def test_mean_average_precision_6():
"""
Multiple wrong prediction because of wrong location (box coordinates), but all with higher scores.
In this case AP should be the precision value @recall=1.0
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.8)
pred2 = PredictedBoundingBox(
clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.85
)
pred3 = PredictedBoundingBox(
clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.86
)
pred4 = PredictedBoundingBox(
clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.87
)
pred5 = PredictedBoundingBox(
clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.88
)
pred6 = PredictedBoundingBox(
clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.89
)
pred7 = PredictedBoundingBox(clazz="a", box=np.array([50, 50, 60, 60]), image_id="1", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2, pred3, pred4, pred5, pred6, pred7}).mAP(
iou_threshold=0.5
)
assert math.isclose(mAP, 1 / 7)
def test_mean_average_precision_8():
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([11, 11, 20, 20]), image_id="1", score=0.9)
pred2 = PredictedBoundingBox(clazz="b", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 0.5
def test_mean_average_precision_5():
"""
One wrong prediction because of wrong location (image_id), but has higher score.
In this case AP should be the precision value @recall=1.0
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.8)
pred2 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="2", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 0.5
def test_mean_average_precision_4():
"""
One wrong prediction because of wrong location (image_id), but has lower score.
In this case AP is still 1.0 because the wrong prediction has lower score
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
pred2 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="2", score=0.8)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 1.0
def test_mean_average_precision_3():
"""
No correct predictions because the class were wrong.
AP should be 0.
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="c", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
pred2 = PredictedBoundingBox(clazz="b", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 0.0
def mAP_from_file(gt_path, prediction_path, str_format, iou_threshold):
with open(prediction_path) as fh:
pred_boxes = {
string_to_boundingbox(l, "pred", str_format="xywh") for l in fh.readlines()
}
with open(gt_path) as fh:
gt_boxes = {
string_to_boundingbox(l, "gt", str_format="xywh") for l in fh.readlines()
}
return BoxMeanAveragePrecision(gt_boxes, pred_boxes).mAP(iou_threshold=iou_threshold)
def test_mean_average_precision_2():
"""
One correct box and one wrong box which got the class wrong
In this case AP should be 0.5 because:
for class "a" AP is 1.0
for class "b" AP is 0.0
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
pred2 = PredictedBoundingBox(clazz="b", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 0.5
def test_mean_average_precision():
"""
Two correct predictions for one ground truths.
In this case even though there should only be one prediction for one ground truth,
the AP should still be 1.0 because:
precision is 1.0 @ recall=1.0
"""
gt = GroundTruthBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1")
pred1 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
pred2 = PredictedBoundingBox(clazz="a", box=np.array([10, 10, 20, 20]), image_id="1", score=0.9)
mAP = BoxMeanAveragePrecision({gt}, {pred1, pred2}).mAP(iou_threshold=0.5)
assert mAP == 1.0