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import spaces
import base64
import cv2
import numpy as np
import gradio as gr
from PIL import Image
from io import BytesIO
@spaces.GPU
def crop_face(base64_image):
# Decode the base64 image
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:
print("Could not decode the image")
return None
# Load the pre-trained face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
# If no faces are detected, return None
if len(faces) == 0:
print("No faces found")
return None
# Crop the first face found
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
def image_to_base64(image):
# Convert PIL Image to Bytes
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
return img_str
# Define the Gradio interface
interface = gr.Interface(
fn=crop_face,
inputs="text",
outputs="text",
title="Face Cropper",
description="Input a base64 encoded image to get a base64 encoded cropped face."
)
interface2 = gr.Interface(
fn=image_to_base64,
inputs=gr.inputs.Image(),
outputs="text",
title="Image to Base64 Converter",
description="Upload an image to convert it to Base64 encoded string."
)
if __name__ == "__main__":
gr.TabbedInterface([interface, interface2], ["Crop Face", "Convert to Base64"]).launch(share=True)
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