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| import cv2 | |
| import torch | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| from super_image import ImageLoader, EdsrModel, MsrnModel, MdsrModel, AwsrnModel, A2nModel, CarnModel, PanModel, \ | |
| HanModel, DrlnModel, RcanModel | |
| title = "super-image" | |
| description = "State of the Art Image Super-Resolution Models." | |
| article = "<p style='text-align: center'><a href='https://github.com/eugenesiow/super-image'>Github Repo</a>" \ | |
| "| <a href='https://eugenesiow.github.io/super-image/'>Documentation</a> " \ | |
| "| <a href='https://github.com/eugenesiow/super-image#scale-x2'>Models</a></p>" | |
| def get_model(model_name, scale): | |
| if model_name == 'EDSR': | |
| model = EdsrModel.from_pretrained('eugenesiow/edsr', scale=scale) | |
| elif model_name == 'MSRN': | |
| model = MsrnModel.from_pretrained('eugenesiow/msrn', scale=scale) | |
| elif model_name == 'MDSR': | |
| model = MdsrModel.from_pretrained('eugenesiow/mdsr', scale=scale) | |
| elif model_name == 'AWSRN-BAM': | |
| model = AwsrnModel.from_pretrained('eugenesiow/awsrn-bam', scale=scale) | |
| elif model_name == 'A2N': | |
| model = A2nModel.from_pretrained('eugenesiow/a2n', scale=scale) | |
| elif model_name == 'CARN': | |
| model = CarnModel.from_pretrained('eugenesiow/carn', scale=scale) | |
| elif model_name == 'PAN': | |
| model = PanModel.from_pretrained('eugenesiow/pan', scale=scale) | |
| elif model_name == 'HAN': | |
| model = HanModel.from_pretrained('eugenesiow/han', scale=scale) | |
| elif model_name == 'DRLN': | |
| model = DrlnModel.from_pretrained('eugenesiow/drln', scale=scale) | |
| elif model_name == 'RCAN': | |
| model = RcanModel.from_pretrained('eugenesiow/rcan', scale=scale) | |
| else: | |
| model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=scale) | |
| return model | |
| def inference(img, scale_str, model_name): | |
| max_res = 1024 | |
| scale = int(scale_str.replace('x', '')) | |
| width, height = img.size | |
| print(width, height) | |
| if width > max_res or height > max_res: | |
| img = img.thumbnail((max_res, max_res), Image.ANTIALIAS) | |
| model = get_model(model_name, scale) | |
| try: | |
| inputs = ImageLoader.load_image(img) | |
| preds = model(inputs) | |
| preds = preds.data.cpu().numpy() | |
| pred = preds[0].transpose((1, 2, 0)) * 255.0 | |
| return Image.fromarray(pred.astype('uint8'), 'RGB') | |
| except Exception as e: | |
| print(e) | |
| return None | |
| torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/baby_mini_d3_gaussian.bmp', | |
| 'baby.bmp') | |
| torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/woman_mini_d3_gaussian.bmp', | |
| 'woman.bmp') | |
| torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/bird_mini_d4_gaussian.bmp', | |
| 'bird.bmp') | |
| # models = ['EDSR-base', 'DRLN', 'EDSR', 'MDSR', 'A2N', 'PAN', 'AWSRN-BAM', 'MSRN'] | |
| models = ['EDSR-base', 'A2N', 'PAN', 'AWSRN-BAM', 'MSRN'] | |
| scales = [2, 3, 4] | |
| for model_name in models: | |
| for scale in scales: | |
| get_model(model_name, scale) | |
| gr.Interface( | |
| inference, | |
| [ | |
| gr.inputs.Image(type="pil", label="Input"), | |
| gr.inputs.Radio(["x2", "x3", "x4"], label='scale'), | |
| gr.inputs.Dropdown(choices=models, | |
| label='Model') | |
| ], | |
| gr.outputs.Image(type="pil", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ['baby.bmp', 'x2', 'EDSR-base'], | |
| ['woman.bmp', 'x3', 'MSRN'], | |
| ['bird.bmp', 'x4', 'PAN'] | |
| ], | |
| allow_flagging='never', | |
| ).launch(debug=False) | |