def denoiseList(self):
img = Image.open(
'Database/waterloo/pristine_images/00001.bmp').convert('RGB')
img = CenterCrop((self.size, self.size))(img)
img.save('outPic/src.bmp', 'bmp', quality=100)
img = Image.open('Database/waterloo/distorted_images/%s/00001_%d.bmp' %
(self.typeDir, 2)).convert('RGB')
img = CenterCrop((self.size, self.size))(img)
img.save('outPic/sorted.bmp', 'bmp', quality=100)
for i in xrange(1, 23):
input = Variable(ToTensor()(img)).view(1, -1, img.size[1],
img.size[0])
input = input.cuda()
if (self.load == 2):
model = torch.load('%s/%sF_ALL.pth' % (self.dir, self.typeDir))
elif (self.load == 1):
model = torch.load('%s/%sF.pth' % (self.dir, self.typeDir))
else:
model = torch.load('%s/%s_%d.pth' %
(self.dir, self.typeDir, i))
model = model.cuda()
out_img = model(input)
out_img = out_img.cpu()
out_img = out_img.data[0]
out_img.clamp_(0.0, 1.0)
# out_img = self.clip(out_img)
out_img = ToPILImage()(out_img)
# out_img.save('outPic/%d.bmp'%i, 'bmp', quality=100)
out_img.save('outPic/%d.bmp' % i, 'bmp', quality=100)
def denoiseList(self):
src = Image.open(
'Database/waterloo/pristine_images/00001.bmp').convert('RGB')
src = CenterCrop((self.size, self.size))(src)
src.save('level_distortion/%s/%s/src.bmp' % (self.dir, self.typeDir),
'bmp',
quality=100)
src = Variable(ToTensor()(src)).view(1, -1, src.size[1], src.size[0])
stock_data = [[], [], [], []]
for i in xrange(1, 11):
print(' checking %dth...' % i)
dis = Image.open('level_distortion/%s/%s/00001_%d.bmp' %
(self.dir, self.typeDir, i)).convert('RGB')
dis = CenterCrop((self.size, self.size))(dis)
dis.save('level_distortion/%s/%s/dis_%d.bmp' %
(self.dir, self.typeDir, i),
'bmp',
quality=100)
dis = Variable(ToTensor()(dis)).view(1, -1, dis.size[1],
dis.size[0])
dis = dis.cpu()
# print (input)
res = self.model(dis)
tmp = {}
loss = self.criterion(src, dis)
tmp[self.src_dis] = loss.data[0]
stock_data[0].append(loss.data[0])
# self.experiment.add_scalar_value(self.src_dis, loss.data[0], i)
loss = self.criterion(res, src)
tmp[self.src_res] = loss.data[0]
stock_data[1].append(loss.data[0])
# self.experiment.add_scalar_value(self.src_res, loss.data[0], i)
loss = self.criterion(res, dis)
tmp[self.dis_res] = loss.data[0]
stock_data[2].append(loss.data[0])
# self.experiment.add_scalar_value(self.dis_res, loss.data[0], i)
stock_data[3].append(stock_data[1][i - 1] + stock_data[2][i - 1])
self.experiment.add_scalar_dict(tmp)
res = res.data[0]
res.clamp_(0.0, 1.0)
res = ToPILImage()(res)
res.save('level_distortion/%s/%s/res_%d.bmp' %
(self.dir, self.typeDir, i),
'bmp',
quality=100)
self.draw(stock_data)
# tmp = self.dis_res_.get_scalar_names()
# print(tmp)
# tmp = self.dis_res_.get_scalar_values(self.dis_res)
# print(tmp)
# print (self.dis_res_.get_histogram_names())
# print (self.dis_res_.get_histogram_values(self.dis_res))
print('Done this work!\n\n')
def func(self):
# img = Image.open('Database/waterloo/distorted_images/gblurConv/00001_1.bmp').convert('RGB')
img = Image.open('Database/waterloo/pristine_images/00001.bmp').convert('RGB')
img = CenterCrop((224, 224))(img)
img.save('outPic/src_pristine.bmp', 'bmp', quality=100)
# img.show()
# print (img.size)
img = Variable(ToTensor()(img)).view(1, -1, img.size[1], img.size[0])
# print(img)
model = torch.load('%s/gblurConv.pth' % self.dir)
model = model.cuda()
input = img.cuda()
out_img = model(input)
out_img = out_img.cpu().data[0]
out_img.clamp_(0.0, 1.0)
out_img = ToPILImage()(out_img)
# out_img.save('outPic/1_1.bmp', 'bmp', quality=100)
out_img.show()
total=len(id_filename)):
path = os.path.join(imdir, filename)
img = Image.open(path).convert('RGBA')
# load alpha
path = os.path.join(maskdir, filename.replace('.jpg', '.png'))
alpha = Image.open(path)
if alpha.mode == 'RGBA':
alpha = alpha.split()[-1]
alpha = alpha.convert('L')
img.putalpha(alpha)
# crop square images using bbox
img = img.crop(boxes[id])
s = max(img.size)
img = CenterCrop(s)(
img
) # CenterCrop pads image to square using zeros (also for alpha)
# composite
img = np.array(img)
alpha = (img[..., 3:4]) > 127 # convert to binary mask
bg = np.array(255 * (1. - alpha), np.uint8)
img = img[..., :3] * alpha + bg
img = Image.fromarray(img)
img.save(os.path.join(outdir, '%06d.png' % i))
print('Preprocessed {} images.'.format(
len(glob.glob(os.path.join(outdir, '*.png')))))
