python 去除 txt 文件中，周围检测框的重复框

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去重 CV 检测框的 50 pix 像素值内的重复框：
如果缺陷类型一样，有重复框则取检测分数最大的框；
如果缺陷类型不一致，则保留两个框；
思想：

先找到每一个缺陷的几何中心 50 pix 内的所有框；
按照框进行缺陷类型分组：做去重处理；
删除位于同一范围内的缺陷；
合并小于 50 像素值和大于 50 pix 的框；

用法：依赖以下 3 个方法，直接调用第 3 个方法 remove_duplicate(txt_path, nearby_pix)
输出是去重后的生成 txt 。原始 txt 格式如下：
id 名称，x, y，w, h，检测分数，缺陷类型，时间
在这里插入图片描述

# txt to df and select the max score
def choose_only_one(nearby_list, defect_list_):df_nearby = pd.DataFrame()id_list = []x_list = []y_list = []w_list = []h_list = []score_list = []defect_list = []time_list = []for ele_nearby in nearby_list:ele_nearby = ele_nearby.split(',')id_list.append(ele_nearby[0])x_list.append(ele_nearby[1])y_list.append(ele_nearby[2])w_list.append(ele_nearby[3])h_list.append(ele_nearby[4])score_list.append(ele_nearby[5])defect_list.append(ele_nearby[6])time_list.append(ele_nearby[7][:-1])# time_list.append(ele_nearby[7])df_nearby['id'] = id_listdf_nearby['x'] = x_listdf_nearby['y'] = y_listdf_nearby['w'] = w_listdf_nearby['h'] = h_listdf_nearby['score'] = score_listdf_nearby['defect'] = defect_listdf_nearby['date'] = time_listresult_list = []df_result = pd.DataFrame()for ele_defect in defect_list_:df_nearby_temp = df_nearby[df_nearby['defect']==str(ele_defect)]df_nearby_max = df_nearby_temp[df_nearby_temp['score'] == np.max(df_nearby_temp['score'])]# result_list.append(df_nearby_max)df_result = pd.concat([df_result, df_nearby_max], axis=0)return df_resultdef txt_to_df(nearby_list):df_nearby = pd.DataFrame()id_list = []x_list = []y_list = []w_list = []h_list = []score_list = []defect_list = []time_list = []for ele_nearby in nearby_list:ele_nearby = ele_nearby.split(',')id_list.append(ele_nearby[0])x_list.append(ele_nearby[1])y_list.append(ele_nearby[2])w_list.append(ele_nearby[3])h_list.append(ele_nearby[4])score_list.append(ele_nearby[5])defect_list.append(ele_nearby[6])time_list.append(ele_nearby[7][:-1])# time_list.append(ele_nearby[7])df_nearby['id'] = id_listdf_nearby['x'] = x_listdf_nearby['y'] = y_listdf_nearby['w'] = w_listdf_nearby['h'] = h_listdf_nearby['score'] = score_listdf_nearby['defect'] = defect_listdf_nearby['date'] = time_listreturn df_nearby# remove dumplicate jiaoji txt
def remove_duplicate(txt_path, nearby_pix):
'''
txt_path : txt 文件的路径
nearby_pix : 邻近范围的像素值
'''work_dir = './factory_result_dashboard_txt/'if(not os.path.exists(work_dir)):os.makedirs(work_dir)f1=open(txt_path,'r')f1_1 = open(txt_path, 'r').read()if(len(f1_1)<=10):if(os.path.exists(txt_path)):shutil.copy(txt_path, work_dir)lines_1_list = list(set(f1.readlines()))lines_2_list = copy.deepcopy(lines_1_list)single_defect_list = []df_single_defect = pd.DataFrame()# single_defect_list_1 = []for line_1 in lines_1_list:line_1_ele = line_1.split(',')line_1_x = float(line_1_ele[1])line_1_y = float(line_1_ele[2])line_1_w = float(line_1_ele[3])line_1_h = float(line_1_ele[4])line_1_score = float(line_1_ele[5])line_1_defect = float(line_1_ele[6])line_1_centre_x = line_1_x + line_1_w // 2line_1_centre_y = line_1_y + line_1_h // 2nearby_list = []defect_list = []for line_2 in lines_2_list:line_2_ele = line_2.split(',')line_2_x = float(line_2_ele[1])line_2_y = float(line_2_ele[2])line_2_w = float(line_2_ele[3])line_2_h = float(line_2_ele[4])line_2_score = float(line_2_ele[5])line_2_defect = float(line_2_ele[6])line_2_centre_x = line_2_x + line_2_w // 2line_2_centre_y = line_2_y + line_2_h // 2if(line_1==line_2):continue# if(line_1 in single_defect_list):#     breakdis = np.sqrt(math.pow(abs(line_1_centre_x - line_2_centre_x), 2) + math.pow(abs(line_1_centre_y - line_2_centre_y), 2))nearby_pix = float(nearby_pix)if(dis <= nearby_pix):if(line_1 not in nearby_list):nearby_list.append(line_1)defect_list.append(line_1_defect)if(line_2 not in nearby_list):nearby_list.append(line_2)defect_list.append(line_2_defect)defect_list = list(set(defect_list))defect_list = map(int, defect_list)df_res = choose_only_one(nearby_list, defect_list) # select only one defect# single_defect_list.extend(df_res)df_single_defect = pd.concat([df_single_defect, df_res], axis=0)nearby_list = map(str, nearby_list)for ele_nearby in nearby_list:lines_1_list.remove(ele_nearby)df_lines_1 = txt_to_df(lines_1_list)df_single_defect = pd.concat([df_single_defect, df_lines_1], axis=0)df_single_defect = df_single_defect.drop_duplicates()# df_single_defect = df_single_defect.drop([0])df_single_defect.to_csv(work_dir + txt_path, sep=',', header=None, index=False)