Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+# Load Haar Cascade for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def process_image(image, operation, canny_threshold1=100, canny_threshold2=200, blur_kernel=5):
+    # Convert Gradio image (PIL) to OpenCV format (BGR)
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    # Initialize output dictionary
+    outputs = {}
+    
+    # Perform selected operation
+    if operation == "Grayscale":
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        outputs["Grayscale Image"] = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
+    
+    elif operation == "Canny Edge Detection":
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        edges = cv2.Canny(gray, canny_threshold1, canny_threshold2)
+        outputs["Edges"] = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    
+    elif operation == "Gaussian Blur":
+        if blur_kernel % 2 == 0:
+            blur_kernel += 1  # Kernel size must be odd
+        blurred = cv2.GaussianBlur(image, (blur_kernel, blur_kernel), 0)
+        outputs["Blurred Image"] = blurred
+    
+    elif operation == "Face Detection":
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+        output_image = image.copy()
+        for (x, y, w, h) in faces:
+            cv2.rectangle(output_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        outputs["Faces Detected"] = output_image
+    
+    # Convert back to RGB for Gradio display
+    for key in outputs:
+        outputs[key] = cv2.cvtColor(outputs[key], cv2.COLOR_BGR2RGB)
+    
+    return outputs
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# OpenCV Feature Demo")
+    gr.Markdown("Upload an image and select an OpenCV operation to apply.")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Image", type="pil")
+            operation = gr.Dropdown(
+                choices=["Grayscale", "Canny Edge Detection", "Gaussian Blur", "Face Detection"],
+                label="Select Operation",
+                value="Grayscale"
+            )
+            canny_threshold1 = gr.Slider(0, 500, value=100, step=10, label="Canny Threshold 1", visible=False)
+            canny_threshold2 = gr.Slider(0, 500, value=200, step=10, label="Canny Threshold 2", visible=False)
+            blur_kernel = gr.Slider(3, 21, value=5, step=2, label="Blur Kernel Size", visible=False)
+            
+            # Show/hide sliders based on operation
+            def update_sliders(op):
+                if op == "Canny Edge Detection":
+                    return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
+                elif op == "Gaussian Blur":
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+                else:
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+            
+            operation.change(update_sliders, inputs=operation, outputs=[canny_threshold1, canny_threshold2, blur_kernel])
+        
+        with gr.Column():
+            output = gr.Gallery(label="Processed Image")
+    
+    submit_button = gr.Button("Process Image")
+    submit_button.click(
+        fn=process_image,
+        inputs=[image_input, operation, canny_threshold1, canny_threshold2, blur_kernel],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()