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94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
import matlab.engine
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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, glob
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import scipy.io as sio
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import cv2
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parser = argparse.ArgumentParser(description="PyTorch SRResNet Eval")
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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("--dataset", default="Set5", type=str, help="dataset name, Default: Set5")
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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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eng = matlab.engine.start_matlab()
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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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image_list = glob.glob("./testsets/" + opt.dataset + "/*.*")
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avg_psnr_predicted = 0.0
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avg_psnr_bicubic = 0.0
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avg_elapsed_time = 0.0
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for image_name in image_list:
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print("Processing ", image_name)
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im_gt_y = sio.loadmat(image_name)['im_gt_y']
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im_b_y = sio.loadmat(image_name)['im_b_y']
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im_l = sio.loadmat(image_name)['im_l']
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im_gt_y = im_gt_y.astype(float)
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im_b_y = im_b_y.astype(float)
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im_l = im_l.astype(float)
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psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=opt.scale)
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avg_psnr_bicubic += psnr_bicubic
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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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HR_4x = model(im_input)
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elapsed_time = time.time() - start_time
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avg_elapsed_time += elapsed_time
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HR_4x = HR_4x.cpu()
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im_h = HR_4x.data[0].numpy().astype(np.float32)
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im_h = im_h*255.
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im_h = np.clip(im_h, 0., 255.)
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im_h = im_h.transpose(1,2,0).astype(np.float32)
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im_h_matlab = matlab.double((im_h / 255.).tolist())
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im_h_ycbcr = eng.rgb2ycbcr(im_h_matlab)
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im_h_ycbcr = np.array(im_h_ycbcr._data).reshape(im_h_ycbcr.size, order='F').astype(np.float32) * 255.
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im_h_y = im_h_ycbcr[:,:,0]
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psnr_predicted = PSNR(im_gt_y, im_h_y,shave_border=opt.scale)
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avg_psnr_predicted += psnr_predicted
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print("Scale=", opt.scale)
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print("Dataset=", opt.dataset)
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print("PSNR_predicted=", avg_psnr_predicted/len(image_list))
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print("PSNR_bicubic=", avg_psnr_bicubic/len(image_list))
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print("It takes average {}s for processing".format(avg_elapsed_time/len(image_list)))
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