Grid Clusters.

datasets/_point_dataset.py

@@ -17,10 +17,6 @@ class _Point_Dataset(ABC, Dataset):
         # FixMe: This does not work when more then x/y tuples are returned
         return self[0][0].shape
 
-    @property
-    def setting(self) -> str:
-        raise NotImplementedError
-
     headers = ['x', 'y', 'z', 'xn', 'yn', 'zn', 'label', 'cl_idx']
     samplers = dict(fps=FarthestpointSampling, rnd=RandomSampling)
 
@@ -28,6 +24,8 @@ class _Point_Dataset(ABC, Dataset):
                  transforms=None, load_preprocessed=True, split='train', dense_output=False, *args, **kwargs):
         super(_Point_Dataset, self).__init__()
 
+        self.setting: str
+
         self.dense_output = dense_output
         self.split = split
         self.norm_as_feature = norm_as_feature
@@ -67,4 +65,23 @@ class _Point_Dataset(ABC, Dataset):
         raise NotImplementedError
 
     def __getitem__(self, item):
-        raise NotImplementedError
+        processed_file_path = self._read_or_load(item)
+
+        with processed_file_path.open('rb') as processed_file:
+            pointcloud = pickle.load(processed_file)
+
+        position = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z']), axis=-1)
+
+        normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
+
+        label = pointcloud['label']
+
+        cl_label = pointcloud['cl_idx']
+
+        sample_idxs = self.sampling(position)
+
+        return (normal[sample_idxs].astype(np.float),
+                position[sample_idxs].astype(np.float),
+                label[sample_idxs].astype(np.int),
+                cl_label[sample_idxs].astype(np.int)
+                )

datasets/full_pointclouds.py

@@ -8,29 +8,11 @@ from ._point_dataset import _Point_Dataset
 
 class FullCloudsDataset(_Point_Dataset):
 
-    setting = 'pc'
     split: str
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, setting='pc', **kwargs):
+        self.setting = setting
         super(FullCloudsDataset, self).__init__(*args, **kwargs)
 
     def __len__(self):
         return len(self._files)
-
-    def __getitem__(self, item):
-        processed_file_path = self._read_or_load(item)
-
-        with processed_file_path.open('rb') as processed_file:
-            pointcloud = pickle.load(processed_file)
-
-        position = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z']), axis=-1)
-
-        normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
-
-        label = pointcloud['label']
-
-        sample_idxs = self.sampling(position)
-
-        return (normal[sample_idxs].astype(np.float),
-                position[sample_idxs].astype(np.float),
-                label[sample_idxs].astype(np.int))

datasets/grid_clustered.py

@@ -1,32 +0,0 @@
-import pickle
-import numpy as np
-
-from ._point_dataset import _Point_Dataset
-
-
-class FullCloudsDataset(_Point_Dataset):
-
-    setting = 'grid'
-
-    def __init__(self, *args, **kwargs):
-        super(FullCloudsDataset, self).__init__(*args, **kwargs)
-
-    def __len__(self):
-        return len(self._files)
-
-    def __getitem__(self, item):
-        processed_file_path = self._read_or_load(item)
-
-        with processed_file_path.open('rb') as processed_file:
-            pointcloud = pickle.load(processed_file)
-        points = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z'],
-                           pointcloud['xn'], pointcloud['yn'], pointcloud['zn']
-                           ),
-                          axis=-1)
-
-        # When yopu want to return points and normal seperately
-        # normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
-        label = pointcloud['cl_idx']
-        sample_idxs = self.sampling(points)
-
-        return points[sample_idxs], label[sample_idxs]

datasets/prim_clustered.py

@@ -1,32 +0,0 @@
-import pickle
-import numpy as np
-
-from ._point_dataset import _Point_Dataset
-
-
-class FullCloudsDataset(_Point_Dataset):
-
-    setting = 'prim'
-
-    def __init__(self, *args, **kwargs):
-        super(FullCloudsDataset, self).__init__(*args, **kwargs)
-
-    def __len__(self):
-        return len(self._files)
-
-    def __getitem__(self, item):
-        processed_file_path = self._read_or_load(item)
-
-        with processed_file_path.open('rb') as processed_file:
-            pointcloud = pickle.load(processed_file)
-        points = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z'],
-                           pointcloud['xn'], pointcloud['yn'], pointcloud['zn']
-                           ),
-                          axis=-1)
-
-        # When yopu want to return points and normal seperately
-        # normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
-        label = pointcloud['cl_idx']
-        sample_idxs = self.sampling(points)
-
-        return points[sample_idxs], label[sample_idxs]

datasets/template_dataset.py

@@ -1,6 +1,7 @@
 from torch.utils.data import Dataset
 from._point_dataset import _Point_Dataset
 
+
 class TemplateDataset(_Point_Dataset):
     def __init__(self, *args, **kwargs):
         super(TemplateDataset, self).__init__()