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import gradio as gr
from PIL import Image, ImageFilter
import numpy as np
import cv2
from skimage import morphology
import matplotlib.pyplot as plt
import io
# Images manipulation functions
#Image loading
def load_image(image_path):
'''Load image with PIL'''
image=Image.open(image_path)
return image
#Negative APPLYING
def apply_negative(image):
'''
input: PIL Image
output : PIL Image
Image loaded with PIL is turned to numpy format. Then, we calculate the new pixels values and image gotten is return to PIL format'''
img_np = np.array(image)
negative = 255 - img_np
return Image.fromarray(negative)
#binarization
def binarize_image(image, threshold_value):
'''
inputs : PIL image ; threshold_value
output : PIL image
Image in PIL format is converted into grayscale format and then into numpy format.Now we make a binary threshold base on threshold value.
Image gotten is returned to Image format'''
img_np = np.array(image.convert('L'))
_, binary = cv2.threshold(img_np, threshold_value, 255, cv2.THRESH_BINARY)
return Image.fromarray(binary)
#image resizing
def resize_image(image, width, height):
'''Resizing is doing by using PIL resizing method'''
return image.resize((width, height))
#image rotation
def rotate_image(image, angle):
'''Rotation is doing by using PIL rotation method'''
return image.rotate(angle)
#Image histogram
def histogram(image):
img = np.array(image.convert('L'))
hist = cv2.calcHist([img],[0],None,[256],[0,256])
plt.plot(hist)
img_buf = io.BytesIO()
plt.savefig(img_buf, format='png')
return Image.open(img_buf)
#Gaussian filter
def g_filter(image):
img_gauss = image.filter(ImageFilter.GaussianBlur(5) )
return img_gauss
#Sobel
def sobel_f(image):
i = np.array(image)
img = cv2.GaussianBlur(i, (3, 3), sigmaX=0, sigmaY=0)
edge_sobel = cv2.Sobel(src=img, ddepth=cv2.CV_8U, dx=1, dy=1, ksize=5)
return Image.fromarray(edge_sobel)
#erosion
def erosion(image):
i=np.array(image.convert('L'))
ero_img= morphology.binary_erosion(i, morphology.disk(1))
return Image.fromarray(ero_img)
#dilatation
def dilatation(image):
i=np.array(image.convert('L'))
ero_img= morphology.binary_dilation(i, morphology.disk(1))
return Image.fromarray(ero_img)
#contour
def contour(image):
return image.filter(ImageFilter.CONTOUR)
#lumineux
def lumineux(image):
return image.filter(ImageFilter.EDGE_ENHANCE)
#Netteté
def nette(image):
return image.filter(ImageFilter.SHARPEN)
# 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":
return histogram(image)
elif operation == "Gaussian Filter":
return g_filter(image)
elif operation == "Sobel":
return sobel_f(image)
elif operation == "Erosion":
return erosion(image)
elif operation == "Dilatation":
return erosion(image)
elif operation == "Contour":
return contour(image)
elif operation == "Luminosité":
return lumineux(image)
elif operation == "Netteté":
return nette(image)
return image
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## Mini photoshop")
with gr.Row():
image_input = gr.Image(type="pil", label="Charger Image")
operation = gr.Radio(["Négatif", "Binarisation", "Redimensionner", "Rotation","Histogramme","Gaussian Filter","Sobel", "Erosion","Dilatation","Luminosité","Contour", "Netteté"], label="Opération")
threshold = gr.Slider(0, 255, 128, label="Seuil de binarisation", visible=True)
width = gr.Number(value=100, label="Largeur de redimensionnement", visible=True)
height = gr.Number(value=100, label="Hauteur de redimensionnement", visible=True)
angle = gr.Number(value=0, label="Angle de Rotation", visible=True)
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)
# Launch application
demo.launch()
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