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test_one_image.py
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# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import scipy.misc
import time
import os
import glob
import cv2
#reader = tf.train.NewCheckpointReader("./checkpoint/CGAN_120/CGAN.model-9")
def imread(path, is_grayscale=True):
"""
Read image using its path.
Default value is gray-scale, and image is read by YCbCr format as the paper said.
"""
if is_grayscale:
#flatten=True 以灰度图的形式读取
return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)
else:
return scipy.misc.imread(path, mode='YCbCr').astype(np.float)
def imsave(image, path):
return scipy.misc.imsave(path, image)
def prepare_data(dataset):
data_dir = os.path.join(os.sep, (os.path.join(os.getcwd(), dataset)))
data = glob.glob(os.path.join(data_dir, "*.jpg"))
data.extend(glob.glob(os.path.join(data_dir, "*.bmp")))
data.sort(key=lambda x:int(x[len(data_dir)+1:-4]))
return data
def lrelu(x, leak=0.2):
return tf.maximum(x, leak * x)
def fusion_model(img):
with tf.variable_scope('fusion_model'):
with tf.variable_scope('layer1'):
weights=tf.get_variable("w1",initializer=tf.constant(reader.get_tensor('fusion_model/layer1/w1')))
bias=tf.get_variable("b1",initializer=tf.constant(reader.get_tensor('fusion_model/layer1/b1')))
conv1_ir= tf.contrib.layers.batch_norm(tf.nn.conv2d(img, weights, strides=[1,1,1,1], padding='VALID') + bias, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True)
conv1_ir = lrelu(conv1_ir)
with tf.variable_scope('layer2'):
weights=tf.get_variable("w2",initializer=tf.constant(reader.get_tensor('fusion_model/layer2/w2')))
bias=tf.get_variable("b2",initializer=tf.constant(reader.get_tensor('fusion_model/layer2/b2')))
conv2_ir= tf.contrib.layers.batch_norm(tf.nn.conv2d(conv1_ir, weights, strides=[1,1,1,1], padding='VALID') + bias, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True)
conv2_ir = lrelu(conv2_ir)
with tf.variable_scope('layer3'):
weights=tf.get_variable("w3",initializer=tf.constant(reader.get_tensor('fusion_model/layer3/w3')))
bias=tf.get_variable("b3",initializer=tf.constant(reader.get_tensor('fusion_model/layer3/b3')))
conv3_ir= tf.contrib.layers.batch_norm(tf.nn.conv2d(conv2_ir, weights, strides=[1,1,1,1], padding='VALID') + bias, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True)
conv3_ir = lrelu(conv3_ir)
with tf.variable_scope('layer4'):
weights=tf.get_variable("w4",initializer=tf.constant(reader.get_tensor('fusion_model/layer4/w4')))
bias=tf.get_variable("b4",initializer=tf.constant(reader.get_tensor('fusion_model/layer4/b4')))
conv4_ir= tf.contrib.layers.batch_norm(tf.nn.conv2d(conv3_ir, weights, strides=[1,1,1,1], padding='VALID') + bias, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True)
conv4_ir = lrelu(conv4_ir)
with tf.variable_scope('layer5'):
weights=tf.get_variable("w5",initializer=tf.constant(reader.get_tensor('fusion_model/layer5/w5')))
bias=tf.get_variable("b5",initializer=tf.constant(reader.get_tensor('fusion_model/layer5/b5')))
conv5_ir= tf.nn.conv2d(conv4_ir, weights, strides=[1,1,1,1], padding='VALID') + bias
conv5_ir=tf.nn.tanh(conv5_ir)
return conv5_ir
def input_setup(index):
padding=6
sub_ir_sequence = []
sub_vi_sequence = []
input_ir=(imread(data_ir[index])-127.5)/127.5
input_ir=np.lib.pad(input_ir,((padding,padding),(padding,padding)),'edge')
w,h=input_ir.shape
input_ir=input_ir.reshape([w,h,1])
input_vi=(imread(data_vi[index])-127.5)/127.5
input_vi=np.lib.pad(input_vi,((padding,padding),(padding,padding)),'edge')
w,h=input_vi.shape
input_vi=input_vi.reshape([w,h,1])
sub_ir_sequence.append(input_ir)
sub_vi_sequence.append(input_vi)
train_data_ir= np.asarray(sub_ir_sequence)
train_data_vi= np.asarray(sub_vi_sequence)
return train_data_ir,train_data_vi
num_epoch=3
while(num_epoch==3):
reader = tf.train.NewCheckpointReader('./checkpoint/CGAN_120/CGAN.model-'+ str(num_epoch))
with tf.name_scope('IR_input'):
#红外图像patch
images_ir = tf.placeholder(tf.float32, [1,None,None,None], name='images_ir')
with tf.name_scope('VI_input'):
#可见光图像patch
images_vi = tf.placeholder(tf.float32, [1,None,None,None], name='images_vi')
#self.labels_vi_gradient=gradient(self.labels_vi)
#将红外和可见光图像在通道方向连起来，第一通道是红外图像，第二通道是可见光图像
with tf.name_scope('input'):
#resize_ir=tf.image.resize_images(images_ir, (512, 512), method=2)
input_image=tf.concat([images_ir,images_vi],axis=-1)
with tf.name_scope('fusion'):
fusion_image=fusion_model(input_image)
with tf.Session() as sess:
init_op=tf.global_variables_initializer()
sess.run(init_op)
data_ir=prepare_data('Test_ir')
data_vi=prepare_data('Test_vi')
for i in range(len(data_ir)):
start=time.time()
train_data_ir,train_data_vi=input_setup(i)
result =sess.run(fusion_image,feed_dict={images_ir: train_data_ir,images_vi: train_data_vi})
result=result*127.5+127.5
result = result.squeeze()
image_path = os.path.join(os.getcwd(), 'result','epoch'+str(num_epoch))
if not os.path.exists(image_path):
os.makedirs(image_path)
if i<=9:
image_path = os.path.join(image_path,'F9_0'+str(i)+".bmp")
else:
image_path = os.path.join(image_path,'F9_'+str(i)+".bmp")
end=time.time()
# print(out.shape)
imsave(result, image_path)
print("Testing [%d] success,Testing time is [%f]"%(i,end-start))
tf.reset_default_graph()
num_epoch=num_epoch+1