# Age and gender prediction on detected faces
# Usage: python run_age_gender.py --image path/to/image.jpg
#        python run_age_gender.py --webcam

import argparse
import os
from pathlib import Path

import cv2

from uniface import SCRFD, AgeGender, RetinaFace
from uniface.visualization import draw_detections


def draw_age_gender_label(image, bbox, gender: str, age: int):
    """Draw age/gender label above the bounding box."""
    x1, y1 = int(bbox[0]), int(bbox[1])
    text = f"{gender}, {age}y"
    (tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
    cv2.rectangle(image, (x1, y1 - th - 10), (x1 + tw + 10, y1), (0, 255, 0), -1)
    cv2.putText(image, text, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)


def process_image(detector, age_gender, image_path: str, save_dir: str = "outputs", threshold: float = 0.6):
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Failed to load image from '{image_path}'")
        return

    faces = detector.detect(image)
    print(f"Detected {len(faces)} face(s)")

    if not faces:
        return

    bboxes = [f["bbox"] for f in faces]
    scores = [f["confidence"] for f in faces]
    landmarks = [f["landmarks"] for f in faces]
    draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)

    for i, face in enumerate(faces):
        gender, age = age_gender.predict(image, face["bbox"])
        print(f"  Face {i + 1}: {gender}, {age} years old")
        draw_age_gender_label(image, face["bbox"], gender, age)

    os.makedirs(save_dir, exist_ok=True)
    output_path = os.path.join(save_dir, f"{Path(image_path).stem}_age_gender.jpg")
    cv2.imwrite(output_path, image)
    print(f"Output saved: {output_path}")


def run_webcam(detector, age_gender, threshold: float = 0.6):
    cap = cv2.VideoCapture(0)  # 0 = default webcam
    if not cap.isOpened():
        print("Cannot open webcam")
        return

    print("Press 'q' to quit")

    while True:
        ret, frame = cap.read()
        frame = cv2.flip(frame, 1)  # mirror for natural interaction
        if not ret:
            break

        faces = detector.detect(frame)

        # unpack face data for visualization
        bboxes = [f["bbox"] for f in faces]
        scores = [f["confidence"] for f in faces]
        landmarks = [f["landmarks"] for f in faces]
        draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)

        for face in faces:
            gender, age = age_gender.predict(frame, face["bbox"])  # predict per face
            draw_age_gender_label(frame, face["bbox"], gender, age)

        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("Age & Gender Detection", frame)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    cap.release()
    cv2.destroyAllWindows()


def main():
    parser = argparse.ArgumentParser(description="Run age and gender detection")
    parser.add_argument("--image", type=str, help="Path to input image")
    parser.add_argument("--webcam", action="store_true", help="Use webcam")
    parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
    parser.add_argument("--threshold", type=float, default=0.6, help="Visualization threshold")
    parser.add_argument("--save_dir", type=str, default="outputs")
    args = parser.parse_args()

    if not args.image and not args.webcam:
        parser.error("Either --image or --webcam must be specified")

    detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
    age_gender = AgeGender()

    if args.webcam:
        run_webcam(detector, age_gender, args.threshold)
    else:
        process_image(detector, age_gender, args.image, args.save_dir, args.threshold)


if __name__ == "__main__":
    main()