Created
April 22, 2016 02:46
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import tensorflow as tf | |
import skimage.transform | |
from skimage.io import imsave, imread | |
import os | |
from os import listdir, path | |
from os.path import isfile, join | |
def get_directory(folder): | |
foundfile = [] | |
for path, subdirs, files in os.walk(folder): | |
for name in files: | |
found = os.path.join(path, name) | |
if name.endswith('.png'): | |
foundfile.append(found) | |
break | |
foundfile.sort() | |
return foundfile | |
def load_image(path): | |
img = imread(path) | |
# crop image from center | |
short_edge = min(img.shape[:2]) | |
yy = int((img.shape[0] - short_edge) / 2) | |
xx = int((img.shape[1] - short_edge) / 2) | |
crop_img = img[yy : yy + short_edge, xx : xx + short_edge] | |
# resize to 224, 224 | |
img = skimage.transform.resize(crop_img, (224, 224)) | |
# desaturate image | |
return (img[:,:,0] + img[:,:,1] + img[:,:,2]) / 3.0 | |
with open("colorize.tfmodel", mode='rb') as f: | |
fileContent = f.read() | |
graph_def = tf.GraphDef() | |
graph_def.ParseFromString(fileContent) | |
grayscale = tf.placeholder("float", [1, 224, 224, 1]) | |
tf.import_graph_def(graph_def, input_map={ "grayscale": grayscale }, name='') | |
images = get_directory("input") | |
for image in images: | |
print(image) | |
scene = load_image(image).reshape(1, 224, 224, 1) | |
with tf.Session() as sess: | |
inferred_rgb = sess.graph.get_tensor_by_name("inferred_rgb:0") | |
inferred_batch = sess.run(inferred_rgb, feed_dict={ grayscale: scene }) | |
filename = "output/"+image | |
imsave(filename, inferred_batch[0]) | |
print("saved " + filename) | |
sess.close() |
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