1、YOLO格式转JSON格式
2、 VOC格式转YOLO格式
3、YOLO格式转VOC格式
4、JSON格式转YOLO格式
1、YOLO格式转JSON格式
""" YOLO转JSON格式 """ import os import cv2 import json import argparse from tqdm import tqdm import xml.etree.ElementTree as ET COCO_DICT=['images','annotations','categories'] IMAGES_DICT=['file_name','height','width','id'] ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id'] CATEGORIES_DICT=['id','name'] #自定义YOLO数据集类别 YOLO_CATEGORIES=['Cahua', 'Crazing'] parser=argparse.ArgumentParser(description='2COCO') #需修改：图片所在路径 parser.add_argument('--image_path',type=str,default=r'D:\desk\defect_dataset\images\test/',help='config file') #需修改：标签所在路径 parser.add_argument('--annotation_path',type=str,default=r'D:\desk\defect_dataset\labels\test/',help='config file') parser.add_argument('--dataset',type=str,default='YOLO',help='config file') #需修改：JSON文件保存位置 parser.add_argument('--save',type=str,default=r'D:\desk\defect_dataset\JASON/instances_test2017.json',help='config file') args=parser.parse_args() def load_json(path): with open(path,'r') as f: json_dict=json.load(f) for i in json_dict: print(i) print(json_dict['annotations']) def save_json(dict,path): print('SAVE_JSON...') with open(path,'w') as f: json.dump(dict,f) print('SUCCESSFUL_SAVE_JSON:',path) def load_image(path): img=cv2.imread(path) return img.shape[0],img.shape[1] def generate_categories_dict(category): #ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id'] print('GENERATE_CATEGORIES_DICT...') return [{CATEGORIES_DICT[0]:category.index(x)+1,CATEGORIES_DICT[1]:x} for x in category] #CATEGORIES_DICT=['id','name'] def generate_images_dict(imagelist,image_path,start_image_id=11725): #IMAGES_DICT=['file_name','height','width','id'] print('GENERATE_IMAGES_DICT...') images_dict=[] with tqdm(total=len(imagelist)) as load_bar: for x in imagelist: #x就是图片的名称 #print(start_image_id) dict={IMAGES_DICT[0]:x,IMAGES_DICT[1]:load_image(image_path+x)[0],\ IMAGES_DICT[2]:load_image(image_path+x)[1],IMAGES_DICT[3]:imagelist.index(x)+start_image_id} load_bar.update(1) images_dict.append(dict) return images_dict def YOLO_Dataset(image_path,annotation_path,start_image_id=0,start_id=0): categories_dict=generate_categories_dict(YOLO_CATEGORIES) imgname=os.listdir(image_path) images_dict=generate_images_dict(imgname,image_path) print('GENERATE_ANNOTATIONS_DICT...') annotations_dict=[] id=start_id for i in images_dict: image_id=i['id'] image_name=i['file_name'] W,H=i['width'],i['height'] annotation_txt=annotation_path+image_name.split('.')[0]+'.txt' txt=open(annotation_txt,'r') lines=txt.readlines() for j in lines: category_id=int(j.split(' ')[0])+1 category=YOLO_CATEGORIES x=float(j.split(' ')[1]) y=float(j.split(' ')[2]) w=float(j.split(' ')[3]) h=float(j.split(' ')[4]) x_min=(x-w/2)*W y_min=(y-h/2)*H w=w*W h=h*H area=w*h bbox=[x_min,y_min,w,h] dict={'image_id':image_id,'iscrowd':0,'area':area,'bbox':bbox,'category_id':category_id,'id':id} annotations_dict.append(dict) id=id+1 print('SUCCESSFUL_GENERATE_YOLO_JSON') return {COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict} if __name__=='__main__': dataset=args.dataset #数据集名字 save=args.save #json的保存路径 image_path=args.image_path #对于coco是图片的路径 annotation_path=args.annotation_path #coco的annotation路径 if dataset=='YOLO': json_dict=YOLO_Dataset(image_path,annotation_path,0) save_json(json_dict,save)
上述代码中仅需要修改以下部分，再运行即可完成YOLO格式转换为JSON格式。
23行 #自定义YOLO数据集类别 YOLO_CATEGORIES=['Cahua', 'Crazing'] 复制27行 #需修改：图片所在路径 parser.add_argument('--image_path',type=str,default=r'D:\desk\defect_dataset\images\test/',help='config file') 复制30行 #需修改：标签所在路径 parser.add_argument('--annotation_path',type=str,default=r'D:\desk\defect_dataset\labels\test/',help='config file') 复制35行 parser.add_argument('--save',type=str,default=r'D:\desk\defect_dataset\JASON/instances_test2017.json',help='config file') 复制
2、 VOC格式转YOLO格式
""" VOC转YOLO格式 """ import xml.etree.ElementTree as ET import os, cv2 import numpy as np from os import listdir from os.path import join #需修改，添加数据集的类别 classes = [] def convert(size, box): dw = 1. / (size[0]) dh = 1. / (size[1]) x = (box[0] + box[1]) / 2.0 - 1 y = (box[2] + box[3]) / 2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) def convert_annotation(xmlpath, xmlname): with open(xmlpath, "r", encoding='utf-8') as in_file: txtname = xmlname[:-4] + '.txt' txtfile = os.path.join(txtpath, txtname) tree = ET.parse(in_file) root = tree.getroot() filename = root.find('filename') img = cv2.imdecode(np.fromfile('{}/{}.{}'.format(imgpath, xmlname[:-4], postfix), np.uint8), cv2.IMREAD_COLOR) h, w = img.shape[:2] res = [] for obj in root.iter('object'): cls = obj.find('name').text if cls not in classes: classes.append(cls) cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) bb = convert((w, h), b) res.append(str(cls_id) + " " + " ".join([str(a) for a in bb])) if len(res) != 0: with open(txtfile, 'w+') as f: f.write('\n'.join(res)) if __name__ == "__main__": #确保图片后缀为统一的jpg格式 postfix = 'jpg' #需修改，改为自己的图片所在位置 imgpath = r'D:\desk\defect_dataset\IMG' #需修改，改为自己的VOC标签格式所在位置 xmlpath = r'D:\desk\defect_dataset\VOC' #需修改，改为自己的想保存YOLO标签格式所在位置 txtpath = r'D:\desk\defect_dataset\TXT' if not os.path.exists(txtpath): os.makedirs(txtpath, exist_ok=True) list = os.listdir(xmlpath) error_file_list = [] for i in range(0, len(list)): try: path = os.path.join(xmlpath, list[i]) if ('.xml' in path) or ('.XML' in path): convert_annotation(path, list[i]) print(f'file {list[i]} convert success.') else: print(f'file {list[i]} is not xml format.') except Exception as e: print(f'file {list[i]} convert error.') print(f'error message:\n{e}') error_file_list.append(list[i]) print(f'this file convert failure\n{error_file_list}') print(f'Dataset Classes:{classes}')
上述代码中仅需要修改以下部分，再运行即可完成VOC格式转换为YOLO格式
7行 # 需修改，添加数据集的类别 classes = []复制54行 # 需修改，改为自己的图片所在位置 imgpath = r'D:\desk\defect_dataset\IMG' 57行 # 需修改，改为自己的VOC标签格式所在位置 xmlpath = r'D:\desk\defect_dataset\VOC' 60行 # 需修改，改为自己的想保存YOLO标签格式所在位置 txtpath = r'D:\desk\defect_dataset\TXT' 复制
3、YOLO格式转VOC格式
""" YOLO转VOC格式 """ from xml.dom.minidom import Document import os import cv2 def makexml(picPath, txtPath, xmlPath): # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径 #需修改，数据集类别，注意一一对应 dic = {'0': "crazing", '1': "patches", '2' :'inclusion', '3': "pitted_surface", '4': "rolled-in_scale", '5': "scratches", } files = os.listdir(txtPath) for i, name in enumerate(files): xmlBuilder = Document() annotation = xmlBuilder.createElement("annotation") # 创建annotation标签 xmlBuilder.appendChild(annotation) txtFile = open(txtPath + name) txtList = txtFile.readlines() img = cv2.imread(picPath + name[0:-4] + ".jpg") Pheight, Pwidth, Pdepth = img.shape folder = xmlBuilder.createElement("folder") # folder标签 foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset") folder.appendChild(foldercontent) annotation.appendChild(folder) # folder标签结束 filename = xmlBuilder.createElement("filename") # filename标签 filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg") filename.appendChild(filenamecontent) annotation.appendChild(filename) # filename标签结束 size = xmlBuilder.createElement("size") # size标签 width = xmlBuilder.createElement("width") # size子标签width widthcontent = xmlBuilder.createTextNode(str(Pwidth)) width.appendChild(widthcontent) size.appendChild(width) # size子标签width结束 height = xmlBuilder.createElement("height") # size子标签height heightcontent = xmlBuilder.createTextNode(str(Pheight)) height.appendChild(heightcontent) size.appendChild(height) # size子标签height结束 depth = xmlBuilder.createElement("depth") # size子标签depth depthcontent = xmlBuilder.createTextNode(str(Pdepth)) depth.appendChild(depthcontent) size.appendChild(depth) # size子标签depth结束 annotation.appendChild(size) # size标签结束 for j in txtList: oneline = j.strip().split(" ") object = xmlBuilder.createElement("object") # object 标签 picname = xmlBuilder.createElement("name") # name标签 namecontent = xmlBuilder.createTextNode(dic[oneline[0]]) picname.appendChild(namecontent) object.appendChild(picname) # name标签结束 pose = xmlBuilder.createElement("pose") # pose标签 posecontent = xmlBuilder.createTextNode("Unspecified") pose.appendChild(posecontent) object.appendChild(pose) # pose标签结束 truncated = xmlBuilder.createElement("truncated") # truncated标签 truncatedContent = xmlBuilder.createTextNode("0") truncated.appendChild(truncatedContent) object.appendChild(truncated) # truncated标签结束 difficult = xmlBuilder.createElement("difficult") # difficult标签 difficultcontent = xmlBuilder.createTextNode("0") difficult.appendChild(difficultcontent) object.appendChild(difficult) # difficult标签结束 bndbox = xmlBuilder.createElement("bndbox") # bndbox标签 xmin = xmlBuilder.createElement("xmin") # xmin标签 mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth) xminContent = xmlBuilder.createTextNode(str(mathData)) xmin.appendChild(xminContent) bndbox.appendChild(xmin) # xmin标签结束 ymin = xmlBuilder.createElement("ymin") # ymin标签 mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight) yminContent = xmlBuilder.createTextNode(str(mathData)) ymin.appendChild(yminContent) bndbox.appendChild(ymin) # ymin标签结束 xmax = xmlBuilder.createElement("xmax") # xmax标签 mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth) xmaxContent = xmlBuilder.createTextNode(str(mathData)) xmax.appendChild(xmaxContent) bndbox.appendChild(xmax) # xmax标签结束 ymax = xmlBuilder.createElement("ymax") # ymax标签 mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight) ymaxContent = xmlBuilder.createTextNode(str(mathData)) ymax.appendChild(ymaxContent) bndbox.appendChild(ymax) # ymax标签结束 object.appendChild(bndbox) # bndbox标签结束 annotation.appendChild(object) # object标签结束 f = open(xmlPath + name[0:-4] + ".xml", 'w') xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8') f.close() if __name__ == "__main__": # 需修改，图片所在文件夹路径 picPath = r"D:\desk\defect_dataset\IMG/" # 需修改，YOLO标签所在文件夹路径 txtPath = r"D:\desk\defect_dataset\TXT/" # 需修改，VOC文件保存路径 xmlPath = r"D:\desk\defect_dataset\VOC/" makexml(picPath, txtPath, xmlPath)
上述代码中仅需要修改以下部分，再运行即可完成YOLO格式转换为VOC格式
11行 #需修改，数据集类别，注意一一对应 dic = {'0': "crazing", '1': "patches", '2' :'inclusion', '3': "pitted_surface", '4': "rolled-in_scale", '5': "scratches", }复制117行 # 需修改，图片所在文件夹路径 picPath = r"D:\desk\defect_dataset\IMG/" 120行 # 需修改，YOLO标签所在文件夹路径 txtPath = r"D:\desk\defect_dataset\TXT/" 123行 # 需修改，VOC文件保存路径 xmlPath = r"D:\desk\defect_dataset\VOC/"复制
4、JSON格式转YOLO格式
# COCO 格式的数据集转化为 YOLO 格式的数据集 # --json_path 输入的json文件路径 # --save_path 保存的文件夹名字，默认为当前目录下的labels。 import os import json from tqdm import tqdm import argparse parser = argparse.ArgumentParser() # 需修改，这里替换自己的json文件位置 parser.add_argument('--json_path', default=r'D:\desk\asssssssssssssss\try\json\instances_val2017.json', type=str, help="input: coco format(json)") # 需修改，这里输出的txt文件保存位置 parser.add_argument('--save_path', default=r'D:\desk\asssssssssssssss\try\txt', type=str, help="specify where to save the output dir of labels") # 需修改，这里设置json标签对应的图片文件位置 parser.add_argument('--img_path', default=r'D:\desk\asssssssssssssss\try\img', type=str, help="all img path") arg = parser.parse_args() def convert(size, box): dw = 1. / (size[0]) dh = 1. / (size[1]) x = box[0] + box[2] / 2.0 y = box[1] + box[3] / 2.0 w = box[2] h = box[3] # round函数确定(xmin, ymin, xmax, ymax)的小数位数 x = round(x * dw, 6) w = round(w * dw, 6) y = round(y * dh, 6) h = round(h * dh, 6) return (x, y, w, h) if __name__ == '__main__': json_file = arg.json_path # COCO Object Instance 类型的标注 ana_txt_save_path = arg.save_path # 保存的路径 data = json.load(open(json_file, 'r')) if not os.path.exists(ana_txt_save_path): os.makedirs(ana_txt_save_path) id_map = {} # coco数据集的id不连续！重新映射一下再输出！ with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f: # 写入classes.txt for i, category in enumerate(data['categories']): f.write(f"{category['name']}\n") id_map[category['id']] = i # print(id_map) # 这里需要根据自己的需要，更改写入图像相对路径的文件位置。 list_file = open(os.path.join(ana_txt_save_path, 'train2017.txt'), 'w') for img in tqdm(data['images']): filename = img["file_name"] img_width = img["width"] img_height = img["height"] img_id = img["id"] head, tail = os.path.splitext(filename) ana_txt_name = head + ".txt" # 对应的txt名字，与jpg一致 f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w') for ann in data['annotations']: if ann['image_id'] == img_id: box = convert((img_width, img_height), ann["bbox"]) f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3])) f_txt.close() list_file.write(os.path.join(arg.img_path,r'%s.jpg\n') % (head))#修改 list_file.close()
上述代码中仅需要修改以下部分，再运行即可完成JSON格式转换为YOLO格式
11行
# 需修改，这里替换自己的json文件位置
parser.add_argument('--json_path',
default=r'D:\desk\asssssssssssssss\try\json\instances_val2017.json', type=str,
help="input: coco format(json)")
16行
# 需修改，这里输出的txt文件保存位置
parser.add_argument('--save_path', default=r'D:\desk\asssssssssssssss\try\txt', type=str,
help="specify where to save the output dir of labels")
20行
# 需修改，这里设置json标签对应的图片文件位置
parser.add_argument('--img_path', default=r'D:\desk\asssssssssssssss\try\img', type=str,
help="all img path")
复制
代码仅用于本人学习使用
