89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
import os
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import cv2
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import json
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from tqdm import tqdm
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from sklearn.model_selection import train_test_split
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import argparse
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# visdrone2019
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classes = ['pedestrain', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
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parser = argparse.ArgumentParser()
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parser.add_argument('--image_path', default='',type=str, help="path of images")
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parser.add_argument('--label_path', default='',type=str, help="path of labels .txt")
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parser.add_argument('--save_path', default='data.json', type=str, help="if not split the dataset, give a path to a json file")
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arg = parser.parse_args()
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def yolo2coco(arg):
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print("Loading data from ", arg.image_path, arg.label_path)
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assert os.path.exists(arg.image_path)
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assert os.path.exists(arg.label_path)
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originImagesDir = arg.image_path
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originLabelsDir = arg.label_path
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# images dir name
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indexes = os.listdir(originImagesDir)
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dataset = {'categories': [], 'annotations': [], 'images': []}
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for i, cls in enumerate(classes, 0):
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dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})
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# 标注的id
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ann_id_cnt = 0
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for k, index in enumerate(tqdm(indexes)):
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# 支持 png jpg 格式的图片.
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txtFile = f'{index[:index.rfind(".")]}.txt'
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stem = index[:index.rfind(".")]
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# 读取图像的宽和高
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try:
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im = cv2.imread(os.path.join(originImagesDir, index))
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height, width, _ = im.shape
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except Exception as e:
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print(f'{os.path.join(originImagesDir, index)} read error.\nerror:{e}')
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# 添加图像的信息
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if not os.path.exists(os.path.join(originLabelsDir, txtFile)):
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# 如没标签，跳过，只保留图片信息.
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continue
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dataset['images'].append({'file_name': index,
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'id': stem,
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'width': width,
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'height': height})
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with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:
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labelList = fr.readlines()
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for label in labelList:
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label = label.strip().split()
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x = float(label[1])
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y = float(label[2])
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w = float(label[3])
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h = float(label[4])
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# convert x,y,w,h to x1,y1,x2,y2
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H, W, _ = im.shape
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x1 = (x - w / 2) * W
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y1 = (y - h / 2) * H
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x2 = (x + w / 2) * W
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y2 = (y + h / 2) * H
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# 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。
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cls_id = int(label[0])
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width = max(0, x2 - x1)
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height = max(0, y2 - y1)
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dataset['annotations'].append({
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'area': width * height,
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'bbox': [x1, y1, width, height],
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'category_id': cls_id,
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'id': ann_id_cnt,
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'image_id': stem,
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'iscrowd': 0,
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# mask, 矩形是从左上角点按顺时针的四个顶点
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'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]
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})
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ann_id_cnt += 1
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# 保存结果
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with open(arg.save_path, 'w') as f:
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json.dump(dataset, f)
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print('Save annotation to {}'.format(arg.save_path))
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if __name__ == "__main__":
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yolo2coco(arg) |