demo.py

import argparse, os
import torch
from torch.autograd import Variable
from scipy.ndimage import imread
from PIL import Image
import numpy as np
import time, math

parser = argparse.ArgumentParser(description="PyTorch VDSR Demo")
parser.add_argument("--cuda", action="store_true", help="use cuda?")
parser.add_argument("--model", default="model/model_epoch_50.pth", type=str, help="model path")
parser.add_argument("--image", default="butterfly_GT", type=str, help="image name")
parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
parser.add_argument("--out_path", default=None, type=str, help="Output image path.")


def PSNR(pred, gt, shave_border=0):
    height, width = pred.shape[:2]
    pred = pred[shave_border:height - shave_border, shave_border:width - shave_border]
    gt = gt[shave_border:height - shave_border, shave_border:width - shave_border]
    imdff = pred - gt
    rmse = math.sqrt(np.mean(imdff ** 2))
    if rmse == 0:
        return 100
    return 20 * math.log10(255.0 / rmse)
    
def colorize(y, ycbcr): 
    img = np.zeros((y.shape[0], y.shape[1], 3), np.uint8)
    img[:,:,0] = y
    img[:,:,1] = ycbcr[:,:,1]
    img[:,:,2] = ycbcr[:,:,2]
    img = Image.fromarray(img, "YCbCr").convert("RGB")
    return img

opt = parser.parse_args()
cuda = opt.cuda

if cuda:
    print("=> use gpu id: '{}'".format(opt.gpus))
    os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
    if not torch.cuda.is_available():
            raise Exception("No GPU found or Wrong gpu id, please run without --cuda")


model = torch.load(opt.model, map_location=lambda storage, loc: storage)["model"]

#im_gt_ycbcr = imread("Set5/" + opt.image + ".bmp", mode="YCbCr")
im_b_ycbcr = imread(opt.image, mode="YCbCr")
    
#im_gt_y = im_gt_ycbcr[:,:,0].astype(float)
im_b_y = im_b_ycbcr[:,:,0].astype(float)

#psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=opt.scale)

im_input = im_b_y/255.

im_input = Variable(torch.from_numpy(im_input).float()).view(1, -1, im_input.shape[0], im_input.shape[1])

if cuda:
    model = model.cuda()
    im_input = im_input.cuda()
else:
    model = model.cpu()

start_time = time.time()
out = model(im_input)
elapsed_time = time.time() - start_time

out = out.cpu()

im_h_y = out.data[0].numpy().astype(np.float32)

im_h_y = im_h_y * 255.
im_h_y[im_h_y < 0] = 0
im_h_y[im_h_y > 255.] = 255.

#psnr_predicted = PSNR(im_gt_y, im_h_y[0,:,:], shave_border=opt.scale)

im_h = colorize(im_h_y[0,:,:], im_b_ycbcr)
#im_gt = Image.fromarray(im_gt_ycbcr, "YCbCr").convert("RGB")
#im_b = Image.fromarray(im_b_ycbcr, "YCbCr").convert("RGB")

# print("Scale=",opt.scale)
# print("PSNR_predicted=", psnr_predicted)
# print("PSNR_bicubic=", psnr_bicubic)
print("It takes {}s for processing".format(elapsed_time))
im_h.save(opt.out_path)

# fig = plt.figure()
# ax = plt.subplot("131")
# ax.imshow(im_gt)
# ax.set_title("GT")
#
# ax = plt.subplot("132")
# ax.imshow(im_b)
# ax.set_title("Input(bicubic)")
#
# ax = plt.subplot("133")
# ax.imshow(im_h)
# ax.set_title("Output(vdsr)")
# plt.show()