使用pytorch加载并读取COCO数据集的详细操作

# 元祖
a = (1, 2)
# 字典
b = {\'username\': \'peipeiwang\', \'code\': \'111\'}
# 数组
c = [1, 2, 3]
print(a[0])
print(c[0])
print(b[\"username\"])

import torchvision
from PIL import ImageDraw
# 导入coco 2017 验证集和对应annotations
coco_dataset = torchvision.datasets.CocoDetection(root=\"COCO_dataset_val_2017/val2017\",
                                                  annFile=\"COCO_dataset_val_2017/annotations_trainval2017/annotations/instances_val2017.json\")
# 图像和annotation分开读取
image, info = coco_dataset[0]
# ImageDraw 画图工具
image_handler = ImageDraw.ImageDraw(image)
for annotation in info:
    # bbox为检测框的位置坐标
    x_min, y_min, width, height = annotation[\'bbox\']
    # ((), ())分别为左上角的坐标对和右上角的坐标对，image_handler.rectangle是指在图片是绘制方框
    image_handler.rectangle(((x_min, y_min), (x_min + width, y_min + height)))
image.show()