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fix: prevent single object from appearing in multiple polygon zones (#1987) #1991

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fix: prevent single object from appearing in multiple polygon zones (#1987) #1991

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e1cb055
fix: prevent single object from appearing in multiple polygon zones
Adithi-Sreenath Oct 14, 2025
6ce5b83
fix(pre_commit): 🎨 auto format pre-commit hooks
pre-commit-ci[bot] Oct 14, 2025
2db7753
fix: typo in polygon zone docstring (simultaneously)
Adithi-Sreenath Oct 14, 2025
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43 changes: 28 additions & 15 deletions supervision/detection/tools/polygon_zone.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,12 @@
from __future__ import annotations

from collections.abc import Iterable
from dataclasses import replace

import cv2
import numpy as np
import numpy.typing as npt

from supervision import Detections
from supervision.detection.utils.boxes import clip_boxes
from supervision.detection.utils.converters import polygon_to_mask
from supervision.draw.color import Color
from supervision.draw.utils import draw_filled_polygon, draw_polygon, draw_text
Expand Down Expand Up @@ -81,6 +79,11 @@ def trigger(self, detections: Detections) -> npt.NDArray[np.bool_]:
"""
Determines if the detections are within the polygon zone.

Anchor points are calculated from original (unclipped) detection boxes.
to ensure a single object can only appear in one zone. This prevents
detections spanning multiple non-overlapping ROIs from being counted
in multiple zones simultaneously.

Parameters:
detections (Detections): The detections
to be checked against the polygon zone
Expand All @@ -89,25 +92,35 @@ def trigger(self, detections: Detections) -> npt.NDArray[np.bool_]:
np.ndarray: A boolean numpy array indicating
if each detection is within the polygon zone
"""
if len(detections) == 0:
return np.array([], dtype=bool)

clipped_xyxy = clip_boxes(
xyxy=detections.xyxy, resolution_wh=self.frame_resolution_wh
)
clipped_detections = replace(detections, xyxy=clipped_xyxy)
all_clipped_anchors = np.array(
all_anchors = np.array(
[
np.ceil(clipped_detections.get_anchors_coordinates(anchor)).astype(int)
for anchor in self.triggering_anchors
np.ceil(detections.get_anchors_coordinates(anchors)).astype(int)
for anchors in self.triggering_anchors
]
)

is_in_zone: npt.NDArray[np.bool_] = (
self.mask[all_clipped_anchors[:, :, 1], all_clipped_anchors[:, :, 0]]
.transpose()
.astype(bool)
)
is_in_zone: npt.NDArray[np.bool_] = np.zeros(len(detections), dtype=bool)

for detection_idx in range(len(detections)):
anchors = all_anchors[:, detection_idx, :]
all_in_zone = True

for anchor in anchors:
x, y = anchor

if x < 0 or y < 0 or x >= self.mask.shape[1] or y >= self.mask.shape[0]:
all_in_zone = False
break

if not self.mask[y, x]:
all_in_zone = False
break

is_in_zone[detection_idx] = all_in_zone

is_in_zone: npt.NDArray[np.bool_] = np.all(is_in_zone, axis=1)
self.current_count = int(np.sum(is_in_zone))
return is_in_zone.astype(bool)

Expand Down