【程序】JSON文件：使用COCO API对标注框bbox的单独可视化

import cv2
import random
import json, os
from pycocotools.coco import COCO
from skimage import io
from matplotlib import pyplot as plt

train_json = './WIDER_train/aaa/trainset.json'
train_path = ''

def visualization_bbox2(num_image, json_path, img_path):
    coco = COCO(json_path)
    img_annIds=coco.getAnnIds(0)
    # print(img_annIds)
    list_imgIds = coco.getImgIds(catIds=1) # 获取含有该给定类别的所有图片的id
    img = coco.loadImgs(list_imgIds[num_image-1])[0]  # 获取满足上述要求，并给定显示第num幅image对应的dict
    image = io.imread(img_path + img['file_name'])  # 读取图像
    print(coco.loadAnns(0))
    image_name = img['file_name'] # 读取图像名字
    image_id = img['id'] # 读取图像id
    for i in range(len(img_annIds)):
        print(i)
        print("***",len(img_annIds))
        x, y, w, h = coco.loadAnns(i)[0]['bbox']  # 读取边框
        image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 255), 2)

    plt.rcParams['figure.figsize'] = (20.0, 20.0)
    # 此处的20.0是由于我的图片是2000*2000，目前还没去研究怎么利用plt自动分辨率。
    plt.imshow(image)
    plt.show()


if __name__ == "__main__":
    visualization_bbox2(0, train_json, train_path)