Upload app2.py

app2.py

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+import cv2
+import gradio as gr
+import numpy as np
+import onnxruntime
+from scipy.ndimage.filters import gaussian_filter
+from skimage.color import rgb2gray
+
+
+def xdog(im, gamma=0.98, phi=200, eps=-0.1, k=1.6, sigma=1):
+    # Source : https://github.com/CemalUnal/XDoG-Filter
+    # Reference : XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization
+    # Link : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.151&rep=rep1&type=pdf
+    if im.shape[2] == 3:
+        im = rgb2gray(im)
+    imf1 = gaussian_filter(im, sigma)
+    imf2 = gaussian_filter(im, sigma * k)
+    imdiff = imf1 - gamma * imf2
+    imdiff = (imdiff < eps) * 1.0 + (imdiff >= eps) * (1.0 + np.tanh(phi * imdiff))
+    imdiff -= imdiff.min()
+    imdiff /= imdiff.max()
+    imdiff *= 255.0
+    imdiff = imdiff.astype('uint8')
+    imdiff = cv2.cvtColor(imdiff, cv2.COLOR_GRAY2BGR)
+    return (imdiff / 255.0).astype('float32')
+
+
+def swin_model(img):
+    h, w = img.shape[0], img.shape[1]
+    factor = max(h / 512, w / 512)
+    if factor > 1:
+        img = cv2.resize(img, (int(h / factor), int(w / factor)))
+    img = np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1)) / 255.0
+    img = img[None, ...].astype(np.float32)
+    mod_pad_h, mod_pad_w = 0, 0
+    window_size = 8
+    if h % window_size != 0:
+        mod_pad_h = window_size - h % window_size
+    if w % window_size != 0:
+        mod_pad_w = window_size - w % window_size
+    img = np.pad(img, (mod_pad_w, mod_pad_h), 'reflect')
+    ort_session = onnxruntime.InferenceSession('model.onnx', providers=['CPUExecutionProvider'])
+    ort_inputs = {ort_session.get_inputs()[0].name: img}
+    output = ort_session.run(None, ort_inputs)[0]
+    _, _, a, b = output.shape
+    output = output[:, :, 0:a - mod_pad_h, 0:b - mod_pad_w]
+    output = np.squeeze(output).clip(0, 1)
+    if factor > 1:
+        output = cv2.resize(output, h, w)
+    return output
+
+
+demo = gr.Blocks()
+with demo:
+    gr.Markdown(
+        """
+        # Hello,World!
+        This is an sketch extraction using in anime art by swin_transformer, you can see the difference between X-dog and learning-based algorithm.
+        """)
+    input_image = gr.Image()
+    output_xdog = gr.Image()
+    output_deep = gr.Image()
+    b_xdog = gr.Button("X_dog")
+    b_learning_based = gr.Button("learning based")
+    b_xdog.click(xdog, inputs=input_image, outputs=output_xdog)
+    b_learning_based.click(swin_model, inputs=input_image, outputs=output_deep)
+demo.launch()