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yolo2coco.py
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import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse
# python yolo2coco.py --root_dir VisDrone2019-DET-train --save_path train.json
# python yolo2coco.py --root_dir VisDrone2019-DET-val --save_path val.json
# python yolo2coco.py --root_dir VisDrone2019-DET-test-dev --save_path test.json
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', default='./dataset/valid',type=str, help="root path of images and labels, include ./images and ./labels and classes.txt")
parser.add_argument('--save_path', type=str,default='./valid.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
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')
# 读取图像的宽和高
im = cv2.imread(os.path.join(root_path, 'images/') + index)
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(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)