Add application file

app.py

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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()