"""Color and pixel analysis utilities.

Provides tools for reading health/mana bars, detecting UI states
via color sampling, and pixel-level game state detection.
"""

from typing import Tuple, Optional, List
import logging

import numpy as np
import cv2

logger = logging.getLogger(__name__)


class ColorAnalyzer:
    """Analyze pixel colors and UI bar states."""
    
    @staticmethod
    def get_pixel_color(screen: np.ndarray, x: int, y: int) -> Tuple[int, int, int]:
        """Get BGR color at pixel position."""
        return tuple(screen[y, x].tolist())
    
    @staticmethod
    def get_pixel_hsv(screen: np.ndarray, x: int, y: int) -> Tuple[int, int, int]:
        """Get HSV color at pixel position."""
        hsv = cv2.cvtColor(screen[y:y+1, x:x+1], cv2.COLOR_BGR2HSV)
        return tuple(hsv[0, 0].tolist())
    
    @staticmethod
    def color_matches(
        color: Tuple[int, int, int],
        target: Tuple[int, int, int],
        tolerance: int = 20,
    ) -> bool:
        """Check if a color matches target within tolerance."""
        return all(abs(c - t) <= tolerance for c, t in zip(color, target))
    
    @staticmethod
    def read_bar_percentage(
        screen: np.ndarray,
        bar_region: Tuple[int, int, int, int],
        filled_color_hsv: Tuple[Tuple[int, int, int], Tuple[int, int, int]],
    ) -> float:
        """Read a horizontal bar's fill percentage (health, mana, xp, etc.).
        
        Args:
            screen: Screenshot in BGR
            bar_region: (x, y, width, height) of the bar
            filled_color_hsv: (lower_hsv, upper_hsv) range of the filled portion
            
        Returns:
            Fill percentage 0.0 to 1.0
        """
        x, y, w, h = bar_region
        bar = screen[y:y+h, x:x+w]
        hsv = cv2.cvtColor(bar, cv2.COLOR_BGR2HSV)
        
        lower, upper = filled_color_hsv
        mask = cv2.inRange(hsv, np.array(lower), np.array(upper))
        
        # Scan columns left to right to find the fill boundary
        col_fill = np.mean(mask, axis=0) / 255.0
        
        # Find the rightmost column that's mostly filled
        threshold = 0.3
        filled_cols = np.where(col_fill > threshold)[0]
        
        if len(filled_cols) == 0:
            return 0.0
        
        return (filled_cols[-1] + 1) / w
    
    @staticmethod
    def sample_region_dominant_color(
        screen: np.ndarray,
        region: Tuple[int, int, int, int],
    ) -> Tuple[int, int, int]:
        """Get the dominant BGR color in a region."""
        x, y, w, h = region
        roi = screen[y:y+h, x:x+w]
        pixels = roi.reshape(-1, 3).astype(np.float32)
        
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
        _, labels, centers = cv2.kmeans(pixels, 1, None, criteria, 3, cv2.KMEANS_RANDOM_CENTERS)
        
        return tuple(centers[0].astype(int).tolist())