Upload app and requierements

app.py

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+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()

requirements.txt

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+gradio==3.40.0
+Pillow==9.4.0
+opencv-python-headless==4.8.0.74
+matplotlib==3.7.1
+numpy==1.24.3