mport scipy.io

import numpy as np

import os

import scipy.misc

import matplotlib.pyplot as plt

import tensorflow as tf

# 进行卷积操作

def _conv_layer(input, weights, bias):

    conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),

                        padding='SAME')

    return tf.nn.bias_add(conv, bias)

# 进行池化操作

def _pool_layer(input):

    return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),

                          padding='SAME')

# 进行去均值的操作

def preprocess(image, mean_pixel):

    return image - mean_pixel

def unprocess(image, mean_pixel):

    return image + mean_pixel

def imread(path):

    return scipy.misc.imread(path).astype(np.float)

def imsave(path, img):

    img = np.clip(img, 0, 255).astype(np.uint8)

    scipy.misc.imsave(path, img)

def net(data_path, input_image):

    layers = (

        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',

        'relu3_3', 'conv3_4', 'relu3_4', 'pool3',

        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',

        'relu4_3', 'conv4_4', 'relu4_4', 'pool4',

        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',

        'relu5_3', 'conv5_4', 'relu5_4'

    )

    # 载入数据

    data = scipy.io.loadmat(data_path)

    mean = data['normalization'][0][0][0]

    mean_pixel = np.mean(mean, axis=(0, 1))

    weights = data['layers'][0]

    net = {}

    current = input_image

    for i, name in enumerate(layers):

        kind = name[:4]

        if kind == 'conv':

            kernels, bias = weights[i][0][0][0][0]

            kernels = np.transpose(kernels, (1, 0, 2, 3))

            # 重构reshape

            bias = bias.reshape(-1)

            current = _conv_layer(current, kernels, bias)

        elif kind == 'relu':

            current = tf.nn.relu(current)

        elif kind == 'pool':

            current = _pool_layer(current)

        # 用来存放对应的处理结果

        net[name] = current

    assert len(net) == len(layers)

    return net, mean_pixel, layers

# 返回当前的路径

cwd = os.getcwd()

# 别人已经训练好的模型

VGG_PATH =cwd + '/data/imagenet-vgg-verydeep-19.mat'

IMG_PATH = cwd + '/data/cat.jpg'

input_image = imread(IMG_PATH)

shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2])

with tf.Session as sess:

    image = tf.placeholder('float', shape=shape)

    #训练模型

    nets, mean_pixel, all_layers = net(VGG_PATH, image)

    # 去除均值

    input_image_pre = np.array([preprocess(input_image, mean_pixel)])

    layers = all_layers

    for i, layer in enumerate(layers):

        # 输出模型的单个特征

        features = nets[layer].eval(feed_dict={image:input_image})

        print(" Type of 'features' is ", type(features))

        print(" Shape of 'features' is %s" % (features.shape,))

    # 画卷积特征图

        if 1:

            plt.figure(i+1, figsize=(10, 5))

            plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)

            plt.title(""+layer)

            plt.colorbar()

            plt.show()