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import cv2
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import os
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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 = Process_tools.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 = Process_tools.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 = Process_tools.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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def para_correction(images_size,bbox,dertpara):
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'''
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修正检测后标注框过小的情况,如果有修正参数则使用修正参数，如果没有就按照坐标值扩大两倍
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'''
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if dertpara:
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pass
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else:
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w = (bbox[2] - bbox[0]) /2
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h = (bbox[3] - bbox[1]) /2
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bbox_extand_list_x = [bbox[0] - w,bbox[2] + w]
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bbox_extand_list_y = [bbox[1] - h,bbox[3] + h]
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bbox_list_x = Process_tools.contrast(size=images_size[0],bbox_extand_list=bbox_extand_list_x)
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bbox_list_y = Process_tools.contrast(size=images_size[1],bbox_extand_list=bbox_extand_list_y)
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bbox_list = bbox_list_x + bbox_list_y
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return bbox_list
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def contrast(size,bbox_extand_list):
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'''
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对比数值是否在这个范围内
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'''
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bbox_list = []
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for x in bbox_extand_list:
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if x in range(size):
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bbox_list.append(x)
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if x > size:
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bbox_list.append(size)
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if x < 0:
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bbox_list.append(0)
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return bbox_list |