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Create app.py
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app.py
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import base64
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import cv2
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import numpy as np
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import gradio as gr
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from io import BytesIO
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def crop_face(base64_image):
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# Decode the base64 image
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img_data = base64.b64decode(base64_image)
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np_arr = np.frombuffer(img_data, np.uint8)
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image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
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if image is None:
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print("Could not decode the image")
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return None
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# Load the pre-trained face detector
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Convert the image to grayscale
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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# If no faces are detected, return None
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if len(faces) == 0:
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print("No faces found")
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return None
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# Crop the first face found
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x, y, w, h = faces[0]
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face_crop = image[y:y+h, x:x+w]
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# Encode the cropped face to base64
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_, buffer = cv2.imencode('.jpg', face_crop)
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face_base64 = base64.b64encode(buffer).decode('utf-8')
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return face_base64
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# Define the Gradio interface
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interface = gr.Interface(
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fn=crop_face,
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inputs="text",
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outputs="text",
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title="Face Cropper",
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description="Input a base64 encoded image to get a base64 encoded cropped face."
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)
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if __name__ == "__main__":
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interface.launch(share=True)
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