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import numpy as np
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import cv2
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import os
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import time
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from tqdm import tqdm
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from ultralytics import YOLO
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from ultralytics.yolo.utils.plotting import Annotator
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import queue
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from yolov8_det import analysis_yolov8
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import threading
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from config import Q_SZ
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class DealVideo():
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def __init__(self,video_file,video_save_file):
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self.video_file = video_file
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self.video_save_file = video_save_file
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# 图片检测后队列
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self.videoQueue = queue.Queue(maxsize=Q_SZ)
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self.frameQueue = 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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# 图像文件夹
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def get_video_list(self):
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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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while True:
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if ~self.videoQueue.empty():
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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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frame_list = []
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count_fps = 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("Ignoring empty camera frame.")
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break
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count_fps += 1
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frame_dict = {'fps':count_fps,'frame':frame}
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frame_list.append(frame_dict)
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video_dict = {'video_path':video_path,'frame_list':frame_list,'cap':cap}
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self.frameQueue.put(video_dict)
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def write_video(self):
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while True:
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if ~self.frameQueue.empty():
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video_frame_dict = self.frameQueue.get()
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video_basename = os.path.basename(video_frame_dict['video_path'])
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video_name_save = os.path.join(self.video_save_file, video_basename)
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# 原视频帧率和尺寸
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cap = video_frame_dict['cap']
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fps = cap.get(cv2.CAP_PROP_FPS)
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size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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# 保存视频
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videoWriter =cv2.VideoWriter(video_name_save,cv2.VideoWriter_fourcc('X','V','I','D'),fps,size)
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frame_list = video_frame_dict['frame_list']
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for i in range(len(frame_list)):
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if frame_list[i]["fps"] == i + 1:
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videoWriter.write(frame_list[i]["frame"])
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else:
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break
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def run(self):
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self.get_video_listThread.start()
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self.get_video_frameThread.start()
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self.write_videoThread.start()
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class Process_tools():
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# 图像文件夹
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def get_video_list(path):
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video_ext = [".mp4", ".avi",".MP4"]
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video_names = []
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for maindir, subdir, file_name_list in os.walk(path):
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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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video_names.append(apath)
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return video_names
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# 截取裁剪需要的视频帧
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def save_seg_video(video_name,frameToStart,frametoStop,videoWriter,bbox):
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cap = cv2.VideoCapture(video_name)
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count = 0
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while True:
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success, frame = cap.read()
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if success:
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count += 1
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if count <= frametoStop and count > frameToStart: # 选取起始帧
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print('correct= ', count)
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#裁剪视频画面
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frame_target = frame[int(bbox[1]):int(bbox[3]), int(bbox[0]):int(bbox[2])] # (split_height, split_width)
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videoWriter.write(frame_target)
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if not success or count >= frametoStop:
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break
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print('end')
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# 获得字典中所有values值（这个值是列表）
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def get_dict_values(lst):
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"""
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获取列表中所有字典的 values 值（如果值是列表）
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参数:
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lst: 包含字典的列表
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返回值:
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values: 包含所有字典的 values 值的列表（如果值是列表）
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"""
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return [value for dictionary in lst for value in dictionary.values() if isinstance(value, list)]
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# 解析检测后的结果，为检测后的结果排序
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def analysis_sort_list(result_dict):
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# print('result_dict:',result_dict)
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# 获得检测列表
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re_list = result_dict['start_bbox']
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# print('re_list:',re_list)
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# 获得列表中所有字典的values值
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re_bbox_list = get_dict_values(re_list)
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# 为检测出来的标注框排序
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sorted_lst = sorted(re_bbox_list, key=lambda x: x[0])
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return sorted_lst
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#对比重叠率高的两个部分，并结合标注框，保存最大的标注框
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def contrast_bbox(e_bbox,r_bbox):
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e_bbox_min = e_bbox[:2]
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r_bbox_min = r_bbox[:2]
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bbox_min = [min(x, y) for x, y in zip(e_bbox_min, r_bbox_min)]
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e_bbox_max = e_bbox[-2:]
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r_bbox_max = r_bbox[-2:]
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bbox_max = [max(x, y) for x, y in zip(e_bbox_max, r_bbox_max)]
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bbox = bbox_min + bbox_max
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return bbox
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# 解析result_list列表
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def analysis_re01_list(example_dict,result_dict):
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# 第一次检测到目标的帧率和信息
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example_dict_fps = list(example_dict.keys())[0]
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example_sorted_lst = Process_tools.analysis_sort_list(example_dict)
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# 当前帧检测结果中所有的检测结果数值
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re_dict_fps = list(result_dict.keys())[0]
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re_dict_sorted_lst = Process_tools.analysis_sort_list(result_dict)
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# 保存前后帧率连续的范围、筛选出相同的部分
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cut_list = []
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example_temp = []
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re_temp = []
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for i,ex_bbox in enumerate(example_sorted_lst):
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for j,re_bbox in enumerate(re_dict_sorted_lst):
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iou = calculate_iou(box1=ex_bbox, box2=re_bbox)
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# print(iou)
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if iou > 0:
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bbox = contrast_bbox(e_bbox=ex_bbox,r_bbox=re_bbox)
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cut_list.append({i:bbox})
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example_temp.append(ex_bbox)
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re_temp.append(re_bbox)
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break
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else:
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continue
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example_sorted_lst = [item for item in example_sorted_lst if item not in example_temp]
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re_dict_sorted_lst = [item for item in re_dict_sorted_lst if item not in re_temp]
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return cut_list,example_sorted_lst,re_dict_sorted_lst
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# 计算前后帧率重叠范围
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def calculate_iou(box1, box2):
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"""
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计算两个边界框之间的IoU值
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参数:
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box1: 边界框1的坐标（x1, y1, x2, y2）
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box2: 边界框2的坐标（x1, y1, x2, y2）
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返回值:
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iou: 两个边界框之间的IoU值
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"""
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x1 = max(box1[0], box2[0])
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y1 = max(box1[1], box2[1])
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x2 = min(box1[2], box2[2])
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y2 = min(box1[3], box2[3])
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# 计算交集区域面积
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intersection_area = max(0, x2 - x1 + 1) * max(0, y2 - y1 + 1)
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# 计算边界框1和边界框2的面积
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box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1)
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box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1)
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# 计算并集区域面积
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union_area = box1_area + box2_area - intersection_area
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# 计算IoU值
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iou = intersection_area / union_area
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return iou
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if __name__ == '__main__':
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# 每个视频的时长（单位秒）
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dertTime = 5
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video = "E:/Bank_files/Bank_02/dataset/video_person/after_1/"
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video_save = 'videos_codes_2'
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# get_seg_video(video_file=video,video_save_path=video_save,dertTime=dertTime)
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deal = DealVideo(video_file=video,video_save_file=video_save)
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deal.run()
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