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| # -*- coding: utf-8 -*- | |
| # file: test.py | |
| # time: 05/12/2022 | |
| # author: yangheng <hy345@exeter.ac.uk> | |
| # github: https://github.com/yangheng95 | |
| # huggingface: https://huggingface.co/yangheng | |
| # google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en | |
| # Copyright (C) 2021. All Rights Reserved. | |
| from pathlib import Path | |
| from typing import Union | |
| import autocuda | |
| import findfile | |
| from pyabsa.utils.pyabsa_utils import fprint | |
| from torchvision import transforms | |
| from .utils.prepare_images import * | |
| from .Models import * | |
| class ImageMagnifier: | |
| def __init__(self): | |
| self.device = autocuda.auto_cuda() | |
| self.model_cran_v2 = CARN_V2(color_channels=3, mid_channels=64, conv=nn.Conv2d, | |
| single_conv_size=3, single_conv_group=1, | |
| scale=2, activation=nn.LeakyReLU(0.1), | |
| SEBlock=True, repeat_blocks=3, atrous=(1, 1, 1)) | |
| self.model_cran_v2 = network_to_half(self.model_cran_v2) | |
| self.checkpoint = findfile.find_cwd_file("CARN_model_checkpoint.pt") | |
| self.model_cran_v2.load_state_dict(torch.load(self.checkpoint, map_location='cpu')) | |
| # if use GPU, then comment out the next line so it can use fp16. | |
| self.model_cran_v2 = self.model_cran_v2.float().to(self.device) | |
| self.model_cran_v2.to(self.device) | |
| def __image_scale(self, img, scale_factor: int = 2): | |
| img_splitter = ImageSplitter(seg_size=64, scale_factor=scale_factor, boarder_pad_size=3) | |
| img_patches = img_splitter.split_img_tensor(img, scale_method=None, img_pad=0) | |
| with torch.no_grad(): | |
| if self.device != 'cpu': | |
| with torch.cuda.amp.autocast(): | |
| out = [self.model_cran_v2(i.to(self.device)) for i in img_patches] | |
| else: | |
| with torch.cpu.amp.autocast(): | |
| out = [self.model_cran_v2(i) for i in img_patches] | |
| img_upscale = img_splitter.merge_img_tensor(out) | |
| final = torch.cat([img_upscale]) | |
| return transforms.ToPILImage()(final[0]) | |
| def magnify(self, img, scale_factor: int = 2): | |
| fprint("scale factor reset to:", scale_factor//2*2) | |
| _scale_factor = scale_factor | |
| while _scale_factor // 2 > 0: | |
| img = self.__image_scale(img, scale_factor=2) | |
| _scale_factor = _scale_factor // 2 | |
| return img | |
| def magnify_from_file(self, img_path: Union[str, Path], scale_factor: int = 2, save_img: bool = True): | |
| if not os.path.exists(img_path): | |
| raise FileNotFoundError("Path is not found.") | |
| if os.path.isfile(img_path): | |
| try: | |
| img = Image.open(img_path) | |
| img = self.magnify(img, scale_factor) | |
| if save_img: | |
| img.save(os.path.join(img_path)) | |
| except Exception as e: | |
| fprint(img_path, e) | |
| fprint(img_path, "Done.") | |
| elif os.path.isdir(img_path): | |
| for path in os.listdir(img_path): | |
| try: | |
| img = Image.open(os.path.join(img_path, path)) | |
| img = self.magnify(img, scale_factor) | |
| if save_img: | |
| img.save(os.path.join(img_path, path)) | |
| except Exception as e: | |
| fprint(path, e) | |
| continue | |
| fprint(path, "Done.") | |
| else: | |
| raise TypeError("Path is not a file or directory.") | |