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')))))