feat: fix latent pylint errors in many source files

dgp/agents/agent_2d.py

@@ -46,12 +46,13 @@ def load(cls, agent_snapshots_pb2, ontology, feature_ontology_table):
         ontology: Ontology
             Ontology for given agent.
 
-        feature_ontology_table: dict, default: None
+        feature_ontology_table: dict, optional
             A dictionary mapping feature type key(s) to Ontology(s), i.e.:
             {
                 "agent_2d": AgentFeatureOntology[<ontology_sha>],
                 "agent_3d": AgentFeatureOntology[<ontology_sha>]
             }
+            Default: None.
 
         Returns
         -------

dgp/agents/agent_3d.py

@@ -47,7 +47,7 @@ def load(cls, agent_snapshots_pb2, ontology, feature_ontology_table):
         ontology: Ontology
             Ontology for given agent.
 
-        feature_ontology_table: dict, default: None
+        feature_ontology_table: dict
             A dictionary mapping feature type key(s) to Ontology(s), i.e.:
             {
                 "agent_2d": AgentFeatureOntology[<ontology_sha>],
@@ -94,17 +94,25 @@ def render(self, image, camera, line_thickness=2, font_scale=0.5):
 
         Parameters
         ----------
-        image: np.uint8 array
-            Image (H, W, C) to render the bounding box onto. We assume the input image is in *RGB* format
+        image: np.ndarray
+            Image (H, W, C) to render the bounding box onto. We assume the input image is in *RGB* format.
+            Data type is uint8.
 
         camera: dgp.utils.camera.Camera
             Camera used to render the bounding box.
 
-        line_thickness: int, default: 2
-            Thickness of bounding box lines.
+        line_thickness: int, optional
+            Thickness of bounding box lines. Default: 2.
 
-        font_scale: float, default: 0.5
-            Font scale used in text labels.
+        font_scale: float, optional
+            Font scale used in text labels. Default: 0.5.
+
+        Raises
+        ------
+        ValueError
+            Raised if `image` is not a 3-channel uint8 numpy array.
+        TypeError
+            Raised if `camera` is not an instance of Camera.
         """
         if (
             not isinstance(image, np.ndarray) or image.dtype != np.uint8 or len(image.shape) != 3 or image.shape[2] != 3

dgp/agents/base_agent.py

@@ -32,18 +32,19 @@ def load(cls, agent_snapshots_pb2, ontology, feature_ontology_table):
 
         Parameters
         ----------
-        agent_snapshots_pb2: agent proto object
-            A proto message holding agent information.
+        agent_snapshots_pb2: object
+            An agent proto message holding agent information.
 
         ontology: Ontology
             Ontology for given agent.
 
-        feature_ontology_table: dict, default: None
+        feature_ontology_table: dict, optional
             A dictionary mapping feature type key(s) to Ontology(s), i.e.:
             {
                 "agent_2d": AgentFeatureOntology[<ontology_sha>],
                 "agent_3d": AgentFeatureOntology[<ontology_sha>]
             }
+            Default: None.
         """
 
     @abstractmethod

dgp/annotations/base_annotation.py

@@ -43,7 +43,7 @@ def save(self, save_dir):
         """Serialize annotation object if possible, and saved to specified directory.
         Annotations are saved in format <save_dir>/<sha>.<ext>
 
-        Paramaters
+        Parameters
         ----------
         save_dir: str
             Path to directory to saved annotation

dgp/annotations/bounding_box_3d_annotation.py

@@ -88,6 +88,11 @@ def to_proto(self):
     def save(self, save_dir):
         """Serialize Annotation object and saved to specified directory. Annotations are saved in format <save_dir>/<sha>.<ext>
 
+        Parameters
+        ----------
+        save_dir: str
+            A pathname to a directory to save the annotation object into.
+
         Returns
         -------
         output_annotation_file: str
@@ -107,17 +112,25 @@ def render(self, image, camera, line_thickness=2, font_scale=0.5):
 
         Parameters
         ----------
-        image: np.uint8 array
-            Image (H, W, C) to render the bounding box onto. We assume the input image is in *RGB* format
+        image: np.uint8
+            Image (H, W, C) to render the bounding box onto. We assume the input image is in *RGB* format.
+            Element type must be uint8.
 
         camera: dgp.utils.camera.Camera
             Camera used to render the bounding box.
 
-        line_thickness: int, default: 2
-            Thickness of bounding box lines.
+        line_thickness: int, optional
+            Thickness of bounding box lines. Default: 2.
 
-        font_scale: float, default: 0.5
-            Font scale used in text labels.
+        font_scale: float, optional
+            Font scale used in text labels. Default: 0.5.
+
+        Raises
+        ------
+        ValueError
+            Raised if image is not a 3-channel uint8 numpy array.
+        TypeError
+            Raised if camera is not an instance of Camera.
         """
         if (
             not isinstance(image, np.ndarray) or image.dtype != np.uint8 or len(image.shape) != 3 or image.shape[2] != 3
@@ -165,5 +178,16 @@ def hexdigest(self):
         return generate_uid_from_pbobject(self.to_proto())
 
     def project(self, camera):
-        """Project bounding boxes into a camera and get back 2D bounding boxes in the frustum."""
+        """Project bounding boxes into a camera and get back 2D bounding boxes in the frustum.
+
+        Parameters
+        ----------
+        camera: Camera
+            The Camera instance to project into.
+
+        Raises
+        ------
+        NotImplementedError
+            Unconditionally.
+        """
         raise NotImplementedError

dgp/annotations/depth_annotation.py

@@ -49,7 +49,7 @@ def save(self, save_dir):
         """Serialize annotation object if possible, and saved to specified directory.
         Annotations are saved in format <save_dir>/<sha>.<ext>
 
-        Paramaters
+        Parameters
         ----------
         save_dir: str
             Path to directory to saved annotation

dgp/annotations/ontology.py

@@ -70,6 +70,13 @@ def load(cls, ontology_file):
         ----------
         ontology_file: str
             Path to ontology JSON
+
+        Raises
+        ------
+        FileNotFoundError
+            Raised if ontology_file does not exist.
+        Exception
+            Raised if we could not open the ontology file for some reason.
         """
         if os.path.exists(ontology_file):
             ontology_pb2 = open_ontology_pbobject(ontology_file)

dgp/annotations/panoptic_segmentation_2d_annotation.py

@@ -1,5 +1,6 @@
 # Copyright 2021 Toyota Research Institute.  All rights reserved.
 import json
+import locale
 import os
 
 import cv2
@@ -98,6 +99,11 @@ def parse_panoptic_image(self):
         -------
         instance_masks: list[InstanceMask2D]
             Instance mask for each instance in panoptic annotation.
+
+        Raises
+        ------
+        ValueError
+            Raised if an instance ID, parsed from a label, is negative.
         """
         instance_masks = []
         for class_name, labels in self.index_to_label.items():
@@ -181,8 +187,8 @@ def load(cls, annotation_file, ontology, panoptic_image_dtype=DEFAULT_PANOPTIC_I
         ontology: Ontology
             Ontology for given annotation
 
-        panoptic_image_dtype: type, default: np.uint16
-            Numpy data type (e.g. np.uint16, np.uint32, etc) of panoptic image.
+        panoptic_image_dtype: type, optional
+            Numpy data type (e.g. np.uint16, np.uint32, etc) of panoptic image. Default: np.uint16.
         """
         panoptic_image = cv2.imread(annotation_file, cv2.IMREAD_UNCHANGED)
         if len(panoptic_image.shape) == 3:
@@ -191,7 +197,7 @@ def load(cls, annotation_file, ontology, panoptic_image_dtype=DEFAULT_PANOPTIC_I
             _L = panoptic_image
             label_map = _L[:, :, 2] + 256 * _L[:, :, 1] + 256 * 256 * _L[:, :, 0]
             panoptic_image = label_map.astype(panoptic_image_dtype)
-        with open('{}.json'.format(os.path.splitext(annotation_file)[0])) as _f:
+        with open('{}.json'.format(os.path.splitext(annotation_file)[0]), encoding=locale.getpreferredencoding()) as _f:
             index_to_label = json.load(_f)
         return cls(ontology, panoptic_image, index_to_label, panoptic_image_dtype)
 
@@ -219,8 +225,8 @@ def from_masklist(cls, masklist, ontology, mask_shape=None, panoptic_image_dtype
         mask_shape: list[int]
             Height and width of the mask. Only used to create an empty panoptic image when masklist is empty.
 
-        panoptic_image_dtype: type, default: np.uint16
-            Numpy data type (e.g. np.uint16, np.uint32, etc) of panoptic image.
+        panoptic_image_dtype: type, optional
+            Numpy data type (e.g. np.uint16, np.uint32, etc) of panoptic image. Default: np.uint16.
         """
         if not masklist:
             panoptic_image = np.ones(mask_shape, panoptic_image_dtype) * ontology.VOID_ID
@@ -283,7 +289,7 @@ def save(self, save_dir, datum=None):
         cv2.imwrite(panoptic_image_path, self.panoptic_image)
 
         index_to_label_path = '{}.json'.format(os.path.splitext(panoptic_image_path)[0])
-        with open(index_to_label_path, 'w') as _f:
+        with open(index_to_label_path, 'w', encoding=locale.getpreferredencoding()) as _f:
             json.dump(self.index_to_label, _f)
 
         return panoptic_image_path

dgp/annotations/transform_utils.py

@@ -18,7 +18,7 @@ def remap_bounding_box_annotations(bounding_box_annotations, lookup_table, origi
     bounding_box_annotations: BoundingBox2DAnnotationList or BoundingBox3DAnnotationList
         Annotations to remap
 
-    lookup: dict
+    lookup_table: dict
         Lookup from old class names to new class names
         e.g.:
             {
@@ -60,10 +60,10 @@ def remap_semantic_segmentation_2d_annotation(
     """
     Parameters
     ----------
-    semantic_segmentation_2d_annotation: SemanticSegmentation2DAnnotation
+    semantic_segmentation_annotation: SemanticSegmentation2DAnnotation
         Annotation to remap
 
-    lookup: dict
+    lookup_table: dict
         Lookup from old class names to new class names
         e.g.:
             {
@@ -103,10 +103,10 @@ def remap_instance_segmentation_2d_annotation(
     """
     Parameters
     ----------
-    instance_segmentation_2d_annotation: PanopticSegmentation2DAnnotation
+    instance_segmentation_annotation: PanopticSegmentation2DAnnotation
         Annotation to remap
 
-    lookup: dict
+    lookup_table: dict
         Lookup from old class names to new class names
         e.g.:
             {

dgp/annotations/transforms.py

@@ -153,41 +153,46 @@ def __init__(self, original_ontology_table, lookup_table, remapped_ontology_tabl
                 class_name in self.remapped_ontology_table[annotation_key].class_names for class_name in lookup.values()
             ]), 'All values in `lookup` need to be valid class names in specified `remapped_ontology`'
 
-    def transform_datum(self, data):
+    def transform_datum(self, datum):
         """
         Parameters
         ----------
-        data: OrderedDict
+        datum: OrderedDict
             Dictionary containing raw data and annotations, with keys such as:
             'rgb', 'intrinsics', 'bounding_box_2d'.
             All annotation_keys in `self.lookup_table` (and `self.remapped_ontology_table`)
             are expected to be contained
 
         Returns
         -------
-        data: OrderedDict
+        datum: OrderedDict
             Same dictionary but with annotations in `self.lookup_table` remapped to desired ontologies
+
+        Raises
+        ------
+        ValueError
+            Raised if the datum to remap does not contain all expected annotations.
         """
-        if not all([annotation_key in data for annotation_key in self.remapped_ontology_table]):
+        if not all([annotation_key in datum for annotation_key in self.remapped_ontology_table]):
             raise ValueError('The data you are trying to remap does not have all annotations it expects')
 
         for annotation_key, remapped_ontology in self.remapped_ontology_table.items():
 
             lookup_table = self.lookup_table[annotation_key]
-            original_ontology = data[annotation_key].ontology
+            original_ontology = datum[annotation_key].ontology
 
             # Need to have specific handlers for each annotation type
             if annotation_key == 'bounding_box_2d' or annotation_key == 'bounding_box_3d':
-                data[annotation_key] = remap_bounding_box_annotations(
-                    data[annotation_key], lookup_table, original_ontology, remapped_ontology
+                datum[annotation_key] = remap_bounding_box_annotations(
+                    datum[annotation_key], lookup_table, original_ontology, remapped_ontology
                 )
             elif annotation_key == 'semantic_segmentation_2d':
-                data[annotation_key] = remap_semantic_segmentation_2d_annotation(
-                    data[annotation_key], lookup_table, original_ontology, remapped_ontology
+                datum[annotation_key] = remap_semantic_segmentation_2d_annotation(
+                    datum[annotation_key], lookup_table, original_ontology, remapped_ontology
                 )
             elif annotation_key == 'instance_segmentation_2d':
-                data[annotation_key] = remap_instance_segmentation_2d_annotation(
-                    data[annotation_key], lookup_table, original_ontology, remapped_ontology
+                datum[annotation_key] = remap_instance_segmentation_2d_annotation(
+                    datum[annotation_key], lookup_table, original_ontology, remapped_ontology
                 )
 
-        return data
+        return datum

dgp/annotations/visibility_filter_transform.py

@@ -45,6 +45,11 @@ def transform_sample(self, sample):
         -------
         new_sample: list[OrderedDict]
             Multimodal sample with all detection annotations are filtered.
+
+        Raises
+        ------
+        ValueError
+            Raised if a 2D or 3D bounding box instance lacks any required instance IDs.
         """
         cam_datums = [datum for datum in sample if datum['datum_name'] in self._camera_datum_names]
 
@@ -189,6 +194,11 @@ def transform_sample(self, sample):
         -------
         new_sample: list[OrderedDict]
             Multimodal sample with updated 3D bounding box annotations.
+
+        Raises
+        ------
+        ValueError
+            Raised if there are multiple instances of the same kind of datum in a sample.
         """
         # Mapping index to datum. The order of datums is preserved in output.
         datums, src_datum_inds, dst_datum_ind = OrderedDict(), [], []

dgp/cli.py

@@ -94,7 +94,7 @@ def cli():
 @cli.command(name='visualize-scene')
 @add_options(options=VISUALIZE_OPTIONS)
 @click.option("--scene-json", required=True, help="Path to Scene JSON")
-def visualize_scene(
+def visualize_scene( # pylint: disable=missing-any-param-doc
     scene_json, annotations, camera_datum_names, dataset_class, show_instance_id, max_num_items, video_fps, dst_dir,
     verbose, lidar_datum_names, render_pointcloud, radar_datum_names, render_radar_pointcloud, render_raw
 ):
@@ -216,7 +216,7 @@ def visualize_scene(
     help="Dataset split to be fetched."
 )
 @add_options(options=VISUALIZE_OPTIONS)
-def visualize_scenes(
+def visualize_scenes( # pylint: disable=missing-any-param-doc
     scene_dataset_json, split, annotations, camera_datum_names, dataset_class, show_instance_id, max_num_items,
     video_fps, dst_dir, verbose, lidar_datum_names, render_pointcloud, radar_datum_names, render_radar_pointcloud,
     render_raw