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from typing import List, Optional, Tuple
import cv2
import numpy as np
from skimage.draw import polygon
class RoomDropoutStrategy:
"""
Strategy for randomly dropping rooms from a density map using ground truth coordinates.
Density map: grayscale image where foreground (rooms) are white points and background is black
GT room coordinates: list of 2D points defining each room's boundary
"""
def __init__(self, density_map: np.ndarray, room_coordinates: List[List[Tuple[int, int]]]):
"""
Initialize the dropout strategy.
Args:
density_map: Grayscale image (H, W) where white pixels represent rooms
room_coordinates: List of rooms, each room is a list of (x, y) coordinate tuples
"""
self.original_density_map = density_map.copy()
self.room_coordinates = room_coordinates
self.num_rooms = len(room_coordinates)
def create_room_masks(self) -> List[np.ndarray]:
"""
Create binary masks for each room using their GT coordinates.
Returns:
List of binary masks, one for each room
"""
h, w = self.original_density_map.shape
room_masks = []
for room_coords in self.room_coordinates:
mask = np.zeros((h, w), dtype=np.uint8)
if len(room_coords) >= 3: # Need at least 3 points for a polygon
# Convert coordinates to numpy array
coords = np.array(room_coords)
x_coords = coords[:, 0]
y_coords = coords[:, 1]
# Create polygon mask using skimage
rr, cc = polygon(y_coords, x_coords, shape=(h, w))
mask[rr, cc] = 1
room_masks.append(mask)
return room_masks
def drop_rooms_random(self, dropout_rate: float = 0.3, seed: Optional[int] = None) -> Tuple[np.ndarray, List[int]]:
"""
Randomly drop rooms from the density map.
Args:
dropout_rate: Fraction of rooms to drop (0.0 to 1.0)
seed: Random seed for reproducibility
Returns:
Tuple of (modified_density_map, list_of_dropped_room_indices)
"""
if seed is not None:
random.seed(seed)
np.random.seed(seed)
# Determine number of rooms to drop
num_to_drop = int(self.num_rooms * dropout_rate)
# Randomly select room indices to drop
room_indices = list(range(self.num_rooms))
dropped_indices = random.sample(room_indices, num_to_drop)
return self._apply_dropout(dropped_indices), dropped_indices
def drop_rooms_by_indices(self, room_indices: List[int]) -> np.ndarray:
"""
Drop specific rooms by their indices.
Args:
room_indices: List of room indices to drop
Returns:
Modified density map with specified rooms removed
"""
return self._apply_dropout(room_indices)
def drop_rooms_by_area(
self, min_area: Optional[int] = None, max_area: Optional[int] = None
) -> Tuple[np.ndarray, List[int]]:
"""
Drop rooms based on their area constraints.
Args:
min_area: Minimum area threshold (drop rooms smaller than this)
max_area: Maximum area threshold (drop rooms larger than this)
Returns:
Tuple of (modified_density_map, list_of_dropped_room_indices)
"""
room_masks = self.create_room_masks()
dropped_indices = []
for i, mask in enumerate(room_masks):
area = np.sum(mask)
should_drop = False
if min_area is not None and area < min_area:
should_drop = True
if max_area is not None and area > max_area:
should_drop = True
if should_drop:
dropped_indices.append(i)
return self._apply_dropout(dropped_indices), dropped_indices
def _apply_dropout(self, room_indices_to_drop: List[int]) -> np.ndarray:
"""
Apply dropout by removing specified rooms from the density map.
Args:
room_indices_to_drop: List of room indices to remove
Returns:
Modified density map with rooms removed
"""
modified_map = self.original_density_map.copy()
room_masks = self.create_room_masks()
# Remove each specified room
for room_idx in room_indices_to_drop:
if 0 <= room_idx < len(room_masks):
mask = room_masks[room_idx]
# Set pixels in the room area to background (black/0)
modified_map[mask == 1] = 0
return modified_map
def visualize_dropout(
self, original_map: np.ndarray, modified_map: np.ndarray, dropped_indices: List[int]
) -> np.ndarray:
"""
Create a visualization showing the dropout effect.
Args:
original_map: Original density map
modified_map: Modified density map after dropout
dropped_indices: Indices of dropped rooms
Returns:
Visualization image with original and modified maps side by side
"""
h, w = original_map.shape
# Create side-by-side comparison
vis = np.zeros((h, w * 2), dtype=np.uint8)
vis[:, :w] = original_map
vis[:, w:] = modified_map
# Highlight dropped rooms in red on the original map
if len(dropped_indices) > 0:
room_masks = self.create_room_masks()
vis_color = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
for idx in dropped_indices:
if 0 <= idx < len(room_masks):
mask = room_masks[idx]
# Highlight in red on the left (original) side
vis_color[mask == 1, 0] = 0 # Blue channel
vis_color[mask == 1, 1] = 0 # Green channel
vis_color[mask == 1, 2] = 255 # Red channel
return vis_color
return cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
# Example usage and testing
def example_usage():
"""
Example of how to use the RoomDropoutStrategy class.
"""
# Create a sample density map (200x200 image)
density_map = np.zeros((200, 200), dtype=np.uint8)
# Create some sample room coordinates (rectangles and polygons)
room_coordinates = [
# Room 1: Rectangle
[(20, 20), (80, 20), (80, 60), (20, 60)],
# Room 2: Another rectangle
[(100, 30), (180, 30), (180, 80), (100, 80)],
# Room 3: L-shaped room
[(30, 100), (90, 100), (90, 130), (60, 130), (60, 160), (30, 160)],
# Room 4: Triangle
[(120, 120), (160, 120), (140, 160)],
# Room 5: Pentagon
[(50, 180), (70, 170), (90, 180), (80, 195), (40, 195)],
]
# Fill the density map with white pixels for each room
for room_coords in room_coordinates:
coords = np.array(room_coords)
x_coords = coords[:, 0]
y_coords = coords[:, 1]
from skimage.draw import polygon
rr, cc = polygon(y_coords, x_coords, shape=density_map.shape)
density_map[rr, cc] = 255 # White pixels for rooms
# Initialize the dropout strategy
dropout_strategy = RoomDropoutStrategy(density_map, room_coordinates)
# Example 1: Random dropout
print("Example 1: Random dropout (30% of rooms)")
modified_map1, dropped_indices1 = dropout_strategy.drop_rooms_random(dropout_rate=0.3, seed=42)
print(f"Dropped rooms: {dropped_indices1}")
# Example 2: Drop specific rooms
print("\nExample 2: Drop specific rooms (indices 0 and 2)")
modified_map2 = dropout_strategy.drop_rooms_by_indices([0, 2])
# Example 3: Drop rooms by area
print("\nExample 3: Drop rooms with area > 3000 pixels")
modified_map3, dropped_indices3 = dropout_strategy.drop_rooms_by_area(max_area=3000)
print(f"Dropped rooms by area: {dropped_indices3}")
return density_map, modified_map1, modified_map2, modified_map3
if __name__ == "__main__":
example_usage()
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