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| import base64 | |
| import numpy as np | |
| import cv2 | |
| import gradio as gr | |
| from PIL import Image | |
| from io import BytesIO | |
| import spaces | |
| def crop_face(base64_image): | |
| try: | |
| # Decode the base64 image to an OpenCV format | |
| img_data = base64.b64decode(base64_image) | |
| np_arr = np.frombuffer(img_data, np.uint8) | |
| image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) | |
| if image is None: | |
| return "Image decoding failed. Check the input format." | |
| # Load the pre-trained face detector | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| # Convert the image to grayscale for face detection | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) | |
| if len(faces) == 0: | |
| return "No faces detected in the image." | |
| # Crop the first detected face | |
| x, y, w, h = faces[0] | |
| face_crop = image[y:y+h, x:x+w] | |
| # Encode the cropped face to base64 | |
| _, buffer = cv2.imencode('.jpg', face_crop) | |
| face_base64 = base64.b64encode(buffer).decode('utf-8') | |
| return face_base64 | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| interface = gr.Interface( | |
| fn=crop_face, | |
| inputs=gr.Textbox(), | |
| outputs="text", | |
| title="Face Cropper", | |
| description="Input a base64 encoded image to get a base64 encoded cropped face." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch(share=True) |