Create app.py

app.py

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+import fastai
+from fastai.vision import *
+from fastai.utils.mem import *
+from fastai.vision import open_image, load_learner, image, torch
+import numpy as np
+import urllib.request
+import PIL.Image
+from io import BytesIO
+import torchvision.transforms as T
+from PIL import Image
+import requests
+from io import BytesIO
+import fastai
+from fastai.vision import *
+from fastai.utils.mem import *
+from fastai.vision import open_image, load_learner, image, torch
+import numpy as np
+import urllib.request
+import PIL.Image
+from PIL import Image
+from io import BytesIO
+import torchvision.transforms as T
+
+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()
+
+def add_margin(pil_img, top, right, bottom, left, color):
+    width, height = pil_img.size
+    new_width = width + right + left
+    new_height = height + top + bottom
+    result = Image.new(pil_img.mode, (new_width, new_height), color)
+    result.paste(pil_img, (left, top))
+    return result
+
+
+MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1 "
+urllib.request.urlretrieve(MODEL_URL, "ArtLine_920.pkl")
+path = Path(".")
+print(os.listdir('.'))
+learn=load_learner(path, 'ArtLine_920.pkl')
+
+
+import gradio as gr
+import cv2
+
+
+def get_filename(prefix="sketch"):
+  from datetime import datetime
+  from pytz import timezone
+  return datetime.now(timezone('Asia/Seoul')).strftime('sketch__%Y-%m-%d %H:%M:%S.jpg')
+
+def predict(img):
+  img = PIL.Image.fromarray(img)
+  img = add_margin(img, 250, 250, 250, 250, (255, 255, 255))
+  img = np.array(img)
+
+  h, w = img.shape[:-1]
+  cv2.imwrite("test.jpg", img)
+  img_test = open_image("test.jpg")
+
+  p,img_hr,b = learn.predict(img_test)
+
+  res = (img_hr / img_hr.max()).numpy()
+  res = res[0]  # take only first channel as result
+  res = cv2.resize(res, (w,h))
+
+  output_file = get_filename()
+
+  cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50])
+
+  return res, output_file
+
+gr.Interface(predict, 
+             inputs="image", 
+             outputs=[gr.Image(label="Sketch Image"), gr.File(label="Result File")],
+             title="Image-to-sketch",
+             description="Transfer any image into BW cartoon-style sketch!").launch(share=True)