""" 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 import numpy as np parser = argparse.ArgumentParser() parser.add_argument('--root_dir', default='/home/shanwei-luo/teamdata/anomaly_detection_active_learning/data0422/smd12_11_12_hard_score_04/train',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(arg): root_path = arg.root_dir 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 ans = 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') print(txtFile) # 读取图像的宽和高 #im = cv2.imread(os.path.join(root_path, 'images/') + index) im = cv2.imdecode(np.fromfile(os.path.join(root_path, 'images/') + index, dtype=np.uint8), cv2.IMREAD_COLOR) 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.readline() labelList = labelList.strip().split(" ") if len(labelList)==1: continue labelList = labelList[1:] for label in labelList: label = label.split(",") cls_id = int(label[4]) - 1 x1 = float(label[0]) y1 = float(label[1]) x2 = float(label[2]) y2 = float(label[3]) # convert x,y,w,h to x1,y1,x2,y2 H, W, _ = im.shape if x1<0: x1 = 0 elif x1>W: x1 = W-1 if x2<0: x2 = 0 elif x2>W: x2 = W-1 if y1<0: y1 = 0 elif y1>H: y1 = H-1 if y2<0: y2 = 0 elif y2>H: y2 = H-1 # 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。 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 print(ann_id_cnt) 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(arg.save_path)) with open(json_name, 'w') as f: json.dump(dataset, f) print('Save annotation to {}'.format(json_name)) if __name__ == "__main__": yolo2coco(arg)