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86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import argparse, os
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import torch
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from torch.autograd import Variable
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import numpy as np
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import time, math
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import scipy.io as sio
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import matplotlib.pyplot as plt
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parser = argparse.ArgumentParser(description="PyTorch SRResNet Demo")
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parser.add_argument("--cuda", action="store_true", help="use cuda?")
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parser.add_argument("--model", default="model/model_srresnet.pth", type=str, help="model path")
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parser.add_argument("--image", default="butterfly_GT", type=str, help="image name")
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parser.add_argument("--dataset", default="Set5", type=str, help="dataset name")
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parser.add_argument("--scale", default=4, type=int, help="scale factor, Default: 4")
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parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
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def PSNR(pred, gt, shave_border=0):
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height, width = pred.shape[:2]
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pred = pred[shave_border:height - shave_border, shave_border:width - shave_border]
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gt = gt[shave_border:height - shave_border, shave_border:width - shave_border]
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imdff = pred - gt
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rmse = math.sqrt(np.mean(imdff ** 2))
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if rmse == 0:
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return 100
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return 20 * math.log10(255.0 / rmse)
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opt = parser.parse_args()
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cuda = opt.cuda
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if cuda:
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print("=> use gpu id: '{}'".format(opt.gpus))
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os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
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if not torch.cuda.is_available():
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raise Exception("No GPU found or Wrong gpu id, please run without --cuda")
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model = torch.load(opt.model)["model"]
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im_gt = sio.loadmat("testsets/" + opt.dataset + "/" + opt.image + ".mat")['im_gt']
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im_b = sio.loadmat("testsets/" + opt.dataset + "/" + opt.image + ".mat")['im_b']
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im_l = sio.loadmat("testsets/" + opt.dataset + "/" + opt.image + ".mat")['im_l']
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im_gt = im_gt.astype(float).astype(np.uint8)
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im_b = im_b.astype(float).astype(np.uint8)
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im_l = im_l.astype(float).astype(np.uint8)
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im_input = im_l.astype(np.float32).transpose(2,0,1)
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im_input = im_input.reshape(1,im_input.shape[0],im_input.shape[1],im_input.shape[2])
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im_input = Variable(torch.from_numpy(im_input/255.).float())
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if cuda:
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model = model.cuda()
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im_input = im_input.cuda()
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else:
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model = model.cpu()
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start_time = time.time()
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out = model(im_input)
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elapsed_time = time.time() - start_time
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out = out.cpu()
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im_h = out.data[0].numpy().astype(np.float32)
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im_h = im_h*255.
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im_h[im_h<0] = 0
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im_h[im_h>255.] = 255.
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im_h = im_h.transpose(1,2,0)
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print("Dataset=",opt.dataset)
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print("Scale=",opt.scale)
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print("It takes {}s for processing".format(elapsed_time))
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fig = plt.figure()
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ax = plt.subplot("131")
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ax.imshow(im_gt)
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ax.set_title("GT")
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ax = plt.subplot("132")
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ax.imshow(im_b)
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ax.set_title("Input(Bicubic)")
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ax = plt.subplot("133")
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ax.imshow(im_h.astype(np.uint8))
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ax.set_title("Output(SRResNet)")
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plt.show()
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