Kitti: Documentation

alodataset/kitti_object.py

@@ -11,6 +11,26 @@
 
 
 class KittiObjectDataset(BaseDataset, SplitMixin):
+    """
+    Object Task from KITTI dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset.
+    right_frame : bool
+        If True, load the right frame.
+    context_images : int
+        Number of image before the main frame to load (max 3).
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Get all the training samples:
+    >>> dataset = KittiObjectDataset(right_frame=True, context_images=3, split=Split.TRAIN)
+    >>> # Get the annotated image from the left camera from the testing set:
+    >>> dataset = KittiObjectDataset(right_frame=False, context_images=0, split=Split.TEST)
+    """
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
     LABELS = ["Car", "Van", "Truck", "Pedestrian", "Person_sitting", "Cyclist", "Tram", "Misc", "DontCare"]
 

alodataset/kitti_odometry.py

@@ -10,6 +10,35 @@
 
 
 class KittiOdometryDataset(BaseDataset, SplitMixin):
+    """
+    Odometry Task from KITTI dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset
+    sequences : str | List[str] | None
+        List of sequences to load. If None, all sequences are loaded.
+    grayscale : bool
+        If True, load grayscale images. Otherwise, load RGB images.
+    right_frame : bool
+        If True, load right frame images.
+    sequence_size : int
+        Number of images per item.
+    skip : int
+        Number of images to skip between each image in the sequence.
+    sequence_skip : int
+        Nunber of sequences to skip between each item.
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load all training sequences with 3 images per item and 3 image from right camera
+    >>> dataset = KittiOdometryDataset(split=Split.TRAIN, sequence_size=3, right_frame=True)
+    >>> # Load all training sequences from sequence 00 and 03 with 5 images per item
+    >>> dataset = KittiOdometryDataset(split=Split.TRAIN, sequences=["00", "03"], sequence_size=5, right_frame=False)
+    """
+
     def __init__(
         self,
         name="kitti_odometry",

alodataset/kitti_road.py

@@ -9,7 +9,34 @@
 
 from alodataset.utils.kitti import load_calib_cam_to_cam
 
+
 class KittiRoadDataset(BaseDataset, SplitMixin):
+    """
+    Road task from Kitti dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset.
+    right_frame : bool
+        If True, load the right frame.
+    grayscale : bool
+        If True, load the grayscale images.
+    environement : str
+        Environement to load. Must be in ['um', 'umm', 'uu'].
+        (urban marked, urban unmarked multiple lanes, urban unmarked)
+    obj : str
+        Can be set to 'lane' if environement is 'um'.
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load urban marked environement with lane object:
+    >>> dataset = KittiRoadDataset(environement="um", obj="lane")
+    >>> # Load urban unmarked environement with road object with grayscale images:
+    >>> dataset = KittiRoadDataset(environement="uu", obj="road", grayscale=True)
+    """
+
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
 
     def __init__(self, name="kitti_road", right_frame=False, grayscale=False, environement="um", obj="road", **kwargs):

alodataset/kitti_semantic.py

@@ -5,11 +5,26 @@
 from typing import Union
 
 from alodataset import BaseDataset, Split, SplitMixin
-from aloscene import Frame, Mask, Labels, BoundingBoxes2D
-from aloscene.camera_calib import CameraIntrinsic, CameraExtrinsic
+from aloscene import Frame, Mask, Labels
 
 
 class KittiSemanticDataset(BaseDataset, SplitMixin):
+    """
+    Semantic task from Kitti dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset.
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load kitti training semantic dataset
+    >>> dataset = KittiSemanticDataset(split=Split.TRAIN)
+    >>> # Load kitti testing semantic dataset
+    >>> dataset = KittiSemanticDataset(split=Split.TEST)
+    """
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
 
     def __init__(self, name="kitti_semantic", **kwargs):

alodataset/kitti_stereo_flow2012.py

@@ -12,6 +12,40 @@
 
 
 class KittiStereoFlow2012(BaseDataset, SplitMixin):
+    """
+    Stereo, Flow tasks from Kitti 2012 dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset
+    grayscale : bool
+        If True, load images in grayscale
+    sequence_start : int
+        20 images are available for each item. Only image 10 is annotated.
+        sequence_start is the first image to load.
+    sequence_end : int
+        sequence_end is the last image to load.
+    load : list
+        List of data to load. Available data are:
+        - right: right image
+        - disp_noc: disparity map without occlusions
+        - disp_occ: disparity map with occlusions
+        - disp_refl_noc: disparity map without occlusions and reflections
+        - disp_refl_occ: disparity map with occlusions and reflections
+        - flow_noc: flow map without occlusions
+        - flow_occ: flow map with occlusions
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load only annotated images with all available data
+    >>> kitti_stereo = KittiStereoFlow2012(sequence_start=10, sequence_end=10, split=Split.TRAIN)
+    >>> # Load only annotated images with no data except right image
+    >>> kitti_stereo = KittiStereoFlow2012(load=["right"], sequence_start=10, sequence_end=10, split=Split.TRAIN)
+    >>> # Load only all trainging images with all available data
+    >>> kitti_stereo = KittiStereoFlow2012(sequence_start=0, sequence_end=20, split=Split.TRAIN)
+    """
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
 
     def __init__(

alodataset/kitti_stereo_flow_sflow2015.py

@@ -12,6 +12,40 @@
 
 
 class KittiStereoFlowSFlow2015(BaseDataset, SplitMixin):
+    """
+    Stereo, Flow, SceneFlow Tasks from Kitti 2015 dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset
+    sequence_start : int
+        20 images are available for each item. Only image 10 and 11 are annotated.
+        sequence_start is the first image to load.
+    sequence_end : int
+        sequence_end is the last image to load.
+    grayscale : bool
+        If True, load images in grayscale.
+    load : List[str]
+        List of data to load. Available data are:
+        - right: right image
+        - disp_noc: disparity map without occlusions
+        - disp_occ: disparity map with occlusions
+        - flow_occ: flow map with occlusions
+        - flow_noc: flow map without occlusions
+        - scene_flow: scene flow map
+        - obj_map: object map
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load dataset with only the 2 annotated images
+    >>> dataset = KittiStereoFlowSFlow2015(sequence_start=10, sequence_end=11)
+    >>> # Load dataset with 3 context images before the 2 annotated images
+    >>> dataset = KittiStereoFlowSFlow2015(sequence_start=7, sequence_end=11)
+    >>> # Load dataset with all the context images
+    >>> dataset = KittiStereoFlowSFlow2015(sequence_start=0, sequence_end=20)
+    """
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
 
     def __init__(

alodataset/kitti_tracking.py

@@ -11,6 +11,32 @@
 
 
 class KittiTrackingDataset(BaseDataset, SplitMixin):
+    """
+    Tracking task from KITTI dataset.
+    Parameters
+    ----------
+    name : str
+        Name of the dataset
+    sequences : int | list[str] | None
+        List of sequences to load. If None, all sequences are loaded.
+    right_frame : bool
+        If True, load the right frame.
+    sequence_size : int
+        Number of frames in a sequence.
+    skip : int
+        Number of frames to skip between two frames in a sequence.
+    sequence_skip : int
+        Nunber of sequences to skip between each item.
+    split : Split
+        Split of the dataset. Can be `Split.TRAIN` or `Split.TEST`.
+
+    Examples
+    --------
+    >>> # Load the first sequence without the right frame
+    >>> dataset = KittiTrackingDataset(sequences=0, right_frame=False)
+    >>> # Load the sequences 0, 1 and 2 with 4 image per item
+    >>> dataset = KittiTrackingDataset(sequences=["0000", "0001", "0002"], sequence_size=4)
+    """
     SPLIT_FOLDERS = {Split.TRAIN: "training", Split.TEST: "testing"}
     LABELS = ["Car", "Van", "Truck", "Pedestrian", "Person_sitting", "Cyclist", "Tram", "Misc", "DontCare"]