Update

app.py

@@ -1,93 +1,111 @@
-import gradio as gr
-from PIL import Image, ImageFilter
-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 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):
-    hist = cv2.calcHist([image], [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_heigth):
-    return cv2.GaussianBlur(image, (kernel_width, kernel_heigth), 0)
-
-def erosion(image, taille):
-    return image.filter(ImageFilter.MinFilter(taille_e))
-
-def dilatation(image, taille):
-    return image.filter(ImageFilter.MaxFilter(taille_d))
-
-def extract_edges(image):
-    image_sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize = 5)
-    image_sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize = 5)
-    return image_sobelx, image_sobely
-
-# 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 des niveaux de gris":
-        return histo_gray(image)
-    elif operation == "Filtre gaussien":
-        return filtre_gauss(image, kernel_width, kernel_heigth)
-    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)       
-
-# 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", "Redimension", "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_heigth = 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")
-
-    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()
+import gradio as gr
+from PIL import Image, ImageFilter
+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 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 image
+
+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)
+    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)
+    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)
+
+
+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)
+
+# 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", "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_visibility(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)
+        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)
+        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)
+        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)
+        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, visible=True)
+        elif operation == "Dilatation":
+            return gr.update(visible=False), gr.update(visible(False), False, stored