keras 特征图可视化

import argparse
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input

from myresnet50 import MyResnet50East
from preprocess import resize_image

from keras import backend as K

def predict(east_detect, img_path):
    img = image.load_img(img_path)
    d_wight, d_height = resize_image(img, 512)
    img = img.resize((d_wight, d_height), Image.NEAREST).convert('RGB')
    img = image.img_to_array(img)
    img = preprocess_input(img, mode='tf')
    x = np.expand_dims(img, axis=0)
    layer_1 = K.function([east_detect.layers[0].input], [east_detect.get_layer('activation_49').output])
    f1 = layer_1([x])[0]
    plt.figure( 1, figsize=(10, 5))
    plt.matshow(f1[0, :, :, 0], cmap=plt.cm.gray, fignum= 1)
    plt.title("test")
    plt.colorbar()
    plt.show()
if __name__ == '__main__':

    img_path = 'demo/icdar/img_38.jpg'
    print(img_path)

    east = MyResnet50East()
    east_detect = east.east_network()
    east_detect.load_weights("saved_model/RCTW2017-resnet50-aspp/east_model_weights_3T512.h5")
    east_detect.summary()
    predict(east_detect, img_path)

附录tensorflow特征图可视化;

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import scipy.misc
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
#定义一些函数
#卷积
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)
#池化
def _pool_layer(input):
       return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
               padding='SAME')
#减像素均值操作
def preprocess(image, mean_pixel):
       return image - mean_pixel
#加像素均值操作
def unprocess(image, mean_pixel):
       return image + mean_pixel
#读
def imread(path):
       return scipy.misc.imread(path).astype(np.float)
#保存
def imsave(path, img):
       img = np.clip(img, 0, 255).astype(np.uint8)
       scipy.misc.imsave(path, img)
print ("Functions for VGG ready")
#定义VGG的网络结构，用来存储网络的权重和偏置参数
def net(data_path, input_image):
        #拿到每一层对应的参数
        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'
        )
        data = scipy.io.loadmat(data_path)
        #原网络在训练的过程中，对每张图片三通道都执行了减均值的操作，这里也要减去均值
        mean = data['normalization'][0][0][0]
        mean_pixel = np.mean(mean, axis=(0, 1))
        #print(mean_pixel)
        #取到权重参数W和b,这里运气好的话，可以查到VGG模型中每层的参数含义，查不到的
        #话可以打印出weights，然后打印每一层的shape，推出其中每一层代表的含义
        weights = data['layers'][0]
        #print(weights)
        net = {}
        current = input_image
        #取到w和b
        for i, name in enumerate(layers):
            #:4的含义是只看每一层的前三个字母，从而进行判断
            kind = name[:4]
            if kind == 'conv':
                kernels, bias = weights[i][0][0][0][0]
                # matconvnet: weights are [width, height, in_channels, out_channels]\n",
                # tensorflow: weights are [height, width, in_channels, out_channels]\n",
                #这里width和height是颠倒的，所以要做一次转置运算
                kernels = np.transpose(kernels, (1, 0, 2, 3))
                #将bias转换为一个维度
                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)
            net[name] = current
        assert len(net) == len(layers)
        return net, mean_pixel, layers
print ("Network for VGG ready")
#cwd  = os.getcwd()
#这里用的是绝对路径
VGG_PATH = "imagenet-vgg-verydeep-19.mat"
#需要可视化的图片路径，这里是一只小猫
IMG_PATH = "./feiji1.png"
input_image = imread(IMG_PATH)
#获取图像shape
shape = (1,input_image.shape[0],input_image.shape[1],input_image.shape[2])
#开始会话
with tf.Session() as sess:
        image = tf.placeholder('float', shape=shape)
        #调用net函数
        nets, mean_pixel, all_layers = net(VGG_PATH, image)
        #减均值操作（由于VGG网络图片传入前都做了减均值操作，所以这里也用相同的预处理
        input_image_pre = np.array([preprocess(input_image, mean_pixel)])
        layers = all_layers # For all layers \n",
        # layers = ('relu2_1', 'relu3_1', 'relu4_1')\n",
        for i, layer in enumerate(layers):
            print ("[%d/%d] %s" % (i+1, len(layers), layer))
            features = nets[layer].eval(feed_dict={image: input_image_pre})
            print (" Type of 'features' is ", type(features))
            print (" Shape of 'features' is %s" % (features.shape,))
            # Plot response \n",
            #画出每一层
            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()