face_extraction.py

import boto3
from PIL import Image
import io

rekognition_client = boto3.client("rekognition")

def get_face_details(image_bytes):
    response = rekognition_client.detect_faces(Image={"Bytes": image_bytes}, Attributes=["ALL"])
    # Assuming the first face is the one we need
    face_details = response["FaceDetails"][0] if response["FaceDetails"] else None
    return face_details


def image_to_bytes(image):
    image_byte_arr = io.BytesIO()
    image.save(image_byte_arr, format="JPEG")
    return image_byte_arr.getvalue()

def rotate_image(image, roll):
    rotated_image = image.rotate(roll)  # Not using negative because PIL's rotate is opposite to AWS's roll
    return rotated_image


# Function to crop image
def crop_face(original_image, face_detail, padding_factor=0.3):
    box = face_detail["BoundingBox"]
    original_size = original_image.size

    padding_width = box["Width"] * original_size[0] * padding_factor
    padding_height = box["Height"] * original_size[1] * padding_factor

    left = max(0, box["Left"] * original_size[0] - padding_width)
    top = max(0, box["Top"] * original_size[1] - padding_height)
    right = min(original_size[0], left + box["Width"] * original_size[0] + 2 * padding_width)
    bottom = min(original_size[1], top + box["Height"] * original_size[1] + 2 * padding_height)

    return original_image.crop((left, top, right, bottom))



def process_image(image_path):
    # Open the image
    with open(image_path, "rb") as img_file:
        image_bytes = img_file.read()

    # First detection
    face_details = get_face_details(image_bytes)
    if not face_details:
        raise ValueError("No face detected in the image")

    roll = face_details["Pose"]["Roll"]

    # Rotate original image
    image = Image.open(io.BytesIO(image_bytes))
    rotated_image = rotate_image(image, roll)
    rotated_image_bytes = image_to_bytes(rotated_image)

    # Second detection on rotated image
    face_details_rotated = get_face_details(rotated_image_bytes)
    if not face_details_rotated:
        raise ValueError("No face detected in the rotated image")

    cropped_image = crop_face(rotated_image, face_details_rotated)
    cropped_image.show()


image_path = "inpu.jpg"
process_image(image_path)