malefs · October 13, 2022 07:54 · Jan 9, 2016 · Jan 9, 2016 · Jan 9, 2016 · Jan 9, 2016
diff --git a/forward.py b/forward.py
@@ -12,7 +12,9 @@
 # 4. Create a directory "kidframe" next to this python file, put your extracted video frames inside. 
 # 5. Make sure to have a directory called "out" next to it. Inside "out" a second directory analogue to first ("kidframe")-
 # 6. Run: python forward.py
-# 7. Grab your rendered frames at "out/kidframe/xxx0001.jpg". ;-)
+# 7. Grab your rendered frames at "out/kidframe/xxx0001.jpg". 
+# 8. Recombine frames with FFMPEG, e.g:
+# cat *.jpg | ffmpeg -f image2pipe -r 25 -vcodec mjpeg -i - -vcodec libx264 out.mp4
 
 import tensorflow as tf
 import skimage.transform

diff --git a/forward.py b/forward.py
@@ -1,5 +1,6 @@
 # "Colorizing B/W Movies with Neural Nets", 
 # Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
+# BACKGROUND: http://tinyclouds.org/colorize/
 # DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
 # USAGE:
 # 1. Download TensorFlow model from: http://tinyclouds.org/colorize/

diff --git a/forward.py b/forward.py
@@ -1,7 +1,8 @@
 # "Colorizing B/W Movies with Neural Nets", 
 # Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
+# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
 # USAGE:
-# 1. Download TensorFlow VGG16 model from: http://tinyclouds.org/colorize/
+# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/
 # 2. Use FFMPEG or such to extract frames from video.
 # 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
 # mogrify -resize 224x224 *.jpg

diff --git a/forward.py b/forward.py
@@ -0,0 +1,67 @@
+# "Colorizing B/W Movies with Neural Nets", 
+# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
+# USAGE:
+# 1. Download TensorFlow VGG16 model from: http://tinyclouds.org/colorize/
+# 2. Use FFMPEG or such to extract frames from video.
+# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
+# mogrify -resize 224x224 *.jpg
+# mogrify -gravity center -background black -extent 224x224 *.jpg
+# mogrify -colorspace sRGB -type TrueColor *.jpg
+# 4. Create a directory "kidframe" next to this python file, put your extracted video frames inside. 
+# 5. Make sure to have a directory called "out" next to it. Inside "out" a second directory analogue to first ("kidframe")-
+# 6. Run: python forward.py
+# 7. Grab your rendered frames at "out/kidframe/xxx0001.jpg". ;-)
+
+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('.jpg'):
+                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("kidframes")
+
+for image in images:
+    print image
+    shark_gray = 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: shark_gray })
+        filename = "out/"+image
+        imsave(filename, inferred_batch[0])
+        print "saved " + filename
+    #sess.close()