def hsl_colors_to_hex_codes(color_list):
rgb_colors = hasel.hsl2rgb(color_list.reshape(-1, 1, 3)).reshape(-1, 3)
rgb_colors = np.clip(rgb_colors, 0, 255)
hex_codes = get_hex_codes(rgb_colors)
return hex_codes
greyscale_image.size, resample=Image.LANCZOS
)
colored_image_np = np.array(colored_image)
# Transfer hue and saturation from the color image to the greyscale image via the
# selected color space
if args.via_color_space == "hsl":
greyscale_image_np_hsl = rgb2hsl(greyscale_image_np)
colored_image_np_hsl = rgb2hsl(colored_image_np)
greyscale_image_np_hsl[:, :, 0] = colored_image_np_hsl[:, :, 0] # hue
greyscale_image_np_hsl[:, :, 1] = colored_image_np_hsl[
:, :, 1
] # saturation
full_res_colored_image = hsl2rgb(greyscale_image_np_hsl)
else:
greyscale_image_np_hsv = rgb2hsv(greyscale_image_np)
colored_image_np_hsv = rgb2hsv(colored_image_np)
greyscale_image_np_hsv[:, :, 0] = colored_image_np_hsv[:, :, 0] # hue
greyscale_image_np_hsv[:, :, 1] = colored_image_np_hsv[
:, :, 1
] # saturation
full_res_colored_image = hsv2rgb(greyscale_image_np_hsv)
full_res_colored_image = (255 * full_res_colored_image).astype(np.uint8)
Image.fromarray(full_res_colored_image).save(
os.path.join(output_path, greyscale_file_path.stem + '.jpg'), quality=95
)
def hsl2rgb_wrap(hsl_mat):
rgb_list = []
for i in range(hsl_mat.shape[0]):
rgb_list.append(hasel.hsl2rgb(hsl_mat[i]))
return np.array(rgb_list)
def hsllist2rl(hsl_color_list):
rgb_colors = hasel.hsl2rgb(hsl_color_list.reshape(-1, 1, 3)).reshape(-1, 3)
rl_values = []
for i in range(rgb_colors.shape[0]):
rl_values.append(rgb2rl(rgb_colors[i]))
return np.array(rl_values)
def hsllist2hex(color_list):
rgb_colors = hasel.hsl2rgb(color_list.reshape(-1, 1, 3)).reshape(-1, 3)
rgb_colors = np.clip(rgb_colors, 0, 255)
hex_codes = rgblist2hex(rgb_colors)
return hex_codes
def hsl2rgb(hsl):
return hasel.hsl2rgb(np.array([hsl]).reshape(-1, 1, 3)).reshape(-1, 3)[0]
dataset = "training" if i < split_index else "validation"
num_examples = 1 if dataset == "training" else 1
for j in range(num_examples):
x1, y1, x2, y2 = get_random_coordinates(target_img)
target_window = target_img[y1:y2, x1:x2, :]
blurry_window = blurry_img[y1:y2, x1:x2, :]
img_hsl = rgb2hsl(target_window)
blurry_img_hsl = rgb2hsl(blurry_window)
# Input image has original lightness channel, but blurry hue and saturation
img_hsl[:, :, 0] = blurry_img_hsl[:, :, 0] # hue
img_hsl[:, :, 1] = blurry_img_hsl[:, :, 1] # saturation
input_window = hsl2rgb(img_hsl)
if random.random() > 0.5:
# Apply horizontal flip to 50 % of the images
input_window = np.fliplr(input_window)
target_window = np.fliplr(target_window)
Image.fromarray(input_window).save(
DATA_DIR
/ "resolution_enhancer_dataset"
/ dataset
/ "input_images"
/ (Path(image_file_paths[i]).stem + "_{}.png".format(j))
)
Image.fromarray(target_window).save(
DATA_DIR
