Create app.py

app.py

@@ -0,0 +1,120 @@
+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import sys 
+from PIL import Image,ImageFilter
+# 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):
+    #image=image.convert('L')
+    return image.resize((width, height))
+
+def rotate_image(image, angle):
+    return image.rotate(angle)
+
+def hister(image):
+    #image=cv2.imread(image,cv2.IMREAD_GRAYSCALE)
+    image=np.array(image.convert('L'))
+    if image is None:
+        print('Image load failed !')
+        sys.exit()
+    hist=cv2.calcHist([image],[0],None,[256],[0,256])
+    #cv2.imshow('Gray Scale Histogram',image)
+    #cv2.waitkey(1)
+    
+    #plt.plot(hist)
+    
+    
+    fig, ax = plt.subplots()
+    ax.plot(hist)
+
+    fig.canvas.draw()
+    img_plot = np.array(fig.canvas.renderer.buffer_rgba())
+
+    #cv2.imshow('Image', cv2.cvtColor(img_plot, cv2.COLOR_RGBA2BGR))
+
+    #cv2.waitKey(0)
+    #image_result=plt.savefig('histogram.png')
+    #plt.show()
+    #image_result=Image.open('histogram.png')
+    #image_result=cv2.imread('histogram.png')
+    return Image.fromarray(img_plot)
+
+def gauss_filterer(image,radius):
+  return image.filter(ImageFilter.GaussianBlur(radius))
+
+def contour_extraction(image):
+  image=image.convert('L')
+  return image.filter(ImageFilter.FIND_EDGES)
+
+def erode(image):
+  return image.filter(ImageFilter.MinFilter(3))
+
+def dilate(image):
+  return image.filter(ImageFilter.MaxFilter(3))
+
+
+
+   
+
+# 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=0,radius=9):
+    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":
+        #print("Yes ....")
+        return hister(image)
+    elif operation=="Filtre":
+      return gauss_filterer(image,radius)
+    elif operation=="Contour":
+      return contour_extraction(image)
+    elif operation=="Erosion":
+      return erode(image)
+    elif operation=="Dilatation":
+      return dilate(image)
+    # Ajouter d'autres conditions pour les autres opérations
+    return image
+
+# Interface Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Image Processing")
+    gr.Markdown("This is the result of my Week 2 work based on image processing and filters .Let me make you discover it!")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Charger Image")
+        operation = gr.Radio(["Négatif", "Binarisation", "Redimensionner", "Rotation","Histogramme","Filtre","Contour","Erosion","Dilatation"], label="Opération")
+        #advanced_operation=gr.Radio(["Histogramme"],label="Advanced")
+        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")
+
+    submit_button = gr.Button("Appliquer")
+    submit_button.click(fn=image_processing, inputs=[image_input, operation, threshold, width, height, angle], outputs=image_output)
+
+# Lancer l'application Gradio
+demo.launch()