kyxt
/
code_test

"""
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)