"""
Image processing utilities for KYC POC.
"""

import cv2
import numpy as np
import base64
from typing import Optional, Tuple
from fastapi import UploadFile, HTTPException


async def read_image_from_upload(file: UploadFile) -> np.ndarray:
    """
    Read uploaded image file into numpy array (OpenCV BGR format).

    Args:
        file: FastAPI UploadFile object

    Returns:
        numpy array in BGR format (OpenCV)

    Raises:
        HTTPException: If image is invalid or cannot be decoded
    """
    contents = await file.read()
    return decode_image_bytes(contents)


def decode_image_bytes(image_bytes: bytes) -> np.ndarray:
    """
    Decode image bytes to numpy array.

    Args:
        image_bytes: Raw image bytes

    Returns:
        numpy array in BGR format (OpenCV)

    Raises:
        HTTPException: If image cannot be decoded
    """
    nparr = np.frombuffer(image_bytes, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    if image is None:
        raise HTTPException(
            status_code=400,
            detail={
                "error_code": "IMAGE_INVALID",
                "message": "Failed to decode image. Please ensure the file is a valid image."
            }
        )

    return image


def decode_base64_image(base64_string: str) -> np.ndarray:
    """
    Decode base64 encoded image string to numpy array.

    Args:
        base64_string: Base64 encoded image string (with or without data URI prefix)

    Returns:
        numpy array in BGR format (OpenCV)

    Raises:
        HTTPException: If base64 string is invalid or image cannot be decoded
    """
    try:
        # Remove data URI prefix if present
        if "," in base64_string:
            base64_string = base64_string.split(",")[1]

        # Decode base64
        image_bytes = base64.b64decode(base64_string)
        return decode_image_bytes(image_bytes)

    except Exception as e:
        raise HTTPException(
            status_code=400,
            detail={
                "error_code": "IMAGE_INVALID",
                "message": f"Failed to decode base64 image: {str(e)}"
            }
        )


def encode_image_to_base64(image: np.ndarray, format: str = ".jpg") -> str:
    """
    Encode numpy array image to base64 string.

    Args:
        image: numpy array in BGR format
        format: Image format (.jpg, .png)

    Returns:
        Base64 encoded string
    """
    _, buffer = cv2.imencode(format, image)
    return base64.b64encode(buffer).decode("utf-8")


def resize_image(
    image: np.ndarray,
    max_size: int = 1024,
    keep_aspect: bool = True
) -> np.ndarray:
    """
    Resize image if it exceeds max size.

    Args:
        image: Input image
        max_size: Maximum dimension size
        keep_aspect: Whether to keep aspect ratio

    Returns:
        Resized image
    """
    height, width = image.shape[:2]

    if max(height, width) <= max_size:
        return image

    if keep_aspect:
        if width > height:
            new_width = max_size
            new_height = int(height * max_size / width)
        else:
            new_height = max_size
            new_width = int(width * max_size / height)
    else:
        new_width = max_size
        new_height = max_size

    return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)


def crop_face_region(
    image: np.ndarray,
    bbox: Tuple[int, int, int, int],
    padding: float = 0.2
) -> np.ndarray:
    """
    Crop face region from image with padding.

    Args:
        image: Input image
        bbox: Face bounding box (x1, y1, x2, y2)
        padding: Padding ratio to add around face

    Returns:
        Cropped face image
    """
    height, width = image.shape[:2]
    x1, y1, x2, y2 = bbox

    # Calculate padding
    face_width = x2 - x1
    face_height = y2 - y1
    pad_x = int(face_width * padding)
    pad_y = int(face_height * padding)

    # Apply padding with bounds checking
    x1 = max(0, x1 - pad_x)
    y1 = max(0, y1 - pad_y)
    x2 = min(width, x2 + pad_x)
    y2 = min(height, y2 + pad_y)

    return image[y1:y2, x1:x2]


def validate_image_size(image_bytes: bytes, max_size_bytes: int) -> None:
    """
    Validate image size doesn't exceed maximum.

    Args:
        image_bytes: Image bytes
        max_size_bytes: Maximum allowed size in bytes

    Raises:
        HTTPException: If image exceeds size limit
    """
    if len(image_bytes) > max_size_bytes:
        max_mb = max_size_bytes / (1024 * 1024)
        actual_mb = len(image_bytes) / (1024 * 1024)
        raise HTTPException(
            status_code=413,
            detail={
                "error_code": "IMAGE_TOO_LARGE",
                "message": f"Image size ({actual_mb:.2f}MB) exceeds maximum allowed ({max_mb:.2f}MB)"
            }
        )


def validate_content_type(content_type: Optional[str], allowed_types: list) -> None:
    """
    Validate image content type.

    Args:
        content_type: MIME type of the file
        allowed_types: List of allowed MIME types

    Raises:
        HTTPException: If content type is not allowed
    """
    if content_type not in allowed_types:
        raise HTTPException(
            status_code=415,
            detail={
                "error_code": "UNSUPPORTED_FORMAT",
                "message": f"Unsupported image format: {content_type}. Allowed: {allowed_types}"
            }
        )