diff --git a/concurrency/FileUploadThread.py b/concurrency/FileUploadThread.py index f328e53..1be5139 100644 --- a/concurrency/FileUploadThread.py +++ b/concurrency/FileUploadThread.py @@ -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: diff --git a/concurrency/IntelligentRecognitionProcess.py b/concurrency/IntelligentRecognitionProcess.py index 7c40632..bec1d9e 100644 --- a/concurrency/IntelligentRecognitionProcess.py +++ b/concurrency/IntelligentRecognitionProcess.py @@ -62,7 +62,7 @@ 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) @@ -91,8 +91,6 @@ class IntelligentRecognitionProcess(Process): hb_thread.start() return hb_thread - - class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess): @@ -113,19 +111,16 @@ 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 ) + 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() @@ -146,7 +141,7 @@ 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 +221,10 @@ 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: @@ -237,19 +232,18 @@ 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) @@ -273,7 +267,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess): raise ServiceException(push_status[1], push_status[2]) # 获取停止指令 event_result = get_no_block_queue(event_queue) - + if event_result: cmdStr = event_result.get("command") #接收到算法开启、或者关闭的命令 @@ -281,7 +275,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess): logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr ) put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True) pull_process.sendCommand({"command": cmdStr}) - + # 接收到停止指令 if "stop" == cmdStr: logger.info("实时任务开始停止, requestId: {}", request_id) @@ -301,20 +295,32 @@ 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 +328,11 @@ 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 +443,23 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess): class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess): __slots__ = () - + 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 ) 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): @@ -602,7 +608,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess): logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr ) put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True) pull_process.sendCommand({"command": cmdStr}) - + pull_result = get_no_block_queue(pull_queue) if pull_result is None: sleep(1) @@ -616,19 +622,32 @@ 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 +764,7 @@ 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 +941,62 @@ class PhotosIntelligentRecognitionProcess(Process): logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id) raise e + #自研车牌模型 + 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 +1011,26 @@ 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 @@ -1125,7 +1220,7 @@ class PhotosIntelligentRecognitionProcess(Process): except requests.exceptions.RequestException as e: # 捕获请求过程中可能出现的异常（如网络问题、超时等） return False,str(e) - + def run(self): fb_queue, msg, analyse_type, context = self._fb_queue, self._msg, self._analyse_type, self._context request_id, logo, image_queue = msg["request_id"], context['logo'], self._image_queue @@ -1136,7 +1231,7 @@ class PhotosIntelligentRecognitionProcess(Process): valFlag=True for url in imageUrls: valFlag,ret = self.check_ImageUrl_Vaild(url,timeout=1) - + if not valFlag: logger.error("图片分析异常: {}, requestId:{},url:{}",ret, request_id,url) #print("AnalysisStatus.FAILED.value:{}，ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0]:{},ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]:{}".format(AnalysisStatus.FAILED.value,ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1] ) ) @@ -1168,6 +1263,15 @@ 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 +1318,7 @@ class ScreenRecordingProcess(Process): put_queue(self._fb_queue, recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]), timeout=1, is_ex=True) - self._storage_source = self._context['service']['storage_source'] + self._storage_source = self._context['service']['storage_source'] def sendEvent(self, result): put_queue(self._event_queue, result, timeout=2, is_ex=True) @@ -1380,17 +1484,14 @@ 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() diff --git a/concurrency/PushVideoStreamProcess.py b/concurrency/PushVideoStreamProcess.py index d10841c..930db86 100644 --- a/concurrency/PushVideoStreamProcess.py +++ b/concurrency/PushVideoStreamProcess.py @@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push from util.ImageUtils import url2Array, add_water_pic from util.LogUtils import init_log -from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, 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 @@ -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._algStatus = False # 默认关闭 self._algSwitch = self._context['service']['algSwitch'] - + #0521: 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）") # 这里放非默认逻辑的代码 self._algSwitch = False - - + print("---line53 :PushVideoStreamProcess.py---",self._algSwitch) - - - - - - - + def build_logo_url(self): logo = None if self._context["video"]["video_add_water"]: @@ -155,17 +148,38 @@ class OnPushStreamProcess(PushStreamProcess): font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"] rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"] for qs in det_result: - 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 + 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] = {} @@ -184,17 +198,17 @@ class OnPushStreamProcess(PushStreamProcess): 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, @@ -207,7 +221,7 @@ class OnPushStreamProcess(PushStreamProcess): # 如果有问题, 走下面的逻辑 if qs_np is not None: if len(qs_np.shape) == 1: - qs_np = qs_np[np.newaxis,...] + qs_np = qs_np[np.newaxis,...] qs_np_id = qs_np.copy() b = np.ones(qs_np_id.shape[0]) qs_np_id = np.column_stack((qs_np_id,b)) @@ -233,7 +247,7 @@ class OnPushStreamProcess(PushStreamProcess): if q[11] >= 1: cls = int(q[9]) if not (cls in new_lab): - continue # 为了防止其他类别被带出 + continue # 为了防止其他类别被带出 code = str(int(q[10])).zfill(3) if det_xywh2.get(code) is None: det_xywh2[code] = {} @@ -246,8 +260,12 @@ class OnPushStreamProcess(PushStreamProcess): is_new = False if q[11] == 1: is_new = True + if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \ + ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\ + ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code): + box = qs if cd is None: - det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] + det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] else: det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new]) if len(det_xywh2) > 0: @@ -268,7 +286,7 @@ class OnPushStreamProcess(PushStreamProcess): ex_status = False logger.info("停止推流进程, requestId: {}", request_id) break - + del push_r else: sleep(1) @@ -363,24 +381,44 @@ 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"] 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 +426,17 @@ class OffPushStreamProcess(PushStreamProcess): if cd is None: det_xywh[code][cls] = [[cls, box, score, label_array, color]] else: - det_xywh[code][cls].append([cls, box, score, label_array, color]) + det_xywh[code][cls].append([cls, box, score, label_array, color]) if qs_np is None: - qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1], - box[2][0], box[2][1], box[3][0], box[3][1], + qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1], + box[2][0], box[2][1], box[3][0], box[3][1], score, cls, code],dtype=np.float32) else: - result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1], + result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1], box[2][0], box[2][1], box[3][0], box[3][1], score, cls, code],dtype=np.float32) qs_np = np.row_stack((qs_np, result_li)) - + if logo: frame = add_water_pic(frame, logo, request_id) copy_frame = add_water_pic(copy_frame, logo, request_id) @@ -407,7 +445,7 @@ 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, @@ -416,10 +454,9 @@ 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)) @@ -446,7 +483,7 @@ class OffPushStreamProcess(PushStreamProcess): if q[11] >= 1: cls = int(q[9]) if not (cls in new_lab): - continue # 为了防止其他类别被带出 + continue # 为了防止其他类别被带出 code = str(int(q[10])).zfill(3) if det_xywh2.get(code) is None: det_xywh2[code] = {} @@ -459,6 +496,11 @@ class OffPushStreamProcess(PushStreamProcess): 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]] else: diff --git a/enums/ModelTypeEnum.py b/enums/ModelTypeEnum.py index 8168344..71afb62 100644 --- a/enums/ModelTypeEnum.py +++ b/enums/ModelTypeEnum.py @@ -9,15 +9,14 @@ 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 segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess 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 p2pNet import p2NnetModel from DMPRUtils.jointUtil import dmpr_yolo_stdc from AI import default_mix from ocr import ocrModel @@ -68,7 +67,7 @@ class ModelType(Enum): '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, @@ -89,7 +88,7 @@ class ModelType(Enum): # }, # 'Segweights': None # }) - + FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: { 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"], @@ -102,10 +101,10 @@ class ModelType(Enum): '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, @@ -117,9 +116,8 @@ class ModelType(Enum): '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: { @@ -131,8 +129,7 @@ class ModelType(Enum): 'seg_nclass': 3, 'segRegionCnt': 2, 'segPar': { - #'modelSize': (640, 360), - 'modelSize': (1920, 1080), + 'modelSize': (640, 360), 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'predResize': True, @@ -141,8 +138,7 @@ class ModelType(Enum): 'mixFunction': { 'function': tracfficAccidentMixFunction, 'pars': { - #'modelSize': (640, 360), - 'modelSize': (1920,1080), + 'modelSize': (640, 360), 'RoadArea': 16000, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75, @@ -166,7 +162,7 @@ class ModelType(Enum): "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], + '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 }) @@ -361,30 +357,27 @@ 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 }) 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':{ '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} + 'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80, + #"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建" + # key:实际训练index value:展示index + 'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7} } }, 'models':[ { - 'weight':'../weights/pth/AIlib2/cityMangement3/yolov5.pt', - #'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName), - 'name':'yolov5', + '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 } } + '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', @@ -392,25 +385,25 @@ class ModelType(Enum): 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640, 'name':'dmpr' }, - 'model':DMPRModel, + 'model':DMPRModel, 'name':'dmpr' }, - { - 'weight':'../weights/pth/AIlib2/cityMangement3/stdc_360X640.pth', + { + 'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName), '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 + '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' - } + 'name':'stdc' + } ], 'postFile': { "name": "post_process", "conf_thres": 0.25, "iou_thres": 0.45, - "classes": 6, + "classes": 8, "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 "pixScale": 1.2, }) @@ -443,9 +436,7 @@ class ModelType(Enum): "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 }) @@ -514,7 +505,7 @@ class ModelType(Enum): 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"], + 'labelnames': ["护栏", "交通标志", "非交通标志", "施工锥桶", "施工水马"], 'trtFlag_seg': False, 'trtFlag_det': True, 'slopeIndex': [], @@ -574,14 +565,12 @@ class ModelType(Enum): }}, '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, @@ -596,7 +585,7 @@ class ModelType(Enum): 'std':[0.5,0.5,0.5], 'dynamic':False, }, - } + } ], 'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]], 'segPar': None, @@ -609,7 +598,7 @@ class ModelType(Enum): }, '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", @@ -640,14 +629,10 @@ class ModelType(Enum): "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}}, @@ -678,7 +663,8 @@ class ModelType(Enum): }) TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: { 'device': str(device), - 'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ], + 'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", + "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ], 'trtFlag_seg': True, 'trtFlag_det': True, 'seg_nclass': 3, @@ -694,12 +680,13 @@ class ModelType(Enum): 'function': tracfficAccidentMixFunction, 'pars': { 'modelSize': (640, 360), - #'modelSize': (1920,1080), 'RoadArea': 16000, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75, 'roundness': 1.0, 'cls': 10, + 'CarId':1, + 'CthcId':1, 'vehicleFactor': 0.1, 'confThres': 0.25, 'roadIou': 0.6, @@ -731,13 +718,13 @@ class ModelType(Enum): '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: { @@ -751,15 +738,15 @@ class ModelType(Enum): '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':{}}, @@ -771,13 +758,13 @@ class ModelType(Enum): '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: { @@ -785,26 +772,24 @@ class ModelType(Enum): 'labelnames': ["抛洒物","车辆"], 'trtFlag_seg': True, 'trtFlag_det': True, - 'seg_nclass': 2, + 'seg_nclass': 3, 'segRegionCnt': 2, 'segPar': { - #'modelSize': (640, 360), - 'modelSize': (1920, 1080), + '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': mixSpillage_postprocess, + 'function': mixTraffic_postprocess, 'pars': { - #'modelSize': (640, 360), - 'modelSize': (1920,1080), + 'modelSize': (640, 360), 'RoadArea': 16000, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75, 'roundness': 1.0, - 'cls': 1, + 'cls': 0, 'vehicleFactor': 0.1, 'confThres': 0.25, 'roadIou': 0.6, @@ -832,26 +817,24 @@ class ModelType(Enum): 'labelnames': ["危化品","罐体","危险标识","普通车"], 'trtFlag_seg': True, 'trtFlag_det': True, - 'seg_nclass': 2, + 'seg_nclass': 3, 'segRegionCnt': 2, 'segPar': { - #'modelSize': (640, 360), - 'modelSize': (1920, 1080), + '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': mixCthc_postprocess, + 'function': mixTraffic_postprocess, 'pars': { - #'modelSize': (640, 360), - 'modelSize': (1920,1080), + 'modelSize': (640, 360), 'RoadArea': 16000, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75, 'roundness': 1.0, - 'cls': 4, + 'cls': 0, 'vehicleFactor': 0.1, 'confThres': 0.25, 'roadIou': 0.6, @@ -865,7 +848,7 @@ class ModelType(Enum): "name": "post_process", "conf_thres": 0.25, "iou_thres": 0.25, - "classes": 1, + "classes": 4, "rainbows": COLOR }, 'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]], @@ -896,34 +879,44 @@ class ModelType(Enum): }) CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: { + 'labelnames': ["车牌"], 'device': str(device), - 'models':{ + 'rainbows': COLOR, + 'models': [ { - 'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', - 'conf_thres': 0.4, - 'iou_thres': 0.45, - 'nc':1, + 'trtFlag_det': False, + 'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit', + 'name': 'yolov5', + 'model': yolov5Model, + 'par': { + 'device': 'cuda:0', + 'half': False, + 'conf_thres': 0.4, + 'iou_thres': 0.45, + 'nc': 1, + 'plate_dilate': (0.5, 0.1) + }, }, { - '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, - } - }, - } - }) + 'trtFlag_ocr': False, + '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: { + CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredPerson', lambda device, gpuName: { 'labelnames': ["行人"], 'postProcess': {'function': default_mix, 'pars': {}}, 'models': @@ -963,7 +956,87 @@ class ModelType(Enum): "rainbows": COLOR }, - }) + }) + + CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: { + 'labelnames': ["人群计数"], + 'device': str(device), + 'rainbows': COLOR, + 'models': [ + { + 'trtFlag_det': False, + 'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径 + 'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径 + 'name': 'p2pnet', + 'model': p2NnetModel, + 'par': { + 'device': 'cuda:0', + 'row': 2, + 'line': 2, + 'point_loss_coef': 0.45, + 'conf': 0.50, + '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 + }, + + }) + + CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: { + 'labelnames': ["建筑物下人群"], + 'device': str(device), + 'rainbows': COLOR, + 'models': [ + { + 'weight': "../weights/trt/AIlib2/perUnderBuild/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径 + 'name': 'yolov5', + 'model': yolov5Model, + 'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45, + '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}}, + }, + { + 'trtFlag_det': False, + 'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径 + 'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径 + 'name': 'p2pnet', + 'model': p2NnetModel, + 'par': { + 'device': 'cuda:0', + 'row': 2, + 'line': 2, + 'point_loss_coef': 0.45, + 'conf': 0.50, + 'gpu_id': 0, + 'eos_coef': '0.5', + 'set_cost_class': 1, + 'set_cost_point': 0.05, + 'backbone': 'vgg16_bn' + }, + }], + }) @staticmethod def checkCode(code): diff --git a/util/ModelUtils.py b/util/ModelUtils.py index 49478f1..4dd2f7e 100644 --- a/util/ModelUtils.py +++ b/util/ModelUtils.py @@ -17,7 +17,7 @@ from util.PlotsUtils import get_label_arrays, get_label_array_dict from util.TorchUtils import select_device sys.path.extend(['..', '../AIlib2']) -from AI import AI_process, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C +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 segutils.segmodel import SegModel from models.experimental import attempt_load @@ -46,8 +46,12 @@ 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: @@ -241,7 +245,7 @@ def channel2_process(args): logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id) raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) - + def get_label_arraylist(*args): width, height, names, rainbows = args # line = int(round(0.002 * (height + width) / 2) + 1) @@ -354,6 +358,73 @@ def im_process(args): raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) +def immulti_process(args): + model_conf, frame, requestId = args + device, modelList, detpar = model_conf[1], model_conf[2], model_conf[3] + try: + # new_device = torch.device(device) + # img, padInfos = pre_process(frame, new_device) + # pred = model(img) + # boxes = post_process(pred, padInfos, device, conf_thres=pardet['conf_thres'], + # iou_thres=pardet['iou_thres'], nc=pardet['nc']) # 后处理 + return AI_process_Ocr([frame], modelList, device, detpar) + except ServiceException as s: + raise s + except Exception: + logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId) + raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], + ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) + +class CARPLATEModel: + __slots__ = "model_conf" + def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, + env=None): + try: + logger.info("########################加载{}########################, requestId:{}", modeType.value[2], + requestId) + par = modeType.value[4](str(device), gpu_name) + modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ] + detpar = par['models'][0]['par'] + # new_device = torch.device(par['device']) + # modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ] + 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 = [pp['par'] for pp in par['models']] + self.model_conf = (modeType, device, models, postPar, rainbows) + except Exception: + logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId) + raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0], + ExceptionType.MODEL_LOADING_EXCEPTION.value[1]) + +def cc_process(args): + model_conf, frame, requestId = args + device, model, postPar = model_conf[1], model_conf[2], model_conf[3] + try: + return AI_process_Crowd([frame], model, device, postPar) + except ServiceException as s: + raise s + except Exception: + logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId) + raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0], + ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1]) + # 百度AI图片识别模型 class BaiduAiImageModel: @@ -470,7 +541,7 @@ MODEL_CONFIG = { lambda x, y, z: one_label(x, y, z), lambda x: detSeg_demo2(x) ), - + # 加载交通模型 ModelType.TRAFFIC_FARM_MODEL.value[1]: ( lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h), @@ -608,27 +679,27 @@ MODEL_CONFIG = { ), # 加载交通模型 ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: ( - lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h), - ModelType.TRAFFIC_FARM_MODEL, + lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFICFORDSJ_FARM_MODEL, t, z, h), + ModelType.TRAFFICFORDSJ_FARM_MODEL, lambda x, y, z: one_label(x, y, z), lambda x: model_process(x) ), - # 加载智慧工地模型 + # 加载智慧工地模型 ModelType.SMARTSITE_MODEL.value[1]: ( lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h), ModelType.SMARTSITE_MODEL, lambda x, y, z: one_label(x, y, z), lambda x: detSeg_demo2(x) ), - - # 加载垃圾模型 + + # 加载垃圾模型 ModelType.RUBBISH_MODEL.value[1]: ( lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h), ModelType.RUBBISH_MODEL, lambda x, y, z: one_label(x, y, z), lambda x: detSeg_demo2(x) ), - + # 加载烟花模型 ModelType.FIREWORK_MODEL.value[1]: ( lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h), @@ -657,6 +728,13 @@ MODEL_CONFIG = { lambda x, y, z: one_label(x, y, z), lambda x: detSeg_demo2(x) ), + # 加载自研车牌检测模型 + ModelType.CITY_CARPLATE_MODEL.value[1]: ( + lambda x, y, r, t, z, h: CARPLATEModel(x, y, r, ModelType.CITY_CARPLATE_MODEL, t, z, h), + ModelType.CITY_CARPLATE_MODEL, + None, + lambda x: immulti_process(x) + ), # 加载红外行人检测模型 ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: ( lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h), @@ -670,5 +748,19 @@ MODEL_CONFIG = { ModelType.CITY_NIGHTFIRESMOKE_MODEL, lambda x, y, z: one_label(x, y, z), lambda x: detSeg_demo2(x) - ), + ), + # 加载密集人群计数检测模型 + ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]: ( + lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_DENSECROWDCOUNT_MODEL, t, z, h), + ModelType.CITY_DENSECROWDCOUNT_MODEL, + None, + lambda x: cc_process(x) + ), + # 加载建筑物下行人检测模型 + ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: ( + lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h), + ModelType.CITY_UNDERBUILDCOUNT_MODEL, + None, + lambda x: cc_process(x) + ), } diff --git a/util/PlotsUtils.py b/util/PlotsUtils.py index ddbbce3..b2a95f9 100644 --- a/util/PlotsUtils.py +++ b/util/PlotsUtils.py @@ -2,6 +2,7 @@ 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") @@ -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 +263,105 @@ 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 \ No newline at end of file + 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 \ No newline at end of file