v1

app.py

@@ -0,0 +1,119 @@
+import os
+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# 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 grayscale(image):
+    return image.convert('L')
+
+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: int, height: int):
+    return image.resize((width, height))
+
+def rotate_image(image, angle):
+    return image.rotate(angle)
+
+def show_histogram(image):
+    grayscale = image.convert("L")
+    plt.hist(grayscale, bins=120)
+    #hist_data = grayscale.histogram()
+    plt.figure()
+    plt.plot(hist_data)
+    plt.title("Histogramme des Niveaux de Gris")
+    plt.show()
+
+def gaussian_filter(image, shape=(3,3)):
+    image = np.array(image)
+    filtered = cv2.GaussianBlur(image, shape, 0)
+    return Image.fromarray(filtered)
+
+def mean_filter(image, shape=(3,3)):
+    image = np.array(image)
+    filtered = cv2.blur(image, shape)
+    return Image.fromarray(filtered)
+
+def sobel_edges(image, k=5):
+    image = np.array(image.convert('L'))
+    sobel_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=k)
+    sobel_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=k)
+    sobel_combined = cv2.magnitude(sobel_x, sobel_y)
+    return Image.fromarray(np.uint8(sobel_combined))
+
+def erosion(image, noyau=(5,5), iterations=3):
+    image = np.array(image.convert("L"))
+    kernel = np.ones(noyau, np.uint8)
+    eroded_image = cv2.erode(image, kernel, iterations=iterations)
+    return Image.fromarray(eroded_image)
+
+def dilatation(image, noyau=(5,5), iterations=3):
+    image = np.array(image.convert("L"))
+    kernel = np.ones(noyau, np.uint8)
+    dilated_image = cv2.dilate(image, kernel, iterations=iterations)
+    return Image.fromarray(dilated_image)
+
+
+# Ajoutez d'autres fonctions pour l'histogramme, le filtrage, Sobel, etc.
+
+# Interface Gradio
+def image_processing(image, operation, threshold=128, width=100, height=100, angle=30, shape=(3,3), noyau=(5,5), k=5, iterations=3):
+    if operation == "Négatif":
+        return apply_negative(image)
+    elif operation == "Image en Gris":
+        return grayscale(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 de Gris':
+        return show_histogram(image)
+    elif operation == 'Filtre Gaussien':
+        return gaussian_filter(image, shape)
+    elif operation == 'Filtre Moyen':
+        return mean_filter(image, shape)
+    elif operation == 'Sobel Edges Extraction':
+        return sobel_edges(image, k)
+    elif operation == 'Erosion':
+        return erosion(image, noyau, iterations)
+    elif operation == 'Dilatation':
+        return dilatation(image)
+    # Ajouter d'autres conditions pour les autres opérations
+    return image
+
+# Interface Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Projet de Traitement d'Image")
+
+    with gr.Row():
+        operation = gr.Radio(["Négatif", "Image en Gris", "Binarisation", "Redimensionner", "Rotation", 'Histogramme de Gris', 
+                            'Filtre Gaussien', 'Filtre Moyen', 'Sobel Edges Extraction', 'Erosion', 'Dilatation'], label="Opération")
+        threshold = gr.Slider(0, 255, 128, label="Seuil de binarisation", visible=True)
+        width = gr.Number(value=100, label="Largeur", visible=True)
+        height = gr.Number(value=100, label="Hauteur", visible=True)
+        angle = gr.Slider(0, 360, 30, label="Angle de Rotation", visible=True)
+        k = gr.Number(value=5, label="k de Sobel", visible=True)
+        iterations = gr.Number(value=3, label="Nombre d'iteration pour les transformations morphologiques", visible=True)
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Charger Image")
+        image_output = gr.Image(type="pil", label="Image Modifiée")
+    submit_button = gr.Button("Appliquer")
+    submit_button.click(image_processing, inputs=[image_input, operation, threshold, width, height, angle], outputs=image_output)
+
+# Lancer l'application Gradio
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio==3.40.0
+Pillow==9.4.0
+opencv-python-headless==4.8.0.74
+matplotlib==3.7.1
+numpy==1.24.3