json格式数据集转yolo txt格式

json文件格式代码txt文件

网上相关代码大多针对COCO数据集的，但是有些非公共数据集没有coco相关的文件，只有每张图片对应的json文件，故本文代码针对每张图片的json文件转换为yolo需要的txt文件。

json文件格式

{"shape": [{"label": "class1","boxes": [367,209,537,339],"points": null},{"label": "class1","boxes": [73,597,230,807],"points": null},{"label": "class1","boxes": [410,680,634,888],"points": null},{"label": "class1","boxes": [705,389,835,753],"points": null},{"label": "class1","boxes": [1073,639,1329,959],"points": null},{"label": "class1","boxes": [1464,499,1616,801],"points": null},{"label": "class1","boxes": [1535,385,1742,513],"points": null},{"label": "class1","boxes": [1321,48,1573,178],"points": null},{"label": "class1","boxes": [1618,120,1757,247],"points": null},{"label": "class1","boxes": [1745,159,1877,386],"points": null},{"label": "class1","boxes": [1810,685,1962,793],"points": null},{"label": "class1","boxes": [1838,770,1972,918],"points": null},{"label": "class1","boxes": [1113,950,1391,1175],"points": null},{"label": "class1","boxes": [83,943,192,1156],"points": null},{"label": "class1","boxes": [110,1132,167,1307],"points": null},{"label": "class1","boxes": [129,1102,216,1302],"points": null},{"label": "class1","boxes": [164,1143,253,1350],"points": null},{"label": "class1","boxes": [218,1137,316,1372],"points": null},{"label": "class1","boxes": [286,1142,383,1369],"points": null},{"label": "class1","boxes": [323,1170,445,1426],"points": null},{"label": "class1","boxes": [359,1172,556,1437],"points": null},{"label": "class1","boxes": [530,1189,648,1447],"points": null},{"label": "class1","boxes": [592,1180,811,1458],"points": null},{"label": "class1","boxes": [651,1097,980,1243],"points": null},{"label": "class1","boxes": [848,1021,1115,1283],"points": null},{"label": "class1","boxes": [1007,1101,1284,1404],"points": null},{"label": "class1","boxes": [1042,1204,1278,1474],"points": null},{"label": "class1","boxes": [243,974,435,1117],"points": null}],"imagePath": "1.jpg"}

代码

import jsonimport cv2import osall_classes={'class1':0,'class2':1} ##类别列表，与训练配置文件中的顺序保持一致savepath="./labels/" #txt文件存放位置jsonpath="./train_json/" #json文件位置imgpath="./train_img/" #图片位置，因为我的json文件中没有图片size，故需要读取图片得到sizejson_files=os.listdir(jsonpath)for i in json_files:infile=jsonpath+iwith open(infile,'r') as load_f:load_dict = json.load(load_f) #打开每个json文件outfile=open(savepath+load_dict["imagePath"][:-4]+'.txt','w')img_path=imgpath+load_dict["imagePath"]img=cv2.imread(img_path)size=img.shapeh_img,w_img=size[0],size[1] #得到图片sizefor item in load_dict["shape"]:print(item)label_int=all_classes[item['label']]print(infile)if not item['boxes']:continuex1,y1,x2,y2=item['boxes']print(label_int)print(x1,y1,x2,y2)x_center=(x1+x2)/2/w_imgy_center=(y1+y2)/2/h_imgw=(x2-x1)/w_imgh=(y2-y1)/h_imgoutfile.write(str(label_int)+" "+str(x_center)+" "+str(y_center)+" "+str(w)+" "+str(h)+'\n')outfile.close()