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model_zalando_refine_test.py
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model_zalando_refine_test.py
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# Copyright 2017 Xintong Han. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
""" Test for Stage 2: from product image + warpped image => refined image.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from utils import *
import collections
from model_zalando_tps_warp import create_refine_generator
import os
import time
from tps_transformer import tps_stn
import numpy as np
import scipy.io as sio
import scipy.misc
import tensorflow as tf
from PIL import Image
FLAGS = tf.app.flags.FLAGS
tf.flags.DEFINE_string("image_dir", "data/women_top/",
"Directory containing product and person images.")
tf.flags.DEFINE_string("test_label",
"data/viton_test_pairs.txt",
"File containing labels for testing.")
tf.flags.DEFINE_string("result_dir", "results/stage2/",
"Folder containing the results of testing.")
tf.flags.DEFINE_string("coarse_result_dir", "results/stage1",
"Folder containing the results of stage1 (coarse) results.")
tf.flags.DEFINE_integer("begin", "0", "")
tf.flags.DEFINE_integer("end", "2032", "")
tf.logging.set_verbosity(tf.logging.INFO)
def deprocess_image(image, mask01=False):
if not mask01:
image = image / 2 + 0.5
return image
def process_one_image(image, resize_height, resize_width, if_zero_one=False):
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
if if_zero_one:
return image
image = tf.image.resize_images(image,
size=[resize_height, resize_width],
method=tf.image.ResizeMethod.BILINEAR)
return (image - 0.5) * 2.0
# preprocess images for testing
def _process_image(image_name, product_image_name, sess,
resize_width=192, resize_height=256):
image_id = image_name[:-4]
image = scipy.misc.imread(FLAGS.image_dir + image_name)
prod_image = scipy.misc.imread(FLAGS.image_dir + product_image_name)
# sorry for the hard coded file path.
coarse_image = scipy.misc.imread(FLAGS.coarse_result_dir +
"/images/00015000_" +
image_name + "_" +
product_image_name + ".png")
mask_output = scipy.misc.imread(FLAGS.coarse_result_dir +
"/images/00015000_" +
image_name + "_" +
product_image_name + "_mask.png")
image = process_one_image(image, resize_height, resize_width)
prod_image = process_one_image(prod_image, resize_height, resize_width)
coarse_image = process_one_image(coarse_image, resize_height, resize_width)
mask_output = process_one_image(mask_output, resize_height,
resize_width, True)
# TPS transform
# Here we use control points to generate
# We tried to learn the control points, but the network refuses to converge.
tps_control_points = sio.loadmat(FLAGS.coarse_result_dir +
"/tps/00015000_" +
image_name + "_" +
product_image_name +
"_tps.mat")
v = tps_control_points["control_points"]
nx = v.shape[1]
ny = v.shape[2]
v = np.reshape(v, -1)
v = np.transpose(v.reshape([1,2,nx*ny]), [0,2,1]) * 2 -1
p = tf.convert_to_tensor(v, dtype=tf.float32)
img = tf.reshape(prod_image, [1,256,192,3])
tps_image = tps_stn(img, nx, ny, p, [256,192,3])
tps_mask = tf.cast(tf.less(tf.reduce_sum(tps_image, -1), 3*0.95), tf.float32)
[image, prod_image, coarse_image, tps_image, mask_output, tps_mask] = sess.run(
[image, prod_image, coarse_image, tps_image, mask_output, tps_mask])
return image, prod_image, coarse_image, tps_image, mask_output, tps_mask
def main(unused_argv):
try:
os.mkdir(FLAGS.result_dir)
except:
pass
try:
os.mkdir(FLAGS.result_dir + "/images/")
except:
pass
batch_size = 1
# Feed into the refine module
image_holder = tf.placeholder(tf.float32, shape=[batch_size,256,192,3])
prod_image_holder = tf.placeholder(tf.float32, shape=[batch_size,256,192,3])
prod_mask_holder = tf.placeholder(tf.float32, shape=[batch_size,256,192,1])
coarse_image_holder = tf.placeholder(tf.float32, shape=[batch_size,256,192,3])
tps_image_holder = tf.placeholder(tf.float32, shape=[batch_size,256,192,3])
with tf.variable_scope("refine_generator") as scope:
select_mask = create_refine_generator(tps_image_holder,
coarse_image_holder)
select_mask = select_mask * prod_mask_holder
model_image_outputs = (select_mask * tps_image_holder +
(1 - select_mask) * coarse_image_holder)
saver = tf.train.Saver(var_list=[var for var in tf.trainable_variables()
if var.name.startswith("refine_generator")])
with tf.Session() as sess:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoint)
if checkpoint == None:
checkpoint = FLAGS.checkpoint
print(checkpoint)
saver.restore(sess, checkpoint)
# reading input data
test_info = open(FLAGS.test_label).read().splitlines()
for i in range(FLAGS.begin, FLAGS.end, batch_size):
# loading batch data
print(i)
images = np.zeros((batch_size,256,192,3))
prod_images = np.zeros((batch_size,256,192,3))
coarse_images = np.zeros((batch_size,256,192,3))
tps_images = np.zeros((batch_size,256,192,3))
mask_outputs = np.zeros((batch_size,256,192,1))
image_names = []
product_image_names = []
for j in range(i, i + batch_size):
info = test_info[j].split()
print(info)
image_name = info[0]
product_image_name = info[1]
image_names.append(image_name)
product_image_names.append(product_image_name)
try:
(image, prod_image, coarse_image,
tps_image, mask_output, tps_mask) = _process_image(image_name,
product_image_name, sess)
except:
continue
images[j-i] = image
prod_images[j-i] = prod_image
coarse_images[j-i] = coarse_image
tps_images[j-i] = tps_image
mask_outputs[j-i] = np.expand_dims(mask_output, -1)
# inference
feed_dict = {
image_holder: images,
prod_image_holder: prod_images,
coarse_image_holder: coarse_images,
tps_image_holder: tps_images,
prod_mask_holder: mask_outputs,
}
[image_output, sel_mask] = sess.run([model_image_outputs, select_mask],
feed_dict=feed_dict)
# write results
for j in range(batch_size):
step = 0
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j] +
"_" + product_image_names[j] + '_tps.png',
(tps_images[j] / 2.0 + 0.5))
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j] +
"_" + product_image_names[j] + '_coarse.png',
(coarse_images[j] / 2.0 + 0.5))
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j] +
"_" + product_image_names[j] + '_mask.png',
np.squeeze(mask_outputs[j]))
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j] +
"_" + product_image_names[j] + '_final.png',
(image_output[j]) / 2.0 + 0.5)
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j] +
"_" + product_image_names[j] + '_sel_mask.png',
np.squeeze(sel_mask[j]))
scipy.misc.imsave(FLAGS.result_dir + "images/" + image_names[j],
(images[j] / 2.0 + 0.5))
scipy.misc.imsave(FLAGS.result_dir + "images/"+ product_image_names[j],
(prod_images[j] / 2.0 + 0.5))
# write html
index_path = os.path.join(FLAGS.result_dir, "index.html")
if os.path.exists(index_path):
index = open(index_path, "a")
else:
index = open(index_path, "w")
index.write("<html><body><table><tr>")
index.write("<th>step</th>")
index.write("<th>name</th><th>input</th>"
"<th>output</th><th>target</th></tr>")
for j in range(batch_size):
index.write("<tr>")
index.write("<td>%d %d</td>" % (step, i + j))
index.write("<td>%s %s</td>" % (image_names[j],
product_image_names[j]))
index.write("<td><img src='images/%s'></td>" % image_names[j])
index.write("<td><img src='images/%s'></td>" % product_image_names[j])
index.write("<td><img src='images/%s'></td>" %
(image_names[j] + "_" + product_image_names[j] + '_tps.png'))
index.write("<td><img src='images/%s'></td>" %
(image_names[j] + "_" + product_image_names[j] + '_coarse.png'))
index.write("<td><img src='images/%s'></td>" %
(image_names[j] + "_" + product_image_names[j] + '_mask.png'))
index.write("<td><img src='images/%s'></td>" %
(image_names[j] + "_" + product_image_names[j] + '_final.png'))
index.write("<td><img src='images/%s'></td>" %
(image_names[j] + "_" + product_image_names[j] + '_sel_mask.png'))
index.write("</tr>")
if __name__ == "__main__":
tf.app.run()