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| import requests | |
| import gradio as gr | |
| import paddle | |
| from paddleseg.cvlibs import Config | |
| from matting.core import predict | |
| from matting.model import * | |
| from matting.dataset import MattingDataset | |
| def download_file(http_address, file_name): | |
| r = requests.get(http_address, allow_redirects=True) | |
| open(file_name, 'wb').write(r.content) | |
| cfg_paths = ['configs/modnet/modnet_mobilenetv2.yml', 'configs/modnet/modnet_resnet50_vd.yml', 'configs/modnet/modnet_hrnet_w18.yml'] | |
| cfgs = [Config(cfg) for cfg in cfg_paths] | |
| download_file('https://paddleseg.bj.bcebos.com/matting/models/modnet-mobilenetv2.pdparams', 'modnet-mobilenetv2.pdparams') | |
| download_file('https://paddleseg.bj.bcebos.com/matting/models/modnet-resnet50_vd.pdparams', 'modnet-resnet50_vd.pdparams') | |
| download_file('https://paddleseg.bj.bcebos.com/matting/models/modnet-hrnet_w18.pdparams', 'modnet-hrnet_w18.pdparams') | |
| models_paths = ['modnet-mobilenetv2.pdparams', 'modnet-resnet50_vd.pdparams', 'modnet-hrnet_w18.pdparams'] | |
| models = [cfg.model for cfg in cfgs] | |
| def inference(image, chosen_model): | |
| paddle.set_device('cpu') | |
| cfg = cfgs[chosen_model] | |
| val_dataset = cfg.val_dataset | |
| img_transforms = val_dataset.transforms | |
| model = models[chosen_model] | |
| alpha_pred = predict(model, | |
| model_path=models_paths[chosen_model], | |
| transforms=img_transforms, | |
| image_list=[image]) | |
| return alpha_pred | |
| inputs = [gr.inputs.Image(label='Input Image'), | |
| gr.inputs.Radio(['MobileNetV2', 'ResNet50_vd', 'HRNet_W18'], label='Model', type='index')] | |
| gr.Interface( | |
| inference, | |
| inputs, | |
| gr.outputs.Image(label='Output'), | |
| title='PaddleSeg - Matting', | |
| examples=[['images/armchair.jpg', 'MobileNetV2'], | |
| ['images/cat.jpg', 'ResNet50_vd'], | |
| ['images/plant.jpg', 'HRNet_W18']] | |
| ).launch() |