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import cv2
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import os
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import time
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import mediapipe as mp
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from ultralytics import YOLO
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import queue
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import threading
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from config import Q_SZ
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from personDet import analysis_yolov8
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import tools_function
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from holisticDet import MediapipeProcess
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import mediapipe_detection_image
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from PP_TSMv2_infer import PP_TSMv2_predict
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import shutil
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import json
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class DealVideo():
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def __init__(self,video_file,video_save_file,person_model,mediapipe_model,pptsmv2_model):
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'''
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加载数据
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'''
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self.video_file = video_file
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self.video_save_file = video_save_file
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# 初始化模型
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self.person_model = person_model
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self.mediapipe_model = mediapipe_model
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self.predictor = pptsmv2_model[1]
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self.infer = pptsmv2_model[0]
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self.batch_size = 1
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# 队列
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self.videoQueue = queue.Queue(maxsize=Q_SZ)
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self.videoQueue2 = queue.Queue(maxsize=Q_SZ)
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self.cutbboxQueue = queue.Queue(maxsize=0)
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self.videodetQueue = queue.Queue(maxsize=0)
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self.videoQueue3 = queue.Queue(maxsize=0)
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self.videoreturnQueue = queue.Queue(maxsize=0)
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#线程
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self.get_video_listThread = threading.Thread(target=self.get_video_list)
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self.get_video_frameThread = threading.Thread(target=self.get_video_frame)
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self.write_videoThread = threading.Thread(target=self.write_video)
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self.head_hands_detThread = threading.Thread(target=self.head_hands_det)
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self.video_select_dectThread = threading.Thread(target=self.video_select_dect)
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self.select_video_pathThread = threading.Thread(target=self.select_video_path)
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self.analysis_return_meassageThread = threading.Thread(target=self.analysis_return_meassage)
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def get_video_list(self):
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'''
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获取数据文件
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'''
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if os.path.isdir(self.video_file):
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video_ext = [".mp4", ".avi",".MP4"]
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for maindir, subdir, file_name_list in os.walk(self.video_file):
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for filename in file_name_list:
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apath = os.path.join(maindir, filename)
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ext = os.path.splitext(apath)[1]
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if ext in video_ext:
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self.videoQueue.put(apath)
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else:
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self.videoQueue.put(self.video_file)
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def get_video_frame(self):
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'''
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对视频进行分帧、每一帧都保存队列
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'''
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while True:
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if self.videoQueue.empty():
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time.sleep(1)
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else:
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video_path = self.videoQueue.get()
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# video_basename = os.path.basename(video_path).split('.')[0]
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# print('video_path:',video_path)
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cap = cv2.VideoCapture(video_path)
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video_fps = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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# frame_list = []
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count_fps = 0
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frame_result_contact = []
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count_fps_del = 0
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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print(video_path,"Ignoring empty camera frame.")
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# print('video_fps:',video_fps,'count_fps:',count_fps)
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break
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# print('count_fps_read_video=',count_fps)
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imgsize = frame.shape
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person_det = analysis_yolov8(frame=frame,
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model_coco=self.person_model,
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confidence_set=0.5)
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person_list = tools_function.get_dict_values(person_det)
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if frame_result_contact:
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start_fps = frame_result_contact[0]['fps']
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else:
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start_fps = count_fps
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if count_fps == (video_fps - 1):
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video_end = True
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else:
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video_end = False
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if person_list:
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count_fps_del_re,updata_result_contact = self.analysis_by_bbox(imgsize=imgsize,
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detect_result=person_list,
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dertpara=4,
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start_fps=start_fps,
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now_fps=count_fps,
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label_name='person',
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video_path=video_path,
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frame_result_contact=frame_result_contact,
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parameter_fps=150,
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count_fps_del=count_fps_del,
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video_end=video_end
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)
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count_fps_del = count_fps_del_re
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frame_result_contact = updata_result_contact
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count_fps += 1
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def head_hands_det(self):
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# print('head_hands_detaohgaogh')
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while True:
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if self.videoQueue3.empty():
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time.sleep(1)
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else:
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t0 = time.time()
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video_path = self.videoQueue3.get()
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# print('video_path_head_hands_det:',video_path)
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cap = cv2.VideoCapture(video_path)
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video_fps = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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# frame_list = []
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count_fps = 0
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head_result_contact = []
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hands_result_contact = []
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count_fps_del_head = 0
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count_fps_del_hand = 0
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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print(video_path,"Ignoring empty camera frame.")
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# print('count_fps:',count_fps,'video_fps:',video_fps)
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break
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# print('count_fps_read_video=',count_fps)
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imgsize = frame.shape
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# 模型推理
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hh_result = MediapipeProcess.mediapipe_det(image=frame,
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holistic=self.mediapipe_model)
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hh_result_dict = MediapipeProcess.get_analysis_result(image=frame,results=hh_result)
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# # 获得当前坐标列表
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head_result = hh_result_dict['face_bbox']
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head_result_1 = tools_function.select_list(head_result)
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hands_result = hh_result_dict['hand_bbox']
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hands_result_1 = tools_function.select_list(hands_result)
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if count_fps == (video_fps - 1):
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# print('count_fps:',count_fps,'video_fps:',video_fps)
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video_end = True
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else:
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video_end = False
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# 统一修正坐标,分别对头和手进行分析
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if head_result_1:
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if head_result_contact:
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start_fps = head_result_contact[0]['fps']
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else:
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start_fps = count_fps
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count_fps_del_re,updata_result_contact = self.analysis_by_bbox(imgsize=imgsize,
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detect_result=head_result_1,
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dertpara=1,
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start_fps=start_fps,
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now_fps=count_fps,
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label_name='head',
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video_path=video_path,
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frame_result_contact=head_result_contact,
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parameter_fps=80,
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count_fps_del=count_fps_del_head,
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video_end=video_end
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)
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count_fps_del_head = count_fps_del_re
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head_result_contact = updata_result_contact
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if hands_result_1:
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if hands_result_contact:
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start_fps = hands_result_contact[0]['fps']
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else:
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start_fps = count_fps
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count_fps_del_re,updata_result_contact = self.analysis_by_bbox(imgsize=imgsize,
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detect_result=hands_result_1,
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dertpara=2,
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start_fps=start_fps,
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now_fps=count_fps,
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label_name='hands',
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video_path=video_path,
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frame_result_contact=hands_result_contact,
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parameter_fps=80,
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count_fps_del=count_fps_del_hand,
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video_end=video_end
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)
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count_fps_del_hand = count_fps_del_re
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hands_result_contact = updata_result_contact
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# print(count_fps,'----------------hands_result_1-------------------------------',hands_result_1)
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# print(count_fps,"-------------------------updata_result_contact----------------------:",updata_result_contact)
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count_fps += 1
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def video_select_dect(self):
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while True:
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if self.videodetQueue.empty():
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time.sleep(5)
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else:
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video_path = self.videodetQueue.get()
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try:
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result_list = PP_TSMv2_predict().predict(input_f=video_path,
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batch_size=self.batch_size,
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predictor=self.predictor,
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InferenceHelper=self.infer)
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if result_list['topk_scores'] > 0.9:
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video_base_name = os.path.basename(video_path)
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video_save_select_path = self.video_save_file + '/' + 'video_select_dect/'+ str(result_list['topk_class'])
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os.makedirs(video_save_select_path, exist_ok=True)
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video_save = os.path.join(video_save_select_path, video_base_name)
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os.rename(video_path, video_save)
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self.videoreturnQueue.put(video_save)
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print("result_list_video_select_dect:",result_list)
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except Exception as e:
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print(e)
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def analysis_by_bbox(self,imgsize,detect_result,dertpara,start_fps,now_fps,label_name,video_path,frame_result_contact,parameter_fps,count_fps_del,video_end):
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'''
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imgsize:图片的尺寸,
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detect_result:检测到的图像的结果,bboxlist
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dertpara:scale_factor：缩放因子，大于 1 表示放大，小于 1 表示缩小
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start_fps: 对比列表中的起始帧
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now_fpsl:当前帧率
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label_name:用于分析的检测类别
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video_path:视频路径
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frame_result_contact:对比列表
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parameter_fps:统计截止时间
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count_fps_del:统计前后帧未出现次数
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'''
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bbox_list = tools_function.para_list_correction(images_size=imgsize,bbox_list=detect_result,dertpara=dertpara)
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count_fps_del_re,update_frame_result_contact = self.get_cut_message(fps1=now_fps,
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label_name = label_name,
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re_list=bbox_list,
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video_path=video_path,
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frame_result_contact=frame_result_contact,
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parameter_fps=parameter_fps,
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count_fps_del=count_fps_del,
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video_end=video_end)
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# count_fps_del_re,updata_result_contact = self.get_continue_keys(count_fps_del=count_fps_del,
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# continue_para=continue_para,
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# start_fps=start_fps,
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# now_fps=now_fps,
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# frame_result_contact=frame_result_contact,
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# update_frame_result_contact=update_frame_result_contact)\
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return count_fps_del_re,update_frame_result_contact
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def get_cut_message(self,fps1,label_name,re_list,video_path,frame_result_contact,parameter_fps,count_fps_del,video_end):
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# continue_para = False
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if not frame_result_contact:
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bbox_list_all = tools_function.change_list_dict(fps1=fps1,re_list=re_list)
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frame_result_contact = bbox_list_all
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# print("frame_result_contact:",frame_result_contact)
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else:
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example_dict_list = frame_result_contact
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cut_list,example_lst,re_dict_lst = tools_function.analysis_re01_list(example_list=example_dict_list,
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result_list=re_list)
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# 有目标减少情况
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if example_lst:
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# 截图保存视频
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# continue_para = True
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# cut_dict = {'video_path':video_path,'label_name':label_name,"stop_fps":fps1,'bbox_list':example_lst}
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start_fps = example_lst[0]['fps']
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if count_fps_del <= 5:
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frame_result_contact = frame_result_contact
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count_fps_del = count_fps_del + 1
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# else:
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if (fps1 - start_fps) < 15:
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frame_result_contact = frame_result_contact
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else:
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cut_dict = {'video_path':video_path,'label_name':label_name,"stop_fps":fps1,'bbox_list':example_lst}
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frame_result_contact = [item for item in frame_result_contact if item not in example_lst]
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self.cutbboxQueue.put(cut_dict)
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# 有新添加目标情况
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if re_dict_lst:
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# 对比示例列表更新
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update_list = tools_function.change_list_dict(fps1=fps1,re_list=re_dict_lst)
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frame_result_contact = frame_result_contact + update_list
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# 统计截止时间
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time_out_list = tools_function.statistics_fps(fps_now=fps1,re_list=frame_result_contact,parameter=parameter_fps)
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if time_out_list:
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# 裁剪保存视频
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# bbox_list = Process_tools.change_dict_list(time_out_list)
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cut_dict = {'video_path':video_path,'label_name':label_name,"stop_fps":fps1,'bbox_list':time_out_list}
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# 添加到新的队列
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self.cutbboxQueue.put(cut_dict)
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# 对比示例列表更新
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frame_result_contact = [item for item in frame_result_contact if item not in time_out_list]
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if video_end:
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cut_dict = {'video_path':video_path,'label_name':label_name,"stop_fps":fps1,'bbox_list':frame_result_contact}
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self.cutbboxQueue.put(cut_dict)
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frame_result_contact.clear()
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# print('frame_result_contact:',frame_result_contact)
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return count_fps_del,frame_result_contact
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def analysis_return_meassage(self):
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# big_add_list = []
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# big_list = []
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while True:
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if self.videoreturnQueue.empty():
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time.sleep(5)
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else:
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video_message_path = self.videoreturnQueue.get()
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directory = os.path.dirname(video_message_path)
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labels_pptsm = directory.split('/')[-1]
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video_basename = os.path.basename(video_message_path).split('.')[0]
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small_anno_infor = video_basename.split('__')[-1]
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big_anno_infor = video_basename.split('__')[-2]
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video_base_name = video_basename.split('__')[0]
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#保存的json文件格式
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file_path = self.video_save_file + '/' + video_base_name + '.json'
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# 对小图上的坐标和帧率进行分析
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small_startfps,small_stopfps,small_fps = small_anno_infor.split('_')[0].split('-')
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small_bbox_0,small_bbox_1,small_bbox_2,small_bbox_3 = small_anno_infor.split('_')[1].split('-')
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big_startfps,big_stopfps,big_fps = big_anno_infor.split('_')[0].split('-')
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big_bbox_0,big_bbox_1,big_bbox_2,big_bbox_3 = big_anno_infor.split('_')[1].split('-')
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big_add_startfps = int(big_startfps) + int(small_startfps)
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big_add_stopfps = int(big_startfps) + int(small_stopfps)
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big_add_bbox_0 = int(big_bbox_0) + int(small_bbox_0)
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big_add_bbox_1 = int(big_bbox_1) + int(small_bbox_1)
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big_add_bbox_2 = int(big_bbox_0) + int(small_bbox_2)
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big_add_bbox_3 = int(big_bbox_1) + int(small_bbox_3)
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big_add_dict = {'labels':labels_pptsm,'startfps':big_add_startfps,'stopfps':big_add_stopfps,'bbox':[big_add_bbox_0,big_add_bbox_1,big_add_bbox_2,big_add_bbox_3]}
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big_person_dict = {'labels':'person','startfps':big_startfps,'stopfps':big_stopfps,'bbox':[big_bbox_0,big_bbox_1,big_bbox_2,big_bbox_3]}
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if os.path.isfile(file_path):
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# 如果文件已存在，读取其中的字典数据
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with open(file_path, "r") as json_file:
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data = json.load(json_file)
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data['big_dict'].append(big_add_dict)
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if tools_function.compare_dicts(data['big_dict'], big_person_dict):
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data['big_dict'].append(big_person_dict)
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with open(file_path, "w") as json_file:
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json.dump(data, json_file)
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# # 访问和处理字典数据
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# print(data)
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else:
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# 如果文件不存在，创建一个新的字典并保存到文件中
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bbox_dict = {'big_dict':[big_add_dict,big_person_dict]}
|
|
|
with open(file_path, "w") as json_file:
|
|
|
json.dump(bbox_dict, json_file)
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def write_video(self):
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# print('write_videoafagragr')
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|
'''
|
|
|
保存成视频
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|
'''
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|
|
while True:
|
|
|
if self.cutbboxQueue.empty():
|
|
|
time.sleep(2)
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|
else:
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|
video_frame_dict = self.cutbboxQueue.get()
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|
|
# 视频路径
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|
|
video_path = video_frame_dict['video_path']
|
|
|
video_basename = os.path.basename(video_path).split('.')[0]
|
|
|
file_name = video_frame_dict['label_name']
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|
|
# 原视频帧率和尺寸
|
|
|
cap = cv2.VideoCapture(video_path)
|
|
|
fps = cap.get(cv2.CAP_PROP_FPS)
|
|
|
video_fps = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
|
|
|
|
# print(video_path,'fps:',fps,'video_fps:',video_fps)
|
|
|
# 获得起始
|
|
|
stop_fps = video_frame_dict['stop_fps']
|
|
|
# 裁剪信息
|
|
|
result_list = video_frame_dict['bbox_list']
|
|
|
if cap.isOpened():
|
|
|
|
|
|
for i,bbox_dict in enumerate(result_list):
|
|
|
start_fps = bbox_dict['fps']
|
|
|
|
|
|
if start_fps >= stop_fps:
|
|
|
|
|
|
# print('start_fps:',start_fps,'stop_fps:',stop_fps)
|
|
|
break
|
|
|
|
|
|
else:
|
|
|
bbox_list = bbox_dict['result']
|
|
|
bbox_int_list = [int(bbox_list[0]),int(bbox_list[1]),int(bbox_list[2]),int(bbox_list[3])]
|
|
|
w = bbox_int_list[2] - bbox_int_list[0]
|
|
|
h = bbox_int_list[3] - bbox_int_list[1]
|
|
|
size = [w,h]
|
|
|
if tools_function.determine_zero(size):
|
|
|
size = (w,h)
|
|
|
|
|
|
# 根据标签保存不同视频分类
|
|
|
# bbox_name = '{}-{}-{}_{}'.format(int(bbox_list[0]), int(bbox_list[1]), int(bbox_list[2]), int(bbox_list[3]))
|
|
|
video_name_save = '{}__{}-{}-{}_{}-{}-{}-{}.avi'.format(video_basename, start_fps, stop_fps, video_fps,int(bbox_list[0]), int(bbox_list[1]), int(bbox_list[2]), int(bbox_list[3]))
|
|
|
video_save_file = self.video_save_file + '/' + file_name
|
|
|
os.makedirs(video_save_file, exist_ok=True)
|
|
|
video_save_path = os.path.join(video_save_file, video_name_save)
|
|
|
videoWriter =cv2.VideoWriter(video_save_path,cv2.VideoWriter_fourcc('X','V','I','D'),fps,size)
|
|
|
|
|
|
tools_function.save_seg_video(video_name=video_path,
|
|
|
frameToStart=start_fps,
|
|
|
frametoStop=stop_fps,
|
|
|
videoWriter=videoWriter,
|
|
|
bbox=bbox_int_list,
|
|
|
size=size)
|
|
|
videoWriter.release()
|
|
|
self.videoQueue2.put(video_save_path)
|
|
|
|
|
|
else:
|
|
|
print('-----------------agrag-----------------',size,'-----------------agag-----------------')
|
|
|
|
|
|
cap.release()
|
|
|
|
|
|
else:
|
|
|
print(video_path)
|
|
|
break
|
|
|
|
|
|
|
|
|
def select_video_path(self):
|
|
|
|
|
|
while True:
|
|
|
|
|
|
if self.videoQueue2.empty():
|
|
|
time.sleep(5)
|
|
|
else:
|
|
|
video_path = self.videoQueue2.get()
|
|
|
directory = os.path.dirname(video_path)
|
|
|
labels = directory.split('/')[-1]
|
|
|
|
|
|
# print('video_pathagfg:',video_path)
|
|
|
|
|
|
# print(labels)
|
|
|
|
|
|
if labels == 'person':
|
|
|
|
|
|
self.videoQueue3.put(video_path)
|
|
|
|
|
|
# if labels == 'head' or labels == 'hands':
|
|
|
if labels == 'hands':
|
|
|
self.videodetQueue.put(video_path)
|
|
|
|
|
|
else:
|
|
|
pass
|
|
|
|
|
|
def run(self):
|
|
|
|
|
|
self.get_video_listThread.start()
|
|
|
self.get_video_frameThread.start()
|
|
|
self.write_videoThread.start()
|
|
|
self.head_hands_detThread.start()
|
|
|
self.video_select_dectThread.start()
|
|
|
self.select_video_pathThread.start()
|
|
|
self.analysis_return_meassageThread.start()
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
t1 = time.time()
|
|
|
video = "E:/Bank_files/Bank_02/dataset/video_kf/02.mp4"
|
|
|
video_save = 'test_video'
|
|
|
|
|
|
# 初始化目标检测
|
|
|
person_model = YOLO("model_file/yolov8x.pt")
|
|
|
|
|
|
# 初始化pptsmv2
|
|
|
config = 'model_file/inference/pptsm_lcnet_k400_16frames_uniform.yaml' # 配置文件地址
|
|
|
model_file = 'model_file/inference_hands_2/ppTSMv2.pdmodel' # 推理模型存放地址
|
|
|
params_file = 'model_file/inference_hands_2/ppTSMv2.pdiparams'
|
|
|
# batch_size= 1
|
|
|
infer,predictor = PP_TSMv2_predict().create_inference_model(config,model_file,params_file)
|
|
|
# PP_TSMv2_predict().predict(config,input_file,batch_size,predictor,infer)
|
|
|
|
|
|
# 初始化mediapipe
|
|
|
mp_holistic = mp.solutions.holistic
|
|
|
holistic = mp_holistic.Holistic(
|
|
|
min_detection_confidence=0.5,
|
|
|
min_tracking_confidence=0.5)
|
|
|
|
|
|
# get_seg_video(video_file=video,video_save_path=video_save,dertTime=dertTime)
|
|
|
|
|
|
deal = DealVideo(video_file=video,video_save_file=video_save,person_model=person_model,mediapipe_model=holistic,pptsmv2_model=[infer,predictor])
|
|
|
deal.run()
|
|
|
|
|
|
t2 = time.time()
|
|
|
|
|
|
# print('总时间：',t2-t1)
|
|
|
|