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Visualization utilities for detection validation.
This module provides functions to visualize detection results,
allowing visual inspection of walls, rooms, doors, and windows.
"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import cv2
def visualize_detections(
image: np.ndarray,
detections: Dict[str, List[np.ndarray]],
output_path: Optional[str] = None,
show_labels: bool = True,
room_alpha: float = 0.3
) -> np.ndarray:
"""
Visualize detection results with color-coded overlays.
This function draws walls, rooms, doors, and windows on the input image
with different colors and styles for easy visual inspection.
Parameters
----------
image : np.ndarray
Original or preprocessed image (grayscale or BGR)
detections : Dict[str, List[np.ndarray]]
Dictionary containing detection results with keys:
- "walls": List of wall polygons (each Nx2 numpy array)
- "rooms": List of room polygons (each Nx2 numpy array)
- "doors": List of door polygons (each Nx2 numpy array)
- "windows": List of window polygons (each Nx2 numpy array)
output_path : str, optional
If provided, save the annotated image to this path
show_labels : bool, default=True
Whether to add text labels for doors and windows
room_alpha : float, default=0.3
Transparency for room fill (0.0 = transparent, 1.0 = opaque)
Returns
-------
np.ndarray
Annotated image with detection overlays
Examples
--------
>>> detections = {
... "walls": [wall_polygon1, wall_polygon2],
... "rooms": [room_polygon1, room_polygon2],
... "doors": [door_polygon1],
... "windows": [window_polygon1, window_polygon2]
... }
>>> annotated = visualize_detections(image, detections, "output.png")
"""
# Convert grayscale to BGR if needed
if len(image.shape) == 2:
viz_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
else:
viz_image = image.copy()
# Ensure image is uint8
if viz_image.dtype != np.uint8:
viz_image = (viz_image * 255).astype(np.uint8) if viz_image.max() <= 1.0 else viz_image.astype(np.uint8)
# Define colors (BGR format)
colors = {
"walls": (0, 255, 0), # Green
"rooms": (144, 238, 144), # Light green
"doors": (255, 0, 0), # Blue
"windows": (0, 0, 255) # Red
}
# Define thicknesses
thicknesses = {
"walls": 2,
"rooms": -1, # Filled
"doors": 2,
"windows": 2
}
# Draw rooms first (with transparency)
if "rooms" in detections and detections["rooms"]:
rooms_overlay = viz_image.copy()
for room_polygon in detections["rooms"]:
if room_polygon is None or len(room_polygon) == 0:
continue
# Convert to integer coordinates
points = _ensure_int_array(room_polygon)
# Draw filled polygon
cv2.fillPoly(rooms_overlay, [points], colors["rooms"])
# Blend with original image for transparency
cv2.addWeighted(rooms_overlay, room_alpha, viz_image, 1 - room_alpha, 0, viz_image)
# Draw walls
if "walls" in detections and detections["walls"]:
for wall_polygon in detections["walls"]:
if wall_polygon is None or len(wall_polygon) == 0:
continue
points = _ensure_int_array(wall_polygon)
cv2.polylines(viz_image, [points], isClosed=True,
color=colors["walls"], thickness=thicknesses["walls"])
# Draw doors with labels
if "doors" in detections and detections["doors"]:
for i, door_polygon in enumerate(detections["doors"]):
if door_polygon is None or len(door_polygon) == 0:
continue
points = _ensure_int_array(door_polygon)
cv2.polylines(viz_image, [points], isClosed=True,
color=colors["doors"], thickness=thicknesses["doors"])
# Add label
if show_labels:
centroid = _compute_centroid(points)
_draw_label(viz_image, f"Door {i+1}", centroid, colors["doors"])
# Draw windows with labels
if "windows" in detections and detections["windows"]:
for i, window_polygon in enumerate(detections["windows"]):
if window_polygon is None or len(window_polygon) == 0:
continue
points = _ensure_int_array(window_polygon)
cv2.polylines(viz_image, [points], isClosed=True,
color=colors["windows"], thickness=thicknesses["windows"])
# Add label
if show_labels:
centroid = _compute_centroid(points)
_draw_label(viz_image, f"Win {i+1}", centroid, colors["windows"])
# Add legend
viz_image = _add_legend(viz_image, colors)
# Save if output path provided
if output_path:
cv2.imwrite(output_path, viz_image)
print(f"Visualization saved to: {output_path}")
return viz_image
def visualize_comparison(
image: np.ndarray,
detections_before: Dict[str, List[np.ndarray]],
detections_after: Dict[str, List[np.ndarray]],
output_path: Optional[str] = None
) -> np.ndarray:
"""
Create side-by-side comparison of detections before and after refinement.
Parameters
----------
image : np.ndarray
Original image
detections_before : Dict[str, List[np.ndarray]]
Detections before refinement (YOLO)
detections_after : Dict[str, List[np.ndarray]]
Detections after refinement
output_path : str, optional
If provided, save the comparison image
Returns
-------
np.ndarray
Side-by-side comparison image
"""
# Create visualizations
viz_before = visualize_detections(image, detections_before, show_labels=False)
viz_after = visualize_detections(image, detections_after, show_labels=True)
# Add titles
viz_before = _add_title(viz_before, "Before Refinement (YOLO)")
viz_after = _add_title(viz_after, "After Refinement (Geometry)")
# Concatenate horizontally
comparison = np.hstack([viz_before, viz_after])
# Save if output path provided
if output_path:
cv2.imwrite(output_path, comparison)
print(f"Comparison saved to: {output_path}")
return comparison
def visualize_vectorization_result(
image: np.ndarray,
vectorization_result,
output_path: Optional[str] = None,
show_labels: bool = True
) -> np.ndarray:
"""
Visualize a VectorizationResult object.
Parameters
----------
image : np.ndarray
Original image
vectorization_result : VectorizationResult
VectorizationResult object from wall_vectorizer
output_path : str, optional
If provided, save the visualization
show_labels : bool, default=True
Whether to show labels
Returns
-------
np.ndarray
Annotated image
"""
# Convert VectorizationResult to detections dict
detections = {
"walls": [np.array(w.points) for w in vectorization_result.walls],
"rooms": [np.array(r.points) for r in vectorization_result.rooms],
"doors": [np.array(d.points) for d in vectorization_result.doors],
"windows": [np.array(w.points) for w in vectorization_result.windows]
}
return visualize_detections(image, detections, output_path, show_labels)
# ── Helper Functions ──────────────────────────────────────────────────────────
def _ensure_int_array(polygon: Union[np.ndarray, List]) -> np.ndarray:
"""Convert polygon to integer numpy array."""
if isinstance(polygon, list):
polygon = np.array(polygon)
# Ensure 2D array
if len(polygon.shape) == 1:
polygon = polygon.reshape(-1, 2)
return polygon.astype(np.int32)
def _compute_centroid(points: np.ndarray) -> Tuple[int, int]:
"""Compute centroid of a polygon."""
centroid = np.mean(points, axis=0)
return (int(centroid[0]), int(centroid[1]))
def _draw_label(
image: np.ndarray,
text: str,
position: Tuple[int, int],
color: Tuple[int, int, int],
font_scale: float = 0.4,
thickness: int = 1
):
"""Draw text label with background."""
font = cv2.FONT_HERSHEY_SIMPLEX
# Get text size
(text_width, text_height), baseline = cv2.getTextSize(
text, font, font_scale, thickness
)
# Draw background rectangle
x, y = position
padding = 2
cv2.rectangle(
image,
(x - padding, y - text_height - padding),
(x + text_width + padding, y + baseline + padding),
(255, 255, 255),
-1
)
# Draw text
cv2.putText(
image,
text,
(x, y),
font,
font_scale,
color,
thickness,
cv2.LINE_AA
)
def _add_legend(
image: np.ndarray,
colors: Dict[str, Tuple[int, int, int]],
position: str = "top-right"
) -> np.ndarray:
"""Add color legend to image."""
legend_items = [
("Walls", colors["walls"]),
("Rooms", colors["rooms"]),
("Doors", colors["doors"]),
("Windows", colors["windows"])
]
# Legend dimensions
item_height = 25
item_width = 120
padding = 10
legend_height = len(legend_items) * item_height + 2 * padding
legend_width = item_width + 2 * padding
# Determine position
h, w = image.shape[:2]
if position == "top-right":
x_start = w - legend_width - 10
y_start = 10
elif position == "top-left":
x_start = 10
y_start = 10
elif position == "bottom-right":
x_start = w - legend_width - 10
y_start = h - legend_height - 10
else: # bottom-left
x_start = 10
y_start = h - legend_height - 10
# Draw legend background
cv2.rectangle(
image,
(x_start, y_start),
(x_start + legend_width, y_start + legend_height),
(255, 255, 255),
-1
)
cv2.rectangle(
image,
(x_start, y_start),
(x_start + legend_width, y_start + legend_height),
(0, 0, 0),
1
)
# Draw legend items
for i, (label, color) in enumerate(legend_items):
y = y_start + padding + i * item_height + item_height // 2
# Draw color box
box_size = 15
cv2.rectangle(
image,
(x_start + padding, y - box_size // 2),
(x_start + padding + box_size, y + box_size // 2),
color,
-1
)
cv2.rectangle(
image,
(x_start + padding, y - box_size // 2),
(x_start + padding + box_size, y + box_size // 2),
(0, 0, 0),
1
)
# Draw label text
cv2.putText(
image,
label,
(x_start + padding + box_size + 10, y + 5),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(0, 0, 0),
1,
cv2.LINE_AA
)
return image
def _add_title(
image: np.ndarray,
title: str,
font_scale: float = 0.7,
thickness: int = 2
) -> np.ndarray:
"""Add title to top of image."""
font = cv2.FONT_HERSHEY_SIMPLEX
# Get text size
(text_width, text_height), baseline = cv2.getTextSize(
title, font, font_scale, thickness
)
# Create space for title
title_height = text_height + baseline + 20
titled_image = np.ones((image.shape[0] + title_height, image.shape[1], 3), dtype=np.uint8) * 255
titled_image[title_height:, :] = image
# Draw title
x = (image.shape[1] - text_width) // 2
y = text_height + 10
cv2.putText(
titled_image,
title,
(x, y),
font,
font_scale,
(0, 0, 0),
thickness,
cv2.LINE_AA
)
return titled_image
def create_detection_report(
image: np.ndarray,
detections: Dict[str, List[np.ndarray]],
output_path: str,
title: str = "Detection Report"
):
"""
Create a comprehensive detection report with statistics.
Parameters
----------
image : np.ndarray
Original image
detections : Dict[str, List[np.ndarray]]
Detection results
output_path : str
Path to save the report image
title : str, default="Detection Report"
Report title
"""
# Create visualization
viz = visualize_detections(image, detections, show_labels=True)
# Add title
viz = _add_title(viz, title)
# Add statistics panel
stats_text = [
f"Walls: {len(detections.get('walls', []))}",
f"Rooms: {len(detections.get('rooms', []))}",
f"Doors: {len(detections.get('doors', []))}",
f"Windows: {len(detections.get('windows', []))}"
]
# Draw statistics
y_offset = 50
for i, text in enumerate(stats_text):
cv2.putText(
viz,
text,
(10, y_offset + i * 25),
cv2.FONT_HERSHEY_SIMPLEX,
0.6,
(0, 0, 0),
2,
cv2.LINE_AA
)
# Save report
cv2.imwrite(output_path, viz)
print(f"Detection report saved to: {output_path}")
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