def test_itself(iou_threshold):
"""Same boxes, with all scores being the same should give 1 as mAP"""
with open("tests/test_jsons/samples_merged.json") as f:
list_boxes = json.load(f)
class_name = "car"
for item in list_boxes:
item["score"] = 1
gt_by_class_name = group_by_key(list_boxes, "name")
pred_by_class_name = group_by_key(list_boxes, "name")
recalls, precisions, average_precision = recall_precision(
gt_by_class_name[class_name], pred_by_class_name[class_name],
iou_threshold)
assert average_precision == 1
def calc_map(gt_file, pred_file, iou_threshold=0.5):
with open(pred_file) as f:
predictions = json.load(f)
with open(gt_file) as f:
gt = json.load(f)
gt_by_class_name = group_by_key(gt, "name")
pred_by_class_name = group_by_key(predictions, "name")
class_names = sorted(gt_by_class_name.keys())
average_precisions = np.zeros(len(class_names))
for class_id, class_name in enumerate(class_names):
if class_name in pred_by_class_name:
_, _, avg_prec = recall_precision(gt_by_class_name[class_name],
pred_by_class_name[class_name],
iou_threshold)
average_precisions[class_id] = avg_prec
mAP = np.mean(average_precisions)
return mAP
def _recall_precision(gt, pred, th, class_name):
recalls, precisions, average_precision = recall_precision(gt, pred, th)
return class_name, average_precision