def __getitem__(self, index):
sample_id = int(self.sample_id_list[index])
if self.mode in ['TRAIN', 'EVAL']:
lidarData = self.get_lidar(sample_id)
objects = self.get_label(sample_id)
calib = self.get_calib(sample_id)
labels, noObjectLabels = bev_utils.read_labels_for_bevbox(objects)
if not noObjectLabels:
labels[:, 1:] = augUtils.camera_to_lidar_box(
labels[:, 1:], calib.V2C, calib.R0,
calib.P) # convert rect cam to velo cord
if self.data_aug and self.mode == 'TRAIN':
lidarData, labels[:,
1:] = augUtils.complex_yolo_pc_augmentation(
lidarData, labels[:, 1:], True)
b = bev_utils.removePoints(lidarData, cnf.boundary)
rgb_map = bev_utils.makeBVFeature(b, cnf.DISCRETIZATION,
cnf.boundary)
target = bev_utils.build_yolo_target(labels)
# img_file = os.path.join(self.image_path, '%06d.png' % sample_id)
img_file = os.path.join(self.image_path, '%06d.jpg' % sample_id)
ntargets = 0
for i, t in enumerate(target):
if t.sum(0):
ntargets += 1
targets = torch.zeros((ntargets, 8))
for i, t in enumerate(target):
if t.sum(0):
targets[i, 1:] = torch.from_numpy(t)
img = torch.from_numpy(rgb_map).type(torch.FloatTensor)
if self.data_aug:
if np.random.random() < 0.5:
img, targets = self.horisontal_flip(img, targets)
return img_file, img, targets
else:
lidarData = self.get_lidar(sample_id)
b = bev_utils.removePoints(lidarData, cnf.boundary)
rgb_map = bev_utils.makeBVFeature(b, cnf.DISCRETIZATION,
cnf.boundary)
# img_file = os.path.join(self.image_path, '%06d.png' % sample_id)
img_file = os.path.join(self.image_path, '%06d.jpg' % sample_id)
return img_file, rgb_map
def predictions_to_kitti_format(img_detections, calib, img_shape_2d, img_size, RGB_Map=None):
predictions = np.zeros([50, 7], dtype=np.float32)
count = 0
for detections in img_detections:
if detections is None:
continue
# Rescale boxes to original image
for x, y, w, l, im, re, conf, cls_conf, cls_pred in detections:
yaw = np.arctan2(im, re)
predictions[count, :] = cls_pred, x/img_size, y/img_size, w/img_size, l/img_size, im, re
count += 1
predictions = bev_utils.inverse_yolo_target(predictions, cnf.boundary)
if predictions.shape[0]:
predictions[:, 1:] = aug_utils.lidar_to_camera_box(predictions[:, 1:], calib.V2C, calib.R0, calib.P)
objects_new = []
corners3d = []
for index, l in enumerate(predictions):
str = "Pedestrian"
if l[0] == 0:str="Car"
elif l[0] == 1:str="Pedestrian"
elif l[0] == 2: str="Cyclist"
else:str = "DontCare"
line = '%s -1 -1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0' % str
obj = kitti_utils.Object3d(line)
obj.t = l[1:4]
obj.h,obj.w,obj.l = l[4:7]
obj.ry = np.arctan2(math.sin(l[7]), math.cos(l[7]))
_, corners_3d = kitti_utils.compute_box_3d(obj, calib.P)
corners3d.append(corners_3d)
objects_new.append(obj)
if len(corners3d) > 0:
corners3d = np.array(corners3d)
img_boxes, _ = calib.corners3d_to_img_boxes(corners3d)
img_boxes[:, 0] = np.clip(img_boxes[:, 0], 0, img_shape_2d[1] - 1)
img_boxes[:, 1] = np.clip(img_boxes[:, 1], 0, img_shape_2d[0] - 1)
img_boxes[:, 2] = np.clip(img_boxes[:, 2], 0, img_shape_2d[1] - 1)
img_boxes[:, 3] = np.clip(img_boxes[:, 3], 0, img_shape_2d[0] - 1)
img_boxes_w = img_boxes[:, 2] - img_boxes[:, 0]
img_boxes_h = img_boxes[:, 3] - img_boxes[:, 1]
box_valid_mask = np.logical_and(img_boxes_w < img_shape_2d[1] * 0.8, img_boxes_h < img_shape_2d[0] * 0.8)
for i, obj in enumerate(objects_new):
x, z, ry = obj.t[0], obj.t[2], obj.ry
beta = np.arctan2(z, x)
alpha = -np.sign(beta) * np.pi / 2 + beta + ry
obj.alpha = alpha
obj.box2d = img_boxes[i, :]
if RGB_Map is not None:
labels, noObjectLabels = kitti_utils.read_labels_for_bevbox(objects_new)
if not noObjectLabels:
labels[:, 1:] = aug_utils.camera_to_lidar_box(labels[:, 1:], calib.V2C, calib.R0, calib.P) # convert rect cam to velo cord
target = bev_utils.build_yolo_target(labels)
utils.draw_box_in_bev(RGB_Map, target)
return objects_new