Merge pull request #1901 from roboflow/fix/inference-overlap-metric-fix
fix: 🐞 update inference_slicer.py for improved detection handling
This commit is contained in:
Piotr Skalski
GitHub
commit
baa0402d5a
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@ -2,7 +2,7 @@
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name = "supervision"
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description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
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license = { text = "MIT" }
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version = "0.26.0"
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version = "0.26.1"
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readme = "README.md"
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requires-python = ">=3.9"
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authors = [
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@ -1939,8 +1939,8 @@ class Detections:
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class_agnostic (bool): Whether to perform class-agnostic
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non-maximum suppression. If True, the class_id of each detection
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will be ignored. Defaults to False.
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overlap_metric (OverlapMetric): Metric used for measuring overlap between
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detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of
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overlap between pairs of masks or boxes (e.g., IoU, IoS).
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Returns:
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Detections: A new Detections object containing the subset of detections
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@ -2003,8 +2003,8 @@ class Detections:
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class_agnostic (bool): Whether to perform class-agnostic
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non-maximum merging. If True, the class_id of each detection
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will be ignored. Defaults to False.
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overlap_metric (OverlapMetric): Metric used for measuring overlap between
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detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of
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overlap between pairs of masks or boxes (e.g., IoU, IoS).
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Returns:
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Detections: A new Detections object containing the subset of detections
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@ -9,7 +9,7 @@ import numpy as np
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from supervision.config import ORIENTED_BOX_COORDINATES
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from supervision.detection.core import Detections
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from supervision.detection.utils.boxes import move_boxes, move_oriented_boxes
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from supervision.detection.utils.iou_and_nms import OverlapFilter
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from supervision.detection.utils.iou_and_nms import OverlapFilter, OverlapMetric
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from supervision.detection.utils.masks import move_masks
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from supervision.utils.image import crop_image
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from supervision.utils.internal import (
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@ -75,8 +75,8 @@ class InferenceSlicer:
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filtering or merging overlapping detections in slices.
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iou_threshold (float): Intersection over Union (IoU) threshold
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used when filtering by overlap.
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match_metric (str): Metric used for matching detections in slices.
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"IOU" or "IOS". Defaults "IOU".
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overlap_metric (Union[OverlapMetric, str]): Metric used for matching detections
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in slices.
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callback (Callable): A function that performs inference on a given image
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slice and returns detections.
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thread_workers (int): Number of threads for parallel execution.
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@ -96,7 +96,7 @@ class InferenceSlicer:
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overlap_wh: tuple[int, int] | None = None,
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overlap_filter: OverlapFilter | str = OverlapFilter.NON_MAX_SUPPRESSION,
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iou_threshold: float = 0.5,
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match_metric: str = "IOU",
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overlap_metric: OverlapMetric | str = OverlapMetric.IOU,
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thread_workers: int = 1,
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):
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if overlap_ratio_wh is not None:
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@ -112,7 +112,7 @@ class InferenceSlicer:
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self.slice_wh = slice_wh
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self.iou_threshold = iou_threshold
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self.match_metric = match_metric
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self.overlap_metric = OverlapMetric.from_value(overlap_metric)
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self.overlap_filter = OverlapFilter.from_value(overlap_filter)
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self.callback = callback
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self.thread_workers = thread_workers
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@ -173,11 +173,11 @@ class InferenceSlicer:
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return merged
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elif self.overlap_filter == OverlapFilter.NON_MAX_SUPPRESSION:
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return merged.with_nms(
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threshold=self.iou_threshold, match_metric=self.match_metric
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threshold=self.iou_threshold, overlap_metric=self.overlap_metric
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)
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elif self.overlap_filter == OverlapFilter.NON_MAX_MERGE:
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return merged.with_nmm(
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threshold=self.iou_threshold, match_metric=self.match_metric
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threshold=self.iou_threshold, overlap_metric=self.overlap_metric
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)
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else:
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warnings.warn(
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@ -164,7 +164,8 @@ def box_iou_batch(
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`shape = (N, 4)` where `N` is number of true objects.
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boxes_detection (np.ndarray): 2D `np.ndarray` representing detection boxes.
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`shape = (M, 4)` where `M` is number of detected objects.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of boxes (e.g., IoU, IoS).
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Returns:
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np.ndarray: Pairwise IoU of boxes from `boxes_true` and `boxes_detection`.
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@ -381,7 +382,8 @@ def _mask_iou_batch_split(
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Args:
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masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
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masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of masks (e.g., IoU, IoS).
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Returns:
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np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
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@ -433,7 +435,8 @@ def mask_iou_batch(
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Args:
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masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
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masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of masks (e.g., IoU, IoS).
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memory_limit (int): memory limit in MB, default is 1024 * 5 MB (5GB).
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Returns:
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@ -492,7 +495,8 @@ def mask_non_max_suppression(
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dimensions of each mask.
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iou_threshold (float): The intersection-over-union threshold
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to use for non-maximum suppression.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of masks (e.g., IoU, IoS).
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mask_dimension (int): The dimension to which the masks should be
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resized before computing IOU values. Defaults to 640.
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@ -543,7 +547,8 @@ def box_non_max_suppression(
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or `(x_min, y_min, x_max, y_max, score, class)`.
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iou_threshold (float): The intersection-over-union threshold
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to use for non-maximum suppression.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of boxes (e.g., IoU, IoS).
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Returns:
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np.ndarray: A boolean array indicating which predictions to keep after n
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@ -603,7 +608,8 @@ def _group_overlapping_masks(
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the predictions.
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iou_threshold (float): The intersection-over-union threshold
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to use for non-maximum suppression. Defaults to 0.5.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of masks (e.g., IoU, IoS).
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Returns:
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list[list[int]]: Groups of prediction indices be merged.
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@ -664,7 +670,8 @@ def mask_non_max_merge(
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to use for non-maximum suppression.
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mask_dimension (int): The dimension to which the masks should be
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resized before computing IOU values. Defaults to 640.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of masks (e.g., IoU, IoS).
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Returns:
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np.ndarray: A boolean array indicating which predictions to keep after
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@ -717,7 +724,8 @@ def _group_overlapping_boxes(
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and the confidence scores.
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iou_threshold (float): The intersection-over-union threshold
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to use for non-maximum suppression. Defaults to 0.5.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of boxes (e.g., IoU, IoS).
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Returns:
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list[list[int]]: Groups of prediction indices be merged.
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@ -765,7 +773,8 @@ def box_non_max_merge(
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detections of different classes to be merged.
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iou_threshold (float): The intersection-over-union threshold
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to use for non-maximum suppression. Defaults to 0.5.
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overlap_metric (OverlapMetric): Metric used for matching detections in slices.
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overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
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between pairs of boxes (e.g., IoU, IoS).
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Returns:
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list[list[int]]: Groups of prediction indices be merged.
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