colorizers/util.py

from PIL import Image
import numpy as np
from skimage import color
import torch
import torch.nn.functional as F

def load_img(img_path):
	img = Image.open(img_path)
	# Convert RGBA to RGB if needed
	if img.mode == 'RGBA':
		img = img.convert('RGB')
	out_np = np.asarray(img)
	if out_np.ndim == 2:
		out_np = np.tile(out_np[:, :, None], 3)
	return out_np

def resize_img(img, HW=(256,256), resample=3):
	return np.asarray(Image.fromarray(img).resize((HW[1],HW[0]), resample=resample))

def preprocess_img(img_rgb_orig, HW=(256,256), resample=3):
	# return original size L and resized L as torch Tensors
	img_rgb_rs = resize_img(img_rgb_orig, HW=HW, resample=resample)
	
	img_lab_orig = color.rgb2lab(img_rgb_orig)
	img_lab_rs = color.rgb2lab(img_rgb_rs)

	img_l_orig = img_lab_orig[:,:,0]
	img_l_rs = img_lab_rs[:,:,0]

	tens_orig_l = torch.Tensor(img_l_orig)[None,None,:,:]
	tens_rs_l = torch.Tensor(img_l_rs)[None,None,:,:]

	return (tens_orig_l, tens_rs_l)

def postprocess_tens(tens_orig_l, out_ab, mode='bilinear'):
	# tens_orig_l 	1 x 1 x H_orig x W_orig
	# out_ab 		1 x 2 x H x W

	HW_orig = tens_orig_l.shape[2:]
	HW = out_ab.shape[2:]

	# call resize function if needed
	if(HW_orig[0]!=HW[0] or HW_orig[1]!=HW[1]):
		out_ab_orig = F.interpolate(out_ab, size=HW_orig, mode='bilinear')
	else:
		out_ab_orig = out_ab

	out_lab_orig = torch.cat((tens_orig_l, out_ab_orig), dim=1)
	return color.lab2rgb(out_lab_orig.data.cpu().numpy()[0,...].transpose((1,2,0)))