【计算机视觉】DINOv2（视觉大模型）代码使用和测试（完整的源代码）

朴灿烈づ我的快乐病毒、 2023-10-13 20:29 86阅读 0赞

#### 文章目录 ####

*  一、环境部署
 *  二、导入原图
 *   *  2.1 使用vit\_s14的模型
 *  三、使用其他模型
 *   *  3.1 使用vit\_b14的模型
     *  3.2 使用vit\_l14的模型
     *  3.3 使用vit\_g14的模型

## 一、环境部署 ##

!git clone https://ghproxy.com/https://github.com/facebookresearch/dinov2.git

输出为：

Cloning into 'dinov2'...
    remote: Enumerating objects: 141, done.
    remote: Counting objects: 100% (96/96), done.
    remote: Compressing objects: 100% (74/74), done.  71% (53/74)
    remote: Total 141 (delta 40), reused 31 (delta 22), pack-reused 45
    Receiving objects: 100% (141/141), 101.01 KiB | 348.00 KiB/s, done.
    Resolving deltas: 100% (42/42), done.

命令是一个Git命令，用于克隆（Clone）名为"dinov2"的存储库。它使用了一个名为"ghproxy.com"的代理，用于加速GitHub的克隆操作。

!pip install -r /kaggle/working/dinov2/requirements.txt

![在这里插入图片描述][41246f9941a84ff1a3537bed08fb1a06.png]  
![在这里插入图片描述][2dc146b4420b425ea49a186881a66b82.png]

!pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple

![在这里插入图片描述][496d5b20e28f4eb6bbb6b88cda50c3a1.png]

## 二、导入原图 ##

%matplotlib inline
    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    
    image = mpimg.imread('/kaggle/input/demo-image/1 (4).png')
    
    plt.imshow(image)
    plt.axis('off')
    plt.show()
    
    # 输出图像尺寸
    print("图像尺寸：{} x {} x {}".format(image.shape[0], image.shape[1], image.shape[2]))

![在这里插入图片描述][d4ae712e63674b42b5c16e5b2c0db24d.png]

图像尺寸：1376 x 920 x 3

我们需要切换为output的路径：

import os
    
    input_path = "/kaggle/working/dinov2"
    os.chdir(input_path)

### 2.1 使用vit\_s14的模型 ###

以下是代码的逐行中文解读：

import torch
    import torchvision.transforms as T
    import matplotlib.pyplot as plt
    import numpy as np
    import matplotlib.image as mpimg 
    from PIL import Image
    from sklearn.decomposition import PCA
    import matplotlib
    
    # 设置补丁(patch)的高度和宽度
    patch_h = 75
    patch_w = 50
    # 特征维度
    feat_dim = 384
    
    # 定义图像转换操作
    transform = T.Compose([
        T.GaussianBlur(9, sigma=(0.1, 2.0)),  # 高斯模糊
        T.Resize((patch_h * 14, patch_w * 14)),  # 调整图像大小
        T.CenterCrop((patch_h * 14, patch_w * 14)),  # 中心裁剪
        T.ToTensor(),  # 转换为张量
        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),  # 标准化
    ])
    
    # 使用torch.hub加载dinov2_vits14模型并移至CUDA设备
    dinov2_vits14 = torch.hub.load('', 'dinov2_vits14', source='local').cuda()
    
    # 创建用于存储特征和图像张量的零张量
    features = torch.zeros(4, patch_h * patch_w, feat_dim)
    imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14).cuda()
    
    # 图像路径
    img_path = f'/kaggle/input/demo-image/1 (4).png'
    # 打开图像并转换为RGB模式
    img = Image.open(img_path).convert('RGB')
    # 对图像进行转换操作，并将其存储在imgs_tensor的第一个位置
    imgs_tensor[0] = transform(img)[:3]
    
    # 禁用梯度计算
    with torch.no_grad():
        # 将图像张量传递给dinov2_vits14模型获取特征
        features_dict = dinov2_vits14.forward_features(imgs_tensor)
        features = features_dict['x_norm_patchtokens']
        
    # 重塑特征形状为(4 * patch_h * patch_w, feat_dim)
    features = features.reshape(4 * patch_h * patch_w, feat_dim).cpu()
    
    # 创建PCA对象并拟合特征
    pca = PCA(n_components=3)
    pca.fit(features)
    
    # 对PCA转换后的特征进行归一化处理
    pca_features = pca.transform(features)
    pca_features[:, 0] = (pca_features[:, 0] - pca_features[:, 0].min()) / (pca_features[:, 0].max() - pca_features[:, 0].min())
    
    # 根据阈值进行前景和背景的区分
    pca_features_fg = pca_features[:, 0] > 0.3
    pca_features_bg = ~pca_features_fg
    
    # 查找背景特征的索引
    b = np.where(pca_features_bg)
    
    # 对前景特征再次进行PCA转换
    pca.fit(features[pca_features_fg])
    pca_features_rem = pca.transform(features[pca_features_fg])
    
    # 对前景特征进行归一化处理
    for i in range(3):
        pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].min()) / (pca_features_rem[:, i].max() - pca_features_rem[:, i].min())
        # 使用均值和标准差进行转换，个人发现这种转换方式可以得到更好的可视化效果
        # pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) + 0.5
    
    # 创建RGB特征数组
    pca_features_rgb = pca_features.copy()
    
    # 替换前景特征为转换后的特征
    pca_features_rgb[pca_features_fg] = pca_features_rem
    
    # 将背景特征设置为0
    pca_features_rgb[b] = 0
    
    # 重塑特征形状为(4, patch_h, patch_w, 3)
    pca_features_rgb = pca_features_rgb.reshape(4, patch_h, patch_w, 3)
    
    # 显示第一个图像的RGB特征
    plt.imshow(pca_features_rgb[0][...,::-1])
    plt.savefig('features.png')
    plt.show()
    plt.close()

这段代码的功能是对给定的图像进行一系列处理和特征提取，并使用PCA对特征进行降维。然后，根据特定阈值对前景和背景进行区分，最后将特征可视化为RGB图像。请注意，其中的具体数值和路径可能需要根据您的实际数据和环境进行调整。

![在这里插入图片描述][221d18457d3c4cd3b16f6980bd9e0b31.png]

print(features)
    print(features.shape)

我们的输出结果为：

tensor([[-1.3500, -4.8793, -1.4393,  ...,  2.3347,  1.6834, -2.9632],
            [-0.4650, -6.4163, -1.5503,  ...,  2.2055,  2.5527, -3.2553],
            [-0.6371, -6.2615, -0.7516,  ...,  3.1827,  2.3861, -2.6838],
            ...,
            [ 1.9385,  0.0726, -0.5395,  ...,  0.3876, -1.4914, -4.5422],
            [ 1.6399, -0.0860,  0.4701,  ...,  1.0180, -0.8897, -5.2614],
            [ 1.6084, -0.0669,  0.7341,  ...,  1.0633, -0.9713, -5.3548]])
    torch.Size([15000, 384])

降维后的特征为：

print(pca_features)
    print(pca_features.shape)

输出的结果为：

[[  0.81004055   2.458559    12.11051576]
     [  0.79562888   5.65071716  10.84007045]
     [  0.82050109   5.55007889   9.05274001]
     ...
     [  0.27618588 -18.96898667  19.48198916]
     [  0.31861323 -12.21414371  14.19802898]
     [  0.34356016 -10.82144825  13.74648131]]
    (15000, 3)

features_dict

我们看一下字典的构成：

{
        'x_norm_clstoken': tensor([[ 2.2549, -1.5661,  4.4978,  ...,  1.4984, -5.8642, -0.8560],
             [ 1.8816,  2.4343,  1.4931,  ..., -1.3401, -2.5460,  1.3967],
             [ 1.8816,  2.4343,  1.4931,  ..., -1.3401, -2.5460,  1.3967],
             [ 1.8816,  2.4343,  1.4931,  ..., -1.3401, -2.5460,  1.3967]],
            device='cuda:0'),
     'x_norm_patchtokens': tensor([[[-1.3500, -4.8793, -1.4393,  ...,  2.3347,  1.6834, -2.9632],
              [-0.4650, -6.4163, -1.5503,  ...,  2.2055,  2.5527, -3.2553],
              [-0.6371, -6.2615, -0.7516,  ...,  3.1827,  2.3861, -2.6838],
              ...,
              [-0.8778, -0.0251, -0.2867,  ...,  4.7801, -2.0887, -4.5910],
              [-1.2309,  0.2852,  0.7693,  ...,  5.0635, -1.1529, -6.0175],
              [-1.7551,  1.1333, -0.0898,  ...,  4.1885, -3.3197, -5.7227]],
     
             [[ 0.9131, -4.9736, -0.6238,  ...,  0.2835, -0.3494, -0.4916],
              [ 1.0967, -6.0392, -0.7900,  ...,  0.2323,  0.0510,  0.0176],
              [ 1.3852, -5.8056, -1.2573,  ...,  0.0549, -0.3270, -0.4510],
              ...,
              [ 1.9385,  0.0726, -0.5395,  ...,  0.3877, -1.4914, -4.5422],
              [ 1.6399, -0.0860,  0.4701,  ...,  1.0180, -0.8897, -5.2614],
              [ 1.6084, -0.0669,  0.7341,  ...,  1.0633, -0.9713, -5.3548]],
     
             [[ 0.9131, -4.9736, -0.6238,  ...,  0.2835, -0.3494, -0.4916],
              [ 1.0967, -6.0392, -0.7900,  ...,  0.2323,  0.0510,  0.0176],
              [ 1.3852, -5.8056, -1.2573,  ...,  0.0549, -0.3270, -0.4510],
              ...,
              [ 1.9385,  0.0726, -0.5395,  ...,  0.3877, -1.4914, -4.5422],
              [ 1.6399, -0.0860,  0.4701,  ...,  1.0180, -0.8897, -5.2614],
              [ 1.6085, -0.0669,  0.7341,  ...,  1.0633, -0.9713, -5.3548]],
     
             [[ 0.9131, -4.9736, -0.6238,  ...,  0.2835, -0.3494, -0.4916],
              [ 1.0967, -6.0392, -0.7900,  ...,  0.2323,  0.0510,  0.0176],
              [ 1.3852, -5.8056, -1.2573,  ...,  0.0549, -0.3270, -0.4511],
              ...,
              [ 1.9385,  0.0726, -0.5395,  ...,  0.3876, -1.4914, -4.5422],
              [ 1.6399, -0.0860,  0.4701,  ...,  1.0180, -0.8897, -5.2614],
              [ 1.6084, -0.0669,  0.7341,  ...,  1.0633, -0.9713, -5.3548]]],
            device='cuda:0'),
     'x_prenorm': tensor([[[ 4.7546e-01, -3.4794e-02,  1.1905e+00,  ...,  3.3896e-01,
               -1.2591e+00, -8.1724e-03],
              [-5.2994e-01, -3.0311e-01, -2.0162e-01,  ...,  9.4372e-01,
                8.7399e-01, -3.2527e-01],
              [-1.5728e-01, -3.9359e-01, -2.1482e-01,  ...,  9.0485e-01,
                1.2325e+00, -3.3923e-01],
              ...,
              [-4.9091e-01,  1.1081e-02,  1.9814e-01,  ...,  2.0630e+00,
               -8.5562e-01, -7.6588e-01],
              [-6.0861e-01,  5.2204e-02,  6.6299e-01,  ...,  2.1127e+00,
               -3.8590e-01, -9.7335e-01],
              [-9.3785e-01,  1.2485e-01,  3.0359e-01,  ...,  1.9137e+00,
               -1.5223e+00, -1.0352e+00]],
     
             [[ 4.4059e-01,  1.4807e-01,  5.9425e-01,  ..., -3.4851e-01,
               -6.1687e-01,  2.0463e-01],
              [ 3.1511e-01, -3.3073e-01,  9.0955e-02,  ...,  1.3627e-01,
                1.8562e-02,  4.2850e-02],
              [ 3.8695e-01, -4.1345e-01,  2.8734e-02,  ...,  1.1916e-01,
                1.8061e-01,  1.2469e-01],
              ...,
              [ 6.3855e-01,  1.9967e-03,  5.6187e-02,  ...,  1.0780e-01,
               -5.0606e-01, -6.6095e-01],
              [ 5.6617e-01,  4.9071e-03,  4.8375e-01,  ...,  3.7527e-01,
               -2.6194e-01, -7.9524e-01],
              [ 5.6790e-01,  1.4408e-02,  6.0538e-01,  ...,  4.0537e-01,
               -2.9182e-01, -8.1226e-01]],
     
             [[ 4.4059e-01,  1.4807e-01,  5.9424e-01,  ..., -3.4851e-01,
               -6.1687e-01,  2.0463e-01],
              [ 3.1511e-01, -3.3073e-01,  9.0957e-02,  ...,  1.3627e-01,
                1.8564e-02,  4.2850e-02],
              [ 3.8695e-01, -4.1345e-01,  2.8733e-02,  ...,  1.1916e-01,
                1.8061e-01,  1.2469e-01],
              ...,
              [ 6.3855e-01,  1.9971e-03,  5.6186e-02,  ...,  1.0780e-01,
               -5.0606e-01, -6.6095e-01],
              [ 5.6617e-01,  4.9067e-03,  4.8375e-01,  ...,  3.7527e-01,
               -2.6194e-01, -7.9524e-01],
              [ 5.6790e-01,  1.4408e-02,  6.0538e-01,  ...,  4.0536e-01,
               -2.9182e-01, -8.1226e-01]],
     
             [[ 4.4059e-01,  1.4807e-01,  5.9424e-01,  ..., -3.4851e-01,
               -6.1687e-01,  2.0463e-01],
              [ 3.1511e-01, -3.3073e-01,  9.0956e-02,  ...,  1.3627e-01,
                1.8562e-02,  4.2849e-02],
              [ 3.8695e-01, -4.1344e-01,  2.8735e-02,  ...,  1.1916e-01,
                1.8061e-01,  1.2469e-01],
              ...,
              [ 6.3855e-01,  1.9964e-03,  5.6189e-02,  ...,  1.0780e-01,
               -5.0607e-01, -6.6095e-01],
              [ 5.6617e-01,  4.9066e-03,  4.8375e-01,  ...,  3.7527e-01,
               -2.6194e-01, -7.9524e-01],
              [ 5.6790e-01,  1.4408e-02,  6.0538e-01,  ...,  4.0537e-01,
               -2.9182e-01, -8.1226e-01]]], device='cuda:0'),
     'masks': None}

我们换一种可视化的方法：

patch_h = 75
    patch_w = 50
    feat_dim = 384
     
    transform = T.Compose([
        T.GaussianBlur(9, sigma=(0.1, 2.0)),
        T.Resize((patch_h * 14, patch_w * 14)),
        T.CenterCrop((patch_h * 14, patch_w * 14)),
        T.ToTensor(),
        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    ])
     
    dinov2_vits14 = torch.hub.load('', 'dinov2_vits14',source='local').cuda()
     
    features = torch.zeros(4, patch_h * patch_w, feat_dim)
    imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14).cuda()
     
    img_path = f'/kaggle/input/demo-image/1 (4).png'
    img = Image.open(img_path).convert('RGB')
    imgs_tensor[0] = transform(img)[:3]
    with torch.no_grad():
        features_dict = dinov2_vits14.forward_features(imgs_tensor)
        features = features_dict['x_norm_patchtokens']
        
    features = features.reshape(4 * patch_h * patch_w, feat_dim).cpu()
    pca = PCA(n_components=3)
    pca.fit(features)
    pca_features = pca.transform(features)
    pca_features[:, 0] = (pca_features[:, 0] - pca_features[:, 0].min()) / (pca_features[:, 0].max() - pca_features[:, 0].min())
     
    pca_features_fg = pca_features[:, 0] > 0.3
    pca_features_bg = ~pca_features_fg
     
    b = np.where(pca_features_bg)
    
    pca.fit(features[pca_features_fg])
    pca_features_rem = pca.transform(features[pca_features_fg])
    for i in range(3):
        # transform using mean and std, I personally found this transformation gives a better visualization
        pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) + 0.5
    
    pca_features_rgb = pca_features.copy()
    pca_features_rgb[pca_features_fg] = pca_features_rem
    pca_features_rgb[b] = 0
    
    pca_features_rgb = pca_features_rgb.reshape(4, patch_h, patch_w, 3)
    plt.imshow(pca_features_rgb[0][...,::-1])
    plt.savefig('features.png')
    plt.show()
    plt.close()

![在这里插入图片描述][3f736f052da54c95a3e3662d2f6d71c2.png]

## 三、使用其他模型 ##

### 3.1 使用vit\_b14的模型 ###

patch_h = 75
    patch_w = 50
    feat_dim = 768
     
    transform = T.Compose([
        T.GaussianBlur(9, sigma=(0.1, 2.0)),
        T.Resize((patch_h * 14, patch_w * 14)),
        T.CenterCrop((patch_h * 14, patch_w * 14)),
        T.ToTensor(),
        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    ])
     
    dinov2_vitb14 = torch.hub.load('', 'dinov2_vitb14',source='local').cuda()
     
    features = torch.zeros(4, patch_h * patch_w, feat_dim)
    imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14).cuda()
     
    img_path = f'/kaggle/input/demo-image/1 (4).png'
    img = Image.open(img_path).convert('RGB')
    imgs_tensor[0] = transform(img)[:3]
    with torch.no_grad():
        features_dict = dinov2_vitb14.forward_features(imgs_tensor)
        features = features_dict['x_norm_patchtokens']
        
    features = features.reshape(4 * patch_h * patch_w, feat_dim).cpu()
    pca = PCA(n_components=3)
    pca.fit(features)
    pca_features = pca.transform(features)
    pca_features[:, 0] = (pca_features[:, 0] - pca_features[:, 0].min()) / (pca_features[:, 0].max() - pca_features[:, 0].min())
     
    pca_features_fg = pca_features[:, 0] > 0.3
    pca_features_bg = ~pca_features_fg
     
    b = np.where(pca_features_bg)
    
    pca.fit(features[pca_features_fg])
    pca_features_rem = pca.transform(features[pca_features_fg])
    for i in range(3):
        # transform using mean and std, I personally found this transformation gives a better visualization
        pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) + 0.5
    
    pca_features_rgb = pca_features.copy()
    pca_features_rgb[pca_features_fg] = pca_features_rem
    pca_features_rgb[b] = 0
    
    pca_features_rgb = pca_features_rgb.reshape(4, patch_h, patch_w, 3)
    plt.imshow(pca_features_rgb[0][...,::-1])
    plt.savefig('features.png')
    plt.show()
    plt.close()

![在这里插入图片描述][4d794d7c6bde43328ee465ac80b86357.png]

### 3.2 使用vit\_l14的模型 ###

patch_h = 75
    patch_w = 50
    feat_dim = 1024
     
    transform = T.Compose([
        T.GaussianBlur(9, sigma=(0.1, 2.0)),
        T.Resize((patch_h * 14, patch_w * 14)),
        T.CenterCrop((patch_h * 14, patch_w * 14)),
        T.ToTensor(),
        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    ])
     
    dinov2_vitl14 = torch.hub.load('', 'dinov2_vitl14',source='local').cuda()
     
    features = torch.zeros(4, patch_h * patch_w, feat_dim)
    imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14).cuda()
     
    img_path = f'/kaggle/input/demo-image/1 (4).png'
    img = Image.open(img_path).convert('RGB')
    imgs_tensor[0] = transform(img)[:3]
    with torch.no_grad():
        features_dict = dinov2_vitl14.forward_features(imgs_tensor)
        features = features_dict['x_norm_patchtokens']
        
    features = features.reshape(4 * patch_h * patch_w, feat_dim).cpu()
    pca = PCA(n_components=3)
    pca.fit(features)
    pca_features = pca.transform(features)
    pca_features[:, 0] = (pca_features[:, 0] - pca_features[:, 0].min()) / (pca_features[:, 0].max() - pca_features[:, 0].min())
     
    pca_features_fg = pca_features[:, 0] > 0.3
    pca_features_bg = ~pca_features_fg
     
    b = np.where(pca_features_bg)
    
    pca.fit(features[pca_features_fg])
    pca_features_rem = pca.transform(features[pca_features_fg])
    for i in range(3):
        # transform using mean and std, I personally found this transformation gives a better visualization
        pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) + 0.5
    
    pca_features_rgb = pca_features.copy()
    pca_features_rgb[pca_features_fg] = pca_features_rem
    pca_features_rgb[b] = 0
    
    pca_features_rgb = pca_features_rgb.reshape(4, patch_h, patch_w, 3)
    plt.imshow(pca_features_rgb[0][...,::-1])
    plt.savefig('features.png')
    plt.show()
    plt.close()

![在这里插入图片描述][71ea5f35cf4142c7841bacb76eecc55f.png]

### 3.3 使用vit\_g14的模型 ###

patch_h = 75
    patch_w = 50
    feat_dim = 1536
     
    transform = T.Compose([
        T.GaussianBlur(9, sigma=(0.1, 2.0)),
        T.Resize((patch_h * 14, patch_w * 14)),
        T.CenterCrop((patch_h * 14, patch_w * 14)),
        T.ToTensor(),
        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    ])
     
    dinov2_vitg14 = torch.hub.load('', 'dinov2_vitg14',source='local').cuda()
     
    features = torch.zeros(4, patch_h * patch_w, feat_dim)
    imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14).cuda()
     
    img_path = f'/kaggle/input/demo-image/1 (4).png'
    img = Image.open(img_path).convert('RGB')
    imgs_tensor[0] = transform(img)[:3]
    with torch.no_grad():
        features_dict = dinov2_vitg14.forward_features(imgs_tensor)
        features = features_dict['x_norm_patchtokens']
        
    features = features.reshape(4 * patch_h * patch_w, feat_dim).cpu()
    pca = PCA(n_components=3)
    pca.fit(features)
    pca_features = pca.transform(features)
    pca_features[:, 0] = (pca_features[:, 0] - pca_features[:, 0].min()) / (pca_features[:, 0].max() - pca_features[:, 0].min())
     
    pca_features_fg = pca_features[:, 0] > 0.3
    pca_features_bg = ~pca_features_fg
     
    b = np.where(pca_features_bg)
    
    pca.fit(features[pca_features_fg])
    pca_features_rem = pca.transform(features[pca_features_fg])
    for i in range(3):
        # transform using mean and std, I personally found this transformation gives a better visualization
        pca_features_rem[:, i] = (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) + 0.5
    
    pca_features_rgb = pca_features.copy()
    pca_features_rgb[pca_features_fg] = pca_features_rem
    pca_features_rgb[b] = 0
    
    pca_features_rgb = pca_features_rgb.reshape(4, patch_h, patch_w, 3)
    plt.imshow(pca_features_rgb[0][...,::-1])
    plt.savefig('features.png')
    plt.show()
    plt.close()

![在这里插入图片描述][f73c5df26f9c40ea8ff6afc25a923401.png]

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[3f736f052da54c95a3e3662d2f6d71c2.png]: https://img-blog.csdnimg.cn/3f736f052da54c95a3e3662d2f6d71c2.png
[4d794d7c6bde43328ee465ac80b86357.png]: https://img-blog.csdnimg.cn/4d794d7c6bde43328ee465ac80b86357.png
[71ea5f35cf4142c7841bacb76eecc55f.png]: https://img-blog.csdnimg.cn/71ea5f35cf4142c7841bacb76eecc55f.png
[f73c5df26f9c40ea8ff6afc25a923401.png]: https://img-blog.csdnimg.cn/f73c5df26f9c40ea8ff6afc25a923401.png