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mice/engine/collision_system.py
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enne2 12836dd2d2 Implement optimized collision detection system using NumPy 
- Introduced a hybrid collision detection approach that utilizes NumPy for vectorized operations, improving performance for games with many entities (200+).
- Added a spatial grid for efficient lookups and AABB (Axis-Aligned Bounding Box) collision detection.
- Implemented a new `CollisionSystem` class with methods for registering units, checking collisions, and managing spatial data.
- Created performance tests to benchmark the new collision system against the old O(n²) method, demonstrating significant speed improvements.
- Updated existing code to integrate the new collision detection system and ensure compatibility with game logic.
2025-10-24 19:13:30 +02:00

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"""
Optimized collision detection system using NumPy for vectorized operations.
This module provides efficient collision detection for games with many entities (200+).
Uses AABB (Axis-Aligned Bounding Box) collision detection with numpy vectorization.
HYBRID APPROACH:
- For < 50 units: Uses simple dictionary-based approach (low overhead)
- For >= 50 units: Uses NumPy vectorization (scales better)
Performance improvements:
- O(n²) → O(n) for spatial queries using grid-based hashing
- Vectorized AABB checks for large unit counts
- Minimal overhead for small unit counts
"""
import numpy as np
from typing import Dict, List, Tuple, Set
from dataclasses import dataclass
# Threshold for switching to NumPy mode
NUMPY_THRESHOLD = 50
@dataclass
class CollisionLayer:
"""Define which types of units can collide with each other."""
RAT = 0
BOMB = 1
GAS = 2
MINE = 3
POINT = 4
EXPLOSION = 5
class CollisionSystem:
"""
Manages collision detection for all game units using NumPy vectorization.
Attributes
----------
cell_size : int
Size of each grid cell in pixels
grid_width : int
Number of cells in grid width
grid_height : int
Number of cells in grid height
"""
def __init__(self, cell_size: int, grid_width: int, grid_height: int):
self.cell_size = cell_size
self.grid_width = grid_width
self.grid_height = grid_height
# Spatial grid for fast lookups
self.spatial_grid: Dict[Tuple[int, int], List] = {}
self.spatial_grid_before: Dict[Tuple[int, int], List] = {}
# Arrays for vectorized operations
self.unit_ids = []
self.bboxes = np.array([], dtype=np.float32).reshape(0, 4) # (x1, y1, x2, y2)
self.positions = np.array([], dtype=np.int32).reshape(0, 2) # (x, y)
self.positions_before = np.array([], dtype=np.int32).reshape(0, 2)
self.layers = np.array([], dtype=np.int8)
# Pre-allocation tracking
self._capacity = 0
self._size = 0
# Collision matrix: which layers collide with which
self.collision_matrix = np.zeros((6, 6), dtype=bool)
self._setup_collision_matrix()
def _setup_collision_matrix(self):
"""Define which collision layers interact with each other."""
L = CollisionLayer
# Rats collide with: Rats, Bombs, Gas, Mines, Points
self.collision_matrix[L.RAT, L.RAT] = True
self.collision_matrix[L.RAT, L.BOMB] = False # Bombs don't kill on contact
self.collision_matrix[L.RAT, L.GAS] = True
self.collision_matrix[L.RAT, L.MINE] = True
self.collision_matrix[L.RAT, L.POINT] = True
self.collision_matrix[L.RAT, L.EXPLOSION] = True
# Gas affects rats
self.collision_matrix[L.GAS, L.RAT] = True
# Mines trigger on rats
self.collision_matrix[L.MINE, L.RAT] = True
# Points collected by rats (handled in point logic)
self.collision_matrix[L.POINT, L.RAT] = True
# Explosions kill rats
self.collision_matrix[L.EXPLOSION, L.RAT] = True
# Make matrix symmetric
self.collision_matrix = np.logical_or(self.collision_matrix,
self.collision_matrix.T)
def clear(self):
"""Clear all collision data for new frame."""
self.spatial_grid.clear()
self.spatial_grid_before.clear()
self.unit_ids = []
self.bboxes = np.array([], dtype=np.float32).reshape(0, 4)
self.positions = np.array([], dtype=np.int32).reshape(0, 2)
self.positions_before = np.array([], dtype=np.int32).reshape(0, 2)
self.layers = np.array([], dtype=np.int8)
def register_unit(self, unit_id, bbox: Tuple[float, float, float, float],
position: Tuple[int, int], position_before: Tuple[int, int],
layer: int):
"""
Register a unit for collision detection this frame.
Parameters
----------
unit_id : UUID
Unique identifier for the unit
bbox : tuple
Bounding box (x1, y1, x2, y2)
position : tuple
Current grid position (x, y)
position_before : tuple
Previous grid position (x, y)
layer : int
Collision layer (from CollisionLayer enum)
"""
idx = len(self.unit_ids)
self.unit_ids.append(unit_id)
# Pre-allocate arrays in batches to reduce overhead
if len(self.bboxes) == 0:
# Initialize with reasonable capacity
self.bboxes = np.empty((100, 4), dtype=np.float32)
self.positions = np.empty((100, 2), dtype=np.int32)
self.positions_before = np.empty((100, 2), dtype=np.int32)
self.layers = np.empty(100, dtype=np.int8)
self._capacity = 100
self._size = 0
elif self._size >= self._capacity:
# Expand capacity
new_capacity = self._capacity * 2
self.bboxes = np.resize(self.bboxes, (new_capacity, 4))
self.positions = np.resize(self.positions, (new_capacity, 2))
self.positions_before = np.resize(self.positions_before, (new_capacity, 2))
self.layers = np.resize(self.layers, new_capacity)
self._capacity = new_capacity
# Add data
self.bboxes[self._size] = bbox
self.positions[self._size] = position
self.positions_before[self._size] = position_before
self.layers[self._size] = layer
self._size += 1
# Add to spatial grids
self.spatial_grid.setdefault(position, []).append(idx)
self.spatial_grid_before.setdefault(position_before, []).append(idx)
def check_aabb_collision(self, idx1: int, idx2: int, tolerance: int = 0) -> bool:
"""
Check AABB collision between two units.
Parameters
----------
idx1, idx2 : int
Indices in the arrays
tolerance : int
Overlap tolerance in pixels (reduces detection zone)
Returns
-------
bool
True if bounding boxes overlap
"""
bbox1 = self.bboxes[idx1]
bbox2 = self.bboxes[idx2]
return (bbox1[0] < bbox2[2] - tolerance and
bbox1[2] > bbox2[0] + tolerance and
bbox1[1] < bbox2[3] - tolerance and
bbox1[3] > bbox2[1] + tolerance)
def check_aabb_collision_vectorized(self, idx: int, indices: np.ndarray,
tolerance: int = 0) -> np.ndarray:
"""
Vectorized AABB collision check between one unit and many others.
Parameters
----------
idx : int
Index of the unit to check
indices : ndarray
Array of indices to check against
tolerance : int
Overlap tolerance in pixels
Returns
-------
ndarray
Boolean array indicating collisions
"""
if len(indices) == 0:
return np.array([], dtype=bool)
# Slice actual data size, not full capacity
bbox = self.bboxes[idx]
other_bboxes = self.bboxes[indices]
# Vectorized AABB check
collisions = (
(bbox[0] < other_bboxes[:, 2] - tolerance) &
(bbox[2] > other_bboxes[:, 0] + tolerance) &
(bbox[1] < other_bboxes[:, 3] - tolerance) &
(bbox[3] > other_bboxes[:, 1] + tolerance)
)
return collisions
def get_collisions_for_unit(self, unit_id, layer: int,
tolerance: int = 0) -> List[Tuple[int, any]]:
"""
Get all units colliding with the specified unit.
Uses hybrid approach: simple method for few units, numpy for many.
Parameters
----------
unit_id : UUID
ID of the unit to check
layer : int
Collision layer of the unit
tolerance : int
Overlap tolerance
Returns
-------
list
List of tuples (index, unit_id) for colliding units
"""
if unit_id not in self.unit_ids:
return []
idx = self.unit_ids.index(unit_id)
position = tuple(self.positions[idx])
position_before = tuple(self.positions_before[idx])
# Get candidate indices from spatial grid
candidates = set()
for pos in [position, position_before]:
candidates.update(self.spatial_grid.get(pos, []))
candidates.update(self.spatial_grid_before.get(pos, []))
# Remove self and out-of-bounds indices
candidates.discard(idx)
candidates = {c for c in candidates if c < self._size}
if not candidates:
return []
# HYBRID APPROACH: Use simple method for few candidates
if len(candidates) < 10:
return self._simple_collision_check(idx, candidates, layer, tolerance)
# NumPy vectorized approach for many candidates
candidates_array = np.array(list(candidates), dtype=np.int32)
candidate_layers = self.layers[candidates_array]
# Check collision matrix
can_collide = self.collision_matrix[layer, candidate_layers]
valid_candidates = candidates_array[can_collide]
if len(valid_candidates) == 0:
return []
# Vectorized AABB check
collisions = self.check_aabb_collision_vectorized(idx, valid_candidates, tolerance)
colliding_indices = valid_candidates[collisions]
# Return list of (index, unit_id) pairs
return [(int(i), self.unit_ids[i]) for i in colliding_indices]
def _simple_collision_check(self, idx: int, candidates: set, layer: int,
tolerance: int) -> List[Tuple[int, any]]:
"""
Simple collision check without numpy overhead.
Used when there are few candidates.
"""
results = []
bbox = self.bboxes[idx]
for other_idx in candidates:
# Check collision layer
if not self.collision_matrix[layer, self.layers[other_idx]]:
continue
# AABB check
other_bbox = self.bboxes[other_idx]
if (bbox[0] < other_bbox[2] - tolerance and
bbox[2] > other_bbox[0] + tolerance and
bbox[1] < other_bbox[3] - tolerance and
bbox[3] > other_bbox[1] + tolerance):
results.append((int(other_idx), self.unit_ids[other_idx]))
return results
def get_units_in_cell(self, position: Tuple[int, int],
use_before: bool = False) -> List[any]:
"""
Get all unit IDs in a specific grid cell.
Parameters
----------
position : tuple
Grid position (x, y)
use_before : bool
If True, use position_before instead of position
Returns
-------
list
List of unit IDs in that cell
"""
grid = self.spatial_grid_before if use_before else self.spatial_grid
indices = grid.get(position, [])
return [self.unit_ids[i] for i in indices]
def get_units_in_area(self, positions: List[Tuple[int, int]],
layer_filter: int = None) -> Set[any]:
"""
Get all units in multiple grid cells (useful for explosions).
Parameters
----------
positions : list
List of grid positions to check
layer_filter : int, optional
If provided, only return units of this layer
Returns
-------
set
Set of unique unit IDs in the area
"""
unit_set = set()
for pos in positions:
# Check both current and previous positions
for grid in [self.spatial_grid, self.spatial_grid_before]:
indices = grid.get(pos, [])
for idx in indices:
if layer_filter is None or self.layers[idx] == layer_filter:
unit_set.add(self.unit_ids[idx])
return unit_set
def check_partial_move_collision(self, unit_id, partial_move: float,
threshold: float = 0.5) -> List[any]:
"""
Check collisions considering partial movement progress.
For units moving between cells, checks if they should be considered
in current or previous cell based on movement progress.
Parameters
----------
unit_id : UUID
Unit to check
partial_move : float
Movement progress (0.0 to 1.0)
threshold : float
Movement threshold for position consideration
Returns
-------
list
List of unit IDs in collision
"""
if unit_id not in self.unit_ids:
return []
idx = self.unit_ids.index(unit_id)
# Choose position based on partial move
if partial_move >= threshold:
position = tuple(self.positions[idx])
else:
position = tuple(self.positions_before[idx])
# Get units in that position
indices = self.spatial_grid.get(position, []) + \
self.spatial_grid_before.get(position, [])
# Remove duplicates and self
indices = list(set(indices))
if idx in indices:
indices.remove(idx)
return [self.unit_ids[i] for i in indices]
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