update

README.md

@@ -9,7 +9,7 @@ Please note research is ongoing, and the neural network works ONLY with color im
 
 - Python 2
 - TensorFlow 1.5
-- PIL
+- Pillow
 
 # Model
 Link coming soon
@@ -18,9 +18,19 @@ Link coming soon
 
 ## I. Decensoring hentai
 
-The decensorship process is fairly involved. A UI will eventually be released to streamline the process.
+The decensorship process is fairly involved. A user interface will eventually be released to streamline the process.
 
-Crop 128 x 128 size images containing the censored regions from your images and save them as new images. For each 128 x 128 image, color the 
+Using image editing software like Photoshop or GIMP, crop 128 x 128 size images containing the censored regions from your images and save them as new ".png" images. For each 128 x 128 cropped image, color the censored regions [tbd].
+
+Move the cropped images to []. Run the command 
+
+```
+$ python decensor.py
+```
+
+Decensored images will be saved to the "output" directory.
+
+Paste the decensored images back into the original image.
 
 ## II. Prepare the training data
 

src/decensor.py

@@ -0,0 +1,95 @@
+import numpy as np
+import tensorflow as tf
+import cv2
+import tqdm
+import os
+import matplotlib.pyplot as plt
+import sys
+sys.path.append('..')
+from model import Model
+
+IMAGE_SIZE = 128
+LOCAL_SIZE = 64
+HOLE_MIN = 24
+HOLE_MAX = 48
+BATCH_SIZE = 16
+
+test_npy = './lfw.npy'
+
+def test():
+    x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
+    mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
+    local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
+    global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
+    local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
+    is_training = tf.placeholder(tf.bool, [])
+
+    model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
+    sess = tf.Session()
+    init_op = tf.global_variables_initializer()
+    sess.run(init_op)
+
+    saver = tf.train.Saver()
+    saver.restore(sess, '../saved_models/latest')
+
+    x_test = np.load(test_npy)
+    np.random.shuffle(x_test)
+    x_test = np.array([a / 127.5 - 1 for a in x_test])
+
+    step_num = int(len(x_test) / BATCH_SIZE)
+
+    cnt = 0
+    for i in tqdm.tqdm(range(step_num)):
+        x_batch = x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
+        _, mask_batch = get_points()
+        completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
+        for i in range(BATCH_SIZE):
+            cnt += 1
+            raw = x_batch[i]
+            raw = np.array((raw + 1) * 127.5, dtype=np.uint8)
+            masked = raw * (1 - mask_batch[i]) + np.ones_like(raw) * mask_batch[i] * 255
+            img = completion[i]
+            img = np.array((img + 1) * 127.5, dtype=np.uint8)
+            dst = './output/{}.jpg'.format("{0:06d}".format(cnt))
+            output_image([['Input', masked], ['Output', img], ['Ground Truth', raw]], dst)
+
+
+def get_points():
+    points = []
+    mask = []
+    for i in range(BATCH_SIZE):
+        x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
+        x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
+        points.append([x1, y1, x2, y2])
+
+        w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
+        p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
+        q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
+        p2 = p1 + w
+        q2 = q1 + h
+        
+        m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
+        m[q1:q2 + 1, p1:p2 + 1] = 1
+        mask.append(m)
+
+    return np.array(points), np.array(mask)
+    
+
+def output_image(images, dst):
+    fig = plt.figure()
+    for i, image in enumerate(images):
+        text, img = image
+        fig.add_subplot(1, 3, i + 1)
+        plt.imshow(img)
+        plt.tick_params(labelbottom='off')
+        plt.tick_params(labelleft='off')
+        plt.gca().get_xaxis().set_ticks_position('none')
+        plt.gca().get_yaxis().set_ticks_position('none')
+        plt.xlabel(text)
+    plt.savefig(dst)
+    plt.close()
+
+
+if __name__ == '__main__':
+    test()
+