def main():
from core.kitti import KITTI, ALL_OBJECTS, CARS_ONLY
DS_DIR = '/home/salam/datasets/KITTI/training'
reader = KITTI(DS_DIR, CARS_ONLY)
ids = reader.get_ids('train')
for t in [ids[4]]:
img = reader.get_image(t)
pts, ref = reader.get_velo(t, use_fov_filter=False)
boxes_2D = reader.get_boxes_2D(t)
boxes_3D = reader.get_boxes_3D(t)
P2 = reader.get_calib(t)[2]
for b in boxes_3D:
b.text = f"{np.random.uniform(0.0, 1.0, (1,))[0]:0.2f}"
imshow(bev(pts, pred_boxes=boxes_3D, title="GT"))
print('world_pts.shape', world_pts.shape)
print('nearest.shape ', nearest.shape)
geo_feature = nearest - world_pts
nearest_projected = project(P2, nearest).astype(
np.int32).T // image_downsampling_factor
return {
'mapping': nearest_projected.reshape((bev_length, bev_width, 2)),
'geo_feature': geo_feature.reshape((bev_length, bev_width, 3))
}
for i, id in enumerate(train_ids + val_ids):
pc, _ = kitti.get_velo(id,
workspace_lim=((-40, 40), (-1, 3), (0, 70)),
use_fov_filter=True)
_, _, P2 = kitti.get_calib(id)
# img = kitti.get_image(id)
# img = cv2.resize(img, (311, 94))
# print('img.shape', img.shape)
bev_shape = (448, 512)
world_pts2x, nearest2x = get_nearest_neighbour(pc, bev_shape[1] // 2,
bev_shape[0] // 2, 2, P2)
# mapping2x[:,:,(0,1)] = mapping2x[:,:,(1,0)]
world_pts4x, nearest4x = get_nearest_neighbour(pc, bev_shape[1] // 4,
bev_shape[0] // 4, 4, P2)
# mapping4x[:,:,(0,1)] = mapping4x[:,:,(1,0)]
world_pts8x, nearest8x = get_nearest_neighbour(pc, bev_shape[1] // 8,
bev_shape[0] // 8, 8, P2)
# mapping8x[:,:,(0,1)] = mapping8x[:,:,(1,0)]