app.py

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=45,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()