app.py

import gradio as gr
from PIL import Image, ImageFilter
import numpy as np
import cv2
import matplotlib.pyplot as plt

def load_image(image):
    return image

def apply_negative(image):
    img_np = np.array(image)
    negative = 255 - img_np
    return Image.fromarray(negative)

def binarize_image(image, threshold):
    img_np = np.array(image.convert('L'))
    _, binary = cv2.threshold(img_np, threshold, 255, cv2.THRESH_BINARY)
    return Image.fromarray(binary)

def resize_image(image, width, height):
    return image.resize((width, height))

def rotate_image(image, angle):
    return image.rotate(angle)

def histo_gray(image):
    img_np = np.array(image.convert('L'))
    hist = cv2.calcHist([img_np], [0], None, [256], [0, 256])
    plt.plot(hist)
    plt.title('Histogramme des niveaux de gris')
    plt.xlabel('Intensité des pixels')
    plt.ylabel('Nombre de pixels')
    plt.show()
    return hist

def filtre_gauss(image, kernel_width, kernel_height):
    img_np = np.array(image)
    blurred = cv2.GaussianBlur(img_np, (kernel_width, kernel_height), 0)
    return Image.fromarray(blurred)

def erosion(image, taille):
    img_np = np.array(image.convert('L'))
    kernel = np.ones((taille, taille), np.uint8)
    eroded = cv2.erode(img_np, kernel, iterations=1)
    return Image.fromarray(eroded)

def dilatation(image, taille):
    img_np = np.array(image.convert('L'))
    kernel = np.ones((taille, taille), np.uint8)
    dilated = cv2.dilate(img_np, kernel, iterations=1)
    return Image.fromarray(dilated)

def extract_edges(image):
    img_np = np.array(image.convert('L'))
    edges = cv2.Canny(img_np, 100, 200)
    return Image.fromarray(edges)

# Interface Gradio
def image_processing(image, operation, threshold=128, width=100, height=100, angle=0, kernel_width=5, kernel_height=5, taille_e=3, taille_d=3):
    if operation == "Négatif":
        return apply_negative(image)
    elif operation == "Binarisation":
        return binarize_image(image, threshold)
    elif operation == "Redimensionner":
        return resize_image(image, width, height)
    elif operation == "Rotation":
        return rotate_image(image, angle)
    elif operation == "Histogramme des niveaux de gris":
        return histo_gray(image)
    elif operation == "Filtre gaussien":
        return filtre_gauss(image, kernel_width, kernel_height)
    elif operation == "Erosion":
        return erosion(image, taille_e)
    elif operation == "Dilatation":
        return dilatation(image, taille_d)
    elif operation == "Extraction de contours":
        return extract_edges(image)


with gr.Blocks() as demo:
    gr.Markdown("## Projet de Traitement d'Image")

    with gr.Row():
        image_input = gr.Image(type="pil", label="Charger Image")
        operation = gr.Radio(["Négatif", "Binarisation", "Redimensionner", "Rotation", "Histogramme des niveaux de gris", "Filtre gaussien", "Extraction de contours", "Erosion", "Dilatation"], label="Opération")
        
        threshold = gr.Slider(0, 255, 128, label="Seuil de binarisation", visible=False)
        width = gr.Number(value=100, label="Largeur", visible=False)
        height = gr.Number(value=100, label="Hauteur", visible=False)
        angle = gr.Number(value=0, label="Angle de Rotation", visible=False)
        kernel_width = gr.Number(value=5, label="Largeur du kernel du filtre gaussien", visible=False)
        kernel_height = gr.Number(value=5, label="Hauteur du kernel du filtre gaussien", visible=False)
        taille_e = gr.Number(value=3, label="Taille du filtre pour l'érosion", visible=False)
        taille_d = gr.Number(value=3, label="Taille du filtre pour la dilatation", visible=False)
        
    image_output = gr.Image(label="Image Modifiée")

    def update_inputs(operation):
        if operation == "Binarisation":
            return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        elif operation == "Redimensionner":
            return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        elif operation == "Rotation":
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        elif operation == "Filtre gaussien":
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
        elif operation == "Erosion":
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
        elif operation == "Dilatation":
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
        else:
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)

    operation.change(update_inputs, inputs=operation, outputs=[threshold, width, height, angle, kernel_width, kernel_height, taille_e, taille_d])

    submit_button = gr.Button("Appliquer")
    submit_button.click(image_processing, inputs=[image_input, operation, threshold, width, height, angle, kernel_width, kernel_height, taille_e, taille_d], outputs=image_output)


demo.launch(share=True)