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More General View Adjusment of Point-cloud BEV #35

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More General View Adjusment of Point-cloud BEV #35

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1987a3a
Add more general way to discretize pointcloud featuremap
Oct 22, 2021
43a046c
Add discretization option for both width & height
Oct 22, 2021
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3 changes: 2 additions & 1 deletion src/config/kitti_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,8 @@
BEV_WIDTH = 608 # across y axis -25m ~ 25m
BEV_HEIGHT = 608 # across x axis 0m ~ 50m

DISCRETIZATION = (boundary["maxX"] - boundary["minX"]) / BEV_HEIGHT
DISCRETIZATION_X = (boundary["maxX"] - boundary["minX"]) / BEV_HEIGHT
DISCRETIZATION_Y = (boundary["maxY"] - boundary["minY"]) / BEV_WIDTH

colors = [[0, 255, 255], [0, 0, 255], [255, 0, 0]]

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10 changes: 7 additions & 3 deletions src/data_process/kitti_bev_utils.py
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Expand Up @@ -34,14 +34,18 @@ def removePoints(PointCloud, BoundaryCond):
return PointCloud


def makeBVFeature(PointCloud_, Discretization, bc):
def makeBVFeature(PointCloud_, Discretization_X, Discretization_Y, bc):
Height = cnf.BEV_HEIGHT + 1
Width = cnf.BEV_WIDTH + 1

# Discretize Feature Map
PointCloud = np.copy(PointCloud_)
PointCloud[:, 0] = np.int_(np.floor(PointCloud[:, 0] / Discretization))
PointCloud[:, 1] = np.int_(np.floor(PointCloud[:, 1] / Discretization) + Width / 2)

offset_x = np.int_(np.floor(bc['minX'] / Discretization_X))
offset_y = np.int_(np.floor(bc['minY'] / Discretization_Y))

PointCloud[:, 0] = np.int_(np.floor(PointCloud[:, 0] / Discretization_X)) - offset_x
PointCloud[:, 1] = np.int_(np.floor(PointCloud[:, 1] / Discretization_Y)) - offset_y

# sort-3times
indices = np.lexsort((-PointCloud[:, 2], PointCloud[:, 1], PointCloud[:, 0]))
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4 changes: 2 additions & 2 deletions src/data_process/kitti_dataset.py
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Expand Up @@ -79,7 +79,7 @@ def load_img_only(self, index):
sample_id = int(self.sample_id_list[index])
lidarData = self.get_lidar(sample_id)
b = kitti_bev_utils.removePoints(lidarData, cnf.boundary)
rgb_map = kitti_bev_utils.makeBVFeature(b, cnf.DISCRETIZATION, cnf.boundary)
rgb_map = kitti_bev_utils.makeBVFeature(b, cnf.DISCRETIZATION_X, cnf.DISCRETIZATION_Y, cnf.boundary)
img_file = os.path.join(self.image_dir, '{:06d}.png'.format(sample_id))

return img_file, rgb_map
Expand All @@ -103,7 +103,7 @@ def load_img_with_targets(self, index):
lidarData, labels[:, 1:] = self.lidar_transforms(lidarData, labels[:, 1:])

b = kitti_bev_utils.removePoints(lidarData, cnf.boundary)
rgb_map = kitti_bev_utils.makeBVFeature(b, cnf.DISCRETIZATION, cnf.boundary)
rgb_map = kitti_bev_utils.makeBVFeature(b, cnf.DISCRETIZATION_X, cnf.DISCRETIZATION_Y, cnf.boundary)
target = kitti_bev_utils.build_yolo_target(labels)
img_file = os.path.join(self.image_dir, '{:06d}.png'.format(sample_id))

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