Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import albumentations as A
+import random
+
+def apply_augmentations(image, flip_h, flip_v, rotate, crop, gray, scale,
+                       prob_flip_h, prob_flip_v, prob_rotate, prob_crop, prob_gray, prob_scale):
+    augmentations = []
+    
+    if flip_h:
+        augmentations.append(A.HorizontalFlip(p=float(prob_flip_h)))
+    if flip_v:
+        augmentations.append(A.VerticalFlip(p=float(prob_flip_v)))
+    if rotate:
+        augmentations.append(A.Rotate(limit=90, p=float(prob_rotate)))
+    if crop:
+        augmentations.append(A.RandomResizedCrop(
+            size=(image.shape[0], image.shape[1]),
+            scale=(0.8, 1.0),
+            p=float(prob_crop)
+        ))
+    if gray:
+        augmentations.append(A.ToGray(p=float(prob_gray)))
+    if scale:
+        scale_factor = random.uniform(0.8, 1.2)
+        augmentations.append(A.Resize(
+            height=int(image.shape[0] * scale_factor),
+            width=int(image.shape[1] * scale_factor),
+            p=float(prob_scale)
+        ))
+
+    transform = A.Compose(augmentations)
+    
+    # Generate 9 augmented images
+    augmented_images = []
+    for _ in range(9):
+        augmented = transform(image=image)
+        augmented_images.append(augmented['image'])
+    
+    # Find maximum dimensions
+    max_height = max(img.shape[0] for img in augmented_images)
+    max_width = max(img.shape[1] for img in augmented_images)
+    
+    # Add padding to all images
+    padded_images = []
+    for img in augmented_images:
+        h, w = img.shape[:2]
+        pad_top = (max_height - h) // 2
+        pad_bottom = max_height - h - pad_top
+        pad_left = (max_width - w) // 2
+        pad_right = max_width - w - pad_left
+        
+        # Handle both RGB and grayscale images
+        if len(img.shape) == 3:
+            padded = cv2.copyMakeBorder(img, pad_top, pad_bottom, pad_left, pad_right, 
+                                      cv2.BORDER_CONSTANT, value=[128, 128, 128])
+        else:
+            padded = cv2.copyMakeBorder(img, pad_top, pad_bottom, pad_left, pad_right, 
+                                      cv2.BORDER_CONSTANT, value=[128])
+        padded_images.append(padded)
+    
+    # Create a 3x3 grid
+    rows = []
+    for i in range(0, 9, 3):
+        row = np.hstack(padded_images[i:i+3])
+        rows.append(row)
+    grid = np.vstack(rows)
+    
+    return grid
+
+def main():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(label="Input Image")
+            with gr.Column():
+                output_image = gr.Image(label="Output Image (3x3 Grid)")
+        
+        with gr.Row():
+            with gr.Column():
+                flip_h = gr.Checkbox(label="Horizontal Flip")
+                prob_flip_h = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+            
+            with gr.Column():
+                flip_v = gr.Checkbox(label="Vertical Flip")
+                prob_flip_v = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+            
+            with gr.Column():
+                rotate = gr.Checkbox(label="Rotate")
+                prob_rotate = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+            
+            with gr.Column():
+                crop = gr.Checkbox(label="Random Crop")
+                prob_crop = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+
+            with gr.Column():
+                gray = gr.Checkbox(label="Grayscale")
+                prob_gray = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+                
+            with gr.Column():
+                scale = gr.Checkbox(label="Random Scale (0.8-1.2x)")
+                prob_scale = gr.Slider(minimum=0, maximum=1, value=0.5, label="Probability")
+
+        with gr.Row():
+            run_button = gr.Button("Apply Augmentations")
+
+        run_button.click(
+            fn=apply_augmentations,
+            inputs=[
+                input_image,
+                flip_h, flip_v, rotate, crop, gray, scale,
+                prob_flip_h, prob_flip_v, prob_rotate, prob_crop, prob_gray, prob_scale
+            ],
+            outputs=output_image
+        )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()