zsl #8
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@ -19,21 +19,20 @@ from util.QueUtil import put_queue, get_no_block_queue, clear_queue
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import io
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import io
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from util.LocationUtils import locate_byMqtt
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from util.LocationUtils import locate_byMqtt
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class FileUpload(Thread):
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class FileUpload(Thread):
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__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg','_mqtt_list')
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__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg', '_mqtt_list')
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def __init__(self, *args):
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def __init__(self, *args):
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super().__init__()
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super().__init__()
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self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type,self._mqtt_list = args
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self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type, self._mqtt_list = args
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self._storage_source = self._context['service']['storage_source']
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self._storage_source = self._context['service']['storage_source']
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self._algStatus = False # 默认关闭
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self._algStatus = False # 默认关闭
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# self._algStatus = True # 默认关闭
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# self._algStatus = True # 默认关闭
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self._algSwitch = self._context['service']['algSwitch']
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self._algSwitch = self._context['service']['algSwitch']
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# 0521:
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#0521:
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default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
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default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
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if default_enabled:
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if default_enabled:
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print("执行默认程序（defaultEnabled=True）")
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print("执行默认程序（defaultEnabled=True）")
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@ -44,15 +43,15 @@ class FileUpload(Thread):
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# 这里放非默认逻辑的代码
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# 这里放非默认逻辑的代码
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self._algSwitch = False
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self._algSwitch = False
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print("---line46 :FileUploadThread.py---",self._algSwitch)
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print("---line46 :FileUploadThread.py---", self._algSwitch)
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#如果任务是在线、离线处理，则用此类
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# 如果任务是在线、离线处理，则用此类
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class ImageFileUpload(FileUpload):
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class ImageFileUpload(FileUpload):
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__slots__ = ()
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__slots__ = ()
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#@staticmethod
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# @staticmethod
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def handle_image(self,frame_msg, frame_step):
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def handle_image(self, frame_msg, frame_step):
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# (high_score_image["code"], all_frames, draw_config["font_config"])
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# (high_score_image["code"], all_frames, draw_config["font_config"])
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# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
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# high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
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det_xywh, frame, current_frame, all_frames, font_config = frame_msg
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det_xywh, frame, current_frame, all_frames, font_config = frame_msg
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@ -65,8 +64,7 @@ class ImageFileUpload(FileUpload):
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模型编号：modeCode
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模型编号：modeCode
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检测目标：detectTargetCode
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检测目标：detectTargetCode
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'''
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'''
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print('*'*100,' mqtt_list:',len(self._mqtt_list))
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print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
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model_info = []
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model_info = []
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# 更加模型编码解析数据
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# 更加模型编码解析数据
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@ -78,12 +76,10 @@ class ImageFileUpload(FileUpload):
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for target in target_list:
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for target in target_list:
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# 自研车牌模型判断
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# 自研车牌模型判断
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if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
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if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
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box = [target[1][0][0], target[1][0][1], target[1][3][0], target[1][3][1]]
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draw_name_ocr(target[1], aFrame, target[4])
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draw_name_ocr(box, aFrame, target[4], target[0])
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elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
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cls = 0
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ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
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elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
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draw_name_crowd(target[1], aFrame, target[4])
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draw_name_crowd(target[3], aFrame, target[4], cls)
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cls = 0
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else:
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else:
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draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
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draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
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target[5])
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target[5])
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@ -115,13 +111,15 @@ class ImageFileUpload(FileUpload):
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image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
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image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
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service_timeout = int(service["timeout"])
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service_timeout = int(service["timeout"])
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frame_step = int(service["filter"]["frame_step"]) + 120
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frame_step = int(service["filter"]["frame_step"]) + 120
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if msg['taskType']==0: self._algStatus = False
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if msg['taskType'] == 0:
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else: self._algStatus = True
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self._algStatus = False
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else:
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self._algStatus = True
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try:
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try:
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with ThreadPoolExecutor(max_workers=2) as t:
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with ThreadPoolExecutor(max_workers=2) as t:
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# 初始化oss客户端
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# 初始化oss客户端
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if self._storage_source==1:
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if self._storage_source == 1:
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minioSdk = MinioSdk(base_dir, env, request_id )
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minioSdk = MinioSdk(base_dir, env, request_id)
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else:
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else:
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aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
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aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
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start_time = time()
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start_time = time()
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@ -137,12 +135,14 @@ class ImageFileUpload(FileUpload):
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if image_msg is not None:
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if image_msg is not None:
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if image_msg[0] == 2:
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if image_msg[0] == 2:
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logger.info("图片上传线程收到命令:{}, requestId: {}",image_msg[1] ,request_id)
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logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
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if 'stop' == image_msg[1]:
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if 'stop' == image_msg[1]:
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logger.info("开始停止图片上传线程, requestId:{}", request_id)
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logger.info("开始停止图片上传线程, requestId:{}", request_id)
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break
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break
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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)
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if 'algStart' == image_msg[1]: self._algStatus = True; logger.info(
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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)
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"图片上传线程,执行算法开启命令, requestId:{}", request_id)
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if 'algStop' == image_msg[1]: self._algStatus = False; logger.info(
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"图片上传线程,执行算法关闭命令, requestId:{}", request_id)
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if image_msg[0] == 1:
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if image_msg[0] == 1:
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image_result = self.handle_image(image_msg[1], frame_step)
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image_result = self.handle_image(image_msg[1], frame_step)
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@ -153,8 +153,8 @@ class ImageFileUpload(FileUpload):
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image_result["last_frame"],
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image_result["last_frame"],
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analyse_type,
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analyse_type,
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"OR", "0", "0", request_id)
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"OR", "0", "0", request_id)
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if self._storage_source==1:
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if self._storage_source == 1:
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or_future = t.submit(minioSdk.put_object, or_image,or_image_name)
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or_future = t.submit(minioSdk.put_object, or_image, or_image_name)
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else:
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else:
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or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
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or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
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task.append(or_future)
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task.append(or_future)
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@ -169,38 +169,38 @@ class ImageFileUpload(FileUpload):
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model_info["modelCode"],
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model_info["modelCode"],
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model_info["detectTargetCode"],
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model_info["detectTargetCode"],
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request_id)
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request_id)
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if self._storage_source==1:
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if self._storage_source == 1:
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ai_future = t.submit(minioSdk.put_object, ai_image,
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ai_future = t.submit(minioSdk.put_object, ai_image,
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ai_image_name)
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ai_image_name)
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else:
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else:
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
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ai_image.tobytes())
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ai_image.tobytes())
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task.append(ai_future)
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task.append(ai_future)
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#msg_list.append(message_feedback(request_id,
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# msg_list.append(message_feedback(request_id,
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# AnalysisStatus.RUNNING.value,
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# AnalysisStatus.RUNNING.value,
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# analyse_type, "", "", "",
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# analyse_type, "", "", "",
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# or_image_name,
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# or_image_name,
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# ai_image_name,
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# ai_image_name,
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# model_info['modelCode'],
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# model_info['modelCode'],
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# model_info['detectTargetCode']))
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# model_info['detectTargetCode']))
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remote_image_list=[]
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remote_image_list = []
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for tk in task:
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for tk in task:
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remote_image_list.append( tk.result())
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remote_image_list.append(tk.result())
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for ii,model_info in enumerate(model_info_list):
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for ii, model_info in enumerate(model_info_list):
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msg_list.append( message_feedback(request_id,
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msg_list.append(message_feedback(request_id,
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AnalysisStatus.RUNNING.value,
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AnalysisStatus.RUNNING.value,
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analyse_type, "", "", "",
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analyse_type, "", "", "",
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remote_image_list[0],
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remote_image_list[0],
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remote_image_list[ii+1],
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remote_image_list[ii + 1],
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model_info['modelCode'],
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model_info['modelCode'],
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model_info['detectTargetCode'],
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model_info['detectTargetCode'],
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longitude=model_info['gps'][0],
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longitude=model_info['gps'][0],
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latitude=model_info['gps'][1],
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latitude=model_info['gps'][1],
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) )
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))
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if (not self._algSwitch) or ( self._algStatus and self._algSwitch):
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if (not self._algSwitch) or (self._algStatus and self._algSwitch):
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for msg in msg_list:
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for msg in msg_list:
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put_queue(fb_queue, msg, timeout=2, is_ex=False)
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put_queue(fb_queue, msg, timeout=2, is_ex=False)
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del task, msg_list
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del task, msg_list
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@ -227,7 +227,7 @@ def build_image_name(*args):
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random_num, mode_type, modeCode, target, image_type)
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random_num, mode_type, modeCode, target, image_type)
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#如果任务是图像处理，则用此类
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# 如果任务是图像处理，则用此类
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class ImageTypeImageFileUpload(Thread):
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class ImageTypeImageFileUpload(Thread):
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__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
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__slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
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@ -235,6 +235,7 @@ class ImageTypeImageFileUpload(Thread):
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super().__init__()
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super().__init__()
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self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
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self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
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self._storage_source = self._context['service']['storage_source']
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self._storage_source = self._context['service']['storage_source']
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@staticmethod
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@staticmethod
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def handle_image(det_xywh, copy_frame, font_config):
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def handle_image(det_xywh, copy_frame, font_config):
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"""
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"""
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@ -256,9 +257,10 @@ class ImageTypeImageFileUpload(Thread):
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for target in target_list:
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for target in target_list:
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# 自研车牌模型判断
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# 自研车牌模型判断
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if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
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if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
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draw_name_ocr(target[1], aiFrame, font_config[cls], target[0])
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draw_name_ocr(target, aiFrame, font_config[cls])
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elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
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elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or \
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draw_name_crowd(target[1],aiFrame,font_config[cls],target[0])
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ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
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draw_name_crowd(target, aiFrame, font_config[cls])
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else:
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else:
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draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
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draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
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@ -287,8 +289,8 @@ class ImageTypeImageFileUpload(Thread):
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with ThreadPoolExecutor(max_workers=2) as t:
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with ThreadPoolExecutor(max_workers=2) as t:
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try:
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try:
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# 初始化oss客户端
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# 初始化oss客户端
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if self._storage_source==1:
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if self._storage_source == 1:
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minioSdk = MinioSdk(base_dir, env, request_id )
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minioSdk = MinioSdk(base_dir, env, request_id)
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else:
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else:
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aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
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aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
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@ -313,14 +315,14 @@ class ImageTypeImageFileUpload(Thread):
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ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
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ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
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result.get("type"), request_id)
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result.get("type"), request_id)
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if self._storage_source==1:
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if self._storage_source == 1:
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ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)
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ai_future = t.submit(minioSdk.put_object, copy_frame, ai_image_name)
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else:
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else:
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
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task.append(ai_future)
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task.append(ai_future)
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remote_names.append(ai_image_name)
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remote_names.append(ai_image_name)
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#msg_list.append(message_feedback(request_id,
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# msg_list.append(message_feedback(request_id,
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# AnalysisStatus.RUNNING.value,
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# AnalysisStatus.RUNNING.value,
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# analyse_type, "", "", "",
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# analyse_type, "", "", "",
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# image_url,
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# image_url,
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@ -336,12 +338,12 @@ class ImageTypeImageFileUpload(Thread):
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if image_url is None:
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if image_url is None:
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or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
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or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
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image_url_0 = build_image_name(image_result.get("current_frame"),
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image_url_0 = build_image_name(image_result.get("current_frame"),
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image_result.get("last_frame"),
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image_result.get("last_frame"),
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analyse_type,
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analyse_type,
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"OR", "0", "O", request_id)
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"OR", "0", "O", request_id)
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if self._storage_source==1:
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if self._storage_source == 1:
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or_future = t.submit(minioSdk.put_object, or_image,image_url_0)
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or_future = t.submit(minioSdk.put_object, or_image, image_url_0)
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else:
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else:
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or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
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or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
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or_image.tobytes())
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or_image.tobytes())
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@ -357,14 +359,14 @@ class ImageTypeImageFileUpload(Thread):
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model_info.get("modelCode"),
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model_info.get("modelCode"),
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model_info.get("detectTargetCode"),
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model_info.get("detectTargetCode"),
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request_id)
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request_id)
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if self._storage_source==1:
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if self._storage_source == 1:
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ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
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ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
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else:
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else:
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
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ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
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ai_image.tobytes())
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ai_image.tobytes())
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task.append(ai_future)
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task.append(ai_future)
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remote_names.append(ai_image_name)
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remote_names.append(ai_image_name)
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#msg_list.append(message_feedback(request_id,
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# msg_list.append(message_feedback(request_id,
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# AnalysisStatus.RUNNING.value,
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# AnalysisStatus.RUNNING.value,
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# analyse_type, "", "", "",
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# analyse_type, "", "", "",
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# image_url,
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# image_url,
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|
|
@ -389,12 +391,12 @@ class ImageTypeImageFileUpload(Thread):
|
||||||
else:
|
else:
|
||||||
if image_result:
|
if image_result:
|
||||||
if image_url is None:
|
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,
|
msg_list.append(message_feedback(request_id,
|
||||||
AnalysisStatus.RUNNING.value,
|
AnalysisStatus.RUNNING.value,
|
||||||
analyse_type, "", "", "",
|
analyse_type, "", "", "",
|
||||||
remote_url_list[0],
|
remote_url_list[0],
|
||||||
remote_url_list[1+ii],
|
remote_url_list[1 + ii],
|
||||||
model_info.get('modelCode'),
|
model_info.get('modelCode'),
|
||||||
model_info.get('detectTargetCode'),
|
model_info.get('detectTargetCode'),
|
||||||
analyse_results=result))
|
analyse_results=result))
|
||||||
|
|
@ -406,7 +408,8 @@ class ImageTypeImageFileUpload(Thread):
|
||||||
image_url,
|
image_url,
|
||||||
remote_url_list[ii],
|
remote_url_list[ii],
|
||||||
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:
|
||||||
|
|
|
||||||
|
|
@ -92,8 +92,6 @@ class IntelligentRecognitionProcess(Process):
|
||||||
return hb_thread
|
return hb_thread
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
__slots__ = ()
|
__slots__ = ()
|
||||||
|
|
||||||
|
|
@ -112,10 +110,9 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
pullProcess.start()
|
pullProcess.start()
|
||||||
return pullProcess
|
return pullProcess
|
||||||
|
|
||||||
|
|
||||||
def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
|
def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
|
||||||
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_or = Common(minioSdk.put_object, orFilePath, "or_online_%s.mp4" % 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)
|
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
|
||||||
else:
|
else:
|
||||||
|
|
@ -123,8 +120,6 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
upload_video_thread_or = Common(aliyunVodSdk.get_play_url, orFilePath, "or_online_%s" % 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_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
upload_video_thread_or.setDaemon(True)
|
upload_video_thread_or.setDaemon(True)
|
||||||
upload_video_thread_ai.setDaemon(True)
|
upload_video_thread_ai.setDaemon(True)
|
||||||
upload_video_thread_or.start()
|
upload_video_thread_or.start()
|
||||||
|
|
@ -247,7 +242,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
# 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
|
||||||
# 启动推流进程
|
# 启动推流进程
|
||||||
push_process = self.start_push_stream(msg, push_queue, image_queue, push_ex_queue, hb_queue, context)
|
push_process = self.start_push_stream(msg, push_queue, image_queue, push_ex_queue, hb_queue, context)
|
||||||
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
|
#print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
|
||||||
|
|
@ -300,7 +295,8 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
for i, model in enumerate(model_array):
|
for i, model in enumerate(model_array):
|
||||||
model_conf, code = model
|
model_conf, code = model
|
||||||
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
||||||
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
if draw_config.get(code) is None:
|
if draw_config.get(code) is None:
|
||||||
draw_config[code] = {}
|
draw_config[code] = {}
|
||||||
draw_config["font_config"] = model_conf[4]
|
draw_config["font_config"] = model_conf[4]
|
||||||
|
|
@ -331,11 +327,11 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
frame_index_list[i], tt, request_id)
|
frame_index_list[i], tt, request_id)
|
||||||
det_array.append(det_result)
|
det_array.append(det_result)
|
||||||
push_objs = [det.result() for det in det_array]
|
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,
|
put_queue(push_queue,
|
||||||
(1, (frame_list, frame_index_list, all_frames, draw_config, push_objs)),
|
(1, (frame_list, frame_index_list, all_frames, draw_config, push_objs)),
|
||||||
timeout=2, is_ex=True)
|
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 det_array, push_objs
|
||||||
del frame_list, frame_index_list, all_frames
|
del frame_list, frame_index_list, all_frames
|
||||||
elif pull_result[0] == 1:
|
elif pull_result[0] == 1:
|
||||||
|
|
@ -447,12 +443,12 @@ 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)
|
||||||
|
|
@ -626,7 +622,8 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
|
||||||
for i, model in enumerate(model_array):
|
for i, model in enumerate(model_array):
|
||||||
model_conf, code = model
|
model_conf, code = model
|
||||||
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
||||||
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
if draw_config.get(code) is None:
|
if draw_config.get(code) is None:
|
||||||
draw_config[code] = {}
|
draw_config[code] = {}
|
||||||
draw_config["font_config"] = model_conf[4]
|
draw_config["font_config"] = model_conf[4]
|
||||||
|
|
@ -943,7 +940,7 @@ 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
|
# 自研究车牌模型
|
||||||
def carplate_rec(self, imageUrl, mod, image_queue, request_id):
|
def carplate_rec(self, imageUrl, mod, image_queue, request_id):
|
||||||
try:
|
try:
|
||||||
# model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
|
# model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
|
||||||
|
|
@ -982,7 +979,6 @@ class PhotosIntelligentRecognitionProcess(Process):
|
||||||
# param = [image, new_device, model, par, img_type, request_id]
|
# param = [image, new_device, model, par, img_type, request_id]
|
||||||
# model_conf, frame, device, requestId
|
# model_conf, frame, device, requestId
|
||||||
dataBack = MODEL_CONFIG[code][3]([[modeType, device, model, postPar], image, request_id])[0][2]
|
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
|
dets[code][0] = dataBack
|
||||||
if not dataBack:
|
if not dataBack:
|
||||||
logger.info("当前页面无人")
|
logger.info("当前页面无人")
|
||||||
|
|
@ -1271,7 +1267,8 @@ class PhotosIntelligentRecognitionProcess(Process):
|
||||||
result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
|
result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
|
||||||
task_list.append(result)
|
task_list.append(result)
|
||||||
# 人群计数模型
|
# 人群计数模型
|
||||||
elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]:
|
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)
|
result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
|
||||||
task_list.append(result)
|
task_list.append(result)
|
||||||
else:
|
else:
|
||||||
|
|
@ -1486,9 +1483,9 @@ 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)
|
||||||
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "%s/ai_online.mp4" % request_id)
|
upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "%s/ai_online.mp4" % request_id)
|
||||||
else:
|
else:
|
||||||
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
|
aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
|
||||||
|
|
|
||||||
|
|
@ -151,35 +151,35 @@ class OnPushStreamProcess(PushStreamProcess):
|
||||||
# 自研车牌模型处理
|
# 自研车牌模型处理
|
||||||
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
|
||||||
cls = 0
|
cls = 0
|
||||||
ocrlabel, xybox = qs
|
box = xy2xyxy(qs[1])
|
||||||
box = xy2xyxy(xybox)
|
|
||||||
score = None
|
score = None
|
||||||
color = rainbows[cls]
|
color = rainbows[cls]
|
||||||
label_array = None
|
label_array = None
|
||||||
rr = t.submit(draw_name_ocr, xybox, copy_frame, color, ocrlabel)
|
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
|
||||||
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
cls = 0
|
cls = 0
|
||||||
crowdlabel, points = qs
|
# crowdlabel, points = qs
|
||||||
box = [(0, 0), (0, 0), (0, 0), (0, 0)]
|
box = [(0, 0), (0, 0), (0, 0), (0, 0)]
|
||||||
score = None
|
score = None
|
||||||
color = rainbows[cls]
|
color = rainbows[cls]
|
||||||
label_array = None
|
label_array = None
|
||||||
rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
|
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
|
||||||
else:
|
else:
|
||||||
try: # 应对NaN情况
|
try: # 应对NaN情况
|
||||||
box, score, cls = xywh2xyxy2(qs)
|
box, score, cls = xywh2xyxy2(qs)
|
||||||
except:
|
except:
|
||||||
continue
|
continue
|
||||||
if cls not in allowedList or score < frame_score:
|
if cls not in allowedList or score < frame_score:
|
||||||
continue
|
continue
|
||||||
label_array, color = label_arrays[cls], rainbows[cls]
|
label_array, color = label_arrays[cls], rainbows[cls]
|
||||||
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
|
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
|
||||||
rr = t.submit(draw_name_joint, box, copy_frame,
|
rr = t.submit(draw_name_joint, box, copy_frame,
|
||||||
draw_config[code]["label_dict"], score, color,
|
draw_config[code]["label_dict"], score, color,
|
||||||
font_config, qs[6])
|
font_config, qs[6])
|
||||||
else:
|
else:
|
||||||
rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
|
rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
|
||||||
score, color, font_config)
|
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:
|
||||||
|
|
@ -261,17 +261,18 @@ 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):
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
||||||
cls = ocrlabel
|
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
cls = crowdlabel
|
box = qs
|
||||||
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:
|
||||||
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
|
det_xywh2[code][cls].append(
|
||||||
|
[cls, box, score, label_array, color, is_new])
|
||||||
if len(det_xywh2) > 0:
|
if len(det_xywh2) > 0:
|
||||||
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
|
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames,
|
||||||
|
draw_config["font_config"]]))
|
||||||
|
|
||||||
push_p = push_stream_result.result(timeout=60)
|
push_p = push_stream_result.result(timeout=60)
|
||||||
ai_video_file = write_ai_video_result.result(timeout=60)
|
ai_video_file = write_ai_video_result.result(timeout=60)
|
||||||
|
|
@ -393,22 +394,21 @@ class OffPushStreamProcess(PushStreamProcess):
|
||||||
# 自研车牌模型处理
|
# 自研车牌模型处理
|
||||||
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
|
||||||
cls = 0
|
cls = 0
|
||||||
ocrlabel, xybox = qs
|
box = xy2xyxy(qs[1])
|
||||||
box = xy2xyxy(xybox)
|
|
||||||
score = None
|
score = None
|
||||||
color = rainbows[cls]
|
color = rainbows[cls]
|
||||||
label_array = None
|
label_array = None
|
||||||
label_arrays = [None]
|
label_arrays = [None]
|
||||||
rr = t.submit(draw_name_ocr,xybox,copy_frame,color,ocrlabel)
|
rr = t.submit(draw_name_ocr, qs, copy_frame, color)
|
||||||
|
|
||||||
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
cls = 0
|
cls = 0
|
||||||
crowdlabel, points = qs
|
|
||||||
box = [(0,0),(0,0),(0,0),(0,0)]
|
box = [(0,0),(0,0),(0,0),(0,0)]
|
||||||
score = None
|
score = None
|
||||||
color = rainbows[cls]
|
color = rainbows[cls]
|
||||||
label_array = None
|
label_array = None
|
||||||
rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
|
rr = t.submit(draw_name_crowd, qs, copy_frame, color)
|
||||||
|
|
||||||
else:
|
else:
|
||||||
box, score, cls = xywh2xyxy2(qs)
|
box, score, cls = xywh2xyxy2(qs)
|
||||||
|
|
@ -500,16 +500,15 @@ class OffPushStreamProcess(PushStreamProcess):
|
||||||
if q[11] == 1:
|
if q[11] == 1:
|
||||||
is_new = True
|
is_new = True
|
||||||
|
|
||||||
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
|
if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
|
||||||
cls = ocrlabel
|
ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
|
||||||
elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
|
ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
|
||||||
cls = crowdlabel
|
box = qs
|
||||||
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:
|
||||||
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
|
det_xywh2[code][cls].append(
|
||||||
|
[cls, box, score, label_array, color, is_new])
|
||||||
if len(det_xywh2) > 0:
|
if len(det_xywh2) > 0:
|
||||||
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
|
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
|
||||||
push_p = push_stream_result.result(timeout=60)
|
push_p = push_stream_result.result(timeout=60)
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,768 @@
|
||||||
|
import sys
|
||||||
|
from enum import Enum, unique
|
||||||
|
|
||||||
|
from common.Constant import COLOR
|
||||||
|
|
||||||
|
sys.path.extend(['..', '../AIlib2'])
|
||||||
|
from DMPR import DMPRModel
|
||||||
|
from DMPRUtils.jointUtil import dmpr_yolo
|
||||||
|
from segutils.segmodel import SegModel
|
||||||
|
from utilsK.queRiver import riverDetSegMixProcess
|
||||||
|
from utilsK.crowdGather import gather_post_process
|
||||||
|
from segutils.trafficUtils import tracfficAccidentMixFunction
|
||||||
|
from utilsK.drownUtils import mixDrowing_water_postprocess
|
||||||
|
from utilsK.noParkingUtils import mixNoParking_road_postprocess
|
||||||
|
from utilsK.illParkingUtils import illParking_postprocess
|
||||||
|
from stdc import stdcModel
|
||||||
|
from yolov5 import yolov5Model
|
||||||
|
from DMPRUtils.jointUtil import dmpr_yolo_stdc
|
||||||
|
from AI import default_mix
|
||||||
|
from ocr import ocrModel
|
||||||
|
from utilsK.channel2postUtils import channel2_post_process
|
||||||
|
|
||||||
|
'''
|
||||||
|
参数说明
|
||||||
|
1. 编号
|
||||||
|
2. 模型编号
|
||||||
|
3. 模型名称
|
||||||
|
4. 选用的模型名称
|
||||||
|
5. 模型配置
|
||||||
|
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
|
||||||
|
'''
|
||||||
|
|
||||||
|
|
||||||
|
@unique
|
||||||
|
class ModelType(Enum):
|
||||||
|
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [5, 6, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
|
||||||
|
# 'device': device,
|
||||||
|
# 'gpu_name': gpuName,
|
||||||
|
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
|
||||||
|
# 'trtFlag_det': True,
|
||||||
|
# 'trtFlag_seg': False,
|
||||||
|
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# 'seg_nclass': 2,
|
||||||
|
# 'segRegionCnt': 0,
|
||||||
|
# 'slopeIndex': [],
|
||||||
|
# 'segPar': None,
|
||||||
|
# 'postFile': {
|
||||||
|
# "name": "post_process",
|
||||||
|
# "conf_thres": 0.25,
|
||||||
|
# "iou_thres": 0.45,
|
||||||
|
# "classes": 6,
|
||||||
|
# "rainbows": COLOR
|
||||||
|
# },
|
||||||
|
# 'Segweights': None
|
||||||
|
# })
|
||||||
|
|
||||||
|
|
||||||
|
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
|
||||||
|
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
|
||||||
|
'postProcess':{'function':default_mix,'pars':{}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
|
||||||
|
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
|
||||||
|
'device': str(device),
|
||||||
|
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 3,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': tracfficAccidentMixFunction,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
#'modelSize': (1920,1080),
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'roadVehicleAngle': 15,
|
||||||
|
'speedRoadVehicleAngleMax': 75,
|
||||||
|
'roundness': 1.0,
|
||||||
|
'cls': 9,
|
||||||
|
'vehicleFactor': 0.1,
|
||||||
|
'confThres': 0.25,
|
||||||
|
'roadIou': 0.6,
|
||||||
|
'radius': 50,
|
||||||
|
'vehicleFlag': False,
|
||||||
|
'distanceFlag': False
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 10,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
|
||||||
|
|
||||||
|
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
|
||||||
|
|
||||||
|
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["车辆"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["行人"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["火焰", "烟雾"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["钓鱼", "游泳"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["违法种植"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
|
||||||
|
'model_size': (608, 608),
|
||||||
|
'K': 100,
|
||||||
|
'conf_thresh': 0.18,
|
||||||
|
'device': 'cuda:%s' % device,
|
||||||
|
'down_ratio': 4,
|
||||||
|
'num_classes': 15,
|
||||||
|
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
|
||||||
|
'dataset': 'dota',
|
||||||
|
'half': False,
|
||||||
|
'mean': (0.5, 0.5, 0.5),
|
||||||
|
'std': (1, 1, 1),
|
||||||
|
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
|
||||||
|
'decoder': None,
|
||||||
|
'test_flag': True,
|
||||||
|
"rainbows": COLOR,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'drawBox': False,
|
||||||
|
'label_array': None,
|
||||||
|
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
|
||||||
|
})
|
||||||
|
|
||||||
|
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
|
||||||
|
|
||||||
|
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["人"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
||||||
|
"蓝藻"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [1, 3, 4, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.3,
|
||||||
|
"ovlap_thres_crossCategory": 0.65,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
|
||||||
|
'labelnames': ["车辆", "垃圾", "商贩", "违停"],
|
||||||
|
'postProcess':{
|
||||||
|
'function':dmpr_yolo_stdc,
|
||||||
|
'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80,'rubCls': 1, 'Rubfilter': 150}
|
||||||
|
},
|
||||||
|
'models':[
|
||||||
|
{
|
||||||
|
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
|
||||||
|
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
|
||||||
|
'par':{
|
||||||
|
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
|
||||||
|
'name':'dmpr'
|
||||||
|
},
|
||||||
|
'model':DMPRModel,
|
||||||
|
'name':'dmpr'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
|
||||||
|
|
||||||
|
'par':{
|
||||||
|
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
|
||||||
|
'model':stdcModel,
|
||||||
|
'name':'stdc'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.5,
|
||||||
|
"iou_thres": 0.5,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
})
|
||||||
|
|
||||||
|
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["人头", "人", "船只"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': mixDrowing_water_postprocess,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
NOPARKING_MODEL = (
|
||||||
|
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["车辆", "违停"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 4,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True, ###分割模型预处理参数
|
||||||
|
'mixFunction': {
|
||||||
|
'function': mixNoParking_road_postprocess,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'roundness': 0.3,
|
||||||
|
'cls': 9,
|
||||||
|
'laneArea': 10,
|
||||||
|
'laneAngleCha': 5,
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'fitOrder':2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["车", "T角点", "L角点", "违停"],
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 4,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'mixFunction': {
|
||||||
|
'function': illParking_postprocess,
|
||||||
|
'pars': {}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.5,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["坑槽"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None,
|
||||||
|
})
|
||||||
|
|
||||||
|
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
|
||||||
|
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只","未封仓"], # 保持原来的标签顺序不变，方便后面业务端增加
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
|
||||||
|
'objs':[2],
|
||||||
|
'wRation':1/6.0,
|
||||||
|
'hRation':1/6.0,
|
||||||
|
'smallId':0, #旗帜
|
||||||
|
'bigId':3, #船只
|
||||||
|
'newId':4, #未挂国旗船只
|
||||||
|
'uncoverId':5, #未封仓标签
|
||||||
|
'recScale':1.2,
|
||||||
|
'target_cls':3.0, #目标种类
|
||||||
|
'filter_cls':4.0 #被过滤的种类
|
||||||
|
}},
|
||||||
|
'models':[
|
||||||
|
{
|
||||||
|
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
|
||||||
|
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
|
||||||
|
|
||||||
|
'weight':'/home/thsw2/jcq/test/AIlib2/weights/channel2/best.pt', # yolov5 原来模型基础上增加了未封仓
|
||||||
|
|
||||||
|
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
|
||||||
|
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
|
||||||
|
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
|
||||||
|
'name':'ocr',
|
||||||
|
'model':ocrModel,
|
||||||
|
'par':{
|
||||||
|
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
|
||||||
|
'mode':'ch',
|
||||||
|
'nc':3,
|
||||||
|
'imgH':32,
|
||||||
|
'imgW':192,
|
||||||
|
'hidden':256,
|
||||||
|
'mean':[0.5,0.5,0.5],
|
||||||
|
'std':[0.5,0.5,0.5],
|
||||||
|
'dynamic':False,
|
||||||
|
},
|
||||||
|
} ,
|
||||||
|
|
||||||
|
|
||||||
|
# {
|
||||||
|
# 'weight':'/home/thsw2/jcq/test/AIlib2/weights1/conf/channel2/yolov5_04.pt', # yolov5_04 添加了uncover 0 4 ；标签 yolov5_jcq
|
||||||
|
# 'name':'yolov5',
|
||||||
|
# 'model':yolov5Model,
|
||||||
|
# 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.15,'iou_thres':0.25,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
|
||||||
|
# }
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None,
|
||||||
|
})
|
||||||
|
|
||||||
|
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
||||||
|
"蓝藻"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [1, 3, 4, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.3,
|
||||||
|
"ovlap_thres_crossCategory": 0.65,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
FORESTCROWD_FARM_MODEL = ("2", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
|
||||||
|
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
|
||||||
|
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
|
||||||
|
|
||||||
|
})
|
||||||
|
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
|
||||||
|
'device': str(device),
|
||||||
|
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 3,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': tracfficAccidentMixFunction,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
#'modelSize': (1920,1080),
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'roadVehicleAngle': 15,
|
||||||
|
'speedRoadVehicleAngleMax': 75,
|
||||||
|
'roundness': 1.0,
|
||||||
|
'cls': 9,
|
||||||
|
'vehicleFactor': 0.1,
|
||||||
|
'confThres': 0.25,
|
||||||
|
'roadIou': 0.6,
|
||||||
|
'radius': 50,
|
||||||
|
'vehicleFlag': False,
|
||||||
|
'distanceFlag': False
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 10,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def checkCode(code):
|
||||||
|
for model in ModelType:
|
||||||
|
if model.value[1] == code:
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
'''
|
||||||
|
参数1: 检测目标名称
|
||||||
|
参数2: 检测目标
|
||||||
|
参数3: 初始化百度检测客户端
|
||||||
|
'''
|
||||||
|
|
||||||
|
|
||||||
|
@unique
|
||||||
|
class BaiduModelTarget(Enum):
|
||||||
|
VEHICLE_DETECTION = (
|
||||||
|
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
|
||||||
|
|
||||||
|
HUMAN_DETECTION = (
|
||||||
|
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
|
||||||
|
|
||||||
|
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
|
||||||
|
|
||||||
|
|
||||||
|
BAIDU_MODEL_TARGET_CONFIG = {
|
||||||
|
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
|
||||||
|
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
|
||||||
|
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
|
||||||
|
}
|
||||||
|
|
||||||
|
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
|
||||||
|
|
||||||
|
|
||||||
|
# 模型分析方式
|
||||||
|
@unique
|
||||||
|
class ModelMethodTypeEnum(Enum):
|
||||||
|
# 方式一: 正常识别方式
|
||||||
|
NORMAL = 1
|
||||||
|
|
||||||
|
# 方式二: 追踪识别方式
|
||||||
|
TRACE = 2
|
||||||
|
|
@ -0,0 +1,807 @@
|
||||||
|
import sys
|
||||||
|
from enum import Enum, unique
|
||||||
|
|
||||||
|
from common.Constant import COLOR
|
||||||
|
|
||||||
|
sys.path.extend(['..', '../AIlib2'])
|
||||||
|
from DMPR import DMPRModel
|
||||||
|
from DMPRUtils.jointUtil import dmpr_yolo
|
||||||
|
from segutils.segmodel import SegModel
|
||||||
|
from utilsK.queRiver import riverDetSegMixProcess
|
||||||
|
from utilsK.crowdGather import gather_post_process
|
||||||
|
from segutils.trafficUtils import tracfficAccidentMixFunction
|
||||||
|
from utilsK.drownUtils import mixDrowing_water_postprocess
|
||||||
|
from utilsK.noParkingUtils import mixNoParking_road_postprocess
|
||||||
|
from utilsK.illParkingUtils import illParking_postprocess
|
||||||
|
from stdc import stdcModel
|
||||||
|
from yolov5 import yolov5Model
|
||||||
|
from DMPRUtils.jointUtil import dmpr_yolo_stdc
|
||||||
|
from AI import default_mix
|
||||||
|
from ocr import ocrModel
|
||||||
|
from utilsK.channel2postUtils import channel2_post_process
|
||||||
|
|
||||||
|
'''
|
||||||
|
参数说明
|
||||||
|
1. 编号
|
||||||
|
2. 模型编号
|
||||||
|
3. 模型名称
|
||||||
|
4. 选用的模型名称
|
||||||
|
5. 模型配置
|
||||||
|
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
|
||||||
|
'''
|
||||||
|
|
||||||
|
|
||||||
|
@unique
|
||||||
|
class ModelType(Enum):
|
||||||
|
WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [5, 6, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
|
||||||
|
# 'device': device,
|
||||||
|
# 'gpu_name': gpuName,
|
||||||
|
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
|
||||||
|
# 'trtFlag_det': True,
|
||||||
|
# 'trtFlag_seg': False,
|
||||||
|
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# 'seg_nclass': 2,
|
||||||
|
# 'segRegionCnt': 0,
|
||||||
|
# 'slopeIndex': [],
|
||||||
|
# 'segPar': None,
|
||||||
|
# 'postFile': {
|
||||||
|
# "name": "post_process",
|
||||||
|
# "conf_thres": 0.25,
|
||||||
|
# "iou_thres": 0.45,
|
||||||
|
# "classes": 6,
|
||||||
|
# "rainbows": COLOR
|
||||||
|
# },
|
||||||
|
# 'Segweights': None
|
||||||
|
# })
|
||||||
|
|
||||||
|
|
||||||
|
FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
|
||||||
|
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
|
||||||
|
'postProcess':{'function':default_mix,'pars':{}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
|
||||||
|
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
|
||||||
|
'device': str(device),
|
||||||
|
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 3,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
#'modelSize': (640, 360),
|
||||||
|
'modelSize': (1920, 1080),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': tracfficAccidentMixFunction,
|
||||||
|
'pars': {
|
||||||
|
#'modelSize': (640, 360),
|
||||||
|
'modelSize': (1920,1080),
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'roadVehicleAngle': 15,
|
||||||
|
'speedRoadVehicleAngleMax': 75,
|
||||||
|
'roundness': 1.0,
|
||||||
|
'cls': 10,
|
||||||
|
'vehicleFactor': 0.1,
|
||||||
|
'confThres': 0.25,
|
||||||
|
'roadIou': 0.6,
|
||||||
|
'radius': 50,
|
||||||
|
'vehicleFlag': False,
|
||||||
|
'distanceFlag': False
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 10,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
|
||||||
|
|
||||||
|
PLATE_MODEL = ("5", "005", "车牌模型", None, None)
|
||||||
|
|
||||||
|
VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["车辆"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["行人"],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["火焰", "烟雾"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["钓鱼", "游泳"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["违法种植"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
|
||||||
|
'model_size': (608, 608),
|
||||||
|
'K': 100,
|
||||||
|
'conf_thresh': 0.18,
|
||||||
|
'device': 'cuda:%s' % device,
|
||||||
|
'down_ratio': 4,
|
||||||
|
'num_classes': 15,
|
||||||
|
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
|
||||||
|
'dataset': 'dota',
|
||||||
|
'half': False,
|
||||||
|
'mean': (0.5, 0.5, 0.5),
|
||||||
|
'std': (1, 1, 1),
|
||||||
|
'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
|
||||||
|
'decoder': None,
|
||||||
|
'test_flag': True,
|
||||||
|
"rainbows": COLOR,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'drawBox': False,
|
||||||
|
'label_array': None,
|
||||||
|
'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
|
||||||
|
})
|
||||||
|
|
||||||
|
BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
|
||||||
|
|
||||||
|
CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["人"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
||||||
|
"蓝藻"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [1, 3, 4, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.3,
|
||||||
|
"ovlap_thres_crossCategory": 0.65,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
|
||||||
|
'labelnames': ["车辆", "垃圾", "商贩", "裸土","占道经营","违停"],
|
||||||
|
'postProcess':{
|
||||||
|
'function':dmpr_yolo_stdc,
|
||||||
|
'pars':{'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80}
|
||||||
|
},
|
||||||
|
'models':[
|
||||||
|
{
|
||||||
|
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
|
||||||
|
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5,"4":0.4,"5":0.5 } }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
|
||||||
|
'par':{
|
||||||
|
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
|
||||||
|
'name':'dmpr'
|
||||||
|
},
|
||||||
|
'model':DMPRModel,
|
||||||
|
'name':'dmpr'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
|
||||||
|
'par':{
|
||||||
|
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
|
||||||
|
'model':stdcModel,
|
||||||
|
'name':'stdc'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
})
|
||||||
|
|
||||||
|
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["人头", "人", "船只"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': mixDrowing_water_postprocess,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
NOPARKING_MODEL = (
|
||||||
|
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["车辆", "违停"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 4,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True, ###分割模型预处理参数
|
||||||
|
'mixFunction': {
|
||||||
|
'function': mixNoParking_road_postprocess,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'roundness': 0.3,
|
||||||
|
'cls': 9,
|
||||||
|
'laneArea': 10,
|
||||||
|
'laneAngleCha': 5,
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'fitOrder':2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["车", "T角点", "L角点", "违停"],
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 4,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'mixFunction': {
|
||||||
|
'function': illParking_postprocess,
|
||||||
|
'pars': {}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 9,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.8,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': None
|
||||||
|
})
|
||||||
|
|
||||||
|
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["坑槽"],
|
||||||
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'slopeIndex': [],
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'trtFlag_seg': False,
|
||||||
|
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None,
|
||||||
|
})
|
||||||
|
|
||||||
|
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'gpu_name': gpuName,
|
||||||
|
'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
|
||||||
|
'segRegionCnt': 0,
|
||||||
|
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
|
||||||
|
'objs':[2],
|
||||||
|
'wRation':1/6.0,
|
||||||
|
'hRation':1/6.0,
|
||||||
|
'smallId':0,
|
||||||
|
'bigId':3,
|
||||||
|
'newId':4,
|
||||||
|
'recScale':1.2}},
|
||||||
|
'models':[
|
||||||
|
{
|
||||||
|
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
|
||||||
|
'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
# 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
|
||||||
|
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
|
||||||
|
'name':'ocr',
|
||||||
|
'model':ocrModel,
|
||||||
|
'par':{
|
||||||
|
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
|
||||||
|
'mode':'ch',
|
||||||
|
'nc':3,
|
||||||
|
'imgH':32,
|
||||||
|
'imgW':192,
|
||||||
|
'hidden':256,
|
||||||
|
'mean':[0.5,0.5,0.5],
|
||||||
|
'std':[0.5,0.5,0.5],
|
||||||
|
'dynamic':False,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
],
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
|
||||||
|
'segPar': None,
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Segweights': None,
|
||||||
|
})
|
||||||
|
|
||||||
|
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
|
||||||
|
'device': device,
|
||||||
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
||||||
|
"蓝藻"],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 2,
|
||||||
|
'segRegionCnt': 1,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': riverDetSegMixProcess,
|
||||||
|
'pars': {
|
||||||
|
'slopeIndex': [1, 3, 4, 7],
|
||||||
|
'riverIou': 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.3,
|
||||||
|
"ovlap_thres_crossCategory": 0.65,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
|
||||||
|
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
|
||||||
|
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
|
||||||
|
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
|
||||||
|
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.45,
|
||||||
|
"classes": 5,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
||||||
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
||||||
|
"pixScale": 1.2,
|
||||||
|
|
||||||
|
|
||||||
|
})
|
||||||
|
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
|
||||||
|
'device': str(device),
|
||||||
|
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
|
||||||
|
'trtFlag_seg': True,
|
||||||
|
'trtFlag_det': True,
|
||||||
|
'seg_nclass': 3,
|
||||||
|
'segRegionCnt': 2,
|
||||||
|
'segPar': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
'mean': (0.485, 0.456, 0.406),
|
||||||
|
'std': (0.229, 0.224, 0.225),
|
||||||
|
'predResize': True,
|
||||||
|
'numpy': False,
|
||||||
|
'RGB_convert_first': True,
|
||||||
|
'mixFunction': {
|
||||||
|
'function': tracfficAccidentMixFunction,
|
||||||
|
'pars': {
|
||||||
|
'modelSize': (640, 360),
|
||||||
|
#'modelSize': (1920,1080),
|
||||||
|
'RoadArea': 16000,
|
||||||
|
'roadVehicleAngle': 15,
|
||||||
|
'speedRoadVehicleAngleMax': 75,
|
||||||
|
'roundness': 1.0,
|
||||||
|
'cls': 9,
|
||||||
|
'vehicleFactor': 0.1,
|
||||||
|
'confThres': 0.25,
|
||||||
|
'roadIou': 0.6,
|
||||||
|
'radius': 50,
|
||||||
|
'vehicleFlag': False,
|
||||||
|
'distanceFlag': False
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'postFile': {
|
||||||
|
"name": "post_process",
|
||||||
|
"conf_thres": 0.25,
|
||||||
|
"iou_thres": 0.25,
|
||||||
|
"classes": 10,
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
|
||||||
|
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
|
||||||
|
})
|
||||||
|
|
||||||
|
SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
|
||||||
|
'labelnames': [ "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
|
||||||
|
'postProcess':{'function':default_mix,'pars':{}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
'postFile': {
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
|
||||||
|
})
|
||||||
|
|
||||||
|
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
|
||||||
|
'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
|
||||||
|
'postProcess':{'function':default_mix,'pars':{}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
'postFile': {
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
|
||||||
|
})
|
||||||
|
|
||||||
|
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
|
||||||
|
'labelnames': [ "烟花"],
|
||||||
|
'postProcess':{'function':default_mix,'pars':{}},
|
||||||
|
'models':
|
||||||
|
[
|
||||||
|
{
|
||||||
|
'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
||||||
|
'name':'yolov5',
|
||||||
|
'model':yolov5Model,
|
||||||
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
],
|
||||||
|
'postFile': {
|
||||||
|
"rainbows": COLOR
|
||||||
|
},
|
||||||
|
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def checkCode(code):
|
||||||
|
for model in ModelType:
|
||||||
|
if model.value[1] == code:
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
'''
|
||||||
|
参数1: 检测目标名称
|
||||||
|
参数2: 检测目标
|
||||||
|
参数3: 初始化百度检测客户端
|
||||||
|
'''
|
||||||
|
|
||||||
|
|
||||||
|
@unique
|
||||||
|
class BaiduModelTarget(Enum):
|
||||||
|
VEHICLE_DETECTION = (
|
||||||
|
"车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
|
||||||
|
|
||||||
|
HUMAN_DETECTION = (
|
||||||
|
"人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
|
||||||
|
|
||||||
|
PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
|
||||||
|
|
||||||
|
|
||||||
|
BAIDU_MODEL_TARGET_CONFIG = {
|
||||||
|
BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
|
||||||
|
BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
|
||||||
|
BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
|
||||||
|
}
|
||||||
|
|
||||||
|
EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
|
||||||
|
|
||||||
|
|
||||||
|
# 模型分析方式
|
||||||
|
@unique
|
||||||
|
class ModelMethodTypeEnum(Enum):
|
||||||
|
# 方式一: 正常识别方式
|
||||||
|
NORMAL = 1
|
||||||
|
|
||||||
|
# 方式二: 追踪识别方式
|
||||||
|
TRACE = 2
|
||||||
|
|
@ -374,8 +374,8 @@ class ModelType(Enum):
|
||||||
},
|
},
|
||||||
'models':[
|
'models':[
|
||||||
{
|
{
|
||||||
'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':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
|
'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 } }
|
||||||
},
|
},
|
||||||
|
|
@ -633,8 +633,6 @@ class ModelType(Enum):
|
||||||
'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
|
'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: {
|
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"],
|
'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}},
|
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
|
||||||
|
|
@ -976,12 +974,14 @@ class ModelType(Enum):
|
||||||
'row': 2,
|
'row': 2,
|
||||||
'line': 2,
|
'line': 2,
|
||||||
'point_loss_coef': 0.45,
|
'point_loss_coef': 0.45,
|
||||||
'conf': 0.25,
|
'conf': 0.65,
|
||||||
'gpu_id': 0,
|
'gpu_id': 0,
|
||||||
'eos_coef': '0.5',
|
'eos_coef': '0.5',
|
||||||
'set_cost_class': 1,
|
'set_cost_class': 1,
|
||||||
'set_cost_point': 0.05,
|
'set_cost_point': 0.05,
|
||||||
'backbone': 'vgg16_bn'
|
'backbone': 'vgg16_bn',
|
||||||
|
'expend': 10,
|
||||||
|
'psize': 2,
|
||||||
},
|
},
|
||||||
}],
|
}],
|
||||||
})
|
})
|
||||||
|
|
@ -1006,6 +1006,42 @@ class ModelType(Enum):
|
||||||
|
|
||||||
})
|
})
|
||||||
|
|
||||||
|
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',
|
||||||
|
'expend': 10,
|
||||||
|
'psize': 5
|
||||||
|
},
|
||||||
|
}],
|
||||||
|
})
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def checkCode(code):
|
def checkCode(code):
|
||||||
for model in ModelType:
|
for model in ModelType:
|
||||||
|
|
|
||||||
|
|
@ -46,8 +46,12 @@ class OneModel:
|
||||||
new_device = select_device(par.get('device'))
|
new_device = select_device(par.get('device'))
|
||||||
half = new_device.type != 'cpu'
|
half = new_device.type != 'cpu'
|
||||||
Detweights = par['Detweights']
|
Detweights = par['Detweights']
|
||||||
with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
|
if par['trtFlag_det']:
|
||||||
model = runtime.deserialize_cuda_engine(f.read())
|
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']
|
par['segPar']['seg_nclass'] = par['seg_nclass']
|
||||||
Segweights = par['Segweights']
|
Segweights = par['Segweights']
|
||||||
if Segweights:
|
if Segweights:
|
||||||
|
|
@ -402,8 +406,8 @@ class DENSECROWDCOUNTModel:
|
||||||
par = modeType.value[4](str(device), gpu_name)
|
par = modeType.value[4](str(device), gpu_name)
|
||||||
rainbows = par["rainbows"]
|
rainbows = par["rainbows"]
|
||||||
models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
|
models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
|
||||||
postPar = par['models'][0]['par']
|
postPar = [pp['par'] for pp in par['models']]
|
||||||
self.model_conf = (modeType, device, models[0], postPar, rainbows)
|
self.model_conf = (modeType, device, models, postPar, rainbows)
|
||||||
except Exception:
|
except Exception:
|
||||||
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
|
logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
|
||||||
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
|
raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
|
||||||
|
|
@ -752,4 +756,11 @@ MODEL_CONFIG = {
|
||||||
None,
|
None,
|
||||||
lambda x: cc_process(x)
|
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)
|
||||||
|
),
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -2,6 +2,7 @@ import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from PIL import Image, ImageDraw, ImageFont
|
from PIL import Image, ImageDraw, ImageFont
|
||||||
import unicodedata
|
import unicodedata
|
||||||
|
from loguru import logger
|
||||||
FONT_PATH = "../AIlib2/conf/platech.ttf"
|
FONT_PATH = "../AIlib2/conf/platech.ttf"
|
||||||
|
|
||||||
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
|
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
|
||||||
|
|
@ -224,11 +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)
|
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):
|
def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
|
||||||
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
|
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
|
||||||
#(color=None, label=None, font=None, fontSize=40, unify=False)
|
# (color=None, label=None, font=None, fontSize=40, unify=False)
|
||||||
label_zh = get_label_array(color, label, font, outfontsize)
|
label_zh = get_label_array(color, box[0], font, outfontsize)
|
||||||
return plot_one_box_auto(box, img, color, line_thickness, label_zh)
|
return plot_one_box_auto(box[1], 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
|
||||||
|
|
@ -316,21 +317,54 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
|
||||||
return img, box
|
return img, box
|
||||||
|
|
||||||
|
|
||||||
def draw_name_crowd(dets, img, color, label, line_thickness=2, outfontsize=20):
|
def draw_name_crowd(dets, img, color, outfontsize=20):
|
||||||
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
|
font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
|
||||||
H,W = img.shape[:2]
|
if len(dets) == 2:
|
||||||
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
label = '当前人数：%d'%len(dets[0])
|
||||||
# img = Image.fromarray(img)
|
detP = dets[0]
|
||||||
# width, height = img.size
|
line = dets[1]
|
||||||
Wrate = W // 128 * 128/W
|
for p in detP:
|
||||||
Hrate = H // 128 * 128/H
|
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
|
||||||
|
label_arr = get_label_array(color, label, font, outfontsize)
|
||||||
|
lh, lw = label_arr.shape[0:2]
|
||||||
|
img[0:lh, 0:lw, :] = label_arr
|
||||||
|
elif len(dets) == 3:
|
||||||
|
detP = dets[1]
|
||||||
|
line = dets[2]
|
||||||
|
for p in detP:
|
||||||
|
img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
|
||||||
|
|
||||||
# img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
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
|
||||||
|
|
||||||
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
|
return img, dets
|
||||||
Loading…
Reference in New Issue