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zsl #7

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jiangchaoqing merged 6 commits from zsl into master 2025-07-11 08:49:15 +08:00
Conversation 0 Commits 6 Files Changed 6 +550 -185
6 changed files with 550 additions and 185 deletions
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108
concurrency/FileUploadThread.py
View File

@ -8,12 +8,13 @@ from loguru import logger
import cv2 import cv2
from entity.FeedBack import message_feedback from entity.FeedBack import message_feedback
from enums.ExceptionEnum import ExceptionType from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType
from exception.CustomerException import ServiceException from exception.CustomerException import ServiceException
from util.AliyunSdk import AliyunOssSdk from util.AliyunSdk import AliyunOssSdk
from util.MinioSdk import MinioSdk from util.MinioSdk import MinioSdk
from util import TimeUtils from util import TimeUtils
from enums.AnalysisStatusEnum import AnalysisStatus 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 from util.QueUtil import put_queue, get_no_block_queue, clear_queue
import io import io
from util.LocationUtils import locate_byMqtt from util.LocationUtils import locate_byMqtt
@ -42,19 +43,11 @@ class FileUpload(Thread):
print("执行替代程序（defaultEnabled=False）") print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码 # 这里放非默认逻辑的代码
self._algSwitch = False self._algSwitch = False
print("---line46 :FileUploadThread.py---",self._algSwitch) print("---line46 :FileUploadThread.py---",self._algSwitch)
#如果任务是在线、离线处理，则用此类
#如果任务是在线、离线处理，则用此类
class ImageFileUpload(FileUpload): class ImageFileUpload(FileUpload):
__slots__ = () __slots__ = ()
@ -74,7 +67,7 @@ class ImageFileUpload(FileUpload):
''' '''
print('*'*100,' mqtt_list:',len(self._mqtt_list)) print('*'*100,' mqtt_list:',len(self._mqtt_list))
model_info = [] model_info = []
# 更加模型编码解析数据 # 更加模型编码解析数据
for code, det_list in det_xywh.items(): for code, det_list in det_xywh.items():
@ -83,15 +76,27 @@ class ImageFileUpload(FileUpload):
if len(target_list) > 0: if len(target_list) > 0:
aFrame = frame.copy() aFrame = frame.copy()
for target in target_list: for target in target_list:
draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5]) # 自研车牌模型判断
igH,igW = aFrame.shape[0:2] if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
if len(self._mqtt_list)>=1: box = [target[1][0][0], target[1][0][1], target[1][3][0], target[1][3][1]]
#camParas = self._mqtt_list[0]['data'] draw_name_ocr(box, aFrame, target[4], target[0])
cls = 0
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
draw_name_crowd(target[3], aFrame, target[4], cls)
cls = 0
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] 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: else:
gps=[None,None] gps = [None, None]
model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame,'gps':gps}) model_info.append(
{"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
if len(model_info) > 0: if len(model_info) > 0:
image_result = { image_result = {
"or_frame": frame, "or_frame": frame,
@ -130,7 +135,7 @@ class ImageFileUpload(FileUpload):
# 获取队列中的消息 # 获取队列中的消息
image_msg = get_no_block_queue(image_queue) image_msg = get_no_block_queue(image_queue)
if image_msg is not None: if image_msg is not None:
if image_msg[0] == 2: 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]: if 'stop' == image_msg[1]:
@ -138,7 +143,7 @@ class ImageFileUpload(FileUpload):
break 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 '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 '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: if image_msg[0] == 1:
image_result = self.handle_image(image_msg[1], frame_step) image_result = self.handle_image(image_msg[1], frame_step)
if image_result is not None: if image_result is not None:
@ -148,7 +153,7 @@ class ImageFileUpload(FileUpload):
image_result["last_frame"], image_result["last_frame"],
analyse_type, analyse_type,
"OR", "0", "0", request_id) "OR", "0", "0", request_id)
if self._storage_source==1: if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,or_image_name) or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
else: else:
or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes()) or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
@ -164,13 +169,13 @@ class ImageFileUpload(FileUpload):
model_info["modelCode"], model_info["modelCode"],
model_info["detectTargetCode"], model_info["detectTargetCode"],
request_id) request_id)
if self._storage_source==1: if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, ai_image, ai_future = t.submit(minioSdk.put_object, ai_image,
ai_image_name) ai_image_name)
else: else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes()) ai_image.tobytes())
task.append(ai_future) task.append(ai_future)
#msg_list.append(message_feedback(request_id, #msg_list.append(message_feedback(request_id,
# AnalysisStatus.RUNNING.value, # AnalysisStatus.RUNNING.value,
@ -194,7 +199,7 @@ class ImageFileUpload(FileUpload):
longitude=model_info['gps'][0], longitude=model_info['gps'][0],
latitude=model_info['gps'][1], 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: for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False) put_queue(fb_queue, msg, timeout=2, is_ex=False)
@ -220,7 +225,7 @@ def build_image_name(*args):
time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S") 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, 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) 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): class ImageTypeImageFileUpload(Thread):
@ -249,12 +254,20 @@ class ImageTypeImageFileUpload(Thread):
if target_list is not None and len(target_list) > 0: if target_list is not None and len(target_list) > 0:
aiFrame = copy_frame.copy() aiFrame = copy_frame.copy()
for target in target_list: for target in target_list:
draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config) # 自研车牌模型判断
model_info.append({ if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
"modelCode": str(code), draw_name_ocr(target[1], aiFrame, font_config[cls], target[0])
"detectTargetCode": str(cls), elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
"frame": aiFrame draw_name_crowd(target[1],aiFrame,font_config[cls],target[0])
}) 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: if len(model_info) > 0:
image_result = { image_result = {
"or_frame": copy_frame, "or_frame": copy_frame,
@ -278,7 +291,7 @@ class ImageTypeImageFileUpload(Thread):
minioSdk = MinioSdk(base_dir, env, request_id ) minioSdk = MinioSdk(base_dir, env, request_id )
else: else:
aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id) aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
start_time = time() start_time = time()
while True: while True:
try: try:
@ -299,12 +312,12 @@ class ImageTypeImageFileUpload(Thread):
if det_xywh is None: if det_xywh is None:
ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"), ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
result.get("type"), request_id) result.get("type"), request_id)
if self._storage_source==1: if self._storage_source==1:
ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name) ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
else: else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame) ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
task.append(ai_future) task.append(ai_future)
remote_names.append(ai_image_name) remote_names.append(ai_image_name)
#msg_list.append(message_feedback(request_id, #msg_list.append(message_feedback(request_id,
@ -318,7 +331,7 @@ class ImageTypeImageFileUpload(Thread):
else: else:
image_result = self.handle_image(det_xywh, copy_frame, font_config) image_result = self.handle_image(det_xywh, copy_frame, font_config)
if image_result: if image_result:
# 图片帧数编码 # 图片帧数编码
if image_url is None: if image_url is None:
or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame")) or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
@ -326,8 +339,8 @@ class ImageTypeImageFileUpload(Thread):
image_result.get("last_frame"), image_result.get("last_frame"),
analyse_type, analyse_type,
"OR", "0", "O", request_id) "OR", "0", "O", request_id)
if self._storage_source==1: if self._storage_source==1:
or_future = t.submit(minioSdk.put_object, or_image,image_url_0) or_future = t.submit(minioSdk.put_object, or_image,image_url_0)
else: else:
or_future = t.submit(aliyunOssSdk.put_object, image_url_0, or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
@ -344,9 +357,9 @@ class ImageTypeImageFileUpload(Thread):
model_info.get("modelCode"), model_info.get("modelCode"),
model_info.get("detectTargetCode"), model_info.get("detectTargetCode"),
request_id) 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_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
else: else:
ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
ai_image.tobytes()) ai_image.tobytes())
task.append(ai_future) task.append(ai_future)
@ -362,9 +375,8 @@ class ImageTypeImageFileUpload(Thread):
remote_url_list = [] remote_url_list = []
for thread_result in task: for thread_result in task:
remote_url_list.append(thread_result.result()) 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: if det_xywh is None:
msg_list.append(message_feedback(request_id, msg_list.append(message_feedback(request_id,
AnalysisStatus.RUNNING.value, AnalysisStatus.RUNNING.value,
@ -396,11 +408,7 @@ class ImageTypeImageFileUpload(Thread):
model_info_list[ii].get('modelCode'), model_info_list[ii].get('modelCode'),
model_info_list[ii].get('detectTargetCode'), model_info_list[ii].get('detectTargetCode'),
analyse_results=result)) analyse_results=result))
for msg in msg_list: for msg in msg_list:
put_queue(fb_queue, msg, timeout=2, is_ex=False) put_queue(fb_queue, msg, timeout=2, is_ex=False)
else: else:

199
concurrency/IntelligentRecognitionProcess.py
View File

@ -91,8 +91,7 @@ class IntelligentRecognitionProcess(Process):
hb_thread.start() hb_thread.start()
return hb_thread return hb_thread
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess): class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
@ -229,7 +228,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
# 事件队列、拉流队列、心跳队列、反馈队列 # 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 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 # 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 push_queue, push_ex_queue, image_queue = self._push_queue, self._push_ex_queue, self._image_queue
try: try:
@ -237,16 +236,15 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
init_log(base_dir, env) init_log(base_dir, env)
# 打印启动日志 # 打印启动日志
logger.info("开始启动实时分析进程！requestId: {}", request_id) 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) # 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 # 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) 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 # 启动心跳线程
hb_thread = self.start_heartbeat(fb_queue, hb_queue, request_id, analyse_type, context) 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 # 加载算法模型
model_array = get_model(msg, context, analyse_type) 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
@ -273,7 +271,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
raise ServiceException(push_status[1], push_status[2]) raise ServiceException(push_status[1], push_status[2])
# 获取停止指令 # 获取停止指令
event_result = get_no_block_queue(event_queue) event_result = get_no_block_queue(event_queue)
if event_result: if event_result:
cmdStr = event_result.get("command") cmdStr = event_result.get("command")
#接收到算法开启、或者关闭的命令 #接收到算法开启、或者关闭的命令
@ -281,7 +279,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr ) 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) put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
pull_process.sendCommand({"command": cmdStr}) pull_process.sendCommand({"command": cmdStr})
# 接收到停止指令 # 接收到停止指令
if "stop" == cmdStr: if "stop" == cmdStr:
logger.info("实时任务开始停止, requestId: {}", request_id) logger.info("实时任务开始停止, requestId: {}", request_id)
@ -301,20 +299,31 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1 task_status[0] = 1
for i, model in enumerate(model_array): for i, model in enumerate(model_array):
model_conf, code = model model_conf, code = model
model_param = model_conf[1] if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
# (modeType, model_param, allowedList, names, rainbows) ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0], if draw_config.get(code) is None:
model_conf) draw_config[code] = {}
if draw_config.get("font_config") is None: draw_config["font_config"] = model_conf[4]
draw_config["font_config"] = model_param['font_config'] draw_config[code]["allowedList"] = 0
if draw_config.get(code) is None: draw_config[code]["label_arrays"] = [None]
draw_config[code] = {} draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["allowedList"] = model_conf[2] else:
draw_config[code]["rainbows"] = model_conf[4] model_param = model_conf[1]
draw_config[code]["label_arrays"] = model_param['label_arraylist'] # (modeType, model_param, allowedList, names, rainbows)
if "label_dict" in model_param: MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
draw_config[code]["label_dict"] = model_param['label_dict'] model_conf)
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) 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 # 多线程并发处理, 经过测试两个线程最优
det_array = [] det_array = []
for i, frame in enumerate(frame_list): for i, frame in enumerate(frame_list):
@ -437,23 +446,23 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess): class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
__slots__ = () __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) 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 = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
if self._storage_source==1: if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id ) minioSdk = MinioSdk(base_dir, env, request_id )
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_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) 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 = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_ai.setDaemon(True) upload_video_thread_ai.setDaemon(True)
upload_video_thread_ai.start() upload_video_thread_ai.start()
ai_url = upload_video_thread_ai.get_result() ai_url = upload_video_thread_ai.get_result()
return ai_url return ai_url
''' '''
@staticmethod @staticmethod
def upload_video(base_dir, env, request_id, aiFilePath): def upload_video(base_dir, env, request_id, aiFilePath):
@ -602,7 +611,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr ) 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) put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
pull_process.sendCommand({"command": cmdStr}) pull_process.sendCommand({"command": cmdStr})
pull_result = get_no_block_queue(pull_queue) pull_result = get_no_block_queue(pull_queue)
if pull_result is None: if pull_result is None:
sleep(1) sleep(1)
@ -616,19 +625,31 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
task_status[0] = 1 task_status[0] = 1
for i, model in enumerate(model_array): for i, model in enumerate(model_array):
model_conf, code = model model_conf, code = model
model_param = model_conf[1] if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
# (modeType, model_param, allowedList, names, rainbows) ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0], if draw_config.get(code) is None:
model_conf) draw_config[code] = {}
if draw_config.get("font_config") is None: draw_config["font_config"] = model_conf[4]
draw_config["font_config"] = model_param['font_config'] draw_config[code]["allowedList"] = 0
if draw_config.get(code) is None: draw_config[code]["label_arrays"] = [None]
draw_config[code] = {} draw_config[code]["rainbows"] = model_conf[4]
draw_config[code]["allowedList"] = model_conf[2]
draw_config[code]["rainbows"] = model_conf[4] else:
draw_config[code]["label_arrays"] = model_param['label_arraylist'] model_param = model_conf[1]
if "label_dict" in model_param: # (modeType, model_param, allowedList, names, rainbows)
draw_config[code]["label_dict"] = model_param['label_dict'] 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 = [] det_array = []
for i, frame in enumerate(frame_list): for i, frame in enumerate(frame_list):
det_result = t.submit(self.obj_det, self, model_array, frame, task_status, det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
@ -922,6 +943,63 @@ class PhotosIntelligentRecognitionProcess(Process):
logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id) logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
raise e 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]
logger.info("当前人数：{}", dataBack[0][0])
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 # 防疫模型
''' '''
@ -936,6 +1014,26 @@ class PhotosIntelligentRecognitionProcess(Process):
for r in obj_list: for r in obj_list:
r.result(60) 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): def image_recognition(self, imageUrl, mod, image_queue, logo, request_id):
try: try:
model_conf, code = mod model_conf, code = mod
@ -1125,7 +1223,7 @@ class PhotosIntelligentRecognitionProcess(Process):
except requests.exceptions.RequestException as e: 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） # 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): def run(self):
fb_queue, msg, analyse_type, context = self._fb_queue, self._msg, self._analyse_type, self._context 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 request_id, logo, image_queue = msg["request_id"], context['logo'], self._image_queue
@ -1136,7 +1234,7 @@ class PhotosIntelligentRecognitionProcess(Process):
valFlag=True valFlag=True
for url in imageUrls: 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: if not valFlag:
logger.error("图片分析异常: {}, requestId:{},url:{}",ret, request_id,url) 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] ) ) #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] ) )
@ -1168,6 +1266,14 @@ class PhotosIntelligentRecognitionProcess(Process):
elif model[1] == ModelType.PLATE_MODEL.value[1]: elif model[1] == ModelType.PLATE_MODEL.value[1]:
result = t.submit(self.epidemicPrevention, imageUrls, model, base_dir, env, request_id) result = t.submit(self.epidemicPrevention, imageUrls, model, base_dir, env, request_id)
task_list.append(result) 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]:
result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
task_list.append(result)
else: else:
result = t.submit(self.publicIdentification, imageUrls, model, image_queue, logo, request_id) result = t.submit(self.publicIdentification, imageUrls, model, image_queue, logo, request_id)
task_list.append(result) task_list.append(result)
@ -1214,7 +1320,7 @@ class ScreenRecordingProcess(Process):
put_queue(self._fb_queue, put_queue(self._fb_queue,
recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]), recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]),
timeout=1, is_ex=True) 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): def sendEvent(self, result):
put_queue(self._event_queue, result, timeout=2, is_ex=True) put_queue(self._event_queue, result, timeout=2, is_ex=True)
@ -1380,9 +1486,6 @@ class ScreenRecordingProcess(Process):
clear_queue(self._hb_queue) clear_queue(self._hb_queue)
clear_queue(self._pull_queue) clear_queue(self._pull_queue)
def upload_video(self,base_dir, env, request_id, orFilePath): def upload_video(self,base_dir, env, request_id, orFilePath):
if self._storage_source==1: if self._storage_source==1:
minioSdk = MinioSdk(base_dir, env, request_id ) minioSdk = MinioSdk(base_dir, env, request_id )
@ -1390,7 +1493,7 @@ class ScreenRecordingProcess(Process):
else: else:
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id) 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 = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
upload_video_thread_ai.setDaemon(True) upload_video_thread_ai.setDaemon(True)
upload_video_thread_ai.start() upload_video_thread_ai.start()
or_url = upload_video_thread_ai.get_result() or_url = upload_video_thread_ai.get_result()

139
concurrency/PushVideoStreamProcess.py
View File

@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
from util.ImageUtils import url2Array, add_water_pic from util.ImageUtils import url2Array, add_water_pic
from util.LogUtils import init_log 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 from util.QueUtil import get_no_block_queue, put_queue, clear_queue
@ -37,7 +37,7 @@ class PushStreamProcess(Process):
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args 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._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch'] self._algSwitch = self._context['service']['algSwitch']
#0521: #0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true" default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
@ -49,16 +49,9 @@ class PushStreamProcess(Process):
print("执行替代程序（defaultEnabled=False）") print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码 # 这里放非默认逻辑的代码
self._algSwitch = False self._algSwitch = False
print("---line53 :PushVideoStreamProcess.py---",self._algSwitch) print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
def build_logo_url(self): def build_logo_url(self):
logo = None logo = None
if self._context["video"]["video_add_water"]: if self._context["video"]["video_add_water"]:
@ -155,17 +148,39 @@ class OnPushStreamProcess(PushStreamProcess):
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"] font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"] rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result: for qs in det_result:
try: # 应对NaN情况 # 自研车牌模型处理
box, score, cls = xywh2xyxy2(qs) if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
except: cls = 0
continue ocrlabel, xybox = qs
if cls not in allowedList or score < frame_score: box = xy2xyxy(xybox)
continue score = None
label_array, color = label_arrays[cls], rainbows[cls] color = rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2: label_array = None
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6]) rr = t.submit(draw_name_ocr, xybox, copy_frame, color, ocrlabel)
elif ModelType.CITY_DENSECROWDCOUNT_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, points, copy_frame, color, crowdlabel)
else: 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) thread_p.append(rr)
if det_xywh.get(code) is None: if det_xywh.get(code) is None:
det_xywh[code] = {} det_xywh[code] = {}
@ -184,17 +199,17 @@ class OnPushStreamProcess(PushStreamProcess):
box[2][0], box[2][1], box[3][0], box[3][1], box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32) score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li)) qs_np = np.row_stack((qs_np, result_li))
if logo: if logo:
frame = add_water_pic(frame, logo, request_id) frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id) copy_frame = add_water_pic(copy_frame, logo, request_id)
if len(thread_p) > 0: if len(thread_p) > 0:
for r in thread_p: for r in thread_p:
r.result() r.result()
#print('----line173:',self._algSwitch,self._algStatus) #print('----line173:',self._algSwitch,self._algStatus)
if self._algSwitch and (not self._algStatus): if self._algSwitch and (not self._algStatus):
frame_merge = video_conjuncing(frame, frame.copy()) frame_merge = video_conjuncing(frame, frame.copy())
else: else:
frame_merge = video_conjuncing(frame, copy_frame) frame_merge = video_conjuncing(frame, copy_frame)
# 写原视频到本地 # 写原视频到本地
write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file, write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
@ -207,7 +222,7 @@ class OnPushStreamProcess(PushStreamProcess):
# 如果有问题, 走下面的逻辑 # 如果有问题, 走下面的逻辑
if qs_np is not None: if qs_np is not None:
if len(qs_np.shape) == 1: if len(qs_np.shape) == 1:
qs_np = qs_np[np.newaxis,...] qs_np = qs_np[np.newaxis,...]
qs_np_id = qs_np.copy() qs_np_id = qs_np.copy()
b = np.ones(qs_np_id.shape[0]) 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))
@ -233,7 +248,7 @@ class OnPushStreamProcess(PushStreamProcess):
if q[11] >= 1: if q[11] >= 1:
cls = int(q[9]) cls = int(q[9])
if not (cls in new_lab): 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) code = str(int(q[10])).zfill(3)
if det_xywh2.get(code) is None: if det_xywh2.get(code) is None:
det_xywh2[code] = {} det_xywh2[code] = {}
@ -246,6 +261,11 @@ class OnPushStreamProcess(PushStreamProcess):
is_new = False is_new = False
if q[11] == 1: if q[11] == 1:
is_new = True is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = ocrlabel
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = crowdlabel
label_array = points
if cd is None: 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: else:
@ -268,7 +288,7 @@ class OnPushStreamProcess(PushStreamProcess):
ex_status = False ex_status = False
logger.info("停止推流进程, requestId: {}", request_id) logger.info("停止推流进程, requestId: {}", request_id)
break break
del push_r del push_r
else: else:
sleep(1) sleep(1)
@ -363,24 +383,45 @@ class OffPushStreamProcess(PushStreamProcess):
qs_reurn = [] qs_reurn = []
for det in push_objs[i]: for det in push_objs[i]:
code, det_result = det code, det_result = det
# 每个单独模型处理 # 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 # 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: if len(det_result) > 0:
font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"] font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"] rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
for qs in det_result: for qs in det_result:
box, score, cls = xywh2xyxy2(qs) # 自研车牌模型处理
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
if cls not in allowedList or score < frame_score: cls = 0
continue ocrlabel, xybox = qs
label_array, color = label_arrays[cls], rainbows[cls] box = xy2xyxy(xybox)
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2: score = None
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6]) color = rainbows[cls]
label_array = None
label_arrays = [None]
rr = t.submit(draw_name_ocr,xybox,copy_frame,color,ocrlabel)
elif ModelType.CITY_DENSECROWDCOUNT_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, points, copy_frame, color, crowdlabel)
else: 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) thread_p.append(rr)
if det_xywh.get(code) is None: if det_xywh.get(code) is None:
det_xywh[code] = {} det_xywh[code] = {}
cd = det_xywh[code].get(cls) cd = det_xywh[code].get(cls)
@ -388,17 +429,17 @@ class OffPushStreamProcess(PushStreamProcess):
if cd is None: if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]] det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else: 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: if qs_np is None:
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1], qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1], box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32) score, cls, code],dtype=np.float32)
else: else:
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1], result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1], box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32) score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li)) qs_np = np.row_stack((qs_np, result_li))
if logo: if logo:
frame = add_water_pic(frame, logo, request_id) frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id) copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -407,7 +448,7 @@ class OffPushStreamProcess(PushStreamProcess):
r.result() r.result()
if self._algSwitch and (not self._algStatus): if self._algSwitch and (not self._algStatus):
frame_merge = video_conjuncing(frame, frame.copy()) frame_merge = video_conjuncing(frame, frame.copy())
else: else:
frame_merge = video_conjuncing(frame, copy_frame) frame_merge = video_conjuncing(frame, copy_frame)
# 写识别视频到本地 # 写识别视频到本地
write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath, write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
@ -416,10 +457,9 @@ class OffPushStreamProcess(PushStreamProcess):
push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url, push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
p_push_status, request_id) p_push_status, request_id)
if qs_np is not None: if qs_np is not None:
if len(qs_np.shape) == 1: if len(qs_np.shape) == 1:
qs_np = qs_np[np.newaxis,...] qs_np = qs_np[np.newaxis,...]
qs_np_id = qs_np.copy() qs_np_id = qs_np.copy()
b = np.ones(qs_np_id.shape[0]) 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))
@ -446,7 +486,7 @@ class OffPushStreamProcess(PushStreamProcess):
if q[11] >= 1: if q[11] >= 1:
cls = int(q[9]) cls = int(q[9])
if not (cls in new_lab): 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) code = str(int(q[10])).zfill(3)
if det_xywh2.get(code) is None: if det_xywh2.get(code) is None:
det_xywh2[code] = {} det_xywh2[code] = {}
@ -459,6 +499,13 @@ class OffPushStreamProcess(PushStreamProcess):
is_new = False is_new = False
if q[11] == 1: if q[11] == 1:
is_new = True is_new = True
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
cls = ocrlabel
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
cls = crowdlabel
label_array = points
if cd is None: 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: else:

112
enums/ModelTypeEnum.py
View File

@ -16,6 +16,7 @@ from utilsK.illParkingUtils import illParking_postprocess
from utilsK.pannelpostUtils import pannel_post_process from utilsK.pannelpostUtils import pannel_post_process
from stdc import stdcModel from stdc import stdcModel
from yolov5 import yolov5Model from yolov5 import yolov5Model
from p2pNet import p2NnetModel
from DMPRUtils.jointUtil import dmpr_yolo_stdc from DMPRUtils.jointUtil import dmpr_yolo_stdc
from AI import default_mix from AI import default_mix
from ocr import ocrModel from ocr import ocrModel
@ -66,7 +67,7 @@ class ModelType(Enum):
'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName, 'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%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: { # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
# 'device': device, # 'device': device,
# 'gpu_name': gpuName, # 'gpu_name': gpuName,
@ -87,7 +88,7 @@ class ModelType(Enum):
# }, # },
# 'Segweights': None # 'Segweights': None
# }) # })
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: { 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"], 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
@ -100,10 +101,10 @@ class ModelType(Enum):
'model':yolov5Model, '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 } }, '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': { 'postFile': {
"name": "post_process", "name": "post_process",
"conf_thres": 0.25, "conf_thres": 0.25,
@ -115,7 +116,7 @@ class ModelType(Enum):
'segRegionCnt':2,###分割模型结果需要保留的等值线数目 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2, "pixScale": 1.2,
}) })
@ -361,14 +362,14 @@ class ModelType(Enum):
}) })
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: { CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土" ], 'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
'postProcess':{ 'postProcess':{
'function':dmpr_yolo_stdc, 'function':dmpr_yolo_stdc,
'pars':{ 'pars':{
'carCls':0 ,'illCls':6,'scaleRatio':0.5,'border':80, 'carCls':0 ,'illCls':7,'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","裸土","占道经营","未覆盖裸土","违建"
#"车辆","垃圾","商贩","违停","占道经营","裸土" # key:实际训练index value:展示index
'classReindex':{ 0:0,1:1,2:2,3:6,4:4,5:5,6:3} 'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
} }
}, },
'models':[ 'models':[
@ -376,7 +377,7 @@ class ModelType(Enum):
'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName), 'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5', 'name':'yolov5',
'model':yolov5Model, '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':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } } 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
}, },
{ {
'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth', 'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
@ -384,25 +385,25 @@ class ModelType(Enum):
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640, 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
'name':'dmpr' 'name':'dmpr'
}, },
'model':DMPRModel, 'model':DMPRModel,
'name':'dmpr' 'name':'dmpr'
}, },
{ {
'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName), 'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
'par':{ 'par':{
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':3},###分割模型预处理参数 'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':3},###分割模型预处理参数
'model':stdcModel, 'model':stdcModel,
'name':'stdc' 'name':'stdc'
} }
], ],
'postFile': { 'postFile': {
"name": "post_process", "name": "post_process",
"conf_thres": 0.25, "conf_thres": 0.25,
"iou_thres": 0.45, "iou_thres": 0.45,
"classes": 6, "classes": 8,
"rainbows": COLOR "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 '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 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
"pixScale": 1.2, "pixScale": 1.2,
}) })
@ -569,7 +570,7 @@ class ModelType(Enum):
'model':yolov5Model, '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} } '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/pth/AIlib2/ocr2/crnn_ch.pth', 'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'name':'ocr', 'name':'ocr',
'model':ocrModel, 'model':ocrModel,
@ -584,7 +585,7 @@ class ModelType(Enum):
'std':[0.5,0.5,0.5], 'std':[0.5,0.5,0.5],
'dynamic':False, 'dynamic':False,
}, },
} }
], ],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]], 'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
'segPar': None, 'segPar': None,
@ -597,7 +598,7 @@ class ModelType(Enum):
}, },
'Segweights': None, 'Segweights': None,
}) })
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: { RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
'device': device, '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", '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",
@ -664,7 +665,8 @@ class ModelType(Enum):
}) })
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: { TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
'device': str(device), 'device': str(device),
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ], 'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
"事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
'trtFlag_seg': True, 'trtFlag_seg': True,
'trtFlag_det': True, 'trtFlag_det': True,
'seg_nclass': 3, 'seg_nclass': 3,
@ -718,13 +720,13 @@ class ModelType(Enum):
'model':yolov5Model, '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 } }, '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': { 'postFile': {
"rainbows": COLOR "rainbows": COLOR
}, },
}) })
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: { RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
@ -738,15 +740,15 @@ class ModelType(Enum):
'model':yolov5Model, '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 } }, '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': { 'postFile': {
"rainbows": COLOR "rainbows": COLOR
}, },
}) })
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: { FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
'labelnames': [ "烟花"], 'labelnames': [ "烟花"],
'postProcess':{'function':default_mix,'pars':{}}, 'postProcess':{'function':default_mix,'pars':{}},
@ -758,13 +760,13 @@ class ModelType(Enum):
'model':yolov5Model, '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 } }, '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': { 'postFile': {
"rainbows": COLOR "rainbows": COLOR
}, },
}) })
TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: { TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
@ -958,6 +960,52 @@ class ModelType(Enum):
}) })
CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
'labelnames': ["人群计数"],
'device': str(device),
'rainbows': COLOR,
'models': [
{
'trtFlag_det': False,
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
'name': 'p2pnet',
'model': p2NnetModel,
'par': {
'device': 'cuda:0',
'row': 2,
'line': 2,
'point_loss_coef': 0.45,
'conf': 0.25,
'gpu_id': 0,
'eos_coef': '0.5',
'set_cost_class': 1,
'set_cost_point': 0.05,
'backbone': 'vgg16_bn'
},
}],
})
CITY_DENSECROWDESTIMATION_MODEL = ("30", "305", "密集人群密度估计", 'DenseCrowdEstimation', lambda device, gpuName: {
'labelnames': ["密度"],
'models':
[
{
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
'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 @staticmethod
def checkCode(code): def checkCode(code):
for model in ModelType: for model in ModelType:

97
util/ModelUtils.py
View File

@ -17,7 +17,7 @@ from util.PlotsUtils import get_label_arrays, get_label_array_dict
from util.TorchUtils import select_device from util.TorchUtils import select_device
sys.path.extend(['..', '../AIlib2']) 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, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C,AI_process_Ocr,AI_process_Crowd
from stdc import stdcModel from stdc import stdcModel
from segutils.segmodel import SegModel from segutils.segmodel import SegModel
from models.experimental import attempt_load from models.experimental import attempt_load
@ -241,7 +241,7 @@ def channel2_process(args):
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id) logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def get_label_arraylist(*args): def get_label_arraylist(*args):
width, height, names, rainbows = args width, height, names, rainbows = args
# line = int(round(0.002 * (height + width) / 2) + 1) # line = int(round(0.002 * (height + width) / 2) + 1)
@ -354,6 +354,73 @@ def im_process(args):
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
def immulti_process(args):
model_conf, frame, requestId = args
device, modelList, detpar = model_conf[1], model_conf[2], model_conf[3]
try:
# new_device = torch.device(device)
# img, padInfos = pre_process(frame, new_device)
# pred = model(img)
# boxes = post_process(pred, padInfos, device, conf_thres=pardet['conf_thres'],
# iou_thres=pardet['iou_thres'], nc=pardet['nc']) # 后处理
return AI_process_Ocr([frame], modelList, device, detpar)
except ServiceException as s:
raise s
except Exception:
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
class CARPLATEModel:
__slots__ = "model_conf"
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
detpar = par['models'][0]['par']
# new_device = torch.device(par['device'])
# modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
logger.info("########################加载 plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
requestId)
self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
class DENSECROWDCOUNTModel:
__slots__ = "model_conf"
def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
try:
logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
requestId)
par = modeType.value[4](str(device), gpu_name)
rainbows = par["rainbows"]
models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
postPar = par['models'][0]['par']
self.model_conf = (modeType, device, models[0], postPar, rainbows)
except Exception:
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
def cc_process(args):
model_conf, frame, requestId = args
device, model, postPar = model_conf[1], model_conf[2], model_conf[3]
try:
return AI_process_Crowd([frame], model, device, postPar)
except ServiceException as s:
raise s
except Exception:
logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
# 百度AI图片识别模型 # 百度AI图片识别模型
class BaiduAiImageModel: class BaiduAiImageModel:
@ -470,7 +537,7 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x) lambda x: detSeg_demo2(x)
), ),
# 加载交通模型 # 加载交通模型
ModelType.TRAFFIC_FARM_MODEL.value[1]: ( ModelType.TRAFFIC_FARM_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h), lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
@ -613,22 +680,22 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x) lambda x: model_process(x)
), ),
# 加载智慧工地模型 # 加载智慧工地模型
ModelType.SMARTSITE_MODEL.value[1]: ( ModelType.SMARTSITE_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h), lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h),
ModelType.SMARTSITE_MODEL, ModelType.SMARTSITE_MODEL,
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x) lambda x: detSeg_demo2(x)
), ),
# 加载垃圾模型 # 加载垃圾模型
ModelType.RUBBISH_MODEL.value[1]: ( ModelType.RUBBISH_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h), lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h),
ModelType.RUBBISH_MODEL, ModelType.RUBBISH_MODEL,
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x) lambda x: detSeg_demo2(x)
), ),
# 加载烟花模型 # 加载烟花模型
ModelType.FIREWORK_MODEL.value[1]: ( ModelType.FIREWORK_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h), lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h),
@ -657,6 +724,13 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x) lambda x: detSeg_demo2(x)
), ),
# 加载自研车牌检测模型
ModelType.CITY_CARPLATE_MODEL.value[1]: (
lambda x, y, r, t, z, h: CARPLATEModel(x, y, r, ModelType.CITY_CARPLATE_MODEL, t, z, h),
ModelType.CITY_CARPLATE_MODEL,
None,
lambda x: immulti_process(x)
),
# 加载红外行人检测模型 # 加载红外行人检测模型
ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: ( ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h), lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h),
@ -670,5 +744,12 @@ MODEL_CONFIG = {
ModelType.CITY_NIGHTFIRESMOKE_MODEL, ModelType.CITY_NIGHTFIRESMOKE_MODEL,
lambda x, y, z: one_label(x, y, z), lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x) lambda x: detSeg_demo2(x)
), ),
# 加载密集人群计数检测模型
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]: (
lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_DENSECROWDCOUNT_MODEL, t, z, h),
ModelType.CITY_DENSECROWDCOUNT_MODEL,
None,
lambda x: cc_process(x)
),
} }

80
util/PlotsUtils.py
View File

@ -67,6 +67,12 @@ def xywh2xyxy2(param):
# return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)] # return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2]) 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): 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 # 识别问题描述图片的高、宽
@ -218,6 +224,11 @@ 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) cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
return img, box return img, box
def draw_name_ocr(box, img, color, label, 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, label, font, outfontsize)
return plot_one_box_auto(box, img, color, line_thickness, label_zh)
def filterBox(det0, det1, pix_dis): def filterBox(det0, det1, pix_dis):
# det0为 (m1, 11) 矩阵 # det0为 (m1, 11) 矩阵
@ -255,4 +266,71 @@ def filterBox(det0, det1, pix_dis):
# 类别相同 & 中心点在上一帧的框内 判断为True # 类别相同 & 中心点在上一帧的框内 判断为True
res = np.sum(mask, axis=1) res = np.sum(mask, axis=1)
det0_copy[..., -1] = res 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, label, line_thickness=2, outfontsize=20):
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
H,W = img.shape[:2]
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = Image.fromarray(img)
# width, height = img.size
Wrate = W // 128 * 128/W
Hrate = H // 128 * 128/H
# img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
for p in dets:
img = cv2.circle(img, (int(p[0]/Wrate), int(p[1]/Hrate)), line_thickness, color, -1)
Calc_label_arr = get_label_array(color, label, font, outfontsize)
lh, lw = Calc_label_arr.shape[0:2]
img[0:lh, 0:lw, :] = Calc_label_arr
return img, dets