Initial project structure: reusable isometric bot engine with D2R implementation

engine/vision/color.py

@@ -0,0 +1,87 @@
+"""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())