app.py

import gradio as gr
import kornia as K
from kornia.core import Tensor
from PIL import Image
import numpy as np

def load_img(file):
    # load the image using PIL and convert to tensor
    img_pil = Image.open(file).convert('RGB')
    img_np = np.array(img_pil)
    img_rgb: Tensor = K.utils.image_to_tensor(img_np).float() / 255.0
    img_rgb = img_rgb.unsqueeze(0)  # add batch dimension
    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=9):
    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 https://kornia.github.io/tutorials/#category=Edge%20Detection' 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="filepath", label="Input Image")
            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"], value="Canny", label="Type of Edge Detector")
        with gr.Column():
            image_output = gr.Image(label="Output Image")
            gr.Examples(examples, inputs=[image_input])

    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])
    image_input.change(fn=Detect_wo_layout, inputs=[image_input, radio, kernel, order, direction], outputs=[image_output])

demo.launch()