1.处理单张图作为网络的输入。
2.根据给定的layer层，获取该层输出结果features。
3.考虑到features的形状为[batch_size, filter_nums, H, W],提取其中第一个过滤器得到的结果的feature。
4.以一张图作为输入的情况下，我们得到的feature即为[H,W]大小的tensor
5.将tensor转为numpy，然后归一化[0,1]，最后乘以255，使得范围为[0,255]。
6.得到灰度图像保存。
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import cv2
import numpy as np
import torch
from torch.autograd import Variable
from torchvision import models
def preprocess_image(cv2im, resize_im=True):
"""
Processes image for CNNs
Args:
PIL_img(PIL_img):Image to process
resize_im(bool):Resize to 224 or not
:return:
im_as_var(Pytorch variable):Variable that contains processed float tensor
"""
# mean and std list for channels (ImageNet)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
# Resize image
if resize_im:
cv2im = cv2.resize(cv2im, (224, 224))
im_as_arr = np.float32(cv2im)
im_as_arr = np.ascontiguousarray(im_as_arr[..., ::-1])
im_as_arr = im_as_arr.transpose(2, 0, 1) # Convert array to D,W,H
# Normalize the channels
for channel, _ in enumerate(im_as_arr):
im_as_arr[channel] /= 255
im_as_arr[channel] -= mean[channel]
im_as_arr[channel] /= std[channel]
# Convert to float tensor
im_as_ten = torch.from_numpy(im_as_arr).float()
# Add one more channel to the beginning. Tensor shape =1,3,224,224
im_as_ten.unsqueeze(0)
# Convert to Pytorch variable
im_as_var = Variable(im_as_ten, requires_grad=True)
return im_as_var
class FeatureVisualization():
def __init__(self, img_path, selected_layer):
self.img_path = img_path
self.selected_layer = selected_layer
self.pretrained_model = models.vgg16(pretrained=True).features
def process_image(self):
img = cv2.imread(self.img_path)
img = preprocess_image(img)
return img
def get_feature(self):
# input = Variable(torch.randn(1, 3, 224, 224))
input = self.process_image()
x = input
x = x.unsqueeze(0)
for index, layer in enumerate(self.pretrained_model):
x = layer(x)
if (index == self.selected_layer):
return x
def get_single_feature(self):
features = self.get_feature()
# print("get_single_features: ", features.shape)
feature = features[:, 0, :, :]
# print("get_single_feature: ", feature.shape)
feature = feature.view(feature.shape[1], feature.shape[2])
return feature
def save_feature_to_img(self, i):
# to numpy
feature = self.get_single_feature()
feature = feature.data.numpy()
# use sigmod to [0,1]
feature = 1.0 / (1 + np.exp(-1 * feature))
# to [0,255]
feature = np.round(feature * 255)
print("ok feature.shape: ", feature.shape)
cv2.imwrite(str(i) + ".jpg", feature)
if __name__ == "__main__":
for i in range(30):
myClass = FeatureVisualization("31.jpg", i)
# print(myClass.pretrained_model)
myClass.save_feature_to_img(i)
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