def track_vis(gt_annos,dt_annos,output_dir, track_id):
root_path = dt_annos[0]["metadata"]["image_prefix"]
plt.style.use(['science','ieee','retro'])
gt_plt = plt.figure()
plt.xlim(-40,40)
plt.ylim(-40,40)
plt.rcParams['figure.dpi'] = 500
plt.rcParams['savefig.dpi'] = 500
gt_ax = gt_plt.add_subplot(1,1,1)
plt.style.use(['science','ieee','retro'])
dt_plt = plt.figure()
plt.xlim(-40,40)
plt.ylim(-40,40)
if gt_annos is None:
plt.xlim(-20,20)
plt.ylim(-20,20)
plt.rcParams['figure.dpi'] = 500
plt.rcParams['savefig.dpi'] = 500
dt_ax = dt_plt.add_subplot(1,1,1)
fig = mlab.figure(figure=None, bgcolor=(0,0,0), fgcolor=None, engine=None, size=(1600, 1000))
pcd = o3d.io.read_point_cloud("/Extra/zhangmeng/3d_detection/BBOX_x4_track/testing/Lidar/PC_0.pcd")
points = np.asarray(pcd.points)
fig = draw_lidar(points,color=None,fig=fig)
if gt_annos is not None:
overlap,_,_,_ = calculate_iou_partly(gt_annos,dt_annos,2,50,2,0.5)
dataset = lvx_object(root_path)
gt_image_idxes = [str(info["token"]) for info in gt_annos]
count = 0
for idx in gt_image_idxes:
gt_objects = dataset.get_label_objects(gt_annos[gt_image_idxes.index(idx)])
dt_objects = dataset.get_label_objects(dt_annos[gt_image_idxes.index(idx)])
show_trajectory(gt_objects,dt_objects,gt_ax,dt_ax,overlap[gt_image_idxes.index(idx)],track_id)
if count%5==0:
fig = draw_track_3d(dt_objects,fig,track_id)
count+=1
else:
dataset = lvx_object(root_path,'testing')
for count,dt_anno in enumerate(dt_annos):
idx = dt_anno['metadata']['token']
dt_objects = dataset.get_label_objects(dt_anno)
show_trajectory([],dt_objects,None,dt_ax,[],track_id)
gt_img_path = os.path.join(output_dir,f"gt_tracject_{track_id}.png")
gt_plt.savefig(gt_img_path)
dt_img_path = os.path.join(output_dir,f"dt_tracject_{track_id}.pdf")
dt_plt.savefig(dt_img_path)
mlab.savefig(gt_img_path)
mlab.clf()
def eval_class_v3(
gt_annos,
dt_annos,
current_classes,
difficultys,
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.
Args:
gt_annos: dict, must from get_label_annos() in kitti_common.py
dt_annos: dict, must from get_label_annos() in kitti_common.py
current_class: int, 0: car, 1: pedestrian, 2: cyclist
difficulty: int. eval difficulty, 0: easy, 1: normal, 2: hard
metric: eval type. 0: bbox, 1: bev, 2: 3d
min_overlap: float, min overlap. official:
[[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.
num_parts: int. a parameter for fast calculate algorithm
Returns:
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)
split_parts = [i for i in split_parts if i != 0]
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_minoverlap = len(min_overlaps)
num_class = len(current_classes)
num_difficulty = len(difficultys)
precision = np.zeros(
[num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
recall = np.zeros(
[num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
aos = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
all_thresholds = np.zeros(
[num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
for m, current_class in enumerate(current_classes):
for l, difficulty in enumerate(difficultys):
rets = prepare_data(
gt_annos,
dt_annos,
current_class,
difficulty=difficulty,
clean_data=clean_data,
)
(
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=0.0,
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)
# print(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)):
# recall[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
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)
# use interp to calculate recall
"""
current_recalls = np.linspace(0, 1, 41)
prec_unique, inds = np.unique(precision[m, l, k], return_index=True)
current_recalls = current_recalls[inds]
f = interp1d(prec_unique, current_recalls)
precs_for_recall = np.linspace(0, 1, 41)
max_prec = np.max(precision[m, l, k])
valid_prec = precs_for_recall < max_prec
num_valid_prec = valid_prec.sum()
recall[m, l, k, :num_valid_prec] = f(precs_for_recall[valid_prec])
"""
ret_dict = {
"recall":
recall, # [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS]
"precision": precision,
"orientation": aos,
"thresholds": all_thresholds,
"min_overlaps": min_overlaps,
}
return ret_dict
def lvx_vis(gt_annos,dt_annos,output_dir):
root_path = dt_annos[0]["metadata"]["image_prefix"]
if gt_annos is not None:
miss = -3*np.ones((22,len(gt_annos)))
overlap,_,_,_ = calculate_iou_partly(gt_annos,dt_annos,2,50,2,0.5)
dataset = lvx_object(root_path)
gt_image_idxes = [str(info["token"]) for info in gt_annos]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vot = cv2.VideoWriter(os.path.join(output_dir,"bev_video.mp4"),fourcc,10,(3200,2400),True)
for idx in gt_image_idxes:
print(idx)
points = dataset.get_lidar(idx)
points = points[points[:,1]<40,:]
points = points[points[:,0]<40,:]
points = points[points[:,1]>-40,:]
points = points[points[:,0]>-40,:]
points = points[points[:,2]>0.1,:]
points_1 = dataset.get_lidar(f'{int(idx)-1}')
points_1 = points_1[points_1[:,1]<40,:]
points_1 = points_1[points_1[:,0]<40,:]
points_1 = points_1[points_1[:,1]>-40,:]
points_1 = points_1[points_1[:,0]>-40,:]
points_1 = points_1[points_1[:,2]>0.1,:]
if (gt_image_idxes.index(idx) < len(gt_image_idxes)-1):
points_2 = dataset.get_lidar(f'{int(idx)+1}')
points_2 = points_2[points_2[:,1]<40,:]
points_2 = points_2[points_2[:,0]<40,:]
points_2 = points_2[points_2[:,1]>-40,:]
points_2 = points_2[points_2[:,0]>-40,:]
points_2 = points_2[points_2[:,2]>0.1,:]
else:
points_2 = []
gt_objects = dataset.get_label_objects(gt_annos[gt_image_idxes.index(idx)])
dt_objects = dataset.get_label_objects(dt_annos[gt_image_idxes.index(idx)])
if not os.path.exists(os.path.join(output_dir,"bev_imgs")):
os.makedirs(os.path.join(output_dir,"bev_imgs"))
img_path = os.path.join(output_dir,f"bev_imgs/lvx_{idx}.png")
show_bev_objects(points,points_1,points_2,gt_objects,dt_objects,img_path,overlap[gt_image_idxes.index(idx)],miss[:,gt_image_idxes.index(idx)])
img = cv2.imread(img_path)
vot.write(img)
vot.release()
np.savetxt(os.path.join(output_dir,'miss.txt'),miss,fmt="%d")
else:
dataset = lvx_object(root_path,split="testing")
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vot = cv2.VideoWriter(os.path.join(output_dir,"beicao_video.mp4"),fourcc,10,(3200,2400),True)
for count,dt_anno in enumerate(dt_annos):
idx = dt_anno['metadata']['token']
print(idx)
points = dataset.get_lidar(idx)
points = points[points[:,1]<40,:]
points = points[points[:,0]<40,:]
points = points[points[:,1]>-40,:]
points = points[points[:,0]>-40,:]
points = points[points[:,2]>0.1,:]
points_1 = dataset.get_lidar(f'{int(idx)-1}')
points_1 = points_1[points_1[:,1]<40,:]
points_1 = points_1[points_1[:,0]<40,:]
points_1 = points_1[points_1[:,1]>-40,:]
points_1 = points_1[points_1[:,0]>-40,:]
points_1 = points_1[points_1[:,2]>0.1,:]
if (count < len(dt_annos)-1):
points_2 = dataset.get_lidar(f'{int(idx)+1}')
points_2 = points_2[points_2[:,1]<40,:]
points_2 = points_2[points_2[:,0]<40,:]
points_2 = points_2[points_2[:,1]>-40,:]
points_2 = points_2[points_2[:,0]>-40,:]
points_2 = points_2[points_2[:,2]>0.1,:]
else:
points_2 = []
dt_objects = dataset.get_label_objects(dt_anno)
if not os.path.exists(os.path.join(output_dir,"beicao_imgs")):
os.makedirs(os.path.join(output_dir,"beicao_imgs"))
img_path = os.path.join(output_dir,f"beicao_imgs/lvx_{idx}.png")
show_bev_objects(points,points_1,points_2,[],dt_objects,img_path,None, None)
img = cv2.imread(img_path)
vot.write(img)
vot.release()