#1 Solution IMGCOL Blitz5

Colorizers library worked well for this competition: https://github.com/richzhang/colorization.git
Below is the code:

import cv2
import glob
from colorizers import *

use_gpu = True

def color(colorizer, img_path):
    # default size to process images is 256x256
    # grab L channel in both original ("orig") and resized ("rs") resolutions
    img = load_img(img_path)
    (tens_l_orig, tens_l_rs) = preprocess_img(img, HW=(256,256))
    if (use_gpu):
            tens_l_rs = tens_l_rs.cuda()

    # colorizer outputs 256x256 ab map
    # resize and concatenate to original L channel
    img_bw = postprocess_tens(tens_l_orig, torch.cat((0*tens_l_orig,0*tens_l_orig),dim=1))
    out_img = postprocess_tens(tens_l_orig, colorizer(tens_l_rs).cpu())
    return out_img

def main():
    # load colorizers
    #colorizer_eccv16 = eccv16(pretrained=True).eval()
    colorizer_siggraph17 = siggraph17(pretrained=True).eval()

    if use_gpu:

    colorizer = colorizer_siggraph17

    stage = 'train'
    data_dir = '{INSERT_YOUR_DATA_DIR}/' + stage + '_black_white_images/' + stage + '_back_white_images/'
    out_dir = '../' + stage + '_color_images/'

    fnames = glob.glob(data_dir + '/*')

    for cnt, fname in enumerate(fnames):
        imagename = fname.split('/')[-1]
        outname = out_dir + imagename
        res = color(colorizer, fname)
        res = res[:,:,[2,1,0]] # reorder channels
        cv2.imwrite(outname, np.clip(res * 256, 0, 255).astype(int))
        if cnt % 100 == 0:


Lol i ran the exact same code and finetuned it a little bit and I was on 12th :frowning:

Why are the channels reordered in the end?

my guess is because the colorizers library opens image using PIL, so it has different convention on channel ordering than cv2.