Initial project structure: reusable isometric bot engine with D2R implementation

engine/vision/detector.py

@@ -0,0 +1,140 @@
+"""Object and UI element detection using computer vision.
+
+Provides high-level detection for game elements using template matching,
+color filtering, and contour analysis.
+"""
+
+from typing import List, Optional, Tuple
+from dataclasses import dataclass
+import logging
+
+import numpy as np
+import cv2
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Detection:
+    """Represents a detected object/element on screen."""
+    
+    x: int
+    y: int
+    width: int
+    height: int
+    confidence: float
+    label: str = ""
+    
+    @property
+    def center(self) -> Tuple[int, int]:
+        return (self.x + self.width // 2, self.y + self.height // 2)
+    
+    @property
+    def bounds(self) -> Tuple[int, int, int, int]:
+        return (self.x, self.y, self.x + self.width, self.y + self.height)
+
+
+class ElementDetector:
+    """Detects game UI elements and objects via computer vision."""
+    
+    def __init__(self, confidence_threshold: float = 0.8):
+        self.confidence_threshold = confidence_threshold
+        self._templates: dict[str, np.ndarray] = {}
+    
+    def load_template(self, name: str, image_path: str) -> None:
+        """Load a template image for matching."""
+        template = cv2.imread(image_path, cv2.IMREAD_COLOR)
+        if template is None:
+            raise FileNotFoundError(f"Template not found: {image_path}")
+        self._templates[name] = template
+        logger.debug(f"Loaded template '{name}': {template.shape}")
+    
+    def find_template(
+        self, screen: np.ndarray, template_name: str,
+        method: int = cv2.TM_CCOEFF_NORMED,
+    ) -> Optional[Detection]:
+        """Find best match of a template in the screen image."""
+        if template_name not in self._templates:
+            logger.error(f"Unknown template: {template_name}")
+            return None
+        
+        template = self._templates[template_name]
+        result = cv2.matchTemplate(screen, template, method)
+        _, max_val, _, max_loc = cv2.minMaxLoc(result)
+        
+        if max_val >= self.confidence_threshold:
+            h, w = template.shape[:2]
+            return Detection(
+                x=max_loc[0], y=max_loc[1],
+                width=w, height=h,
+                confidence=max_val, label=template_name,
+            )
+        return None
+    
+    def find_all_templates(
+        self, screen: np.ndarray, template_name: str,
+        method: int = cv2.TM_CCOEFF_NORMED,
+    ) -> List[Detection]:
+        """Find all matches of a template above confidence threshold."""
+        if template_name not in self._templates:
+            return []
+        
+        template = self._templates[template_name]
+        h, w = template.shape[:2]
+        result = cv2.matchTemplate(screen, template, method)
+        
+        locations = np.where(result >= self.confidence_threshold)
+        detections = []
+        
+        for pt in zip(*locations[::-1]):
+            detections.append(Detection(
+                x=pt[0], y=pt[1], width=w, height=h,
+                confidence=result[pt[1], pt[0]], label=template_name,
+            ))
+        
+        # Non-maximum suppression (simple distance-based)
+        return self._nms(detections, distance_threshold=min(w, h) // 2)
+    
+    def find_by_color(
+        self, screen: np.ndarray, lower_hsv: Tuple[int, int, int],
+        upper_hsv: Tuple[int, int, int], min_area: int = 100,
+        label: str = "",
+    ) -> List[Detection]:
+        """Find objects by HSV color range."""
+        hsv = cv2.cvtColor(screen, cv2.COLOR_BGR2HSV)
+        mask = cv2.inRange(hsv, np.array(lower_hsv), np.array(upper_hsv))
+        
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        detections = []
+        for contour in contours:
+            area = cv2.contourArea(contour)
+            if area >= min_area:
+                x, y, w, h = cv2.boundingRect(contour)
+                detections.append(Detection(
+                    x=x, y=y, width=w, height=h,
+                    confidence=area / (w * h), label=label,
+                ))
+        
+        return detections
+    
+    def _nms(self, detections: List[Detection], distance_threshold: int) -> List[Detection]:
+        """Simple non-maximum suppression by distance."""
+        if not detections:
+            return []
+        
+        detections.sort(key=lambda d: d.confidence, reverse=True)
+        kept = []
+        
+        for det in detections:
+            too_close = False
+            for k in kept:
+                dx = abs(det.center[0] - k.center[0])
+                dy = abs(det.center[1] - k.center[1])
+                if dx < distance_threshold and dy < distance_threshold:
+                    too_close = True
+                    break
+            if not too_close:
+                kept.append(det)
+        
+        return kept