from typing import List, Optional, Sequence, Tuple, Union
import cv2
import numpy as np
def _box_iou(
box1: Union[Tuple[float, float, float, float], np.ndarray],
box2: Union[Tuple[float, float, float, float], np.ndarray]
) -> float:
"""
Calculate Intersection over Union (IoU) for two axis-aligned bounding boxes.
"""
x1_min, y1_min, x1_max, y1_max = box1
x2_min, y2_min, x2_max, y2_max = box2
inter_x_min = max(x1_min, x2_min)
inter_y_min = max(y1_min, y2_min)
inter_x_max = min(x1_max, x2_max)
inter_y_max = min(y1_max, y2_max)
if inter_x_max <= inter_x_min or inter_y_max <= inter_y_min:
return 0.0
inter_area = (inter_x_max - inter_x_min) * (inter_y_max - inter_y_min)
area1 = (x1_max - x1_min) * (y1_max - y1_min)
area2 = (x2_max - x2_min) * (y2_max - y2_min)
union_area = area1 + area2 - inter_area
if union_area <= 0:
return 0.0
return inter_area / union_area
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def polygon_to_bbox(
polygon: Union[np.ndarray, Tuple[Tuple[float, float], ...]],
image_shape: Optional[Tuple[int, ...]] = None,
pad: float = 0.0,
) -> Optional[Tuple[int, int, int, int]]:
"""
Convert a polygon with any number of vertices to a clipped axis-aligned
bounding box.
Parameters
----------
polygon : array-like of shape (N, 2)
Polygon vertices in image coordinates.
image_shape : tuple, optional
Source image shape used for clipping.
pad : float, optional
Extra padding in pixels around the polygon. Default is ``0``.
Returns
-------
tuple or None
Bounding box as ``(x1, y1, x2, y2)`` or ``None`` if invalid.
"""
pts = np.asarray(polygon, dtype=np.float32)
if pts.ndim != 2 or pts.shape[1] != 2 or pts.size == 0:
return None
x_min, y_min = np.min(pts, axis=0)
x_max, y_max = np.max(pts, axis=0)
if pad:
x1 = int(np.floor(x_min - pad))
y1 = int(np.floor(y_min - pad))
x2 = int(np.ceil(x_max + pad))
y2 = int(np.ceil(y_max + pad))
else:
# Preserve legacy crop behavior for the default bbox preset.
x1 = int(x_min)
y1 = int(y_min)
x2 = int(x_max)
y2 = int(y_max)
if image_shape is not None:
height, width = image_shape[:2]
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(width, x2)
y2 = min(height, y2)
if x2 <= x1 or y2 <= y1:
return None
return x1, y1, x2, y2
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def crop_axis_aligned(
image: np.ndarray,
polygon: Union[np.ndarray, Tuple[Tuple[float, float], ...]],
pad: float = 0.0,
) -> Optional[np.ndarray]:
"""
Crop an axis-aligned rectangle covering the polygon.
Parameters
----------
image : numpy.ndarray
Source image.
polygon : array-like of shape (N, 2)
Polygon vertices in image coordinates.
pad : float, optional
Extra padding in pixels around the polygon. Default is ``0``.
Returns
-------
numpy.ndarray or None
Image crop, or ``None`` if the bounding box is invalid.
"""
bbox = polygon_to_bbox(polygon, image_shape=image.shape, pad=pad)
if bbox is None:
return None
x1, y1, x2, y2 = bbox
crop = image[y1:y2, x1:x2]
if crop.size == 0:
return None
return crop.copy()
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def crop_polygon_mask(
image: np.ndarray,
polygon: Union[np.ndarray, Tuple[Tuple[float, float], ...]],
pad: float = 0.0,
background: int = 255,
) -> Optional[np.ndarray]:
"""
Crop the polygon bounding box and mask pixels outside the polygon.
Works with arbitrary polygons of shape ``(N, 2)``.
Parameters
----------
image : numpy.ndarray
Source image.
polygon : array-like of shape (N, 2)
Polygon vertices in image coordinates.
pad : float, optional
Extra padding in pixels around the polygon. Default is ``0``.
background : int, optional
Background pixel value outside the polygon. Default is ``255``.
Returns
-------
numpy.ndarray or None
Masked image crop, or ``None`` if the bounding box is invalid.
"""
pts = np.asarray(polygon, dtype=np.float32)
bbox = polygon_to_bbox(pts, image_shape=image.shape, pad=pad)
if bbox is None:
return None
x1, y1, x2, y2 = bbox
crop = image[y1:y2, x1:x2].copy()
if crop.size == 0:
return None
shifted = pts.copy()
shifted[:, 0] -= x1
shifted[:, 1] -= y1
mask = np.zeros(crop.shape[:2], dtype=np.uint8)
cv2.fillPoly(mask, [shifted.astype(np.int32)], 255)
result = np.full_like(crop, background)
if crop.ndim == 2:
result[mask == 255] = crop[mask == 255]
else:
result[mask == 255] = crop[mask == 255]
return result
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def order_quad_points(
points: Union[np.ndarray, Tuple[Tuple[float, float], ...]]
) -> np.ndarray:
"""
Order exactly 4 polygon points as top-left, top-right, bottom-right, bottom-left.
Parameters
----------
points : array-like of shape (4, 2)
Quadrilateral vertices.
Returns
-------
numpy.ndarray
Ordered vertices as float32.
Raises
------
ValueError
If exactly four points are not provided.
"""
pts = np.asarray(points, dtype=np.float32)
if pts.shape != (4, 2):
raise ValueError(f"Expected 4 points with shape (4, 2), got: {pts.shape}")
if not np.isfinite(pts).all():
raise ValueError("Quadrilateral vertices must be finite")
hull = cv2.convexHull(pts).reshape(-1, 2)
if len(hull) != 4 or abs(cv2.contourArea(hull)) <= 1e-6:
raise ValueError("Expected a nondegenerate convex quadrilateral")
centered = hull - hull.mean(axis=0)
rect = hull[np.argsort(np.arctan2(centered[:, 1], centered[:, 0]))]
first = np.lexsort((rect[:, 0], rect[:, 1], rect.sum(axis=1)))[0]
return np.roll(rect, -int(first), axis=0)
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def warp_quad(
image: np.ndarray,
polygon: Union[np.ndarray, Tuple[Tuple[float, float], ...]],
output_size: Optional[Tuple[int, int]] = None,
background: int = 255,
) -> Optional[np.ndarray]:
"""
Perspective-warp a quadrilateral polygon into a rectified crop.
This helper is intentionally quad-specific. For non-quad polygons it returns
``None`` so callers may choose a fallback strategy.
Parameters
----------
image : numpy.ndarray
Source image.
polygon : array-like of shape (4, 2)
Quadrilateral vertices in image coordinates.
output_size : tuple of int, optional
Crop size ``(width, height)``. If ``None``, estimate it from polygon edges.
Returns
-------
numpy.ndarray or None
Rectified image crop, or ``None`` for an invalid quadrilateral.
"""
pts = np.asarray(polygon, dtype=np.float32)
if pts.shape != (4, 2):
return None
try:
rect = order_quad_points(pts)
except ValueError:
return None
if output_size is None:
top_width = np.linalg.norm(rect[1] - rect[0])
bottom_width = np.linalg.norm(rect[2] - rect[3])
left_height = np.linalg.norm(rect[3] - rect[0])
right_height = np.linalg.norm(rect[2] - rect[1])
width = max(int(round(max(top_width, bottom_width))), 1)
height = max(int(round(max(left_height, right_height))), 1)
else:
width = max(int(output_size[0]), 1)
height = max(int(output_size[1]), 1)
dst = np.array(
[[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]],
dtype=np.float32,
)
matrix = cv2.getPerspectiveTransform(rect, dst)
border_value = background if image.ndim == 2 else (background, background, background)
warped = cv2.warpPerspective(
image,
matrix,
(width, height),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=border_value,
)
if warped.size == 0:
return None
return warped
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def merge_polygons(
polygons: Sequence[Union[np.ndarray, Tuple[Tuple[float, float], ...]]],
method: str = "bbox",
) -> Optional[List[Tuple[float, float]]]:
"""
Merge multiple polygons into a single polygon.
Parameters
----------
polygons : sequence of array-like polygons
Input polygons with shape ``(N, 2)``.
method : {"bbox", "convex_hull"}, optional
Merge strategy. ``"bbox"`` returns an axis-aligned rectangle covering
all points. ``"convex_hull"`` returns a convex hull over all points.
Returns
-------
list of tuple or None
Merged polygon, or ``None`` when ``polygons`` is empty.
"""
if not polygons:
return None
normalized = []
for polygon in polygons:
pts = np.asarray(polygon, dtype=np.float32)
if pts.ndim != 2 or pts.shape[1] != 2 or pts.size == 0:
raise ValueError("Each polygon must have shape (N, 2)")
normalized.append(pts)
points = np.concatenate(normalized, axis=0)
if method == "bbox":
x_min, y_min = np.min(points, axis=0)
x_max, y_max = np.max(points, axis=0)
return [
(float(x_min), float(y_min)),
(float(x_max), float(y_min)),
(float(x_max), float(y_max)),
(float(x_min), float(y_max)),
]
if method == "convex_hull":
hull = cv2.convexHull(points)
if hull is None or hull.size == 0:
return None
return [(float(x), float(y)) for x, y in hull.reshape(-1, 2)]
raise ValueError(f"method must be 'bbox' or 'convex_hull', got: {method}")
def boxes_to_polygons(boxes: np.ndarray) -> np.ndarray:
"""Convert [N, 4+] xyxy boxes to [N, 4, 2] float32 polygons."""
if len(boxes) == 0:
return np.zeros((0, 4, 2), dtype=np.float32)
polygons = np.zeros((len(boxes), 4, 2), dtype=np.float32)
polygons[:, 0, 0] = boxes[:, 0]
polygons[:, 0, 1] = boxes[:, 1]
polygons[:, 1, 0] = boxes[:, 2]
polygons[:, 1, 1] = boxes[:, 1]
polygons[:, 2, 0] = boxes[:, 2]
polygons[:, 2, 1] = boxes[:, 3]
polygons[:, 3, 0] = boxes[:, 0]
polygons[:, 3, 1] = boxes[:, 3]
return polygons
def clip_polygons(
polygons: np.ndarray,
image_hw: Tuple[int, int],
) -> np.ndarray:
"""Clamp polygon vertices to image bounds without changing the input."""
if len(polygons) == 0:
return polygons
clipped = polygons.copy()
image_h, image_w = image_hw
clipped[:, :, 0] = np.clip(clipped[:, :, 0], 0, image_w)
clipped[:, :, 1] = np.clip(clipped[:, :, 1], 0, image_h)
return clipped
def polygon_areas(polygons: np.ndarray) -> np.ndarray:
"""Compute absolute shoelace areas for a batch of polygons."""
if len(polygons) == 0:
return np.zeros((0,), dtype=np.float32)
shifted = np.roll(polygons, shift=-1, axis=1)
cross = polygons[:, :, 0] * shifted[:, :, 1] - shifted[:, :, 0] * polygons[:, :, 1]
return 0.5 * np.abs(cross.sum(axis=1)).astype(np.float32)
def box_intersection_area(box_a: np.ndarray, box_b: np.ndarray) -> float:
"""Return intersection area of two xyxy boxes."""
x1 = max(float(box_a[0]), float(box_b[0]))
y1 = max(float(box_a[1]), float(box_b[1]))
x2 = min(float(box_a[2]), float(box_b[2]))
y2 = min(float(box_a[3]), float(box_b[3]))
return max(0.0, x2 - x1) * max(0.0, y2 - y1)
def polygons_to_boxes(polygons: np.ndarray) -> np.ndarray:
"""Return float32 xyxy bounds for [N, vertices, 2] polygons.
Unlike polygon_to_bbox, this batch operation preserves fractional
coordinates and does not apply crop rounding, padding or clipping.
"""
if len(polygons) == 0:
return np.zeros((0, 4), dtype=np.float32)
return np.column_stack([polygons.min(axis=1), polygons.max(axis=1)]).astype(np.float32)
def repair_detection_polygon(points, *, fallback=None):
"""Preserve a usable detection outline, repairing topology before a box fallback.
Disconnected repairs use a rectangle covering the entire original outline;
they never silently select only the largest component. None means that even
the available fallback has no two-dimensional extent.
"""
from shapely.geometry import Polygon
from shapely.errors import ShapelyError
points = np.asarray(points, dtype=np.float64)
if points.ndim != 2 or points.shape[1] != 2 or not np.isfinite(points).all():
raise ValueError("Detection polygon must contain finite XY points")
if len(points) >= 4:
geometry = Polygon(points)
if geometry.is_valid and geometry.area > 0:
return points
try:
repaired = geometry.buffer(0)
except ShapelyError:
repaired = None
if (repaired is not None and repaired.geom_type == "Polygon"
and repaired.is_valid and repaired.area > 0):
result = np.asarray(repaired.exterior.coords[:-1])
if len(result) >= 4:
return result
source = points if fallback is None else np.asarray(fallback, dtype=np.float64)
if source.ndim != 2 or source.shape[1] != 2 or not np.isfinite(source).all():
raise ValueError("Detection fallback must contain finite XY points")
if not len(source):
return None
result = cv2.boxPoints(cv2.minAreaRect(source.astype(np.float32))).astype(np.float64)
if Polygon(result).is_valid and Polygon(result).area > 0:
return result
return None