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import os
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import math
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import time
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import cv2
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import json
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from queue import Queue, Empty
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from threading import Thread
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from log import logger
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from ultralytics import YOLO
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from yolov8_det import analysis_yolov8
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from capture_queue import CAMERA_QUEUE, camera_mul_thread
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with open('cfg.json', 'r') as f:
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cfg_dict = json.load(f)
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class FrameToVideo(Thread):
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"""
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农商行员工打瞌睡，玩手机分析类
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"""
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def __init__(self, camera_name):
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super(FrameToVideo, self).__init__()
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self.camera = camera_name
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# self.queue_img = CAMERA_QUEUE[camera_name]
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# self.yolo_model = {'person': YOLO(cfg_dict['model_path']['person']),
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# 'head': YOLO(cfg_dict['model_path']['head'])}
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self.yolo_model = YOLO(cfg_dict['model_path']['all'])
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self.person_target_list = []
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self.head_target_list = []
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self.phone_target_list = []
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@staticmethod
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def save_video(frames, fps, fourcc, video_path, w_h_size):
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"""
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截取后图像保存视频
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"""
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encoding = list(fourcc)
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video_fourcc = cv2.VideoWriter_fourcc(encoding[0], encoding[1], encoding[2], encoding[3])
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video_write_obj = cv2.VideoWriter(video_path, video_fourcc, fps, w_h_size)
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for frame in frames:
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video_write_obj.write(frame)
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logger.info(f'生成视频：{video_path}')
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@staticmethod
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def boundary_treat(frame_x, frame_y, coord):
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"""
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边界处理，裁剪可能会超出范围
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"""
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y_min = coord[1] - cfg_dict['error_y']
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y_max = coord[3] + cfg_dict['error_y']
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x_min = coord[0] - cfg_dict['error_x']
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x_max = coord[2] + cfg_dict['error_x']
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split_y = {'min': int(y_min) if y_min >= 0 else 0,
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'max': int(y_max) if y_max <= frame_y else frame_y}
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split_x = {'min': int(x_min) if y_min >= 0 else 0,
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'max': int(x_max) if x_max <= frame_x else frame_x}
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return split_x, split_y
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@staticmethod
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def target_match(target_list, coord, frame_img, new_target_list):
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"""
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遍历目标进行匹配
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"""
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match_flag = False
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for target in target_list:
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if target['flag']:
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continue
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if all([abs(coord[n] - target['coord'][n]) <= 5 for n in range(4)]): # 误差判断
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frame_split = frame_img[target['split_y']['min']:target['split_y']['max'],
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target['split_x']['min']:target['split_x']['max']]
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# cv2.imshow('yang', frame_split)
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# cv2.waitKey(2000)
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target['frame'].append(frame_split)
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target['count'] += 1
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target['flag'] = True
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# new_target_list.append(target)
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match_flag = True
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break
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else:
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continue
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return match_flag
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def target_analysis(self, target_list, new_target_list, person_coord_list, frame_x, frame_y, frame_img, label):
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if not target_list:
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for line in person_coord_list:
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# label that self define maybe different from model
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if label == 'head':
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coord = line['head']
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elif label == 'person':
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coord = line['person']
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else:
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coord = line['phone']
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split_x, split_y = self.boundary_treat(frame_x, frame_y, coord)
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# 裁剪大一圈,固定裁剪范围
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frame_split = frame_img[split_y['min']:split_y['max'], split_x['min']:split_x['max']]
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target_list.append({'frame': [frame_split], 'coord': coord, 'count': 0, 'split_x': split_x,
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'split_y': split_y, 'flag': False})
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else:
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for line in person_coord_list:
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coord = line[label]
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match_flag = self.target_match(target_list, coord, frame_img, new_target_list)
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if not match_flag:
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split_x, split_y = self.boundary_treat(frame_x, frame_y, coord)
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# 裁剪大一圈,固定裁剪范围
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frame_split = frame_img[split_y['min']:split_y['max'], split_x['min']:split_x['max']]
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new_target_list.append({'frame': [frame_split], 'coord': coord, 'count': 0, 'split_x': split_x,
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'split_y': split_y, 'flag': False})
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# 判断帧数，生成视频
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for target in target_list:
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if len(target['frame']) == cfg_dict['video_length']:
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frame_w = target['split_x']['max'] - target['split_x']['min']
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frame_h = target['split_y']['max'] - target['split_y']['min']
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logger.info(f'开始输出视频：{label}')
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self.save_video(target['frame'], cfg_dict['fps'], cfg_dict['video_encoding'],
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cfg_dict['video_path'][label] + self.camera + str(int(time.time())) + '.mp4v',
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(frame_w, frame_h))
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logger.info(f'输出视频结束：{label}')
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continue
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# 过滤中断没有匹配到的目标
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if target['flag']:
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target['flag'] = False
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new_target_list.append(target)
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if label == 'person':
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self.person_target_list = new_target_list
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else:
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self.head_target_list = new_target_list
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def frame_analysis(self):
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video_capture = cv2.VideoCapture(cfg_dict['test_video_path']) # 本地测试用
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while True:
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result, frame_img = video_capture.read() # 本地测试用
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# try:
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# frame_img = self.queue_img.get_nowait()
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# except Empty:
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# time.sleep(0.01)
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# continue
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new_person_target_list = []
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new_head_target_list = []
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new_phone_target_list = []
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# 调用模型，逐帧检测
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# person_coord_list = analysis_yolov8(frame=frame_img, model_coco=self.yolo_model['person'],
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# confidence=cfg_dict['confidence']['person'])
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# head_coord_list = analysis_yolov8(frame=frame_img, model_coco=self.yolo_model['head'],
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# confidence=cfg_dict['confidence']['head'])
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person_coord_list, head_coord_list, phone_coord_list = analysis_yolov8(frame=frame_img,
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model_coco=self.yolo_model,
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confidence=cfg_dict['confidence'])
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frame_y, frame_x, _ = frame_img.shape
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logger.debug(f'帧尺寸，y:{frame_y},x:{frame_x}')
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self.target_analysis(self.person_target_list, new_person_target_list, person_coord_list,
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frame_x, frame_y, frame_img, 'person')
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self.target_analysis(self.head_target_list, new_head_target_list, head_coord_list, frame_x,
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frame_y, frame_img, 'head')
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self.target_analysis(self.phone_target_list, new_phone_target_list, phone_coord_list, frame_x,
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frame_y, frame_img, 'phone')
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def run(self):
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self.frame_analysis()
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class ViolationJudgmentSend(Thread):
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"""
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农商行员工打瞌睡，玩手机，结果分析、发送类
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开线程运行
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"""
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def __int__(self):
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super(ViolationJudgmentSend, self).__init__()
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self.action_model = cfg_dict['model_path']['action']
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def video_analysis_sand(self):
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while True:
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pass
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def run(self):
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self.video_analysis_sand()
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# 程序启动
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def process_run():
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logger.info('程序启动')
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# 接入监控线程
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camera_mul_thread()
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# 截取视频线程
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frame_to_video_obj = [FrameToVideo(camera) for camera in CAMERA_QUEUE]
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for line in frame_to_video_obj:
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line.start()
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# 发送结果线程
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send_obj = ViolationJudgmentSend()
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send_obj.start()
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send_obj.join()
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if __name__ == '__main__':
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fv = FrameToVideo('camera_01')
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fv.frame_analysis()
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# process_run()
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