#!/usr/bin/env python3
import argparse
from pathlib import Path

import cv2 as cv


default_cascade = (
    Path(cv.data.haarcascades) / "haarcascade_frontalface_default.xml"
)

parser = argparse.ArgumentParser(
    description="Detect frontal faces with an OpenCV Haar cascade."
)
parser.add_argument("image", help="input image path")
parser.add_argument(
    "--output",
    default="output/faces.jpg",
    help="annotated image path to write",
)
parser.add_argument(
    "--cascade",
    default=str(default_cascade),
    help="Haar cascade XML file",
)
parser.add_argument(
    "--scale-factor",
    type=float,
    default=1.1,
    help="image pyramid step used by detectMultiScale",
)
parser.add_argument(
    "--min-neighbors",
    type=int,
    default=5,
    help="neighbor count required to keep a detection",
)
args = parser.parse_args()

image_path = Path(args.image)
output_path = Path(args.output)
cascade_path = Path(args.cascade)

image = cv.imread(str(image_path))
if image is None:
    raise SystemExit(f"Could not read image: {image_path}")

classifier = cv.CascadeClassifier(str(cascade_path))
if classifier.empty():
    raise SystemExit(f"Could not load cascade: {cascade_path}")

gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
gray = cv.equalizeHist(gray)

faces = classifier.detectMultiScale(
    gray,
    scaleFactor=args.scale_factor,
    minNeighbors=args.min_neighbors,
    minSize=(30, 30),
)

annotated = image.copy()
for x, y, width, height in faces:
    cv.rectangle(
        annotated,
        (x, y),
        (x + width, y + height),
        (0, 255, 0),
        3,
    )

output_path.parent.mkdir(parents=True, exist_ok=True)
if not cv.imwrite(str(output_path), annotated):
    raise SystemExit(f"Could not write output image: {output_path}")

height, width = image.shape[:2]
print(f"cascade: {cascade_path.name}")
print(f"image size: {width}x{height}")
print(f"faces detected: {len(faces)}")

for index, (x, y, face_width, face_height) in enumerate(faces, start=1):
    print(
        f"face {index}: x={x} y={y} "
        f"width={face_width} height={face_height}"
    )

print(f"wrote: {output_path}")