Query trained model

[foromer4]

HI, first a big thank you for publishing this work.
I am trying to use a trained model and query it with a new probe image.
It seems to me a very imprtant functionality , after all that is what you train the network for, right?
But I couldn't find it anywhere. I tried writing something, but I get poor results.
here is what I came up with:
any insights would be most appreciated.
thanks,
Omer

import os
import cv2
import numpy as np
from model import DCGAN
from utils import get_image, image_save, save_images
import tensorflow as tf
from scipy.misc import imresize

flags = tf.app.flags
flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")
flags.DEFINE_integer("image_size", 128, "The size of image to use")
flags.DEFINE_string("checkpoint_dir", "/home/omer/work/sub_pixel/models",
                    "Directory name to read the checkpoints [checkpoint]")
flags.DEFINE_string("test_image_dir", "/home/omer/work/sub_pixel/data/celebA/valid",
                    "Directory name of the images to evaluate")
flags.DEFINE_string("out_dir", "/home/omer/work/sub_pixel/out", "Directory name of to save results in")

FLAGS = flags.FLAGS


def doresize(x, shape):
    x = np.copy((x + 1.) * 127.5).astype("uint8")
    y = imresize(x, shape)
    return y


def main():
    with tf.Session() as sess:
        dcgan = DCGAN(sess, image_size=FLAGS.image_size, image_shape=[FLAGS.image_size, FLAGS.image_size, 3],
                      batch_size=FLAGS.batch_size,
                      dataset_name='celebA', is_crop=False, checkpoint_dir=FLAGS.checkpoint_dir)
        res = dcgan.load(FLAGS.checkpoint_dir)
        if not res:
            print ("failed loading model from path:" + FLAGS.checkpoint_dir)
            return

        i = 0
        files = []
        num_batches = len(os.listdir(FLAGS.test_image_dir)) / FLAGS.batch_size
        completed_batches = 0
        input_images = np.zeros(shape=(FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3))
        for f in os.listdir(FLAGS.test_image_dir):
            try:
                img_path = os.path.join(FLAGS.test_image_dir, f)
                if os.path.isdir(img_path):
                    i += 1
                    continue
                img = get_image(img_path, FLAGS.image_size, False)
                files.append(f)
                input_images[i] = img

                if i == FLAGS.batch_size - 1 or i == len(os.listdir(FLAGS.test_image_dir)) - 1:
                    batch_ready(dcgan, input_images, sess, files)

                    i = 0
                    input_images = np.zeros(shape=(FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3))
                    files = []
                    completed_batches += 1
                    print('done batch {0} out of {1}'.format(completed_batches, num_batches))
                else:
                    i += 1
            except Exception as e:
                print("problem working on:" + f)
                print (str(e))
                i += 1


def batch_ready(dcgan, input_images, sess, files):
    input_resized = [doresize(xx, (32, 32, 3)) for xx in input_images]
    sample_input_resized = np.array(input_resized).astype(np.float32)
    sample_input_images = np.array(input_images).astype(np.float32)
    output_images = sess.run(fetches=[dcgan.G],
                             feed_dict={dcgan.inputs: sample_input_resized, dcgan.images: sample_input_images})
    save_results(output_images, files)


def save_results(output_images, files):
    for k in range(0, len(files)):
        out_path = os.path.join(FLAGS.out_dir, files[k] + '_.png')
        out_img = output_images[0][k]

        # out_correct = ((out_img + 1) * 127.5).astype(np.uint8)
        # out_correct = cv2.cvtColor(out_correct, cv2.COLOR_RGB2BGR)
        # cv2.imshow('image', out_correct)
        # cv2.waitKey(0)

        image_save(out_img, out_path)


if __name__ == '__main__':
    main()