def match(boxes, prior_boxes, iou_threshold=0.5):
"""Matches each prior box with a ground truth box (box from `boxes`).
It then selects which matched box will be considered positive e.g. iou > .5
and returns for each prior box a ground truth box that is either positive
(with a class argument different than 0) or negative.
# Arguments
boxes: Numpy array of shape `(num_ground_truh_boxes, 4 + 1)`,
where the first the first four coordinates correspond to
box coordinates and the last coordinates is the class
argument. This boxes should be the ground truth boxes.
prior_boxes: Numpy array of shape `(num_prior_boxes, 4)`.
where the four coordinates are in center form coordinates.
iou_threshold: Float between [0, 1]. Intersection over union
used to determine which box is considered a positive box.
# Returns
numpy array of shape `(num_prior_boxes, 4 + 1)`.
where the first the first four coordinates correspond to point
form box coordinates and the last coordinates is the class
argument.
"""
ious = compute_ious(boxes, to_corner_form(np.float32(prior_boxes)))
per_prior_which_box_iou = np.max(ious, axis=0)
per_prior_which_box_arg = np.argmax(ious, 0)
# overwriting per_prior_which_box_arg if they are the best prior box
per_box_which_prior_arg = np.argmax(ious, 1)
per_prior_which_box_iou[per_box_which_prior_arg] = 2
for box_arg in range(len(per_box_which_prior_arg)):
best_prior_box_arg = per_box_which_prior_arg[box_arg]
per_prior_which_box_arg[best_prior_box_arg] = box_arg
matches = boxes[per_prior_which_box_arg]
matches[per_prior_which_box_iou < iou_threshold, 4] = 0
return matches
def test_compute_ious_shape(boxes):
box_A, box_B = boxes
ious = compute_ious(box_A, box_B)
target_shape = (box_A.shape[0], box_B.shape[0])
assert ious.shape == target_shape
def test_compute_ious(boxes, target):
box_A, box_B = boxes
result = compute_ious(box_A[1:2, :], box_B)
assert np.allclose(result, target)