pytorch-cpn可视化标注信息

pytorch-cpn项目的代码对coco标注进行了重新组装，但是基本的标注内容并没有改变，关键点的坐标依然是以图片左上角为坐标原点,标注格式依然为[x1,y1,v1，x2,y2,v2,…,x17,y17,v17]。在其mscocoMulti.py文件中，以下为核心可视化代码的部分。使用以下代码，即可实现原图和标注在同一个plt画布中显示。

   		plt.figure()
        c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
        plt.plot(x[v > 0], y[v > 0], 'o', markersize=10, markerfacecolor=c, markeredgecolor='k', markeredgewidth=2)
        img = Image.open(os.path.join(img_folder, image_name))
        plt.imshow(img)
        plt.show()

完整加入以下代码，即可可视化第一个标注信息。

if __name__ == '__main__':
    import argparse
    parser = argparse.ArgumentParser(description='PyTorch CPN Training')
    parser.add_argument('-j', '--workers', default=12, type=int, metavar='N',
                        help='number of data loading workers (default: 12)')
    parser.add_argument('-g', '--num_gpus', default=1, type=int, metavar='N',
                        help='number of GPU to use (default: 1)')
    parser.add_argument('--epochs', default=32, type=int, metavar='N',
                        help='number of total epochs to run (default: 32)')
    parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                        help='manual epoch number (useful on restarts)')
    parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
                        help='path to save checkpoint (default: checkpoint)')
    parser.add_argument('--resume', default='', type=str, metavar='PATH',
                        help='path to latest checkpoint')
    args = parser.parse_args()

    import sys

    sys.path.append('../256.192.model')
    from config import cfg
    from PIL import Image
    import matplotlib.pyplot as plt

    img_folder =  cfg.img_path
    with open(cfg.gt_path) as anno_file:
        anno = json.load(anno_file)
        for i, item in enumerate(anno):
            print(i, item)
            if (i == 5):
                print(item.keys())
                break;

        num_class = 17
        a = anno[0]
        image_name = a['imgInfo']['img_paths']
        points = np.array(a['unit']['keypoints']).reshape(num_class, 3).astype(np.float32)
        gt_bbox = a['unit']['GT_bbox']
        points = points.flatten()
        x = points[0::3]
        y = points[1::3]
        v = points[2::3]
        print(points)
        print(x)
        print(y)
        print(v)
        print(x[v>0])
        print(y[v>0])
        plt.figure()
        c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
        plt.plot(x[v > 0], y[v > 0], 'o', markersize=10, markerfacecolor=c, markeredgecolor='k', markeredgewidth=2)
        img = Image.open(os.path.join(img_folder, image_name))
        plt.imshow(img)
        plt.show()

本文地址：https://blog.csdn.net/qq_37025073/article/details/107350206