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| import gradio as gr | |
| import kornia as K | |
| from kornia.core import Tensor | |
| def load_img(file): | |
| # load the image using the rust backend | |
| img_rgb: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32) | |
| img_rgb = img_rgb[None] | |
| img_gray = K.color.rgb_to_grayscale(img_rgb) | |
| return img_gray | |
| def canny_edge_detector(file): | |
| x_gray = load_img(file) | |
| x_canny: Tensor = K.filters.canny(x_gray)[0] | |
| img_out = 1.0 - x_canny.clamp(0.0, 1.0) | |
| return K.utils.tensor_to_image(img_out) | |
| def sobel_edge_detector(file): | |
| x_gray = load_img(file) | |
| x_sobel: Tensor = K.filters.sobel(x_gray) | |
| img_out = 1.0 - x_sobel | |
| return K.utils.tensor_to_image(img_out) | |
| def simple_edge_detector(file, order, direction): | |
| x_gray = load_img(file) | |
| grads: Tensor = K.filters.spatial_gradient( | |
| x_gray, order=order | |
| ) # BxCx2xHxW | |
| grads_x = grads[:, :, 0] | |
| grads_y = grads[:, :, 1] | |
| if direction == "x": | |
| img_out = 1.0 - grads_x.clamp(0.0, 1.0) | |
| else: | |
| img_out = 1.0 - grads_y.clamp(0.0, 1.0) | |
| return K.utils.tensor_to_image(img_out) | |
| def laplacian_edge_detector(file, kernel): | |
| x_gray = load_img(file) | |
| x_laplacian: Tensor = K.filters.laplacian(x_gray, kernel_size=kernel) | |
| img_out = 1.0 - x_laplacian.clamp(0.0, 1.0) | |
| return K.utils.tensor_to_image(img_out) | |
| examples = [["examples/doraemon.png"], ["examples/kornia.png"]] | |
| title = "Kornia Edge Detector" | |
| 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>" | |
| 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>" | |
| def change_layout(choice): | |
| kernel = gr.update(visible=False) | |
| order = gr.update(visible=False) | |
| direction = gr.update(visible=False) | |
| if choice == "Laplacian": | |
| return [gr.update(value=3, visible=True), order, direction] | |
| elif choice == "Simple": | |
| return [ | |
| kernel, | |
| gr.update(value=2, visible=True), | |
| gr.update(value="x", visible=True), | |
| ] | |
| return [kernel, order, direction] | |
| def Detect(file, choice): | |
| layout = change_layout(choice) | |
| if choice == "Canny": | |
| img = canny_edge_detector(file) | |
| elif choice == "Sobel": | |
| img = sobel_edge_detector(file) | |
| elif choice == "Laplacian": | |
| img = laplacian_edge_detector(file, 5) | |
| else: | |
| img = simple_edge_detector(file, 1, "x") | |
| layout.extend([img]) | |
| return layout | |
| def Detect_wo_layout(file, choice, kernel, order, direction): | |
| if choice == "Canny": | |
| img = canny_edge_detector(file) | |
| elif choice == "Sobel": | |
| img = sobel_edge_detector(file) | |
| elif choice == "Laplacian": | |
| img = laplacian_edge_detector(file, kernel) | |
| else: | |
| img = simple_edge_detector(file, order, direction) | |
| return img | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(type="file") | |
| kernel = gr.Slider( | |
| minimum=1, | |
| maximum=7, | |
| step=2, | |
| value=3, | |
| label="kernel_size", | |
| visible=False, | |
| ) | |
| order = gr.Radio( | |
| [1, 2], value=1, label="Derivative Order", visible=False | |
| ) | |
| direction = gr.Radio( | |
| ["x", "y"], | |
| value="x", | |
| label="Derivative Direction", | |
| visible=False, | |
| ) | |
| radio = gr.Radio( | |
| ["Canny", "Simple", "Sobel", "Laplacian"], | |
| label="Type of Edge Detector", | |
| ) | |
| gr.Examples(examples, inputs=[image_input]) | |
| image_output = gr.Image(shape=(256, 256)) | |
| radio.change( | |
| fn=Detect, | |
| inputs=[image_input, radio], | |
| outputs=[kernel, order, direction, image_output], | |
| ) | |
| kernel.change( | |
| fn=Detect_wo_layout, | |
| inputs=[image_input, radio, kernel, order, direction], | |
| outputs=[image_output], | |
| ) | |
| order.change( | |
| fn=Detect_wo_layout, | |
| inputs=[image_input, radio, kernel, order, direction], | |
| outputs=[image_output], | |
| ) | |
| direction.change( | |
| fn=Detect_wo_layout, | |
| inputs=[image_input, radio, kernel, order, direction], | |
| outputs=[image_output], | |
| ) | |
| demo.launch() | |