def toVox()

in datasets/depth_dataset.py [0:0]

• 30 lines of code
• 9 McCabe index (conditional complexity)

    def toVox(self, coords, feats, labels):
        if "Lidar" in self.cfg:
            voxel_output = self.voxel_generator.generate(coords)
            if isinstance(voxel_output, dict):
                voxels, coordinates, num_points = \
                                                  voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']
            else:
                voxels, coordinates, num_points = voxel_output

            data_dict = {}
            data_dict['voxels'] = voxels
            data_dict['voxel_coords'] = coordinates
            data_dict['voxel_num_points'] = num_points
            return data_dict
        else:
            precoords = np.copy(coords)
            prefeats = np.copy(feats)
            if (self.split == "TRAIN") and (self.prevoxel_transform is not None):
                coords, feats, labels = self.prevoxel_transform(coords, feats, labels)
            coords, feats, labels, transformation = self.voxelizer.voxelize(coords, feats, labels)
            if (self.split == "TRAIN") and (self.input_transforms is not None):
                try:
                    coords, feats, labels = self.input_transforms(coords, feats, labels)
                except:
                    print ("error with: ", coords.shape)
                    coords = np.zeros((100,3),dtype=np.int32)
                    feats = np.zeros((100,3),dtype=np.float64)
                    labels = np.zeros((100,),dtype=np.int32)
            if (self.split == "TRAIN") and (self.AUGMENT_COORDS_TO_FEATS):
                coords, feats, labels = self._augment_coords_to_feats(coords, feats, labels)
            return (coords, feats, labels)