FEN_Colorization/testor.py at main · JstYY/FEN_Colorization · GitHub

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import os
import numpy as np
from skimage import color, io

import torch
import torch.nn.functional as F

from PIL import Image
from models import ColorEncoder, ColorUNet

os.environ["CUDA_VISIBLE_DEVICES"] = '0'


def mkdirs(path):
    if not os.path.exists(path):
        os.makedirs(path)


def Lab2RGB_out(img_lab):
    img_lab = img_lab.detach().cpu()
    img_l = img_lab[:, :1, :, :]
    img_ab = img_lab[:, 1:, :, :]
    # print(torch.max(img_l), torch.min(img_l))
    # print(torch.max(img_ab), torch.min(img_ab))
    img_l = img_l + 50
    pred_lab = torch.cat((img_l, img_ab), 1)[0, ...].numpy()
    # grid_lab = utils.make_grid(pred_lab, nrow=1).numpy().astype("float64")
    # print(grid_lab.shape)
    out = (np.clip(color.lab2rgb(pred_lab.transpose(1, 2, 0)), 0, 1) * 255).astype("uint8")
    return out


def RGB2Lab(inputs):
    return color.rgb2lab(inputs)


def Normalize(inputs):
    l = inputs[:, :, 0:1]
    ab = inputs[:, :, 1:3]
    l = l - 50
    lab = np.concatenate((l, ab), 2)

    return lab.astype('float32')


def numpy2tensor(inputs):
    out = torch.from_numpy(inputs.transpose(2, 0, 1))
    return out


def tensor2numpy(inputs):
    out = inputs[0, ...].detach().cpu().numpy().transpose(1, 2, 0)
    return out


def preprocessing(inputs):
    # input: rgb, [0, 255], uint8
    img_lab = Normalize(RGB2Lab(inputs))
    img = np.array(inputs, 'float32')  # [0, 255]
    img = numpy2tensor(img)
    img_lab = numpy2tensor(img_lab)
    return img.unsqueeze(0), img_lab.unsqueeze(0)


if __name__ == "__main__":
    device = "cuda"

    model_name = 'Color2Embed_C_9.5w'
    ckpt_path = 'experiments/Color2Embed_1/095000.pt'
    test_dir_path = 'E:\Python_Project\Color2Embed-main\\test_datasets\contact'
    out_dir_path = 'results/gray2color/exemplar_based/' + model_name
    imgs_num = len(os.listdir(test_dir_path)) // 2
    imgsize = 256

    mkdirs(out_dir_path)

    ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)

    colorEncoder = ColorEncoder().to(device)
    colorEncoder.load_state_dict(ckpt["colorEncoder"])
    colorEncoder.eval()

    colorUNet = ColorUNet().to(device)
    colorUNet.load_state_dict(ckpt["colorUNet"])
    colorUNet.eval()

    imgs = []
    imgs_lab = []
    cos_d_avg = 0

    for i in range(imgs_num):
        idx = i
        print('Image', idx, 'Input Image', 'in%d.JPEG' % idx, 'Ref Image', 'ref%d.JPEG' % idx)

        img_path = os.path.join(test_dir_path, '%03d_in.jpg' % idx)
        ref_img_path = os.path.join(test_dir_path, '%03d_ref.jpg' % idx)
        out_img_path = os.path.join(out_dir_path, 'in%d_ref%d.png' % (idx, idx))

        img1 = Image.open(img_path).convert("RGB")
        width, height = img1.size
        img2 = Image.open(ref_img_path).convert("RGB")

        img1, img1_lab = preprocessing(img1)
        img2, img2_lab = preprocessing(img2)

        img1 = img1.to(device)
        img1_lab = img1_lab.to(device)
        img2 = img2.to(device)
        img2_lab = img2_lab.to(device)

        # print('-------',torch.max(img1_lab[:,:1,:,:]), torch.min(img1_lab[:,1:,:,:]))

        with torch.no_grad():
            img2_resize = F.interpolate(img2 / 255., size=(imgsize, imgsize), mode='bilinear',
                                        recompute_scale_factor=False, align_corners=False)
            img1_L_resize = F.interpolate(img1_lab[:, :1, :, :] / 50., size=(imgsize, imgsize), mode='bilinear',
                                          recompute_scale_factor=False, align_corners=False)

            color_vector = colorEncoder(img2_resize)

            fake_ab = colorUNet((img1_L_resize, color_vector))
            fake_ab = F.interpolate(fake_ab * 110, size=(height, width), mode='bilinear', recompute_scale_factor=False,
                                    align_corners=False)

            fake_img = torch.cat((img1_lab[:, :1, :, :], fake_ab), 1)
            fake_img = Lab2RGB_out(fake_img)
            io.imsave(out_img_path, fake_img)

            re_img, re_img_lab = preprocessing(fake_img)
            re_img = re_img.to(device)
            re_img_resize = F.interpolate(re_img / 255., size=(imgsize, imgsize), mode='bilinear',
                                          recompute_scale_factor=False, align_corners=False)
            re_color_vector = colorEncoder(re_img_resize)

            cos_d = torch.cosine_similarity(color_vector, re_color_vector, dim=1)
            cos_d_avg += cos_d

    print('Average Cosine Distance is: ', cos_d_avg / imgs_num)