publicIdentification, imageUrls, model, image_queue, logo, request_id)
task_list.append(result)
@ -1322,8 +1214,7 @@ class ScreenRecordingProcess(Process):
put_queue(self._fb_queue,
recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]),
timeout=1, is_ex=True)
self._storage_source = self._context['service']['storage_source']
self._storage_source = self._context['service']['storage_source']
def sendEvent(self, result):
put_queue(self._event_queue, result, timeout=2, is_ex=True)
@ -1489,19 +1380,21 @@ class ScreenRecordingProcess(Process):
clear_queue(self._hb_queue)
clear_queue(self._pull_queue)
def upload_video(self, base_dir, env, request_id, orFilePath):
if self._storage_source == 1:
minioSdk = MinioSdk(base_dir, env, request_id)
def upload_video(self,base_dir, env, request_id, orFilePath):
if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "%s/ai_online.mp4" % request_id)
else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_ai.setDaemon(True)
upload_video_thread_ai.start()
or_url = upload_video_thread_ai.get_result()
return or_url
'''
@staticmethod
def upload_video(base_dir, env, request_id, orFilePath):

1444
concurrency/IntelligentRecognitionProcess_20250106.py Normal file
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303
concurrency/PullMqttThread.py
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@ -1,163 +1,142 @@
# -*- coding: utf-8 -*-
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
from common.YmlConstant import mqtt_yml_path
from util.RWUtils import getConfigs
from common.Constant import init_progess
from enums.AnalysisStatusEnum import AnalysisStatus
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
from multiprocessing import Process, Queue
import paho.mqtt.client as mqtt
import json,os
class PullMqtt(Thread):
__slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context" ,'__business')
def __init__(self, *args):
super().__init__()
self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context, self.__business = args
base_dir, env = self._context["base_dir"], self._context["env"]
self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
if self.__business == 0:
self.__broker = self.__config['location']["broker"]
self.__port = self.__config['location']["port"]
self.__topic = self.__config['location']["topic"]
elif self.__business == 1:
self.__broker = self.__config['invade']["broker"]
self.__port = self.__config['invade']["port"]
self.__topic = self.__config['invade']["topic"]
self.__lengthMqttList = self.__config["length"]
def put_queue(self,__queue,data):
if __queue.full():
a = __queue.get()
__queue.put( data,block=True, timeout=2 )
def on_connect(self,client,userdata,flags,rc):
client.subscribe(self.__topic)
# 当接收到MQTT消息时，回调函数
def on_location(self,client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
#logger.info(str(data))
# 解析位姿信息
lon = data.get("lon")
lat = data.get("lat")
alt = data.get("alt")
yaw = data.get("yaw")
pitch = data.get("pitch")
roll = data.get("roll")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append([self.__business,data])
# 打印无人机的位姿信息
#print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def on_invade(self, client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
# logger.info(str(data))
# 解析位姿信息
points = data.get("points")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append([self.__business,points])
# 打印无人机的位姿信息
# print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def mqtt_connect(self):
# 创建客户端
self.client = mqtt.Client()
self.client.on_connect = self.on_connect
if self.__business == 0:
# 设置回调函数
self.client.on_message = self.on_location
elif self.__business == 1:
# 设置回调函数
self.client.on_message = self.on_invade
# 连接到 Broker
self.client.connect(self.__broker, self.__port)
# 订阅主题
self.client.subscribe(self.__topic)
# 循环等待并处理网络事件
self.client.loop_forever()
def mqtt_disconnect(self):
start_time = time()
while True:
if time() - start_time > service_timeout:
logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
client.loop_stop() # 停止循环
client.disconnect() # 断开连接
def run(self):
request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
#service_timeout = int(self.__config["service"]["timeout"]) + 120
try:
logger.info("开始MQTT读取线程！requestId:{}", request_id)
mqtt_init_num = 0
self.mqtt_connect()
except Exception:
logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
mqtt_list = []
logger.info("MQTT线程停止完成！requestId:{}", request_id)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
def start_PullVideo(mqtt_list):
for i in range(1000):
sleep(1)
if len(mqtt_list)>=10:
print( mqtt_list[4])
print(i,len(mqtt_list))
if __name__=="__main__":
#context = {'service':{'timeout':3600},'mqtt':{
# 'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
# }
context = {
'base_dir':'/home/th/WJ/test/tuoheng_algN',
'env':'test'
}
analyse_type = '1'
request_id = '123456789'
event_queue, pull_queue, mqtt_list, image_queue, push_queue, push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
fb_queue = Queue()
mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
start_PullVideo(mqtt_list)
print('---line117--')
#mqtt_thread.join()
# -*- coding: utf-8 -*-
from threading import Thread
from time import sleep, time
from traceback import format_exc
from loguru import logger
from common.YmlConstant import mqtt_yml_path
from util.RWUtils import getConfigs
from common.Constant import init_progess
from enums.AnalysisStatusEnum import AnalysisStatus
from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType
from exception.CustomerException import ServiceException
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
from multiprocessing import Process, Queue
import paho.mqtt.client as mqtt
import json,os
class PullMqtt(Thread):
__slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context")
def __init__(self, *args):
super().__init__()
self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context = args
base_dir, env = self._context["base_dir"], self._context["env"]
self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
self.__broker = self.__config["broker"]
self.__port = self.__config["port"]
self.__topic = self.__config["topic"]
self.__lengthMqttList = self.__config["length"]
def put_queue(self,__queue,data):
if __queue.full():
a = __queue.get()
__queue.put( data,block=True, timeout=2 )
def on_connect(self,client,userdata,flags,rc):
client.subscribe(self.__topic)
# 当接收到MQTT消息时，回调函数
def on_message(self,client, userdata, msg):
# 将消息解码为JSON格式
payload = msg.payload.decode('utf-8')
data = json.loads(payload)
#logger.info(str(data))
# 解析位姿信息
lon = data.get("lon")
lat = data.get("lat")
alt = data.get("alt")
yaw = data.get("yaw")
pitch = data.get("pitch")
roll = data.get("roll")
if len(self.__mqtt_list) == self.__lengthMqttList:
self.__mqtt_list.pop(0)
self.__mqtt_list.append(data)
# 打印无人机的位姿信息
#print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
def mqtt_connect(self):
# 创建客户端
self.client = mqtt.Client()
self.client.on_connect = self.on_connect
# 设置回调函数
self.client.on_message = self.on_message
# 连接到 Broker
self.client.connect(self.__broker, self.__port)
# 订阅主题
self.client.subscribe(self.__topic)
# 循环等待并处理网络事件
self.client.loop_forever()
def mqtt_disconnect(self):
start_time = time()
while True:
if time() - start_time > service_timeout:
logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
client.loop_stop() # 停止循环
client.disconnect() # 断开连接
def run(self):
request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
#service_timeout = int(self.__config["service"]["timeout"]) + 120
try:
logger.info("开始MQTT读取线程！requestId:{}", request_id)
mqtt_init_num = 0
self.mqtt_connect()
except Exception:
logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
finally:
mqtt_list = []
logger.info("MQTT线程停止完成！requestId:{}", request_id)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
def start_PullVideo(mqtt_list):
for i in range(1000):
sleep(1)
if len(mqtt_list)>=10:
print( mqtt_list[4])
print(i,len(mqtt_list))
if __name__=="__main__":
#context = {'service':{'timeout':3600},'mqtt':{
# 'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
# }
context = {
'base_dir':'/home/th/WJ/test/tuoheng_algN',
'env':'test'
}
analyse_type = '1'
request_id = '123456789'
event_queue, pull_queue, mqtt_list, image_queue, push_queue, push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
fb_queue = Queue()
mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
start_PullVideo(mqtt_list)
print('---line117--')
#mqtt_thread.join()

40
concurrency/PullVideoStreamProcess.py
View File

@ -35,15 +35,15 @@ class PullVideoStreamProcess(Process):
put_queue(self._command_queue, result, timeout=2, is_ex=True)
@staticmethod
def start_File_upload(fb_queue, context, msg, image_queue, analyse_type):
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type)
def start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list):
image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
image_thread.setDaemon(True)
image_thread.start()
return image_thread
@staticmethod
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business)
def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
mqtt_thread.setDaemon(True)
mqtt_thread.start()
return mqtt_thread
@ -81,14 +81,13 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
# 初始化日志
init_log(base_dir, env)
logger.info("开启启动实时视频拉流进程, requestId:{},pid:{},ppid:{}", request_id,os.getpid(),os.getppid())
# 开启mqtt
if service['mqtt']["flag"] == 1:
business = service['mqtt']["business"]
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
#开启mqtt
if service["mqtt_flag"]==1:
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
start_time, pull_stream_start_time, read_start_time, full_timeout = time(), None, None, None
while True:
@ -130,7 +129,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if pull_queue.full():
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
@ -172,7 +171,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
sleep(1)
continue
init_pull_num, read_start_time = 1, None
frame_list.append([frame, mqtt_list])
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
@ -223,11 +222,10 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
def run(self):
msg, context, frame_num, analyse_type = self._msg, self._context, self._frame_num, self._analyse_type
request_id, base_dir, env, pull_url, service = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"], context["service"]
request_id, base_dir, env, pull_url = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"]
ex, service_timeout, full_timeout = None, int(context["service"]["timeout"]) + 120, None
command_queue, pull_queue, image_queue, fb_queue, mqtt_list = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue, self._mqtt_list
command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
self._fb_queue
image_thread, pull_p = None, None
width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
frame_list, frame_index_list = [], []
@ -237,12 +235,8 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
init_log(base_dir, env)
logger.info("开启离线视频拉流进程, requestId:{}", request_id)
#开启mqtt
if service['mqtt']["flag"]==1:
business = service['mqtt']["business"]
mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
# 开启图片上传线程
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,[])
# 初始化拉流工具类
cv2_init_num, concurrent_frame = 0, 1
@ -275,7 +269,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
continue
if pull_queue.full():
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
@ -312,7 +306,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
logger.info("离线拉流线程结束, requestId: {}", request_id)
break
frame_list.append([frame,mqtt_list])
frame_list.append(frame)
frame_index_list.append(concurrent_frame)
if len(frame_list) >= frame_num:
put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)

238
concurrency/PushVideoStreamProcess.py
View File

@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
from util.ImageUtils import url2Array, add_water_pic
from util.LogUtils import init_log
from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd,draw_transparent_red_polygon
from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
@ -36,21 +36,8 @@ class PushStreamProcess(Process):
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
self._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
# 0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
print("执行默认程序（defaultEnabled=True）")
self._algSwitch = True
# 这里放默认逻辑的代码
else:
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self._algSwitch = False
print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
self._algSwitch = self._context['service']['algSwitch']
def build_logo_url(self):
logo = None
if self._context["video"]["video_add_water"]:
@ -130,26 +117,15 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r is not None:
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每1帧
for i, [frame,mqtt_list] in enumerate(frame_list):
# mqtt传参
border = None
mqttPares = None
if len(mqtt_list) >= 1:
mqttPares = mqtt_list[0]
if mqttPares[0] == 1:
border = mqttPares[1]
for i, frame in enumerate(frame_list):
pix_dis = int((frame.shape[0]//10)*1.2)
# 复制帧用来画图
copy_frame = frame.copy()
if border is not None:
copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
det_xywh, thread_p = {}, []
det_xywh2 = {'mqttPares':mqttPares}
det_xywh2 = {}
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
@ -158,42 +134,17 @@ class OnPushStreamProcess(PushStreamProcess):
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
# 自研车牌模型处理
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = 0
box = xy2xyxy(qs[1])
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
cls = 0
# crowdlabel, points = qs
box = [(0, 0), (0, 0), (0, 0), (0, 0)]
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
except:
continue
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
else:
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
# 借score作为points点集
box.append(qs[-1])
except:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame,
draw_config[code]["label_dict"], score, color,
font_config, qs[6])
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
@ -202,34 +153,27 @@ class OnPushStreamProcess(PushStreamProcess):
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
det_xywh[code][cls].append([cls, box, score, label_array, color])
if qs_np is None:
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
else:
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0:
for r in thread_p:
r.result()
#print('----line173:',self._algSwitch,self._algStatus)
#print('----line173:',self._algSwitch,self._algStatus)
if self._algSwitch and (not self._algStatus):
frame_merge = video_conjuncing(frame, frame.copy())
else:
else:
frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
@ -242,7 +186,7 @@ class OnPushStreamProcess(PushStreamProcess):
# 如果有问题, 走下面的逻辑
if qs_np is not None:
if len(qs_np.shape) == 1:
qs_np = qs_np[np.newaxis,...]
qs_np = qs_np[np.newaxis,...]
qs_np_id = qs_np.copy()
b = np.ones(qs_np_id.shape[0])
qs_np_id = np.column_stack((qs_np_id,b))
@ -250,7 +194,7 @@ class OnPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -268,7 +212,7 @@ class OnPushStreamProcess(PushStreamProcess):
if q[11] >= 1:
cls = int(q[9])
if not (cls in new_lab):
continue # 为了防止其他类别被带出
continue # 为了防止其他类别被带出
code = str(int(q[10])).zfill(3)
if det_xywh2.get(code) is None:
det_xywh2[code] = {}
@ -276,29 +220,17 @@ class OnPushStreamProcess(PushStreamProcess):
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
is_new = False
if q[11] == 1:
is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
box = qs
if cd is None:
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
else:
det_xywh2[code][cls].append(
[cls, box, score, label_array, color, is_new])
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 0:
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames,
draw_config["font_config"]]))
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
@ -307,7 +239,7 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
@ -315,7 +247,7 @@ class OnPushStreamProcess(PushStreamProcess):
ex_status = False
logger.info("停止推流进程, requestId: {}", request_id)
break
del push_r
else:
sleep(1)
@ -394,72 +326,40 @@ class OffPushStreamProcess(PushStreamProcess):
# [(2, 操作指令)] 指令操作
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
# 处理每一帧图片
for i, [frame,mqtt_list] in enumerate(frame_list):
# mqtt传参
border = None
mqttPares = None
if len(mqtt_list) >= 1:
mqttPares = mqtt_list[0]
if mqttPares[0] == 1:
border = mqttPares[1]
for i, frame in enumerate(frame_list):
pix_dis = int((frame.shape[0]//10)*1.2)
if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
# 复制帧用来画图
copy_frame = frame.copy()
if border is not None:
copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
# 所有问题记录字典
det_xywh, thread_p = {}, []
det_xywh2 = {'mqttPares':mqttPares}
det_xywh2 = {}
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result:
# 自研车牌模型处理
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = 0
box = xy2xyxy(qs[1])
score = None
color = rainbows[cls]
label_array = None
label_arrays = [None]
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
cls = 0
box = [(0,0),(0,0),(0,0),(0,0)]
score = None
color = rainbows[cls]
label_array = None
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
else:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
@ -467,24 +367,17 @@ class OffPushStreamProcess(PushStreamProcess):
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
det_xywh[code][cls].append([cls, box, score, label_array, color])
if qs_np is None:
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
else:
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1]== str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -493,7 +386,7 @@ class OffPushStreamProcess(PushStreamProcess):
r.result()
if self._algSwitch and (not self._algStatus):
frame_merge = video_conjuncing(frame, frame.copy())
else:
else:
frame_merge = video_conjuncing(frame, copy_frame)
# 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
@ -502,9 +395,10 @@ class OffPushStreamProcess(PushStreamProcess):
push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status, request_id)
if qs_np is not None:
if len(qs_np.shape) == 1:
qs_np = qs_np[np.newaxis,...]
qs_np = qs_np[np.newaxis,...]
qs_np_id = qs_np.copy()
b = np.ones(qs_np_id.shape[0])
qs_np_id = np.column_stack((qs_np_id,b))
@ -512,7 +406,7 @@ class OffPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -531,7 +425,7 @@ class OffPushStreamProcess(PushStreamProcess):
if q[11] >= 1:
cls = int(q[9])
if not (cls in new_lab):
continue # 为了防止其他类别被带出
continue # 为了防止其他类别被带出
code = str(int(q[10])).zfill(3)
if det_xywh2.get(code) is None:
det_xywh2[code] = {}
@ -539,28 +433,16 @@ class OffPushStreamProcess(PushStreamProcess):
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
is_new = False
if q[11] == 1:
is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
box = qs
if cd is None:
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
else:
det_xywh2[code][cls].append(
[cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 1:
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
if len(det_xywh2) > 0:
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
push_p = push_stream_result.result(timeout=60)
ai_video_file = write_ai_video_result.result(timeout=60)
@ -568,7 +450,7 @@ class OffPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break

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2
config/logger/dsp_prod_logger.yml
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@ -5,6 +5,6 @@ log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "15 days"
retention: "7 days"
encoding: "utf8"

2
config/logger/dsp_test_logger.yml
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@ -5,6 +5,6 @@ log_name: "dsp.log"
log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
level: "INFO"
rotation: "00:00"
retention: "7 days"
retention: "3 days"
encoding: "utf8"

7
config/minio/dsp_test_minio.yml
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@ -1,7 +1,8 @@
endpoint: 'minio.t-aaron.com:9000'
endpoint: 'minio.t-aaron.com'
domain: 'https://minio.t-aaron.com'
access_key: 'IKf3A0ZSXsR1m0oalMjV'
secret_key: 'yoC6qRo2hlyZu8Pdbt6eh9TVaTV4gD7KRudromrk'
secure: false
image_bucket: 'image'
video_bucket: 'video'
image_bucket: 'th-airport'
video_bucket: 'th-airport'
file_dir: 'testFile'

25
config/mqtt/dsp_test_mqtt.yml
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@ -1,21 +1,10 @@
mqtt_flag: true
# 业务0为经纬度定位，业务1为入侵算法开关
business: 1
# 经纬度定位
location:
broker : "58.213.148.44"
port : 1883
username: "admin"
password: "admin##123"
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
# 入侵
invade:
broker : "192.168.11.8"
port : 2883
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "test000/topic"
broker : "58.213.148.44"
port : 1883
username: "admin"
password: "admin##123"
#topic: "/topic/v1/airportFly/%s/aiDroneData"
topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
# 存储多少条消息到list里
length: 10
length: 30

9
config/service/dsp_test_service.yml
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@ -31,10 +31,9 @@ service:
image:
limit: 20
#storage source,0--aliyun,1--minio
storage_source: 0
storage_source: 1
#是否启用mqtt，0--不用，1--启用
mqtt:
flag: 0
business: 1
mqtt_flag: 0
#是否启用alg控制功能
algSwitch: False
algSwitch: false

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768
enums/ModelTypeEnum-jcq.py
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@ -1,768 +0,0 @@
import sys
from enum import Enum, unique
from common.Constant import COLOR
sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
from utilsK.channel2postUtils import channel2_post_process
'''
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. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''
@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device,
# 'gpu_name': gpuName,
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
# 'segPar': None,
# 'postFile': {
# "name": "post_process",
# "conf_thres": 0.25,
# "iou_thres": 0.45,
# "classes": 6,
# "rainbows": COLOR
# },
# 'Segweights': None
# })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆"],
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["行人"],
'seg_nclass': 2,
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰", "烟雾"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["钓鱼", "游泳"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["违法种植"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.18,
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
'decoder': None,
'test_flag': True,
"rainbows": COLOR,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'label_array': None,
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
})
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["人"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80,'rubCls': 1, 'Rubfilter': 150}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
'par':{
'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,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.5,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360)
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True, ###分割模型预处理参数
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {
'modelSize': (640, 360),
'roundness': 0.3,
'cls': 9,
'laneArea': 10,
'laneAngleCha': 5,
'RoadArea': 16000,
'fitOrder':2
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车", "T角点", "L角点", "违停"],
'trtFlag_seg': False,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'mixFunction': {
'function': illParking_postprocess,
'pars': {}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["坑槽"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只","未封仓"], # 保持原来的标签顺序不变，方便后面业务端增加
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0, #旗帜
'bigId':3, #船只
'newId':4, #未挂国旗船只
'uncoverId':5, #未封仓标签
'recScale':1.2,
'target_cls':3.0, #目标种类
'filter_cls':4.0 #被过滤的种类
}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'weight':'/home/thsw2/jcq/test/AIlib2/weights/channel2/best.pt', # yolov5 原来模型基础上增加了未封仓
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
},
{
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
'imgW':192,
'hidden':256,
'mean':[0.5,0.5,0.5],
'std':[0.5,0.5,0.5],
'dynamic':False,
},
} ,
# {
# 'weight':'/home/thsw2/jcq/test/AIlib2/weights1/conf/channel2/yolov5_04.pt', # yolov5_04 添加了uncover 0 4 ；标签 yolov5_jcq
# 'name':'yolov5',
# 'model':yolov5Model,
# 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.15,'iou_thres':0.25,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
# }
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("2", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
'models':
[
{
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
@staticmethod
def checkCode(code):
for model in ModelType:
if model.value[1] == code:
return True
return False
'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''
@unique
class BaiduModelTarget(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
BAIDU_MODEL_TARGET_CONFIG = {
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
# 模型分析方式
@unique
class ModelMethodTypeEnum(Enum):
# 方式一: 正常识别方式
NORMAL = 1
# 方式二: 追踪识别方式
TRACE = 2

807
enums/ModelTypeEnum-raw.py
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@ -1,807 +0,0 @@
import sys
from enum import Enum, unique
from common.Constant import COLOR
sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
from utilsK.channel2postUtils import channel2_post_process
'''
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. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''
@unique
class ModelType(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'seg_nclass': 2,
'trtFlag_seg': True,
'trtFlag_det': True,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [5, 6, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device,
# 'gpu_name': gpuName,
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
# 'segPar': None,
# 'postFile': {
# "name": "post_process",
# "conf_thres": 0.25,
# "iou_thres": 0.45,
# "classes": 6,
# "rainbows": COLOR
# },
# 'Segweights': None
# })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["车辆"],
'seg_nclass': 2,
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["行人"],
'seg_nclass': 2,
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰", "烟雾"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["钓鱼", "游泳"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["违法种植"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.18,
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
'decoder': None,
'test_flag': True,
"rainbows": COLOR,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'label_array': None,
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
})
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["人"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None
})
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': ["车辆", "垃圾", "商贩", "裸土","占道经营","违停"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5,"4":0.4,"5":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
'par':{
'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,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixDrowing_water_postprocess,
'pars': {
'modelSize': (640, 360)
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True, ###分割模型预处理参数
'mixFunction': {
'function': mixNoParking_road_postprocess,
'pars': {
'modelSize': (640, 360),
'roundness': 0.3,
'cls': 9,
'laneArea': 10,
'laneAngleCha': 5,
'RoadArea': 16000,
'fitOrder':2
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车", "T角点", "L角点", "违停"],
'trtFlag_seg': False,
'trtFlag_det': True,
'seg_nclass': 4,
'segRegionCnt': 2,
'segPar': {
'mixFunction': {
'function': illParking_postprocess,
'pars': {}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.8,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["坑槽"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0,
'bigId':3,
'newId':4,
'recScale':1.2}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
},
{
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
'imgW':192,
'hidden':256,
'mean':[0.5,0.5,0.5],
'std':[0.5,0.5,0.5],
'dynamic':False,
},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
'models':
[
{
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
'labelnames': [ "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
'labelnames': [ "烟花"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
@staticmethod
def checkCode(code):
for model in ModelType:
if model.value[1] == code:
return True
return False
'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''
@unique
class BaiduModelTarget(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
BAIDU_MODEL_TARGET_CONFIG = {
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
# 模型分析方式
@unique
class ModelMethodTypeEnum(Enum):
# 方式一: 正常识别方式
NORMAL = 1
# 方式二: 追踪识别方式
TRACE = 2

541
enums/ModelTypeEnum.py
View File

@ -9,15 +9,12 @@ from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess
from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from utilsK.pannelpostUtils import pannel_post_process
from utilsK.securitypostUtils import security_post_process
from stdc import stdcModel
from yolov5 import yolov5Model
from p2pNet import p2NnetModel
from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
@ -64,18 +61,17 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'fiterList':[2],
'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
})
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device,
# 'gpu_name': gpuName,
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
@ -89,7 +85,7 @@ class ModelType(Enum):
# },
# 'Segweights': None
# })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
@ -97,13 +93,15 @@ class ModelType(Enum):
'models':
[
{
'weight':"../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
@ -111,23 +109,25 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3},
'fiterList': [5],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
"事故","抛撒物", "危化品车辆", "虚标线","其他标线","其他","桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露"],
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
@ -136,14 +136,13 @@ class ModelType(Enum):
'mixFunction': {
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'CarId':1,
'CthcId':12,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
@ -160,10 +159,8 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'score_byClass':{11:0.75,12:0.75},
'fiterList': [13,14,15,16,17,18,19,20,21,22],
'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
@ -179,7 +176,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/vehicle/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -199,7 +196,7 @@ class ModelType(Enum):
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
@ -220,7 +217,7 @@ class ModelType(Enum):
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
@ -230,7 +227,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
'Segweights': None
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
@ -242,7 +239,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -263,7 +260,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -282,7 +279,7 @@ class ModelType(Enum):
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../weights/trt/AIlib2/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
@ -314,7 +311,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -344,8 +341,7 @@ class ModelType(Enum):
'function': riverDetSegMixProcess,
'pars': {
'slopeIndex': [1, 3, 4, 7],
'riverIou': 0.1,
'scale': 0.25
'riverIou': 0.1
}
}
},
@ -357,54 +353,56 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/river2/stdc_360X640_%s_fp16.engine' % gpuName
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土" ],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{
'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80,
#"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建"
# key:实际训练index value:展示index
'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80,
#车辆","垃圾","商贩","裸土","占道经营","违停"--->
#"车辆","垃圾","商贩","违停","占道经营","裸土"
'classReindex':{ 0:0,1:1,2:2,3:5,4:4,5:3}
}
},
'models':[
{
'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True}
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../weights/trt/AIlib2/cityMangement3/dmpr_3090.engine',
#'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
'par':{
'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,'seg_nclass':3},###分割模型预处理参数
'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,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
'name':'stdc'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 8,
"classes": 5,
"rainbows": COLOR
},
"score_byClass":{0:0.8, 1:0.4, 2:0.5, 3:0.5},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
})
@ -437,8 +435,10 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
@ -476,8 +476,8 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/noParking/stdc_360X640_%s_fp16.engine' % gpuName
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
})
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
@ -500,13 +500,13 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/illParking/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["护栏", "交通标志", "非交通标志", "施工锥桶", "施工水马"],
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
@ -520,7 +520,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
@ -533,7 +533,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/pothole/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -548,35 +548,31 @@ class ModelType(Enum):
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
# 'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
'labelnames': ["国旗", "浮标", "船名", "船只", "未挂国旗船只","未封仓船只","未挂国旗且未封仓船只"],
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'flagId':0,
'boatId':3,
'unflagId': 4, # 未挂国旗船只
'uncoverId': 5, # 未封仓
'unflagAndcoverId': 6, # 未挂国旗且未封仓
'recScale':1.2,
'target_cls': 3, # 船只目标种类
'filter_cls': 4 # 被过滤的种类,模型文件中未封仓实际index
}},
'smallId':0,
'bigId':3,
'newId':4,
'recScale':1.2}},
'models':[
{
'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False}
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
},
{
'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
{
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/conf/ocr2/benchmark.txt',
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
@ -586,8 +582,9 @@ class ModelType(Enum):
'std':[0.5,0.5,0.5],
'dynamic':False,
},
}
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'segPar': None,
'postFile': {
"name": "post_process",
@ -597,10 +594,8 @@ class ModelType(Enum):
"rainbows": COLOR
},
'Segweights': None,
"score_byClass": {0: 0.7, 1: 0.7, 2: 0.8, 3: 0.6}
})
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
@ -631,20 +626,24 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
'models':
[
{
'weight':"../weights/trt/AIlib2/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
}
@ -657,7 +656,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
"score_byClass":{0:0.25,1:0.25,2:0.6,3:0.6,4:0.6 ,5:0.6},
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2,
@ -665,8 +664,7 @@ class ModelType(Enum):
})
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
"事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
@ -682,13 +680,12 @@ class ModelType(Enum):
'function': tracfficAccidentMixFunction,
'pars': {
'modelSize': (640, 360),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'CarId':1,
'CthcId':1,
'cls': 9,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
@ -705,9 +702,8 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'fiterltList': [11,12,13,14,15,16,17],
'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
@ -716,19 +712,18 @@ class ModelType(Enum):
'models':
[
{
'weight':"../weights/trt/AIlib2/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
})
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
@ -737,360 +732,40 @@ class ModelType(Enum):
'models':
[
{
'weight':"../weights/trt/AIlib2/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
"score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
})
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
'labelnames': [ "烟花"],
'postProcess':{'function':default_mix,'pars':{}},
'models':
[
{
'weight':"../weights/trt/AIlib2/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False },
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
}
],
'postFile': {
"rainbows": COLOR
},
})
TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
'device': str(device),
'labelnames': ["抛洒物","车辆"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixTraffic_postprocess,
'pars': {
'modelSize': (640, 360),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 0,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 2,
"rainbows": COLOR
},
'fiterList': [1],
###控制哪些检测类别显示、输出
'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
})
TRAFFIC_CTHC_MODEL = ("50", "502", "高速公路危化品模型", 'highWayCthc', lambda device, gpuName: {
'device': str(device),
'labelnames': ["危化品","罐体","危险标识","普通车"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
'modelSize': (640, 360),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixTraffic_postprocess,
'pars': {
'modelSize': (640, 360),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 0,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
'radius': 50,
'vehicleFlag': False,
'distanceFlag': False
}
}
},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 4,
"rainbows": COLOR
},
'fiterList':[1,2,3],
###控制哪些检测类别显示、输出
'Detweights': "../weights/trt/AIlib2/highWayCthc/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWayCthc/stdc_360X640_%s_fp16.engine' % gpuName
})
TRAFFIC_PANNEL_MODEL = ("50", "503", "光伏板模型", 'pannel', lambda device, gpuName: {
'labelnames': ["光伏板","覆盖物","裂缝"],
'postProcess': {'function': pannel_post_process, 'pars': {'objs': [0]}},
'models':
[
{
'weight': "../weights/trt/AIlib2/pannel/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
'fiterList':[0]
})
CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
'labelnames': ["车牌"],
'device': str(device),
'rainbows': COLOR,
'models': [
{
#'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
'weight': '../weights/trt/AIlib2/carplate/yolov5_%s_fp16.engine' % (gpuName),
'name': 'yolov5',
'model': yolov5Model,
'par': {
'trtFlag_det': True,
'device': 'cuda:0',
'half': True,
'conf_thres': 0.4,
'iou_thres': 0.45,
'nc': 1,
'plate':8,
'plate_dilate': (0.5, 0.1)
},
},
{
'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
'name': 'ocr',
'model': ocrModel,
'par': {
'trtFlag_ocr': True,
'char_file': '../AIlib2/conf/ocr2/benchmark.txt',
'mode': 'ch',
'nc': 3,
'imgH': 32,
'imgW': 192,
'hidden': 256,
'mean': [0.5, 0.5, 0.5],
'std': [0.5, 0.5, 0.5],
'dynamic': False,
}
}],
})
CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredPerson', lambda device, gpuName: {
'labelnames': ["行人"],
'postProcess': {'function': default_mix, 'pars': {}},
'models':
[
{
'weight': "../weights/trt/AIlib2/infraredPerson/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True,'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
})
CITY_NIGHTFIRESMOKE_MODEL = ("30", "303", "夜间烟火模型", 'nightFireSmoke', lambda device, gpuName: {
'labelnames': ["火","烟雾"],
'postProcess': {'function': default_mix, 'pars': {}},
'models':
[
{
'weight': "../weights/trt/AIlib2/nightFireSmoke/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
})
CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
'labelnames': ["人群计数"],
'device': str(device),
'rainbows': COLOR,
'models': [
{
'trtFlag_det': False,
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
'name': 'p2pnet',
'model': p2NnetModel,
'par': {
'device': 'cuda:0',
'row': 2,
'line': 2,
'point_loss_coef': 0.45,
'conf': 0.65,
'gpu_id': 0,
'eos_coef': '0.5',
'set_cost_class': 1,
'set_cost_point': 0.05,
'backbone': 'vgg16_bn',
'expend': 10,
'psize': 2,
},
}],
})
CITY_DENSECROWDESTIMATION_MODEL = ("30", "305", "密集人群密度估计", 'DenseCrowdEstimation', lambda device, gpuName: {
'labelnames': ["密度"],
'models':
[
{
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
})
CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: {
'labelnames': ["建筑物下人群"],
'device': str(device),
'rainbows': COLOR,
'models': [
{
'weight': "../weights/trt/AIlib2/perUnderBuild/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
},
{
'trtFlag_det': False,
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
'name': 'p2pnet',
'model': p2NnetModel,
'par': {
'device': 'cuda:0',
'row': 2,
'line': 2,
'point_loss_coef': 0.45,
'conf': 0.50,
'gpu_id': 0,
'eos_coef': '0.5',
'set_cost_class': 1,
'set_cost_point': 0.05,
'backbone': 'vgg16_bn',
'expend': 10,
'psize': 5
},
}],
})
CITY_FIREAREA_MODEL = ("30", "307", "火焰面积模型", 'FireArea', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName, # 0：fire 1：smoke
'Samweights': "../weights/pth/AIlib2/firearea/sam_vit_b_01ec64.pth", #分割模型
'ksize':(7,7),
'sam_type':'vit_b',
'slopeIndex': [],
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'Segweights': None,
'fiterList':[1],
"score_byClass": {0: 0.1}
})
CITY_SECURITY_MODEL = ("30", "308", "安防模型", 'SECURITY', lambda device, gpuName: {
'labelnames': ["带安全帽","安全帽","攀爬","斗殴","未戴安全帽"],
'postProcess': {'function': security_post_process, 'pars': {'objs': [0,1],'iou':0.25,'unhelmet':4}},
'models':
[
{
'weight': "../weights/trt/AIlib2/security/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
'fiterList': [0,1],
"score_byClass": {"0": 0.50}
})
@staticmethod
def checkCode(code):

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@ -0,0 +1,10 @@
1.2025.01.21把之前的tuoheng alg仓库代码重新开个仓库
(1)在config/service/dsp_test_service.yml里面添加参数，控制存储用的oss还是minio
storage_source: 1
2.2025.02.06
(1)修改代码，把mqtt读取加入到系统中。config/service/dsp_test_service.yml，中添加mqtt_flag,决定是否启用。
(2)修改了minio情况下的，文件名命名方式。
3.2025.02.12
(1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。
4.2025.04.26
(1)代码更新路径到gitadmin

124
service/Dispatcher.py
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@ -187,9 +187,6 @@ class DispatcherService:
except Exception:
logger.error("主线程异常：{}", format_exc())
def identify_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
@ -247,30 +244,9 @@ class DispatcherService:
# 开启实时进程
def startOnlineProcess(self, msg, analysisType):
#0521:
default_enabled = str(msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
print("执行默认程序（defaultEnabled=True）")
self.__context['service']['algSwitch'] = True
# 这里放默认逻辑的代码
else:
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self.__context['service']['algSwitch'] = False
print("---line264-Dispatcher.py---",self.__context)
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
@ -350,23 +326,8 @@ class DispatcherService:
校验kafka消息
'''
@staticmethod
def check_msg(msg, schema):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# default_enabled = str(msg1.get("defaultEnabled", "True")).lower() == "true"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled and msg1["command"] == "algStart" :
# msg1["command"] = "algStop"
# msg = msg1
try:
v = Validator(schema, allow_unknown=True)
result = v.validate(msg)
@ -415,104 +376,23 @@ class DispatcherService:
在线分析逻辑
'''
#0520：主要是在线分析 -- "algStart","algStop" 外部多增加一层逻辑
# def online(self, message, analysisType):
# if "start" == message.get("command"):
# self.check_msg(message, ONLINE_START_SCHEMA)
# if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
# raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
# ExceptionType.NO_RESOURCES.value[1])
# self.startOnlineProcess(message, analysisType)
# elif message.get("command") in ["algStart","algStop"]:
# self.sendCmdToChildProcess(message,cmd=message.get("command"))
# elif "stop" == message.get("command"):
# self.check_msg(message, ONLINE_STOP_SCHEMA)
# self.stopOnlineProcess(message)
# else:
# raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
# ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def online(self, message, analysisType):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# #逻辑还是有问题 - 肯定是先判断是否为 true
# default_enabled = str(message1.get("defaultEnabled", "True")).lower() == "True"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled :
# message.get("command")
# message = message1
# message = message
# print("line429",message)
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
# elif (message.get("command") in ["algStart","algStop"] ) and (message.get("defaultEnabled",True)):
# self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif (
message is not None # 防止 message 为 None
and isinstance(message, dict) # 确保 message 是字典
and (command := message.get("command")) in ["algStart", "algStop"] # Python 3.8+ 海象运算符
and message.get("defaultEnabled", True) is not False # 显式排除 False
):
self.sendCmdToChildProcess(message, cmd=command)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def offline(self, message, analysisType):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# default_enabled = str(message.get("defaultEnabled", "True")).lower() == "true"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled and message["command"] == "algStart" :
# message["command"] = "algStop"
# message = message
# print("line429",message)
if "start" == message.get("command"):
self.check_msg(message, OFFLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):

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0
requirements.txt → t.txt
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1
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@ -0,0 +1 @@
test wj

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8
util/Cv2Utils.py
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@ -456,8 +456,8 @@ class Cv2Util:
# '-sc_threshold', '0',
'-pix_fmt', 'yuv420p',
# '-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,
# '-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',
'-f', 'flv',
self.pushUrl]
@ -876,8 +876,8 @@ def build_push_p(push_url, width, height, requestId):
# '-zerolatency', '1',
'-pix_fmt', 'yuv420p',
# '-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,
# '-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',
'-f', 'flv',
push_url]

4
util/MinioSdk.py
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@ -57,8 +57,8 @@ class MinioSdk:
self.create_bucknet(bucketName)
if '/' not in remotePath:
remoteUrl=join(request_id,remotePath )
else: remoteUrl = remotePath
remoteUrl=join( self.__config["file_dir"] , request_id,remotePath )
else: remoteUrl = join(self.__config["file_dir"] , remotePath)
max_retries = 3
retry_count = 0
while True:

232
util/ModelUtils.py
View File

@ -17,7 +17,7 @@ from util.PlotsUtils import get_label_arrays, get_label_array_dict
from util.TorchUtils import select_device
sys.path.extend(['..', '../AIlib2'])
from AI import AI_process, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C,AI_process_Ocr,AI_process_Crowd
from AI import AI_process, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C
from stdc import stdcModel
from segutils.segmodel import SegModel
from models.experimental import attempt_load
@ -27,7 +27,6 @@ import torch
import tensorrt as trt
from utilsK.jkmUtils import pre_process, post_process, get_return_data
from DMPR import DMPRModel
from segment_anything import SamPredictor, sam_model_registry
FONT_PATH = "../AIlib2/conf/platech.ttf"
@ -37,7 +36,6 @@ class OneModel:
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
try:
start = time.time()
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
@ -48,12 +46,8 @@ class OneModel:
new_device = select_device(par.get('device'))
half = new_device.type != 'cpu'
Detweights = par['Detweights']
if par['trtFlag_det']:
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
else:
model = attempt_load(Detweights, map_location=new_device) # load FP32 model
if half: model.half()
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
par['segPar']['seg_nclass'] = par['seg_nclass']
Segweights = par['Segweights']
if Segweights:
@ -69,12 +63,10 @@ class OneModel:
'conf_thres': postFile["conf_thres"],
'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
'iou_thres': postFile["iou_thres"],
# 对高速模型进行过滤
'allowedList': [],
'segRegionCnt': par['segRegionCnt'],
'trtFlag_det': par['trtFlag_det'],
'trtFlag_seg': par['trtFlag_seg'],
'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
'trtFlag_seg': par['trtFlag_seg']
}
model_param = {
"model": model,
@ -89,8 +81,7 @@ class OneModel:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
# 纯分类模型
class cityManagementModel:
__slots__ = "model_conf"
@ -107,31 +98,37 @@ class cityManagementModel:
model_param = {
"modelList": modelList,
"postProcess": postProcess,
"score_byClass":par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList":par['fiterList'] if 'fiterList' in par.keys() else [],
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
def detSeg_demo2(args):
model_conf, frame, request_id = args
modelList, postProcess,score_byClass,fiterList = (
model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
try:
result = [[ None, None, AI_process_N([frame], modelList, postProcess,score_byClass,fiterList)[0] ] ] # 为了让返回值适配统一的接口而写的shi
result = [[ None, None, AI_process_N([frame], modelList, postProcess)[0] ] ] # 为了让返回值适配统一的接口而写的shi
return result
except ServiceException as s:
raise s
except Exception:
# self.num += 1
# cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def model_process(args):
model_conf, frame, request_id = args
model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
# modeType, model_param, allowedList, names, rainbows = model_conf
# segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
# model_param['digitFont'] = digitFont
# model_param['label_arraylist'] = label_arraylist
# model_param['font_config'] = font_config
try:
return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
@ -164,13 +161,7 @@ class TwoModel:
Detweights = par['Detweights']
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
if modeType == ModelType.CITY_FIREAREA_MODEL:
sam = sam_model_registry[par['sam_type']](checkpoint=par['Samweights'])
sam.to(device=device)
segmodel = SamPredictor(sam)
else:
segmodel = None
segmodel = None
postFile = par['postFile']
conf_thres = postFile["conf_thres"]
iou_thres = postFile["iou_thres"]
@ -184,10 +175,7 @@ class TwoModel:
"conf_thres": conf_thres,
"iou_thres": iou_thres,
"trtFlag_det": par['trtFlag_det'],
"otc": otc,
"ksize":par['ksize'] if 'ksize' in par.keys() else None,
"score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
"otc": otc
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
@ -195,15 +183,16 @@ class TwoModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def forest_process(args):
model_conf, frame, request_id = args
model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
try:
return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
model_param['conf_thres'], model_param['iou_thres'],font=model_param['digitFont'],
trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'],ksize = model_param['ksize'],
score_byClass=model_param['score_byClass'],fiterList=model_param['fiterList'])
model_param['conf_thres'], model_param['iou_thres'], [], font=model_param['digitFont'],
trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'])
except ServiceException as s:
raise s
except Exception:
@ -212,6 +201,7 @@ def forest_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
class MultiModel:
__slots__ = "model_conf"
@ -230,8 +220,6 @@ class MultiModel:
model_param = {
"modelList": modelList,
"postProcess": postProcess,
"score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
"fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
@ -239,13 +227,13 @@ class MultiModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def channel2_process(args):
model_conf, frame, request_id = args
modelList, postProcess,score_byClass,fiterList = (
model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
try:
start = time.time()
result = [[None, None, AI_process_C([frame], modelList, postProcess,score_byClass,fiterList)[0]]] # 为了让返回值适配统一的接口而写的shi
result = [[None, None, AI_process_C([frame], modelList, postProcess)[0]]] # 为了让返回值适配统一的接口而写的shi
# print("AI_process_C use time = {}".format(time.time()-start))
return result
except ServiceException as s:
@ -254,6 +242,7 @@ def channel2_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def get_label_arraylist(*args):
width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
@ -274,6 +263,8 @@ def get_label_arraylist(*args):
'label_location': 'leftTop'}
label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
# 船只模型
class ShipModel:
__slots__ = "model_conf"
@ -299,6 +290,8 @@ class ShipModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
def obb_process(args):
model_conf, frame, request_id = args
model_param = model_conf[1]
@ -313,6 +306,8 @@ def obb_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
# 车牌分割模型、健康码、行程码分割模型
class IMModel:
__slots__ = "model_conf"
@ -326,8 +321,8 @@ class IMModel:
if ModelType.PLATE_MODEL == modeType:
img_type = 'plate'
par = {
'code': {'weights': '../weights/pth/AIlib2/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
'plate': {'weights': '../weights/pth/AIlib2/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
'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,
@ -336,7 +331,7 @@ class IMModel:
new_device = torch.device(par['device'])
model = torch.jit.load(par[img_type]['weights'])
logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
logger.info("########################加载 ../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
requestId)
self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
except Exception:
@ -344,6 +339,7 @@ class IMModel:
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
def im_process(args):
frame, device, model, par, img_type, requestId = args
try:
@ -361,70 +357,6 @@ def im_process(args):
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def immulti_process(args):
model_conf, frame, requestId = args
device, modelList, detpar = model_conf[1], model_conf[2], model_conf[3]
try:
# new_device = torch.device(device)
# img, padInfos = pre_process(frame, new_device)
# pred = model(img)
# boxes = post_process(pred, padInfos, device, conf_thres=pardet['conf_thres'],
# iou_thres=pardet['iou_thres'], nc=pardet['nc']) # 后处理
return AI_process_Ocr([frame], modelList, device, detpar)
except ServiceException as s:
raise s
except Exception:
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
class CARPLATEModel:
__slots__ = "model_conf"
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
detpar = par['models'][0]['par']
# new_device = torch.device(par['device'])
# modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
class DENSECROWDCOUNTModel:
__slots__ = "model_conf"
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
rainbows = par["rainbows"]
models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
postPar = [pp['par'] for pp in par['models']]
self.model_conf = (modeType, device, models, postPar, rainbows)
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
def cc_process(args):
model_conf, frame, requestId = args
device, model, postPar = model_conf[1], model_conf[2], model_conf[3]
try:
return AI_process_Crowd([frame], model, device, postPar)
except ServiceException as s:
raise s
except Exception:
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
# 百度AI图片识别模型
class BaiduAiImageModel:
@ -541,7 +473,7 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载交通模型
ModelType.TRAFFIC_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
@ -679,27 +611,27 @@ MODEL_CONFIG = {
),
# 加载交通模型
ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFICFORDSJ_FARM_MODEL, t, z, h),
ModelType.TRAFFICFORDSJ_FARM_MODEL,
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
ModelType.TRAFFIC_FARM_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载智慧工地模型
# 加载智慧工地模型
ModelType.SMARTSITE_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h),
ModelType.SMARTSITE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载垃圾模型
# 加载垃圾模型
ModelType.RUBBISH_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h),
ModelType.RUBBISH_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载烟花模型
ModelType.FIREWORK_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h),
@ -707,74 +639,6 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载高速公路抛撒物模型
ModelType.TRAFFIC_SPILL_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_SPILL_MODEL, t, z, h),
ModelType.TRAFFIC_SPILL_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载高速公路危化品模型
ModelType.TRAFFIC_CTHC_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_CTHC_MODEL, t, z, h),
ModelType.TRAFFIC_CTHC_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载光伏板异常检测模型
ModelType.TRAFFIC_PANNEL_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.TRAFFIC_PANNEL_MODEL, t, z, h),
ModelType.TRAFFIC_PANNEL_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载自研车牌检测模型
ModelType.CITY_CARPLATE_MODEL.value[1]: (
lambda x, y, r, t, z, h: CARPLATEModel(x, y, r, ModelType.CITY_CARPLATE_MODEL, t, z, h),
ModelType.CITY_CARPLATE_MODEL,
None,
lambda x: immulti_process(x)
),
# 加载红外行人检测模型
ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h),
ModelType.CITY_INFRAREDPERSON_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载夜间烟火检测模型
ModelType.CITY_NIGHTFIRESMOKE_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_NIGHTFIRESMOKE_MODEL, t, z, h),
ModelType.CITY_NIGHTFIRESMOKE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载密集人群计数检测模型
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]: (
lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_DENSECROWDCOUNT_MODEL, t, z, h),
ModelType.CITY_DENSECROWDCOUNT_MODEL,
None,
lambda x: cc_process(x)
),
# 加载建筑物下行人检测模型
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: (
lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h),
ModelType.CITY_UNDERBUILDCOUNT_MODEL,
None,
lambda x: cc_process(x)
),
# 加载火焰面积模型
ModelType.CITY_FIREAREA_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITY_FIREAREA_MODEL, t, z, h),
ModelType.CITY_FIREAREA_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)
),
# 加载安防模型
ModelType.CITY_SECURITY_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
ModelType.CITY_SECURITY_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
}

300
util/PlotsUtils.py
View File

@ -2,10 +2,9 @@ import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import unicodedata
from loguru import logger
FONT_PATH = "../AIlib2/conf/platech.ttf"
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False):
if unify:
@ -24,6 +23,7 @@ def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False)
im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
return im_array
def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
label_arraylist = [get_label_array(colors[i % 20], label_name, font, fontSize) for i, label_name in
@ -49,9 +49,40 @@ def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
zh_dict[code] = arr
return zh_dict
def get_label_left(x0,y1,label_array,img):
imh, imw = img.shape[0:2]
def xywh2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
xc, yc, w, h = int(box[0]), int(box[1]), int(box[2]), int(box[3])
bw, bh = int(w / 2), int(h / 2)
lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
box = [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return box
def xywh2xyxy2(param):
if not isinstance(param[0], (list, tuple, np.ndarray)):
xc, yc, x2, y2 = int(param[0]), int(param[1]), int(param[2]), int(param[3])
return [(xc, yc), (x2, yc), (x2, y2), (xc, y2)], float(param[4]), int(param[5])
# bw, bh = int(w / 2), int(h / 2)
# lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
# return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False):
# 识别问题描述图片的高、宽
lh, lw = label_array.shape[0:2]
# 图片的长度和宽度
imh, imw = img.shape[0:2]
box = xywh2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
# if score_location == 'leftTop':
# x0, y1 = box[0][0], box[0][1]
# # 框框左下的位置
# elif score_location == 'leftBottom':
# x0, y1 = box[3][0], box[3][1]
# else:
# x0, y1 = box[0][0], box[0][1]
# x1 框框左上x位置 + 描述的宽
# y0 框框左上y位置 - 描述的高
x1, y0 = x0 + lw, y1 - lh
@ -73,67 +104,6 @@ def get_label_left(x0,y1,label_array,img):
if x1 > imw:
x1 = imw
x0 = x1 - lw
return x0,y0,x1,y1
def get_label_right(x1,y0,label_array):
lh, lw = label_array.shape[0:2]
# x1 框框右上x位置 + 描述的宽
# y0 框框右上y位置 - 描述的高
x0, y1 = x1 - lw, y0 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0 or y1 < 0:
y1 = 0
# y1等于文字高度
y0 = y1 + lh
# 如果x0小于0
if x0 < 0 or x1 < 0:
x0 = 0
x1 = x0 + lw
return x0,y1,x1,y0
def xywh2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
xc, yc, w, h = int(box[0]), int(box[1]), int(box[2]), int(box[3])
bw, bh = int(w / 2), int(h / 2)
lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
box = [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return box
def xywh2xyxy2(param):
if not isinstance(param[0], (list, tuple, np.ndarray)):
xc, yc, x2, y2 = int(param[0]), int(param[1]), int(param[2]), int(param[3])
return [(xc, yc), (x2, yc), (x2, y2), (xc, y2)], float(param[4]), int(param[5])
# bw, bh = int(w / 2), int(h / 2)
# lt, yt, rt, yr = xc - bw, yc - bh, xc + bw, yc + bh
# return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
def xy2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
# 顺时针
box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
return box
def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False, border=None):
# 识别问题描述图片的高、宽
# 图片的长度和宽度
if border is not None:
border = np.array(border,np.int32)
color,label_array=draw_name_border(box,color,label_array,border)
#img = draw_transparent_red_polygon(img,border,'',alpha=0.1)
lh, lw = label_array.shape[0:2]
tl = config[0]
if isinstance(box[-1], np.ndarray):
return draw_name_points(img,box,color)
label = ' %.2f' % score
box = xywh2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
'''
1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
@ -143,12 +113,14 @@ def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=Non
5. thickness（int）：画线的粗细
6. shift：顶点坐标中小数的位数
'''
img[y0:y1, x0:x1, :] = label_array
tl = config[0]
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
img[y0:y1, x0:x1, :] = label_array
pts_cls = [(x0, y0), (x1, y1)]
# 把英文字符score画到类别旁边
# tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
label = ' %.2f' % score
# tf = max(tl, 1)
# fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
# fontScale = tl * 0.33
@ -246,11 +218,7 @@ def draw_name_joint(box, img, label_array_dict, score=0.5, color=None, config=No
cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
return img, box
def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
# (color=None, label=None, font=None, fontSize=40, unify=False)
label_zh = get_label_array(color, box[0], font, outfontsize)
return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
def filterBox(det0, det1, pix_dis):
# det0为 (m1, 11) 矩阵
# det1为 (m2, 12) 矩阵
@ -283,194 +251,8 @@ def filterBox(det0, det1, pix_dis):
x_c, y_c = (x3+x4)//2, (y3+y4)//2
dis = (x2_c - x_c)**2 + (y2_c - y_c)**2
mask = (joint_det[..., 9] == joint_det[..., 20]) & (dis <= pix_dis**2)
# 类别相同 & 中心点在上一帧的框内 判断为True
res = np.sum(mask, axis=1)
det0_copy[..., -1] = res
return det0_copy
def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
# print("省略 :%s, box:%s"%('+++' * 10, box))
# 识别问题描述图片的高、宽
lh, lw = label_array.shape[0:2]
# print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
# 图片的长度和宽度
imh, imw = img.shape[0:2]
points = None
box = xy2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > imh:
# y1等于图片高度
y1 = imh
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > imw:
x1 = imw
x0 = x1 - lw
# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
'''
1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
2. box（array）：为所画多边形的顶点坐标
3. 所画四边形是否闭合，通常为True
4. color（tuple）：BGR三个通道的值
5. thickness（int）：画线的粗细
6. shift：顶点坐标中小数的位数
'''
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
img[y0:y1, x0:x1, :] = label_array
return img, box
def draw_name_crowd(dets, img, color, outfontsize=20):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
if len(dets) == 2:
label = '当前人数：%d'%len(dets[0])
detP = dets[0]
line = dets[1]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
label_arr = get_label_array(color, label, font, outfontsize)
lh, lw = label_arr.shape[0:2]
img[0:lh, 0:lw, :] = label_arr
elif len(dets) == 3:
detP = dets[1]
line = dets[2]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
detM = dets[0]
h, w = img.shape[:2]
for b in detM:
label = '该建筑下行人及数量：%d'%(int(b[4]))
label_arr = get_label_array(color, label, font, outfontsize)
lh, lw = label_arr.shape[0:2]
# 框框左上的位置
x0, y1 = int(b[0]), int(b[1])
# print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > h:
# y1等于图片高度
y1 = h
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > w:
x1 = w
x0 = x1 - lw
cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
img[y0:y1, x0:x1, :] = label_arr
return img, dets
def draw_name_points(img,box,color):
font = ImageFont.truetype(FONT_PATH, 6, encoding='utf-8')
points = box[-1]
arrea = cv2.contourArea(points)
label = '火焰'
arealabel = '面积:%s' % f"{arrea:.1e}"
label_array_area = get_label_array(color, arealabel, font, 10)
label_array = get_label_array(color, label, font, 10)
lh_area, lw_area = label_array_area.shape[0:2]
box = box[:4]
# 框框左上的位置
x0, y1 = box[0][0], max(box[0][1] - lh_area - 3, 0)
x1, y0 = box[1][0], box[1][1]
x0_label, y0_label, x1_label, y1_label = get_label_left(x0, y1, label_array, img)
x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
img[y0_label:y1_label, x0_label:x1_label, :] = label_array
img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
# cv2.drawContours(img, points, -1, color, tl)
cv2.polylines(img, [points], False, color, 2)
if lw_area < box[1][0] - box[0][0]:
box = [(x0, y1), (x1, y1), (x1, box[2][1]), (x0, box[2][1])]
else:
box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
box = np.asarray(box, np.int32)
cv2.polylines(img, [box], True, color, 2)
return img, box
def draw_name_border(box,color,label_array,border):
box = xywh2xyxy(box[:4])
cx, cy = int((box[0][0] + box[2][0]) / 2), int((box[0][1] + box[2][1]) / 2)
flag = cv2.pointPolygonTest(border, (int(cx), int(cy)),
False) # 若为False，会找点是否在内，外，或轮廓上
if flag == 1:
color = [0, 0, 255]
# 纯白色是(255, 255, 255)，根据容差定义白色范围
lower_white = np.array([255 - 30] * 3, dtype=np.uint8)
upper_white = np.array([255, 255, 255], dtype=np.uint8)
# 创建白色区域的掩码（白色区域为True，非白色为False）
white_mask = cv2.inRange(label_array, lower_white, upper_white)
# 创建与原图相同大小的目标颜色图像
target_img = np.full_like(label_array, color, dtype=np.uint8)
# 先将非白色区域设为目标颜色，再将白色区域覆盖回原图颜色
label_array = np.where(white_mask[..., None], label_array, target_img)
return color,label_array
def draw_transparent_red_polygon(img, points, alpha=0.5):
"""
在图像中指定的多边形区域绘制半透明红色
参数:
image_path: 原始图像路径
points: 多边形顶点坐标列表，格式为[(x1,y1), (x2,y2), ..., (xn,yn)]
output_path: 输出图像路径
alpha: 透明度系数，0-1之间，值越小透明度越高
"""
# 读取原始图像
if img is None:
raise ValueError(f"无法读取图像")
# 创建与原图大小相同的透明图层（RGBA格式）
overlay = np.zeros((img.shape[0], img.shape[1], 4), dtype=np.uint8)
# 将点列表转换为适合cv2.fillPoly的格式
#pts = np.array(points, np.int32)
pts = points.reshape((-1, 1, 2))
# 在透明图层上绘制红色多边形（BGR为0,0,255）
# 最后一个通道是Alpha值，控制透明度，黄色rgb
cv2.fillPoly(overlay, [pts], (255, 0, 0, int(alpha * 255)))
# 将透明图层转换为BGR格式（用于与原图混合）
overlay_bgr = cv2.cvtColor(overlay, cv2.COLOR_RGBA2BGR)
# 创建掩码，用于提取红色区域
mask = overlay[:, :, 3] / 255.0
mask = np.stack([mask] * 3, axis=-1) # 转换为3通道
# 混合原图和透明红色区域
img = img * (1 - mask) + overlay_bgr * mask
img = img.astype(np.uint8)
# # 保存结果
# cv2.imwrite(output_path, result)
return img
return det0_copy

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