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16 bits png support. #97

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16 bits png support. #97

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42 changes: 33 additions & 9 deletions pix2pix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,8 @@
parser.add_argument("--flip", dest="flip", action="store_true", help="flip images horizontally")
parser.add_argument("--no_flip", dest="flip", action="store_false", help="don't flip images horizontally")
parser.set_defaults(flip=True)
parser.add_argument("--png16bits", dest="png16bits", action="store_true", help="use png 16 bits images encoder and decoders")
parser.set_defaults(png16bits=False)
parser.add_argument("--lr", type=float, default=0.0002, help="initial learning rate for adam")
parser.add_argument("--beta1", type=float, default=0.5, help="momentum term of adam")
parser.add_argument("--l1_weight", type=float, default=100.0, help="weight on L1 term for generator gradient")
Expand Down Expand Up @@ -258,7 +260,10 @@ def get_name(path):
path_queue = tf.train.string_input_producer(input_paths, shuffle=a.mode == "train")
reader = tf.WholeFileReader()
paths, contents = reader.read(path_queue)
raw_input = decode(contents)
if a.png16bits:
raw_input = decode(contents,dtype=tf.uint16)
else:
raw_input = decode(contents)
raw_input = tf.image.convert_image_dtype(raw_input, dtype=tf.float32)

assertion = tf.assert_equal(tf.shape(raw_input)[2], 3, message="image does not have 3 channels")
Expand Down Expand Up @@ -586,7 +591,11 @@ def main():
with tf.variable_scope("generator"):
batch_output = deprocess(create_generator(preprocess(batch_input), 3))

output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
if a.png16bits:
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint16, saturate=True)[0]
else:
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]

if a.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif a.output_filetype == "jpeg":
Expand Down Expand Up @@ -656,7 +665,10 @@ def convert(image):
size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)

return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
if a.png16bits:
return tf.image.convert_image_dtype(image, dtype=tf.uint16, saturate=True)
else:
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)

# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
Expand All @@ -677,21 +689,33 @@ def convert(image):
}

# summaries
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)
if not a.png16bits:
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)

with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)

with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)

else:
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", tf.image.convert_image_dtype(converted_inputs,dtype=tf.uint8))

with tf.name_scope("targets_summary"):
tf.summary.image("targets", tf.image.convert_image_dtype(converted_targets,dtype=tf.uint8))

with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", tf.image.convert_image_dtype(converted_outputs,dtype=tf.uint8))

with tf.name_scope("predict_real_summary"):
tf.summary.image("predict_real", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))

with tf.name_scope("predict_fake_summary"):
tf.summary.image("predict_fake", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))


tf.summary.scalar("discriminator_loss", model.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)
Expand Down