def get_example(self, i):
pc = load_pointcloud_from_bin(self.velo_list[i])
calib = read_calib_file(self.calib_list[i])
proj_velo = proj_img_to_velo(calib) # R0_rect / Tr_velo_to_cam
self.rect_cam = calib["P2"].reshape(3, 4)[:, :3]
self.proj_cam = np.linalg.inv(proj_velo)
random_indexes = np.random.permutation(pc.shape[0])
pc = pc[random_indexes]
d, h, w = self.voxel_shape
d_res, h_res, w_res = self.resolution
x_min, x_max = self.x_range
y_min, y_max = self.y_range
z_min, z_max = self.z_range
rotate = 0
pc = np.ascontiguousarray(pc, dtype=np.float32)
create_input = create_feature_input_rotate if self.norm_input else create_feature_input
s = time.time()
feature_input, counter, indexes, n_no_empty = \
create_input(pc, d_res, h_res, w_res, self.t,
d, h, w, x_min, x_max, y_min, y_max, z_min, z_max,
self.thres_t, 0, 92)
print("create input", time.time() - s)
area_mask = create_mask(0, 90, d, h, w,
self.scale_label).astype(np.int8)
return (feature_input, counter, indexes, np.array([indexes.shape[0]]),
np.array([n_no_empty]), area_mask)
def _transform(inputs,
crop_size=(512, 512),
g_scale=[0.95, 1.05],
l_rotate=None,
g_rotate=None,
resolution=None,
voxel_shape=None,
x_range=None,
y_range=None,
z_range=None,
t=35,
thres_t=None,
anchor_size=(1.56, 1.6, 3.9),
anchor_center=(-1.0, 0., 0.),
fliplr=False,
n_class=20,
scale_label=1,
norm_input=False):
pc, places, rotates, size = inputs
d, h, w = voxel_shape
d_res, h_res, w_res = resolution
x_min, x_max = x_range
y_min, y_max = y_range
z_min, z_max = z_range
places[:, 2] += size[:, 0] / 2.
s = time.time()
create_input = create_feature_input_rotate if norm_input else create_feature_input
feature_input, counter, indexes, n_no_empty = \
create_input(pc, d_res, h_res, w_res, t, d, h, w,
x_min, x_max, y_min, y_max, z_min, z_max, thres_t, 0, 92)
print("Convert raw data to input", time.time() - s)
del pc
anchor_z, anchor_y, anchor_x = anchor_center
anchor_h, anchor_w, anchor_l = anchor_size
area_mask = create_mask(0, 90, d, h, w, scale_label)
# label_creator = create_label_rotate if label_rotate else create_label
gt_obj, gt_reg, _ = create_label_rotate(places, rotates, size, d_res,
h_res, w_res, t, d, h, w, x_min,
x_max, y_min, y_max, z_min, z_max,
thres_t, anchor_l, anchor_w,
anchor_h, anchor_x, anchor_y,
anchor_z, scale_label, 0.8)
return (feature_input, counter, indexes, gt_obj, gt_reg, area_mask[None],
np.array([indexes.shape[0]]), np.array([n_no_empty]))
def _transform(inputs,
crop_size=(512, 512),
g_scale=[0.95, 1.05],
l_rotate=None,
g_rotate=None,
resolution=None,
voxel_shape=None,
x_range=None,
y_range=None,
z_range=None,
t=35,
thres_t=None,
anchor_size=(1.56, 1.6, 3.9),
anchor_center=(-1.0, 0., 0.),
fliplr=False,
n_class=20,
scale_label=1,
norm_input=False,
label_rotate=False,
angle=90,
surround_prob=1):
pc, places, rotates, size = inputs
del inputs
places[:, 2] += size[:, 0] / 2.
rotates = np.pi / 2 - rotates
rotates = aug_rotate(rotates, 0)
# Local rotation (Label and local points) # TODO
if l_rotate:
l_rotate = np.random.uniform(l_rotate[0], l_rotate[1])
# Global scaling
if g_scale:
scale = np.random.uniform(g_scale[0], g_scale[1], 4)
pc *= scale
places *= scale[:3]
size *= scale[:3]
# Global rotation
r = 0
if g_rotate:
g_rotate = np.random.uniform(g_rotate[0], g_rotate[1])
r = float(g_rotate / 180 * np.pi)
rotate_matrix = np.array([[np.cos(r), -np.sin(r), 0],
[np.sin(r), np.cos(r), 0], [0, 0, 1]])
pc[:, :3] = np.dot(pc[:, :3], rotate_matrix.transpose())
places = np.dot(places, rotate_matrix.transpose()).astype("f")
rotates = aug_rotate(rotates, r)
# Flip
if fliplr:
if np.random.rand() > 0.5:
pc[:, 1] = pc[:, 1] * -1
places[:, 1] = places[:, 1] * -1
rotates = rotates * -1
r = r * -1
random_indexes = np.random.permutation(pc.shape[0])
pc = pc[random_indexes]
d, h, w = voxel_shape
d_res, h_res, w_res = resolution
x_min, x_max = x_range
y_min, y_max = y_range
z_min, z_max = z_range
area_mask = create_mask(r, angle, d, h, w, scale_label)
create_input = create_feature_input_rotate if norm_input else create_feature_input
pc = np.ascontiguousarray(pc, dtype=np.float32)
feature_input, counter, indexes, n_no_empty = \
create_input(pc, d_res, h_res, w_res, t, d, h, w,
x_min, x_max, y_min, y_max, z_min, z_max, thres_t, r, angle)
del pc
anchor_z, anchor_y, anchor_x = anchor_center
anchor_h, anchor_w, anchor_l = anchor_size
# bird_corners = get_bird_boxcorners(places, rotates, size)
label_creator = create_label_rotate #if label_rotate else create_label
gt_obj, gt_reg, gt_obj_for_reg = \
label_creator(places, rotates, size, d_res, h_res, w_res, t, d, h, w,
x_min, x_max, y_min, y_max, z_min, z_max, thres_t,
anchor_l, anchor_w, anchor_h,
anchor_x, anchor_y, anchor_z, scale_label, surround_prob)
return (feature_input, counter, indexes, gt_obj, gt_reg, gt_obj_for_reg,
area_mask[None], np.array([indexes.shape[0]]),
np.array([n_no_empty]))