def do_for_file(self, file_path, output_folder="output"):
org_image = util.load_image(file_path)
filename, extension = os.path.splitext(os.path.basename(file_path))
output_folder += "/" + self.name + "/"
util.save_image(output_folder + filename + extension, org_image)
#Esta linea para solo blanco y negro
org_image = util.convert_rgb_to_y(org_image)
util.save_image(output_folder + filename + "_y" + extension, org_image)
if len(org_image.shape) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
input_y_image = util.convert_rgb_to_y(org_image)
scaled_image = util.resize_image_by_pil(input_y_image, self.scale, resampling_method=self.resampling_method)
util.save_image(output_folder + filename + "_bicubic_y" + extension, scaled_image)
output_y_image = self.do(input_y_image)
util.save_image(output_folder + filename + "_result_y" + extension, output_y_image)
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(
util.resize_image_by_pil(org_image, self.scale, self.resampling_method))
image = util.convert_y_and_cbcr_to_rgb(output_y_image, scaled_ycbcr_image[:, :, 1:3])
else:
scaled_image = util.resize_image_by_pil(org_image, self.scale, resampling_method=self.resampling_method)
util.save_image(output_folder + filename + "_bicubic_y" + extension, scaled_image)
image = self.do(org_image)
util.save_image(output_folder + filename + "_residual_y" + extension, image)
final_image = scaled_image + image
util.save_image(output_folder + filename + "_result" + extension, final_image)
def do_util(src, dest):
with Timer('util: load'):
inp = util.load_image(src)
with Timer('util: resize'):
resized = util.resize_image_by_pil(inp, 2)
with Timer('util: extract Y'):
only_y = util.convert_rgb_to_y(inp)
only_y = util.convert_rgb_to_y(resized) # simulate upscale
with Timer('util: rgb => YCbCr'):
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(resized)
with Timer('util: Y + YCbCr -> rgb'):
image = util.convert_y_and_cbcr_to_rgb(only_y, scaled_ycbcr_image[:, :,
1:3])
with Timer('util: save'):
util.save_image(dest, image)
def build_input_image(image,
width=0,
height=0,
channels=1,
scale=1,
alignment=0,
convert_ycbcr=True):
"""
build input image from file.
crop, adjust the image alignment for the scale factor, resize, convert color space.
"""
if width != 0 and height != 0:
if image.shape[0] != height or image.shape[1] != width:
x = (image.shape[1] - width) // 2
y = (image.shape[0] - height) // 2
image = image[y:y + height, x:x + width, :]
if alignment > 1:
image = util.set_image_alignment(image, alignment)
if channels == 1 and image.shape[2] == 3:
if convert_ycbcr:
image = util.convert_rgb_to_y(image)
else:
if convert_ycbcr:
image = util.convert_rgb_to_ycbcr(image)
if scale != 1:
image = util.resize_image_by_pil(image, 1.0 / scale)
return image
def do_for_evaluate(self, file_path, print_console=False):
true_image = util.set_image_alignment(util.load_image(file_path, print_console=False), self.scale)
if true_image.shape[2] == 3 and self.channels == 1:
# for color images
input_y_image = loader.build_input_image(true_image, channels=self.channels, scale=self.scale,
alignment=self.scale, convert_ycbcr=True)
true_y_image = util.convert_rgb_to_y(true_image)
input_bicubic_y_image = util.resize_image_by_pil(input_y_image, self.scale,
resampling_method=self.resampling_method)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
mse = util.compute_mse(true_y_image, output_y_image, border_size=self.psnr_calc_border_size)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = loader.build_input_image(true_image, channels=self.channels, scale=self.scale,
alignment=self.scale)
input_bicubic_y_image = util.resize_image_by_pil(input_image, self.scale,
resampling_method=self.resampling_method)
output_image = self.do(input_image, input_bicubic_y_image)
mse = util.compute_mse(true_image, output_image, border_size=self.psnr_calc_border_size)
else:
mse = 0
if print_console:
print("MSE:%f, PSNR:%f" % (mse, util.get_psnr(mse)))
return mse
def evaluate_bicubic(self, file_path, print_console=False):
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), self.scale)
if true_image.shape[2] == 3 and self.channels == 1:
input_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale,
convert_ycbcr=True)
true_image = util.convert_rgb_to_y(true_image)
elif true_image.shape[2] == 1 and self.channels == 1:
input_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale)
else:
return None, None
input_bicubic_image = util.resize_image_by_pil(
input_image, self.scale, resampling_method=self.resampling_method)
psnr, ssim = util.compute_psnr_and_ssim(
true_image,
input_bicubic_image,
border_size=self.psnr_calc_border_size)
if print_console:
print("PSNR:%f, SSIM:%f" % (psnr, ssim))
return psnr, ssim
def log_to_tensorboard(self, test_filename, psnr, save_meta_data=True):
if self.enable_log is False:
return
# todo
save_meta_data = False
org_image = util.set_image_alignment(util.load_image(test_filename, print_console=False), self.scale)
if len(org_image.shape) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
org_image = util.convert_rgb_to_y(org_image)
input_image = util.resize_image_by_pil(org_image, 1.0 / self.scale, resampling_method=self.resampling_method)
bicubic_image = util.resize_image_by_pil(input_image, self.scale, resampling_method=self.resampling_method)
feed_dict = {self.x: input_image.reshape([1, input_image.shape[0], input_image.shape[1], input_image.shape[2]]),
self.x2: bicubic_image.reshape(
[1, bicubic_image.shape[0], bicubic_image.shape[1], bicubic_image.shape[2]]),
self.y: org_image.reshape([1, org_image.shape[0], org_image.shape[1], org_image.shape[2]]),
self.dropout: 1.0,
self.is_training: 0}
if save_meta_data:
# profiler = tf.profiler.Profile(self.sess.graph)
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
summary_str, _ = self.sess.run([self.summary_op, self.loss], feed_dict=feed_dict, options=run_options,
run_metadata=run_metadata)
self.test_writer.add_run_metadata(run_metadata, "step%d" % self.epochs_completed)
filename = self.checkpoint_dir + "/" + self.name + "_metadata.txt"
with open(filename, "w") as out:
out.write(str(run_metadata))
# filename = self.checkpoint_dir + "/" + self.name + "_memory.txt"
# tf.profiler.write_op_log(
# tf.get_default_graph(),
# log_dir=self.checkpoint_dir,
# #op_log=op_log,
# run_meta=run_metadata)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(), run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
else:
summary_str, _ = self.sess.run([self.summary_op, self.loss], feed_dict=feed_dict)
self.train_writer.add_summary(summary_str, self.epochs_completed)
if not self.use_l1_loss:
util.log_scalar_value(self.train_writer, 'PSNR', self.training_psnr_sum / self.training_step,
self.epochs_completed)
util.log_scalar_value(self.train_writer, 'LR', self.lr, self.epochs_completed)
self.train_writer.flush()
util.log_scalar_value(self.test_writer, 'PSNR', psnr, self.epochs_completed)
self.test_writer.flush()
def build_image_set(file_path, channels=1, scale=1, convert_ycbcr=True, resampling_method="bicubic",
print_console=True):
true_image = util.set_image_alignment(util.load_image(file_path, print_console=print_console), scale)
if channels == 1 and true_image.shape[2] == 3 and convert_ycbcr:
true_image = util.convert_rgb_to_y(true_image)
input_image = util.resize_image_by_pil(true_image, 1.0 / scale, resampling_method=resampling_method)
input_interpolated_image = util.resize_image_by_pil(input_image, scale, resampling_method=resampling_method)
return input_image, input_interpolated_image, true_image
def doframe(self, org_image):
input_y_image = util.convert_rgb_to_y(org_image)
scaled_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
output_y_image = self.do(input_y_image)
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(
util.resize_image_by_pil(org_image, self.scale,
self.resampling_method))
return util.convert_y_and_cbcr_to_rgb(output_y_image,
scaled_ycbcr_image[:, :, 1:3])
def load_batch_image(self, max_value):
image = None
file_path = None
while image is None:
file_path = self.filenames[self.get_next_image_no()]
if self.image_maker.patch_scale_max > 1.0:
patch_scale_value = random.randrange(100, self.image_maker.patch_scale_max * 100) / 100.
else:
patch_scale_value = 1.
image = self.load_random_patch(file_path, patch_scale_value)
input_image = self.image_maker.make_input_image(file_path, image, scale=self.scale,
print_console=False)
flip = random.randrange(0, 4)
if flip == 1 or flip == 3:
input_image = np.flipud(input_image)
image = np.flipud(image)
if flip == 2 or flip == 3:
input_image = np.fliplr(input_image)
image = np.fliplr(image)
rot90 = random.randrange(0, 2)
if rot90 == 1:
input_image = np.rot90(input_image)
image = np.rot90(image)
input_image = util.convert_rgb_to_y(input_image)
input_bicubic_image = util.resize_image_by_pil(input_image, self.scale)
if max_value != 255:
scale = max_value / 255.0
input_image = np.multiply(input_image, scale)
input_bicubic_image = np.multiply(input_bicubic_image, scale)
image = np.multiply(image, scale)
image = util.convert_rgb_to_y(image)
return input_image, input_bicubic_image, image
def predict_im(self, org_image):
if len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
input_y_image = util.convert_rgb_to_y(org_image)
output_y_image = self.do(input_y_image)
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(
util.resize_image_by_pil(org_image, self.scale,
self.resampling_method))
image = util.convert_y_and_cbcr_to_rgb(
output_y_image, scaled_ycbcr_image[:, :, 1:3])
else:
image = self.do(org_image)
return image
def do_for_evaluate(self, file_path, output_directory="output", output=True, print_console=True):
filename, extension = os.path.splitext(file_path)
output_directory += "/"
true_image = util.set_image_alignment(util.load_image(file_path), self.scale)
if true_image.shape[2] == 3 and self.channels == 1:
input_y_image = loader.build_input_image(true_image, channels=self.channels, scale=self.scale,
alignment=self.scale, convert_ycbcr=True, jpeg_mode=self.jpeg_mode)
# for color images
if output:
input_bicubic_y_image = util.resize_image_by_pil(input_y_image, self.scale)
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image, jpeg_mode=self.jpeg_mode)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
mse = util.compute_mse(true_ycbcr_image[:, :, 0:1], output_y_image, border_size=self.scale)
loss_image = util.get_loss_image(true_ycbcr_image[:, :, 0:1], output_y_image, border_size=self.scale)
output_color_image = util.convert_y_and_cbcr_to_rgb(output_y_image, true_ycbcr_image[:, :, 1:3],
jpeg_mode=self.jpeg_mode)
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_input" + extension, input_y_image)
util.save_image(output_directory + filename + "_input_bicubic" + extension, input_bicubic_y_image)
util.save_image(output_directory + filename + "_true_y" + extension, true_ycbcr_image[:, :, 0:1])
util.save_image(output_directory + filename + "_result" + extension, output_y_image)
util.save_image(output_directory + filename + "_result_c" + extension, output_color_image)
util.save_image(output_directory + filename + "_loss" + extension, loss_image)
else:
true_y_image = util.convert_rgb_to_y(true_image, jpeg_mode=self.jpeg_mode)
output_y_image = self.do(input_y_image)
mse = util.compute_mse(true_y_image, output_y_image, border_size=self.scale)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = loader.build_input_image(true_image, channels=self.channels, scale=self.scale, alignment=self.scale)
output_image = self.do(input_image)
mse = util.compute_mse(true_image, output_image, border_size=self.scale)
if output:
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_result" + extension, output_image)
if print_console:
print("MSE:%f PSNR:%f" % (mse, util.get_psnr(mse)))
return mse
def upscale(self, org_image):
assert len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1
input_y_image = util.convert_rgb_to_y(org_image)
big_blurry_input_image = util.resize_image_by_pil(
org_image, self.scale)
big_blurry_input_ycbcr_image = util.convert_rgb_to_ycbcr(
big_blurry_input_image)
bbi_input_y_image, bbi_input_cbcr_image = util.convert_ycbcr_to_y_cbcr(
big_blurry_input_ycbcr_image)
output_y_image = self.do(input_y_image, bbi_input_y_image)
output_image = util.convert_y_and_cbcr_to_rgb(output_y_image,
bbi_input_cbcr_image)
return output_image
def do_for_file(self, file_path, output_folder="output"):
filename, extension = os.path.splitext(file_path)
output_folder += "/"
org_image = util.load_image(file_path)
util.save_image(output_folder + file_path, org_image)
if len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
input_y_image = util.convert_rgb_to_y(org_image,
jpeg_mode=self.jpeg_mode)
scaled_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
util.save_image(
output_folder + filename + "_bicubic_y" + extension,
scaled_image)
output_y_image = self.do(input_y_image)
util.save_image(output_folder + filename + "_result_y" + extension,
output_y_image)
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(
util.resize_image_by_pil(org_image, self.scale,
self.resampling_method),
jpeg_mode=self.jpeg_mode)
image = util.convert_y_and_cbcr_to_rgb(output_y_image,
scaled_ycbcr_image[:, :,
1:3],
jpeg_mode=self.jpeg_mode)
else:
scaled_image = util.resize_image_by_pil(
org_image,
self.scale,
resampling_method=self.resampling_method)
util.save_image(
output_folder + filename + "_bicubic_y" + extension,
scaled_image)
image = self.do(org_image)
util.save_image(output_folder + filename + "_result" + extension,
image)
def do_for_file(self, file_path, output_folder="output"):
org_image = cv2.imread(file_path)
assert len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1
input_y_image = util.convert_rgb_to_y(org_image)
big_blurry_input_image = util.resize_image_by_pil(
org_image, self.scale)
big_blurry_input_ycbcr_image = util.convert_rgb_to_ycbcr(
big_blurry_input_image)
bbi_input_y_image, bbi_input_cbcr_image = util.convert_ycbcr_to_y_cbcr(
big_blurry_input_ycbcr_image)
output_y_image = self.do(input_y_image, bbi_input_y_image)
output_image = util.convert_y_and_cbcr_to_rgb(output_y_image,
bbi_input_cbcr_image)
target_path = os.path.basename(file_path)
cv2.imwrite(os.path.join(output_folder, target_path), output_image)
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
print("Building Y channel data...")
training_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" +
FLAGS.dataset + "/")
target_dir = FLAGS.data_dir + "/" + FLAGS.dataset + "_y/"
util.make_dir(target_dir)
for file_path in training_filenames:
org_image = util.load_image(file_path)
if org_image.shape[2] == 3:
org_image = util.convert_rgb_to_y(org_image)
filename = os.path.basename(file_path)
filename, extension = os.path.splitext(filename)
new_filename = target_dir + filename
util.save_image(new_filename + ".bmp", org_image)
def do_for_file(self, file_path, output_folder="output"):
org_image = util.load_image(file_path)
filename, extension = os.path.splitext(os.path.basename(file_path))
output_folder += "/" + self.name + "/"
# util.save_image(output_folder + filename + extension, org_image)
if os.path.exists(output_folder + filename + extension):
print("File already exists in the target directory")
return
if org_image.shape[0] + org_image.shape[1] >= 1024:
print("Image is too big: ", org_image.shape)
image = org_image
elif len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
input_y_image = util.convert_rgb_to_y(org_image)
# scaled_image = util.resize_image_by_pil(input_y_image, self.scale, resampling_method=self.resampling_method)
# util.save_image(output_folder + filename + "_bicubic_y" + extension, scaled_image)
output_y_image = self.do(input_y_image)
# util.save_image(output_folder + filename + "_result_y" + extension, output_y_image)
scaled_ycbcr_image = util.convert_rgb_to_ycbcr(
util.resize_image_by_pil(org_image, self.scale,
self.resampling_method))
image = util.convert_y_and_cbcr_to_rgb(
output_y_image, scaled_ycbcr_image[:, :, 1:3])
else:
# scaled_image = util.resize_image_by_pil(org_image, self.scale, resampling_method=self.resampling_method)
# util.save_image(output_folder + filename + "_bicubic_y" + extension, scaled_image)
image = self.do(org_image)
# util.save_image(output_folder + filename + "_result" + extension, image)
file_path = output_folder + filename + extension
util.save_image(file_path, image)
print("Saved at ", file_path)
def do_for_evaluate_with_output(self,
file_path,
output_directory=None,
print_console=False):
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), self.scale)
# Assuming the image is color
assert true_image.shape[
2] == 3 and self.channels == 1, "Only 3-channel images are supported"
input_image = loader.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
input_y_image = util.convert_rgb_to_y(input_image)
true_y_image = util.convert_rgb_to_y(true_image)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
psnr, ssim = util.compute_psnr_and_ssim(
true_y_image,
output_y_image,
border_size=self.psnr_calc_border_size)
if output_directory:
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
_, true_cbcr = util.convert_ycbcr_to_y_cbcr(true_ycbcr_image)
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_cbcr)
loss_image = util.get_loss_image(
true_y_image,
output_y_image,
border_size=self.psnr_calc_border_size)
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_input" + extension,
input_y_image)
util.save_image(
output_directory + filename + "_input_bicubic" + extension,
input_bicubic_y_image)
util.save_image(
output_directory + filename + "_true_y" + extension,
true_ycbcr_image[:, :, 0:1])
util.save_image(
output_directory + filename + "_result" + extension,
output_y_image)
util.save_image(
output_directory + filename + "_result_c" + extension,
output_color_image)
util.save_image(output_directory + filename + "_loss" + extension,
loss_image)
if print_console:
print("[%s] PSNR:%f, SSIM:%f" % (filename, psnr, ssim))
return psnr, ssim
