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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() |