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Parent(s):
dac2479
Update app.py
Browse files
app.py
CHANGED
@@ -1,36 +1,33 @@
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import gradio as gr
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import kornia as K
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from kornia.core import Tensor
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def load_img(file):
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# load the image using
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img_gray = K.color.rgb_to_grayscale(img_rgb)
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return img_gray
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def canny_edge_detector(file):
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x_gray = load_img(file)
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x_canny: Tensor = K.filters.canny(x_gray)[0]
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img_out = 1.0 - x_canny.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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def sobel_edge_detector(file):
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x_gray = load_img(file)
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x_sobel: Tensor = K.filters.sobel(x_gray)
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img_out = 1.0 - x_sobel
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return K.utils.tensor_to_image(img_out)
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def simple_edge_detector(file, order, direction):
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x_gray = load_img(file)
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grads: Tensor = K.filters.spatial_gradient(
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x_gray, order=order
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) # BxCx2xHxW
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grads_x = grads[:, :, 0]
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grads_y = grads[:, :, 1]
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if direction == "x":
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@@ -39,21 +36,18 @@ def simple_edge_detector(file, order, direction):
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img_out = 1.0 - grads_y.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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def laplacian_edge_detector(file, kernel=9):
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x_gray = load_img(file)
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x_laplacian: Tensor = K.filters.laplacian(x_gray, kernel_size=kernel)
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img_out = 1.0 - x_laplacian.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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examples = [["examples/doraemon.png"], ["examples/kornia.png"]]
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title = "Kornia Edge Detector"
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description = "<p style='text-align: center'>This is a Gradio demo for Kornia's Edge Detector.</p><p style='text-align: center'>To use it, simply upload your image, or click one of the examples to load them, and use the sliders to enhance! Read more at the links at the bottom.</p>"
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article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Kornia Docs</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://kornia-tutorials.readthedocs.io/en/latest/image_enhancement.html' target='_blank'>Kornia Enhancements Tutorial</a></p>"
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def change_layout(choice):
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kernel = gr.update(visible=False)
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order = gr.update(visible=False)
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@@ -61,14 +55,9 @@ def change_layout(choice):
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if choice == "Laplacian":
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return [gr.update(value=3, visible=True), order, direction]
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elif choice == "Simple":
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return [
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kernel,
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gr.update(value=2, visible=True),
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gr.update(value="x", visible=True),
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]
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return [kernel, order, direction]
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def Detect(file, choice):
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layout = change_layout(choice)
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if choice == "Canny":
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layout.extend([img])
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return layout
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def Detect_wo_layout(file, choice, kernel, order, direction):
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if choice == "Canny":
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img = canny_edge_detector(file)
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img = simple_edge_detector(file, order, direction)
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return img
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(type="
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kernel = gr.Slider(
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value=3,
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label="kernel_size",
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visible=False,
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)
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order = gr.Radio(
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[1, 2], value=1, label="Derivative Order", visible=False
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)
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direction = gr.Radio(
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["x", "y"],
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value="x",
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label="Derivative Direction",
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visible=False,
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)
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radio = gr.Radio(
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["Canny", "Simple", "Sobel", "Laplacian"],
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value="Canny",
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label="Type of Edge Detector",
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)
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with gr.Column():
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image_output = gr.Image(
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gr.Examples(examples, inputs=[image_input])
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kernel.change(
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fn=Detect_wo_layout,
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inputs=[image_input, radio, kernel, order, direction],
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outputs=[image_output],
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)
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order.change(
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fn=Detect_wo_layout,
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inputs=[image_input, radio, kernel, order, direction],
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outputs=[image_output],
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)
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direction.change(
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fn=Detect_wo_layout,
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inputs=[image_input, radio, kernel, order, direction],
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outputs=[image_output],
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)
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image_input.change(
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fn=Detect_wo_layout,
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inputs=[image_input, radio, kernel, order, direction],
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outputs=[image_output],
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)
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demo.launch()
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import gradio as gr
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import kornia as K
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from kornia.core import Tensor
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from PIL import Image
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import numpy as np
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def load_img(file):
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# load the image using PIL and convert to tensor
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img_pil = Image.open(file).convert('RGB')
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img_np = np.array(img_pil)
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img_rgb: Tensor = K.utils.image_to_tensor(img_np).float() / 255.0
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img_rgb = img_rgb.unsqueeze(0) # add batch dimension
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img_gray = K.color.rgb_to_grayscale(img_rgb)
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return img_gray
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def canny_edge_detector(file):
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x_gray = load_img(file)
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x_canny: Tensor = K.filters.canny(x_gray)[0]
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img_out = 1.0 - x_canny.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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def sobel_edge_detector(file):
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x_gray = load_img(file)
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x_sobel: Tensor = K.filters.sobel(x_gray)
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img_out = 1.0 - x_sobel
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return K.utils.tensor_to_image(img_out)
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def simple_edge_detector(file, order, direction):
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x_gray = load_img(file)
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grads: Tensor = K.filters.spatial_gradient(x_gray, order=order) # BxCx2xHxW
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grads_x = grads[:, :, 0]
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grads_y = grads[:, :, 1]
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if direction == "x":
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img_out = 1.0 - grads_y.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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def laplacian_edge_detector(file, kernel=9):
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x_gray = load_img(file)
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x_laplacian: Tensor = K.filters.laplacian(x_gray, kernel_size=kernel)
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img_out = 1.0 - x_laplacian.clamp(0.0, 1.0)
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return K.utils.tensor_to_image(img_out)
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examples = [["examples/doraemon.png"], ["examples/kornia.png"]]
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title = "Kornia Edge Detector"
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description = "<p style='text-align: center'>This is a Gradio demo for Kornia's Edge Detector.</p><p style='text-align: center'>To use it, simply upload your image, or click one of the examples to load them, and use the sliders to enhance! Read more at the links at the bottom.</p>"
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article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Kornia Docs</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://kornia-tutorials.readthedocs.io/en/latest/image_enhancement.html' target='_blank'>Kornia Enhancements Tutorial</a></p>"
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def change_layout(choice):
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kernel = gr.update(visible=False)
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order = gr.update(visible=False)
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if choice == "Laplacian":
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return [gr.update(value=3, visible=True), order, direction]
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elif choice == "Simple":
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return [kernel, gr.update(value=2, visible=True), gr.update(value="x", visible=True)]
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return [kernel, order, direction]
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def Detect(file, choice):
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layout = change_layout(choice)
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if choice == "Canny":
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layout.extend([img])
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return layout
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def Detect_wo_layout(file, choice, kernel, order, direction):
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if choice == "Canny":
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img = canny_edge_detector(file)
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img = simple_edge_detector(file, order, direction)
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return img
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(type="filepath", label="Input Image")
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kernel = gr.Slider(minimum=1, maximum=7, step=2, value=3, label="kernel_size", visible=False)
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order = gr.Radio([1, 2], value=1, label="Derivative Order", visible=False)
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direction = gr.Radio(["x", "y"], value="x", label="Derivative Direction", visible=False)
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radio = gr.Radio(["Canny", "Simple", "Sobel", "Laplacian"], value="Canny", label="Type of Edge Detector")
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with gr.Column():
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image_output = gr.Image(label="Output Image")
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gr.Examples(examples, inputs=[image_input])
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radio.change(fn=Detect, inputs=[image_input, radio], outputs=[kernel, order, direction, image_output])
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kernel.change(fn=Detect_wo_layout, inputs=[image_input, radio, kernel, order, direction], outputs=[image_output])
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order.change(fn=Detect_wo_layout, inputs=[image_input, radio, kernel, order, direction], outputs=[image_output])
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direction.change(fn=Detect_wo_layout, inputs=[image_input, radio, kernel, order, direction], outputs=[image_output])
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image_input.change(fn=Detect_wo_layout, inputs=[image_input, radio, kernel, order, direction], outputs=[image_output])
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demo.launch()
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