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import gradio as gr
from PIL import Image
import requests
import numpy as np
import urllib.request
from urllib.request import urlretrieve
import PIL.Image
import torchvision.transforms as T
import fastai
from fastai.vision import *
from fastai.utils.mem import *
class FeatureLoss(nn.Module):
def __init__(self, m_feat, layer_ids, layer_wgts):
super().__init__()
self.m_feat = m_feat
self.loss_features = [self.m_feat[i] for i in layer_ids]
self.hooks = hook_outputs(self.loss_features, detach=False)
self.wgts = layer_wgts
self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
] + [f'gram_{i}' for i in range(len(layer_ids))]
def make_features(self, x, clone=False):
self.m_feat(x)
return [(o.clone() if clone else o) for o in self.hooks.stored]
def forward(self, input, target):
out_feat = self.make_features(target, clone=True)
in_feat = self.make_features(input)
self.feat_losses = [base_loss(input,target)]
self.feat_losses += [base_loss(f_in, f_out)*w
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
self.metrics = dict(zip(self.metric_names, self.feat_losses))
return sum(self.feat_losses)
def __del__(self): self.hooks.remove()
MODEL_URL = "https://www.dropbox.com/s/1u7jg12zn35er9q/bt.pkl?dl=0"
urllib.request.urlretrieve(MODEL_URL, "bt.pkl")
path = Path(".")
learn=load_learner(path, 'bt.pkl')
urlretrieve("https://www.independent.ie/incoming/714c6/29308190.ece/AUTOCROP/h530/melanie-griffiths_2391378a.jpg","socce1.jpg")
urlretrieve("https://media.okmagazine.com/brand-img/IEPXUdkY7/0x0/2015/06/celebrity-tattoos-16-splash.jpg","socce2.jpg")
urlretrieve("https://newsmeter.in/wp-content/uploads/2020/06/Ajay-Devgn-Tattoo.jpg","basebal.jpg")
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")
sample_images = [["socce1.jpg"],
["socce2.jpg"],
["basebal.jpg"],
["baseb.jpg"]]
def predict(input):
size = input.size
img_t = T.ToTensor()(input)
img_fast = Image(img_t)
p,img_hr,b = learn.predict(img_fast)
x = np.minimum(np.maximum(image2np(img_hr.data*255), 0), 255).astype(np.uint8)
img = PIL.Image.fromarray(x)
im = img.resize(size)
return im
gr_interface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="image", title='Skin-Deep',examples=sample_images).launch(); |