gitadmin
/
tuoheng_algN
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

巴中水利分支

This commit is contained in:
zhoushuliang 2025-07-25 19:41:32 +08:00
parent 12a4b296e1
commit 3fad23e9e6
81 changed files with 625 additions and 4578 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

203
concurrency/FileUploadThread.py
View File

@ -8,32 +8,32 @@ 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
from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd
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','_mqtt_list')
__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,self._mqtt_list = 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 = 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"
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
@ -42,24 +42,16 @@ class FileUpload(Thread):
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
print("---line46 :FileUploadThread.py---", self._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
@ -72,9 +64,8 @@ class ImageFileUpload(FileUpload):
模型编号：modeCode
检测目标：detectTargetCode
'''
print('*'*100,' mqtt_list:',len(self._mqtt_list))
print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
model_info = []
# 更加模型编码解析数据
for code, det_list in det_xywh.items():
@ -83,15 +74,25 @@ class ImageFileUpload(FileUpload):
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])
igH,igW = aFrame.shape[0:2]
if len(self._mqtt_list)>=1:
#camParas = self._mqtt_list[0]['data']
# 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])
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')
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})
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,
@ -110,13 +111,15 @@ 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()
@ -130,15 +133,17 @@ 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:
@ -148,8 +153,8 @@ 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)
@ -164,38 +169,38 @@ 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
@ -220,9 +225,9 @@ 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')
@ -230,6 +235,7 @@ 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):
"""
@ -249,12 +255,21 @@ class ImageTypeImageFileUpload(Thread):
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
})
# 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
})
if len(model_info) > 0:
image_result = {
"or_frame": copy_frame,
@ -274,11 +289,11 @@ 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:
@ -299,15 +314,15 @@ 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,
@ -318,17 +333,17 @@ 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())
@ -344,14 +359,14 @@ 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,
@ -362,9 +377,8 @@ 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,
@ -377,12 +391,12 @@ 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))
@ -394,13 +408,10 @@ 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:

302
concurrency/IntelligentRecognitionProcess.py
View File

@ -46,7 +46,9 @@ from util.PlotsUtils import xywh2xyxy2
from util.QueUtil import put_queue, get_no_block_queue, clear_queue
from util.TimeUtils import now_date_to_str, YMDHMSF
from util.CpuUtils import print_cpu_status
import inspect
import inspect
class IntelligentRecognitionProcess(Process):
__slots__ = ('_fb_queue', '_msg', '_analyse_type', '_context', 'event_queue', '_pull_queue', '_hb_queue',
"_image_queue", "_push_queue", '_push_ex_queue')
@ -62,8 +64,9 @@ 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)
@ -91,9 +94,6 @@ class IntelligentRecognitionProcess(Process):
hb_thread.start()
return hb_thread
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = ()
@ -113,19 +113,16 @@ 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 )
def upload_video(self, base_dir, env, request_id, orFilePath, aiFilePath):
if self._storage_source == 1:
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()
@ -133,6 +130,7 @@ 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 upload_video(base_dir, env, request_id, orFilePath, aiFilePath):
@ -146,7 +144,7 @@ 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):
@ -226,10 +224,10 @@ 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:
@ -237,22 +235,21 @@ 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)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
# 第一个参数: 模型是否初始化 0:未初始化 1:初始化
# 第二个参数: 检测是否有问题 0: 没有问题, 1: 有问题
task_status = [0, 0]
@ -268,27 +265,27 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
pull_queue, request_id)
# 检查推流是否异常
push_status = get_no_block_queue(push_ex_queue)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5,11.2
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5,11.2
if push_status is not None and push_status[0] == 1:
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
if cmdStr in ['algStart' , 'algStop' ]:
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
# 接收到算法开启、或者关闭的命令
if cmdStr in ['algStart', 'algStop']:
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)
pull_process.sendCommand({"command": 'stop'})
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
pull_result = get_no_block_queue(pull_queue)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
# print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
if pull_result is None:
sleep(1)
continue
@ -301,20 +298,32 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1
for i, model in enumerate(model_array):
model_conf, code = model
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)
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].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):
@ -322,11 +331,11 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
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:
@ -437,23 +446,23 @@ 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):
@ -464,6 +473,7 @@ 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
@ -598,11 +608,11 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
if "stop" == cmdStr:
logger.info("离线任务开始停止, requestId: {}", request_id)
pull_process.sendCommand({"command": 'stop'})
if cmdStr in ['algStart' , 'algStop' ]:
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
if cmdStr in ['algStart', 'algStop']:
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)
@ -616,19 +626,32 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1
for i, model in enumerate(model_array):
model_conf, code = model
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']
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].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):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
@ -745,7 +768,7 @@ 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):
@ -922,6 +945,62 @@ 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
'''
@ -936,6 +1015,26 @@ 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
@ -943,7 +1042,7 @@ class PhotosIntelligentRecognitionProcess(Process):
image = url2Array(imageUrl)
MODEL_CONFIG[code][2](image.shape[1], image.shape[0], model_conf)
p_result = MODEL_CONFIG[code][3]([model_conf, image, request_id])[0]
#print(' line872:p_result[2]:',p_result[2] )
# print(' line872:p_result[2]:',p_result[2] )
if p_result is None or len(p_result) < 3 or p_result[2] is None or len(p_result[2]) == 0:
return
if logo:
@ -966,7 +1065,7 @@ class PhotosIntelligentRecognitionProcess(Process):
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
#print('ai_result_list:{},allowlist:{}'.format(ai_result_list,allowedList ))
# print('ai_result_list:{},allowlist:{}'.format(ai_result_list,allowedList ))
if len(det_xywh) > 0:
put_queue(image_queue, (1, (det_xywh, imageUrl, image, font_config, "")), timeout=2, is_ex=False)
except ServiceException as s:
@ -1114,18 +1213,19 @@ 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
if response.status_code == 200:
return True,url
return True, url
else:
return False,f"图片地址无效，状态码：{response.status_code}"
return False, f"图片地址无效，状态码：{response.status_code}"
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)
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
@ -1133,24 +1233,23 @@ class PhotosIntelligentRecognitionProcess(Process):
imageUrls = msg["image_urls"]
image_thread = None
init_log(base_dir, env)
valFlag=True
valFlag = True
for url in imageUrls:
valFlag,ret = self.check_ImageUrl_Vaild(url,timeout=1)
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] ) )
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] ) )
put_queue(fb_queue, message_feedback(request_id, AnalysisStatus.FAILED.value,
analyse_type,
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2)
return
analyse_type,
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2)
return
with ThreadPoolExecutor(max_workers=1) as t:
try:
#init_log(base_dir, env)
# init_log(base_dir, env)
logger.info("开始启动图片识别进程, requestId: {}", request_id)
model_array = get_model(msg, context, analyse_type)
image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
@ -1168,6 +1267,15 @@ 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)
@ -1214,7 +1322,8 @@ 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)
@ -1380,21 +1489,19 @@ 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):
@ -1406,6 +1513,7 @@ class ScreenRecordingProcess(Process):
return or_url
'''
"""
"models": [{
"code": "模型编号",

260
concurrency/PushVideoStreamProcess.py
View File

@ -1,4 +1,4 @@
#ne -*- coding: utf-8 -*-
# ne -*- coding: utf-8 -*-
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Process
@ -23,7 +23,8 @@ 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, draw_name_joint
from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, \
draw_name_ocr, draw_name_crowd
from util.QueUtil import get_no_block_queue, put_queue, clear_queue
@ -35,12 +36,11 @@ class PushStreamProcess(Process):
super().__init__()
# 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"
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
@ -49,16 +49,9 @@ class PushStreamProcess(Process):
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)
print("---line53 :PushVideoStreamProcess.py---", self._algSwitch)
def build_logo_url(self):
logo = None
if self._context["video"]["video_add_water"]:
@ -114,7 +107,7 @@ class OnPushStreamProcess(PushStreamProcess):
pix_dis = 60
try:
init_log(base_dir, env)
logger.info("开始实时启动推流进程！requestId:{},pid:{}, ppid:{}", request_id,os.getpid(),os.getppid())
logger.info("开始实时启动推流进程！requestId:{},pid:{}, ppid:{}", request_id, os.getpid(), os.getppid())
with ThreadPoolExecutor(max_workers=2) as t:
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
@ -139,7 +132,7 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r[0] == 1:
frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
for i, frame in enumerate(frame_list):
pix_dis = int((frame.shape[0]//10)*1.2)
pix_dis = int((frame.shape[0] // 10) * 1.2)
# 复制帧用来画图
copy_frame = frame.copy()
det_xywh, thread_p = {}, []
@ -152,49 +145,73 @@ class OnPushStreamProcess(PushStreamProcess):
# 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"]
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:
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])
# 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)
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
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:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2) :
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],
score, cls, code],dtype=np.float32)
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],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
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 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,
@ -207,19 +224,19 @@ 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))
qs_np_id = np.column_stack((qs_np_id, b))
if qs_np_tmp is None:
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:
qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
qs_np_tmp = np.append(qs_np_tmp, qs_np_id, axis=0)
qs_np_tmp[:, 11] += 1
qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
has = False
@ -233,34 +250,43 @@ 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] = {}
cd = det_xywh2[code].get(cls)
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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]))]
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)
or_video_file = write_or_video_result.result(timeout=60)
# 接收停止指令
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)
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 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
@ -268,7 +294,7 @@ class OnPushStreamProcess(PushStreamProcess):
ex_status = False
logger.info("停止推流进程, requestId: {}", request_id)
break
del push_r
else:
sleep(1)
@ -317,12 +343,14 @@ class OffPushStreamProcess(PushStreamProcess):
picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
qs_np_tmp = None
pix_dis = 60
if msg['taskType']==0: self._algStatus = False
else: self._algStatus = True
if msg['taskType'] == 0:
self._algStatus = False
else:
self._algStatus = True
try:
init_log(base_dir, env)
logger.info("开始启动离线推流进程！requestId:{}", request_id)
with ThreadPoolExecutor(max_workers=2) as t:
with (ThreadPoolExecutor(max_workers=2) as t):
# 定义三种推流、写原视频流、写ai视频流策略
# 第一个参数时间, 第二个参数重试次数
p_push_status, ai_write_status = [0, 0], [0, 0]
@ -348,8 +376,10 @@ class OffPushStreamProcess(PushStreamProcess):
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
# 每100帧上传一次
ncount = 0
for i, frame in enumerate(frame_list):
pix_dis = int((frame.shape[0]//10)*1.2)
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)
@ -363,24 +393,49 @@ class OffPushStreamProcess(PushStreamProcess):
qs_reurn = []
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"]
font_config, allowedList = draw_config["font_config"], draw_config[code][
"allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code][
"label_arrays"]
for qs in det_result:
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
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])
# 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)
else:
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
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:
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)
@ -388,17 +443,17 @@ 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],
score, cls, code],dtype=np.float32)
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],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
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 logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -407,7 +462,7 @@ 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,
@ -416,22 +471,21 @@ 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))
qs_np_id = np.column_stack((qs_np_id, b))
if qs_np_tmp is None:
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:
qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
qs_np_tmp = np.append(qs_np_tmp, qs_np_id, axis=0)
qs_np_tmp[:, 11] += 1
qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
has = False
@ -446,32 +500,42 @@ 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] = {}
cd = det_xywh2[code].get(cls)
score = q[8]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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]))]
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)
# 接收停止指令
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)
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 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break

1
config/service/dsp_prod_service.yml
View File

@ -3,6 +3,7 @@ video:
file_path: "../dsp/video/"
# 是否添加水印
video_add_water: false
role : 1
service:
filter:
# 图片得分多少分以上返回图片

768
enums/ModelTypeEnum-jcq.py
View File

@ -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
View File

@ -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

948
enums/ModelTypeEnum.py
View File

@ -4,24 +4,7 @@ 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 utilsK.spillUtils import mixSpillage_postprocess
from utilsK.cthcUtils import mixCthc_postprocess
from utilsK.pannelpostUtils import pannel_post_process
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
@ -36,307 +19,15 @@ from utilsK.channel2postUtils import channel2_post_process
@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': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/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,
# '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':"../weights/trt/AIlib2/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","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"],
'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,
'CarId':1,
'CthcId':12,
'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
},
'allowedList':[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,18,19,20,21,22],
'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': '../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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,
# 实际具体服务器显卡数
'device': '0,1',
'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,
'trtFlag_seg': False,
'trtFlag_det': False,
'seg_nclass': 2,
'segRegionCnt': 1,
'segPar': {
@ -361,610 +52,10 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../weights/trt/AIlib2/river2/stdc_360X640_%s_fp16.engine' % gpuName
'Detweights': "../weights/pth/AIlib2/river2/yolov5.pt",
'Segweights': '../weights/pth/AIlib2/river2/stdc_360X640.pth'
})
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" ],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{
'carCls':0 ,'illCls':6,'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':{ 0:0,1:1,2:2,3:6,4:4,5:5,6:3}
}
},
'models':[
{
'weight':'../weights/pth/AIlib2/cityMangement3/yolov5.pt',
#'weight':'../weights/trt/AIlib2/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],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../weights/pth/AIlib2/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':'../weights/pth/AIlib2/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": 6,
"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
},
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../weights/trt/AIlib2/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': "../weights/trt/AIlib2/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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': "../weights/trt/AIlib2/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"],
'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"],
'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
}},
'models':[
{
#'weight':'../weights/pth/AIlib2/channel2/yolov5.pt',
'weight':'../weights/trt/AIlib2/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' : '../weights/trt/AIlib2/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'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,
},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
'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
},
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../weights/trt/AIlib2/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
'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': 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': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/highWay2T/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':"../weights/trt/AIlib2/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':"../weights/trt/AIlib2/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':"../weights/trt/AIlib2/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
},
})
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': 2,
'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': mixSpillage_postprocess,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 1,
'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
},
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
###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': 2,
'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': mixCthc_postprocess,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 4,
'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": 1,
"rainbows": COLOR
},
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
###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,
'allowedList': [0,1,2], '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
},
})
CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
'device': str(device),
'models':{
{
'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit',
'conf_thres': 0.4,
'iou_thres': 0.45,
'nc':1,
},
{
'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'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,
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
}
],
'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,
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
}
],
'postFile': {
"rainbows": COLOR
},
})
@staticmethod
def checkCode(code):
for model in ModelType:
@ -973,33 +64,6 @@ class ModelType(Enum):
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):

762
enums/ModelTypeEnum2.py
View File

@ -1,762 +0,0 @@
import sys
from enum import Enum, unique
from common.Constant import COLOR
sys.path.extend(['..', '../AIlib2'])
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess_N
from segutils.trafficUtils import tracfficAccidentMixFunction_N
from utilsK.drownUtils import mixDrowing_water_postprocess_N
from utilsK.noParkingUtils import mixNoParking_road_postprocess_N
from utilsK.illParkingUtils import illParking_postprocess
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from yolov5 import yolov5Model
from stdc import stdcModel
from AI import default_mix
from DMPRUtils.jointUtil import dmpr_yolo_stdc
'''
参数说明
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
'''
@unique
class ModelType2(Enum):
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
'device': device,
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7] ],###控制哪些检测类别显示、输出
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 10, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 29, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
},
'models':
[
{
'weight':"../AIlib2/weights/river/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 } },
},
{
'weight':'../AIlib2/weights/conf/river/stdc_360X640.pth',
'par':{
'modelSize':(640,360),
'mean':(0.485, 0.456, 0.406),
'std' :(0.229, 0.224, 0.225),
'numpy':False,
'RGB_convert_first':True,
'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
})
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
'device': device,
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾"],
'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 }
},
}
],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
'device': device,
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "事故"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
'postProcess':{
'function':tracfficAccidentMixFunction_N,
'pars':{
'RoadArea': 16000,
'vehicleArea': 10,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'radius': 50 ,
'roundness': 1.0,
'cls': 9,
'vehicleFactor': 0.1,
'cls':9,
'confThres':0.25,
'roadIou':0.6,
'vehicleFlag':False,
'distanceFlag': False,
'modelSize':(640,360),
}
},
'models':
[
{
'weight':"../AIlib2/weights/highWay2/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 } },
},
{
'weight':'../AIlib2/weights/conf/highWay2/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
'model':stdcModel,
'name':'stdc'
}
],
'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
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
})
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/vehicle/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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 3
}
})
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
'device': device,
'labelnames': ["行人"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/pedestrian/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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
'device': device,
'labelnames': ["烟雾", "火焰"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/smogfire/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
#'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business'] ),
'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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
'device': device,
'labelnames': ["钓鱼", "游泳"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/AnglerSwimmer/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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
},
})
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
'device': device,
'labelnames': ["违法种植"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/countryRoad/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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
'obbModelPar': {
'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"],
'model_size': (608, 608),
'K': 100,
'conf_thresh': 0.3,
'down_ratio': 4,
'num_classes': 15,
'dataset': 'dota',
'heads': {
'hm': None,
'wh': 10,
'reg': 2,
'cls_theta': 1
},
'mean': (0.5, 0.5, 0.5),
'std': (1, 1, 1),
'half': False,
'test_flag': True,
'decoder': None,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName
},
'trackPar': {
'sort_max_age': 2, # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
'sort_min_hits': 3, # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
'sort_iou_thresh': 0.2, # 检测最小的置信度。
'det_cnt': 10, # 每隔几次做一个跟踪和检测，默认10。
'windowsize': 29, # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
'patchCnt': 100, # 每次送入图像的数量，不宜少于100帧。
},
'device': "cuda:%s" % device,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'drawBox': False,
'drawPar': {
"rainbows": COLOR,
'digitWordFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'wordSize': 40,
'fontSize': 1.0,
'label_location': 'leftTop'
}
},
'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"]
})
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
'device': device,
'labelnames': ["人"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
#'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business'] ),
'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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
'device': device,
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
"蓝藻"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数
'models':
[
{
'weight':"../AIlib2/weights/river2/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 } },
},
{
'weight':'../AIlib2/weights/conf/river2/stdc_360X640.pth',
'par':{
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.3,
"ovlap_thres_crossCategory": 0.65,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 80,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
},
'models':[
{
'weight':"../AIlib2/weights/cityMangement3/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":0.8,"1":0.5,"2":0.5,"3":0.5 } }
},
{
'weight':"../AIlib2/weights/cityMangement3/dmpr_%s.engine"% gpuName,###DMPR模型路径
'par':{
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
'name':'dmpr'
},
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':"../AIlib2/weights/cityMangement3/stdc_360X640_%s_fp16.engine"% gpuName,###分割模型路径
'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'
}
],
'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
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 2
}
})
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
'device': device,
'labelnames': ["人头", "人", "船只"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':mixDrowing_water_postprocess_N,
'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/drowning/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 } },
},
{
'weight':'../AIlib2/weights/conf/drowning/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'
}
],
'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
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
})
NOPARKING_MODEL = (
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
'device': device,
'labelnames': ["车辆", "违停"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':mixNoParking_road_postprocess_N,
'pars': { 'roundness': 0.3, 'cls': 9, 'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2, 'modelSize':(640,360)}
} ,
'models':
[
{
'weight':"../AIlib2/weights/noParking/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 } },
},
{
'weight':'../AIlib2/weights/conf/noParking/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':4},###分割模型预处理参数
'model':stdcModel,
'name':'stdc'
}
],
'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
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.25,
"classes": 9,
"rainbows": COLOR
},
'txtFontSize': 20,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': 2
}
}
)
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"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.8,'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 } },
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.8,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
}
})
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: { # 目前集成到另外的模型中去了 不单独使用
'device': device,
'labelnames': ["坑槽"],
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':3,'windowsize':29,'patchCnt':100},
'postProcess':{'function':default_mix,'pars':{ }},
'models':
[
{
'weight':"../AIlib2/weights/pothole/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}},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0]],###控制哪些检测类别显示、输出
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"rainbows": COLOR
},
'txtFontSize': 40,
'digitFont': {
'line_thickness': 2,
'boxLine_thickness': 1,
'fontSize': 1.0,
'segLineShow': False,
'waterLineColor': (0, 255, 255),
'waterLineWidth': 3
},
})
@staticmethod
def checkCode(code):
for model in ModelType2:
if model.value[1] == code:
return True
return False
'''
参数1: 检测目标名称
参数2: 检测目标
参数3: 初始化百度检测客户端
'''
@unique
class BaiduModelTarget2(Enum):
VEHICLE_DETECTION = (
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
HUMAN_DETECTION = (
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
BAIDU_MODEL_TARGET_CONFIG2 = {
BaiduModelTarget2.VEHICLE_DETECTION.value[1]: BaiduModelTarget2.VEHICLE_DETECTION,
BaiduModelTarget2.HUMAN_DETECTION.value[1]: BaiduModelTarget2.HUMAN_DETECTION,
BaiduModelTarget2.PEOPLE_COUNTING.value[1]: BaiduModelTarget2.PEOPLE_COUNTING
}
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
# 模型分析方式
@unique
class ModelMethodTypeEnum2(Enum):
# 方式一: 正常识别方式
NORMAL = 1
# 方式二: 追踪识别方式
TRACE = 2

592
util/ModelUtils.py
View File

@ -2,33 +2,44 @@
import sys
from pickle import dumps, loads
from traceback import format_exc
import time
import cv2
from loguru import logger
from common.Constant import COLOR
from enums.BaiduSdkEnum import VehicleEnum
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType, BAIDU_MODEL_TARGET_CONFIG
from enums.ModelTypeEnum import ModelType
from exception.CustomerException import ServiceException
from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient
from util.PlotsUtils import get_label_arrays, get_label_array_dict
from util.PlotsUtils import get_label_arrays
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
from AI import AI_process
from stdc import stdcModel
from segutils.segmodel import SegModel
from models.experimental import attempt_load
from obbUtils.shipUtils import OBB_infer
from obbUtils.load_obb_model import load_model_decoder_OBB
import torch
import tensorrt as trt
from utilsK.jkmUtils import pre_process, post_process, get_return_data
from DMPR import DMPRModel
FONT_PATH = "../AIlib2/conf/platech.ttf"
def get_label_arraylist(*args):
width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
line = max(1, int(round(width / 1920 * 3)))
label = ' 0.95'
tf = max(line - 1, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
# fontsize = int(width / 1920 * 40)
numFontSize = float(format(width / 1920 * 1.1, '.1f'))
digitFont = {'line_thickness': line,
'boxLine_thickness': line,
'fontSize': numFontSize,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': line,
'wordSize': text_height,
'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)
# 河道模型、河道检测模型、交通模型、人员落水模型、城市违章公共模型
class OneModel:
@ -46,8 +57,12 @@ class OneModel:
new_device = select_device(par.get('device'))
half = new_device.type != 'cpu'
Detweights = par['Detweights']
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
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()
par['segPar']['seg_nclass'] = par['seg_nclass']
Segweights = par['Segweights']
if Segweights:
@ -82,43 +97,6 @@ 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])
# 纯分类模型
class cityManagementModel:
__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)
postProcess = par['postProcess']
names = par['labelnames']
postFile = par['postFile']
rainbows = postFile["rainbows"]
modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
model_param = {
"modelList": modelList,
"postProcess": postProcess,
}
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 = model_conf[1]['modelList'], model_conf[1]['postProcess']
try:
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
@ -142,276 +120,6 @@ def model_process(args):
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
# 森林模型、车辆模型、行人模型、烟火模型、 钓鱼模型、航道模型、乡村模型、城管模型公共模型
class TwoModel:
__slots__ = "model_conf"
def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
s = time.time()
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device1), gpu_name)
device = select_device(par.get('device'))
names = par['labelnames']
half = device.type != 'cpu'
Detweights = par['Detweights']
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
segmodel = None
postFile = par['postFile']
conf_thres = postFile["conf_thres"]
iou_thres = postFile["iou_thres"]
rainbows = postFile["rainbows"]
otc = postFile.get("ovlap_thres_crossCategory")
model_param = {
"model": model,
"segmodel": segmodel,
"half": half,
"device": device,
"conf_thres": conf_thres,
"iou_thres": iou_thres,
"trtFlag_det": par['trtFlag_det'],
"otc": otc
}
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])
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'])
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])
class MultiModel:
__slots__ = "model_conf"
def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
s = time.time()
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device1), gpu_name)
postProcess = par['postProcess']
names = par['labelnames']
postFile = par['postFile']
rainbows = postFile["rainbows"]
modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
model_param = {
"modelList": modelList,
"postProcess": postProcess,
}
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])
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 = model_conf[1]['modelList'], model_conf[1]['postProcess']
try:
start = time.time()
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:
raise s
except Exception:
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)
line = max(1, int(round(width / 1920 * 3)))
label = ' 0.95'
tf = max(line - 1, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
# fontsize = int(width / 1920 * 40)
numFontSize = float(format(width / 1920 * 1.1, '.1f'))
digitFont = {'line_thickness': line,
'boxLine_thickness': line,
'fontSize': numFontSize,
'waterLineColor': (0, 255, 255),
'segLineShow': False,
'waterLineWidth': line,
'wordSize': text_height,
'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"
def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
s = time.time()
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device1), gpu_name)
model, decoder2 = load_model_decoder_OBB(par)
par['decoder'] = decoder2
names = par['labelnames']
rainbows = par['postFile']["rainbows"]
model_param = {
"model": model,
"par": par
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
logger.exception("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() - s, requestId)
def obb_process(args):
model_conf, frame, request_id = args
model_param = model_conf[1]
# font_config, frame, names, label_arrays, rainbows, model, par, requestId = args
try:
return OBB_infer(model_param["model"], frame, model_param["par"])
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])
# 车牌分割模型、健康码、行程码分割模型
class IMModel:
__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)
img_type = 'code'
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},
'conf_thres': 0.4,
'iou_thres': 0.45,
'device': 'cuda:%s' % device,
'plate_dilate': (0.5, 0.3)
}
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:{}",
requestId)
self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
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 im_process(args):
frame, device, model, par, img_type, requestId = args
try:
img, padInfos = pre_process(frame, device)
pred = model(img)
boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
iou_thres=par['iou_thres'], nc=par[img_type]['nc']) # 后处理
dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
print('-------line351----:',dataBack)
return dataBack
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:
__slots__ = "model_conf"
def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
# 人体检测与属性识别、 人流量统计客户端
aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
# 车辆检测检测客户端
aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
rainbows = COLOR
vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
person_names = ['人']
self.model_conf = (modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
vehicle_names, person_names, requestId)
except Exception:
logger.exception("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 get_baidu_label_arraylist(*args):
width, height, vehicle_names, person_names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
line = max(1, int(round(width / 1920 * 3) + 1))
label = ' 0.97'
tf = max(line, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
font_config = (line, text_width, text_height, fontScale, tf)
return vehicle_label_arrays, person_label_arrays, font_config
def baidu_process(args):
target, url, aipImageClassifyClient, aipBodyAnalysisClient, request_id = args
try:
# [target, url, aipImageClassifyClient, aipBodyAnalysisClient, requestId]
baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
if baiduEnum is None:
raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
+ " target: " + target)
return baiduEnum.value[2](aipImageClassifyClient, aipBodyAnalysisClient, url, request_id)
except ServiceException as s:
raise s
except Exception:
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 one_label(width, height, model_conf):
# modeType, model_param, allowedList, names, rainbows = model_conf
names = model_conf[3]
@ -422,253 +130,13 @@ def one_label(width, height, model_conf):
model_param['label_arraylist'] = label_arraylist
model_param['font_config'] = font_config
def dynamics_label(width, height, model_conf):
# modeType, model_param, allowedList, names, rainbows = model_conf
names = model_conf[3]
rainbows = model_conf[4]
model_param = model_conf[1]
digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
line = max(1, int(round(width / 1920 * 3)))
label = ' 0.95'
tf = max(line - 1, 1)
fontScale = line * 0.33
_, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
label_dict = get_label_array_dict(rainbows, fontSize=text_height, fontPath=FONT_PATH)
model_param['digitFont'] = digitFont
model_param['label_arraylist'] = label_arraylist
model_param['font_config'] = font_config
model_param['label_dict'] = label_dict
def baidu_label(width, height, model_conf):
# modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
# vehicle_names, person_names, requestId
vehicle_names = model_conf[5]
person_names = model_conf[6]
rainbows = model_conf[4]
vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
person_names, rainbows)
return vehicle_label_arrays, person_label_arrays, font_config
MODEL_CONFIG = {
# 加载河道模型
ModelType.WATER_SURFACE_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.WATER_SURFACE_MODEL, t, z, h),
ModelType.WATER_SURFACE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载森林模型
# ModelType.FOREST_FARM_MODEL.value[1]: (
# lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
# ModelType.FOREST_FARM_MODEL,
# lambda x, y, z: one_label(x, y, z),
# lambda x: forest_process(x)
# ),
ModelType.FOREST_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
ModelType.FOREST_FARM_MODEL,
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),
ModelType.TRAFFIC_FARM_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载防疫模型
ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]: (
lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.EPIDEMIC_PREVENTION_MODEL, t, z, h),
ModelType.EPIDEMIC_PREVENTION_MODEL,
None,
lambda x: im_process(x)),
# 加载车牌模型
ModelType.PLATE_MODEL.value[1]: (
lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.PLATE_MODEL, t, z, h),
ModelType.PLATE_MODEL,
None,
lambda x: im_process(x)),
# 加载车辆模型
ModelType.VEHICLE_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.VEHICLE_MODEL, t, z, h),
ModelType.VEHICLE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)
),
# 加载行人模型
ModelType.PEDESTRIAN_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.PEDESTRIAN_MODEL, t, z, h),
ModelType.PEDESTRIAN_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 加载烟火模型
ModelType.SMOGFIRE_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.SMOGFIRE_MODEL, t, z, h),
ModelType.SMOGFIRE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 加载钓鱼游泳模型
ModelType.ANGLERSWIMMER_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.ANGLERSWIMMER_MODEL, t, z, h),
ModelType.ANGLERSWIMMER_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 加载乡村模型
ModelType.COUNTRYROAD_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.COUNTRYROAD_MODEL, t, z, h),
ModelType.COUNTRYROAD_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 加载船只模型
ModelType.SHIP_MODEL.value[1]: (
lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType.SHIP_MODEL, t, z, h),
ModelType.SHIP_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: obb_process(x)),
# 百度AI图片识别模型
ModelType.BAIDU_MODEL.value[1]: (
lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType.BAIDU_MODEL, t, z, h),
ModelType.BAIDU_MODEL,
lambda x, y, z: baidu_label(x, y, z),
lambda x: baidu_process(x)),
# 航道模型
ModelType.CHANNEL_EMERGENCY_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CHANNEL_EMERGENCY_MODEL, t, z, h),
ModelType.CHANNEL_EMERGENCY_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 河道检测模型
ModelType.RIVER2_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVER2_MODEL, t, z, h),
ModelType.RIVER2_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 城管模型
ModelType.CITY_MANGEMENT_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_MANGEMENT_MODEL, t, z, h),
ModelType.CITY_MANGEMENT_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 人员落水模型
ModelType.DROWING_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.DROWING_MODEL, t, z, h),
ModelType.DROWING_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 城市违章模型
ModelType.NOPARKING_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.NOPARKING_MODEL, t, z, h),
ModelType.NOPARKING_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 车辆违停模型
ModelType.ILLPARKING_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.ILLPARKING_MODEL, t, z, h),
ModelType.ILLPARKING_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 城市公路模型
ModelType.CITYROAD_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITYROAD_MODEL, t, z, h),
ModelType.CITYROAD_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)),
# 加载坑槽模型
ModelType.POTHOLE_MODEL.value[1]: (
lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.POTHOLE_MODEL, t, z, h),
ModelType.POTHOLE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)
),
# 加载船只综合检测模型
ModelType.CHANNEL2_MODEL.value[1]: (
lambda x, y, r, t, z, h: MultiModel(x, y, r, ModelType.CHANNEL2_MODEL, t, z, h),
ModelType.CHANNEL2_MODEL,
lambda x, y, z: dynamics_label(x, y, z),
lambda x: channel2_process(x)
),
# 河道检测模型
ModelType.RIVERT_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVERT_MODEL, t, z, h),
ModelType.RIVERT_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 加载森林人群模型
ModelType.FORESTCROWD_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FORESTCROWD_FARM_MODEL, t, z, h),
ModelType.FORESTCROWD_FARM_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载交通模型
ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: (
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
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
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),
ModelType.FIREWORK_MODEL,
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)
),
}

442
util/ModelUtils2.py
View File

@ -1,442 +0,0 @@
# -*- coding: utf-8 -*-
import sys
from json import dumps, loads
from traceback import format_exc
import cv2
from loguru import logger
from common.Constant import COLOR
from enums.BaiduSdkEnum import VehicleEnum
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum2 import ModelType2, BAIDU_MODEL_TARGET_CONFIG2
from exception.CustomerException import ServiceException
from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient
from util.PlotsUtils import get_label_arrays
from util.TorchUtils import select_device
import time
import torch
import tensorrt as trt
sys.path.extend(['..', '../AIlib2'])
from AI import AI_process, get_postProcess_para, get_postProcess_para_dic, AI_det_track, AI_det_track_batch, AI_det_track_batch_N
from stdc import stdcModel
from utilsK.jkmUtils import pre_process, post_process, get_return_data
from obbUtils.shipUtils import OBB_infer, OBB_tracker, draw_obb, OBB_tracker_batch
from obbUtils.load_obb_model import load_model_decoder_OBB
from trackUtils.sort import Sort
from trackUtils.sort_obb import OBB_Sort
from DMPR import DMPRModel
FONT_PATH = "../AIlib2/conf/platech.ttf"
class Model:
__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)
trackPar = par['trackPar']
names = par['labelnames']
detPostPar = par['postFile']
rainbows = detPostPar["rainbows"]
#第一步加载模型
modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
#第二步准备跟踪参数
trackPar=par['trackPar']
sort_tracker = Sort(max_age=trackPar['sort_max_age'],
min_hits=trackPar['sort_min_hits'],
iou_threshold=trackPar['sort_iou_thresh'])
postProcess = par['postProcess']
model_param = {
"modelList": modelList,
"postProcess": postProcess,
"sort_tracker": sort_tracker,
"trackPar": trackPar,
}
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 get_label_arraylist(*args):
width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
line = max(1, int(round(width / 1920 * 3)))
tf = max(line, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(' 0.95', 0, fontScale=fontScale, thickness=tf)[0]
label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
return label_arraylist, (line, text_width, text_height, fontScale, tf)
"""
输入：
imgarray_list--图像列表
iframe_list -- 帧号列表
modelPar--模型参数,字典,modelPar={'det_Model':,'seg_Model':}
processPar--字典，存放检测相关参数，'half', 'device', 'conf_thres', 'iou_thres','trtFlag_det'
sort_tracker--对象，初始化的跟踪对象。为了保持一致，即使是单帧也要有。
trackPar--跟踪参数，关键字包括：det_cnt，windowsize
segPar--None,分割模型相关参数。如果用不到，则为None
输入：retResults,timeInfos
retResults：list
retResults[0]--imgarray_list
retResults[1]--所有结果用numpy格式，所有的检测结果，包括8类，每列分别是x1, y1, x2, y2, conf, detclass,iframe,trackId
retResults[2]--所有结果用list表示,其中每一个元素为一个list，表示每一帧的检测结果，每一个结果是由多个list构成，每个list表示一个框，格式为[ cls , x0 ,y0 ,x1 ,y1 ,conf,ifrmae,trackId ]，如 retResults[2][j][k]表示第j帧的第k个框。
"""
def model_process(args):
# (modeType, model_param, allowedList, names, rainbows)
imgarray_list, iframe_list, model_param, request_id = args
try:
return AI_det_track_batch_N(imgarray_list, iframe_list,
model_param['modelList'],
model_param['postProcess'],
model_param['sort_tracker'],
model_param['trackPar'])
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])
# 船只模型
class ShipModel:
__slots__ = "model_conf"
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
s = time.time()
try:
logger.info("########################加载船只模型########################, requestId:{}", requestId)
par = modeType.value[4](str(device), gpu_name)
obbModelPar = par['obbModelPar']
model, decoder2 = load_model_decoder_OBB(obbModelPar)
obbModelPar['decoder'] = decoder2
names = par['labelnames']
rainbows = par['postFile']["rainbows"]
trackPar = par['trackPar']
sort_tracker = OBB_Sort(max_age=trackPar['sort_max_age'], min_hits=trackPar['sort_min_hits'],
iou_threshold=trackPar['sort_iou_thresh'])
modelPar = {'obbmodel': model}
segPar = None
model_param = {
"modelPar": modelPar,
"obbModelPar": obbModelPar,
"sort_tracker": sort_tracker,
"trackPar": trackPar,
"segPar": segPar
}
self.model_conf = (modeType, model_param, allowedList, names, rainbows)
except Exception:
logger.exception("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() - s, requestId)
def obb_process(args):
imgarray_list, iframe_list, model_param, request_id = args
try:
return OBB_tracker_batch(imgarray_list, iframe_list, model_param['modelPar'], model_param['obbModelPar'],
model_param['sort_tracker'], model_param['trackPar'], model_param['segPar'])
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])
# 车牌分割模型、健康码、行程码分割模型
class IMModel:
__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)
img_type = 'code'
if ModelType2.PLATE_MODEL == modeType:
img_type = 'plate'
par = {
'code': {'weights': '../AIlib2/weights/conf/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
'plate': {'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
'conf_thres': 0.4,
'iou_thres': 0.45,
'device': 'cuda:%s' % device,
'plate_dilate': (0.5, 0.3)
}
new_device = torch.device(par['device'])
model = torch.jit.load(par[img_type]['weights'])
model_param = {
"device": new_device,
"model": model,
"par": par,
"img_type": img_type
}
self.model_conf = (modeType, model_param, allowedList)
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 im_process(args):
model_param, frame, request_id = args
device, par, img_type = model_param['device'], model_param['par'], model_param['img_type']
try:
img, padInfos = pre_process(frame, device)
pred = model_param['model'](img)
boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
iou_thres=par['iou_thres'], nc=par[img_type]['nc']) # 后处理
dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
return dataBack
except ServiceException as s:
raise s
except Exception:
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])
# 百度AI图片识别模型
class BaiduAiImageModel:
__slots__ = "model_conf"
def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
rainbows = COLOR
vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
person_names = ['人']
model_param = {
"vehicle_client": aipImageClassifyClient,
"person_client": aipBodyAnalysisClient,
}
self.model_conf = (modeType, model_param, allowedList, (vehicle_names, person_names), rainbows)
except Exception:
logger.exception("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 baidu_process(args):
model_param, target, url, request_id = args
try:
baiduEnum = BAIDU_MODEL_TARGET_CONFIG2.get(target)
if baiduEnum is None:
raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
+ " target: " + target)
return baiduEnum.value[2](model_param['vehicle_client'], model_param['person_client'], url, request_id)
except ServiceException as s:
raise s
except Exception:
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_baidu_label_arraylist(*args):
width, height, vehicle_names, person_names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1)
line = max(1, int(round(width / 1920 * 3) + 1))
label = ' 0.97'
tf = max(line, 1)
fontScale = line * 0.33
text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
font_config = (line, text_width, text_height, fontScale, tf)
return vehicle_label_arrays, person_label_arrays, font_config
def one_label(width, height, model_config):
# (modeType, model_param, allowedList, names, rainbows)
names = model_config[3]
rainbows = model_config[4]
label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
model_config[1]['label_arraylist'] = label_arraylist
model_config[1]['font_config'] = font_config
def baidu_label(width, height, model_config):
# modeType, model_param, allowedList, (vehicle_names, person_names), rainbows
vehicle_names = model_config[3][0]
person_names = model_config[3][1]
rainbows = model_config[4]
vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
person_names, rainbows)
model_config[1]['vehicle_label_arrays'] = vehicle_label_arrays
model_config[1]['person_label_arrays'] = person_label_arrays
model_config[1]['font_config'] = font_config
def model_process1(args):
imgarray_list, iframe_list, model_param, request_id = 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'],
segPar=loads(dumps(model_param['segPar'])), mode=model_param['mode'],
postPar=model_param['postPar'])
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])
MODEL_CONFIG2 = {
# 加载河道模型
ModelType2.WATER_SURFACE_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.WATER_SURFACE_MODEL, t, z, h),
ModelType2.WATER_SURFACE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载森林模型
ModelType2.FOREST_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.FOREST_FARM_MODEL, t, z, h),
ModelType2.FOREST_FARM_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载交通模型
ModelType2.TRAFFIC_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.TRAFFIC_FARM_MODEL, t, z, h),
ModelType2.TRAFFIC_FARM_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载防疫模型
ModelType2.EPIDEMIC_PREVENTION_MODEL.value[1]: (
lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType2.EPIDEMIC_PREVENTION_MODEL, t, z, h),
ModelType2.EPIDEMIC_PREVENTION_MODEL,
None,
lambda x: im_process(x)),
# 加载车牌模型
ModelType2.PLATE_MODEL.value[1]: (
lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType2.PLATE_MODEL, t, z, h),
ModelType2.PLATE_MODEL,
None,
lambda x: im_process(x)),
# 加载车辆模型
ModelType2.VEHICLE_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.VEHICLE_MODEL, t, z, h),
ModelType2.VEHICLE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载行人模型
ModelType2.PEDESTRIAN_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.PEDESTRIAN_MODEL, t, z, h),
ModelType2.PEDESTRIAN_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 加载烟火模型
ModelType2.SMOGFIRE_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.SMOGFIRE_MODEL, t, z, h),
ModelType2.SMOGFIRE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 加载钓鱼游泳模型
ModelType2.ANGLERSWIMMER_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.ANGLERSWIMMER_MODEL, t, z, h),
ModelType2.ANGLERSWIMMER_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 加载乡村模型
ModelType2.COUNTRYROAD_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.COUNTRYROAD_MODEL, t, z, h),
ModelType2.COUNTRYROAD_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 加载船只模型
ModelType2.SHIP_MODEL.value[1]: (
lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType2.SHIP_MODEL, t, z, h),
ModelType2.SHIP_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: obb_process(x)),
# 百度AI图片识别模型
ModelType2.BAIDU_MODEL.value[1]: (
lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType2.BAIDU_MODEL, t, z, h),
ModelType2.BAIDU_MODEL,
lambda x, y, z: baidu_label(x, y, z),
lambda x: baidu_process(x)),
# 航道模型
ModelType2.CHANNEL_EMERGENCY_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CHANNEL_EMERGENCY_MODEL, t, z, h),
ModelType2.CHANNEL_EMERGENCY_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 河道检测模型
ModelType2.RIVER2_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.RIVER2_MODEL, t, z, h),
ModelType2.RIVER2_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)),
# 城管模型
ModelType2.CITY_MANGEMENT_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CITY_MANGEMENT_MODEL, t, z, h),
ModelType2.CITY_MANGEMENT_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 人员落水模型
ModelType2.DROWING_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.DROWING_MODEL, t, z, h),
ModelType2.DROWING_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 城市违章模型
ModelType2.NOPARKING_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.NOPARKING_MODEL, t, z, h),
ModelType2.NOPARKING_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 城市公路模型
ModelType2.CITYROAD_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CITYROAD_MODEL, t, z, h),
ModelType2.CITYROAD_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载坑槽模型
ModelType2.POTHOLE_MODEL.value[1]: (
lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.POTHOLE_MODEL, t, z, h),
ModelType2.POTHOLE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
}

116
util/PlotsUtils.py
View File

@ -2,9 +2,10 @@ 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:
@ -67,6 +68,12 @@ def xywh2xyxy2(param):
# 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):
# 识别问题描述图片的高、宽
@ -218,6 +225,11 @@ 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
@ -251,8 +263,106 @@ 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
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]
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, line_thickness=2, outfontsize=20):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
if len(dets) == 1:
label = '当前人数：%d'%len(dets[0])
detP = dets[0]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line_thickness, 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) == 2:
detP = dets[1]
for p in detP:
img = cv2.circle(img, (int(p[0]), int(p[1])), line_thickness, 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

2
util/TorchUtils.py
View File

@ -70,7 +70,7 @@ def select_device(device='0'):
# 设置环境变量
os.environ['CUDA_VISIBLE_DEVICES'] = device
assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'
return torch.device('cuda:%s' % device)
return torch.device('cuda:1')
# def select_device(device='', batch_size=None):

BIN
vodsdk/common/__pycache__/Constant.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/common/__pycache__/__init__.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/IntelligentRecognitionProcess2.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/Pull2PushStreamProcess.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/Pull2PushStreamThread.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/PullMqttThread.cpython-38.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/PullVideoStreamProcess2.cpython-38.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/PushVideoStreamProcess.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/PushVideoStreamProcess2.cpython-38.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/concurrency/__pycache__/uploadGPU.cpython-38.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/enums/__pycache__/BaiduSdkEnum.cpython-310.pyc
View File

Binary file not shown.

BIN
vodsdk/enums/__pycache__/BaiduSdkEnum.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/enums/__pycache__/ExceptionEnum.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/enums/__pycache__/ModelTypeEnum2.cpython-38.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/enums/__pycache__/StatusEnum.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/enums/__pycache__/__init__.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/exception/__pycache__/CustomerException.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/exception/__pycache__/__init__.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/service/__pycache__/Dispatcher.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/service/__pycache__/__init__.cpython-310.pyc
View File

Binary file not shown.

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

Binary file not shown.

BIN
vodsdk/util/__pycache__/AliyunSdk.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/CpuUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/Cv2Utils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/FileUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/GPUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/ImageUtils.cpython-310.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/ImageUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/ImgBaiduSdk.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/KafkaUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/LocationUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/LogUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/MinioSdk.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/ModelUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/ModelUtils2.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/OcrBaiduSdk.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/PlotsUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/PushStreamUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/QueUtil.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/RWUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/TimeUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/TorchUtils.cpython-38.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/__init__.cpython-310.pyc
View File

Binary file not shown.

BIN
vodsdk/util/__pycache__/__init__.cpython-38.pyc
View File

Binary file not shown.