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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() |