mirror of
https://github.com/Deepshift/DeepCreamPy.git
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96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
import numpy as np
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import tensorflow as tf
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from PIL import Image
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import tqdm
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import os
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import matplotlib.pyplot as plt
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import sys
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sys.path.append('..')
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from model import Model
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IMAGE_SIZE = 128
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LOCAL_SIZE = 64
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HOLE_MIN = 24
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HOLE_MAX = 48
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BATCH_SIZE = 16
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image_path = './lfw.npy'
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def test():
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x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
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mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
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local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
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global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
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local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
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is_training = tf.placeholder(tf.bool, [])
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model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
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sess = tf.Session()
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init_op = tf.global_variables_initializer()
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sess.run(init_op)
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saver = tf.train.Saver()
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saver.restore(sess, './models/latest')
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x_test = np.load(test_npy)
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np.random.shuffle(x_test)
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x_test = np.array([a / 127.5 - 1 for a in x_test])
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step_num = int(len(x_test) / BATCH_SIZE)
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cnt = 0
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for i in tqdm.tqdm(range(step_num)):
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x_batch = x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
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_, mask_batch = get_points()
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completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
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for i in range(BATCH_SIZE):
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cnt += 1
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raw = x_batch[i]
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raw = np.array((raw + 1) * 127.5, dtype=np.uint8)
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masked = raw * (1 - mask_batch[i]) + np.ones_like(raw) * mask_batch[i] * 255
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img = completion[i]
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img = np.array((img + 1) * 127.5, dtype=np.uint8)
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dst = './output/{}.jpg'.format("{0:06d}".format(cnt))
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output_image([['Input', masked], ['Output', img], ['Ground Truth', raw]], dst)
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def get_points():
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points = []
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mask = []
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for i in range(BATCH_SIZE):
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x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
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x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
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points.append([x1, y1, x2, y2])
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w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
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p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
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q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
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p2 = p1 + w
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q2 = q1 + h
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m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
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m[q1:q2 + 1, p1:p2 + 1] = 1
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mask.append(m)
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return np.array(points), np.array(mask)
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def output_image(images, dst):
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fig = plt.figure()
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for i, image in enumerate(images):
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text, img = image
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fig.add_subplot(1, 3, i + 1)
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plt.imshow(img)
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plt.tick_params(labelbottom='off')
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plt.tick_params(labelleft='off')
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plt.gca().get_xaxis().set_ticks_position('none')
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plt.gca().get_yaxis().set_ticks_position('none')
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plt.xlabel(text)
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plt.savefig(dst)
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plt.close()
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if __name__ == '__main__':
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test()
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