# Source code for monai.apps.detection.metrics.matching

```
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# =========================================================================
# Adapted from https://github.com/MIC-DKFZ/nnDetection/blob/main/nndet/evaluator/detection/matching.py
# which has the following license...
# https://github.com/MIC-DKFZ/nnDetection/blob/main/LICENSE
#
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
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# =========================================================================
# Adapted from https://github.com/cocodataset/cocoapi
# which has the following license...
# https://github.com/cocodataset/cocoapi/blob/master/license.txt
# Copyright (c) 2014, Piotr Dollar and Tsung-Yi Lin
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"""
This script is almost same with https://github.com/MIC-DKFZ/nnDetection/blob/main/nndet/evaluator/detection/matching.py
The changes include 1) code reformatting, 2) docstrings,
3) allow input args gt_ignore to be optional. (If so, no GT boxes will be ignored.)
"""
from __future__ import annotations
from collections.abc import Callable, Sequence
import numpy as np
__all__ = ["matching_batch"]
[docs]
def matching_batch(
iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray],
iou_thresholds: Sequence[float],
pred_boxes: Sequence[np.ndarray],
pred_classes: Sequence[np.ndarray],
pred_scores: Sequence[np.ndarray],
gt_boxes: Sequence[np.ndarray],
gt_classes: Sequence[np.ndarray],
gt_ignore: Sequence[Sequence[bool]] | Sequence[np.ndarray] | None = None,
max_detections: int = 100,
) -> list[dict[int, dict[str, np.ndarray]]]:
"""
Match boxes of a batch to corresponding ground truth for each category
independently.
Args:
iou_fn: compute overlap for each pair
iou_thresholds: defined which IoU thresholds should be evaluated
pred_boxes: predicted boxes from single batch; List[[D, dim * 2]],
D number of predictions
pred_classes: predicted classes from a single batch; List[[D]],
D number of predictions
pred_scores: predicted score for each bounding box; List[[D]],
D number of predictions
gt_boxes: ground truth boxes; List[[G, dim * 2]], G number of ground
truth
gt_classes: ground truth classes; List[[G]], G number of ground truth
gt_ignore: specified if which ground truth boxes are not counted as
true positives. If not given, when use all the gt_boxes.
(detections which match theses boxes are not counted as false
positives either); List[[G]], G number of ground truth
max_detections: maximum number of detections which should be evaluated
Returns:
List[Dict[int, Dict[str, np.ndarray]]], each Dict[str, np.ndarray] corresponds to an image.
Dict has the following keys.
- `dtMatches`: matched detections [T, D], where T = number of
thresholds, D = number of detections
- `gtMatches`: matched ground truth boxes [T, G], where T = number
of thresholds, G = number of ground truth
- `dtScores`: prediction scores [D] detection scores
- `gtIgnore`: ground truth boxes which should be ignored
[G] indicate whether ground truth should be ignored
- `dtIgnore`: detections which should be ignored [T, D],
indicate which detections should be ignored
Example:
.. code-block:: python
from monai.data.box_utils import box_iou
from monai.apps.detection.metrics.coco import COCOMetric
from monai.apps.detection.metrics.matching import matching_batch
# 3D example outputs of one image from detector
val_outputs_all = [
{"boxes": torch.tensor([[1,1,1,3,4,5]],dtype=torch.float16),
"labels": torch.randint(3,(1,)),
"scores": torch.randn((1,)).absolute()},
]
val_targets_all = [
{"boxes": torch.tensor([[1,1,1,2,6,4]],dtype=torch.float16),
"labels": torch.randint(3,(1,))},
]
coco_metric = COCOMetric(
classes=['c0','c1','c2'], iou_list=[0.1], max_detection=[10]
)
results_metric = matching_batch(
iou_fn=box_iou,
iou_thresholds=coco_metric.iou_thresholds,
pred_boxes=[val_data_i["boxes"].numpy() for val_data_i in val_outputs_all],
pred_classes=[val_data_i["labels"].numpy() for val_data_i in val_outputs_all],
pred_scores=[val_data_i["scores"].numpy() for val_data_i in val_outputs_all],
gt_boxes=[val_data_i["boxes"].numpy() for val_data_i in val_targets_all],
gt_classes=[val_data_i["labels"].numpy() for val_data_i in val_targets_all],
)
val_metric_dict = coco_metric(results_metric)
print(val_metric_dict)
"""
results = []
if gt_ignore is None:
gt_ignore = [np.full_like(gt_c, False) for gt_c in gt_classes]
# iterate over images/batches
for pboxes, pclasses, pscores, gboxes, gclasses, gignore in zip(
pred_boxes, pred_classes, pred_scores, gt_boxes, gt_classes, gt_ignore
):
# for each image
img_classes = np.union1d(pclasses, gclasses) # possible class labels
result = {} # dict contains results for each class in one image
for c in img_classes:
pred_mask = pclasses == c # bool mask predictions with current class
gt_mask = gclasses == c # bool mask ground truth with current class
if not np.any(gt_mask): # no ground truth
result[c] = _matching_no_gt(
iou_thresholds=iou_thresholds, pred_scores=pscores[pred_mask], max_detections=max_detections
)
elif not np.any(pred_mask): # no predictions
result[c] = _matching_no_pred(iou_thresholds=iou_thresholds, gt_ignore=gignore[gt_mask])
else: # at least one prediction and one ground truth
result[c] = _matching_single_image_single_class(
iou_fn=iou_fn,
pred_boxes=pboxes[pred_mask],
pred_scores=pscores[pred_mask],
gt_boxes=gboxes[gt_mask],
gt_ignore=gignore[gt_mask],
max_detections=max_detections,
iou_thresholds=iou_thresholds,
)
results.append(result)
return results
def _matching_no_gt(
iou_thresholds: Sequence[float], pred_scores: np.ndarray, max_detections: int
) -> dict[str, np.ndarray]:
"""
Matching result with not ground truth in image
Args:
iou_thresholds: defined which IoU thresholds should be evaluated
dt_scores: predicted scores
max_detections: maximum number of allowed detections per image.
This functions uses this parameter to stay consistent with
the actual matching function which needs this limit.
Returns:
computed matching, a Dict[str, np.ndarray]
- `dtMatches`: matched detections [T, D], where T = number of
thresholds, D = number of detections
- `gtMatches`: matched ground truth boxes [T, G], where T = number
of thresholds, G = number of ground truth
- `dtScores`: prediction scores [D] detection scores
- `gtIgnore`: ground truth boxes which should be ignored
[G] indicate whether ground truth should be ignored
- `dtIgnore`: detections which should be ignored [T, D],
indicate which detections should be ignored
"""
dt_ind = np.argsort(-pred_scores, kind="mergesort")
dt_ind = dt_ind[:max_detections]
dt_scores = pred_scores[dt_ind]
num_preds = len(dt_scores)
gt_match: np.ndarray = np.array([[]] * len(iou_thresholds))
dt_match: np.ndarray = np.zeros((len(iou_thresholds), num_preds))
dt_ignore: np.ndarray = np.zeros((len(iou_thresholds), num_preds))
return {
"dtMatches": dt_match, # [T, D], where T = number of thresholds, D = number of detections
"gtMatches": gt_match, # [T, G], where T = number of thresholds, G = number of ground truth
"dtScores": dt_scores, # [D] detection scores
"gtIgnore": np.array([]).reshape(-1), # [G] indicate whether ground truth should be ignored
"dtIgnore": dt_ignore, # [T, D], indicate which detections should be ignored
}
def _matching_no_pred(iou_thresholds: Sequence[float], gt_ignore: np.ndarray) -> dict[str, np.ndarray]:
"""
Matching result with no predictions
Args:
iou_thresholds: defined which IoU thresholds should be evaluated
gt_ignore: specified if which ground truth boxes are not counted as
true positives (detections which match theses boxes are not
counted as false positives either); [G], G number of ground truth
Returns:
dict: computed matching
- `dtMatches`: matched detections [T, D], where T = number of
thresholds, D = number of detections
- `gtMatches`: matched ground truth boxes [T, G], where T = number
of thresholds, G = number of ground truth
- `dtScores`: prediction scores [D] detection scores
- `gtIgnore`: ground truth boxes which should be ignored
[G] indicate whether ground truth should be ignored
- `dtIgnore`: detections which should be ignored [T, D],
indicate which detections should be ignored
"""
dt_scores: np.ndarray = np.array([])
dt_match: np.ndarray = np.array([[]] * len(iou_thresholds))
dt_ignore: np.ndarray = np.array([[]] * len(iou_thresholds))
n_gt = 0 if gt_ignore.size == 0 else gt_ignore.shape[0]
gt_match = np.zeros((len(iou_thresholds), n_gt))
return {
"dtMatches": dt_match, # [T, D], where T = number of thresholds, D = number of detections
"gtMatches": gt_match, # [T, G], where T = number of thresholds, G = number of ground truth
"dtScores": dt_scores, # [D] detection scores
"gtIgnore": gt_ignore.reshape(-1), # [G] indicate whether ground truth should be ignored
"dtIgnore": dt_ignore, # [T, D], indicate which detections should be ignored
}
def _matching_single_image_single_class(
iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray],
pred_boxes: np.ndarray,
pred_scores: np.ndarray,
gt_boxes: np.ndarray,
gt_ignore: np.ndarray,
max_detections: int,
iou_thresholds: Sequence[float],
) -> dict[str, np.ndarray]:
"""
Adapted from https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py
Args:
iou_fn: compute overlap for each pair
iou_thresholds: defined which IoU thresholds should be evaluated
pred_boxes: predicted boxes from single batch; [D, dim * 2], D number
of predictions
pred_scores: predicted score for each bounding box; [D], D number of
predictions
gt_boxes: ground truth boxes; [G, dim * 2], G number of ground truth
gt_ignore: specified if which ground truth boxes are not counted as
true positives (detections which match theses boxes are not
counted as false positives either); [G], G number of ground truth
max_detections: maximum number of detections which should be evaluated
Returns:
dict: computed matching
- `dtMatches`: matched detections [T, D], where T = number of
thresholds, D = number of detections
- `gtMatches`: matched ground truth boxes [T, G], where T = number
of thresholds, G = number of ground truth
- `dtScores`: prediction scores [D] detection scores
- `gtIgnore`: ground truth boxes which should be ignored
[G] indicate whether ground truth should be ignored
- `dtIgnore`: detections which should be ignored [T, D],
indicate which detections should be ignored
"""
# filter for max_detections highest scoring predictions to speed up computation
dt_ind = np.argsort(-pred_scores, kind="mergesort")
dt_ind = dt_ind[:max_detections]
pred_boxes = pred_boxes[dt_ind]
pred_scores = pred_scores[dt_ind]
# sort ignored ground truth to last positions
gt_ind = np.argsort(gt_ignore, kind="mergesort")
gt_boxes = gt_boxes[gt_ind]
gt_ignore = gt_ignore[gt_ind]
# ious between sorted(!) predictions and ground truth
ious = iou_fn(pred_boxes, gt_boxes) # array sized (num_preds, num_gts)
num_preds, num_gts = ious.shape[0], ious.shape[1]
gt_match = np.zeros((len(iou_thresholds), num_gts))
dt_match = np.zeros((len(iou_thresholds), num_preds))
dt_ignore = np.zeros((len(iou_thresholds), num_preds))
for tind, t in enumerate(iou_thresholds):
for dind, _d in enumerate(pred_boxes): # iterate detections starting from highest scoring one
# information about best match so far (m=-1 -> unmatched)
iou = min([t, 1 - 1e-10])
m = -1
for gind, _g in enumerate(gt_boxes): # iterate ground truth
# if this gt already matched, continue
if gt_match[tind, gind] > 0:
continue
# if dt matched to reg gt, and on ignore gt, stop
if m > -1 and gt_ignore[m] == 0 and gt_ignore[gind] == 1:
break
# continue to next gt unless better match made
if ious[dind, gind] < iou:
continue
# if match successful and best so far, store appropriately
iou = ious[dind, gind]
m = gind
# if match made, store id of match for both dt and gt
if m == -1:
continue
else:
dt_ignore[tind, dind] = int(gt_ignore[m])
dt_match[tind, dind] = 1
gt_match[tind, m] = 1
# store results for given image and category
return {
"dtMatches": dt_match, # [T, D], where T = number of thresholds, D = number of detections
"gtMatches": gt_match, # [T, G], where T = number of thresholds, G = number of ground truth
"dtScores": pred_scores, # [D] detection scores
"gtIgnore": gt_ignore.reshape(-1), # [G] indicate whether ground truth should be ignored
"dtIgnore": dt_ignore, # [T, D], indicate which detections should be ignored
}
```