def test__apply_threshold(od_sample_detection_bboxes):
""" Test `_apply_threshold` and verify it works at different thresholds. """
det_bboxes = _apply_threshold(od_sample_detection_bboxes, threshold=0.5)
assert len(det_bboxes) == 3
det_bboxes = _apply_threshold(od_sample_detection_bboxes, threshold=0.01)
assert len(det_bboxes) == 5
det_bboxes = _apply_threshold(od_sample_detection_bboxes, threshold=0.995)
assert len(det_bboxes) == 2
def od_sample_detection(od_sample_raw_preds, od_detection_mask_dataset):
labels = ["one", "two", "three", "four"]
detections = _extract_od_results(
_apply_threshold(od_sample_raw_preds[0], threshold=0.001),
labels,
od_detection_mask_dataset.im_paths[0],
)
detections["idx"] = 0
del detections["keypoints"]
return detections
def test__apply_threshold(od_sample_output):
""" Test `_apply_threshold` and verify it works at different thresholds. """
# test cases: [(threshold, num, mask_pixels)]
test_cases = [
(0.5, 3, (21146, 28098, 28458)),
(0.01, 5, (21146, 28098, 28458, 28356, 21311)),
(0.995, 2, (21146, 28098)),
]
res = {k: v.detach().cpu().numpy() for k, v in od_sample_output.items()}
for threshold, num, mask_pixels in test_cases:
pred = _apply_threshold(res, threshold=threshold)
for v in pred.values():
assert len(v) == num
for mask, num_pixels in zip(pred["masks"], mask_pixels):
assert np.sum(mask) == num_pixels
