app.py

import gradio as gr
import improcess
import numpy as np


# Fonction pour choisir un filtre et l'appliquer
def choose_filter(input_img, filter):
    if input_img is None:
        return None
    match filter:
        case "Gray Scale":
            return improcess.gray_scale(input_img)
        case "Gaussian Blur":
            return improcess.gaussian_blur(input_img)
        case "Binarize":
            return improcess.binarize(input_img)
        case "Negative":
            return improcess.negative(input_img)


# Fonction pour manipuler le contraste
def handle_contrast(output_image, contrast: list):
    if output_image is None:
        return None
    for trans in contrast:
        if trans == "Dark Contrast":
            output_image = improcess.log_trans(output_image)
        elif trans == "White Contrast":
            output_image = improcess.exp_trans(output_image)
    return output_image


# Fonction pour modifier la morphologie
def handle_morphology(image, morphology):
    if image is None:
        return None
    for trans in morphology:
        if trans == "Erosion":
            image = improcess.erosion(image)
        elif trans == "Dilatation":
            image = improcess.dilatation(image)
    return image


# Fonction pour egaliser l'histogramme
def equalize_hist(image, equalize):
    if equalize == True:
        return improcess.equalize(image)
    return image


def detect_feat(input_img, feature):
    if input_img is None:
        return None
    match feature:
        case "Edges(canny)":
            input_img = improcess.find_edges(input_img)
            return input_img
        case "Edges(laplacian)":
            input_img = improcess.laplacian(input_img)
            return input_img
        case "Contours":
            input_img = improcess.find_contours(input_img)
            return input_img


# Fonction pour réinitialiser l'image
def reset_img(input_img):
    return input_img


# Interface Gradio
with gr.Blocks(theme="Zarkel/IBM_Carbon_Theme") as demo:
    gr.HTML(
        """
        <h1 style='text-align: center'>Image processing lab</h1>
        """
    )
    with gr.Row():
        input_img = gr.Image(
            "profile.jpeg", label="Original image", scale=1, interactive=True
        )
        with gr.Group():
            output_img = gr.Image(
                "profile.jpeg",
                label="Transformed image",
                scale=2,
                interactive=False,
                format="png",
            )
            btn = gr.Button(value="Reset")  # Bouton de réinitialisation
    with gr.Row(variant="panel"):
        with gr.Group():
            morphology = gr.CheckboxGroup(
                choices=["Dilatation", "Erosion"], label="Morphology"
            )
            # equalize_img = gr.Checkbox(label="Equalize image")
            gamma = gr.Slider(
                minimum=0, maximum=1, label="Luminosity", interactive=True, value=0
            )

            contrast = gr.CheckboxGroup(
                choices=["Dark Contrast", "White Contrast"], label="Contrast"
            )
            angle = gr.Slider(label="Rotation (deg)", minimum=0, maximum=360)
        with gr.Group():
            scale = gr.Slider(
                minimum=0.1, maximum=1.5, value=1, label="Resize", info="Choose a scale"
            )
            filter = gr.Dropdown(
                interactive=True,
                label="Apply filter",
                choices=["Gray Scale", "Binarize", "Negative", "Gaussian Blur"],
            )
            features = gr.Dropdown(
                choices=["Contours", "Edges(canny)", "Edges(laplacian)"],
                interactive=True,
                label="Extract feature",
            )
            show_histogram = gr.Button(value="Show Histogram")

    # Mise à jour de l'image lorsqu'un filtre est appliqué
    filter.change(fn=choose_filter, inputs=[input_img, filter], outputs=output_img)

    # Mise à jour de l'image avec redimensionnement
    scale.change(fn=improcess.resize, inputs=[input_img, scale], outputs=output_img)

    # Bouton de réinitialisation de l'image
    btn.click(fn=reset_img, inputs=[input_img], outputs=[output_img])

    # Mise à jour de l'image avec correction de gamma
    gamma.change(
        fn=improcess.gamma_trans, inputs=[input_img, gamma], outputs=[output_img]
    )

    # Mise à jour de l'image avec contraste
    contrast.change(
        fn=handle_contrast, inputs=[output_img, contrast], outputs=output_img
    )
    # Mise à jour de l'image avec une transformation morphologique
    morphology.change(
        fn=handle_morphology, inputs=[input_img, morphology], outputs=output_img
    )
    # Egaliser l'histogramme de l'image
    # equalize_img.change(fn=equalize_hist, inputs=[input_img], outputs=[output_img])
    # Effectuer une rotation de l'image
    angle.change(fn=improcess.rotate, inputs=[input_img, angle], outputs=[output_img])

    # Extraire les caractéristiques
    features.select(fn=detect_feat, inputs=[input_img, features], outputs=[output_img])

    # Afficher l'histogram
    show_histogram.click(
        fn=improcess.return_histogram_path, inputs=[input_img], outputs=[output_img]
    )
# Lancer l'application
if __name__ == "__main__":
    demo.launch()