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import cv2
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import os
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from deepdiff import DeepDiff
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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 = video_name
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# cap.set(cv2.CAP_PROP_POS_FRAMES, frameToStart) # 设置初始帧数
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# count = frameToStart
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# while True:
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# success, frame = cap.read()
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# if not success or count > frametoStop:
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# break
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# if count >= frameToStart:
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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])]
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# videoWriter.write(frame_target)
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# count += 1
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# cap.release()
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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,size):
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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[bbox[1]:bbox[3], bbox[0]:bbox[2]] # (split_height, split_width)
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try:
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frame_target = cv2.resize(frame_target,size)
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videoWriter.write(frame_target)
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except Exception as e:
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print('----------------------------------',size,'----------------------------------')
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print(e)
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if not success or count >= frametoStop:
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break
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videoWriter.release()
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cap.release()
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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(re_list):
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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_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_list,result_list):
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'''
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example_dict:对比的参照
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result_dict: 需要与参照对比的结果
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example_sorted_lst：返回值中，原先有现在没有部分
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re_dict_sorted_lst：返回值中，现在有原先没有部分
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cut_list：原先有，现在也有的部分
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'''
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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_list)
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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_list)
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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_dict in enumerate(example_list):
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ex_bbox = ex_bbox_dict['result']
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for j,re_bbox in enumerate(result_list):
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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 > 0.5:
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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:re_bbox})
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cut_list.append(re_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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# print('example_temp:',example_temp)
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# print('re_temp:',re_temp)
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example_sorted_lst = [item for item in example_list if item['result'] not in example_temp]
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re_dict_sorted_lst = [item for item in result_list 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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# 修正坐标参数
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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]) / int(dertpara)
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h = (bbox[3] - bbox[1]) / int(dertpara)
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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 = contrast(size=images_size[0],bbox_extand_list=bbox_extand_list_x)
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bbox_list_y = contrast(size=images_size[1],bbox_extand_list=bbox_extand_list_y)
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bbox_list = [bbox_list_x[0],bbox_list_y[0],bbox_list_x[1],bbox_list_y[1]]
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return bbox_list
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def para_correction_back(x1, y1, x2, y2):
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'''
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修正检测后标注框还原
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'''
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# if dertpara:
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# pass
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# else:
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# x1, y1, x2, y2 = bbox[0], bbox[1],bbox[2],bbox[3] # 原始坐标
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width = x2 - x1
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height = y2 - y1
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scaled_width = int(width * 1.2)
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scaled_height = int(height * 1.2)
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restored_width = int(scaled_width / 1.2)
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restored_height = int(scaled_height / 1.2)
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restored_x1 = x1 + (width - restored_width) // 2
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restored_y1 = y1 + (height - restored_height) // 2
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restored_x2 = restored_x1 + restored_width
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restored_y2 = restored_y1 + restored_height
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bbox_list = [restored_x1,restored_y1,restored_x2,restored_y2]
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return bbox_list
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def para_list_correction(images_size,bbox_list,dertpara):
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updata_result_list = []
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for bbox in bbox_list:
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updata_bbox = para_correction(images_size,bbox,dertpara)
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updata_result_list.append(updata_bbox)
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return updata_result_list
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# 对比数值是否在这个范围内
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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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# print('bbox_extand_list:',bbox_extand_list)
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# print('size:',size)
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bbox_list = []
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for x in bbox_extand_list:
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# print('size:',size)
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if 0 <= int(x) <= int(size):
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# print('in:',x,size)
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bbox_list.append(x)
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if int(x) > int(size):
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# print('>:',x,size)
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bbox_list.append(size)
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if int(x) < 0:
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# print('<:',x,size)
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bbox_list.append(0)
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# print('bbox_list:',bbox_list)
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return bbox_list
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def change_list_dict(fps1,re_list):
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'''
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给列表的结果设置对应帧率
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'''
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bbox_list_all = []
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for bbox_list in re_list:
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bbox_dict = {'fps':fps1,'result':bbox_list}
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bbox_list_all.append(bbox_dict)
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return bbox_list_all
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def statistics_fps(fps_now,re_list,parameter):
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'''
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统计时长，返回时间满足截取要求的目标坐标
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'''
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time_out_list = []
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for bbox_dict in re_list:
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con_fps = int(fps_now) - int(bbox_dict["fps"])
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if con_fps > parameter:
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time_out_list.append(bbox_dict)
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return time_out_list
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def change_dict_list(dict_list):
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'''
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从字典列表得到bbox列表
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'''
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bbox_list = []
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for dicts1 in dict_list:
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bbox_list.append(dicts1['result'])
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return bbox_list
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def select_list(result_list):
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'''
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筛选列表中的空列表
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'''
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if result_list:
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result_only = []
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for result in result_list:
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if result == None :
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pass
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else:
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# result_bbox = select_bbox(result)
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result_only.append(result)
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return result_only
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def select_bbox(bbox_list):
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# bbox_list_return = []
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# print('bbox:',bbox_list)
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left_top = [min(bbox_list, key=lambda p: p[0])[0], min(bbox_list, key=lambda p: p[1])[1]]
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right_bottom = [max(bbox_list, key=lambda p: p[0])[0], max(bbox_list, key=lambda p: p[1])[1]]
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bbox_list_return = left_top + right_bottom
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# print('bbox_list:',bbox_list_return)
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return bbox_list_return
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# 对比两字典中的值是否完全一致
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def compare_dicts(dict1, list_of_dicts):
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for d in list_of_dicts:
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diff = DeepDiff(dict1, d)
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if not diff:
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return False
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return True |