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import gradio as gr |
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from PIL import Image |
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import requests |
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import numpy as np |
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import urllib.request |
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from urllib.request import urlretrieve |
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import PIL.Image |
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import torchvision.transforms as T |
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import fastai |
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from fastai.vision import * |
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from fastai.utils.mem import * |
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class FeatureLoss(nn.Module): |
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def __init__(self, m_feat, layer_ids, layer_wgts): |
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super().__init__() |
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self.m_feat = m_feat |
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self.loss_features = [self.m_feat[i] for i in layer_ids] |
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self.hooks = hook_outputs(self.loss_features, detach=False) |
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self.wgts = layer_wgts |
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self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids)) |
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] + [f'gram_{i}' for i in range(len(layer_ids))] |
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def make_features(self, x, clone=False): |
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self.m_feat(x) |
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return [(o.clone() if clone else o) for o in self.hooks.stored] |
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def forward(self, input, target): |
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out_feat = self.make_features(target, clone=True) |
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in_feat = self.make_features(input) |
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self.feat_losses = [base_loss(input,target)] |
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self.feat_losses += [base_loss(f_in, f_out)*w |
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for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] |
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self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3 |
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for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] |
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self.metrics = dict(zip(self.metric_names, self.feat_losses)) |
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return sum(self.feat_losses) |
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def __del__(self): self.hooks.remove() |
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MODEL_URL = "https://www.dropbox.com/s/rz9nt35um1agf5y/t10T.pkl?dl=1" |
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urllib.request.urlretrieve(MODEL_URL, "t10T.pkl") |
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path = Path(".") |
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learn=load_learner(path, 't10T.pkl') |
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urlretrieve("https://www.independent.ie/incoming/714c6/29308190.ece/AUTOCROP/h530/melanie-griffiths_2391378a.jpg","socce1.jpg") |
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urlretrieve("https://media.okmagazine.com/brand-img/IEPXUdkY7/0x0/2015/06/celebrity-tattoos-16-splash.jpg","socce2.jpg") |
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urlretrieve("https://newsmeter.in/wp-content/uploads/2020/06/Ajay-Devgn-Tattoo.jpg","basebal.jpg") |
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urlretrieve("https://akns-images.eonline.com/eol_images/Entire_Site/2014617/rs_600x600-140717150632-600-vin-los-tattoo-model.ls.71714_copy.jpg?fit=around%7C600:600&output-quality=90&crop=600:600;center,top","baseb.jpg") |
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sample_images = [["socce1.jpg"], |
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["socce2.jpg"], |
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["basebal.jpg"], |
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["baseb.jpg"]] |
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def predict(input): |
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size = input.size |
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img_t = T.ToTensor()(input) |
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img_fast = Image(img_t) |
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p,img_hr,b = learn.predict(img_fast) |
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x = np.minimum(np.maximum(image2np(img_hr.data*255), 0), 255).astype(np.uint8) |
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img = PIL.Image.fromarray(x) |
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im1 = img.resize(size) |
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im2 = im1.resize(300) |
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return im2 |
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gr_interface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="image", title='Skin-Deep',examples=sample_images).launch(); |