国际网站建设,ppt制作模板免费下载,wordpress导航栏制作教程,北京网站建设 shwl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#xff1a;
Cloning in… 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!pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple二、导入原图
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimgimage 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]))图像尺寸1376 x 920 x 3我们需要切换为output的路径
import osinput_path /kaggle/working/dinov2
os.chdir(input_path)2.1 使用vit_s14的模型
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 matplotlibpatch_h 75
patch_w 50
feat_dim 384transform 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,sourcelocal).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_components3)
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_fgb 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):pca_features_rem[:, i] (pca_features_rem[:, i] - pca_features_rem[:, i].min()) / (pca_features_rem[:, i].max() - pca_features_rem[:, i].min())# 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) 0.5pca_features_rgb pca_features.copy()
pca_features_rgb[pca_features_fg] pca_features_rem
pca_features_rgb[b] 0pca_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()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, sourcelocal).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_components3)
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。 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]],devicecuda: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]]],devicecuda:0),x_prenorm: tensor([[[ 4.7546e-01, -3.4794e-02, 1.1905e00, ..., 3.3896e-01,-1.2591e00, -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-01,1.2325e00, -3.3923e-01],...,[-4.9091e-01, 1.1081e-02, 1.9814e-01, ..., 2.0630e00,-8.5562e-01, -7.6588e-01],[-6.0861e-01, 5.2204e-02, 6.6299e-01, ..., 2.1127e00,-3.8590e-01, -9.7335e-01],[-9.3785e-01, 1.2485e-01, 3.0359e-01, ..., 1.9137e00,-1.5223e00, -1.0352e00]],[[ 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]]], devicecuda:0),masks: None}我们换一种可视化的方法
patch_h 75
patch_w 50
feat_dim 384transform 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,sourcelocal).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_components3)
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_fgb 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 visualizationpca_features_rem[:, i] (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) 0.5pca_features_rgb pca_features.copy()
pca_features_rgb[pca_features_fg] pca_features_rem
pca_features_rgb[b] 0pca_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()三、使用其他模型
3.1 使用vit_b14的模型
patch_h 75
patch_w 50
feat_dim 768transform 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,sourcelocal).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_components3)
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_fgb 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 visualizationpca_features_rem[:, i] (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) 0.5pca_features_rgb pca_features.copy()
pca_features_rgb[pca_features_fg] pca_features_rem
pca_features_rgb[b] 0pca_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()3.2 使用vit_l14的模型
patch_h 75
patch_w 50
feat_dim 1024transform 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,sourcelocal).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_components3)
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_fgb 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 visualizationpca_features_rem[:, i] (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) 0.5pca_features_rgb pca_features.copy()
pca_features_rgb[pca_features_fg] pca_features_rem
pca_features_rgb[b] 0pca_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()3.3 使用vit_g14的模型
patch_h 75
patch_w 50
feat_dim 1536transform 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,sourcelocal).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_components3)
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_fgb 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 visualizationpca_features_rem[:, i] (pca_features_rem[:, i] - pca_features_rem[:, i].mean()) / (pca_features_rem[:, i].std() ** 2) 0.5pca_features_rgb pca_features.copy()
pca_features_rgb[pca_features_fg] pca_features_rem
pca_features_rgb[b] 0pca_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()
