def convert_app(det_seq, trk_seq, save_path):
for fr_idx, (det_frm, trk_frm) in enumerate(zip(det_seq, trk_seq)):
cam_calib = np.array(det_frm['intrinsics']['cali'])
for det_idx, det_out in enumerate(det_frm['prediction']):
depth_pd = det_out['box3d']['location'][2]
for td in trk_frm:
match = np.where(
np.sum(bh.get_box2d_array([det_out]) == np.array(
td['det_box']),
axis=1) == 5)[0]
if len(match):
#print(match, depth_pd, td['depth'], det_out['box3d']['xc'], td['x'], det_out['box3d']['yc'], td['y'])
depth_pd = td['depth']
det_out['id'] = td['id']
det_out['box3d']['xc'] = td['x']
det_out['box3d']['yc'] = td['y']
# In camera coordinates
location = tu.imagetocamera(
np.array([[det_out['box3d']['xc'], det_out['box3d']['yc']]]),
np.array([depth_pd]), cam_calib)
det_out['box3d']['location'] = location.tolist()
print("Saving updated tracking results with {} frames at {}...".format(
len(det_seq), save_path))
dump_output_json(save_path, det_seq)
def convert_app(det_placeholder, det_out):
if len(det_out['rois_pd']):
depth_pd = det_out['depth_pd'].copy()
rot_y = tu.alpha2rot_y(det_out['alpha_pd'],
det_out['center_pd'][:, 0] - cfg.GTA.W // 2,
cfg.GTA.FOCAL_LENGTH)
# In camera coordinates
location = tu.imagetocamera(det_out['center_pd'],
depth_pd,
det_out['cam_calib'])
for idx, prediction in enumerate(det_placeholder['prediction']):
if len(det_out['rois_pd']):
pred_box2d = bh.get_box2d_array([prediction])
pred_cen = bh.get_cen_array([prediction])
roi_match = np.sum(det_out['rois_pd'] == pred_box2d, axis=1) == 5
cen_match = np.sum(det_out['center_pd'] == pred_cen, axis=1) == 2
match = np.where(roi_match * cen_match)[0]
if len(match) == 0:
continue
box = det_out['rois_pd'][match].reshape(5)
cen = det_out['center_pd'][match].reshape(2)
alpha = det_out['alpha_pd'][match].reshape(1)
dim = det_out['dim_pd'][match].reshape(3)
orient = rot_y[match].reshape(1)
loc = location[match].reshape(3)
prediction['box2d']['x1'] = int(box[0])
prediction['box2d']['y1'] = int(box[1])
prediction['box2d']['x2'] = int(box[2])
prediction['box2d']['y2'] = int(box[3])
prediction['box2d']['confidence'] = float(box[4])
# 3D box projected center
prediction['box3d']['xc'] = int(cen[0])
prediction['box3d']['yc'] = int(cen[1])
prediction['box3d']['alpha'] = float(alpha)
prediction['box3d']['dimension'] = dim.tolist()
prediction['box3d']['location'] = loc.tolist()
prediction['box3d']['orientation'] = orient.tolist()
return det_placeholder
def get_3d_box_from_2d(box_2d_cen, depth, rot_y, dim, cam_calib, cam_pose):
points_cam = tu.imagetocamera(box_2d_cen, depth, cam_calib)
vol_box = tu.computeboxes([rot_y], dim, points_cam)
return vol_box