YOLO系列算法训练数据格式转换脚本

""" YOLO 格式的数据集转化为 COCO 格式的数据集 --root_dir 输入根路径 --save_path 保存文件的名字(没有random_split时使用) --random_split 有则会随机划分数据集，然后再分别保存为3个文件。 --split_by_file 按照 ./train.txt ./val.txt ./test.txt 来对数据集进行划分。 """ import os import cv2 import json from tqdm import tqdm #from sklearn.model_selection import train_test_split import argparse # parser = argparse.ArgumentParser() # parser.add_argument('--root_dir', default='./data',type=str, help="root path of images and labels, include ./images and ./labels and classes.txt") # parser.add_argument('--save_path', type=str,default='./train.json', help="if not split the dataset, give a path to a json file") # parser.add_argument('--random_split', action='store_true', help="random split the dataset, default ratio is 8:1:1") # parser.add_argument('--split_by_file', action='store_true', help="define how to split the dataset, include ./train.txt ./val.txt ./test.txt ") # arg = parser.parse_args() # def train_test_val_split_random(img_paths,ratio_train=0.8,ratio_test=0.1,ratio_val=0.1): # # 这里可以修改数据集划分的比例。 # assert int(ratio_train+ratio_test+ratio_val) == 1 # train_img, middle_img = train_test_split(img_paths,test_size=1-ratio_train, random_state=233) # ratio=ratio_val/(1-ratio_train) # val_img, test_img =train_test_split(middle_img,test_size=ratio, random_state=233) # print("NUMS of train:val:test = {}:{}:{}".format(len(train_img), len(val_img), len(test_img))) # return train_img, val_img, test_img # def train_test_val_split_by_files(img_paths, root_dir): # # 根据文件 train.txt, val.txt, test.txt（里面写的都是对应集合的图片名字） 来定义训练集、验证集和测试集 # phases = ['train', 'val', 'test'] # img_split = [] # for p in phases: # define_path = os.path.join(root_dir, f'{p}.txt') # print(f'Read {p} dataset definition from {define_path}') # assert os.path.exists(define_path) # with open(define_path, 'r') as f: # img_paths = f.readlines() # # img_paths = [os.path.split(img_path.strip())[1] for img_path in img_paths] # NOTE 取消这句备注可以读取绝对地址。 # img_split.append(img_paths) # return img_split[0], img_split[1], img_split[2] def yolo2coco(): #root_path = arg.root_dir root_path = "D:\\vision\\driver-action\\yolo_data" print("Loading data from ",root_path) assert os.path.exists(root_path) originLabelsDir = os.path.join(root_path, 'labels') originImagesDir = os.path.join(root_path, 'images') with open(os.path.join(root_path, 'classes.txt')) as f: classes = f.read().strip().split() # images dir name indexes = os.listdir(originImagesDir) # if arg.random_split or arg.split_by_file: # # 用于保存所有数据的图片信息和标注信息 # train_dataset = {'categories': [], 'annotations': [], 'images': []} # val_dataset = {'categories': [], 'annotations': [], 'images': []} # test_dataset = {'categories': [], 'annotations': [], 'images': []} # # 建立类别标签和数字id的对应关系, 类别id从0开始。 # for i, cls in enumerate(classes, 0): # train_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'}) # val_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'}) # test_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'}) # if arg.random_split: # print("spliting mode: random split") # train_img, val_img, test_img = train_test_val_split_random(indexes,0.8,0.1,0.1) # elif arg.split_by_file: # print("spliting mode: split by files") # train_img, val_img, test_img = train_test_val_split_by_files(indexes, root_path) #else: dataset = {'categories': [], 'annotations': [], 'images': []} for i, cls in enumerate(classes, 0): dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'}) # 标注的id ann_id_cnt = 0 for k, index in enumerate(tqdm(indexes)): # 支持 png jpg 格式的图片。 txtFile = index.replace('images','txt').replace('.jpg','.txt').replace('.png','.txt').replace('.jpeg','.txt') # 读取图像的宽和高 impath =root_path+'\\images\\' + index print(impath) im = cv2.imread(impath) height, width, _ = im.shape # if arg.random_split or arg.split_by_file: # # 切换dataset的引用对象，从而划分数据集 # if index in train_img: # dataset = train_dataset # elif index in val_img: # dataset = val_dataset # elif index in test_img: # dataset = test_dataset # 添加图像的信息 dataset['images'].append({'file_name': index, 'id': k, 'width': width, 'height': height}) if not os.path.exists(os.path.join(originLabelsDir, txtFile)): # 如没标签，跳过，只保留图片信息。 continue with open(os.path.join(originLabelsDir, txtFile), 'r') as fr: labelList = fr.readlines() for label in labelList: label = label.strip().split() x = float(label[1]) y = float(label[2]) w = float(label[3]) h = float(label[4]) # convert x,y,w,h to x1,y1,x2,y2 H, W, _ = im.shape x1 = (x - w / 2) * W y1 = (y - h / 2) * H x2 = (x + w / 2) * W y2 = (y + h / 2) * H # 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。 cls_id = int(label[0]) width = max(0, x2 - x1) height = max(0, y2 - y1) dataset['annotations'].append({ 'area': width * height, 'bbox': [x1, y1, width, height], 'category_id': cls_id, 'id': ann_id_cnt, 'image_id': k, 'iscrowd': 0, # mask, 矩形是从左上角点按顺时针的四个顶点 'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]] }) ann_id_cnt += 1 # 保存结果 folder = os.path.join(root_path, 'annotations') if not os.path.exists(folder): os.makedirs(folder) # if arg.random_split or arg.split_by_file: # for phase in ['train','val','test']: # json_name = os.path.join(root_path, 'annotations/{}.json'.format(phase)) # with open(json_name, 'w') as f: # if phase == 'train': # json.dump(train_dataset, f) # elif phase == 'val': # json.dump(val_dataset, f) # elif phase == 'test': # json.dump(test_dataset, f) # print('Save annotation to {}'.format(json_name)) # else: json_name = os.path.join(root_path, 'annotations/{}'.format("train.json")) with open(json_name, 'w') as f: json.dump(dataset, f) print('Save annotation to {}'.format(json_name)) if __name__ == "__main__": yolo2coco()