import cv2
import os
from PIL import Image
import sys
import cv2.data
import numpy as np

# Locate the standard frontal face XML classifier provided by OpenCV
cascade_path = os.path.join(
    cv2.data.haarcascades, "haarcascade_frontalface_default.xml"
)
face_cascade = cv2.CascadeClassifier(cascade_path)


def _load_image_exif_safe(image_path):
    """Loads an image using PIL, handles EXIF orientation, and converts to OpenCV BGR."""
    try:
        from PIL import ImageOps
        pil_img = Image.open(image_path)
        pil_img = ImageOps.exif_transpose(pil_img)
        # Convert to BGR for OpenCV
        return cv2.cvtColor(np.array(pil_img.convert("RGB")), cv2.COLOR_RGB2BGR)
    except Exception as e:
        print(f"Error loading image safe: {e}")
        return None


def get_auto_crop_rect(image_path):
    """
    Detects a face and calculates the 5:7 crop rectangle.
    Returns (x1, y1, x2, y2) in original image coordinates or None.
    """
    image = _load_image_exif_safe(image_path)
    if image is None:
        return None
    h, w, _ = image.shape
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

    if len(faces) == 0:
        # Fallback: Center crop if no face found
        aspect_ratio = 5 / 7
        crop_h = int(h * 0.8)
        crop_w = int(crop_h * aspect_ratio)
        x1 = (w - crop_w) // 2
        y1 = (h - crop_h) // 2
        return (x1, y1, x1 + crop_w, y1 + crop_h)

    faces = sorted(faces, key=lambda x: x[2] * x[3], reverse=True)
    (x, y, fw, fh) = faces[0]
    cx, cy = x + fw // 2, y + fh // 2
    aspect_ratio = 5 / 7

    crop_height = int(min(h, w / aspect_ratio) * 0.7)
    crop_width = int(crop_height * aspect_ratio)

    head_top = y - int(fh * 0.35)
    HEAD_SPACE_RATIO = 0.10
    y1 = max(0, head_top - int(crop_height * HEAD_SPACE_RATIO))
    x1 = max(0, cx - crop_width // 2)

    x2 = min(w, x1 + crop_width)
    y2 = min(h, y1 + crop_height)

    # Adjust to maintain size
    if x2 - x1 < crop_width:
        x1 = max(0, x2 - crop_width)
    if y2 - y1 < crop_height:
        y1 = max(0, y2 - crop_height)

    return (int(x1), int(y1), int(x1 + crop_width), int(y1 + crop_height))


def apply_custom_crop(image_path, output_path, rect):
    """
    Applies a specific (x1, y1, x2, y2) crop and resizes to 10x14cm @ 300DPI.
    """
    x1, y1, x2, y2 = rect
    try:
        image = _load_image_exif_safe(image_path)
        if image is None: return False

        cropped = image[y1:y2, x1:x2]
        # Use Lanczos resampling for better quality
        final = cv2.resize(cropped, (1181, 1654), interpolation=cv2.INTER_LANCZOS4)
        final_rgb = cv2.cvtColor(final, cv2.COLOR_BGR2RGB)
        pil_img = Image.fromarray(final_rgb)
        
        ext = os.path.splitext(output_path)[1].lower()
        if ext == ".png":
            pil_img.save(output_path, dpi=(300, 300), compress_level=1) # Low compression for speed, lossless
        else:
            pil_img.save(output_path, dpi=(300, 300), quality=100, subsampling=0)
        return True
    except Exception as e:
        print(f"Error applying custom crop: {e}")
        return False


def crop_to_4x6_opencv(image_path, output_path):
    """Standard AI auto-crop."""
    rect = get_auto_crop_rect(image_path)
    if rect:
        return apply_custom_crop(image_path, output_path, rect)
    return False


def batch_process(input_folder, output_folder):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    files = [
        f
        for f in os.listdir(input_folder)
        if f.lower().endswith((".jpg", ".jpeg", ".png"))
    ]
    for filename in files:
        crop_to_4x6_opencv(
            os.path.join(input_folder, filename), os.path.join(output_folder, filename)
        )