mirror of
https://github.com/kritiksoman/GIMP-ML
synced 2024-10-31 09:20:18 +00:00
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
import matlab.engine
|
|
import argparse, os
|
|
import torch
|
|
from torch.autograd import Variable
|
|
import numpy as np
|
|
import time, math, glob
|
|
import scipy.io as sio
|
|
import cv2
|
|
|
|
parser = argparse.ArgumentParser(description="PyTorch SRResNet Eval")
|
|
parser.add_argument("--cuda", action="store_true", help="use cuda?")
|
|
parser.add_argument("--model", default="model/model_srresnet.pth", type=str, help="model path")
|
|
parser.add_argument("--dataset", default="Set5", type=str, help="dataset name, Default: Set5")
|
|
parser.add_argument("--scale", default=4, type=int, help="scale factor, Default: 4")
|
|
parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
|
|
|
|
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)
|
|
|
|
opt = parser.parse_args()
|
|
cuda = opt.cuda
|
|
eng = matlab.engine.start_matlab()
|
|
|
|
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)["model"]
|
|
|
|
image_list = glob.glob("./testsets/" + opt.dataset + "/*.*")
|
|
|
|
avg_psnr_predicted = 0.0
|
|
avg_psnr_bicubic = 0.0
|
|
avg_elapsed_time = 0.0
|
|
|
|
for image_name in image_list:
|
|
print("Processing ", image_name)
|
|
im_gt_y = sio.loadmat(image_name)['im_gt_y']
|
|
im_b_y = sio.loadmat(image_name)['im_b_y']
|
|
im_l = sio.loadmat(image_name)['im_l']
|
|
|
|
im_gt_y = im_gt_y.astype(float)
|
|
im_b_y = im_b_y.astype(float)
|
|
im_l = im_l.astype(float)
|
|
|
|
psnr_bicubic = PSNR(im_gt_y, im_b_y,shave_border=opt.scale)
|
|
avg_psnr_bicubic += psnr_bicubic
|
|
|
|
im_input = im_l.astype(np.float32).transpose(2,0,1)
|
|
im_input = im_input.reshape(1,im_input.shape[0],im_input.shape[1],im_input.shape[2])
|
|
im_input = Variable(torch.from_numpy(im_input/255.).float())
|
|
|
|
if cuda:
|
|
model = model.cuda()
|
|
im_input = im_input.cuda()
|
|
else:
|
|
model = model.cpu()
|
|
|
|
start_time = time.time()
|
|
HR_4x = model(im_input)
|
|
elapsed_time = time.time() - start_time
|
|
avg_elapsed_time += elapsed_time
|
|
|
|
HR_4x = HR_4x.cpu()
|
|
|
|
im_h = HR_4x.data[0].numpy().astype(np.float32)
|
|
|
|
im_h = im_h*255.
|
|
im_h = np.clip(im_h, 0., 255.)
|
|
im_h = im_h.transpose(1,2,0).astype(np.float32)
|
|
|
|
im_h_matlab = matlab.double((im_h / 255.).tolist())
|
|
im_h_ycbcr = eng.rgb2ycbcr(im_h_matlab)
|
|
im_h_ycbcr = np.array(im_h_ycbcr._data).reshape(im_h_ycbcr.size, order='F').astype(np.float32) * 255.
|
|
im_h_y = im_h_ycbcr[:,:,0]
|
|
|
|
psnr_predicted = PSNR(im_gt_y, im_h_y,shave_border=opt.scale)
|
|
avg_psnr_predicted += psnr_predicted
|
|
|
|
print("Scale=", opt.scale)
|
|
print("Dataset=", opt.dataset)
|
|
print("PSNR_predicted=", avg_psnr_predicted/len(image_list))
|
|
print("PSNR_bicubic=", avg_psnr_bicubic/len(image_list))
|
|
print("It takes average {}s for processing".format(avg_elapsed_time/len(image_list)))
|