Moving iou_with_jaccard and jaccard from mean_average_precision.py to detection/utils.py
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@@ -1325,3 +1325,73 @@ def spread_out_boxes(
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xyxy_padded[:, [2, 3]] += force_vectors
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return pad_boxes(xyxy_padded, px=-1)
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def _jaccard(box_a: List[float], box_b: List[float], is_crowd: bool) -> float:
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"""
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Calculate the Jaccard index (intersection over union) between two bounding boxes.
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If a gt object is marked as "iscrowd", a dt is allowed to match any subregion
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of the gt. Choosing gt' in the crowd gt that best matches the dt can be done using
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gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
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iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
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Args:
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box_a (List[float]): Box coordinates in the format [x, y, width, height].
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box_b (List[float]): Box coordinates in the format [x, y, width, height].
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iscrowd (bool): Flag indicating if the second box is a crowd region or not.
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Returns:
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float: Jaccard index between the two bounding boxes.
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"""
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# Smallest number to avoid division by zero
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EPS = np.spacing(1)
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xa, ya, x2a, y2a = box_a[0], box_a[1], box_a[0] + box_a[2], box_a[1] + box_a[3]
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xb, yb, x2b, y2b = box_b[0], box_b[1], box_b[0] + box_b[2], box_b[1] + box_b[3]
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# Innermost left x
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xi = max(xa, xb)
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# Innermost right x
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x2i = min(x2a, x2b)
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# Same for y
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yi = max(ya, yb)
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y2i = min(y2a, y2b)
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# Calculate areas
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Aa = max(x2a - xa, 0.0) * max(y2a - ya, 0.0)
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Ab = max(x2b - xb, 0.0) * max(y2b - yb, 0.0)
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Ai = max(x2i - xi, 0.0) * max(y2i - yi, 0.0)
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if is_crowd:
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return Ai / (Aa + EPS)
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return Ai / (Aa + Ab - Ai + EPS)
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def iou_with_jaccard(
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dt: List[List[float]], gt: List[List[float]], is_crowd: List[bool]
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) -> np.ndarray:
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"""
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Calculate the intersection over union (IoU) between detection bounding boxes (dt)
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and ground-truth bounding boxes (gt).
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Reference: https://github.com/rafaelpadilla/review_object_detection_metrics
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Args:
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dt (List[List[float]]): List of detection bounding boxes in the \
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format [x, y, width, height].
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gt (List[List[float]]): List of ground-truth bounding boxes in the \
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format [x, y, width, height].
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is_crowd (List[bool]): List indicating if each ground-truth bounding box \
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is a crowd region or not.
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Returns:
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np.ndarray: Array of IoU values of shape (len(dt), len(gt)).
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"""
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assert len(is_crowd) == len(gt), "iou(iscrowd=) must have the same length as gt"
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if len(dt) == 0 or len(gt) == 0:
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return np.array([])
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ious = np.zeros((len(dt), len(gt)), dtype=np.float64)
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for g_idx, g in enumerate(gt):
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for d_idx, d in enumerate(dt):
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ious[d_idx, g_idx] = _jaccard(d, g, is_crowd[g_idx])
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return ious
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@@ -16,6 +16,7 @@ from supervision.detection.core import Detections
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from supervision.draw.color import LEGACY_COLOR_PALETTE
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from supervision.metrics.core import Metric, MetricTarget
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from supervision.metrics.utils.utils import ensure_pandas_installed
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from supervision.detection.utils import iou_with_jaccard
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if TYPE_CHECKING:
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import pandas as pd
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@@ -99,10 +100,10 @@ class MeanAveragePrecisionResult:
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f"maxDets=100 ] = {self.map75:.3f}\n"
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f"Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] "
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f"= {self.small_objects.map50_95:.3f}\n"
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f"Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] "
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f"= {self.medium_objects.map50_95:.3f}\n"
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f"Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] "
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f"= {self.large_objects.map50_95:.3f}"
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f"Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] \
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= {self.medium_objects.map50_95:.3f}\n"
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f"Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] \
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= {self.large_objects.map50_95:.3f}"
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)
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def to_pandas(self) -> "pd.DataFrame":
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@@ -485,73 +486,6 @@ class ObjectSize(Enum):
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LARGE = "large"
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def _iou_with_jaccard(
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dt: List[List[float]], gt: List[List[float]], is_crowd: List[bool]
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) -> np.ndarray:
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"""
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Calculate the intersection over union (IoU) between detection bounding boxes (dt)
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and ground-truth bounding boxes (gt).
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Reference: https://github.com/rafaelpadilla/review_object_detection_metrics
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Args:
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dt (List[List[float]]): List of detection bounding boxes in the \
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format [x, y, width, height].
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gt (List[List[float]]): List of ground-truth bounding boxes in the \
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format [x, y, width, height].
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is_crowd (List[bool]): List indicating if each ground-truth bounding box \
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is a crowd region or not.
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Returns:
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np.ndarray: Array of IoU values of shape (len(dt), len(gt)).
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"""
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assert len(is_crowd) == len(gt), "iou(iscrowd=) must have the same length as gt"
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if len(dt) == 0 or len(gt) == 0:
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return np.array([])
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ious = np.zeros((len(dt), len(gt)), dtype=np.float64)
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for g_idx, g in enumerate(gt):
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for d_idx, d in enumerate(dt):
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ious[d_idx, g_idx] = _jaccard(d, g, is_crowd[g_idx])
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return ious
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def _jaccard(box_a: List[float], box_b: List[float], is_crowd: bool) -> float:
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"""
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Calculate the Jaccard index (intersection over union) between two bounding boxes.
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If a gt object is marked as "iscrowd", a dt is allowed to match any subregion
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of the gt. Choosing gt' in the crowd gt that best matches the dt can be done using
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gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
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iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
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Args:
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box_a (List[float]): Box coordinates in the format [x, y, width, height].
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box_b (List[float]): Box coordinates in the format [x, y, width, height].
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iscrowd (bool): Flag indicating if the second box is a crowd region or not.
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Returns:
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float: Jaccard index between the two bounding boxes.
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"""
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xa, ya, x2a, y2a = box_a[0], box_a[1], box_a[0] + box_a[2], box_a[1] + box_a[3]
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xb, yb, x2b, y2b = box_b[0], box_b[1], box_b[0] + box_b[2], box_b[1] + box_b[3]
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# Innermost left x
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xi = max(xa, xb)
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# Innermost right x
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x2i = min(x2a, x2b)
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# Same for y
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yi = max(ya, yb)
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y2i = min(y2a, y2b)
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# Calculate areas
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Aa = max(x2a - xa, 0.0) * max(y2a - ya, 0.0)
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Ab = max(x2b - xb, 0.0) * max(y2b - yb, 0.0)
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Ai = max(x2i - xi, 0.0) * max(y2i - yi, 0.0)
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if is_crowd:
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return Ai / (Aa + EPS)
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return Ai / (Aa + Ab - Ai + EPS)
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class COCOEvaluatorParameters:
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"""
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Parameters for COCOEvaluator
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@@ -689,7 +623,7 @@ class COCOEvaluator:
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# Get the iscrowd flag for each gt
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is_crowd = [int(o["iscrowd"]) for o in gt]
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# Compute iou between each prediction a and gt region
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iou = _iou_with_jaccard(dt_boxes, gt_boxes, is_crowd)
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iou = iou_with_jaccard(dt_boxes, gt_boxes, is_crowd)
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return iou
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def _evaluate_image(
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