聚类算法

  • 1. 直接聚类后裁剪
  • 2. 分步骤进行聚类、裁剪、可视化图片
    • （1）使用DBSCAN进行聚类
    • （2）在原图上裁剪，根据聚类中心框出600*600的矩阵，保存标注格式为xml
    • （3）可视化裁剪的标注格式是否正确
  • 3. 将裁剪后的图片用yolov4进行预测，得到result.json文件。再将预测出来的目标进行拼接回原图。

1. 直接聚类后裁剪

  • clus_output：聚类后二值图片
  • img：原始图片
  • output：根据目标坐标和有无目标——》二值图片
  • txt：原始标注
  • txt_del：删掉有无目标的标注
# -*- coding: UTF-8 -*-
# 聚类代码
import cv2
import os
import sys
from sklearn import cluster
import numpy as np
import matplotlib.pyplot as pltdef dot_Visualization(txt_path, img_path, save_path):  # 可视化像素点name_id = os.listdir(txt_path)img_id = os.listdir(img_path)image_total = []for i, image_id in enumerate(img_id):data = []img = cv2.imread(img_path + img_id[i])w, h = img.shape[0], img.shape[1]image = np.zeros((w, h), np.uint8)txt_file = open(txt_path + name_id[i], "r")for j, line in enumerate(txt_file):data.append(line)x = int(data[j].split(',')[0])y = int(data[j].split(',')[1])if data[j].split(',')[2] == '1\n' or data[j].split(',')[2] == '1':cv2.circle(image, (x, y), 7, (255,255,255), -1)cv2.imwrite(save_path + img_id[i], image)else:continueimage_total.append(image)return image_totaldef del_zeros(txt_path, save_del_path):  # 去掉坐标后面的0和1，只用坐标来聚类name_id = os.listdir(txt_path)data = []new_lines = []for i, image_id in enumerate(name_id):file_ori = open(txt_path + name_id[i], "r")file_new = open(save_del_path + name_id[i], "w")lines = file_ori.readlines()for line in lines:if line.split(',')[2] == '0' or line.split(',')[2] == '0\n':del lineelse: if lines[-1] == line:new_line = line[:-2]file_new.write(new_line) else:new_line = line[:-3] + '\n' file_new.write(new_line)def cluster_method(save_del_path, img_path, save_cluster_path, image):  # 聚类操作name_id = os.listdir(save_del_path)img_id = os.listdir(img_path)data = []data_change = []for i, image_id in enumerate(name_id):txt_file = open(save_del_path + name_id[i], "r")for j, xy in enumerate(txt_file):data.append(xy)for val in data:arr = val.split(',')arr = [int(i) for i in arr]data_change.append(arr)data_arr = np.array(data_change)# import pdb# pdb.set_trace()[centroid, label, inertial] = cluster.k_means(data_arr, n_clusters=3)cluster_1 = centroid[0].astype(np.int).tolist()cluster_2 = centroid[1].astype(np.int).tolist()# cluster_1_right = cluster_1[0] + 300# cluster_1_left = cluster_1[0] - 300# cluster_1_top = cluster_1[1] + 300# cluster_1_bottom = cluster_1[1] - 300# cluster_2_right = cluster_2[0] + 300# cluster_2_left = cluster_2[0] - 300# cluster_2_top = cluster_2[1] + 300# cluster_2_bottom = cluster_2[1] - 300cluster_1_x1 = cluster_1[0] - 300cluster_1_y1 = cluster_1[1] + 300cluster_1_x2 = cluster_1[0] + 300cluster_1_y2 = cluster_1[1] - 300cluster_2_x1 = cluster_2[0] - 300cluster_2_y1 = cluster_2[1] + 300cluster_2_x2 = cluster_2[0] + 300cluster_2_y2 = cluster_2[1] - 300cv2.circle(image[i], (cluster_1[0], cluster_1[1]), 5, (125,125,125), -1)cv2.circle(image[i], (cluster_2[0], cluster_2[1]), 5, (125,125,125), -1)cv2.rectangle(image[i], (cluster_1_x1, cluster_1_y1), (cluster_1_x2, cluster_1_y2), (75,75,75), 2)cv2.rectangle(image[i], (cluster_2_x1, cluster_2_y1), (cluster_2_x2, cluster_2_y2), (75,75,75), 2)cv2.imwrite(save_cluster_path + img_id[i], image[i])if __name__ == '__main__':# txt存放的路径txt_path = "D:/code/cluster/txt/"# 原图片路径 img_path = "D:/code/cluster/img/"# 画出来的图片保存的路径save_path = "D:/code/cluster/output/"save_cluster_path = "D:/code/cluster/clus_output/"save_del_path = "D:/code/cluster/txt_del/"image = dot_Visualization(txt_path, img_path, save_path)del_zeros(txt_path, save_del_path)cluster_method(save_del_path, img_path, save_cluster_path, image)print("All Done....")

2. 分步骤进行聚类、裁剪、可视化图片

（1）使用DBSCAN进行聚类

final_cluster.py

# -*- coding: UTF-8 -*-
# 聚类代码
import cv2
import os
import sys
from sklearn import cluster
import numpy as np
import matplotlib.pyplot as plt
import time
import copy
from xml.dom.minidom import Document
import scipy.misc as miscdef dot_Visualization(img_data, txt, save_path, idx):  # 可视化像素点image_total = []data = []w, h = img_data.shape[0], img_data.shape[1]image = np.zeros((w, h), np.uint8)for j, line in enumerate(txt):data.append(line)x = int(data[j].split(',')[0])y = int(data[j].split(',')[1])cv2.circle(image, (x, y), 2, (255,255,255), -1)cv2.imwrite(save_path + idx, image)if line.isspace():  # 判断当前行是不是空continueimage_total.append(image)return image_totaldef cluster_method(txt, image, img_data, idx):  # 聚类操作start_time = time.time()data = []data_change = []data0_xy = []data1_xy = []data2_xy = []data3_xy = []# data4_xy = []# data5_xy = []center0 = []center1 = []center2 = []center3 = []# center4 = []# center5 = []data_xy = []for j, xy in enumerate(txt):data.append(xy)for val in data:arr = val.split(',')arr = [int(i) for i in arr]data_change.append(arr)data_arr = np.array(data_change)# 随机抽取某1000行row_rand_arr = np.arange(data_arr.shape[0])np.random.shuffle(row_rand_arr)row_rand = data_arr[row_rand_arr[0:1000]]# k-means# [centroid, label, inertial] = cluster.k_means(data_arr, n_clusters=3)# print(centroid)# centroid = centroid.tolist()# mini-batch kmeans# centroid = cluster.MiniBatchKMeans(n_clusters=2, batch_size=16).fit(data_arr)# centroid = centroid.cluster_centers_# ----------------dbscan——start---------------------# [centroid, label, inertial] = cluster.dbscan(data_arr)# centroid = cluster.DBSCAN(eps=40, min_samples=50).fit_predict(row_rand)  # clus_output  eps=40, min_samples=50centroid = cluster.DBSCAN(eps=40, min_samples=50).fit_predict(row_rand)  centroid = centroid.tolist()print(centroid)data = row_rand.tolist()for i in range(len(centroid)):if centroid[i] == 0:center0.append(data[i])elif centroid[i] == 1:center1.append(data[i])elif centroid[i] == 2:center2.append(data[i])elif centroid[i] == 3:center3.append(data[i])# elif centroid[i] == 4:#     center4.append(data[i])# elif centroid[i] == 5:#     center5.append(data[i])cen0_x, cen0_y, cen1_x, cen1_y = 0, 0, 0, 0cen2_x, cen2_y, cen3_x, cen3_y = 0, 0, 0, 0# cen4_x, cen4_y, cen5_x, cen5_y = 0, 0, 0, 0for c0 in center0:cen0_x += c0[0]cen0_y += c0[1]cen0_x = cen0_x / len(center0)cen0_y = cen0_y / len(center0)for c1 in center1:cen1_x += c1[0]cen1_y += c1[1]cen1_x = cen1_x / len(center1)cen1_y = cen1_y / len(center1)if center2:for c2 in center2:cen2_x += c2[0]cen2_y += c2[1]cen2_x = cen2_x / len(center2)cen2_y = cen2_y / len(center2)if center3:for c3 in center3:cen3_x += c3[0]cen3_y += c3[1]cen3_x = cen3_x / len(center3)cen3_y = cen3_y / len(center3)# if center4:#     for c4 in center4:#         cen4_x += c4[0]#         cen4_y += c4[1]#     cen4_x = cen4_x / len(center4)#     cen4_y = cen4_y / len(center4)# if center5:#     for c5 in center5:#         cen5_x += c5[0]#         cen5_y += c5[1]#     cen5_x = cen5_x / len(center5)#     cen5_y = cen5_y / len(center5)x01 = int(cen0_x) - 300y01 = int(cen0_y) - 300x02 = int(cen0_x) + 300y02 = int(cen0_y) + 300data0_xy.append([x01, y01, x02, y02])data0_xy = [x for z in data0_xy for x in z]print(data0_xy)x11 = int(cen1_x) - 300y11 = int(cen1_y) - 300x12 = int(cen1_x) + 300y12 = int(cen1_y) + 300data1_xy.append([x11, y11, x12, y12])data1_xy = [x for z in data1_xy for x in z]print(data1_xy)x21 = int(cen2_x) - 300y21 = int(cen2_y) - 300x22 = int(cen2_x) + 300y22 = int(cen2_y) + 300data2_xy.append([x21, y21, x22, y22])data2_xy = [x for z in data2_xy for x in z]print(data2_xy)x31 = int(cen3_x) - 300y31 = int(cen3_y) - 300x32 = int(cen3_x) + 300y32 = int(cen3_y) + 300data3_xy.append([x31, y31, x32, y32])data3_xy = [x for z in data3_xy for x in z]print(data3_xy)# x41 = int(cen4_x) - 300# y41 = int(cen4_y) - 300# x42 = int(cen4_x) + 300# y42 = int(cen4_y) + 300# data4_xy.append([x41, y41, x42, y42])# data4_xy = [x for z in data4_xy for x in z]# print(data4_xy)# x51 = int(cen5_x) - 300# y51 = int(cen5_y) - 300# x52 = int(cen5_x) + 300# y52 = int(cen5_y) + 300# data5_xy.append([x51, y51, x52, y52])# data5_xy = [x for z in data5_xy for x in z]# print(data5_xy)cv2.circle(image[0], (int(cen0_x), int(cen0_y)), 5, (125,125,125), -1)cv2.rectangle(image[0], (x01, y01), (x02, y02), (75,75,75), 2)cv2.circle(image[0], (int(cen1_x), int(cen1_y)), 5, (125,125,125), -1)cv2.rectangle(image[0], (x11, y11), (x12, y12), (75,75,75), 2)cv2.circle(image[0], (int(cen2_x), int(cen2_y)), 5, (125,125,125), -1)cv2.rectangle(image[0], (x21, y21), (x22, y22), (75,75,75), 2)cv2.circle(image[0], (int(cen3_x), int(cen3_y)), 5, (125,125,125), -1)cv2.rectangle(image[0], (x31, y31), (x32, y32), (75,75,75), 2)# cv2.circle(image[0], (int(cen4_x), int(cen4_y)), 5, (125,125,125), -1)# cv2.rectangle(image[0], (x41, y41), (x42, y42), (75,75,75), 2)# cv2.circle(image[0], (int(cen5_x), int(cen5_y)), 5, (125,125,125), -1)# cv2.rectangle(image[0], (x51, y51), (x52, y52), (75,75,75), 2)cv2.imwrite(save_cluster_path + idx, image[0])data_xy.append(data0_xy)data_xy.append(data1_xy)data_xy.append(data2_xy)data_xy.append(data3_xy)# data_xy.append(data4_xy)# data_xy.append(data5_xy)# ----------------dbscan——end---------------------print("ok!")end_time = time.time()   print("take %f second" % (end_time - start_time))return np.array(data_xy)def text_save(filename, data):#filename为写入txt文件的路径，data为要写入数据列表.file = open(filename,'w')from itertools import chains = '\n'.join([' '.join(chain([filename[-10:-4] + '0' + str(i)], map(str, j))) for i, j in enumerate(data)]) + '\n'file.write(s)file.close()print(" %s 保存文件成功"  %  filename[-10:] ) if __name__ == '__main__':# txt存放的路径txt_path = "/home/jjliao/cluster/gt_map_copy/"# 原图片路径 img_path = "/home/jjliao/cluster/gt_img_copy/"# 画出来的图片保存的路径save_path = "/home/jjliao/cluster/output/"save_cluster_path = "/home/jjliao/cluster/clus_output/"output_path = "/home/jjliao/cluster/cluster_output/"class_list = ['pedestrian','people','bicycle','car','van','truck','tricycle','awning-tricycle','bus','motor']images = [i for i in os.listdir(img_path) if '.jpg' in i]labels = [i for i in os.listdir(txt_path) if 'txt' in i]print('find image', len(images))print('find label', len(labels))width, height = 600, 600for idx, img in enumerate(images):start_time = time.time()   print(idx, 'read image', img)img_data = misc.imread(os.path.join(img_path, img))txt = open(os.path.join(txt_path, img.replace('jpg', 'txt')), 'r').readlines()image = dot_Visualization(img_data, txt, save_path, img)box = cluster_method(txt, image, img_data, img)text_save(output_path + img.replace('jpg', 'txt'), box)# clip_image(img.strip('jpg'), img_data, box, width, height)  # 代码内裁剪end_time = time.time()   print(img, "take %f second" % (end_time - start_time))print("All Done....")

可视化聚类中心及框：

（2）在原图上裁剪，根据聚类中心框出600*600的矩阵，保存标注格式为xml

crop_visdrone.py

import os
from xml.dom.minidom import Document
import copy
import numpy as np
from scipy import misc
import cv2class_list = ['ignored regions', 'pedestrian', 'people', 'bicycle', 'car', 'van','truck', 'tricycle', 'awning-tricycle', 'bus', 'motor', 'others'
]raw_data = '/home/jjliao/Visdrone_yolo_cluster/VisDrone2019-DET-train/'
raw_images_dir = os.path.join(raw_data, 'images')
raw_label_dir = os.path.join(raw_data, 'annotations')
input_path = "/home/jjliao/cluster/cluster_output/"
out_images_dir = os.path.join(raw_data, 'images_cluster')
out_label_dir = os.path.join(raw_data, 'annotations_cluster_xml')def format_label(txt_list):format_data = []for i in txt_list[0:]:format_data.append([int(xy) for xy in i.split(',')[:8]])return np.array(format_data)def save_to_xml(save_path,im_width,im_height,objects_axis,label_name,name,hbb=True):im_depth = 0object_num = len(objects_axis)doc = Document()annotation = doc.createElement('annotataion')doc.appendChild(annotation)folder = doc.createElement('folder')folder_name = doc.createTextNode('Visdrone')folder.appendChild(folder_name)annotation.appendChild(folder)filename = doc.createElement('filename')filename_name = doc.createTextNode(name)filename.appendChild(filename_name)annotation.appendChild(filename)source = doc.createElement('source')annotation.appendChild(source)database = doc.createElement('database')database.appendChild(doc.createTextNode('The Visdrone Database'))source.appendChild(database)annotation_s = doc.createElement('annotation')annotation_s.appendChild(doc.createTextNode('Visdrone'))source.appendChild(annotation_s)image = doc.createElement('image')image.appendChild(doc.createTextNode('flickr'))source.appendChild(image)flickrid = doc.createElement('flickrid')flickrid.appendChild(doc.createTextNode('322409915'))source.appendChild(flickrid)owner = doc.createElement('owner')annotation.appendChild(owner)flickrid_o = doc.createElement('flickrid')flickrid_o.appendChild(doc.createTextNode('knautia'))owner.appendChild(flickrid_o)name_o = doc.createElement('name')name_o.appendChild(doc.createTextNode('yang'))owner.appendChild(name_o)size = doc.createElement('size')annotation.appendChild(size)width = doc.createElement('width')width.appendChild(doc.createTextNode(str(im_width)))height = doc.createElement('height')height.appendChild(doc.createTextNode(str(im_height)))depth = doc.createElement('depth')depth.appendChild(doc.createTextNode(str(im_depth)))size.appendChild(width)size.appendChild(height)size.appendChild(depth)segmented = doc.createElement('segmented')segmented.appendChild(doc.createTextNode('0'))annotation.appendChild(segmented)for i in range(object_num):objects = doc.createElement('object')annotation.appendChild(objects)object_name = doc.createElement('name')object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][5])]))objects.appendChild(object_name)pose = doc.createElement('pose')pose.appendChild(doc.createTextNode('Unspecified'))objects.appendChild(pose)truncated = doc.createElement('truncated')truncated.appendChild(doc.createTextNode('1'))objects.appendChild(truncated)difficult = doc.createElement('difficult')difficult.appendChild(doc.createTextNode('0'))objects.appendChild(difficult)bndbox = doc.createElement('bndbox')objects.appendChild(bndbox)if hbb:x0 = doc.createElement('xmin')x0.appendChild(doc.createTextNode(str((objects_axis[i][0]))))bndbox.appendChild(x0)y0 = doc.createElement('ymin')y0.appendChild(doc.createTextNode(str((objects_axis[i][1]))))bndbox.appendChild(y0)x1 = doc.createElement('xmax')x1.appendChild(doc.createTextNode(str((objects_axis[i][2]))))bndbox.appendChild(x1)y1 = doc.createElement('ymax')y1.appendChild(doc.createTextNode(str((objects_axis[i][3]))))bndbox.appendChild(y1)else:x0 = doc.createElement('x0')x0.appendChild(doc.createTextNode(str((objects_axis[i][0]))))bndbox.appendChild(x0)y0 = doc.createElement('y0')y0.appendChild(doc.createTextNode(str((objects_axis[i][1]))))bndbox.appendChild(y0)x1 = doc.createElement('x1')x1.appendChild(doc.createTextNode(str((objects_axis[i][2]))))bndbox.appendChild(x1)y1 = doc.createElement('y1')y1.appendChild(doc.createTextNode(str((objects_axis[i][3]))))bndbox.appendChild(y1)x2 = doc.createElement('x2')x2.appendChild(doc.createTextNode(str((objects_axis[i][4]))))bndbox.appendChild(x2)y2 = doc.createElement('y2')y2.appendChild(doc.createTextNode(str((objects_axis[i][5]))))bndbox.appendChild(y2)x3 = doc.createElement('x3')x3.appendChild(doc.createTextNode(str((objects_axis[i][6]))))bndbox.appendChild(x3)y3 = doc.createElement('y3')y3.appendChild(doc.createTextNode(str((objects_axis[i][7]))))bndbox.appendChild(y3)f = open(save_path, 'w')f.write(doc.toprettyxml(indent=''))f.close()def clip_image(name_new, path_new_xml, img_old, data_new, boxes_all):name_new, x1, y1, x2, y2 = data_newif len(boxes_all) > 0:shape = img_old.shapewidth, height = x2 - x1, y2 - y1# print(width, height)assert (width == 600 and height == 600)boxes = copy.deepcopy(boxes_all)boxes_new = np.zeros_like(boxes_all)top_left_col, top_left_row = max(x1, 0), max(y1, 0)bottom_right_col, bottom_right_row = max(x2, 0), max(y2, 0)img_new = img_old[top_left_row:bottom_right_row, top_left_col:bottom_right_col]boxes_new[:, 0] = boxes[:, 0] - top_left_colboxes_new[:, 2] = boxes[:, 0] + boxes[:, 2] - top_left_colboxes_new[:, 4] = boxes[:, 4]boxes_new[:, 1] = boxes[:, 1] - top_left_rowboxes_new[:, 3] = boxes[:, 1] + boxes[:, 3] - top_left_rowboxes_new[:, 5] = boxes[:, 5]center_y = 0.5 * (boxes_new[:, 1] + boxes_new[:, 3])center_x = 0.5 * (boxes_new[:, 0] + boxes_new[:, 2])cond1 = np.intersect1d(np.where(center_y[:] >= 0)[0],np.where(center_x[:] >= 0)[0])cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0],np.where(center_x[:] <= (bottom_right_col - top_left_col))[0])idx = np.intersect1d(cond1, cond2)if len(idx) > 0:save_to_xml(path_new_xml, img_new.shape[1], img_new.shape[0], boxes_new[idx,:], class_list,name_new + '.jpg')if img_new.shape[0] > 5 and img_new.shape[1] > 5:img = os.path.join(out_images_dir, name_new + '.jpg')cv2.imwrite(img, img_new)# return img_new.shape[1], img_new.shape[0], boxes_newif __name__ == '__main__':for i in [i for i in os.listdir(input_path) if i[-4:] == '.txt']:print(i)with open(os.path.join(input_path, i), 'r', encoding='utf8') as f:lines = [i.split() for i in f.readlines()]drawer = {}for line in lines:name_new, x1, y1, x2, y2 = linex1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)name_old = name_new[:6]if name_old in drawer:drawer[name_old].append((name_new, x1, y1, x2, y2))else:drawer[name_old] = [(name_new, x1, y1, x2, y2)]for name_old, datas in drawer.items():path_old = os.path.join(raw_images_dir, name_old + '.jpg')img_data = misc.imread(path_old)txt_data = open(os.path.join(raw_label_dir, name_old + '.txt'),'r').readlines()boxes = format_label(txt_data)for data_new in datas:name_new, x1, y1, x2, y2 = data_newpath_new = os.path.join(out_images_dir, name_new + '.jpg')path_new_xml = os.path.join(out_label_dir,name_new + '.xml')clip_image(name_new, path_new_xml, img_data, data_new, boxes)# save_to_xml(path_new_xml, w, h, boxes_new, class_list,#             name_new + '.jpg')

（3）可视化裁剪的标注格式是否正确

xml_drawbox_cluster.py

import os
import os.path
import numpy as np
import xml.etree.ElementTree as xmlET
from PIL import Image, ImageDrawclasses = ('__background__', # always index 0'ignored regions','pedestrian','people','bicycle','car','van','truck','tricycle','awning-tricycle','bus','motor','others')# 把下面的路径改为自己的路径即可
file_path_img = '/home/jjliao/Visdrone_yolo_cluster/VisDrone2019-DET-train/images_cluster'
file_path_xml = '/home/jjliao/Visdrone_yolo_cluster/VisDrone2019-DET-train/annotations_cluster_xml'
save_file_path = '/home/jjliao/Visdrone_yolo_cluster/VisDrone2019-DET-train/visual_cluster'pathDir = os.listdir(file_path_xml)
for idx in range(len(pathDir)): filename = pathDir[idx]tree = xmlET.parse(os.path.join(file_path_xml, filename))objs = tree.findall('object')        num_objs = len(objs)boxes = np.zeros((num_objs, 5), dtype=np.uint16)for ix, obj in enumerate(objs):bbox = obj.find('bndbox')# Make pixel indexes 0-basedx1 = max(float(bbox.find('xmin').text), 0) y1 = max(float(bbox.find('ymin').text), 0) x2 = float(bbox.find('xmax').text) y2 = float(bbox.find('ymax').text)cla = obj.find('name').textlabel = classes.index(cla)boxes[ix, 0:4] = [x1, y1, x2, y2]boxes[ix, 4] = labelimage_name = os.path.splitext(filename)[0]# if image_name == '10380201':#     import pdb#     pdb.set_trace()img = Image.open(os.path.join(file_path_img, image_name + '.jpg'))draw = ImageDraw.Draw(img)for ix in range(len(boxes)):xmin = int(boxes[ix, 0])ymin = int(boxes[ix, 1])xmax = int(boxes[ix, 2])ymax = int(boxes[ix, 3])draw.rectangle([xmin, ymin, xmax, ymax], outline=(255, 0, 0))draw.text([xmin, ymin], classes[boxes[ix, 4]], (255, 0, 0))img.save(os.path.join(save_file_path, image_name + '.jpg'))

3. 将裁剪后的图片用yolov4进行预测，得到result.json文件。再将预测出来的目标进行拼接回原图。

splice.py

# coding:utf-8
import json
import shutil
import cv2
import osjson_old_path = "/home/jjliao/code/PyTorch_yolov4_visdrone_cluster/results_cluster.json"
json_new_path = "/home/jjliao/code/PyTorch_yolov4_visdrone_cluster/results_cluster_new.json"
txt_path = "/home/jjliao/cluster/cluster_output/"def select(json_old, json_new, txt):json_old = open(json_old, 'r')infos = json.load(json_old)new_json = []for i in [i for i in os.listdir(txt) if i[-4:] == '.txt']:print(i)with open(os.path.join(txt, i), 'r', encoding='utf8') as f:lines = [i.split() for i in f.readlines()]for line in lines:name, x1, y1, x2, y2 = line # x1, y1, x2, y2x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)  for j in infos:images = j["image_id"]if images == name:x1_hat, y1_hat = int(j['bbox'][0]), int(j['bbox'][1])j['bbox'][0], j['bbox'][1] = x1 + x1_hat, y1 + y1_hatj["image_id"] = images[:-2]if os.path.exists(json_new_path):with open(json_new_path, 'w', encoding='utf-8') as ff:new_json.append(j)new = json.dump(new_json, ff, indent=4)ff.close()print(len(new_json))if __name__ == "__main__":select(json_old_path, json_new_path, txt_path)

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