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eval.py
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eval.py
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# ref: https://github.com/traveller59/kitti-object-eval-python/blob/master/eval.py
import io as sysio
import numba
import numpy as np
from .iou import rotate_iou_gpu_eval
__all__ = ['get_official_eval_result']
def get_map(prec):
sums = 0
for i in range(0, prec.shape[-1], 4):
sums = sums + prec[..., i]
return sums / 11 * 100
def get_split_parts(num, num_part):
same_part = num // num_part
remain_num = num % num_part
if remain_num == 0:
return [same_part] * num_part
else:
return [same_part] * num_part + [remain_num]
@numba.jit(nopython=True)
def image_box_overlap(boxes, query_boxes, criterion=-1):
N = boxes.shape[0]
K = query_boxes.shape[0]
overlaps = np.zeros((N, K), dtype=boxes.dtype)
for k in range(K):
qbox_area = (query_boxes[k, 2] - query_boxes[k, 0]) * (query_boxes[k, 3] - query_boxes[k, 1])
for n in range(N):
iw = (min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]))
if iw > 0:
ih = (min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]))
if ih > 0:
if criterion == -1:
ua = (boxes[n, 2] - boxes[n, 0]) * (boxes[n, 3] - boxes[n, 1]) + qbox_area - iw * ih
elif criterion == 0:
ua = (boxes[n, 2] - boxes[n, 0]) * (boxes[n, 3] - boxes[n, 1])
elif criterion == 1:
ua = qbox_area
else:
ua = 1.0
overlaps[n, k] = iw * ih / ua
return overlaps
def bev_box_overlap(boxes, qboxes, criterion=-1):
return rotate_iou_gpu_eval(boxes, qboxes, criterion)
@numba.jit(nopython=True)
def d3_box_overlap_kernel(boxes, qboxes, rinc, criterion=-1, z_axis=1, z_center=1.0):
"""
:param boxes:
:param qboxes:
:param rinc:
:param criterion:
:param z_axis: the z (height) axis
:param z_center: unified z (height) center of box
:return:
"""
N, K = boxes.shape[0], qboxes.shape[0]
for i in range(N):
for j in range(K):
if rinc[i, j] > 0:
min_z = min(boxes[i, z_axis] + boxes[i, z_axis + 3] * (1 - z_center),
qboxes[j, z_axis] + qboxes[j, z_axis + 3] * (1 - z_center))
max_z = max(boxes[i, z_axis] - boxes[i, z_axis + 3] * z_center,
qboxes[j, z_axis] - qboxes[j, z_axis + 3] * z_center)
iw = min_z - max_z
if iw > 0:
area1 = boxes[i, 3] * boxes[i, 4] * boxes[i, 5]
area2 = qboxes[j, 3] * qboxes[j, 4] * qboxes[j, 5]
inc = iw * rinc[i, j]
if criterion == -1:
ua = (area1 + area2 - inc)
elif criterion == 0:
ua = area1
elif criterion == 1:
ua = area2
else:
ua = 1.0
rinc[i, j] = inc / ua
else:
rinc[i, j] = 0.0
def d3_box_overlap(boxes, qboxes, criterion=-1, z_axis=1, z_center=1.0):
"""
kitti camera format z_axis=1.
"""
bev_axes = list(range(7))
bev_axes.pop(z_axis + 3)
bev_axes.pop(z_axis)
rinc = rotate_iou_gpu_eval(boxes[:, bev_axes], qboxes[:, bev_axes], 2)
d3_box_overlap_kernel(boxes, qboxes, rinc, criterion, z_axis, z_center)
return rinc
def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50, z_axis=1, z_center=1.0):
"""
fast iou algorithm. this function can be used independently to do result analysis.
:param gt_annos: must from get_label_annos() in kitti_common.py, dict
:param dt_annos: must from get_label_annos() in kitti_common.py, dict
:param metric: eval type, 0: bbox, 1: bev, 2: 3d
:param num_parts: a parameter for fast calculate algorithm, int
:param z_axis: height axis, kitti camera use 1, lidar use 2.
:param z_center:
:return:
"""
assert len(gt_annos) == len(dt_annos)
total_dt_num = np.stack([len(a['name']) for a in dt_annos], 0)
total_gt_num = np.stack([len(a['name']) for a in gt_annos], 0)
num_examples = len(gt_annos)
split_parts = get_split_parts(num_examples, num_parts)
parted_overlaps = []
example_idx = 0
bev_axes = list(range(3))
bev_axes.pop(z_axis)
for num_part in split_parts:
gt_annos_part = gt_annos[example_idx:example_idx + num_part]
dt_annos_part = dt_annos[example_idx:example_idx + num_part]
if metric == 0:
gt_boxes = np.concatenate([a['bbox'] for a in gt_annos_part], 0)
dt_boxes = np.concatenate([a['bbox'] for a in dt_annos_part], 0)
overlap_part = image_box_overlap(gt_boxes, dt_boxes)
elif metric == 1:
loc = np.concatenate([a['location'][:, bev_axes] for a in gt_annos_part], 0)
dims = np.concatenate([a['dimensions'][:, bev_axes] for a in gt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0)
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1)
loc = np.concatenate([a['location'][:, bev_axes] for a in dt_annos_part], 0)
dims = np.concatenate([a['dimensions'][:, bev_axes] for a in dt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1)
overlap_part = bev_box_overlap(gt_boxes, dt_boxes).astype(np.float64)
elif metric == 2:
loc = np.concatenate([a['location'] for a in gt_annos_part], 0)
dims = np.concatenate([a['dimensions'] for a in gt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0)
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1)
loc = np.concatenate([a['location'] for a in dt_annos_part], 0)
dims = np.concatenate([a['dimensions'] for a in dt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1)
overlap_part = d3_box_overlap(gt_boxes, dt_boxes, z_axis=z_axis, z_center=z_center).astype(np.float64)
else:
raise ValueError('unknown metric')
parted_overlaps.append(overlap_part)
example_idx += num_part
overlaps = []
example_idx = 0
for j, num_part in enumerate(split_parts):
gt_num_idx, dt_num_idx = 0, 0
for i in range(num_part):
gt_box_num = total_gt_num[example_idx + i]
dt_box_num = total_dt_num[example_idx + i]
overlaps.append(parted_overlaps[j][gt_num_idx:gt_num_idx + gt_box_num, dt_num_idx:dt_num_idx + dt_box_num])
gt_num_idx += gt_box_num
dt_num_idx += dt_box_num
example_idx += num_part
return overlaps, parted_overlaps, total_gt_num, total_dt_num
def clean_data(gt_anno, dt_anno, current_class, difficulty):
_class_names = ['car', 'pedestrian', 'cyclist', 'van', 'person_sitting', 'car', 'tractor', 'trailer']
_min_height = [40, 25, 25]
_max_occlusion = [0, 1, 2]
_max_truncation = [0.15, 0.3, 0.5]
dc_bboxes, ignored_gt, ignored_dt = [], [], []
current_cls_name = _class_names[current_class].lower()
num_gt = len(gt_anno['name'])
num_dt = len(dt_anno['name'])
num_valid_gt = 0
for i in range(num_gt):
bbox = gt_anno['bbox'][i]
gt_name = gt_anno['name'][i].lower()
height = bbox[3] - bbox[1]
if gt_name == current_cls_name:
valid_class = 1
elif current_cls_name == 'Pedestrian'.lower() and 'Person_sitting'.lower() == gt_name:
valid_class = 0
elif current_cls_name == 'Car'.lower() and 'Van'.lower() == gt_name:
valid_class = 0
else:
valid_class = -1
ignore = False
if ((gt_anno['occluded'][i] > _max_occlusion[difficulty])
or (gt_anno['truncated'][i] > _max_truncation[difficulty])
or (height <= _min_height[difficulty])):
ignore = True
if valid_class == 1 and not ignore:
ignored_gt.append(0)
num_valid_gt += 1
elif valid_class == 0 or (ignore and (valid_class == 1)):
ignored_gt.append(1)
else:
ignored_gt.append(-1)
if gt_anno['name'][i] == 'DontCare':
dc_bboxes.append(gt_anno['bbox'][i])
for i in range(num_dt):
if dt_anno['name'][i].lower() == current_cls_name:
valid_class = 1
else:
valid_class = -1
height = abs(dt_anno['bbox'][i, 3] - dt_anno['bbox'][i, 1])
if height < _min_height[difficulty]:
ignored_dt.append(1)
elif valid_class == 1:
ignored_dt.append(0)
else:
ignored_dt.append(-1)
return num_valid_gt, ignored_gt, ignored_dt, dc_bboxes
def _prepare_data(gt_annos, dt_annos, current_class, difficulty):
gt_datas_list = []
dt_datas_list = []
total_dc_num = []
ignored_gts, ignored_dets, dontcares = [], [], []
total_num_valid_gt = 0
for i in range(len(gt_annos)):
rets = clean_data(gt_annos[i], dt_annos[i], current_class, difficulty)
num_valid_gt, ignored_gt, ignored_det, dc_bboxes = rets
ignored_gts.append(np.array(ignored_gt, dtype=np.int64))
ignored_dets.append(np.array(ignored_det, dtype=np.int64))
if len(dc_bboxes) == 0:
dc_bboxes = np.zeros((0, 4)).astype(np.float64)
else:
dc_bboxes = np.stack(dc_bboxes, 0).astype(np.float64)
total_dc_num.append(dc_bboxes.shape[0])
dontcares.append(dc_bboxes)
total_num_valid_gt += num_valid_gt
gt_datas = np.concatenate([gt_annos[i]['bbox'], gt_annos[i]['alpha'][..., np.newaxis]], 1)
dt_datas = np.concatenate([dt_annos[i]['bbox'], dt_annos[i]['alpha'][..., np.newaxis],
dt_annos[i]['score'][..., np.newaxis]], 1)
gt_datas_list.append(gt_datas)
dt_datas_list.append(dt_datas)
total_dc_num = np.stack(total_dc_num, axis=0)
return gt_datas_list, dt_datas_list, ignored_gts, ignored_dets, dontcares, total_dc_num, total_num_valid_gt
@numba.jit(nopython=True)
def compute_statistics_jit(overlaps, gt_datas, dt_datas, ignored_gt, ignored_det, dc_bboxes, metric, min_overlap,
thresh=0, compute_fp=False, compute_aos=False):
det_size = dt_datas.shape[0]
gt_size = gt_datas.shape[0]
dt_scores = dt_datas[:, -1]
dt_alphas = dt_datas[:, 4]
gt_alphas = gt_datas[:, 4]
dt_bboxes = dt_datas[:, :4]
assigned_detection = [False] * det_size
ignored_threshold = [False] * det_size
if compute_fp:
for i in range(det_size):
if dt_scores[i] < thresh:
ignored_threshold[i] = True
_no_detection = -10000000
tp, fp, fn, similarity = 0, 0, 0, 0
thresholds = np.zeros((gt_size, ))
thresh_idx = 0
delta = np.zeros((gt_size, ))
delta_idx = 0
for i in range(gt_size):
if ignored_gt[i] == -1:
continue
det_idx = -1
valid_detection = _no_detection
max_overlap = 0
assigned_ignored_det = False
for j in range(det_size):
if ignored_det[j] == -1:
continue
if assigned_detection[j]:
continue
if ignored_threshold[j]:
continue
overlap = overlaps[j, i]
dt_score = dt_scores[j]
if not compute_fp and (overlap > min_overlap) and dt_score > valid_detection:
det_idx = j
valid_detection = dt_score
elif (compute_fp and (overlap > min_overlap) and (overlap > max_overlap or assigned_ignored_det)
and ignored_det[j] == 0):
max_overlap = overlap
det_idx = j
valid_detection = 1
assigned_ignored_det = False
elif compute_fp and (overlap > min_overlap) and (valid_detection == _no_detection) and ignored_det[j] == 1:
det_idx = j
valid_detection = 1
assigned_ignored_det = True
if (valid_detection == _no_detection) and ignored_gt[i] == 0:
fn += 1
elif (valid_detection != _no_detection) and (ignored_gt[i] == 1 or ignored_det[det_idx] == 1):
assigned_detection[det_idx] = True
elif valid_detection != _no_detection:
# only a tp add a threshold.
tp += 1
thresholds[thresh_idx] = dt_scores[det_idx]
thresh_idx += 1
if compute_aos:
delta[delta_idx] = gt_alphas[i] - dt_alphas[det_idx]
delta_idx += 1
assigned_detection[det_idx] = True
if compute_fp:
for i in range(det_size):
if not (assigned_detection[i] or ignored_det[i] == -1 or ignored_det[i] == 1 or ignored_threshold[i]):
fp += 1
nstuff = 0
if metric == 0:
overlaps_dt_dc = image_box_overlap(dt_bboxes, dc_bboxes, 0)
for i in range(dc_bboxes.shape[0]):
for j in range(det_size):
if assigned_detection[j]:
continue
if ignored_det[j] == -1 or ignored_det[j] == 1:
continue
if ignored_threshold[j]:
continue
if overlaps_dt_dc[j, i] > min_overlap:
assigned_detection[j] = True
nstuff += 1
fp -= nstuff
if compute_aos:
tmp = np.zeros((fp + delta_idx, ))
for i in range(delta_idx):
tmp[i + fp] = (1.0 + np.cos(delta[i])) / 2.0
if tp > 0 or fp > 0:
similarity = np.sum(tmp)
else:
similarity = -1
return tp, fp, fn, similarity, thresholds[:thresh_idx]
@numba.jit
def get_thresholds(scores: np.ndarray, num_gt, num_sample_pts=41):
scores.sort()
scores = scores[::-1]
current_recall = 0
thresholds = []
for i, score in enumerate(scores):
l_recall = (i + 1) / num_gt
if i < (len(scores) - 1):
r_recall = (i + 2) / num_gt
else:
r_recall = l_recall
if (((r_recall - current_recall) < (current_recall - l_recall))
and (i < (len(scores) - 1))):
continue
thresholds.append(score)
current_recall += 1 / (num_sample_pts - 1.0)
return thresholds
@numba.jit(nopython=True)
def fused_compute_statistics(overlaps, pr, gt_nums, dt_nums, dc_nums, gt_datas, dt_datas, dontcares,
ignored_gts, ignored_dets, metric, min_overlap, thresholds, compute_aos=False):
gt_num = 0
dt_num = 0
dc_num = 0
for i in range(gt_nums.shape[0]):
for t, thresh in enumerate(thresholds):
overlap = overlaps[dt_num:dt_num + dt_nums[i], gt_num:gt_num + gt_nums[i]]
gt_data = gt_datas[gt_num:gt_num + gt_nums[i]]
dt_data = dt_datas[dt_num:dt_num + dt_nums[i]]
ignored_gt = ignored_gts[gt_num:gt_num + gt_nums[i]]
ignored_det = ignored_dets[dt_num:dt_num + dt_nums[i]]
dontcare = dontcares[dc_num:dc_num + dc_nums[i]]
tp, fp, fn, similarity, _ = compute_statistics_jit(
overlap, gt_data, dt_data, ignored_gt, ignored_det, dontcare, metric,
min_overlap=min_overlap, thresh=thresh, compute_fp=True, compute_aos=compute_aos)
pr[t, 0] += tp
pr[t, 1] += fp
pr[t, 2] += fn
if similarity != -1:
pr[t, 3] += similarity
gt_num += gt_nums[i]
dt_num += dt_nums[i]
dc_num += dc_nums[i]
def eval_class(gt_annos, dt_annos, current_classes, difficulties, metric, min_overlaps, compute_aos=False, z_axis=1,
z_center=1.0, num_parts=50):
"""
Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.
:param gt_annos: must from get_label_annos() in kitti_common.py, dict
:param dt_annos: must from get_label_annos() in kitti_common.py, dict
:param current_classes: 0: car, 1: pedestrian, 2: cyclist, int
:param difficulties: eval difficulty, 0: easy, 1: normal, 2: hard, int
:param metric: eval type, 0: bbox, 1: bev, 2: 3d, int
:param min_overlaps: [[0.7, 0.5, 0.5], [0.7, 0.5, 0.5], [0.7, 0.5, 0.5]] format: [metric, class]
choose one from matrix above, float
:param compute_aos:
:param z_axis:
:param z_center:
:param num_parts:
:return: dict of recall, precision and aos
"""
assert len(gt_annos) == len(dt_annos)
num_examples = len(gt_annos)
split_parts = get_split_parts(num_examples, num_parts)
rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts, z_axis=z_axis, z_center=z_center)
overlaps, parted_overlaps, total_dt_num, total_gt_num = rets
_n_sample_pts = 41
num_min_overlap = len(min_overlaps)
num_class = len(current_classes)
num_difficulty = len(difficulties)
precision = np.zeros([num_class, num_difficulty, num_min_overlap, _n_sample_pts])
aos = np.zeros([num_class, num_difficulty, num_min_overlap, _n_sample_pts])
all_thresholds = np.zeros([num_class, num_difficulty, num_min_overlap, _n_sample_pts])
for m, current_class in enumerate(current_classes):
for l, difficulty in enumerate(difficulties):
rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)
(gt_datas_list, dt_datas_list, ignored_gts, ignored_dets,
dontcares, total_dc_num, total_num_valid_gt) = rets
for k, min_overlap in enumerate(min_overlaps[:, metric, m]):
thresholdss = []
for i in range(len(gt_annos)):
rets = compute_statistics_jit(overlaps[i], gt_datas_list[i], dt_datas_list[i], ignored_gts[i],
ignored_dets[i], dontcares[i], metric, min_overlap=min_overlap,
thresh=0, compute_fp=False)
tp, fp, fn, similarity, thresholds = rets
thresholdss += thresholds.tolist()
thresholdss = np.array(thresholdss)
thresholds = get_thresholds(thresholdss, total_num_valid_gt)
thresholds = np.array(thresholds)
all_thresholds[m, l, k, :len(thresholds)] = thresholds
pr = np.zeros([len(thresholds), 4])
idx = 0
for j, num_part in enumerate(split_parts):
gt_datas_part = np.concatenate(gt_datas_list[idx:idx + num_part], 0)
dt_datas_part = np.concatenate(dt_datas_list[idx:idx + num_part], 0)
dc_datas_part = np.concatenate(dontcares[idx:idx + num_part], 0)
ignored_dets_part = np.concatenate(ignored_dets[idx:idx + num_part], 0)
ignored_gts_part = np.concatenate(ignored_gts[idx:idx + num_part], 0)
fused_compute_statistics(parted_overlaps[j], pr, total_gt_num[idx:idx + num_part],
total_dt_num[idx:idx + num_part], total_dc_num[idx:idx + num_part],
gt_datas_part, dt_datas_part, dc_datas_part, ignored_gts_part,
ignored_dets_part, metric, min_overlap=min_overlap, thresholds=thresholds,
compute_aos=compute_aos)
idx += num_part
for i in range(len(thresholds)):
precision[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])
if compute_aos:
aos[m, l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
for i in range(len(thresholds)):
precision[m, l, k, i] = np.max(
precision[m, l, k, i:], axis=-1)
if compute_aos:
aos[m, l, k, i] = np.max(aos[m, l, k, i:], axis=-1)
ret_dict = {'precision': precision, 'orientation': aos, 'thresholds': all_thresholds, 'min_overlaps': min_overlaps}
return ret_dict
def do_eval(gt_annos, dt_annos, current_classes, min_overlaps, compute_aos=False, difficulties=(0, 1, 2),
z_axis=1, z_center=1.0):
types = ['bbox', 'bev', '3d']
metrics = {}
for i in range(3):
metrics[types[i]] = eval_class(gt_annos, dt_annos, current_classes, difficulties, i, min_overlaps, compute_aos,
z_axis=z_axis, z_center=z_center)
return metrics
def print_str(value, *arg, sstream=None):
if sstream is None:
sstream = sysio.StringIO()
sstream.truncate(0)
sstream.seek(0)
print(value, *arg, file=sstream)
return sstream.getvalue()
def get_official_eval_result(gt_annos, dt_annos, current_classes, difficulties=(0, 1, 2), z_axis=1, z_center=1.0):
"""
:param gt_annos: must contains following keys: [bbox, location, dimensions, rotation_y, score]
:param dt_annos: must contains following keys: [bbox, location, dimensions, rotation_y, score]
:param current_classes:
:param difficulties:
:param z_axis:
:param z_center:
:return:
"""
min_overlaps = np.array([[[0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
[0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
[0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7]]])
class_to_name = {
0: 'Car',
1: 'Pedestrian',
2: 'Cyclist',
3: 'Van',
4: 'Person_sitting',
5: 'car',
6: 'tractor',
7: 'trailer',
}
name_to_class = {v: n for n, v in class_to_name.items()}
if not isinstance(current_classes, (list, tuple)):
current_classes = [current_classes]
current_classes_int = []
for cur_cls in current_classes:
if isinstance(cur_cls, str):
current_classes_int.append(name_to_class[cur_cls])
else:
current_classes_int.append(cur_cls)
current_classes = current_classes_int
min_overlaps = min_overlaps[:, :, current_classes]
# check whether alpha is valid
compute_aos = False
for anno in dt_annos:
if anno['alpha'].shape[0] != 0:
if anno['alpha'][0] != -10:
compute_aos = True
break
metrics = do_eval(gt_annos, dt_annos, current_classes, min_overlaps, compute_aos, difficulties,
z_axis=z_axis, z_center=z_center)
results_str = ''
results = dict()
for j, cur_cls in enumerate(current_classes):
cur_cls_name = class_to_name[cur_cls]
# mAP threshold array: [num_min_overlap, metric, class]
# mAP result: [num_class, num_diff, num_min_overlap]
map_bbox = get_map(metrics['bbox']['precision'][j, :, 0])
map_bbox_str = ', '.join(f'{v:.2f}' for v in map_bbox)
map_bev = get_map(metrics['bev']['precision'][j, :, 0])
map_bev_str = ', '.join(f'{v:.2f}' for v in map_bev)
map_3d = get_map(metrics['3d']['precision'][j, :, 0])
map_3d_str = ', '.join(f'{v:.2f}' for v in map_3d)
results_str += print_str((f'{cur_cls_name}'
' AP(Average Precision)@{:.2f}, {:.2f}, {:.2f}:'.format(*min_overlaps[0, :, j])))
results_str += print_str(f'bbox AP:{map_bbox_str}')
results_str += print_str(f'bev AP:{map_bev_str}')
results_str += print_str(f'3d AP:{map_3d_str}')
if compute_aos:
map_aos = get_map(metrics['bbox']['orientation'][j, :, 0])
map_aos = ', '.join(f'{v:.2f}' for v in map_aos)
results_str += print_str(f'aos AP:{map_aos}')
results[cur_cls_name] = {'bbox': map_bbox, 'bev': map_bev, '3d': map_3d}
return metrics, results, results_str