def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
training_filenames = util.get_files_in_directory(
"/media/data1/ww/sr_data/DIV2K_aug2/DIV2K_train_HR")
# target_dir_x2 = "/media/data1/ww/sr_data/DIV2K_aug2/291_LR_bicubic_X2/291_LR_bicubic/X2"
target_dir_x3 = "/media/data1/ww/sr_data/DIV2K_aug2/DIV2K_train_LR_bicubic_X3/DIV2K_train_LR_bicubic/X3"
target_dir_x4 = "/media/data1/ww/sr_data/DIV2K_aug2/DIV2K_train_LR_bicubic_X4/DIV2K_train_LR_bicubic/X4"
# util.make_dir(target_dir_x2)
util.make_dir(target_dir_x3)
util.make_dir(target_dir_x4)
for file_path in training_filenames:
org_image = util.load_image(file_path)
filename = os.path.basename(file_path)
filename, extension = os.path.splitext(filename)
# new_filename_x2 = target_dir_x2 + '/' +filename + 'x{}'.format(2)
new_filename_x3 = target_dir_x3 + '/' + filename + 'x{}'.format(3)
new_filename_x4 = target_dir_x4 + '/' + filename + 'x{}'.format(4)
# bicubic_image_x2 = util.resize_image_by_pil(org_image, 1 / 2)
bicubic_image_x3 = util.resize_image_by_pil(org_image, 1 / 3)
bicubic_image_x4 = util.resize_image_by_pil(org_image, 1 / 4)
# util.save_image(new_filename_x2 + extension, bicubic_image_x2)
util.save_image(new_filename_x3 + extension, bicubic_image_x3)
util.save_image(new_filename_x4 + extension, bicubic_image_x4)
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 make_input_image(self, file_path, true_image, scale=1, print_console=True):
if true_image is not None:
assert len(true_image.shape) == 3 and true_image.shape[2] == 3
fname, fext = os.path.splitext(file_path)
input_image = None
if fname.lower().endswith(TRUE_SUFFIX):
target = fname[:-len(TRUE_SUFFIX)] + INPUT_SUFFIX + fext
head, tail = os.path.split(target)
target = os.path.join(head, 'input', tail)
if os.path.exists(target):
input_image = util.set_image_alignment(util.load_image(target, print_console=print_console), scale)
if input_image is None and true_image is not None:
input_image = util.resize_image_by_pil(true_image, 1.0 / scale, resampling_method=self.resampling_method)
hblur_radius = random.randrange(0, self.hblur_max) / 100.
vblur_radius = random.randrange(0, self.vblur_max) / 100.
qua = random.randrange(self.jpegify[0], self.jpegify[1])
# if vblur_radius > 0:
# input_image = gaussian_filter1d(input_image, sigma=vblur_radius, axis=0)
# if hblur_radius > 0:
# input_image = gaussian_filter1d(input_image, sigma=hblur_radius, axis=1)
kx = int((hblur_radius + 0.5) * 2)
if kx % 2 == 0:
kx += 1
ky = int((vblur_radius + 0.5) * 2)
if ky % 2 == 0:
ky += 1
cv2.GaussianBlur(src=input_image, ksize=(kx, ky), sigmaX=hblur_radius, dst=input_image, sigmaY=vblur_radius)
if qua < 100:
tmpname = tempfile.mktemp(suffix='.jpg')
util.save_image(tmpname, input_image, qua, print_console=False)
input_image = util.load_image(tmpname, print_console=False)
os.unlink(tmpname)
return input_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 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(self,
file_path,
output_directory=None,
print_console=False,
save_output_images=True):
if save_output_images:
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
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)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
start = time.time()
output_y_image = self.do(input_y_image, input_bicubic_y_image)
end = time.time()
psnr, ssim = util.compute_psnr_and_ssim(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.psnr_calc_border_size)
loss_image = util.get_loss_image(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.psnr_calc_border_size)
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_ycbcr_image[:, :, 1:3])
if save_output_images:
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)
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)
start = time.time()
output_image = self.do(input_image, input_bicubic_y_image)
end = time.time()
psnr, ssim = util.compute_psnr_and_ssim(
true_image,
output_image,
border_size=self.psnr_calc_border_size)
if save_output_images:
util.save_image(output_directory + file_path, true_image)
util.save_image(
output_directory + filename + "_result" + extension,
output_image)
else:
return None, None
if print_console:
print("[%s] PSNR:%f, SSIM:%f" % (filename, psnr, ssim))
elapsed_time = end - start
return psnr, ssim, elapsed_time
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
print("Building x%d augmented data." % FLAGS.augment_level)
training_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" +
FLAGS.dataset + "/")
target_dir = FLAGS.data_dir + "/" + FLAGS.dataset + ("_%d/" %
FLAGS.augment_level)
util.make_dir(target_dir)
for file_path in training_filenames:
org_image = util.load_image(file_path)
filename = os.path.basename(file_path)
filename, extension = os.path.splitext(filename)
new_filename = target_dir + filename
util.save_image(new_filename + extension, org_image)
if FLAGS.augment_level >= 2:
ud_image = np.flipud(org_image)
util.save_image(new_filename + "_v" + extension, ud_image)
if FLAGS.augment_level >= 3:
lr_image = np.fliplr(org_image)
util.save_image(new_filename + "_h" + extension, lr_image)
if FLAGS.augment_level >= 4:
lr_image = np.fliplr(org_image)
lrud_image = np.flipud(lr_image)
util.save_image(new_filename + "_hv" + extension, lrud_image)
if FLAGS.augment_level >= 5:
rotated_image1 = np.rot90(org_image)
util.save_image(new_filename + "_r1" + extension, rotated_image1)
if FLAGS.augment_level >= 6:
rotated_image2 = np.rot90(org_image, -1)
util.save_image(new_filename + "_r2" + extension, rotated_image2)
if FLAGS.augment_level >= 7:
rotated_image1 = np.rot90(org_image)
ud_image = np.flipud(rotated_image1)
util.save_image(new_filename + "_r1_v" + extension, ud_image)
if FLAGS.augment_level >= 8:
rotated_image2 = np.rot90(org_image, -1)
ud_image = np.flipud(rotated_image2)
util.save_image(new_filename + "_r2_v" + extension, ud_image)
def save_true_batch_image(self, image_number, image):
return util.save_image(
self.batch_dir + "/" + TRUE_IMAGE_DIR + "/%06d.bmp" % image_number,
image)
def save_interpolated_batch_image(self, image_number, image):
return util.save_image(
self.batch_dir + "/" + INTERPOLATED_IMAGE_DIR +
"/%06d.bmp" % image_number, image)
def save_input_batch_image(self, image_number, image):
return util.save_image(
self.batch_dir + "/" + INPUT_IMAGE_DIR +
"/%06d.bmp" % image_number, image)
def build_batch(self, data_dir, batch_dir):
""" load from input files. Then save batch images on file to reduce memory consumption. """
print("Building batch images for %s..." % batch_dir)
filenames = util.get_files_in_directory(data_dir)
images_count = 0
util.make_dir(batch_dir)
util.clean_dir(batch_dir)
util.make_dir(batch_dir + "/" + INPUT_IMAGE_DIR)
util.make_dir(batch_dir + "/" + INTERPOLATED_IMAGE_DIR)
util.make_dir(batch_dir + "/" + TRUE_IMAGE_DIR)
for filename in filenames:
output_window_size = self.batch_image_size * self.scale
output_window_stride = self.stride * self.scale
input_image, input_bicubic_image = self.input.load_input_image(
filename,
rescale=True,
resampling_method=self.resampling_method)
test_image = self.true.load_test_image(filename)
# split into batch images
input_batch_images = util.get_split_images(input_image,
self.batch_image_size,
stride=self.stride)
input_bicubic_batch_images = util.get_split_images(
input_bicubic_image,
output_window_size,
stride=output_window_stride)
if input_batch_images is None or input_bicubic_batch_images is None:
continue
input_count = input_batch_images.shape[0]
test_batch_images = util.get_split_images(
test_image, output_window_size, stride=output_window_stride)
for i in range(input_count):
# util.save_image_data(batch_dir + "/" + INPUT_IMAGE_DIR + "/%06d.npy" % images_count, input_batch_images[i])
# util.save_image_data(batch_dir + "/" + INTERPOLATED_IMAGE_DIR + "/%06d.npy" % images_count,
# input_bicubic_batch_images[i])
# util.save_image_data(batch_dir + "/" + TRUE_IMAGE_DIR + "/%06d.npy" % images_count, test_batch_images[i])
util.save_image(
batch_dir + "/" + INPUT_IMAGE_DIR +
"/%06d.bmp" % images_count, input_batch_images[i])
util.save_image(
batch_dir + "/" + INTERPOLATED_IMAGE_DIR +
"/%06d.bmp" % images_count, input_bicubic_batch_images[i])
util.save_image(
batch_dir + "/" + TRUE_IMAGE_DIR +
"/%06d.bmp" % images_count, test_batch_images[i])
images_count += 1
print("%d mini-batch images are built(saved)." % images_count)
config = configparser.ConfigParser()
config.add_section("batch")
config.set("batch", "count", str(images_count))
config.set("batch", "scale", str(self.scale))
config.set("batch", "batch_image_size", str(self.batch_image_size))
config.set("batch", "stride", str(self.stride))
config.set("batch", "channels", str(self.channels))
config.set("batch", "jpeg_mode", str(self.jpeg_mode))
config.set("batch", "max_value", str(self.max_value))
with open(batch_dir + "/batch_images.ini", "w") as configfile:
config.write(configfile)
def load_and_evaluate_tflite_graph(
output_dir,
data_dir,
test_data,
model_path=os.path.join(os.getcwd(),
'model_to_freeze/converted_model.tflite')):
# https://stackoverflow.com/questions/50443411/how-to-load-a-tflite-model-in-script
# https://www.tensorflow.org/lite/convert/python_api#tensorflow_lite_python_interpreter_
output_directory = output_dir
output_directory += "/" + "tflite" + "/"
util.make_dir(output_directory)
test_filepaths = util.get_files_in_directory(data_dir + "/" + test_data)
total_psnr = total_ssim = total_time = 0
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
# interpreter = tf.contrib.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
for file_path in test_filepaths:
# split filename from extension
filename, extension = os.path.splitext(file_path)
# prepare true image
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), FLAGS.scale)
# start the timer
if true_image.shape[2] == 3 and FLAGS.channels == 1:
# prepare input and ground truth images
input_y_image = loader.build_input_image(true_image,
channels=FLAGS.channels,
scale=FLAGS.scale,
alignment=FLAGS.scale,
convert_ycbcr=True)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
FLAGS.scale,
resampling_method=FLAGS.resampling_method)
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
# pass inputs through the model (need to recast and reshape inputs)
input_y_image_reshaped = input_y_image.astype('float32')
input_y_image_reshaped = input_y_image_reshaped.reshape(
1, input_y_image.shape[0], input_y_image.shape[1],
FLAGS.channels)
input_bicubic_y_image_reshaped = input_bicubic_y_image.astype(
'float32')
input_bicubic_y_image_reshaped = input_bicubic_y_image_reshaped.reshape(
1, input_bicubic_y_image.shape[0],
input_bicubic_y_image.shape[1], FLAGS.channels)
interpreter.set_tensor(input_details[0]['index'],
input_y_image_reshaped) # pass x
interpreter.set_tensor(input_details[1]['index'],
input_bicubic_y_image_reshaped) # pass x2
start = time.time()
interpreter.invoke()
end = time.time()
output_y_image = interpreter.get_tensor(
output_details[0]['index']) # get y
# resize the output into an image
output_y_image = output_y_image.reshape(output_y_image.shape[1],
output_y_image.shape[2],
FLAGS.channels)
# calculate psnr and ssim for the output
psnr, ssim = util.compute_psnr_and_ssim(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=FLAGS.psnr_calc_border_size)
# get the loss image
loss_image = util.get_loss_image(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=FLAGS.psnr_calc_border_size)
# get output color image
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_ycbcr_image[:, :, 1:3])
# save all images
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)
elapsed_time = end - start
total_psnr += psnr
total_ssim += ssim
total_time += elapsed_time
testSize = len(test_filepaths)
print("Model Average [%s] PSNR:%f, SSIM:%f, Elapsed Time:%f" %
(test_data, total_psnr / testSize, total_ssim / testSize,
total_time / testSize))
def do_for_evaluate_with_output(self,
file_path,
output_directory,
print_console=False):
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
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 == 3:
# for color images
input_image = util.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
input_bicubic_image = util.resize_image_by_pil(
input_image,
self.scale,
resampling_method=self.resampling_method)
output_image, spend_time = self.do(input_image) # SR
SR_y = eva.convert_rgb_to_y(output_image)
HR_y = eva.convert_rgb_to_y(true_image)
psnr_predicted = eva.PSNR(np.uint8(HR_y),
np.uint8(SR_y),
shave_border=self.psnr_calc_border_size)
ssim_predicted = eva.compute_ssim(np.squeeze(HR_y),
np.squeeze(SR_y))
mse = util.compute_mse(HR_y,
SR_y,
border_size=self.psnr_calc_border_size)
loss_image = util.get_loss_image(
HR_y, SR_y, border_size=self.psnr_calc_border_size)
util.save_image(output_directory + file_path[29:], true_image)
util.save_image(
output_directory + filename[28:] + "_input" + extension,
input_image)
util.save_image(
output_directory + filename[28:] + "_input_bicubic" +
extension, input_bicubic_image)
util.save_image(
output_directory + filename[28:] + "_sr" + extension,
output_image)
util.save_image(
output_directory + filename[28:] + "_loss" + extension,
loss_image)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = util.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
output_image, spend_time = self.do(input_image)
psnr_predicted = eva.PSNR(np.uint8(true_image),
np.uint8(output_image),
shave_border=self.psnr_calc_border_size)
ssim_predicted = eva.compute_ssim(np.squeeze(true_image),
np.squeeze(output_image))
mse = util.compute_mse(true_image,
output_image,
border_size=self.psnr_calc_border_size)
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_sr" + extension,
output_image)
else:
psnr_predicted = 0.0
ssim_predicted = 0.0
mse = 0.0
spend_time = 0.0
if print_console:
print("[%s] psnr:%f, ssim:%f, time:%f" %
(filename, psnr_predicted, ssim_predicted, spend_time))
return mse, psnr_predicted, ssim_predicted, spend_time
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_for_evaluate(self,
file_path,
output_directory="output",
output=True,
print_console=False):
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
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_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,
resampling_method=self.resampling_method)
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=6 + 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)
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=6 + 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)
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=6 + self.scale)
if output:
util.save_image(output_directory + file_path, true_image)
util.save_image(
output_directory + filename + "_result" + extension,
output_image)
else:
mse = 0
if print_console:
print("MSE:%f, PSNR:%f" % (mse, util.get_psnr(mse)))
return mse
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
