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import gradio as gr
from PIL import Image
import numpy as np
import cv2
# Fonction pour charger et afficher l'image
def load_image(image):
return image
# Fonction pour appliquer un négatif
def apply_negative(image):
img_np = np.array(image)
negative = 255 - img_np
return Image.fromarray(negative)
# Fonction pour binariser une image
def binarize_image(image, threshold):
img_np = np.array(image.convert('L')) # Convertir en niveaux de gris
_, binary = cv2.threshold(img_np, threshold, 255, cv2.THRESH_BINARY)
return Image.fromarray(binary)
# Interface Gradio
def image_processing(image, operation, threshold=128):
if operation == "Négatif":
return apply_negative(image)
elif operation == "Binarisation":
return binarize_image(image, threshold)
return image
# Interface Gradio
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"], label="Opération à effectuer", value="Négatif")
threshold = gr.Slider(0, 255, 128, label="Seuil de binarisation", visible=False)
image_output = gr.Image(label="Image Modifiée")
def update_threshold(operation):
if operation == "Binarisation":
return gr.update(visible=True)
return gr.update(visible=False)
operation.change(update_threshold, inputs=operation, outputs=threshold)
submit_button = gr.Button("Appliquer")
submit_button.click(image_processing, inputs=[image_input, operation, threshold], outputs=image_output)
# Lancer l'application Gradio
demo.launch()
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