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extract_cnn_vgg16_keras.py
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# extract_cnn_vgg16_keras.py
# -*- coding: utf-8 -*-
import numpy as np
from memory_profiler import profile
from numpy import linalg as LA
from keras.applications.vgg16 import VGG16
# from keras.applications.resnet50 import ResNet50
# from keras.applications.densenet import DenseNet121
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg
# from keras.applications.resnet50 import preprocess_input as preprocess_input_resnet
from keras.applications.densenet import preprocess_input as preprocess_input_densenet
class VGGNet:
def __init__(self):
# weights: 'imagenet'
# pooling: 'max' or 'avg'
# input_shape: (width, height, 3), width and height should >= 48
self.input_shape = (224, 224, 3)
self.weight = 'imagenet'
self.pooling = 'max'
# include_top：是否保留顶层的3个全连接网络
# weights：None代表随机初始化，即不加载预训练权重。'imagenet'代表加载预训练权重
# input_tensor：可填入Keras tensor作为模型的图像输出tensor
# input_shape：可选，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于48，如(200,200,3)
#pooling：当include_top = False时，该参数指定了池化方式。None代表不池化，最后一个卷积层的输出为4D张量。‘avg’代表全局平均池化，‘max’代表全局最大值池化。
#classes：可选，图片分类的类别数，仅当include_top = True并且不加载预训练权重时可用。
self.model_vgg = VGG16(weights = self.weight, input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling = self.pooling, include_top = False)
# self.model_resnet = ResNet50(weights = self.weight, input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling = self.pooling, include_top = False)
# self.model_densenet = DenseNet121(weights = self.weight, input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling = self.pooling, include_top = False)
self.model_vgg.predict(np.zeros((1, 224, 224, 3)))
# self.model_resnet.predict(np.zeros((1, 224, 224, 3)))
# self.model_densenet.predict(np.zeros((1, 224, 224, 3)))
'''
Use vgg16/Resnet model to extract features
Output normalized feature vector
'''
#提取vgg16最后一层卷积特征
def vgg_extract_feat(self, img_path):
img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input_vgg(img)
feat = self.model_vgg.predict(img)
# print(feat.shape)
norm_feat = feat[0]/LA.norm(feat[0])
return norm_feat
#提取resnet50最后一层卷积特征
# def resnet_extract_feat(self, img_path):
# img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))
# img = image.img_to_array(img)
# img = np.expand_dims(img, axis=0)
# img = preprocess_input_resnet(img)
# feat = self.model_resnet.predict(img)
# # print(feat.shape)
# norm_feat = feat[0]/LA.norm(feat[0])
# return norm_feat
#提取densenet121最后一层卷积特征
# def densenet_extract_feat(self, img_path):
# img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))
# img = image.img_to_array(img)
# img = np.expand_dims(img, axis=0)
# img = preprocess_input_densenet(img)
# feat = self.model_densenet.predict(img)
# # print(feat.shape)
# norm_feat = feat[0]/LA.norm(feat[0])
# return norm_feat