def prepForPredicting(fileidx, objects_to_suppress):
data = np.fromfile(lidar_files.format(fileidx), dtype=np.float32).reshape(
(-1, 4))[:, :3]
calib_extrinsic = calib_extrinsics[calib_map[fileidx]].copy()
calib_extrinsic[2, 3] += 1.65
data = data.dot(calib_extrinsic[:3, :3].T) + calib_extrinsic[:3, 3]
# get ground
ground = np.load(ground_planes_by_file.format(fileidx))
pts, tileidxs = tilePoints(data, grndstart, grndstep, grndlen)
groundTs = planes2Transforms(ground)
# get suppressed objects
suppress_start, suppress_end = np.searchsorted(objects_to_suppress[:, 0],
[fileidx, fileidx + 1])
#pts2suppress = objects_to_suppress[suppress_start:suppress_end, 1:3].copy()
pts2suppress = np.zeros((suppress_end - suppress_start, 4))
pts2suppress[:, :2] = objects_to_suppress[suppress_start:suppress_end, 1:3]
pts2suppress[:,
2] = np.cos(objects_to_suppress[suppress_start:suppress_end,
0])
pts2suppress[:,
2] = np.sin(objects_to_suppress[suppress_start:suppress_end,
0])
return pts, tileidxs, pts2suppress, groundTs
files2use = np.random.choice(np.arange(training_file_start,
training_file_end),
nfilesfornegatives,
replace=False)
objects_to_suppress = np.load(present_boxes_file)
#anchoroverlaps = prepLocalNms()
globaldetections = np.zeros(
(nnegatives, anchorlen[0], anchorlen[1], anchorlen[2]), dtype=bool)
detectioncount = 0
for file_idx in files2use:
# load relevant data
data = np.fromfile(lidar_files.format(file_idx),
dtype=np.float32).reshape((-1, 4))[:, :3]
calib_extrinsic = calib_extrinsics[calib_map[file_idx]].copy()
calib_extrinsic[2, 3] += 1.65
data = data.dot(calib_extrinsic[:3, :3].T) + calib_extrinsic[:3, 3]
# get ground
ground = np.load(ground_planes_by_file.format(file_idx))
pts, tileidxs = tilePoints(data, grndstart, grndstep, grndlen)
groundTs = planes2Transforms(ground)
# get suppressed objects
suppress_start, suppress_end = np.searchsorted(
objects_to_suppress[:, 0], [file_idx, file_idx + 1])
pts2suppress = objects_to_suppress[suppress_start:suppress_end,
1:3].copy()
maxnotscoredcars = maxncars * 2
maxnpoints = estimate_npoints_per_car * maxncars
out_pts = np.zeros((maxnpoints, 3))
out_idxs = np.zeros((maxncars, 3), dtype=int)
out_positiveboxes = np.zeros((maxnotscoredcars, 7))
ncars = 0
npoints = 0
nnotscoredcars = 0
for file_idx in training_split:
if file_idx % 1000 == 0: print("file {:d}".format(file_idx))
calib_extrinsic = calib_extrinsics[calib_map[file_idx]].copy()
calib_extrinsic[2, 3] += 1.65
# load relevant data
data = np.fromfile(lidar_files.format(file_idx), dtype=np.float32).reshape(
(-1, 4))[:, :3]
with open(gt_files.format(file_idx), 'r') as fd:
gtstr = fd.read()
gtstuff = readGroundTruthFile(gtstr, ('Car', 'Van'))
gt = [{
'box': gtstuff[1][gtidx],
'scored': gtstuff[4][gtidx]
} for gtidx in xrange(len(gtstuff[0]))]
ground = np.load(ground_planes_by_file.format(file_idx))
# get ground
full_data_xyz = data.dot(calib_extrinsic[:3, :3].T) + calib_extrinsic[:3,
3]
full_data_tiled, data_tile_idxs = tilePoints(full_data_xyz, grndstart,
grndstep, grndlen)