Ajout de l'application Gradio

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+from skimage import exposure
+from scipy import signal
+from skimage.color import rgb2gray
+
+# Fonctions de traitement d'image
+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 histogram(image):
+    img_np = np.array(image)
+    gray = rgb2gray(img_np)
+    
+    hist, _ = exposure.histogram(gray)
+    plt.figure(figsize=(6, 4))
+    plt.title('Histogramme en nuances de gris')
+    plt.xlabel('Intensité des pixels')
+    plt.ylabel('Nombre de pixels')
+    plt.bar(np.arange(len(hist)), hist, width=0.5, color='gray')
+    plt.tight_layout()
+    plt.show()
+
+def mean_filter(image):
+    img_np = np.array(image)
+    mean_filtered = cv2.blur(img_np, (5, 5))  # Filtre moyen 5x5
+    return Image.fromarray(mean_filtered)
+
+def gaussian_filter(image):
+    img_np = np.array(image)
+    gaussian_filtered = cv2.GaussianBlur(img_np, (5, 5), 0)  # Filtre gaussien 5x5
+    return Image.fromarray(gaussian_filtered)
+
+def contour_extract(image):
+    img_np = np.array(image.convert('L'))
+    kernel = np.array([[0, 0, 0], [-1, 1, 0], [0, 0, 0]])
+    img_contour = signal.convolve2d(img_np, kernel, boundary='symm', mode='same')
+    return Image.fromarray(np.uint8(np.absolute(img_contour)))
+
+def morph_erosion(image):
+    img_np = np.array(image.convert('L'))  # Conversion en niveaux de gris
+    kernel = np.ones((5, 5), np.uint8)  # Noyau 5x5 pour l'érosion
+    erosion = cv2.erode(img_np, kernel, iterations=1)
+    return Image.fromarray(erosion)
+
+def morph_dilation(image):
+    img_np = np.array(image.convert('L'))  # Conversion en niveaux de gris
+    kernel = np.ones((5, 5), np.uint8)  # Noyau 5x5 pour la dilatation
+    dilation = cv2.dilate(img_np, kernel, iterations=1)
+    return Image.fromarray(dilation)
+
+# Sauvegarder l'image sur l'ordinateur
+def save_image(image):
+    img_np = np.array(image)
+    save_path = "image_modifiee.png"
+    cv2.imwrite(save_path, cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR))  # Sauvegarder en format PNG
+    return save_path
+
+# Interface Gradio
+def image_processing(image, operation, threshold=128, width=100, height=100, angle=0):
+    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":
+        histogram(image)  # Affichage seulement
+        return image
+    elif operation == "Filtre moyen":
+        return mean_filter(image)
+    elif operation == "Filtre gaussien":
+        return gaussian_filter(image)
+    elif operation == "Contours":
+        return contour_extract(image)
+    elif operation == "Erosion":
+        return morph_erosion(image)
+    elif operation == "Dilatation":
+        return morph_dilation(image)
+    return image
+
+# Mise à jour dynamique de la visibilité des champs en fonction de l'opération
+def update_visibility(operation):
+    return {
+        "threshold": operation == "Binarisation",
+        "width": operation == "Redimensionner",
+        "height": operation == "Redimensionner",
+        "angle": operation == "Rotation"
+    }
+
+# Interface Gradio avec style
+custom_css = """
+#main-header {
+    color: #4CAF50;  /* Vert clair */
+    font-family: 'Arial', sans-serif;
+    text-align: center;
+}
+
+#upload-area {
+    border: 2px solid #FF9800;  /* Orange */
+    background-color: #FFF3E0;
+}
+
+#result-area {
+    border: 2px solid #4CAF50;
+    background-color: #E8F5E9;
+}
+
+#process-button {
+    background-color: #FF9800;  /* Orange */
+    color: white;
+    font-size: 16px;
+}
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("## 🌈 PixelCrafter: Transformez vos images avec style 🎨")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Charger Image")
+        operation = gr.Radio(
+            ["Négatif", "Binarisation", "Redimensionner", "Rotation", "Histogramme", "Filtre moyen", "Filtre gaussien", "Contours", "Erosion", "Dilatation"],
+            label="Opération", value="Négatif"
+        )
+
+    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)
+    image_output = gr.Image(label="Image Modifiée")
+
+    # Mise à jour de la visibilité des champs
+    operation.change(update_visibility, inputs=operation, outputs=[threshold, width, height, angle])
+
+    # Bouton d'application
+    submit_button = gr.Button("Appliquer")
+    submit_button.click(image_processing, inputs=[image_input, operation, threshold, width, height, angle], outputs=image_output)
+
+    # Sauvegarde de l'image
+    save_button = gr.Button("Sauvegarder l'image")
+    save_button.click(save_image, inputs=image_output, outputs=gr.File(label="Télécharger l'image"))
+
+# Lancer l'application Gradio
+demo.launch()

requirements.txt

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+gradio
+numpy
+Pillow
+opencv-python
+matplotlib