def intersection(boxlist1, boxlist2):
"""Compute pairwise intersection areas between boxes.
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding M boxes
Returns:
a numpy array with shape [N*M] representing pairwise intersection area
"""
return np_box_ops.intersection(boxlist1.get(), boxlist2.get())
def intersection(boxlist1, boxlist2):
"""Compute pairwise intersection areas between boxes.
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding M boxes
Returns:
a numpy array with shape [N*M] representing pairwise intersection area
"""
return np_box_ops.intersection(boxlist1.get(), boxlist2.get())
def testIntersection(self):
intersection = np_box_ops.intersection(self.boxes1, self.boxes2)
expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]],
dtype=float)
self.assertAllClose(intersection, expected_intersection)
def testIntersection(self):
intersection = np_box_ops.intersection(self.boxes1, self.boxes2)
expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]],
dtype=float)
self.assertAllClose(intersection, expected_intersection)
def get_mtl_metrics(result_lists):
mtl_metrics = dict()
gt_boxes_list = result_lists[fields.InputDataFields.groundtruth_boxes]
detection_box_list = result_lists['detection_boxes']
b_window = False
b_closeness = False
b_edgemask = False
if 'window_classes_gt' in result_lists.keys():
window_classes_gt_list = result_lists['window_classes_gt']
window_classes_dt_list = result_lists['window_classes_dt']
b_window = True
if 'closeness_gt' in result_lists.keys():
closeness_gt_list = result_lists['closeness_gt']
closeness_dt_list = result_lists['closeness_dt']
b_closeness = True
if 'edgemask_gt' in result_lists.keys():
edgemask_gt_list = result_lists['edgemask_gt']
edgemask_dt_list = result_lists['edgemask_dt']
b_edgemask = True
if b_window:
map_list = []
for window_classes_gt, window_classes_dt in zip(
window_classes_gt_list, window_classes_dt_list):
ap_list = []
for window_class_gt, window_class_dt in zip(
window_classes_gt, window_classes_dt):
window_class_dt = _softmax(window_class_dt)
window_class_gt = [
float(val_str) for val_str in window_class_gt.split(' ')
]
scores = window_class_dt
tp_fp_labels = np.asarray([gt > 0 for gt in window_class_gt],
dtype=np.bool)
num_gt = int(np.sum(np.asarray(tp_fp_labels, dtype=np.int32)))
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels, num_gt)
average_precision = metrics.compute_average_precision(
precision, recall)
ap_list.append(average_precision)
map_list.append(float(np.mean(ap_list)))
window_map = float(np.mean(map_list))
mtl_metrics['mtl/window_map'] = window_map
gt_dt_index_list = []
for gt_boxes, dt_boxes in zip(gt_boxes_list, detection_box_list):
intersection = np_box_ops.intersection(gt_boxes, dt_boxes)
gt_dt_index = np.argmax(intersection, axis=1)
gt_dt_index_list.append(gt_dt_index)
if b_closeness:
diff_list = []
for closeness_gt, gt_dt_indices, closeness_dt_image in zip(
closeness_gt_list, gt_dt_index_list, closeness_dt_list):
ap_list = []
for gt, gt_dt_index in zip(closeness_gt, gt_dt_indices):
closeness_dt = _sigmoid(closeness_dt_image[gt_dt_index])
closeness_gt = np.asarray(
[float(val_str) for val_str in gt.split(' ')],
dtype=np.float32)
num_non_zeros = int(np.sum(closeness_gt != 0))
if num_non_zeros == 0:
continue
argmax_dt = np.argmax(closeness_dt[1:])
argmax_gt = np.argmax(closeness_gt[1:])
ap_list.append(float(argmax_dt == argmax_gt))
if ap_list:
diff_list.append(float(np.mean(ap_list)))
if diff_list:
closeness_diff = float(np.mean(diff_list))
else:
closeness_diff = 0.0
mtl_metrics['mtl/closeness_diff'] = closeness_diff
if b_edgemask:
ap_list = []
for edgemask_gt, edgemask_dt in zip(edgemask_gt_list,
edgemask_dt_list):
edgemask_gt = edgemask_gt[0]
edgemask_dt = edgemask_dt[0]
shape_gt = edgemask_gt.shape
edgemask_dt_resize = resize(edgemask_dt,
list(shape_gt) + [2]).astype(
np.float32)
edgemask_dt_resize = (edgemask_dt_resize[:, :, 0] <
edgemask_dt_resize[:, :, 1]).astype(
np.float32)
edgemask_precision = np.mean(edgemask_dt_resize == edgemask_gt)
ap_list.append(edgemask_precision)
if ap_list:
mtl_metrics['mtl/edgemask_ap'] = float(np.mean(ap_list))
else:
mtl_metrics['mtl/edgemask_ap'] = float(0)
return mtl_metrics
