Add KITTI 3D format support

.github/workflows/codeql.yml

@@ -52,7 +52,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@429e1977040da7a23b6822b13c129cd1ba93dbb2 # v3.26.2
+      uses: github/codeql-action/init@4dd16135b69a43b6c8efb853346f8437d92d3c93 # v3.26.6
       with:
         languages: ${{ matrix.language }}
         # If you wish to specify custom queries, you can do so here or in a config file.
@@ -73,7 +73,7 @@ jobs:
         python -m build
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@429e1977040da7a23b6822b13c129cd1ba93dbb2 # v3.26.2
+      uses: github/codeql-action/analyze@4dd16135b69a43b6c8efb853346f8437d92d3c93 # v3.26.6
       with:
         category: "/language:${{matrix.language}}"
     - name: Generate Security Report

.github/workflows/publish_to_pypi.yml

@@ -80,12 +80,12 @@ jobs:
         file_glob: true
     - name: Publish package distributions to PyPI
       if: ${{ steps.check-tag.outputs.match != '' }}
-      uses: pypa/gh-action-pypi-publish@v1.9.0
+      uses: pypa/gh-action-pypi-publish@v1.10.1
       with:
         password: ${{ secrets.PYPI_API_TOKEN }}
     - name: Publish package distributions to TestPyPI
       if: ${{ steps.check-tag.outputs.match == '' }}
-      uses: pypa/gh-action-pypi-publish@v1.9.0
+      uses: pypa/gh-action-pypi-publish@v1.10.1
       with:
         password: ${{ secrets.TESTPYPI_API_TOKEN }}
         repository-url: https://test.pypi.org/legacy/

.github/workflows/scorecard.yml

@@ -67,6 +67,6 @@ jobs:
 
       # Upload the results to GitHub's code scanning dashboard.
       - name: "Upload to code-scanning"
-        uses: github/codeql-action/upload-sarif@429e1977040da7a23b6822b13c129cd1ba93dbb2 # v3.26.2
+        uses: github/codeql-action/upload-sarif@4dd16135b69a43b6c8efb853346f8437d92d3c93 # v3.26.6
         with:
           sarif_file: results.sarif

src/datumaro/components/environment.py

@@ -275,7 +275,7 @@ def merge(cls, envs: Sequence["Environment"]) -> "Environment":
         merged = Environment()
 
         def _register(registry: PluginRegistry):
-            merged.register_plugins(plugin for plugin in registry)
+            merged.register_plugins(list(registry._items.values()))
 
         for env in envs:
             _register(env.extractors)

src/datumaro/components/hl_ops/__init__.py

@@ -282,13 +282,14 @@ def merge(
         merger = get_merger(merge_policy, **kwargs)
         merged = merger(*datasets)
         env = Environment.merge(
-            dataset.env
-            for dataset in datasets
-            if hasattr(
-                dataset, "env"
-            )  # TODO: Sometimes, there is dataset which is not exactly "Dataset",
-            # e.g., VocClassificationBase. this should be fixed and every object from
-            # Dataset.import_from should have "Dataset" type.
+            [
+                dataset.env
+                for dataset in datasets
+                if hasattr(dataset, "env")
+                # TODO: Sometimes, there is dataset which is not exactly "Dataset",
+                # e.g., VocClassificationBase. this should be fixed and every object from
+                # Dataset.import_from should have "Dataset" type.
+            ]
         )
         if report_path:
             merger.save_merge_report(report_path)

src/datumaro/plugins/data_formats/kitti_3d/base.py

@@ -0,0 +1,122 @@
+# Copyright (C) 2024 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import glob
+import os.path as osp
+from typing import List, Optional, Type, TypeVar
+
+from datumaro.components.annotation import AnnotationType, Bbox, LabelCategories
+from datumaro.components.dataset_base import DatasetItem, SubsetBase
+from datumaro.components.errors import InvalidAnnotationError
+from datumaro.components.importer import ImportContext
+from datumaro.components.media import Image
+from datumaro.util.image import find_images
+
+from .format import Kitti3dPath
+
+T = TypeVar("T")
+
+
+class Kitti3dBase(SubsetBase):
+    # http://www.cvlibs.net/datasets/kitti/raw_data.php
+    # https://s3.eu-central-1.amazonaws.com/avg-kitti/devkit_raw_data.zip
+    # Check cpp header implementation for field meaning
+
+    def __init__(
+        self,
+        path: str,
+        *,
+        subset: Optional[str] = None,
+        ctx: Optional[ImportContext] = None,
+    ):
+        assert osp.isfile(path), path
+        super().__init__(subset=subset, ctx=ctx)
+
+        self._path = path
+        self._categories = {AnnotationType.label: LabelCategories()}
+        self._items = self._load()
+
+    def _load(self) -> List[DatasetItem]:
+        items = []
+        image_dir = osp.join(self._path, Kitti3dPath.IMAGE_DIR)
+        image_path_by_id = {
+            osp.splitext(osp.relpath(p, image_dir))[0]: p
+            for p in find_images(image_dir, recursive=True)
+        }
+
+        ann_dir = osp.join(self._path, Kitti3dPath.LABEL_DIR)
+        label_categories = self._categories[AnnotationType.label]
+        for labels_path in sorted(glob.glob(osp.join(ann_dir, "*.txt"), recursive=True)):
+            item_id = osp.splitext(osp.relpath(labels_path, ann_dir))[0]
+            anns = []
+
+            with open(labels_path, "r", encoding="utf-8") as f:
+                lines = f.readlines()
+
+            for line_idx, line in enumerate(lines):
+                line = line.split()
+                assert len(line) == 15 or len(line) == 16
+
+                label_name = line[0]
+                label_id = label_categories.find(label_name)[0]
+                if label_id is None:
+                    label_id = label_categories.add(label_name)
+
+                x1 = self._parse_field(line[4], float, "bbox left-top x")
+                y1 = self._parse_field(line[5], float, "bbox left-top y")
+                x2 = self._parse_field(line[6], float, "bbox right-bottom x")
+                y2 = self._parse_field(line[7], float, "bbox right-bottom y")
+
+                attributes = {}
+                attributes["truncated"] = self._parse_field(line[1], float, "truncated")
+                attributes["occluded"] = self._parse_field(line[2], int, "occluded")
+                attributes["alpha"] = self._parse_field(line[3], float, "alpha")
+
+                height_3d = self._parse_field(line[8], float, "height (in meters)")
+                width_3d = self._parse_field(line[9], float, "width (in meters)")
+                length_3d = self._parse_field(line[10], float, "length (in meters)")
+
+                x_3d = self._parse_field(line[11], float, "x (in meters)")
+                y_3d = self._parse_field(line[12], float, "y (in meters)")
+                z_3d = self._parse_field(line[13], float, "z (in meters)")
+
+                yaw_angle = self._parse_field(line[14], float, "rotation_y")
+
+                attributes["dimensions"] = [height_3d, width_3d, length_3d]
+                attributes["location"] = [x_3d, y_3d, z_3d]
+                attributes["rotation_y"] = yaw_angle
+
+                if len(line) == 16:
+                    attributes["score"] = self._parse_field(line[15], float, "score")
+
+                anns.append(
+                    Bbox(
+                        x=x1,
+                        y=y1,
+                        w=x2 - x1,
+                        h=y2 - y1,
+                        id=line_idx,
+                        attributes=attributes,
+                        label=label_id,
+                    )
+                )
+                self._ann_types.add(AnnotationType.bbox)
+
+            image = image_path_by_id.pop(item_id, None)
+            if image:
+                image = Image.from_file(path=image)
+
+            items.append(
+                DatasetItem(id=item_id, annotations=anns, media=image, subset=self._subset)
+            )
+
+        return items
+
+    def _parse_field(self, value: str, desired_type: Type[T], field_name: str) -> T:
+        try:
+            return desired_type(value)
+        except Exception as e:
+            raise InvalidAnnotationError(
+                f"Can't parse {field_name} from '{value}'. Expected {desired_type}"
+            ) from e

src/datumaro/plugins/data_formats/kitti_3d/format.py

@@ -0,0 +1,12 @@
+# Copyright (C) 2024 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import os.path as osp
+
+
+class Kitti3dPath:
+    PCD_DIR = osp.join("velodyne_points", "data")
+    IMAGE_DIR = "image_2"
+    LABEL_DIR = "label_2"
+    CALIB_DIR = "calib"

src/datumaro/plugins/data_formats/kitti_3d/importer.py

@@ -0,0 +1,43 @@
+# Copyright (C) 2024 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+from typing import List
+
+from datumaro.components.errors import DatasetImportError
+from datumaro.components.format_detection import FormatDetectionConfidence, FormatDetectionContext
+from datumaro.components.importer import Importer
+
+from .format import Kitti3dPath
+
+
+class Kitti3dImporter(Importer):
+    _ANNO_EXT = ".txt"
+
+    @classmethod
+    def detect(cls, context: FormatDetectionContext) -> FormatDetectionConfidence:
+        with context.require_any():
+            with context.alternative():
+                cls._check_ann_file(context.require_file(f"{Kitti3dPath.LABEL_DIR}/*.txt"), context)
+
+        return FormatDetectionConfidence.MEDIUM
+
+    @classmethod
+    def _check_ann_file(cls, fpath: str, context: FormatDetectionContext) -> bool:
+        with context.probe_text_file(
+            fpath, "Requirements for the annotation file of Kitti 3D format"
+        ) as fp:
+            for line in fp:
+                fields = line.rstrip("\n").split(" ")
+                if len(fields) == 15 or len(fields) == 16:
+                    return True
+                raise DatasetImportError(
+                    f"Kitti 3D format txt file should have 15 or 16 fields for "
+                    f"each line, but the read line has {len(fields)} fields: "
+                    f"fields={fields}."
+                )
+            raise DatasetImportError("Empty file is not allowed.")
+
+    @classmethod
+    def get_file_extensions(cls) -> List[str]:
+        return [cls._ANNO_EXT]

src/datumaro/plugins/specs.json

@@ -1,13 +1,4 @@
 [
-  {
-    "import_path": "datumaro.plugins.accuracy_checker_plugin.ac_launcher.AcLauncher",
-    "plugin_name": "ac",
-    "plugin_type": "Launcher",
-    "extra_deps": [
-      "tensorflow",
-      "openvino.tools.accuracy_checker"
-    ]
-  },
   {
     "import_path": "datumaro.plugins.configurable_validator.ConfigurableValidator",
     "plugin_name": "configurable",
@@ -799,6 +790,21 @@
       ]
     }
   },
+  {
+    "import_path": "datumaro.plugins.data_formats.kitti_3d.base.Kitti3dBase",
+    "plugin_name": "kitti3d",
+    "plugin_type": "DatasetBase"
+  },
+  {
+    "import_path": "datumaro.plugins.data_formats.kitti_3d.importer.Kitti3dImporter",
+    "plugin_name": "kitti3d",
+    "plugin_type": "Importer",
+    "metadata": {
+      "file_extensions": [
+        ".txt"
+      ]
+    }
+  },
   {
     "import_path": "datumaro.plugins.data_formats.kitti_raw.base.KittiRawBase",
     "plugin_name": "kitti_raw",
@@ -1855,13 +1861,13 @@
     "plugin_type": "Transform"
   },
   {
-    "import_path": "datumaro.plugins.transforms.Correct",
-    "plugin_name": "correct",
+    "import_path": "datumaro.plugins.transforms.Clean",
+    "plugin_name": "clean",
     "plugin_type": "Transform"
   },
   {
-    "import_path": "datumaro.plugins.transforms.Clean",
-    "plugin_name": "clean",
+    "import_path": "datumaro.plugins.transforms.Correct",
+    "plugin_name": "correct",
     "plugin_type": "Transform"
   },
   {

tests/unit/test_environment.py

@@ -5,7 +5,8 @@
 import pytest
 
 import datumaro.components.lazy_plugin
-from datumaro.components.environment import Environment, PluginRegistry
+from datumaro.components.environment import DEFAULT_ENVIRONMENT, Environment, PluginRegistry
+from datumaro.components.exporter import Exporter
 
 real_find_spec = datumaro.components.lazy_plugin.find_spec
 
@@ -77,3 +78,17 @@ def test_extra_deps_req(self, fxt_tf_failure_env):
         )
 
         assert "tf_detection_api" not in loaded_plugin_names
+
+    def test_merge_default_env(self):
+        merged_env = Environment.merge([DEFAULT_ENVIRONMENT, DEFAULT_ENVIRONMENT])
+        assert merged_env is DEFAULT_ENVIRONMENT
+
+    def test_merge_custom_env(self):
+        class TestPlugin(Exporter):
+            pass
+
+        envs = [Environment(), Environment()]
+        envs[0].exporters.register("test_plugin", TestPlugin)
+
+        merged = Environment.merge(envs)
+        assert "test_plugin" in merged.exporters