[Datumaro] Plugins and transforms (#1126)

* Fix model run command

* Rename annotation types, update class interfaces

* Fix random cvat format test fails

* Mask operations and dataset format fixes

* Update tests, extract format testing functions

* Add transform interface

* Implement plugin system

* Update tests with plugins

* Fix logging

* Add transfroms

* Update cvat integration

cvat/apps/dataset_manager/bindings.py

@@ -156,35 +156,31 @@ class CvatTaskExtractor(datumaro.Extractor):
 
         for tag_obj in cvat_anno.tags:
             anno_group = tag_obj.group
-            if isinstance(anno_group, int):
-                anno_group = anno_group
             anno_label = map_label(tag_obj.label)
             anno_attr = convert_attrs(tag_obj.label, tag_obj.attributes)
 
-            anno = datumaro.LabelObject(label=anno_label,
+            anno = datumaro.Label(label=anno_label,
                 attributes=anno_attr, group=anno_group)
             item_anno.append(anno)
 
         for shape_obj in cvat_anno.labeled_shapes:
             anno_group = shape_obj.group
-            if isinstance(anno_group, int):
-                anno_group = anno_group
             anno_label = map_label(shape_obj.label)
             anno_attr = convert_attrs(shape_obj.label, shape_obj.attributes)
 
             anno_points = shape_obj.points
             if shape_obj.type == ShapeType.POINTS:
-                anno = datumaro.PointsObject(anno_points,
+                anno = datumaro.Points(anno_points,
                     label=anno_label, attributes=anno_attr, group=anno_group)
             elif shape_obj.type == ShapeType.POLYLINE:
-                anno = datumaro.PolyLineObject(anno_points,
+                anno = datumaro.PolyLine(anno_points,
                     label=anno_label, attributes=anno_attr, group=anno_group)
             elif shape_obj.type == ShapeType.POLYGON:
-                anno = datumaro.PolygonObject(anno_points,
+                anno = datumaro.Polygon(anno_points,
                     label=anno_label, attributes=anno_attr, group=anno_group)
             elif shape_obj.type == ShapeType.RECTANGLE:
                 x0, y0, x1, y1 = anno_points
-                anno = datumaro.BboxObject(x0, y0, x1 - x0, y1 - y0,
+                anno = datumaro.Bbox(x0, y0, x1 - x0, y1 - y0,
                     label=anno_label, attributes=anno_attr, group=anno_group)
             else:
                 raise Exception("Unknown shape type '%s'" % (shape_obj.type))

cvat/apps/dataset_manager/export_templates/plugins/cvat_rest_api_task_images.py

@@ -28,7 +28,7 @@ DEFAULT_CONFIG = Config({
     'server_port': 80
 }, schema=CONFIG_SCHEMA, mutable=False)
 
-class cvat_rest_api_task_images(datumaro.Extractor):
+class cvat_rest_api_task_images(datumaro.SourceExtractor):
     def _image_local_path(self, item_id):
         task_id = self._config.task_id
         return osp.join(self._cache_dir,
@@ -53,7 +53,7 @@ class cvat_rest_api_task_images(datumaro.Extractor):
 
         session = None
         try:
-            print("Enter credentials for '%s:%s':" % \
+            print("Enter credentials for '%s:%s' to read task data:" % \
                 (self._config.server_host, self._config.server_port))
             username = input('User: ')
             password = getpass.getpass()

cvat/apps/dataset_manager/task.py

@@ -15,13 +15,13 @@ from django.utils import timezone
 import django_rq
 
 from cvat.apps.engine.log import slogger
-from cvat.apps.engine.models import Task, ShapeType
+from cvat.apps.engine.models import Task
 from .util import current_function_name, make_zip_archive
 
 _CVAT_ROOT_DIR = __file__[:__file__.rfind('cvat/')]
 _DATUMARO_REPO_PATH = osp.join(_CVAT_ROOT_DIR, 'datumaro')
 sys.path.append(_DATUMARO_REPO_PATH)
-from datumaro.components.project import Project
+from datumaro.components.project import Project, Environment
 import datumaro.components.extractor as datumaro
 from .bindings import CvatImagesDirExtractor, CvatTaskExtractor
 
@@ -132,83 +132,7 @@ class TaskProject:
         return categories
 
     def put_annotations(self, annotations):
-        patch = {}
+        raise NotImplementedError()
-
-        categories = self._dataset.categories()
-        label_cat = categories[datumaro.AnnotationType.label]
-
-        label_map = {}
-        attr_map = {}
-        db_labels = self._db_task.label_set.all()
-        for db_label in db_labels:
-            label_map[db_label.id] = label_cat.find(db_label.name)
-
-            db_attributes = db_label.attributespec_set.all()
-            for db_attr in db_attributes:
-                attr_map[(db_label.id, db_attr.id)] = db_attr.name
-        map_label = lambda label_db_id: label_map[label_db_id]
-        map_attr = lambda label_db_id, attr_db_id: \
-            attr_map[(label_db_id, attr_db_id)]
-
-        for tag_obj in annotations['tags']:
-            item_id = str(tag_obj['frame'])
-            item_anno = patch.get(item_id, [])
-
-            anno_group = tag_obj['group']
-            if isinstance(anno_group, int):
-                anno_group = [anno_group]
-            anno_label = map_label(tag_obj['label_id'])
-            anno_attr = {}
-            for attr in tag_obj['attributes']:
-                attr_name = map_attr(tag_obj['label_id'], attr['id'])
-                anno_attr[attr_name] = attr['value']
-
-            anno = datumaro.LabelObject(label=anno_label,
-                attributes=anno_attr, group=anno_group)
-            item_anno.append(anno)
-
-            patch[item_id] = item_anno
-
-        for shape_obj in annotations['shapes']:
-            item_id = str(shape_obj['frame'])
-            item_anno = patch.get(item_id, [])
-
-            anno_group = shape_obj['group']
-            if isinstance(anno_group, int):
-                anno_group = [anno_group]
-            anno_label = map_label(shape_obj['label_id'])
-            anno_attr = {}
-            for attr in shape_obj['attributes']:
-                attr_name = map_attr(shape_obj['label_id'], attr['id'])
-                anno_attr[attr_name] = attr['value']
-
-            anno_points = shape_obj['points']
-            if shape_obj['type'] == ShapeType.POINTS:
-                anno = datumaro.PointsObject(anno_points,
-                    label=anno_label, attributes=anno_attr, group=anno_group)
-            elif shape_obj['type'] == ShapeType.POLYLINE:
-                anno = datumaro.PolyLineObject(anno_points,
-                    label=anno_label, attributes=anno_attr, group=anno_group)
-            elif shape_obj['type'] == ShapeType.POLYGON:
-                anno = datumaro.PolygonObject(anno_points,
-                    label=anno_label, attributes=anno_attr, group=anno_group)
-            elif shape_obj['type'] == ShapeType.RECTANGLE:
-                x0, y0, x1, y1 = anno_points
-                anno = datumaro.BboxObject(x0, y0, x1 - x0, y1 - y0,
-                    label=anno_label, attributes=anno_attr, group=anno_group)
-            else:
-                raise Exception("Unknown shape type '%s'" % (shape_obj['type']))
-
-            item_anno.append(anno)
-
-            patch[item_id] = item_anno
-
-        # TODO: support track annotations
-
-        patch = [datumaro.DatasetItem(id=id_, annotations=anno) \
-            for id_, ann in patch.items()]
-
-        self._dataset.update(patch)
 
     def save(self, save_dir=None, save_images=False):
         if self._dataset is not None:
@@ -296,10 +220,10 @@ class TaskProject:
             osp.join(templates_dir, 'README.md'),
             osp.join(target_dir, 'README.md'))
 
-        templates_dir = osp.join(templates_dir, 'extractors')
+        templates_dir = osp.join(templates_dir, 'plugins')
         target_dir = osp.join(target_dir,
             exported_project.config.env_dir,
-            exported_project.env.config.extractors_dir)
+            exported_project.config.plugins_dir)
         os.makedirs(target_dir, exist_ok=True)
         shutil.copyfile(
             osp.join(templates_dir, _TASK_IMAGES_REMOTE_EXTRACTOR + '.py'),
@@ -409,9 +333,9 @@ EXPORT_FORMATS = [
 ]
 
 def get_export_formats():
-    from datumaro.components import converters
+    converters = Environment().converters
 
-    available_formats = set(name for name, _ in converters.items)
+    available_formats = set(converters.items)
     available_formats.add(EXPORT_FORMAT_DATUMARO_PROJECT)
 
     public_formats = []

datumaro/datumaro/cli/__main__.py

@@ -5,6 +5,8 @@
 
 import argparse
 import logging as log
+import logging.handlers
+import os
 import sys
 
 from . import contexts, commands
@@ -81,15 +83,70 @@ def make_parser():
 
     return parser
 
-def set_up_logger(args):
+class _LogManager:
-    log.basicConfig(format='%(asctime)s %(levelname)s: %(message)s',
+    _LOGLEVEL_ENV_NAME = '_DATUMARO_INIT_LOGLEVEL'
-        level=args.loglevel)
+    _BUFFER_SIZE = 1000
+    _root = None
+    _init_handler = None
+    _default_handler = None
+
+    @classmethod
+    def init_basic_logger(cls):
+        base_loglevel = os.getenv(cls._LOGLEVEL_ENV_NAME, 'info')
+        base_loglevel = loglevel(base_loglevel)
+        root = log.getLogger()
+        root.setLevel(base_loglevel)
+
+        # NOTE: defer use of this handler until the logger
+        # is properly initialized, but keep logging enabled before this.
+        # Store messages obtained during initialization and print them after
+        # if necessary.
+        default_handler = log.StreamHandler()
+        default_handler.setFormatter(
+            log.Formatter('%(asctime)s %(levelname)s: %(message)s'))
+
+        init_handler = logging.handlers.MemoryHandler(cls._BUFFER_SIZE,
+            target=default_handler)
+        root.addHandler(init_handler)
+
+        cls._root = root
+        cls._init_handler = init_handler
+        cls._default_handler = default_handler
+
+    @classmethod
+    def set_up_logger(cls, level):
+        log.getLogger().setLevel(level)
+
+        if cls._init_handler:
+            # NOTE: Handlers are not capable of filtering with loglevel
+            # despite a level can be set for a handler. The level is checked
+            # by Logger. However, handler filters are checked at handler level.
+            class LevelFilter:
+                def __init__(self, level):
+                    super().__init__()
+                    self.level = level
+
+                def filter(self, record):
+                    return record.levelno >= self.level
+            filt = LevelFilter(level)
+            cls._default_handler.addFilter(filt)
+
+            cls._root.removeHandler(cls._init_handler)
+            cls._init_handler.close()
+            del cls._init_handler
+            cls._init_handler = None
+
+            cls._default_handler.removeFilter(filt)
+
+            cls._root.addHandler(cls._default_handler)
 
 def main(args=None):
+    _LogManager.init_basic_logger()
+
     parser = make_parser()
     args = parser.parse_args(args)
 
-    set_up_logger(args)
+    _LogManager.set_up_logger(args.loglevel)
 
     if 'command' not in args:
         parser.print_help()

datumaro/datumaro/cli/contexts/project/__init__.py

@@ -10,10 +10,11 @@ import os
 import os.path as osp
 import shutil
 
-from datumaro.components.project import Project
+from datumaro.components.project import Project, Environment
 from datumaro.components.comparator import Comparator
 from datumaro.components.dataset_filter import DatasetItemEncoder
 from datumaro.components.extractor import AnnotationType
+from datumaro.components.cli_plugin import CliPlugin
 from .diff import DiffVisualizer
 from ...util import add_subparser, CliException, MultilineFormatter
 from ...util.project import make_project_path, load_project, \
@@ -75,8 +76,7 @@ def create_command(args):
     return 0
 
 def build_import_parser(parser_ctor=argparse.ArgumentParser):
-    import datumaro.components.importers as importers_module
+    builtins = sorted(Environment().importers.items)
-    builtin_importers = [name for name, cls in importers_module.items]
 
     parser = parser_ctor(help="Create project from existing dataset",
         description="""
@@ -104,7 +104,7 @@ def build_import_parser(parser_ctor=argparse.ArgumentParser):
             <project_dir>/.datumaro/extractors
             and <project_dir>/.datumaro/importers.|n
             |n
-            List of supported dataset formats: %s|n
+            List of builtin dataset formats: %s|n
             |n
             Examples:|n
             - Create a project from VOC dataset in the current directory:|n
@@ -112,7 +112,7 @@ def build_import_parser(parser_ctor=argparse.ArgumentParser):
             |n
             - Create a project from COCO dataset in other directory:|n
             |s|simport -f coco -i path/to/coco -o path/I/like/
-        """ % ', '.join(builtin_importers),
+        """ % ', '.join(builtins),
         formatter_class=MultilineFormatter)
 
     parser.add_argument('-o', '--output-dir', default='.', dest='dst_dir',
@@ -129,8 +129,8 @@ def build_import_parser(parser_ctor=argparse.ArgumentParser):
         help="Path to import project from")
     parser.add_argument('-f', '--format', required=True,
         help="Source project format")
-    # parser.add_argument('extra_args', nargs=argparse.REMAINDER,
+    parser.add_argument('extra_args', nargs=argparse.REMAINDER,
-    #     help="Additional arguments for importer (pass '-- -h' for help)")
+        help="Additional arguments for importer (pass '-- -h' for help)")
     parser.set_defaults(command=import_command)
 
     return parser
@@ -155,11 +155,21 @@ def import_command(args):
     if project_name is None:
         project_name = osp.basename(project_dir)
 
+    extra_args = {}
+    try:
+        env = Environment()
+        importer = env.importers.get(args.format)
+        if hasattr(importer, 'from_cmdline'):
+            extra_args = importer.from_cmdline(args.extra_args)
+    except KeyError:
+        raise CliException("Importer for format '%s' is not found" % \
+            args.format)
+
     log.info("Importing project from '%s' as '%s'" % \
         (args.source, args.format))
 
     source = osp.abspath(args.source)
-    project = Project.import_from(source, args.format)
+    project = importer(source, **extra_args)
     project.config.project_name = project_name
     project.config.project_dir = project_dir
 
@@ -217,8 +227,7 @@ class FilterModes(Enum):
         return [m.name.replace('_', '+') for m in cls]
 
 def build_export_parser(parser_ctor=argparse.ArgumentParser):
-    import datumaro.components.converters as converters_module
+    builtins = sorted(Environment().converters.items)
-    builtin_converters = [name for name, cls in converters_module.items]
 
     parser = parser_ctor(help="Export project",
         description="""
@@ -237,7 +246,7 @@ def build_export_parser(parser_ctor=argparse.ArgumentParser):
             To do this, you need to put a Converter
             definition script to <project_dir>/.datumaro/converters.|n
             |n
-            List of supported dataset formats: %s|n
+            List of builtin dataset formats: %s|n
             |n
             Examples:|n
             - Export project as a VOC-like dataset, include images:|n
@@ -245,7 +254,7 @@ def build_export_parser(parser_ctor=argparse.ArgumentParser):
             |n
             - Export project as a COCO-like dataset in other directory:|n
             |s|sexport -f coco -o path/I/like/
-        """ % ', '.join(builtin_converters),
+        """ % ', '.join(builtins),
         formatter_class=MultilineFormatter)
 
     parser.add_argument('-e', '--filter', default=None,
@@ -282,8 +291,10 @@ def export_command(args):
     dst_dir = osp.abspath(dst_dir)
 
     try:
-        converter = project.env.make_converter(args.format,
+        converter = project.env.converters.get(args.format)
-            cmdline_args=args.extra_args)
+        if hasattr(converter, 'from_cmdline'):
+            extra_args = converter.from_cmdline(args.extra_args)
+            converter = converter(**extra_args)
     except KeyError:
         raise CliException("Converter for format '%s' is not found" % \
             args.format)
@@ -494,8 +505,7 @@ def diff_command(args):
             second_project.config.project_name)
         )
     dst_dir = osp.abspath(dst_dir)
-    if dst_dir:
+    log.info("Saving diff to '%s'" % dst_dir)
-        log.info("Saving diff to '%s'" % dst_dir)
 
     visualizer = DiffVisualizer(save_dir=dst_dir, comparator=comparator,
         output_format=args.format)
@@ -506,13 +516,19 @@ def diff_command(args):
     return 0
 
 def build_transform_parser(parser_ctor=argparse.ArgumentParser):
+    builtins = sorted(Environment().transforms.items)
+
     parser = parser_ctor(help="Transform project",
         description="""
             Applies some operation to dataset items in the project
-            and produces a new project.
+            and produces a new project.|n
-
+            |n
-            [NOT IMPLEMENTED YET]
+            Builtin transforms: %s|n
-        """,
+            |n
+            Examples:|n
+            - Convert instance polygons to masks:|n
+            |s|stransform -n polygons_to_masks
+        """ % ', '.join(builtins),
         formatter_class=MultilineFormatter)
 
     parser.add_argument('-t', '--transform', required=True,
@@ -523,30 +539,46 @@ def build_transform_parser(parser_ctor=argparse.ArgumentParser):
         help="Overwrite existing files in the save directory")
     parser.add_argument('-p', '--project', dest='project_dir', default='.',
         help="Directory of the project to operate on (default: current dir)")
+    parser.add_argument('extra_args', nargs=argparse.REMAINDER, default=None,
+        help="Additional arguments for transformation (pass '-- -h' for help)")
     parser.set_defaults(command=transform_command)
 
     return parser
 
 def transform_command(args):
-    raise NotImplementedError("Not implemented yet.")
+    project = load_project(args.project_dir)
 
-    # project = load_project(args.project_dir)
+    dst_dir = args.dst_dir
+    if dst_dir:
+        if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir):
+            raise CliException("Directory '%s' already exists "
+                "(pass --overwrite to force creation)" % dst_dir)
+    else:
+        dst_dir = generate_next_dir_name('%s-transform' % \
+            project.config.project_name)
+    dst_dir = osp.abspath(dst_dir)
 
-    # dst_dir = args.dst_dir
+    extra_args = {}
-    # if dst_dir:
+    try:
-    #     if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir):
+        transform = project.env.transforms.get(args.transform)
-    #         raise CliException("Directory '%s' already exists "
+        if hasattr(transform, 'from_cmdline'):
-    #             "(pass --overwrite to force creation)" % dst_dir)
+            extra_args = transform.from_cmdline(args.extra_args)
-    # dst_dir = osp.abspath(args.dst_dir)
+    except KeyError:
+        raise CliException("Transform '%s' is not found" % args.transform)
+
+    log.info("Loading the project...")
+    dataset = project.make_dataset()
 
-    # project.make_dataset().transform_project(
+    log.info("Transforming the project...")
-    #     method=args.transform,
+    dataset.transform_project(
-    #     save_dir=dst_dir
+        method=transform,
-    # )
+        save_dir=dst_dir,
+        **extra_args
+    )
 
-    # log.info("Transform results saved to '%s'" % dst_dir)
+    log.info("Transform results have been saved to '%s'" % dst_dir)
 
-    # return 0
+    return 0
 
 def build_info_parser(parser_ctor=argparse.ArgumentParser):
     parser = parser_ctor(help="Get project info",

datumaro/datumaro/cli/contexts/source/__init__.py

@@ -9,13 +9,13 @@ import os
 import os.path as osp
 import shutil
 
+from datumaro.components.project import Environment
 from ...util import add_subparser, CliException, MultilineFormatter
 from ...util.project import load_project
 
 
 def build_add_parser(parser_ctor=argparse.ArgumentParser):
-    import datumaro.components.extractors as extractors_module
+    builtins = sorted(Environment().extractors.items)
-    extractors_list = [name for name, cls in extractors_module.items]
 
     base_parser = argparse.ArgumentParser(add_help=False)
     base_parser.add_argument('-n', '--name', default=None,
@@ -53,7 +53,7 @@ def build_add_parser(parser_ctor=argparse.ArgumentParser):
             To do this, you need to put an Extractor
             definition script to <project_dir>/.datumaro/extractors.|n
             |n
-            List of supported source formats: %s|n
+            List of builtin source formats: %s|n
             |n
             Examples:|n
             - Add a local directory with VOC-like dataset:|n
@@ -61,7 +61,7 @@ def build_add_parser(parser_ctor=argparse.ArgumentParser):
             - Add a local file with CVAT annotations, call it 'mysource'|n
             |s|s|s|sto the project somewhere else:|n
             |s|sadd path path/to/cvat.xml -f cvat -n mysource -p somewhere/else/
-        """ % ('%(prog)s SOURCE_TYPE --help', ', '.join(extractors_list)),
+        """ % ('%(prog)s SOURCE_TYPE --help', ', '.join(builtins)),
         formatter_class=MultilineFormatter,
         add_help=False)
     parser.set_defaults(command=add_command)

datumaro/datumaro/components/cli_plugin.py

@@ -0,0 +1,56 @@
+
+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import argparse
+
+from datumaro.cli.util import MultilineFormatter
+
+
+class CliPlugin:
+    @staticmethod
+    def _get_name(cls):
+        return getattr(cls, 'NAME',
+            remove_plugin_type(to_snake_case(cls.__name__)))
+
+    @staticmethod
+    def _get_doc(cls):
+        return getattr(cls, '__doc__', "")
+
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        args = {
+            'prog': cls._get_name(cls),
+            'description': cls._get_doc(cls),
+            'formatter_class': MultilineFormatter,
+        }
+        args.update(kwargs)
+
+        return argparse.ArgumentParser(**args)
+
+    @classmethod
+    def from_cmdline(cls, args=None):
+        if args and args[0] == '--':
+            args = args[1:]
+        parser = cls.build_cmdline_parser()
+        args = parser.parse_args(args)
+        return vars(args)
+
+def remove_plugin_type(s):
+    for t in {'transform', 'extractor', 'converter', 'launcher', 'importer'}:
+        s = s.replace('_' + t, '')
+    return s
+
+def to_snake_case(s):
+    if not s:
+        return ''
+
+    name = [s[0].lower()]
+    for char in s[1:]:
+        if char.isalpha() and char.isupper():
+            name.append('_')
+            name.append(char.lower())
+        else:
+            name.append(char)
+    return ''.join(name)

datumaro/datumaro/components/config_model.py

@@ -30,40 +30,22 @@ class Model(Config):
         super().__init__(config, schema=MODEL_SCHEMA)
 
 
-ENV_SCHEMA = _SchemaBuilder() \
-    .add('models_dir', str) \
-    .add('importers_dir', str) \
-    .add('launchers_dir', str) \
-    .add('converters_dir', str) \
-    .add('extractors_dir', str) \
-    \
-    .add('models', lambda: _DefaultConfig(
-        lambda v=None: Model(v))) \
-    .build()
-
-ENV_DEFAULT_CONFIG = Config({
-    'models_dir': 'models',
-    'importers_dir': 'importers',
-    'launchers_dir': 'launchers',
-    'converters_dir': 'converters',
-    'extractors_dir': 'extractors',
-}, mutable=False, schema=ENV_SCHEMA)
-
-
 PROJECT_SCHEMA = _SchemaBuilder() \
     .add('project_name', str) \
     .add('format_version', int) \
     \
-    .add('sources_dir', str) \
-    .add('dataset_dir', str) \
-    .add('build_dir', str) \
     .add('subsets', list) \
     .add('sources', lambda: _DefaultConfig(
         lambda v=None: Source(v))) \
+    .add('models', lambda: _DefaultConfig(
+        lambda v=None: Model(v))) \
     \
+    .add('models_dir', str, internal=True) \
+    .add('plugins_dir', str, internal=True) \
+    .add('sources_dir', str, internal=True) \
+    .add('dataset_dir', str, internal=True) \
     .add('project_filename', str, internal=True) \
     .add('project_dir', str, internal=True) \
-    .add('env_filename', str, internal=True) \
     .add('env_dir', str, internal=True) \
     .build()
 
@@ -73,10 +55,10 @@ PROJECT_DEFAULT_CONFIG = Config({
 
     'sources_dir': 'sources',
     'dataset_dir': 'dataset',
-    'build_dir': 'build',
+    'models_dir': 'models',
+    'plugins_dir': 'plugins',
 
     'project_filename': 'config.yaml',
     'project_dir': '',
-    'env_filename': 'datumaro.yaml',
     'env_dir': '.datumaro',
 }, mutable=False, schema=PROJECT_SCHEMA)

datumaro/datumaro/components/converters/__init__.py

@@ -1,53 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-
-from datumaro.components.converters.datumaro import DatumaroConverter
-
-from datumaro.components.converters.coco import (
-    CocoConverter,
-    CocoImageInfoConverter,
-    CocoCaptionsConverter,
-    CocoInstancesConverter,
-    CocoPersonKeypointsConverter,
-    CocoLabelsConverter,
-)
-
-from datumaro.components.converters.voc import (
-    VocConverter,
-    VocClassificationConverter,
-    VocDetectionConverter,
-    VocLayoutConverter,
-    VocActionConverter,
-    VocSegmentationConverter,
-)
-
-from datumaro.components.converters.yolo import YoloConverter
-from datumaro.components.converters.tfrecord import DetectionApiConverter
-from datumaro.components.converters.cvat import CvatConverter
-
-
-items = [
-    ('datumaro', DatumaroConverter),
-
-    ('coco', CocoConverter),
-    ('coco_images', CocoImageInfoConverter),
-    ('coco_captions', CocoCaptionsConverter),
-    ('coco_instances', CocoInstancesConverter),
-    ('coco_person_kp', CocoPersonKeypointsConverter),
-    ('coco_labels', CocoLabelsConverter),
-
-    ('voc', VocConverter),
-    ('voc_cls', VocClassificationConverter),
-    ('voc_det', VocDetectionConverter),
-    ('voc_segm', VocSegmentationConverter),
-    ('voc_action', VocActionConverter),
-    ('voc_layout', VocLayoutConverter),
-
-    ('yolo', YoloConverter),
-
-    ('tf_detection_api', DetectionApiConverter),
-
-    ('cvat', CvatConverter),
-]

datumaro/datumaro/components/dataset_filter.py

@@ -6,8 +6,7 @@
 from lxml import etree as ET # NOTE: lxml has proper XPath implementation
 from datumaro.components.extractor import (DatasetItem, Extractor,
     Annotation, AnnotationType,
-    LabelObject, MaskObject, PointsObject, PolygonObject,
+    Label, Mask, Points, Polygon, PolyLine, Bbox, Caption,
-    PolyLineObject, BboxObject, CaptionObject,
 )
 
 
@@ -100,7 +99,7 @@ class DatasetItemEncoder:
         ET.SubElement(ann_elem, 'y').text = str(obj.y)
         ET.SubElement(ann_elem, 'w').text = str(obj.w)
         ET.SubElement(ann_elem, 'h').text = str(obj.h)
-        ET.SubElement(ann_elem, 'area').text = str(obj.area())
+        ET.SubElement(ann_elem, 'area').text = str(obj.get_area())
 
         return ann_elem
 
@@ -191,19 +190,19 @@ class DatasetItemEncoder:
 
     @classmethod
     def encode_annotation(cls, o, categories=None):
-        if isinstance(o, LabelObject):
+        if isinstance(o, Label):
             return cls.encode_label_object(o, categories)
-        if isinstance(o, MaskObject):
+        if isinstance(o, Mask):
             return cls.encode_mask_object(o, categories)
-        if isinstance(o, BboxObject):
+        if isinstance(o, Bbox):
             return cls.encode_bbox_object(o, categories)
-        if isinstance(o, PointsObject):
+        if isinstance(o, Points):
             return cls.encode_points_object(o, categories)
-        if isinstance(o, PolyLineObject):
+        if isinstance(o, PolyLine):
             return cls.encode_polyline_object(o, categories)
-        if isinstance(o, PolygonObject):
+        if isinstance(o, Polygon):
             return cls.encode_polygon_object(o, categories)
-        if isinstance(o, CaptionObject):
+        if isinstance(o, Caption):
             return cls.encode_caption_object(o)
         raise NotImplementedError("Unexpected annotation object passed: %s" % o)
 

datumaro/datumaro/components/extractor.py

@@ -35,7 +35,9 @@ class Annotation:
             attributes = dict(attributes)
         self.attributes = attributes
 
-        if group is not None:
+        if group is None:
+            group = 0
+        else:
             group = int(group)
         self.group = group
     # pylint: enable=redefined-builtin
@@ -95,6 +97,8 @@ class LabelCategories(Categories):
                 attributes = set(attributes)
             for attr in attributes:
                 assert isinstance(attr, str)
+        if parent is None:
+            parent = ''
 
         index = len(self.items)
         self.items.append(self.Category(name, parent, attributes))
@@ -112,12 +116,15 @@ class LabelCategories(Categories):
         return \
             (self.items == other.items)
 
-class LabelObject(Annotation):
+class Label(Annotation):
     # pylint: disable=redefined-builtin
     def __init__(self, label=None,
             id=None, attributes=None, group=None):
         super().__init__(id=id, type=AnnotationType.label,
             attributes=attributes, group=group)
+
+        if label is not None:
+            label = int(label)
         self.label = label
     # pylint: enable=redefined-builtin
 
@@ -157,54 +164,60 @@ class MaskCategories(Categories):
                 return False
         return True
 
-class MaskObject(Annotation):
+class Mask(Annotation):
     # pylint: disable=redefined-builtin
     def __init__(self, image=None, label=None, z_order=None,
             id=None, attributes=None, group=None):
-        super().__init__(id=id, type=AnnotationType.mask,
+        super().__init__(type=AnnotationType.mask,
-            attributes=attributes, group=group)
+            id=id, attributes=attributes, group=group)
+
         self._image = image
+
+        if label is not None:
+            label = int(label)
         self._label = label
 
         if z_order is None:
             z_order = 0
+        else:
+            z_order = int(z_order)
         self._z_order = z_order
     # pylint: enable=redefined-builtin
 
-    @property
-    def label(self):
-        return self._label
-
     @property
     def image(self):
         if callable(self._image):
             return self._image()
         return self._image
 
-    def painted_data(self, colormap):
+    @property
-        raise NotImplementedError()
+    def label(self):
+        return self._label
 
-    def area(self):
+    @property
-        if self._label is None:
+    def z_order(self):
-            raise NotImplementedError()
+        return self._z_order
-        return np.count_nonzero(self.image)
 
-    def extract(self, class_id):
+    def as_class_mask(self, label_id=None):
-        raise NotImplementedError()
+        from datumaro.util.mask_tools import make_index_mask
+        if label_id is None:
+            label_id = self.label
+        return make_index_mask(self.image, label_id)
+
+    def as_instance_mask(self, instance_id):
+        from datumaro.util.mask_tools import make_index_mask
+        return make_index_mask(self.image, instance_id)
+
+    def get_area(self):
+        return np.count_nonzero(self.image)
 
     def get_bbox(self):
-        if self._label is None:
+        from datumaro.util.mask_tools import find_mask_bbox
-            raise NotImplementedError()
+        return find_mask_bbox(self.image)
-        image = self.image
-        cols = np.any(image, axis=0)
-        rows = np.any(image, axis=1)
-        x0, x1 = np.where(cols)[0][[0, -1]]
-        y0, y1 = np.where(rows)[0][[0, -1]]
-        return [x0, y0, x1 - x0, y1 - y0]
 
-    @property
+    def paint(self, colormap):
-    def z_order(self):
+        from datumaro.util.mask_tools import paint_mask
-        return self._z_order
+        return paint_mask(self.as_class_mask(), colormap)
 
     def __eq__(self, other):
         if not super().__eq__(other):
@@ -215,7 +228,7 @@ class MaskObject(Annotation):
             (self.image is not None and other.image is not None and \
                 np.array_equal(self.image, other.image))
 
-class RleMask(MaskObject):
+class RleMask(Mask):
     # pylint: disable=redefined-builtin
     def __init__(self, rle=None, label=None, z_order=None,
             id=None, attributes=None, group=None):
@@ -231,11 +244,11 @@ class RleMask(MaskObject):
         from pycocotools import mask as mask_utils
         return lambda: mask_utils.decode(rle).astype(np.bool)
 
-    def area(self):
+    def get_area(self):
         from pycocotools import mask as mask_utils
         return mask_utils.area(self._rle)
 
-    def bbox(self):
+    def get_bbox(self):
         from pycocotools import mask as mask_utils
         return mask_utils.toBbox(self._rle)
 
@@ -248,6 +261,73 @@ class RleMask(MaskObject):
             return super().__eq__(other)
         return self._rle == other._rle
 
+class CompiledMask:
+    @staticmethod
+    def from_instance_masks(instance_masks,
+            instance_ids=None, instance_labels=None):
+        from datumaro.util.mask_tools import merge_masks
+
+        if instance_ids is not None:
+            assert len(instance_ids) == len(instance_masks)
+        else:
+            instance_ids = [1 + i for i in range(len(instance_masks))]
+
+        if instance_labels is not None:
+            assert len(instance_labels) == len(instance_masks)
+        else:
+            instance_labels = [None] * len(instance_masks)
+
+        instance_masks = sorted(instance_masks, key=lambda m: m.z_order)
+
+        instance_mask = [m.as_instance_mask(id) for m, id in
+            zip(instance_masks, instance_ids)]
+        instance_mask = merge_masks(instance_mask)
+
+        cls_mask = [m.as_class_mask(c) for m, c in
+            zip(instance_masks, instance_labels)]
+        cls_mask = merge_masks(cls_mask)
+        return __class__(class_mask=cls_mask, instance_mask=instance_mask)
+
+    def __init__(self, class_mask=None, instance_mask=None):
+        self._class_mask = class_mask
+        self._instance_mask = instance_mask
+
+    @staticmethod
+    def _get_image(image):
+        if callable(image):
+            return image()
+        return image
+
+    @property
+    def class_mask(self):
+        return self._get_image(self._class_mask)
+
+    @property
+    def instance_mask(self):
+        return self._get_image(self._instance_mask)
+
+    @property
+    def instance_count(self):
+        return int(self.instance_mask.max())
+
+    def get_instance_labels(self, class_count=None):
+        if class_count is None:
+            class_count = np.max(self.class_mask) + 1
+
+        m = self.class_mask * class_count + self.instance_mask
+        m = m.astype(int)
+        keys = np.unique(m)
+        instance_labels = {k % class_count: k // class_count
+            for k in keys if k % class_count != 0
+        }
+        return instance_labels
+
+    def extract(self, instance_id):
+        return self.instance_mask == instance_id
+
+    def lazy_extract(self, instance_id):
+        return lambda: self.extract(instance_id)
+
 def compute_iou(bbox_a, bbox_b):
     aX, aY, aW, aH = bbox_a
     bX, bY, bW, bH = bbox_b
@@ -266,29 +346,43 @@ def compute_iou(bbox_a, bbox_b):
 
     return intersection / max(1.0, union)
 
-class ShapeObject(Annotation):
+class _Shape(Annotation):
     # pylint: disable=redefined-builtin
     def __init__(self, type, points=None, label=None, z_order=None,
             id=None, attributes=None, group=None):
         super().__init__(id=id, type=type,
             attributes=attributes, group=group)
-        self.points = points
+        self._points = points
-        self.label = label
+
+        if label is not None:
+            label = int(label)
+        self._label = label
 
         if z_order is None:
             z_order = 0
+        else:
+            z_order = int(z_order)
         self._z_order = z_order
     # pylint: enable=redefined-builtin
 
-    def area(self):
+    @property
-        raise NotImplementedError()
+    def points(self):
+        return self._points
+
+    @property
+    def label(self):
+        return self._label
 
-    def get_polygon(self):
+    @property
+    def z_order(self):
+        return self._z_order
+
+    def get_area(self):
         raise NotImplementedError()
 
     def get_bbox(self):
-        points = self.get_points()
+        points = self.points
-        if not self.points:
+        if not points:
             return None
 
         xs = [p for p in points[0::2]]
@@ -299,22 +393,15 @@ class ShapeObject(Annotation):
         y1 = max(ys)
         return [x0, y0, x1 - x0, y1 - y0]
 
-    def get_points(self):
-        return self.points
-
-    @property
-    def z_order(self):
-        return self._z_order
-
     def __eq__(self, other):
         if not super().__eq__(other):
             return False
         return \
-            (self.points == other.points) and \
+            (np.array_equal(self.points, other.points)) and \
             (self.z_order == other.z_order) and \
             (self.label == other.label)
 
-class PolyLineObject(ShapeObject):
+class PolyLine(_Shape):
     # pylint: disable=redefined-builtin
     def __init__(self, points=None, label=None, z_order=None,
             id=None, attributes=None, group=None):
@@ -323,35 +410,34 @@ class PolyLineObject(ShapeObject):
             id=id, attributes=attributes, group=group)
     # pylint: enable=redefined-builtin
 
-    def get_polygon(self):
+    def as_polygon(self):
-        return self.get_points()
+        return self.points[:]
 
-    def area(self):
+    def get_area(self):
         return 0
 
-class PolygonObject(ShapeObject):
+class Polygon(_Shape):
     # pylint: disable=redefined-builtin
     def __init__(self, points=None, z_order=None,
             label=None, id=None, attributes=None, group=None):
         if points is not None:
+            # keep the message on the single line to produce
+            # informative output
             assert len(points) % 2 == 0 and 3 <= len(points) // 2, "Wrong polygon points: %s" % points
         super().__init__(type=AnnotationType.polygon,
             points=points, label=label, z_order=z_order,
             id=id, attributes=attributes, group=group)
     # pylint: enable=redefined-builtin
 
-    def get_polygon(self):
+    def get_area(self):
-        return self.get_points()
-
-    def area(self):
         import pycocotools.mask as mask_utils
 
         _, _, w, h = self.get_bbox()
-        rle = mask_utils.frPyObjects([self.get_points()], h, w)
+        rle = mask_utils.frPyObjects([self.points], h, w)
         area = mask_utils.area(rle)[0]
         return area
 
-class BboxObject(ShapeObject):
+class Bbox(_Shape):
     # pylint: disable=redefined-builtin
     def __init__(self, x=0, y=0, w=0, h=0, label=None, z_order=None,
             id=None, attributes=None, group=None):
@@ -376,13 +462,13 @@ class BboxObject(ShapeObject):
     def h(self):
         return self.points[3] - self.points[1]
 
-    def area(self):
+    def get_area(self):
         return self.w * self.h
 
     def get_bbox(self):
         return [self.x, self.y, self.w, self.h]
 
-    def get_polygon(self):
+    def as_polygon(self):
         x, y, w, h = self.get_bbox()
         return [
             x, y,
@@ -417,7 +503,7 @@ class PointsCategories(Categories):
         return \
             (self.items == other.items)
 
-class PointsObject(ShapeObject):
+class Points(_Shape):
     Visibility = Enum('Visibility', [
         ('absent', 0),
         ('hidden', 1),
@@ -447,7 +533,7 @@ class PointsObject(ShapeObject):
         self.visibility = visibility
     # pylint: enable=redefined-builtin
 
-    def area(self):
+    def get_area(self):
         return 0
 
     def get_bbox(self):
@@ -467,7 +553,7 @@ class PointsObject(ShapeObject):
         return \
             (self.visibility == other.visibility)
 
-class CaptionObject(Annotation):
+class Caption(Annotation):
     # pylint: disable=redefined-builtin
     def __init__(self, caption=None,
             id=None, attributes=None, group=None):
@@ -476,6 +562,8 @@ class CaptionObject(Annotation):
 
         if caption is None:
             caption = ''
+        else:
+            caption = str(caption)
         self.caption = caption
     # pylint: enable=redefined-builtin
 
@@ -642,5 +730,39 @@ class Extractor(_ExtractorBase):
         return DatasetIteratorWrapper(
             _DatasetFilter(self, pred), self.categories(), self.subsets())
 
+DEFAULT_SUBSET_NAME = 'default'
+
+
+class SourceExtractor(Extractor):
+    pass
+
+class Importer:
+    def __call__(self, path, **extra_params):
+        raise NotImplementedError()
+
+class Transform(Extractor):
+    @classmethod
+    def wrap_item(cls, item, **kwargs):
+        expected_args = {'id', 'annotations', 'subset', 'path', 'image'}
+        for k in expected_args:
+            if k not in kwargs:
+                if k == 'image' and item.has_image:
+                    kwargs[k] = lambda: item.image
+                else:
+                    kwargs[k] = getattr(item, k)
+        return DatasetItem(**kwargs)
+
+    def __init__(self, extractor):
+        super().__init__()
+
+        self._extractor = extractor
+
+    def __iter__(self):
+        for item in self._extractor:
+            yield self.transform_item(item)
+
+    def categories(self):
+        return self._extractor.categories()
 
-DEFAULT_SUBSET_NAME = 'default'
+    def transform_item(self, item):
+        raise NotImplementedError()

datumaro/datumaro/components/extractors/__init__.py

@@ -1,62 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-
-from datumaro.components.extractors.datumaro import DatumaroExtractor
-
-from datumaro.components.extractors.coco import (
-    CocoImageInfoExtractor,
-    CocoCaptionsExtractor,
-    CocoInstancesExtractor,
-    CocoLabelsExtractor,
-    CocoPersonKeypointsExtractor,
-)
-
-from datumaro.components.extractors.voc import (
-    VocClassificationExtractor,
-    VocDetectionExtractor,
-    VocSegmentationExtractor,
-    VocLayoutExtractor,
-    VocActionExtractor,
-    VocComp_1_2_Extractor,
-    VocComp_3_4_Extractor,
-    VocComp_5_6_Extractor,
-    VocComp_7_8_Extractor,
-    VocComp_9_10_Extractor,
-)
-
-from datumaro.components.extractors.yolo import YoloExtractor
-from datumaro.components.extractors.tfrecord import DetectionApiExtractor
-from datumaro.components.extractors.cvat import CvatExtractor
-from datumaro.components.extractors.image_dir import ImageDirExtractor
-
-items = [
-    ('datumaro', DatumaroExtractor),
-
-    ('coco_images', CocoImageInfoExtractor),
-    ('coco_captions', CocoCaptionsExtractor),
-    ('coco_instances', CocoInstancesExtractor),
-    ('coco_person_kp', CocoPersonKeypointsExtractor),
-    ('coco_labels', CocoLabelsExtractor),
-
-    ('voc_cls', VocClassificationExtractor),
-    ('voc_det', VocDetectionExtractor),
-    ('voc_segm', VocSegmentationExtractor),
-    ('voc_layout', VocLayoutExtractor),
-    ('voc_action', VocActionExtractor),
-
-    ('voc_comp_1_2', VocComp_1_2_Extractor),
-    ('voc_comp_3_4', VocComp_3_4_Extractor),
-    ('voc_comp_5_6', VocComp_5_6_Extractor),
-    ('voc_comp_7_8', VocComp_7_8_Extractor),
-    ('voc_comp_9_10', VocComp_9_10_Extractor),
-
-    ('yolo', YoloExtractor),
-
-    ('tf_detection_api', DetectionApiExtractor),
-
-    ('cvat', CvatExtractor),
-
-    ('image_dir', ImageDirExtractor),
-]

datumaro/datumaro/components/formats/__init__.py

@@ -1,5 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-

datumaro/datumaro/components/importers/__init__.py

@@ -1,30 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-
-from datumaro.components.importers.datumaro import DatumaroImporter
-from datumaro.components.importers.coco import CocoImporter
-from datumaro.components.importers.voc import VocImporter, VocResultsImporter
-from datumaro.components.importers.tfrecord import DetectionApiImporter
-from datumaro.components.importers.yolo import YoloImporter
-from datumaro.components.importers.cvat import CvatImporter
-from datumaro.components.importers.image_dir import ImageDirImporter
-
-
-items = [
-    ('datumaro', DatumaroImporter),
-
-    ('coco', CocoImporter),
-
-    ('voc', VocImporter),
-    ('voc_results', VocResultsImporter),
-
-    ('yolo', YoloImporter),
-
-    ('tf_detection_api', DetectionApiImporter),
-
-    ('cvat', CvatImporter),
-
-    ('image_dir', ImageDirImporter),
-]

datumaro/datumaro/components/importers/image_dir.py

@@ -1,26 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-
-import os.path as osp
-
-
-class ImageDirImporter:
-    EXTRACTOR_NAME = 'image_dir'
-
-    def __call__(self, path, **extra_params):
-        from datumaro.components.project import Project # cyclic import
-        project = Project()
-
-        if not osp.isdir(path):
-            raise Exception("Can't find a directory at '%s'" % path)
-
-        source_name = osp.basename(osp.normpath(path))
-        project.add_source(source_name, {
-            'url': source_name,
-            'format': self.EXTRACTOR_NAME,
-            'options': dict(extra_params),
-        })
-
-        return project

datumaro/datumaro/components/launchers/__init__.py

@@ -1,13 +0,0 @@
-
-# Copyright (C) 2019 Intel Corporation
-#
-# SPDX-License-Identifier: MIT
-
-items = [
-]
-
-try:
-    from datumaro.components.launchers.openvino import OpenVinoLauncher
-    items.append(('openvino', OpenVinoLauncher))
-except ImportError:
-    pass

datumaro/datumaro/components/project.py

@@ -4,9 +4,11 @@
 # SPDX-License-Identifier: MIT
 
 from collections import OrderedDict, defaultdict
+from functools import reduce
 import git
+from glob import glob
 import importlib
-from functools import reduce
+import inspect
 import logging as log
 import os
 import os.path as osp
@@ -20,16 +22,16 @@ from datumaro.components.dataset_filter import \
     XPathDatasetFilter, XPathAnnotationsFilter
 
 
-def import_foreign_module(name, path):
+def import_foreign_module(name, path, package=None):
     module = None
     default_path = sys.path.copy()
     try:
         sys.path = [ osp.abspath(path), ] + default_path
         sys.modules.pop(name, None) # remove from cache
-        module = importlib.import_module(name)
+        module = importlib.import_module(name, package=package)
         sys.modules.pop(name) # remove from cache
-    except ImportError as e:
+    except Exception:
-        log.warn("Failed to import module '%s': %s" % (name, e))
+        raise
     finally:
         sys.path = default_path
     return module
@@ -81,19 +83,21 @@ class SourceRegistry(Registry):
             for name, source in config.sources.items():
                 self.register(name, source)
 
-
+class PluginRegistry(Registry):
-class ModuleRegistry(Registry):
     def __init__(self, config=None, builtin=None, local=None):
         super().__init__(config)
 
+        from datumaro.components.cli_plugin import CliPlugin
+
         if builtin is not None:
-            for k, v in builtin:
+            for v in builtin:
+                k = CliPlugin._get_name(v)
                 self.register(k, v)
         if local is not None:
-            for k, v in local:
+            for v in local:
+                k = CliPlugin._get_name(v)
                 self.register(k, v)
 
-
 class GitWrapper:
     def __init__(self, config=None):
         self.repo = None
@@ -135,103 +139,134 @@ def load_project_as_dataset(url):
     raise NotImplementedError()
 
 class Environment:
+    _builtin_plugins = None
     PROJECT_EXTRACTOR_NAME = 'project'
 
     def __init__(self, config=None):
         config = Config(config,
             fallback=PROJECT_DEFAULT_CONFIG, schema=PROJECT_SCHEMA)
 
-        env_dir = osp.join(config.project_dir, config.env_dir)
+        self.models = ModelRegistry(config)
-        env_config_path = osp.join(env_dir, config.env_filename)
-        env_config = Config(fallback=ENV_DEFAULT_CONFIG, schema=ENV_SCHEMA)
-        if osp.isfile(env_config_path):
-            env_config.update(Config.parse(env_config_path))
-
-        self.config = env_config
-
-        self.models = ModelRegistry(env_config)
         self.sources = SourceRegistry(config)
 
-        import datumaro.components.importers as builtin_importers
+        self.git = GitWrapper(config)
-        builtin_importers = builtin_importers.items
+
-        custom_importers = self._get_custom_module_items(
+        env_dir = osp.join(config.project_dir, config.env_dir)
-            env_dir, env_config.importers_dir)
+        builtin = self._load_builtin_plugins()
-        self.importers = ModuleRegistry(config,
+        custom = self._load_plugins2(osp.join(env_dir, config.plugins_dir))
-            builtin=builtin_importers, local=custom_importers)
+        select = lambda seq, t: [e for e in seq if issubclass(e, t)]
-
+        from datumaro.components.extractor import Transform
-        import datumaro.components.extractors as builtin_extractors
+        from datumaro.components.extractor import SourceExtractor
-        builtin_extractors = builtin_extractors.items
+        from datumaro.components.extractor import Importer
-        custom_extractors = self._get_custom_module_items(
+        from datumaro.components.converter import Converter
-            env_dir, env_config.extractors_dir)
+        from datumaro.components.launcher import Launcher
-        self.extractors = ModuleRegistry(config,
+        self.extractors = PluginRegistry(
-            builtin=builtin_extractors, local=custom_extractors)
+            builtin=select(builtin, SourceExtractor),
+            local=select(custom, SourceExtractor)
+        )
         self.extractors.register(self.PROJECT_EXTRACTOR_NAME,
             load_project_as_dataset)
 
-        import datumaro.components.launchers as builtin_launchers
+        self.importers = PluginRegistry(
-        builtin_launchers = builtin_launchers.items
+            builtin=select(builtin, Importer),
-        custom_launchers = self._get_custom_module_items(
+            local=select(custom, Importer)
-            env_dir, env_config.launchers_dir)
+        )
-        self.launchers = ModuleRegistry(config,
+        self.launchers = PluginRegistry(
-            builtin=builtin_launchers, local=custom_launchers)
+            builtin=select(builtin, Launcher),
-
+            local=select(custom, Launcher)
-        import datumaro.components.converters as builtin_converters
+        )
-        builtin_converters = builtin_converters.items
+        self.converters = PluginRegistry(
-        custom_converters = self._get_custom_module_items(
+            builtin=select(builtin, Converter),
-            env_dir, env_config.converters_dir)
+            local=select(custom, Converter)
-        if custom_converters is not None:
+        )
-            custom_converters = custom_converters.items
+        self.transforms = PluginRegistry(
-        self.converters = ModuleRegistry(config,
+            builtin=select(builtin, Transform),
-            builtin=builtin_converters, local=custom_converters)
+            local=select(custom, Transform)
-
+        )
-        self.statistics = ModuleRegistry(config)
-        self.visualizers = ModuleRegistry(config)
-        self.git = GitWrapper(config)
 
-    def _get_custom_module_items(self, module_dir, module_name):
+    @staticmethod
-        items = None
+    def _find_plugins(plugins_dir):
+        plugins = []
+        if not osp.exists(plugins_dir):
+            return plugins
+
+        for plugin_name in os.listdir(plugins_dir):
+            p = osp.join(plugins_dir, plugin_name)
+            if osp.isfile(p) and p.endswith('.py'):
+                plugins.append((plugins_dir, plugin_name, None))
+            elif osp.isdir(p):
+                plugins += [(plugins_dir,
+                        osp.splitext(plugin_name)[0] + '.' + osp.basename(p),
+                        osp.splitext(plugin_name)[0]
+                    )
+                    for p in glob(osp.join(p, '*.py'))]
+        return plugins
 
-        module = None
+    @classmethod
-        if osp.exists(osp.join(module_dir, module_name)):
+    def _import_module(cls, module_dir, module_name, types, package=None):
-            module = import_foreign_module(module_name, module_dir)
+        module = import_foreign_module(osp.splitext(module_name)[0], module_dir,
-        if module is not None:
+            package=package)
-            if hasattr(module, 'items'):
-                items = module.items
-            else:
-                items = self._find_custom_module_items(
-                    osp.join(module_dir, module_name))
 
-        return items
+        exports = []
+        if hasattr(module, 'exports'):
+            exports = module.exports
+        else:
+            for symbol in dir(module):
+                if symbol.startswith('_'):
+                    continue
+                exports.append(getattr(module, symbol))
 
-    @staticmethod
+        exports = [s for s in exports
-    def _find_custom_module_items(module_dir):
+            if inspect.isclass(s) and issubclass(s, types) and not s in types]
-        files = [p for p in os.listdir(module_dir)
-            if p.endswith('.py') and p != '__init__.py']
-
-        all_items = []
-        for f in files:
-            name = osp.splitext(f)[0]
-            module = import_foreign_module(name, module_dir)
-
-            items = []
-            if hasattr(module, 'items'):
-                items = module.items
-            else:
-                if hasattr(module, name):
-                    items = [ (name, getattr(module, name)) ]
-                else:
-                    log.warn("Failed to import custom module '%s'."
-                        " Custom module is expected to provide 'items' "
-                        "list or have an item matching its file name."
-                        " Skipping this module." % \
-                        (module_dir + '.' + name))
 
-            all_items.extend(items)
+        return exports
+
+    @classmethod
+    def _load_plugins(cls, plugins_dir, types):
+        types = tuple(types)
+
+        plugins = cls._find_plugins(plugins_dir)
+
+        all_exports = []
+        for module_dir, module_name, package in plugins:
+            try:
+                exports = cls._import_module(module_dir, module_name, types,
+                    package)
+            except ImportError as e:
+                log.debug("Failed to import module '%s': %s" % (module_name, e))
+                continue
+
+            log.debug("Imported the following symbols from %s: %s" % \
+                (
+                    module_name,
+                    ', '.join(s.__name__ for s in exports)
+                )
+            )
+            all_exports.extend(exports)
+
+        return all_exports
 
-        return all_items
+    @classmethod
+    def _load_builtin_plugins(cls):
+        if not cls._builtin_plugins:
+            plugins_dir = osp.join(
+                __file__[: __file__.rfind(osp.join('datumaro', 'components'))],
+                osp.join('datumaro', 'plugins')
+            )
+            assert osp.isdir(plugins_dir), plugins_dir
+            cls._builtin_plugins = cls._load_plugins2(plugins_dir)
+        return cls._builtin_plugins
+
+    @classmethod
+    def _load_plugins2(cls, plugins_dir):
+        from datumaro.components.extractor import Transform
+        from datumaro.components.extractor import SourceExtractor
+        from datumaro.components.extractor import Importer
+        from datumaro.components.converter import Converter
+        from datumaro.components.launcher import Launcher
+        types = [SourceExtractor, Converter, Importer, Launcher, Transform]
 
-    def save(self, path):
+        return cls._load_plugins(plugins_dir, types)
-        self.config.dump(path)
 
     def make_extractor(self, name, *args, **kwargs):
         return self.extractors.get(name)(*args, **kwargs)
@@ -246,11 +281,9 @@ class Environment:
         return self.converters.get(name)(*args, **kwargs)
 
     def register_model(self, name, model):
-        self.config.models[name] = model
         self.models.register(name, model)
 
     def unregister_model(self, name):
-        self.config.models.remove(name)
         self.models.unregister(name)
 
 
@@ -648,15 +681,21 @@ class ProjectDataset(Dataset):
 
         dst_dataset.save(save_dir=save_dir, merge=True)
 
-    def transform_project(self, method, *args, save_dir=None, **kwargs):
+    def transform_project(self, method, save_dir=None, **method_kwargs):
         # NOTE: probably this function should be in the ViewModel layer
-        transformed = self.transform(method, *args, **kwargs)
+        if isinstance(method, str):
+            method = self.env.make_transform(method)
+
+        transformed = self.transform(method, **method_kwargs)
         self._save_branch_project(transformed, save_dir=save_dir)
 
-    def apply_model(self, model_name, save_dir=None):
+    def apply_model(self, model, save_dir=None, batch_size=1):
         # NOTE: probably this function should be in the ViewModel layer
-        launcher = self._project.make_executable_model(model_name)
+        if isinstance(model, str):
-        self.transform_project(InferenceWrapper, launcher, save_dir=save_dir)
+            launcher = self._project.make_executable_model(model)
+
+        self.transform_project(InferenceWrapper, launcher=launcher,
+            save_dir=save_dir, batch_size=batch_size)
 
     def export_project(self, save_dir, converter,
             filter_expr=None, filter_annotations=False, remove_empty=False):
@@ -698,12 +737,8 @@ class Project:
         if save_dir is None:
             assert config.project_dir
             save_dir = osp.abspath(config.project_dir)
+        os.makedirs(save_dir, exist_ok=True)
         config_path = osp.join(save_dir, config.project_filename)
-
-        env_dir = osp.join(save_dir, config.env_dir)
-        os.makedirs(env_dir, exist_ok=True)
-        self.env.save(osp.join(env_dir, config.env_filename))
-
         config.dump(config_path)
 
     @staticmethod
@@ -757,6 +792,7 @@ class Project:
         if isinstance(value, (dict, Config)):
             value = Model(value)
         self.env.register_model(name, value)
+        self.config.models[name] = value
 
     def get_model(self, name):
         try:
@@ -765,6 +801,7 @@ class Project:
             raise KeyError("Model '%s' is not found" % name)
 
     def remove_model(self, name):
+        self.config.models.remove(name)
         self.env.unregister_model(name)
 
     def make_executable_model(self, name):
@@ -785,7 +822,7 @@ class Project:
 
     def local_model_dir(self, model_name):
         return osp.join(
-            self.config.env_dir, self.env.config.models_dir, model_name)
+            self.config.env_dir, self.config.models_dir, model_name)
 
     def local_source_dir(self, source_name):
         return osp.join(self.config.sources_dir, source_name)

datumaro/datumaro/plugins/coco_format/converter.py

@@ -13,14 +13,16 @@ import os.path as osp
 import pycocotools.mask as mask_utils
 
 from datumaro.components.converter import Converter
-from datumaro.components.extractor import (
+from datumaro.components.extractor import (DEFAULT_SUBSET_NAME,
-    DEFAULT_SUBSET_NAME, AnnotationType, PointsObject, MaskObject
+    AnnotationType, Points, Mask
 )
-from datumaro.components.formats.coco import CocoTask, CocoPath
+from datumaro.components.cli_plugin import CliPlugin
 from datumaro.util import find
 from datumaro.util.image import save_image
 import datumaro.util.mask_tools as mask_tools
 
+from .format import CocoTask, CocoPath
+
 
 def _cast(value, type_conv, default=None):
     if value is None:
@@ -202,7 +204,7 @@ class _InstancesConverter(_TaskConverter):
         leader = self.find_group_leader(anns)
         bbox = self.compute_bbox(anns)
         mask = None
-        polygons = [p.get_polygon() for p in polygons]
+        polygons = [p.points for p in polygons]
 
         if self._context._segmentation_mode == SegmentationMode.guess:
             use_masks = True == leader.attributes.get('is_crowd',
@@ -237,7 +239,7 @@ class _InstancesConverter(_TaskConverter):
 
     @staticmethod
     def find_group_leader(group):
-        return max(group, key=lambda x: x.area())
+        return max(group, key=lambda x: x.get_area())
 
     @staticmethod
     def merge_masks(masks):
@@ -245,7 +247,7 @@ class _InstancesConverter(_TaskConverter):
             return None
 
         def get_mask(m):
-            if isinstance(m, MaskObject):
+            if isinstance(m, Mask):
                 return m.image
             else:
                 return m
@@ -278,7 +280,7 @@ class _InstancesConverter(_TaskConverter):
 
         ann_groups = []
         for g_id, group in groupby(instance_anns, lambda a: a.group):
-            if g_id is None:
+            if not g_id:
                 ann_groups.extend(([a] for a in group))
             else:
                 ann_groups.append(list(group))
@@ -395,7 +397,7 @@ class _KeypointsConverter(_InstancesConverter):
         solitary_points = []
 
         for g_id, group in groupby(annotations, lambda a: a.group):
-            if g_id is not None and not cls.find_instance_anns(group):
+            if g_id and not cls.find_instance_anns(group):
                 group = [a for a in group if a.type == AnnotationType.points]
             solitary_points.extend(group)
 
@@ -404,7 +406,7 @@ class _KeypointsConverter(_InstancesConverter):
     @staticmethod
     def convert_points_object(ann):
         keypoints = []
-        points = ann.get_points()
+        points = ann.points
         visibility = ann.visibility
         for index in range(0, len(points), 2):
             kp = points[index : index + 2]
@@ -412,7 +414,7 @@ class _KeypointsConverter(_InstancesConverter):
             keypoints.extend([*kp, state])
 
         num_annotated = len([v for v in visibility \
-            if v != PointsObject.Visibility.absent])
+            if v != Points.Visibility.absent])
 
         return {
             'keypoints': keypoints,
@@ -543,8 +545,11 @@ class _Converter:
                 filename = ''
                 if item.has_image:
                     filename = str(item.id) + CocoPath.IMAGE_EXT
-                    if self._save_images:
+                if self._save_images:
+                    if item.has_image:
                         self.save_image(item, filename)
+                    else:
+                        log.debug("Item '%s' has no image" % item.id)
                 for task_conv in task_converters.values():
                     task_conv.save_image_info(item, filename)
                     task_conv.save_annotations(item)
@@ -554,46 +559,29 @@ class _Converter:
                     task_conv.write(osp.join(self._ann_dir,
                         '%s_%s.json' % (task.name, subset_name)))
 
-class CocoConverter(Converter):
+class CocoConverter(Converter, CliPlugin):
-    def __init__(self,
-            tasks=None, save_images=False, segmentation_mode=None,
-            crop_covered=False,
-            cmdline_args=None):
-        super().__init__()
-
-        self._options = {
-            'tasks': tasks,
-            'save_images': save_images,
-            'segmentation_mode': segmentation_mode,
-            'crop_covered': crop_covered,
-        }
-
-        if cmdline_args is not None:
-            self._options.update(self._parse_cmdline(cmdline_args))
-
     @staticmethod
     def _split_tasks_string(s):
         return [CocoTask[i.strip()] for i in s.split(',')]
 
     @classmethod
-    def build_cmdline_parser(cls, parser=None):
+    def build_cmdline_parser(cls, **kwargs):
-        import argparse
+        parser = super().build_cmdline_parser(**kwargs)
-        if not parser:
-            parser = argparse.ArgumentParser(prog='coco')
-
         parser.add_argument('--save-images', action='store_true',
             help="Save images (default: %(default)s)")
         parser.add_argument('--segmentation-mode',
             choices=[m.name for m in SegmentationMode],
             default=SegmentationMode.guess.name,
-            help="Save mode for instance segmentation: "
+            help="""
-                "- '{sm.guess.name}': guess the mode for each instance, "
+                Save mode for instance segmentation:|n
-                    "use 'is_crowd' attribute as hint; "
+                - '{sm.guess.name}': guess the mode for each instance,|n
-                "- '{sm.polygons.name}': save polygons, "
+                |s|suse 'is_crowd' attribute as hint|n
-                    "merge and convert masks, prefer polygons; "
+                - '{sm.polygons.name}': save polygons,|n
-                "- '{sm.mask.name}': save masks, "
+                |s|smerge and convert masks, prefer polygons|n
-                    "merge and convert polygons, prefer masks; "
+                - '{sm.mask.name}': save masks,|n
-                "(default: %(default)s)".format(sm=SegmentationMode))
+                |s|smerge and convert polygons, prefer masks|n
+                Default: %(default)s.
+                """.format(sm=SegmentationMode))
         parser.add_argument('--crop-covered', action='store_true',
             help="Crop covered segments so that background objects' "
                 "segmentation was more accurate (default: %(default)s)")
@@ -601,24 +589,40 @@ class CocoConverter(Converter):
             default=None,
             help="COCO task filter, comma-separated list of {%s} "
                 "(default: all)" % ', '.join([t.name for t in CocoTask]))
-
         return parser
 
+    def __init__(self,
+            tasks=None, save_images=False, segmentation_mode=None,
+            crop_covered=False):
+        super().__init__()
+
+        self._options = {
+            'tasks': tasks,
+            'save_images': save_images,
+            'segmentation_mode': segmentation_mode,
+            'crop_covered': crop_covered,
+        }
+
     def __call__(self, extractor, save_dir):
         converter = _Converter(extractor, save_dir, **self._options)
         converter.convert()
 
-def CocoInstancesConverter(**kwargs):
+class CocoInstancesConverter(CocoConverter):
-    return CocoConverter(CocoTask.instances, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(CocoTask.instances, **kwargs)
 
-def CocoImageInfoConverter(**kwargs):
+class CocoImageInfoConverter(CocoConverter):
-    return CocoConverter(CocoTask.image_info, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(CocoTask.image_info, **kwargs)
 
-def CocoPersonKeypointsConverter(**kwargs):
+class CocoPersonKeypointsConverter(CocoConverter):
-    return CocoConverter(CocoTask.person_keypoints, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(CocoTask.person_keypoints, **kwargs)
 
-def CocoCaptionsConverter(**kwargs):
+class CocoCaptionsConverter(CocoConverter):
-    return CocoConverter(CocoTask.captions, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(CocoTask.captions, **kwargs)
 
-def CocoLabelsConverter(**kwargs):
+class CocoLabelsConverter(CocoConverter):
-    return CocoConverter(CocoTask.labels, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(CocoTask.labels, **kwargs)

datumaro/datumaro/plugins/coco_format/extractor.py

@@ -4,22 +4,23 @@
 # SPDX-License-Identifier: MIT
 
 from collections import OrderedDict
+import logging as log
 import os.path as osp
 
 from pycocotools.coco import COCO
 import pycocotools.mask as mask_utils
 
-from datumaro.components.extractor import (Extractor, DatasetItem,
+from datumaro.components.extractor import (SourceExtractor,
-    DEFAULT_SUBSET_NAME, AnnotationType,
+    DEFAULT_SUBSET_NAME, DatasetItem,
-    LabelObject, RleMask, PointsObject, PolygonObject,
+    AnnotationType, Label, RleMask, Points, Polygon, Bbox, Caption,
-    BboxObject, CaptionObject,
     LabelCategories, PointsCategories
 )
-from datumaro.components.formats.coco import CocoTask, CocoPath
 from datumaro.util.image import lazy_image
 
+from .format import CocoTask, CocoPath
 
-class CocoExtractor(Extractor):
+
+class _CocoExtractor(SourceExtractor):
     def __init__(self, path, task, merge_instance_polygons=False):
         super().__init__()
 
@@ -156,7 +157,7 @@ class CocoExtractor(Extractor):
                 points = [p for i, p in enumerate(keypoints) if i % 3 != 2]
                 visibility = keypoints[2::3]
                 parsed_annotations.append(
-                    PointsObject(points, visibility, label=label_id,
+                    Points(points, visibility, label=label_id,
                         id=ann_id, attributes=attributes, group=group)
                 )
 
@@ -165,14 +166,14 @@ class CocoExtractor(Extractor):
                 rle = None
 
                 if isinstance(segmentation, list):
-                    # polygon - a single object can consist of multiple parts
+                    if not self._merge_instance_polygons:
-                    for polygon_points in segmentation:
+                        # polygon - a single object can consist of multiple parts
-                        parsed_annotations.append(PolygonObject(
+                        for polygon_points in segmentation:
-                            points=polygon_points, label=label_id,
+                            parsed_annotations.append(Polygon(
-                            id=ann_id, attributes=attributes, group=group
+                                points=polygon_points, label=label_id,
-                        ))
+                                id=ann_id, attributes=attributes, group=group
-
+                            ))
-                    if self._merge_instance_polygons:
+                    else:
                         # merge all parts into a single mask RLE
                         img_h = image_info['height']
                         img_w = image_info['width']
@@ -180,8 +181,19 @@ class CocoExtractor(Extractor):
                         rle = mask_utils.merge(rles)
                 elif isinstance(segmentation['counts'], list):
                     # uncompressed RLE
-                    img_h, img_w = segmentation['size']
+                    img_h = image_info['height']
-                    rle = mask_utils.frPyObjects([segmentation], img_h, img_w)[0]
+                    img_w = image_info['width']
+                    mask_h, mask_w = segmentation['size']
+                    if img_h == mask_h and img_w == mask_w:
+                        rle = mask_utils.frPyObjects(
+                            [segmentation], mask_h, mask_w)[0]
+                    else:
+                        log.warning("item #%s: mask #%s "
+                            "does not match image size: %s vs. %s. "
+                            "Skipping this annotation.",
+                            image_info['id'], ann_id,
+                            (mask_h, mask_w), (img_h, img_w)
+                        )
                 else:
                     # compressed RLE
                     rle = segmentation
@@ -190,21 +202,21 @@ class CocoExtractor(Extractor):
                     parsed_annotations.append(RleMask(rle=rle, label=label_id,
                         id=ann_id, attributes=attributes, group=group
                     ))
-
+            else:
-            parsed_annotations.append(
+                parsed_annotations.append(
-                BboxObject(x, y, w, h, label=label_id,
+                    Bbox(x, y, w, h, label=label_id,
-                    id=ann_id, attributes=attributes, group=group)
+                        id=ann_id, attributes=attributes, group=group)
-            )
+                )
         elif self._task is CocoTask.labels:
             label_id = self._get_label_id(ann)
             parsed_annotations.append(
-                LabelObject(label=label_id,
+                Label(label=label_id,
                     id=ann_id, attributes=attributes, group=group)
             )
         elif self._task is CocoTask.captions:
             caption = ann['caption']
             parsed_annotations.append(
-                CaptionObject(caption,
+                Caption(caption,
                     id=ann_id, attributes=attributes, group=group)
             )
         else:
@@ -222,23 +234,22 @@ class CocoExtractor(Extractor):
             if osp.exists(image_path):
                 return lazy_image(image_path)
 
-class CocoImageInfoExtractor(CocoExtractor):
+class CocoImageInfoExtractor(_CocoExtractor):
     def __init__(self, path, **kwargs):
         super().__init__(path, task=CocoTask.image_info, **kwargs)
 
-class CocoCaptionsExtractor(CocoExtractor):
+class CocoCaptionsExtractor(_CocoExtractor):
     def __init__(self, path, **kwargs):
         super().__init__(path, task=CocoTask.captions, **kwargs)
 
-class CocoInstancesExtractor(CocoExtractor):
+class CocoInstancesExtractor(_CocoExtractor):
     def __init__(self, path, **kwargs):
         super().__init__(path, task=CocoTask.instances, **kwargs)
 
-class CocoPersonKeypointsExtractor(CocoExtractor):
+class CocoPersonKeypointsExtractor(_CocoExtractor):
     def __init__(self, path, **kwargs):
-        super().__init__(path, task=CocoTask.person_keypoints,
+        super().__init__(path, task=CocoTask.person_keypoints, **kwargs)
-            **kwargs)
 
-class CocoLabelsExtractor(CocoExtractor):
+class CocoLabelsExtractor(_CocoExtractor):
     def __init__(self, path, **kwargs):
         super().__init__(path, task=CocoTask.labels, **kwargs)

datumaro/datumaro/plugins/coco_format/importer.py

@@ -8,16 +8,18 @@ from glob import glob
 import logging as log
 import os.path as osp
 
-from datumaro.components.formats.coco import CocoTask, CocoPath
+from datumaro.components.extractor import Importer
 
+from .format import CocoTask, CocoPath
 
-class CocoImporter:
+
+class CocoImporter(Importer):
     _COCO_EXTRACTORS = {
         CocoTask.instances: 'coco_instances',
-        CocoTask.person_keypoints: 'coco_person_kp',
+        CocoTask.person_keypoints: 'coco_person_keypoints',
         CocoTask.captions: 'coco_captions',
         CocoTask.labels: 'coco_labels',
-        CocoTask.image_info: 'coco_images',
+        CocoTask.image_info: 'coco_image_info',
     }
 
     def __call__(self, path, **extra_params):

datumaro/datumaro/plugins/cvat_format/converter.py

@@ -4,15 +4,18 @@
 # SPDX-License-Identifier: MIT
 
 from collections import OrderedDict
+import logging as log
 import os
 import os.path as osp
 from xml.sax.saxutils import XMLGenerator
 
+from datumaro.components.cli_plugin import CliPlugin
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import DEFAULT_SUBSET_NAME, AnnotationType
-from datumaro.components.formats.cvat import CvatPath
 from datumaro.util.image import save_image
 
+from .format import CvatPath
+
 
 def _cast(value, type_conv, default=None):
     if value is None:
@@ -156,7 +159,10 @@ class _SubsetWriter:
 
         for item in self._extractor:
             if self._context._save_images:
-                self._save_image(item)
+                if item.has_image:
+                    self._save_image(item)
+                else:
+                    log.debug("Item '%s' has no image" % item.id)
             self._write_item(item)
 
         self._writer.close_root()
@@ -235,13 +241,12 @@ class _SubsetWriter:
             ("occluded", str(int(shape.attributes.get('occluded', False)))),
         ])
 
-        points = shape.get_points()
         if shape.type == AnnotationType.bbox:
             shape_data.update(OrderedDict([
-                ("xtl", "{:.2f}".format(points[0])),
+                ("xtl", "{:.2f}".format(shape.points[0])),
-                ("ytl", "{:.2f}".format(points[1])),
+                ("ytl", "{:.2f}".format(shape.points[1])),
-                ("xbr", "{:.2f}".format(points[2])),
+                ("xbr", "{:.2f}".format(shape.points[2])),
-                ("ybr", "{:.2f}".format(points[3]))
+                ("ybr", "{:.2f}".format(shape.points[3]))
             ]))
         else:
             shape_data.update(OrderedDict([
@@ -249,12 +254,12 @@ class _SubsetWriter:
                     ','.join((
                         "{:.2f}".format(x),
                         "{:.2f}".format(y)
-                    )) for x, y in pairwise(points))
+                    )) for x, y in pairwise(shape.points))
                 )),
             ]))
 
         shape_data['z_order'] = str(int(shape.attributes.get('z_order', 0)))
-        if shape.group is not None:
+        if shape.group:
             shape_data['group_id'] = str(shape.group)
 
         if shape.type == AnnotationType.bbox:
@@ -320,28 +325,21 @@ class _Converter:
                 writer = _SubsetWriter(f, subset_name, subset, self)
                 writer.write()
 
-class CvatConverter(Converter):
+class CvatConverter(Converter, CliPlugin):
-    def __init__(self, save_images=False, cmdline_args=None):
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser = super().__init__(**kwargs)
+        parser.add_argument('--save-images', action='store_true',
+            help="Save images (default: %(default)s)")
+        return parser
+
+    def __init__(self, save_images=False):
         super().__init__()
 
         self._options = {
             'save_images': save_images,
         }
 
-        if cmdline_args is not None:
-            self._options.update(self._parse_cmdline(cmdline_args))
-
-    @classmethod
-    def build_cmdline_parser(cls, parser=None):
-        import argparse
-        if not parser:
-            parser = argparse.ArgumentParser(prog='cvat')
-
-        parser.add_argument('--save-images', action='store_true',
-            help="Save images (default: %(default)s)")
-
-        return parser
-
     def __call__(self, extractor, save_dir):
         converter = _Converter(extractor, save_dir, **self._options)
         converter.convert()

datumaro/datumaro/plugins/cvat_format/extractor.py

@@ -7,16 +7,17 @@ from collections import OrderedDict
 import os.path as osp
 import xml.etree as ET
 
-from datumaro.components.extractor import (Extractor, DatasetItem,
+from datumaro.components.extractor import (SourceExtractor,
-    DEFAULT_SUBSET_NAME, AnnotationType,
+    DEFAULT_SUBSET_NAME, DatasetItem,
-    PointsObject, PolygonObject, PolyLineObject, BboxObject,
+    AnnotationType, Points, Polygon, PolyLine, Bbox,
     LabelCategories
 )
-from datumaro.components.formats.cvat import CvatPath
 from datumaro.util.image import lazy_image
 
+from .format import CvatPath
 
-class CvatExtractor(Extractor):
+
+class CvatExtractor(SourceExtractor):
     _SUPPORTED_SHAPES = ('box', 'polygon', 'polyline', 'points')
 
     def __init__(self, path):
@@ -242,8 +243,6 @@ class CvatExtractor(Extractor):
             attributes['keyframe'] = ann.get('keyframe', False)
 
         group = ann.get('group')
-        if group == 0:
-            group = None
 
         label = ann.get('label')
         label_id = categories[AnnotationType.label].find(label)[0]
@@ -251,21 +250,21 @@ class CvatExtractor(Extractor):
         points = ann.get('points', [])
 
         if ann_type == 'polyline':
-            return PolyLineObject(points, label=label_id,
+            return PolyLine(points, label=label_id,
                 id=ann_id, attributes=attributes, group=group)
 
         elif ann_type == 'polygon':
-            return PolygonObject(points, label=label_id,
+            return Polygon(points, label=label_id,
                 id=ann_id, attributes=attributes, group=group)
 
         elif ann_type == 'points':
-            return PointsObject(points, label=label_id,
+            return Points(points, label=label_id,
                 id=ann_id, attributes=attributes, group=group)
 
         elif ann_type == 'box':
             x, y = points[0], points[1]
             w, h = points[2] - x, points[3] - y
-            return BboxObject(x, y, w, h, label=label_id,
+            return Bbox(x, y, w, h, label=label_id,
                 id=ann_id, attributes=attributes, group=group)
 
         else:

datumaro/datumaro/plugins/cvat_format/importer.py

@@ -7,10 +7,12 @@ from glob import glob
 import logging as log
 import os.path as osp
 
-from datumaro.components.formats.cvat import CvatPath
+from datumaro.components.extractor import Importer
 
+from .format import CvatPath
 
-class CvatImporter:
+
+class CvatImporter(Importer):
     EXTRACTOR_NAME = 'cvat'
 
     def __call__(self, path, **extra_params):

datumaro/datumaro/plugins/datumaro_format/converter.py

@@ -12,12 +12,13 @@ import os.path as osp
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import (
     DEFAULT_SUBSET_NAME, Annotation,
-    LabelObject, MaskObject, PointsObject, PolygonObject,
+    Label, Mask, Points, Polygon, PolyLine, Bbox, Caption,
-    PolyLineObject, BboxObject, CaptionObject,
     LabelCategories, MaskCategories, PointsCategories
 )
-from datumaro.components.formats.datumaro import DatumaroPath
 from datumaro.util.image import save_image
+from datumaro.components.cli_plugin import CliPlugin
+
+from .format import DatumaroPath
 
 
 def _cast(value, type_conv, default=None):
@@ -60,19 +61,19 @@ class _SubsetWriter:
         self.items.append(item_desc)
 
         for ann in item.annotations:
-            if isinstance(ann, LabelObject):
+            if isinstance(ann, Label):
                 converted_ann = self._convert_label_object(ann)
-            elif isinstance(ann, MaskObject):
+            elif isinstance(ann, Mask):
                 converted_ann = self._convert_mask_object(ann)
-            elif isinstance(ann, PointsObject):
+            elif isinstance(ann, Points):
                 converted_ann = self._convert_points_object(ann)
-            elif isinstance(ann, PolyLineObject):
+            elif isinstance(ann, PolyLine):
                 converted_ann = self._convert_polyline_object(ann)
-            elif isinstance(ann, PolygonObject):
+            elif isinstance(ann, Polygon):
                 converted_ann = self._convert_polygon_object(ann)
-            elif isinstance(ann, BboxObject):
+            elif isinstance(ann, Bbox):
                 converted_ann = self._convert_bbox_object(ann)
-            elif isinstance(ann, CaptionObject):
+            elif isinstance(ann, Caption):
                 converted_ann = self._convert_caption_object(ann)
             else:
                 raise NotImplementedError()
@@ -101,7 +102,7 @@ class _SubsetWriter:
             'id': _cast(obj.id, int),
             'type': _cast(obj.type.name, str),
             'attributes': obj.attributes,
-            'group': _cast(obj.group, int, None),
+            'group': _cast(obj.group, int, 0),
         }
         return ann_json
 
@@ -148,7 +149,7 @@ class _SubsetWriter:
 
         converted.update({
             'label_id': _cast(obj.label, int),
-            'points': [float(p) for p in obj.get_points()],
+            'points': [float(p) for p in obj.points],
         })
         return converted
 
@@ -157,7 +158,7 @@ class _SubsetWriter:
 
         converted.update({
             'label_id': _cast(obj.label, int),
-            'points': [float(p) for p in obj.get_points()],
+            'points': [float(p) for p in obj.points],
         })
         return converted
 
@@ -272,28 +273,20 @@ class _Converter:
             str(item.id) + DatumaroPath.IMAGE_EXT)
         save_image(image_path, image)
 
-class DatumaroConverter(Converter):
+class DatumaroConverter(Converter, CliPlugin):
-    def __init__(self, save_images=False, cmdline_args=None):
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser.add_argument('--save-images', action='store_true',
+            help="Save images (default: %(default)s)")
+        return parser
+
+    def __init__(self, save_images=False):
         super().__init__()
 
         self._options = {
             'save_images': save_images,
         }
 
-        if cmdline_args is not None:
-            self._options.update(self._parse_cmdline(cmdline_args))
-
-    @classmethod
-    def build_cmdline_parser(cls, parser=None):
-        import argparse
-        if not parser:
-            parser = argparse.ArgumentParser(prog='datumaro')
-
-        parser.add_argument('--save-images', action='store_true',
-            help="Save images (default: %(default)s)")
-
-        return parser
-
     def __call__(self, extractor, save_dir):
         converter = _Converter(extractor, save_dir, **self._options)
-        converter.convert()
+        converter.convert()

datumaro/datumaro/plugins/datumaro_format/extractor.py

@@ -7,18 +7,18 @@ import json
 import logging as log
 import os.path as osp
 
-from datumaro.components.extractor import (Extractor, DatasetItem,
+from datumaro.components.extractor import (SourceExtractor,
-    DEFAULT_SUBSET_NAME, AnnotationType,
+    DEFAULT_SUBSET_NAME, DatasetItem,
-    LabelObject, MaskObject, PointsObject, PolygonObject,
+    AnnotationType, Label, Mask, Points, Polygon, PolyLine, Bbox, Caption,
-    PolyLineObject, BboxObject, CaptionObject,
     LabelCategories, MaskCategories, PointsCategories
 )
-from datumaro.components.formats.datumaro import DatumaroPath
 from datumaro.util.image import lazy_image
 from datumaro.util.mask_tools import lazy_mask
 
+from .format import DatumaroPath
 
-class DatumaroExtractor(Extractor):
+
+class DatumaroExtractor(SourceExtractor):
     def __init__(self, path):
         super().__init__()
 
@@ -120,7 +120,7 @@ class DatumaroExtractor(Extractor):
 
             if ann_type == AnnotationType.label:
                 label_id = ann.get('label_id')
-                loaded.append(LabelObject(label=label_id,
+                loaded.append(Label(label=label_id,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.mask:
@@ -137,36 +137,36 @@ class DatumaroExtractor(Extractor):
                     log.warn("Not found mask image file '%s', skipped." % \
                         mask_path)
 
-                loaded.append(MaskObject(label=label_id, image=mask,
+                loaded.append(Mask(label=label_id, image=mask,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.polyline:
                 label_id = ann.get('label_id')
                 points = ann.get('points')
-                loaded.append(PolyLineObject(points, label=label_id,
+                loaded.append(PolyLine(points, label=label_id,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.polygon:
                 label_id = ann.get('label_id')
                 points = ann.get('points')
-                loaded.append(PolygonObject(points, label=label_id,
+                loaded.append(Polygon(points, label=label_id,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.bbox:
                 label_id = ann.get('label_id')
                 x, y, w, h = ann.get('bbox')
-                loaded.append(BboxObject(x, y, w, h, label=label_id,
+                loaded.append(Bbox(x, y, w, h, label=label_id,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.points:
                 label_id = ann.get('label_id')
                 points = ann.get('points')
-                loaded.append(PointsObject(points, label=label_id,
+                loaded.append(Points(points, label=label_id,
                     id=ann_id, attributes=attributes, group=group))
 
             elif ann_type == AnnotationType.caption:
                 caption = ann.get('caption')
-                loaded.append(CaptionObject(caption,
+                loaded.append(Caption(caption,
                     id=ann_id, attributes=attributes, group=group))
 
             else:

datumaro/datumaro/plugins/datumaro_format/importer.py

@@ -7,10 +7,12 @@ from glob import glob
 import logging as log
 import os.path as osp
 
-from datumaro.components.formats.datumaro import DatumaroPath
+from datumaro.components.extractor import Importer
 
+from .format import DatumaroPath
 
-class DatumaroImporter:
+
+class DatumaroImporter(Importer):
     EXTRACTOR_NAME = 'datumaro'
 
     def __call__(self, path, **extra_params):

datumaro/datumaro/plugins/image_dir.py

@@ -1,5 +1,5 @@
 
-# Copyright (C) 2018 Intel Corporation
+# Copyright (C) 2019 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
@@ -7,11 +7,31 @@ from collections import OrderedDict
 import os
 import os.path as osp
 
-from datumaro.components.extractor import DatasetItem, Extractor
+from datumaro.components.extractor import DatasetItem, SourceExtractor, Importer
 from datumaro.util.image import lazy_image
 
 
-class ImageDirExtractor(Extractor):
+class ImageDirImporter(Importer):
+    EXTRACTOR_NAME = 'image_dir'
+
+    def __call__(self, path, **extra_params):
+        from datumaro.components.project import Project # cyclic import
+        project = Project()
+
+        if not osp.isdir(path):
+            raise Exception("Can't find a directory at '%s'" % path)
+
+        source_name = osp.basename(osp.normpath(path))
+        project.add_source(source_name, {
+            'url': source_name,
+            'format': self.EXTRACTOR_NAME,
+            'options': dict(extra_params),
+        })
+
+        return project
+
+
+class ImageDirExtractor(SourceExtractor):
     _SUPPORTED_FORMATS = ['.png', '.jpg']
 
     def __init__(self, url):

datumaro/datumaro/plugins/tf_detection_api_format/converter.py

@@ -5,16 +5,19 @@
 
 import codecs
 from collections import OrderedDict
+import logging as log
 import os
 import os.path as osp
 import string
 
 from datumaro.components.extractor import AnnotationType, DEFAULT_SUBSET_NAME
 from datumaro.components.converter import Converter
-from datumaro.components.formats.tfrecord import DetectionApiPath
+from datumaro.components.cli_plugin import CliPlugin
 from datumaro.util.image import encode_image
 from datumaro.util.tf_util import import_tf as _import_tf
 
+from .format import DetectionApiPath
+
 
 # we need it to filter out non-ASCII characters, otherwise training will crash
 _printable = set(string.printable)
@@ -56,14 +59,17 @@ def _make_tf_example(item, get_label_id, get_label, save_images=False):
         'image/width': int64_feature(width),
     })
 
-    if save_images and item.has_image:
+    if save_images:
-        fmt = DetectionApiPath.IMAGE_FORMAT
+        if item.has_image:
-        buffer = encode_image(item.image, DetectionApiPath.IMAGE_EXT)
+            fmt = DetectionApiPath.IMAGE_FORMAT
+            buffer = encode_image(item.image, DetectionApiPath.IMAGE_EXT)
 
-        features.update({
+            features.update({
-            'image/encoded': bytes_feature(buffer),
+                'image/encoded': bytes_feature(buffer),
-            'image/format': bytes_feature(fmt.encode('utf-8')),
+                'image/format': bytes_feature(fmt.encode('utf-8')),
-        })
+            })
+        else:
+            log.debug("Item '%s' has no image" % item.id)
 
     xmins = [] # List of normalized left x coordinates in bounding box (1 per box)
     xmaxs = [] # List of normalized right x coordinates in bounding box (1 per box)
@@ -98,29 +104,19 @@ def _make_tf_example(item, get_label_id, get_label, save_images=False):
 
     return tf_example
 
-class DetectionApiConverter(Converter):
+class TfDetectionApiConverter(Converter, CliPlugin):
-    def __init__(self, save_images=False, cmdline_args=None):
-        super().__init__()
-
-        self._save_images = save_images
-
-        if cmdline_args is not None:
-            options = self._parse_cmdline(cmdline_args)
-            for k, v in options.items():
-                if hasattr(self, '_' + str(k)):
-                    setattr(self, '_' + str(k), v)
-
     @classmethod
-    def build_cmdline_parser(cls, parser=None):
+    def build_cmdline_parser(cls, **kwargs):
-        import argparse
+        parser = super().build_cmdline_parser(**kwargs)
-        if not parser:
-            parser = argparse.ArgumentParser(prog='tf_detection_api')
-
         parser.add_argument('--save-images', action='store_true',
             help="Save images (default: %(default)s)")
-
         return parser
 
+    def __init__(self, save_images=False):
+        super().__init__()
+
+        self._save_images = save_images
+
     def __call__(self, extractor, save_dir):
         tf = _import_tf()
 

datumaro/datumaro/plugins/tf_detection_api_format/extractor.py

@@ -8,60 +8,68 @@ import numpy as np
 import os.path as osp
 import re
 
-from datumaro.components.extractor import AnnotationType, DEFAULT_SUBSET_NAME, \
+from datumaro.components.extractor import (SourceExtractor,
-    LabelCategories, BboxObject, DatasetItem, Extractor
+    DEFAULT_SUBSET_NAME, DatasetItem,
-from datumaro.components.formats.tfrecord import DetectionApiPath
+    AnnotationType, Bbox, LabelCategories
+)
 from datumaro.util.image import lazy_image, decode_image
 from datumaro.util.tf_util import import_tf as _import_tf
 
+from .format import DetectionApiPath
+
 
 def clamp(value, _min, _max):
     return max(min(_max, value), _min)
 
-class DetectionApiExtractor(Extractor):
+class TfDetectionApiExtractor(SourceExtractor):
-    class Subset(Extractor):
+    def __init__(self, path):
-        def __init__(self, name, parent):
-            super().__init__()
-            self._name = name
-            self._parent = parent
-            self.items = OrderedDict()
-
-        def __iter__(self):
-            for item in self.items.values():
-                yield item
-
-        def __len__(self):
-            return len(self.items)
-
-        def categories(self):
-            return self._parent.categories()
-
-    def __init__(self, path, images_dir=None):
         super().__init__()
 
+        assert osp.isfile(path)
+        images_dir = ''
         root_dir = osp.dirname(osp.abspath(path))
         if osp.basename(root_dir) == DetectionApiPath.ANNOTATIONS_DIR:
             root_dir = osp.dirname(root_dir)
             images_dir = osp.join(root_dir, DetectionApiPath.IMAGES_DIR)
             if not osp.isdir(images_dir):
-                images_dir = None
+                images_dir = ''
-        self._images_dir = images_dir
-
-        self._subsets = {}
 
         subset_name = osp.splitext(osp.basename(path))[0]
         if subset_name == DEFAULT_SUBSET_NAME:
             subset_name = None
-        subset = DetectionApiExtractor.Subset(subset_name, self)
+        self._subset_name = subset_name
+
         items, labels = self._parse_tfrecord_file(path, subset_name, images_dir)
-        subset.items = items
+        self._items = items
-        self._subsets[subset_name] = subset
+        self._categories = self._load_categories(labels)
+
+    def categories(self):
+        return self._categories
+
+    def __iter__(self):
+        for item in self._items:
+            yield item
+
+    def __len__(self):
+        return len(self._items)
+
+    def subsets(self):
+        if self._subset_name:
+            return [self._subset_name]
+        return None
 
+    def get_subset(self, name):
+        if name != self._subset_name:
+            return None
+        return self
+
+    @staticmethod
+    def _load_categories(labels):
         label_categories = LabelCategories()
         labels = sorted(labels.items(), key=lambda item: item[1])
         for label, _ in labels:
             label_categories.add(label)
-        self._categories = {
+        return {
             AnnotationType.label: label_categories
         }
 
@@ -114,7 +122,7 @@ class DetectionApiExtractor(Extractor):
                 for label, id in cls._parse_labelmap(labelmap_text).items()
             })
 
-        dataset_items = OrderedDict()
+        dataset_items = []
 
         for record in dataset:
             parsed_record = tf.io.parse_single_example(record, features)
@@ -163,7 +171,7 @@ class DetectionApiExtractor(Extractor):
                 y = clamp(shape[2] * frame_height, 0, frame_height)
                 w = clamp(shape[3] * frame_width, 0, frame_width) - x
                 h = clamp(shape[4] * frame_height, 0, frame_height) - y
-                annotations.append(BboxObject(x, y, w, h,
+                annotations.append(Bbox(x, y, w, h,
                     label=dataset_labels.get(label, None), id=index
                 ))
 
@@ -175,32 +183,7 @@ class DetectionApiExtractor(Extractor):
                 if osp.exists(image_path):
                     image = lazy_image(image_path)
 
-            dataset_items[item_id] = DatasetItem(id=item_id, subset=subset_name,
+            dataset_items.append(DatasetItem(id=item_id, subset=subset_name,
-                image=image, annotations=annotations)
+                image=image, annotations=annotations))
 
         return dataset_items, dataset_labels
-
-    def categories(self):
-        return self._categories
-
-    def __iter__(self):
-        for subset in self._subsets.values():
-            for item in subset:
-                yield item
-
-    def __len__(self):
-        length = 0
-        for subset in self._subsets.values():
-            length += len(subset)
-        return length
-
-    def subsets(self):
-        return list(self._subsets)
-
-    def get_subset(self, name):
-        return self._subsets[name]
-
-    def get(self, item_id, subset=None, path=None):
-        if path is not None:
-            return None
-        return self.get_subset(subset).items.get(item_id, None)

datumaro/datumaro/plugins/tf_detection_api_format/importer.py

@@ -7,8 +7,10 @@ from glob import glob
 import logging as log
 import os.path as osp
 
+from datumaro.components.extractor import Importer
 
-class DetectionApiImporter:
+
+class TfDetectionApiImporter(Importer):
     EXTRACTOR_NAME = 'tf_detection_api'
 
     def __call__(self, path, **extra_params):

datumaro/datumaro/plugins/transforms.py

@@ -0,0 +1,255 @@
+
+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+from itertools import groupby
+import logging as log
+
+import pycocotools.mask as mask_utils
+
+from datumaro.components.extractor import (Transform, AnnotationType,
+    Mask, RleMask, Polygon)
+from datumaro.components.cli_plugin import CliPlugin
+import datumaro.util.mask_tools as mask_tools
+
+
+class CropCoveredSegments(Transform, CliPlugin):
+    def transform_item(self, item):
+        annotations = []
+        segments = []
+        for ann in item.annotations:
+            if ann.type in {AnnotationType.polygon, AnnotationType.mask}:
+                segments.append(ann)
+            else:
+                annotations.append(ann)
+        if not segments:
+            return item
+
+        if not item.has_image:
+            raise Exception("Image info is required for this transform")
+        h, w = item.image.shape[:2]
+        segments = self.crop_segments(segments, w, h)
+
+        annotations += segments
+        return self.wrap_item(item, annotations=annotations)
+
+    @classmethod
+    def crop_segments(cls, segment_anns, img_width, img_height):
+        segment_anns = sorted(segment_anns, key=lambda x: x.z_order)
+
+        segments = []
+        for s in segment_anns:
+            if s.type == AnnotationType.polygon:
+                segments.append(s.points)
+            elif s.type == AnnotationType.mask:
+                if isinstance(s, RleMask):
+                    rle = s._rle
+                else:
+                    rle = mask_tools.mask_to_rle(s.image)
+                segments.append(rle)
+
+        segments = mask_tools.crop_covered_segments(
+            segments, img_width, img_height)
+
+        new_anns = []
+        for ann, new_segment in zip(segment_anns, segments):
+            fields = {'z_order': ann.z_order, 'label': ann.label,
+                'id': ann.id, 'group': ann.group, 'attributes': ann.attributes
+            }
+            if ann.type == AnnotationType.polygon:
+                if fields['group'] is None:
+                    fields['group'] = cls._make_group_id(
+                        segment_anns + new_anns, fields['id'])
+                for polygon in new_segment:
+                    new_anns.append(Polygon(points=polygon, **fields))
+            else:
+                rle = mask_tools.mask_to_rle(new_segment)
+                rle = mask_utils.frPyObjects(rle, *rle['size'])
+                new_anns.append(RleMask(rle=rle, **fields))
+
+        return new_anns
+
+    @staticmethod
+    def _make_group_id(anns, ann_id):
+        if ann_id:
+            return ann_id
+        max_gid = max(anns, default=0, key=lambda x: x.group)
+        return max_gid + 1
+
+class MergeInstanceSegments(Transform, CliPlugin):
+    """
+    Replaces instance masks and, optionally, polygons with a single mask.
+    """
+
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser = super().build_cmdline_parser(**kwargs)
+        parser.add_argument('--include-polygons', action='store_true',
+            help="Include polygons")
+        return parser
+
+    def __init__(self, extractor, include_polygons=False):
+        super().__init__(extractor)
+
+        self._include_polygons = include_polygons
+
+    def transform_item(self, item):
+        annotations = []
+        segments = []
+        for ann in item.annotations:
+            if ann.type in {AnnotationType.polygon, AnnotationType.mask}:
+                segments.append(ann)
+            else:
+                annotations.append(ann)
+        if not segments:
+            return item
+
+        if not item.has_image:
+            raise Exception("Image info is required for this transform")
+        h, w = item.image.shape[:2]
+        instances = self.find_instances(segments)
+        segments = [self.merge_segments(i, w, h, self._include_polygons)
+            for i in instances]
+        segments = sum(segments, [])
+
+        annotations += segments
+        return self.wrap_item(item, annotations=annotations)
+
+    @classmethod
+    def merge_segments(cls, instance, img_width, img_height,
+            include_polygons=False):
+        polygons = [a for a in instance if a.type == AnnotationType.polygon]
+        masks = [a for a in instance if a.type == AnnotationType.mask]
+        if not polygons and not masks:
+            return []
+
+        leader = cls.find_group_leader(polygons + masks)
+        instance = []
+
+        # Build the resulting mask
+        mask = None
+
+        if include_polygons and polygons:
+            polygons = [p.points for p in polygons]
+            mask = mask_tools.rles_to_mask(polygons, img_width, img_height)
+        else:
+            instance += polygons # keep unused polygons
+
+        if masks:
+            if mask is not None:
+                masks += [mask]
+            mask = cls.merge_masks(masks)
+
+        if mask is None:
+            return instance
+
+        mask = mask_tools.mask_to_rle(mask)
+        mask = mask_utils.frPyObjects(mask, *mask['size'])
+        instance.append(
+            RleMask(rle=mask, label=leader.label, z_order=leader.z_order,
+                id=leader.id, attributes=leader.attributes, group=leader.group
+            )
+        )
+        return instance
+
+    @staticmethod
+    def find_group_leader(group):
+        return max(group, key=lambda x: x.get_area())
+
+    @staticmethod
+    def merge_masks(masks):
+        if not masks:
+            return None
+
+        def get_mask(m):
+            if isinstance(m, Mask):
+                return m.image
+            else:
+                return m
+
+        binary_mask = get_mask(masks[0])
+        for m in masks[1:]:
+            binary_mask |= get_mask(m)
+
+        return binary_mask
+
+    @staticmethod
+    def find_instances(annotations):
+        segment_anns = (a for a in annotations
+            if a.type in {AnnotationType.polygon, AnnotationType.mask}
+        )
+
+        ann_groups = []
+        for g_id, group in groupby(segment_anns, lambda a: a.group):
+            if g_id is None:
+                ann_groups.extend(([a] for a in group))
+            else:
+                ann_groups.append(list(group))
+
+        return ann_groups
+
+class PolygonsToMasks(Transform, CliPlugin):
+    def transform_item(self, item):
+        annotations = []
+        for ann in item.annotations:
+            if ann.type == AnnotationType.polygon:
+                if not item.has_image:
+                    raise Exception("Image info is required for this transform")
+                h, w = item.image.shape[:2]
+                annotations.append(self.convert_polygon(ann, h, w))
+            else:
+                annotations.append(ann)
+
+        return self.wrap_item(item, annotations=annotations)
+
+    @staticmethod
+    def convert_polygon(polygon, img_h, img_w):
+        rle = mask_utils.frPyObjects([polygon.points], img_h, img_w)[0]
+
+        return RleMask(rle=rle, label=polygon.label, z_order=polygon.z_order,
+            id=polygon.id, attributes=polygon.attributes, group=polygon.group)
+
+class MasksToPolygons(Transform, CliPlugin):
+    def transform_item(self, item):
+        annotations = []
+        for ann in item.annotations:
+            if ann.type == AnnotationType.mask:
+                polygons = self.convert_mask(ann)
+                if not polygons:
+                    log.debug("[%s]: item %s: "
+                        "Mask conversion to polygons resulted in too "
+                        "small polygons, which were discarded" % \
+                        (self.NAME, item.id))
+                annotations.extend(polygons)
+            else:
+                annotations.append(ann)
+
+        return self.wrap_item(item, annotations=annotations)
+
+    @staticmethod
+    def convert_mask(mask):
+        polygons = mask_tools.mask_to_polygons(mask.image)
+
+        return [
+            Polygon(points=p, label=mask.label, z_order=mask.z_order,
+                id=mask.id, attributes=mask.attributes, group=mask.group)
+            for p in polygons
+        ]
+
+class Reindex(Transform, CliPlugin):
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser = super().build_cmdline_parser(**kwargs)
+        parser.add_argument('-s', '--start', type=int, default=1,
+            help="Start value for item ids")
+        return parser
+
+    def __init__(self, extractor, start=1):
+        super().__init__(extractor)
+
+        self._start = start
+
+    def __iter__(self):
+        for i, item in enumerate(self._extractor):
+            yield self.wrap_item(item, id=i + self._start)

datumaro/datumaro/plugins/voc_format/converter.py

@@ -11,16 +11,18 @@ from lxml import etree as ET
 import os
 import os.path as osp
 
+from datumaro.components.cli_plugin import CliPlugin
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import (DEFAULT_SUBSET_NAME, AnnotationType,
-    LabelCategories
+    LabelCategories, CompiledMask,
 )
-from datumaro.components.formats.voc import (VocTask, VocPath,
+from datumaro.util.image import save_image
+from datumaro.util.mask_tools import paint_mask, remap_mask
+
+from .format import (VocTask, VocPath,
     VocInstColormap, VocPose,
     parse_label_map, make_voc_label_map, make_voc_categories, write_label_map
 )
-from datumaro.util.image import save_image
-from datumaro.util.mask_tools import paint_mask, remap_mask
 
 
 def _convert_attr(name, attributes, type_conv, default=None, warn=True):
@@ -131,13 +133,15 @@ class _Converter:
             segm_list = OrderedDict()
 
             for item in subset:
-                item_id = str(item.id)
+                log.debug("Converting item '%s'", item.id)
+
                 if self._save_images:
-                    data = item.image
+                    if item.has_image:
-                    if data is not None:
                         save_image(osp.join(self._images_dir,
-                                str(item_id) + VocPath.IMAGE_EXT),
+                                item.id + VocPath.IMAGE_EXT),
-                            data)
+                            item.image)
+                    else:
+                        log.debug("Item '%s' has no image" % item.id)
 
                 labels = []
                 bboxes = []
@@ -152,13 +156,13 @@ class _Converter:
 
                 if len(bboxes) != 0:
                     root_elem = ET.Element('annotation')
-                    if '_' in item_id:
+                    if '_' in item.id:
-                        folder = item_id[ : item_id.find('_')]
+                        folder = item.id[ : item.id.find('_')]
                     else:
                         folder = ''
                     ET.SubElement(root_elem, 'folder').text = folder
                     ET.SubElement(root_elem, 'filename').text = \
-                        item_id + VocPath.IMAGE_EXT
+                        item.id + VocPath.IMAGE_EXT
 
                     source_elem = ET.SubElement(root_elem, 'source')
                     ET.SubElement(source_elem, 'database').text = 'Unknown'
@@ -198,17 +202,25 @@ class _Converter:
                         obj_label =  self.get_label(obj.label)
                         ET.SubElement(obj_elem, 'name').text = obj_label
 
-                        pose = _convert_attr('pose', attr, lambda v: VocPose[v],
+                        if 'pose' in attr:
-                            VocPose.Unspecified)
+                            pose = _convert_attr('pose', attr,
-                        ET.SubElement(obj_elem, 'pose').text = pose.name
+                                lambda v: VocPose[v], VocPose.Unspecified)
+                            ET.SubElement(obj_elem, 'pose').text = pose.name
 
-                        truncated = _convert_attr('truncated', attr, int, 0)
+                        if 'truncated' in attr:
-                        ET.SubElement(obj_elem, 'truncated').text = \
+                            truncated = _convert_attr('truncated', attr, int, 0)
-                            '%d' % truncated
+                            ET.SubElement(obj_elem, 'truncated').text = \
+                                '%d' % truncated
 
-                        difficult = _convert_attr('difficult', attr, int, 0)
+                        if 'difficult' in attr:
-                        ET.SubElement(obj_elem, 'difficult').text = \
+                            difficult = _convert_attr('difficult', attr, int, 0)
-                            '%d' % difficult
+                            ET.SubElement(obj_elem, 'difficult').text = \
+                                '%d' % difficult
+
+                        if 'occluded' in attr:
+                            occluded = _convert_attr('occluded', attr, int, 0)
+                            ET.SubElement(obj_elem, 'occluded').text = \
+                                '%d' % occluded
 
                         bbox = obj.get_bbox()
                         if bbox is not None:
@@ -226,12 +238,14 @@ class _Converter:
                         label_actions = self._get_actions(obj_label)
                         actions_elem = ET.Element('actions')
                         for action in label_actions:
-                            presented = _convert_attr(action, attr,
+                            present = 0
-                                lambda v: int(v == True), 0)
+                            if action in attr:
-                            ET.SubElement(actions_elem, action).text = \
+                                present = _convert_attr(action, attr,
-                                '%d' % presented
+                                    lambda v: int(v == True), 0)
-
+                                ET.SubElement(actions_elem, action).text = \
-                            objects_with_actions[new_obj_id][action] = presented
+                                    '%d' % present
+
+                            objects_with_actions[new_obj_id][action] = present
                         if len(actions_elem) != 0:
                             obj_elem.append(actions_elem)
 
@@ -239,41 +253,44 @@ class _Converter:
                             VocTask.detection,
                             VocTask.person_layout,
                             VocTask.action_classification]):
-                        with open(osp.join(self._ann_dir, item_id + '.xml'), 'w') as f:
+                        with open(osp.join(self._ann_dir, item.id + '.xml'), 'w') as f:
                             f.write(ET.tostring(root_elem,
                                 encoding='unicode', pretty_print=True))
 
-                    clsdet_list[item_id] = True
+                    clsdet_list[item.id] = True
-                    layout_list[item_id] = objects_with_parts
+                    layout_list[item.id] = objects_with_parts
-                    action_list[item_id] = objects_with_actions
+                    action_list[item.id] = objects_with_actions
 
-                for label_obj in labels:
+                for label_ann in labels:
-                    label = self.get_label(label_obj.label)
+                    label = self.get_label(label_ann.label)
                     if not self._is_label(label):
                         continue
-                    class_list = class_lists.get(item_id, set())
+                    class_list = class_lists.get(item.id, set())
-                    class_list.add(label_obj.label)
+                    class_list.add(label_ann.label)
-                    class_lists[item_id] = class_list
+                    class_lists[item.id] = class_list
 
-                    clsdet_list[item_id] = True
+                    clsdet_list[item.id] = True
 
-                for mask_obj in masks:
+                if masks:
-                    if mask_obj.attributes.get('class') == True:
+                    compiled_mask = CompiledMask.from_instance_masks(masks,
-                        self.save_segm(osp.join(self._segm_dir,
+                        instance_labels=[self._label_id_mapping(m.label)
-                                item_id + VocPath.SEGM_EXT),
+                            for m in masks])
-                            mask_obj)
-                    if mask_obj.attributes.get('instances') == True:
-                        self.save_segm(osp.join(self._inst_dir,
-                                item_id + VocPath.SEGM_EXT),
-                            mask_obj, VocInstColormap)
 
-                    segm_list[item_id] = True
+                    self.save_segm(
+                        osp.join(self._segm_dir, item.id + VocPath.SEGM_EXT),
+                        compiled_mask.class_mask)
+                    self.save_segm(
+                        osp.join(self._inst_dir, item.id + VocPath.SEGM_EXT),
+                        compiled_mask.instance_mask,
+                        colormap=VocInstColormap)
+
+                    segm_list[item.id] = True
 
                 if len(item.annotations) == 0:
-                    clsdet_list[item_id] = None
+                    clsdet_list[item.id] = None
-                    layout_list[item_id] = None
+                    layout_list[item.id] = None
-                    action_list[item_id] = None
+                    action_list[item.id] = None
-                    segm_list[item_id] = None
+                    segm_list[item.id] = None
 
                 if set(self._tasks) & set([None,
                         VocTask.classification,
@@ -361,14 +378,12 @@ class _Converter:
                 else:
                     f.write('%s\n' % (item))
 
-    def save_segm(self, path, annotation, colormap=None):
+    def save_segm(self, path, mask, colormap=None):
-        data = annotation.image
         if self._apply_colormap:
             if colormap is None:
                 colormap = self._categories[AnnotationType.mask].colormap
-            data = self._remap_mask(data)
+            mask = paint_mask(mask, colormap)
-            data = paint_mask(data, colormap)
+        save_image(path, mask)
-        save_image(path, data)
 
     def save_label_map(self):
         path = osp.join(self._save_dir, VocPath.LABELMAP_FILE)
@@ -468,30 +483,25 @@ class _Converter:
         if void_labels:
             log.warning("The following labels are remapped to background: %s" %
                 ', '.join(void_labels))
+        log.debug("Saving segmentations with the following label mapping: \n%s" %
+            '\n'.join(["#%s '%s' -> #%s '%s'" %
+                (
+                    src_id, src_label, id_mapping[src_id],
+                    self._categories[AnnotationType.label] \
+                        .items[id_mapping[src_id]].name
+                )
+                for src_id, src_label in source_labels.items()
+            ])
+        )
 
         def map_id(src_id):
-            return id_mapping[src_id]
+            return id_mapping.get(src_id, 0)
         return map_id
 
     def _remap_mask(self, mask):
         return remap_mask(mask, self._label_id_mapping)
 
-class VocConverter(Converter):
+class VocConverter(Converter, CliPlugin):
-    def __init__(self,
-            tasks=None, save_images=False, apply_colormap=False, label_map=None,
-            cmdline_args=None):
-        super().__init__()
-
-        self._options = {
-            'tasks': tasks,
-            'save_images': save_images,
-            'apply_colormap': apply_colormap,
-            'label_map': label_map,
-        }
-
-        if cmdline_args is not None:
-            self._options.update(self._parse_cmdline(cmdline_args))
-
     @staticmethod
     def _split_tasks_string(s):
         return [VocTask[i.strip()] for i in s.split(',')]
@@ -503,10 +513,8 @@ class VocConverter(Converter):
         return LabelmapType[s].name
 
     @classmethod
-    def build_cmdline_parser(cls, parser=None):
+    def build_cmdline_parser(cls, **kwargs):
-        import argparse
+        parser = super().build_cmdline_parser(**kwargs)
-        if not parser:
-            parser = argparse.ArgumentParser(prog='voc')
 
         parser.add_argument('--save-images', action='store_true',
             help="Save images (default: %(default)s)")
@@ -523,21 +531,37 @@ class VocConverter(Converter):
 
         return parser
 
+    def __init__(self, tasks=None, save_images=False,
+            apply_colormap=False, label_map=None):
+        super().__init__()
+
+        self._options = {
+            'tasks': tasks,
+            'save_images': save_images,
+            'apply_colormap': apply_colormap,
+            'label_map': label_map,
+        }
+
     def __call__(self, extractor, save_dir):
         converter = _Converter(extractor, save_dir, **self._options)
         converter.convert()
 
-def VocClassificationConverter(**kwargs):
+class VocClassificationConverter(VocConverter):
-    return VocConverter(VocTask.classification, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(VocTask.classification, **kwargs)
 
-def VocDetectionConverter(**kwargs):
+class VocDetectionConverter(VocConverter):
-    return VocConverter(VocTask.detection, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(VocTask.detection, **kwargs)
 
-def VocLayoutConverter(**kwargs):
+class VocLayoutConverter(VocConverter):
-    return VocConverter(VocTask.person_layout, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(VocTask.person_layout, **kwargs)
 
-def VocActionConverter(**kwargs):
+class VocActionConverter(VocConverter):
-    return VocConverter(VocTask.action_classification, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(VocTask.action_classification, **kwargs)
 
-def VocSegmentationConverter(**kwargs):
+class VocSegmentationConverter(VocConverter):
-    return VocConverter(VocTask.segmentation, **kwargs)
+    def __init__(self, **kwargs):
+        super().__init__(VocTask.segmentation, **kwargs)

datumaro/datumaro/plugins/voc_format/extractor.py

@@ -8,20 +8,22 @@ import os
 import os.path as osp
 from xml.etree import ElementTree as ET
 
-from datumaro.components.extractor import (Extractor, DatasetItem,
+from datumaro.components.extractor import (SourceExtractor, Extractor,
-    AnnotationType, LabelObject, MaskObject, BboxObject,
+    DEFAULT_SUBSET_NAME, DatasetItem,
-)
+    AnnotationType, Label, Mask, Bbox, CompiledMask
-from datumaro.components.formats.voc import (
-    VocTask, VocPath, VocInstColormap, parse_label_map, make_voc_categories
 )
 from datumaro.util import dir_items
 from datumaro.util.image import lazy_image
 from datumaro.util.mask_tools import lazy_mask, invert_colormap
 
+from .format import (
+    VocTask, VocPath, VocInstColormap, parse_label_map, make_voc_categories
+)
+
 
 _inverse_inst_colormap = invert_colormap(VocInstColormap)
 
-class VocExtractor(Extractor):
+class VocExtractor(SourceExtractor):
     class Subset(Extractor):
         def __init__(self, name, parent):
             super().__init__()
@@ -45,20 +47,25 @@ class VocExtractor(Extractor):
 
         subsets = {}
         for subset_name in subset_names:
+            subset_file_name = subset_name
+            if subset_name == DEFAULT_SUBSET_NAME:
+                subset_name = None
             subset = __class__.Subset(subset_name, self)
 
-            with open(osp.join(subsets_dir, subset_name + '.txt'), 'r') as f:
+            with open(osp.join(subsets_dir, subset_file_name + '.txt'), 'r') as f:
                 subset.items = [line.split()[0] for line in f]
 
             subsets[subset_name] = subset
         return subsets
 
     def _load_cls_annotations(self, subsets_dir, subset_names):
+        subset_file_names = [n if n else DEFAULT_SUBSET_NAME
+            for n in subset_names]
         dir_files = dir_items(subsets_dir, '.txt', truncate_ext=True)
 
         label_annotations = defaultdict(list)
         label_anno_files = [s for s in dir_files \
-            if '_' in s and s[s.rfind('_') + 1:] in subset_names]
+            if '_' in s and s[s.rfind('_') + 1:] in subset_file_names]
         for ann_filename in label_anno_files:
             with open(osp.join(subsets_dir, ann_filename + '.txt'), 'r') as f:
                 label = ann_filename[:ann_filename.rfind('_')]
@@ -139,43 +146,77 @@ class VocExtractor(Extractor):
         assert label_id is not None
         return label_id
 
-    def _get_annotations(self, item):
+    @staticmethod
+    def _lazy_extract_mask(mask, c):
+        return lambda: mask == c
+
+    def _get_annotations(self, item_id):
         item_annotations = []
 
         if self._task is VocTask.segmentation:
+            class_mask = None
             segm_path = osp.join(self._path, VocPath.SEGMENTATION_DIR,
-                item + VocPath.SEGM_EXT)
+                item_id + VocPath.SEGM_EXT)
             if osp.isfile(segm_path):
                 inverse_cls_colormap = \
                     self._categories[AnnotationType.mask].inverse_colormap
-                item_annotations.append(MaskObject(
+                class_mask = lazy_mask(segm_path, inverse_cls_colormap)
-                    image=lazy_mask(segm_path, inverse_cls_colormap),
-                    attributes={ 'class': True }
-                ))
 
+            instances_mask = None
             inst_path = osp.join(self._path, VocPath.INSTANCES_DIR,
-                item + VocPath.SEGM_EXT)
+                item_id + VocPath.SEGM_EXT)
             if osp.isfile(inst_path):
-                item_annotations.append(MaskObject(
+                instances_mask = lazy_mask(inst_path, _inverse_inst_colormap)
-                    image=lazy_mask(inst_path, _inverse_inst_colormap),
+
-                    attributes={ 'instances': True }
+            if instances_mask is not None:
-                ))
+                compiled_mask = CompiledMask(class_mask, instances_mask)
+
+                if class_mask is not None:
+                    label_cat = self._categories[AnnotationType.label]
+                    instance_labels = compiled_mask.get_instance_labels(
+                        class_count=len(label_cat.items))
+                else:
+                    instance_labels = {i: None
+                        for i in range(compiled_mask.instance_count)}
+
+                for instance_id, label_id in instance_labels.items():
+                    image = compiled_mask.lazy_extract(instance_id)
+
+                    attributes = dict()
+                    if label_id is not None:
+                        actions = {a: False
+                            for a in label_cat.items[label_id].attributes
+                        }
+                        attributes.update(actions)
+
+                    item_annotations.append(Mask(
+                        image=image, label=label_id,
+                        attributes=attributes, group=instance_id
+                    ))
+            elif class_mask is not None:
+                log.warn("item '%s': has only class segmentation, "
+                    "instance masks will not be available" % item_id)
+                classes = class_mask.image.unique()
+                for label_id in classes:
+                    image = self._lazy_extract_mask(class_mask, label_id)
+                    item_annotations.append(Mask(image=image, label=label_id))
 
         cls_annotations = self._annotations.get(VocTask.classification)
         if cls_annotations is not None and \
                 self._task is VocTask.classification:
-            item_labels = cls_annotations.get(item)
+            item_labels = cls_annotations.get(item_id)
             if item_labels is not None:
                 for label_id in item_labels:
-                    item_annotations.append(LabelObject(label_id))
+                    item_annotations.append(Label(label_id))
 
         det_annotations = self._annotations.get(VocTask.detection)
         if det_annotations is not None:
-            det_annotations = det_annotations.get(item)
+            det_annotations = det_annotations.get(item_id)
         if det_annotations is not None:
             root_elem = ET.fromstring(det_annotations)
 
             for obj_id, object_elem in enumerate(root_elem.findall('object')):
+                obj_id += 1
                 attributes = {}
                 group = None
 
@@ -225,24 +266,22 @@ class VocExtractor(Extractor):
                 for part_elem in object_elem.findall('part'):
                     part = part_elem.find('name').text
                     part_label_id = self._get_label_id(part)
-                    bbox = self._parse_bbox(part_elem)
+                    part_bbox = self._parse_bbox(part_elem)
                     group = obj_id
 
                     if self._task is not VocTask.person_layout:
                         break
-                    if bbox is None:
+                    if part_bbox is None:
                         continue
-                    item_annotations.append(BboxObject(
+                    item_annotations.append(Bbox(*part_bbox, label=part_label_id,
-                        *bbox, label=part_label_id,
+                        group=group))
-                        group=obj_id))
 
-                if self._task is VocTask.person_layout and group is None:
+                if self._task is VocTask.person_layout and not group:
                     continue
                 if self._task is VocTask.action_classification and not actions:
                     continue
 
-                item_annotations.append(BboxObject(
+                item_annotations.append(Bbox(*obj_bbox, label=obj_label_id,
-                    *obj_bbox, label=obj_label_id,
                     attributes=attributes, id=obj_id, group=group))
 
         return item_annotations
@@ -482,7 +521,7 @@ class VocComp_1_2_Extractor(VocResultsExtractor):
         if cls_ann is not None:
             for desc in cls_ann:
                 label_id, conf = desc
-                annotations.append(LabelObject(
+                annotations.append(Label(
                     int(label_id),
                     attributes={ 'score': float(conf) }
                 ))
@@ -508,7 +547,7 @@ class VocComp_3_4_Extractor(VocResultsExtractor):
         if det_ann is not None:
             for desc in det_ann:
                 label_id, conf, left, top, right, bottom = desc
-                annotations.append(BboxObject(
+                annotations.append(Bbox(
                     x=float(left), y=float(top),
                     w=float(right) - float(left), h=float(bottom) - float(top),
                     label=int(label_id),
@@ -560,7 +599,7 @@ class VocComp_5_6_Extractor(VocResultsExtractor):
         if cls_image_path and osp.isfile(cls_image_path):
             inverse_cls_colormap = \
                 self._categories[AnnotationType.mask].inverse_colormap
-            annotations.append(MaskObject(
+            annotations.append(Mask(
                 image=lazy_mask(cls_image_path, inverse_cls_colormap),
                 attributes={ 'class': True }
             ))
@@ -568,7 +607,7 @@ class VocComp_5_6_Extractor(VocResultsExtractor):
         inst_ann = self._annotations[subset_name]
         inst_image_path = inst_ann.get(item)
         if inst_image_path and osp.isfile(inst_image_path):
-            annotations.append(MaskObject(
+            annotations.append(Mask(
                 image=lazy_mask(inst_image_path, _inverse_inst_colormap),
                 attributes={ 'instances': True }
             ))
@@ -641,7 +680,7 @@ class VocComp_7_8_Extractor(VocResultsExtractor):
 
                 for part in parts:
                     label_id, bbox = part
-                    annotations.append(BboxObject(
+                    annotations.append(Bbox(
                         *bbox, label=label_id,
                         attributes=attributes))
 
@@ -672,7 +711,7 @@ class VocComp_9_10_Extractor(VocResultsExtractor):
         if action_ann is not None:
             for desc in action_ann:
                 action_id, obj_id, conf = desc
-                annotations.append(LabelObject(
+                annotations.append(Label(
                     action_id,
                     attributes={
                         'score': conf,

datumaro/datumaro/plugins/voc_format/importer.py

@@ -6,15 +6,17 @@
 import os
 import os.path as osp
 
-from datumaro.components.formats.voc import VocTask, VocPath
+from datumaro.components.extractor import Importer
 from datumaro.util import find
 
+from .format import VocTask, VocPath
 
-class VocImporter:
+
+class VocImporter(Importer):
     _TASKS = [
-        (VocTask.classification, 'voc_cls', 'Main'),
+        (VocTask.classification, 'voc_classification', 'Main'),
-        (VocTask.detection, 'voc_det', 'Main'),
+        (VocTask.detection, 'voc_detection', 'Main'),
-        (VocTask.segmentation, 'voc_segm', 'Segmentation'),
+        (VocTask.segmentation, 'voc_segmentation', 'Segmentation'),
         (VocTask.person_layout, 'voc_layout', 'Layout'),
         (VocTask.action_classification, 'voc_action', 'Action'),
     ]

datumaro/datumaro/plugins/yolo_format/converter.py

@@ -10,9 +10,11 @@ import os.path as osp
 
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import AnnotationType
-from datumaro.components.formats.yolo import YoloPath
+from datumaro.components.cli_plugin import CliPlugin
 from datumaro.util.image import save_image
 
+from .format import YoloPath
+
 
 def _make_yolo_bbox(img_size, box):
     # https://github.com/pjreddie/darknet/blob/master/scripts/voc_label.py
@@ -24,30 +26,20 @@ def _make_yolo_bbox(img_size, box):
     h = (box[3] - box[1]) / img_size[1]
     return x, y, w, h
 
-class YoloConverter(Converter):
+class YoloConverter(Converter, CliPlugin):
     # https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects
 
-    def __init__(self, save_images=False, cmdline_args=None):
-        super().__init__()
-        self._save_images = save_images
-
-        if cmdline_args is not None:
-            options = self._parse_cmdline(cmdline_args)
-            for k, v in options.items():
-                if hasattr(self, '_' + str(k)):
-                    setattr(self, '_' + str(k), v)
-
     @classmethod
-    def build_cmdline_parser(cls, parser=None):
+    def build_cmdline_parser(cls, **kwargs):
-        import argparse
+        parser = super().build_cmdline_parser(**kwargs)
-        if not parser:
-            parser = argparse.ArgumentParser(prog='yolo')
-
         parser.add_argument('--save-images', action='store_true',
             help="Save images (default: %(default)s)")
-
         return parser
 
+    def __init__(self, save_images=False):
+        super().__init__()
+        self._save_images = save_images
+
     def __call__(self, extractor, save_dir):
         os.makedirs(save_dir, exist_ok=True)
 
@@ -88,9 +80,10 @@ class YoloConverter(Converter):
                     osp.basename(subset_dir), image_name)
 
                 if self._save_images:
-                    image_path = osp.join(subset_dir, image_name)
+                    if item.has_image:
-                    if not osp.exists(image_path):
+                        save_image(osp.join(subset_dir, image_name), item.image)
-                        save_image(image_path, item.image)
+                    else:
+                        log.debug("Item '%s' has no images" % item.id)
 
                 height, width = item.image.shape[:2]
 

datumaro/datumaro/plugins/yolo_format/extractor.py

@@ -7,14 +7,15 @@ from collections import OrderedDict
 import os.path as osp
 import re
 
-from datumaro.components.extractor import (Extractor, DatasetItem,
+from datumaro.components.extractor import (SourceExtractor, Extractor,
-    AnnotationType, BboxObject, LabelCategories
+    DatasetItem, AnnotationType, Bbox, LabelCategories
 )
-from datumaro.components.formats.yolo import YoloPath
 from datumaro.util.image import lazy_image
 
+from .format import YoloPath
 
-class YoloExtractor(Extractor):
+
+class YoloExtractor(SourceExtractor):
     class Subset(Extractor):
         def __init__(self, name, parent):
             super().__init__()
@@ -124,9 +125,9 @@ class YoloExtractor(Extractor):
                 h = float(h)
                 x = float(xc) - w * 0.5
                 y = float(yc) - h * 0.5
-                annotations.append(BboxObject(
+                annotations.append(Bbox(
-                    x * image_width, y * image_height,
+                    round(x * image_width, 1), round(y * image_height, 1),
-                    w * image_width, h * image_height,
+                    round(w * image_width, 1), round(h * image_height, 1),
                     label=label_id
                 ))
         return annotations
@@ -137,7 +138,7 @@ class YoloExtractor(Extractor):
 
         with open(names_path, 'r') as f:
             for label in f:
-                label_categories.add(label)
+                label_categories.add(label.strip())
 
         return label_categories
 

datumaro/datumaro/plugins/yolo_format/importer.py

@@ -7,8 +7,10 @@ from glob import glob
 import logging as log
 import os.path as osp
 
+from datumaro.components.extractor import Importer
 
-class YoloImporter:
+
+class YoloImporter(Importer):
     def __call__(self, path, **extra_params):
         from datumaro.components.project import Project # cyclic import
         project = Project()

datumaro/datumaro/util/image.py

@@ -136,7 +136,7 @@ class lazy_image:
 
     def __call__(self):
         image = None
-        image_id = id(self) # path is not necessary hashable or a file path
+        image_id = hash(self) # path is not necessary hashable or a file path
 
         cache = self._get_cache()
         if cache is not None:
@@ -155,3 +155,6 @@ class lazy_image:
         elif cache == False:
             return None
         return cache
+
+    def __hash__(self):
+        return hash((id(self), self.path, self.loader))

datumaro/datumaro/util/mask_tools.py

@@ -93,9 +93,16 @@ def remap_mask(mask, map_fn):
 
     return np.array([map_fn(c) for c in range(256)], dtype=np.uint8)[mask]
 
+def make_index_mask(binary_mask, index):
+    return np.choose(binary_mask, np.array([0, index], dtype=np.uint8))
+
+def make_binary_mask(mask):
+    return np.nonzero(mask)
+
 
 def load_mask(path, inverse_colormap=None):
     mask = load_image(path)
+    mask = mask.astype(np.uint8)
     if inverse_colormap is not None:
         if len(mask.shape) == 3 and mask.shape[2] != 1:
             mask = unpaint_mask(mask, inverse_colormap)
@@ -250,3 +257,23 @@ def rles_to_mask(rles, width, height):
     rles = mask_utils.merge(rles)
     mask = mask_utils.decode(rles)
     return mask
+
+def find_mask_bbox(mask):
+    cols = np.any(mask, axis=0)
+    rows = np.any(mask, axis=1)
+    x0, x1 = np.where(cols)[0][[0, -1]]
+    y0, y1 = np.where(rows)[0][[0, -1]]
+    return [x0, y0, x1 - x0, y1 - y0]
+
+def merge_masks(masks):
+    """
+        Merges masks into one, mask order is resposible for z order.
+    """
+    if not masks:
+        return None
+
+    merged_mask = masks[0]
+    for m in masks[1:]:
+        merged_mask = np.where(m != 0, m, merged_mask)
+
+    return merged_mask

datumaro/datumaro/util/test_utils.py

@@ -9,6 +9,9 @@ import os.path as osp
 import shutil
 import tempfile
 
+from datumaro.components.extractor import AnnotationType
+from datumaro.util import find
+
 
 def current_function_name(depth=1):
     return inspect.getouterframes(inspect.currentframe())[depth].function
@@ -20,7 +23,7 @@ class FileRemover:
         self.ignore_errors = ignore_errors
 
     def __enter__(self):
-        return self
+        return self.path
 
     # pylint: disable=redefined-builtin
     def __exit__(self, type=None, value=None, traceback=None):
@@ -51,4 +54,46 @@ def item_to_str(item):
             'ann[%s]: %s' % (i, ann_to_str(a))
             for i, a in enumerate(item.annotations)
         ]
-    )
+    )
+
+def compare_categories(test, expected, actual):
+    test.assertEqual(
+        sorted(expected, key=lambda t: t.value),
+        sorted(actual, key=lambda t: t.value)
+    )
+
+    if AnnotationType.label in expected:
+        test.assertEqual(
+            expected[AnnotationType.label].items,
+            actual[AnnotationType.label].items,
+        )
+    if AnnotationType.mask in expected:
+        test.assertEqual(
+            expected[AnnotationType.mask].colormap,
+            actual[AnnotationType.mask].colormap,
+        )
+    if AnnotationType.points in expected:
+        test.assertEqual(
+            expected[AnnotationType.points].items,
+            actual[AnnotationType.points].items,
+        )
+
+def compare_datasets(test, expected, actual):
+    compare_categories(test, expected.categories(), actual.categories())
+
+    test.assertEqual(sorted(expected.subsets()), sorted(actual.subsets()))
+    test.assertEqual(len(expected), len(actual))
+    for item_a in expected:
+        item_b = find(actual, lambda x: x.id == item_a.id)
+        test.assertFalse(item_b is None, item_a.id)
+        test.assertEqual(len(item_a.annotations), len(item_b.annotations))
+        for ann_a in item_a.annotations:
+            # We might find few corresponding items, so check them all
+            ann_b_matches = [x for x in item_b.annotations
+                if x.id == ann_a.id and \
+                    x.type == ann_a.type and x.group == ann_a.group]
+            test.assertFalse(len(ann_b_matches) == 0, 'ann id: %s' % ann_a.id)
+
+            ann_b = find(ann_b_matches, lambda x: x == ann_a)
+            test.assertEqual(ann_a, ann_b, 'ann: %s' % ann_to_str(ann_a))
+            item_b.annotations.remove(ann_b) # avoid repeats

datumaro/docs/user_manual.md

@@ -470,7 +470,7 @@ def process_outputs(inputs, outputs):
                y = max(int(det[4] * input_height), 0)
                w = min(int(det[5] * input_width - x), input_width)
                h = min(int(det[6] * input_height - y), input_height)
-               image_results.append(BboxObject(x, y, w, h,
+               image_results.append(Bbox(x, y, w, h,
                     label=label, attributes={'score': conf} ))
 
                results.append(image_results[:max_det])

datumaro/tests/test_RISE.py

@@ -4,7 +4,7 @@ import numpy as np
 
 from unittest import TestCase
 
-from datumaro.components.extractor import LabelObject, BboxObject
+from datumaro.components.extractor import Label, Bbox
 from datumaro.components.launcher import Launcher
 from datumaro.components.algorithms.rise import RISE
 
@@ -32,7 +32,7 @@ class RiseTest(TestCase):
                 other_conf = (1.0 - cls_conf) / (self.class_count - 1)
 
                 return [
-                    LabelObject(i, attributes={
+                    Label(i, attributes={
                         'score': cls_conf if cls == i else other_conf }) \
                     for i in range(self.class_count)
                 ]
@@ -94,7 +94,7 @@ class RiseTest(TestCase):
                     if roi.threshold < roi_sum / roi_base_sum:
                         cls = roi.label
                         detections.append(
-                            BboxObject(roi.x, roi.y, roi.w, roi.h,
+                            Bbox(roi.x, roi.y, roi.w, roi.h,
                                 label=cls, attributes={'score': cls_conf})
                         )
 
@@ -108,7 +108,7 @@ class RiseTest(TestCase):
                         box = [roi.x, roi.y, roi.w, roi.h]
                         offset = (np.random.rand(4) - 0.5) * self.pixel_jitter
                         detections.append(
-                            BboxObject(*(box + offset),
+                            Bbox(*(box + offset),
                                 label=cls, attributes={'score': cls_conf})
                         )
 
@@ -189,7 +189,7 @@ class RiseTest(TestCase):
         detections = []
         for i, roi in enumerate(rois):
             detections.append(
-                BboxObject(roi.x, roi.y, roi.w, roi.h,
+                Bbox(roi.x, roi.y, roi.w, roi.h,
                     label=roi.label, attributes={'score': roi.conf})
             )
 
@@ -199,7 +199,7 @@ class RiseTest(TestCase):
                 box = [roi.x, roi.y, roi.w, roi.h]
                 offset = (np.random.rand(4) - 0.5) * pixel_jitter
                 detections.append(
-                    BboxObject(*(box + offset),
+                    Bbox(*(box + offset),
                         label=cls, attributes={'score': cls_conf})
                 )
 

datumaro/tests/test_coco_format.py

@@ -2,17 +2,15 @@ import json
 import numpy as np
 import os
 import os.path as osp
-from PIL import Image
 
 from unittest import TestCase
 
 from datumaro.components.project import Project
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, LabelObject, MaskObject, PointsObject, PolygonObject,
+    AnnotationType, Label, Mask, Points, Polygon, Bbox, Caption,
-    BboxObject, CaptionObject,
     LabelCategories, PointsCategories
 )
-from datumaro.components.converters.coco import (
+from datumaro.plugins.coco_format.converter import (
     CocoConverter,
     CocoImageInfoConverter,
     CocoCaptionsConverter,
@@ -20,8 +18,10 @@ from datumaro.components.converters.coco import (
     CocoPersonKeypointsConverter,
     CocoLabelsConverter,
 )
+from datumaro.plugins.coco_format.importer import CocoImporter
+from datumaro.util.image import save_image
 from datumaro.util import find
-from datumaro.util.test_utils import TestDir
+from datumaro.util.test_utils import TestDir, compare_datasets
 
 
 class CocoImporterTest(TestCase):
@@ -59,8 +59,8 @@ class CocoImporterTest(TestCase):
         })
         annotation['images'].append({
             "id": 1,
-            "width": 10,
+            "width": 5,
-            "height": 5,
+            "height": 10,
             "file_name": '000000000001.jpg',
             "license": 0,
             "flickr_url": '',
@@ -101,8 +101,7 @@ class CocoImporterTest(TestCase):
         os.makedirs(ann_dir)
 
         image = np.ones((10, 5, 3), dtype=np.uint8)
-        image = Image.fromarray(image).convert('RGB')
+        save_image(osp.join(img_dir, '000000000001.jpg'), image)
-        image.save(osp.join(img_dir, '000000000001.jpg'))
 
         annotation = self.generate_annotation()
 
@@ -110,9 +109,9 @@ class CocoImporterTest(TestCase):
             json.dump(annotation, outfile)
 
     def test_can_import(self):
-        with TestDir() as temp_dir:
+        with TestDir() as test_dir:
-            self.COCO_dataset_generate(temp_dir.path)
+            self.COCO_dataset_generate(test_dir)
-            project = Project.import_from(temp_dir.path, 'coco')
+            project = Project.import_from(test_dir, 'coco')
             dataset = project.make_dataset()
 
             self.assertListEqual(['val'], sorted(dataset.subsets()))
@@ -121,7 +120,7 @@ class CocoImporterTest(TestCase):
             item = next(iter(dataset))
             self.assertTrue(item.has_image)
             self.assertEqual(np.sum(item.image), np.prod(item.image.shape))
-            self.assertEqual(4, len(item.annotations))
+            self.assertEqual(2, len(item.annotations))
 
             ann_1 = find(item.annotations, lambda x: x.id == 1)
             ann_1_poly = find(item.annotations, lambda x: \
@@ -137,38 +136,17 @@ class CocoImporterTest(TestCase):
 
 class CocoConverterTest(TestCase):
     def _test_save_and_load(self, source_dataset, converter, test_dir,
-            importer_params=None, target_dataset=None):
+            target_dataset=None, importer_args=None):
-        converter(source_dataset, test_dir.path)
+        converter(source_dataset, test_dir)
-
+
-        if not importer_params:
+        if importer_args is None:
-            importer_params = {}
+            importer_args = {}
-        project = Project.import_from(test_dir.path, 'coco',
+        parsed_dataset = CocoImporter()(test_dir, **importer_args).make_dataset()
-            **importer_params)
+
-        parsed_dataset = project.make_dataset()
+        if target_dataset is None:
-
+            target_dataset = source_dataset
-        if target_dataset is not None:
+
-            source_dataset = target_dataset
+        compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
-        self.assertListEqual(
-            sorted(source_dataset.subsets()),
-            sorted(parsed_dataset.subsets()),
-        )
-
-        self.assertEqual(len(source_dataset), len(parsed_dataset))
-
-        for item_a in source_dataset:
-            item_b = find(parsed_dataset, lambda x: x.id == item_a.id)
-            self.assertFalse(item_b is None)
-            self.assertEqual(len(item_a.annotations), len(item_b.annotations))
-            for ann_a in item_a.annotations:
-                # We might find few corresponding items, so check them all
-                ann_b_matches = [x for x in item_b.annotations
-                    if x.id == ann_a.id and \
-                        x.type == ann_a.type and x.group == ann_a.group]
-                self.assertFalse(len(ann_b_matches) == 0, 'aid: %s' % ann_a.id)
-
-                ann_b = find(ann_b_matches, lambda x: x == ann_a)
-                self.assertEqual(ann_a, ann_b, 'aid: %s' % ann_a.id)
-                item_b.annotations.remove(ann_b) # avoid repeats
 
     def test_can_save_and_load_captions(self):
         class TestExtractor(Extractor):
@@ -176,17 +154,17 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, subset='train',
                         annotations=[
-                            CaptionObject('hello', id=1, group=1),
+                            Caption('hello', id=1, group=1),
-                            CaptionObject('world', id=2, group=2),
+                            Caption('world', id=2, group=2),
                         ]),
                     DatasetItem(id=2, subset='train',
                         annotations=[
-                            CaptionObject('test', id=3, group=3),
+                            Caption('test', id=3, group=3),
                         ]),
 
                     DatasetItem(id=3, subset='val',
                         annotations=[
-                            CaptionObject('word', id=1, group=1),
+                            Caption('word', id=1, group=1),
                         ]
                     ),
                 ])
@@ -207,39 +185,76 @@ class CocoConverterTest(TestCase):
                     DatasetItem(id=1, subset='train', image=np.ones((4, 4, 3)),
                         annotations=[
                             # Bbox + single polygon
-                            BboxObject(0, 1, 2, 2,
+                            Bbox(0, 1, 2, 2,
                                 label=2, group=1, id=1,
                                 attributes={ 'is_crowd': False }),
-                            PolygonObject([0, 1, 2, 1, 2, 3, 0, 3],
+                            Polygon([0, 1, 2, 1, 2, 3, 0, 3],
                                 attributes={ 'is_crowd': False },
                                 label=2, group=1, id=1),
                         ]),
                     DatasetItem(id=2, subset='train', image=np.ones((4, 4, 3)),
                         annotations=[
                             # Mask + bbox
-                            MaskObject(np.array([
+                            Mask(np.array([
                                     [0, 1, 0, 0],
                                     [0, 1, 0, 0],
                                     [0, 1, 1, 1],
                                     [0, 0, 0, 0]],
-                                    ),
+                                ),
                                 attributes={ 'is_crowd': True },
                                 label=4, group=3, id=3),
-                            BboxObject(1, 0, 2, 2, label=4, group=3, id=3,
+                            Bbox(1, 0, 2, 2, label=4, group=3, id=3,
                                 attributes={ 'is_crowd': True }),
                         ]),
 
                     DatasetItem(id=3, subset='val', image=np.ones((4, 4, 3)),
                         annotations=[
                             # Bbox + mask
-                            BboxObject(0, 1, 2, 2, label=4, group=3, id=3,
+                            Bbox(0, 1, 2, 2, label=4, group=3, id=3,
                                 attributes={ 'is_crowd': True }),
-                            MaskObject(np.array([
+                            Mask(np.array([
+                                    [0, 0, 0, 0],
+                                    [1, 1, 1, 0],
+                                    [1, 1, 0, 0],
+                                    [0, 0, 0, 0]],
+                                ),
+                                attributes={ 'is_crowd': True },
+                                label=4, group=3, id=3),
+                        ]),
+                ])
+
+            def categories(self):
+                return categories
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='train', image=np.ones((4, 4, 3)),
+                        annotations=[
+                            Polygon([0, 1, 2, 1, 2, 3, 0, 3],
+                                attributes={ 'is_crowd': False },
+                                label=2, group=1, id=1),
+                        ]),
+                    DatasetItem(id=2, subset='train', image=np.ones((4, 4, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 1, 0, 0],
+                                    [0, 1, 0, 0],
+                                    [0, 1, 1, 1],
+                                    [0, 0, 0, 0]],
+                                ),
+                                attributes={ 'is_crowd': True },
+                                label=4, group=3, id=3),
+                        ]),
+
+                    DatasetItem(id=3, subset='val', image=np.ones((4, 4, 3)),
+                        annotations=[
+                            Mask(np.array([
                                     [0, 0, 0, 0],
                                     [1, 1, 1, 0],
                                     [1, 1, 0, 0],
                                     [0, 0, 0, 0]],
-                                    ),
+                                ),
                                 attributes={ 'is_crowd': True },
                                 label=4, group=3, id=3),
                         ]),
@@ -250,7 +265,8 @@ class CocoConverterTest(TestCase):
 
         with TestDir() as test_dir:
             self._test_save_and_load(TestExtractor(),
-                CocoInstancesConverter(), test_dir)
+                CocoInstancesConverter(), test_dir,
+                target_dataset=DstExtractor())
 
     def test_can_merge_polygons_on_loading(self):
         label_categories = LabelCategories()
@@ -263,9 +279,9 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, image=np.zeros((6, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, id=4, group=4),
-                            PolygonObject([5, 0, 9, 0, 5, 5],
+                            Polygon([5, 0, 9, 0, 5, 5],
                                 label=3, id=4, group=4),
                         ]
                     ),
@@ -278,16 +294,7 @@ class CocoConverterTest(TestCase):
             def __iter__(self):
                 items = list(super().__iter__())
                 items[0]._annotations = [
-                    BboxObject(0, 0, 9, 5,
+                    Mask(np.array([
-                        label=3, id=4, group=4,
-                        attributes={ 'is_crowd': False }),
-                    PolygonObject([0, 0, 4, 0, 4, 4],
-                        label=3, id=4, group=4,
-                        attributes={ 'is_crowd': False }),
-                    PolygonObject([5, 0, 9, 0, 5, 5],
-                        label=3, id=4, group=4,
-                        attributes={ 'is_crowd': False }),
-                    MaskObject(np.array([
                             [0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
                             [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
                             [0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
@@ -305,7 +312,7 @@ class CocoConverterTest(TestCase):
         with TestDir() as test_dir:
             self._test_save_and_load(TestExtractor(),
                 CocoInstancesConverter(), test_dir,
-                importer_params={'merge_instance_polygons': True},
+                importer_args={'merge_instance_polygons': True},
                 target_dataset=TargetExtractor())
 
     def test_can_crop_covered_segments(self):
@@ -315,56 +322,47 @@ class CocoConverterTest(TestCase):
 
         class SrcTestExtractor(Extractor):
             def __iter__(self):
-                items = [
+                return iter([
                     DatasetItem(id=1, image=np.zeros((5, 5, 3)),
                         annotations=[
-                            MaskObject(np.array([
+                            Mask(np.array([
                                     [0, 0, 1, 1, 1],
                                     [0, 0, 1, 1, 1],
                                     [1, 1, 0, 1, 1],
                                     [1, 1, 1, 0, 0],
                                     [1, 1, 1, 0, 0]],
-                                    ),
+                                ),
                                 label=2, id=1, z_order=0),
-                            PolygonObject([1, 1, 4, 1, 4, 4, 1, 4],
+                            Polygon([1, 1, 4, 1, 4, 4, 1, 4],
                                 label=1, id=2, z_order=1),
                         ]
                     ),
-                ]
+                ])
-                return iter(items)
 
             def categories(self):
                 return { AnnotationType.label: label_categories }
 
         class DstTestExtractor(Extractor):
             def __iter__(self):
-                items = [
+                return iter([
                     DatasetItem(id=1, image=np.zeros((5, 5, 3)),
                         annotations=[
-                            BboxObject(0, 0, 4, 4,
+                            Mask(np.array([
-                                label=2, id=1, group=1,
-                                attributes={ 'is_crowd': True }),
-                            MaskObject(np.array([
                                     [0, 0, 1, 1, 1],
                                     [0, 0, 0, 0, 1],
                                     [1, 0, 0, 0, 1],
                                     [1, 0, 0, 0, 0],
                                     [1, 1, 1, 0, 0]],
-                                    ),
+                                ),
                                 attributes={ 'is_crowd': True },
                                 label=2, id=1, group=1),
 
-                            BboxObject(1, 1, 3, 3,
+                            Polygon([1, 1, 4, 1, 4, 4, 1, 4],
-                                label=1, id=2, group=2,
-                                attributes={ 'is_crowd': False }),
-                            PolygonObject([1, 1, 4, 1, 4, 4, 1, 4],
                                 label=1, id=2, group=2,
                                 attributes={ 'is_crowd': False }),
-                            # NOTE: Why it's 4 in COCOapi?..
                         ]
                     ),
-                ]
+                ])
-                return iter(items)
 
             def categories(self):
                 return { AnnotationType.label: label_categories }
@@ -384,9 +382,9 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, image=np.zeros((6, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, id=4, group=4),
-                            PolygonObject([5, 0, 9, 0, 5, 5],
+                            Polygon([5, 0, 9, 0, 5, 5],
                                 label=3, id=4, group=4),
                         ]
                     ),
@@ -400,9 +398,7 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, image=np.zeros((6, 10, 3)),
                         annotations=[
-                            BboxObject(0, 0, 9, 5, label=3, id=4, group=4,
+                            Mask(np.array([
-                                attributes={ 'is_crowd': True }),
-                            MaskObject(np.array([
                                     [0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
                                     [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
                                     [0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
@@ -411,7 +407,7 @@ class CocoConverterTest(TestCase):
                                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
                                     # only internal fragment (without the border),
                                     # but not everywhere...
-                                    ),
+                                ),
                                 attributes={ 'is_crowd': True },
                                 label=3, id=4, group=4),
                         ]
@@ -433,22 +429,20 @@ class CocoConverterTest(TestCase):
 
         class SrcTestExtractor(Extractor):
             def __iter__(self):
-                items = [
+                return iter([
                     DatasetItem(id=1, image=np.zeros((5, 10, 3)),
                         annotations=[
-                            MaskObject(np.array([
+                            Mask(np.array([
-                                        [0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
+                                    [0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
-                                        [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
+                                    [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
-                                        [0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
+                                    [0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
-                                        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
-                                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                                    ],
+                                ]),
-                                    ),
                                 label=3, id=4, group=4),
                         ]
                     ),
-                ]
+                ])
-                return iter(items)
 
             def categories(self):
                 return { AnnotationType.label: label_categories }
@@ -458,13 +452,11 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, image=np.zeros((5, 10, 3)),
                         annotations=[
-                            BboxObject(1, 0, 7, 3, label=3, id=4, group=4,
+                            Polygon(
-                                attributes={ 'is_crowd': False }),
-                            PolygonObject(
                                 [3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5],
                                 label=3, id=4, group=4,
                                 attributes={ 'is_crowd': False }),
-                            PolygonObject(
+                            Polygon(
                                 [5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5],
                                 label=3, id=4, group=4,
                                 attributes={ 'is_crowd': False }),
@@ -504,17 +496,17 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, subset='train',
                         annotations=[
-                            LabelObject(4, id=1, group=1),
+                            Label(4, id=1, group=1),
-                            LabelObject(9, id=2, group=2),
+                            Label(9, id=2, group=2),
                         ]),
                     DatasetItem(id=2, subset='train',
                         annotations=[
-                            LabelObject(4, id=4, group=4),
+                            Label(4, id=4, group=4),
                         ]),
 
                     DatasetItem(id=3, subset='val',
                         annotations=[
-                            LabelObject(2, id=1, group=1),
+                            Label(2, id=1, group=1),
                         ]),
                 ])
 
@@ -547,25 +539,25 @@ class CocoConverterTest(TestCase):
                     DatasetItem(id=1, subset='train', image=np.zeros((5, 5, 3)),
                         annotations=[
                             # Full instance annotations: polygon + keypoints
-                            PointsObject([0, 0, 0, 2, 4, 1], [0, 1, 2],
+                            Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
                                 label=3, group=1, id=1),
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, group=1, id=1),
 
                             # Full instance annotations: bbox + keypoints
-                            PointsObject([1, 2, 3, 4, 2, 3], group=2, id=2),
+                            Points([1, 2, 3, 4, 2, 3], group=2, id=2),
-                            BboxObject(1, 2, 2, 2, group=2, id=2),
+                            Bbox(1, 2, 2, 2, group=2, id=2),
                         ]),
                     DatasetItem(id=2, subset='train',
                         annotations=[
                             # Solitary keypoints
-                            PointsObject([1, 2, 0, 2, 4, 1], label=5, id=3),
+                            Points([1, 2, 0, 2, 4, 1], label=5, id=3),
                         ]),
 
                     DatasetItem(id=3, subset='val',
                         annotations=[
                             # Solitary keypoints with no label
-                            PointsObject([0, 0, 1, 2, 3, 4], [0, 1, 2], id=3),
+                            Points([0, 0, 1, 2, 3, 4], [0, 1, 2], id=3),
                         ]),
                 ])
 
@@ -577,48 +569,36 @@ class CocoConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=1, subset='train', image=np.zeros((5, 5, 3)),
                         annotations=[
-                            PointsObject([0, 0, 0, 2, 4, 1], [0, 1, 2],
+                            Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
-                                label=3, group=1, id=1,
-                                attributes={'is_crowd': False}),
-                            PolygonObject([0, 0, 4, 0, 4, 4],
                                 label=3, group=1, id=1,
                                 attributes={'is_crowd': False}),
-                            BboxObject(0, 0, 4, 4,
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, group=1, id=1,
                                 attributes={'is_crowd': False}),
 
-                            PointsObject([1, 2, 3, 4, 2, 3],
+                            Points([1, 2, 3, 4, 2, 3],
-                                group=2, id=2,
-                                attributes={'is_crowd': False}),
-                            PolygonObject([1, 2, 3, 2, 3, 4, 1, 4],
                                 group=2, id=2,
                                 attributes={'is_crowd': False}),
-                            BboxObject(1, 2, 2, 2,
+                            Polygon([1, 2, 3, 2, 3, 4, 1, 4],
                                 group=2, id=2,
                                 attributes={'is_crowd': False}),
                         ]),
                     DatasetItem(id=2, subset='train',
                         annotations=[
-                            PointsObject([1, 2, 0, 2, 4, 1],
+                            Points([1, 2, 0, 2, 4, 1],
                                 label=5, group=3, id=3,
                                 attributes={'is_crowd': False}),
-                            PolygonObject([0, 1, 4, 1, 4, 2, 0, 2],
+                            Polygon([0, 1, 4, 1, 4, 2, 0, 2],
-                                label=5, group=3, id=3,
-                                attributes={'is_crowd': False}),
-                            BboxObject(0, 1, 4, 1,
                                 label=5, group=3, id=3,
                                 attributes={'is_crowd': False}),
                         ]),
 
                     DatasetItem(id=3, subset='val',
                         annotations=[
-                            PointsObject([0, 0, 1, 2, 3, 4], [0, 1, 2],
+                            Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
-                                group=3, id=3,
-                                attributes={'is_crowd': False}),
-                            PolygonObject([1, 2, 3, 2, 3, 4, 1, 4],
                                 group=3, id=3,
                                 attributes={'is_crowd': False}),
-                            BboxObject(1, 2, 2, 2,
+                            Polygon([1, 2, 3, 2, 3, 4, 1, 4],
                                 group=3, id=3,
                                 attributes={'is_crowd': False}),
                         ]),
@@ -634,11 +614,11 @@ class CocoConverterTest(TestCase):
             def __iter__(self):
                 return iter([
                     DatasetItem(id=1, annotations=[
-                        LabelObject(2, id=1, group=1),
+                        Label(2, id=1, group=1),
                     ]),
 
                     DatasetItem(id=2, annotations=[
-                        LabelObject(3, id=2, group=2),
+                        Label(3, id=2, group=2),
                     ]),
                 ])
 

datumaro/tests/test_command_targets.py

@@ -21,7 +21,7 @@ class CommandTargetsTest(TestCase):
 
     def test_image_false_when_false(self):
         with TestDir() as test_dir:
-            path = osp.join(test_dir.path, 'test.jpg')
+            path = osp.join(test_dir, 'test.jpg')
             with open(path, 'w+') as f:
                 f.write('qwerty123')
 
@@ -33,7 +33,7 @@ class CommandTargetsTest(TestCase):
 
     def test_image_true_when_true(self):
         with TestDir() as test_dir:
-            path = osp.join(test_dir.path, 'test.jpg')
+            path = osp.join(test_dir, 'test.jpg')
             image = np.random.random_sample([10, 10, 3])
             cv2.imwrite(path, image)
 
@@ -60,7 +60,7 @@ class CommandTargetsTest(TestCase):
 
     def test_project_true_when_project_file(self):
         with TestDir() as test_dir:
-            path = osp.join(test_dir.path, 'test.jpg')
+            path = osp.join(test_dir, 'test.jpg')
             Project().save(path)
 
             target = ProjectTarget()
@@ -91,9 +91,9 @@ class CommandTargetsTest(TestCase):
 
         self.assertFalse(status)
 
-    def test_project_true_when_not_project_file(self):
+    def test_project_false_when_not_project_file(self):
         with TestDir() as test_dir:
-            path = osp.join(test_dir.path, 'test.jpg')
+            path = osp.join(test_dir, 'test.jpg')
             with open(path, 'w+') as f:
                 f.write('wqererw')
 

datumaro/tests/test_cvat_format.py

@@ -6,22 +6,21 @@ from xml.etree import ElementTree as ET
 from unittest import TestCase
 
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, PointsObject, PolygonObject, PolyLineObject, BboxObject,
+    AnnotationType, Points, Polygon, PolyLine, Bbox,
     LabelCategories,
 )
-from datumaro.components.importers.cvat import CvatImporter
+from datumaro.plugins.cvat_format.importer import CvatImporter
-from datumaro.components.converters.cvat import CvatConverter
+from datumaro.plugins.cvat_format.converter import CvatConverter
-from datumaro.components.project import Project
+from datumaro.plugins.cvat_format.format import CvatPath
-import datumaro.components.formats.cvat as Cvat
 from datumaro.util.image import save_image
-from datumaro.util.test_utils import TestDir, item_to_str
+from datumaro.util.test_utils import TestDir, compare_datasets
 
 
 class CvatExtractorTest(TestCase):
     @staticmethod
     def generate_dummy_cvat(path):
-        images_dir = osp.join(path, Cvat.CvatPath.IMAGES_DIR)
+        images_dir = osp.join(path, CvatPath.IMAGES_DIR)
-        anno_dir = osp.join(path, Cvat.CvatPath.ANNOTATIONS_DIR)
+        anno_dir = osp.join(path, CvatPath.ANNOTATIONS_DIR)
 
         os.makedirs(images_dir)
         os.makedirs(anno_dir)
@@ -103,80 +102,55 @@ class CvatExtractorTest(TestCase):
         class TestExtractor(Extractor):
             def __iter__(self):
                 return iter([
-                    DatasetItem(id=1, subset='train', image=np.ones((8, 8, 3)),
+                    DatasetItem(id=0, subset='train', image=np.ones((8, 8, 3)),
                         annotations=[
-                            BboxObject(0, 2, 4, 2, label=0,
+                            Bbox(0, 2, 4, 2, label=0,
                                 attributes={
                                     'occluded': True, 'z_order': 1,
                                     'a1': True, 'a2': 'v3'
                                 }),
-                            PolyLineObject([1, 2, 3, 4, 5, 6, 7, 8],
+                            PolyLine([1, 2, 3, 4, 5, 6, 7, 8],
                                 attributes={'occluded': False, 'z_order': 0}),
                         ]),
-                    DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
+                    DatasetItem(id=1, subset='train', image=np.ones((10, 10, 3)),
                         annotations=[
-                            PolygonObject([1, 2, 3, 4, 6, 5],
+                            Polygon([1, 2, 3, 4, 6, 5],
                                 attributes={'occluded': False, 'z_order': 1}),
-                            PointsObject([1, 2, 3, 4, 5, 6], label=1,
+                            Points([1, 2, 3, 4, 5, 6], label=1,
                                 attributes={'occluded': False, 'z_order': 2}),
                         ]),
                 ])
 
             def categories(self):
                 label_categories = LabelCategories()
-                for i in range(10):
+                label_categories.add('label1', attributes={'a1', 'a2'})
-                    label_categories.add('label_' + str(i))
+                label_categories.add('label2')
                 return {
                     AnnotationType.label: label_categories,
                 }
 
         with TestDir() as test_dir:
-            self.generate_dummy_cvat(test_dir.path)
+            self.generate_dummy_cvat(test_dir)
             source_dataset = TestExtractor()
 
-            parsed_dataset = CvatImporter()(test_dir.path).make_dataset()
+            parsed_dataset = CvatImporter()(test_dir).make_dataset()
 
-            self.assertListEqual(
+            compare_datasets(self, source_dataset, parsed_dataset)
-                sorted(source_dataset.subsets()),
-                sorted(parsed_dataset.subsets()),
-            )
-            self.assertEqual(len(source_dataset), len(parsed_dataset))
-            for subset_name in source_dataset.subsets():
-                source_subset = source_dataset.get_subset(subset_name)
-                parsed_subset = parsed_dataset.get_subset(subset_name)
-                for item_a, item_b in zip(source_subset, parsed_subset):
-                    self.assertEqual(len(item_a.annotations), len(item_b.annotations))
-                    for ann_a, ann_b in zip(item_a.annotations, item_b.annotations):
-                        self.assertEqual(ann_a, ann_b)
 
 
 class CvatConverterTest(TestCase):
     def _test_save_and_load(self, source_dataset, converter, test_dir,
-            importer_params=None, target_dataset=None):
+            target_dataset=None, importer_args=None):
-        converter(source_dataset, test_dir.path)
+        converter(source_dataset, test_dir)
-
+
-        if not importer_params:
+        if importer_args is None:
-            importer_params = {}
+            importer_args = {}
-        project = Project.import_from(test_dir.path, 'cvat', **importer_params)
+        parsed_dataset = CvatImporter()(test_dir, **importer_args).make_dataset()
-        parsed_dataset = project.make_dataset()
+
-
+        if target_dataset is None:
-        if target_dataset is not None:
+            target_dataset = source_dataset
-            source_dataset = target_dataset
+
-        self.assertListEqual(
+        compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
-            sorted(source_dataset.subsets()),
-            sorted(parsed_dataset.subsets()),
-        )
-
-        self.assertEqual(len(source_dataset), len(parsed_dataset))
-
-        for subset_name in source_dataset.subsets():
-            source_subset = source_dataset.get_subset(subset_name)
-            parsed_subset = parsed_dataset.get_subset(subset_name)
-            self.assertEqual(len(source_subset), len(parsed_subset))
-            for idx, (item_a, item_b) in enumerate(
-                    zip(source_subset, parsed_subset)):
-                self.assertEqual(item_a, item_b, '%s:\n%s\nvs.\n%s\n' % \
-                    (idx, item_to_str(item_a), item_to_str(item_b)))
 
     def test_can_save_and_load(self):
         label_categories = LabelCategories()
@@ -190,32 +164,32 @@ class CvatConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=1, group=4,
                                 attributes={ 'occluded': True }),
-                            PolygonObject([5, 0, 9, 0, 5, 5],
+                            Polygon([5, 0, 9, 0, 5, 5],
                                 label=2, group=4,
                                 attributes={ 'unknown': 'bar' }),
-                            PointsObject([1, 1, 3, 2, 2, 3],
+                            Points([1, 1, 3, 2, 2, 3],
                                 label=2,
                                 attributes={ 'a1': 'x', 'a2': 42 }),
                         ]
                     ),
                     DatasetItem(id=1, subset='s1',
                         annotations=[
-                            PolyLineObject([0, 0, 4, 0, 4, 4],
+                            PolyLine([0, 0, 4, 0, 4, 4],
                                 label=3, id=4, group=4),
-                            BboxObject(5, 0, 1, 9,
+                            Bbox(5, 0, 1, 9,
                                 label=3, id=4, group=4),
                         ]
                     ),
 
                     DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, group=4,
                                 attributes={ 'z_order': 1, 'occluded': False }),
-                            PolyLineObject([5, 0, 9, 0, 5, 5]), # will be skipped as no label
+                            PolyLine([5, 0, 9, 0, 5, 5]), # will be skipped as no label
                         ]
                     ),
                 ])
@@ -228,13 +202,13 @@ class CvatConverterTest(TestCase):
                 return iter([
                     DatasetItem(id=0, subset='s1', image=np.zeros((5, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=1, group=4,
                                 attributes={ 'z_order': 0, 'occluded': True }),
-                            PolygonObject([5, 0, 9, 0, 5, 5],
+                            Polygon([5, 0, 9, 0, 5, 5],
                                 label=2, group=4,
                                 attributes={ 'z_order': 0, 'occluded': False }),
-                            PointsObject([1, 1, 3, 2, 2, 3],
+                            Points([1, 1, 3, 2, 2, 3],
                                 label=2,
                                 attributes={ 'z_order': 0, 'occluded': False,
                                     'a1': 'x', 'a2': 42 }),
@@ -242,10 +216,10 @@ class CvatConverterTest(TestCase):
                     ),
                     DatasetItem(id=1, subset='s1',
                         annotations=[
-                            PolyLineObject([0, 0, 4, 0, 4, 4],
+                            PolyLine([0, 0, 4, 0, 4, 4],
                                 label=3, group=4,
                                 attributes={ 'z_order': 0, 'occluded': False }),
-                            BboxObject(5, 0, 1, 9,
+                            Bbox(5, 0, 1, 9,
                                 label=3, group=4,
                                 attributes={ 'z_order': 0, 'occluded': False }),
                         ]
@@ -253,7 +227,7 @@ class CvatConverterTest(TestCase):
 
                     DatasetItem(id=2, subset='s2', image=np.ones((5, 10, 3)),
                         annotations=[
-                            PolygonObject([0, 0, 4, 0, 4, 4],
+                            Polygon([0, 0, 4, 0, 4, 4],
                                 label=3, group=4,
                                 attributes={ 'z_order': 1, 'occluded': False }),
                         ]

datumaro/tests/test_datumaro_format.py

@@ -4,11 +4,11 @@ from unittest import TestCase
 
 from datumaro.components.project import Project
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, LabelObject, MaskObject, PointsObject, PolygonObject,
+    AnnotationType, Label, Mask, Points, Polygon,
-    PolyLineObject, BboxObject, CaptionObject,
+    PolyLine, Bbox, Caption,
     LabelCategories, MaskCategories, PointsCategories
 )
-from datumaro.components.converters.datumaro import DatumaroConverter
+from datumaro.plugins.datumaro_format.converter import DatumaroConverter
 from datumaro.util.test_utils import TestDir, item_to_str
 from datumaro.util.mask_tools import generate_colormap
 
@@ -19,30 +19,30 @@ class DatumaroConverterTest(TestCase):
             return iter([
                 DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)),
                     annotations=[
-                        CaptionObject('hello', id=1),
+                        Caption('hello', id=1),
-                        CaptionObject('world', id=2, group=5),
+                        Caption('world', id=2, group=5),
-                        LabelObject(2, id=3, attributes={
+                        Label(2, id=3, attributes={
                             'x': 1,
                             'y': '2',
                         }),
-                        BboxObject(1, 2, 3, 4, label=4, id=4, attributes={
+                        Bbox(1, 2, 3, 4, label=4, id=4, attributes={
                             'score': 1.0,
                         }),
-                        BboxObject(5, 6, 7, 8, id=5, group=5),
+                        Bbox(5, 6, 7, 8, id=5, group=5),
-                        PointsObject([1, 2, 2, 0, 1, 1], label=0, id=5),
+                        Points([1, 2, 2, 0, 1, 1], label=0, id=5),
-                        MaskObject(label=3, id=5, image=np.ones((2, 3))),
+                        Mask(label=3, id=5, image=np.ones((2, 3))),
                     ]),
                 DatasetItem(id=21, subset='train',
                     annotations=[
-                        CaptionObject('test'),
+                        Caption('test'),
-                        LabelObject(2),
+                        Label(2),
-                        BboxObject(1, 2, 3, 4, 5, id=42, group=42)
+                        Bbox(1, 2, 3, 4, 5, id=42, group=42)
                     ]),
 
                 DatasetItem(id=2, subset='val',
                     annotations=[
-                        PolyLineObject([1, 2, 3, 4, 5, 6, 7, 8], id=11),
+                        PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11),
-                        PolygonObject([1, 2, 3, 4, 5, 6, 7, 8], id=12),
+                        Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12),
                     ]),
 
                 DatasetItem(id=42, subset='test'),
@@ -74,9 +74,9 @@ class DatumaroConverterTest(TestCase):
             source_dataset = self.TestExtractor()
 
             converter = DatumaroConverter(save_images=True)
-            converter(source_dataset, test_dir.path)
+            converter(source_dataset, test_dir)
 
-            project = Project.import_from(test_dir.path, 'datumaro')
+            project = Project.import_from(test_dir, 'datumaro')
             parsed_dataset = project.make_dataset()
 
             self.assertListEqual(

datumaro/tests/test_diff.py

@@ -1,6 +1,6 @@
 from unittest import TestCase
 
-from datumaro.components.extractor import DatasetItem, LabelObject, BboxObject
+from datumaro.components.extractor import DatasetItem, Label, Bbox
 from datumaro.components.comparator import Comparator
 
 
@@ -8,7 +8,7 @@ class DiffTest(TestCase):
     def test_no_bbox_diff_with_same_item(self):
         detections = 3
         anns = [
-            BboxObject(i * 10, 10, 10, 10, label=i,
+            Bbox(i * 10, 10, 10, 10, label=i,
                     attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections)
         ]
@@ -38,12 +38,12 @@ class DiffTest(TestCase):
         detections = 3
         class_count = 2
         item1 = DatasetItem(id=1, annotations=[
-            BboxObject(i * 10, 10, 10, 10, label=i,
+            Bbox(i * 10, 10, 10, 10, label=i,
                     attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections)
         ])
         item2 = DatasetItem(id=2, annotations=[
-            BboxObject(i * 10, 10, 10, 10, label=(i + 1) % class_count,
+            Bbox(i * 10, 10, 10, 10, label=(i + 1) % class_count,
                     attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections)
         ])
@@ -72,12 +72,12 @@ class DiffTest(TestCase):
         detections = 3
         class_count = 2
         item1 = DatasetItem(id=1, annotations=[
-            BboxObject(i * 10, 10, 10, 10, label=i,
+            Bbox(i * 10, 10, 10, 10, label=i,
                     attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections) if i % 2 == 0
         ])
         item2 = DatasetItem(id=2, annotations=[
-            BboxObject(i * 10, 10, 10, 10, label=(i + 1) % class_count,
+            Bbox(i * 10, 10, 10, 10, label=(i + 1) % class_count,
                     attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections) if i % 2 == 1
         ])
@@ -102,7 +102,7 @@ class DiffTest(TestCase):
     def test_no_label_diff_with_same_item(self):
         detections = 3
         anns = [
-            LabelObject(i, attributes={'score': (1.0 + i) / detections}) \
+            Label(i, attributes={'score': (1.0 + i) / detections}) \
                 for i in range(detections)
         ]
         item = DatasetItem(id=1, annotations=anns)
@@ -121,14 +121,14 @@ class DiffTest(TestCase):
 
     def test_can_find_wrong_label(self):
         item1 = DatasetItem(id=1, annotations=[
-            LabelObject(0),
+            Label(0),
-            LabelObject(1),
+            Label(1),
-            LabelObject(2),
+            Label(2),
         ])
         item2 = DatasetItem(id=2, annotations=[
-            LabelObject(2),
+            Label(2),
-            LabelObject(3),
+            Label(3),
-            LabelObject(4),
+            Label(4),
         ])
 
         conf_thresh = 0.5

datumaro/tests/test_image.py

@@ -23,7 +23,7 @@ class ImageTest(TestCase):
                     src_image = np.random.randint(0, 255 + 1, (2, 4))
                 else:
                     src_image = np.random.randint(0, 255 + 1, (2, 4, c))
-                path = osp.join(test_dir.path, 'img.png') # lossless
+                path = osp.join(test_dir, 'img.png') # lossless
 
                 image_module._IMAGE_BACKEND = save_backend
                 image_module.save_image(path, src_image)

datumaro/tests/test_image_dir_format.py

@@ -5,7 +5,7 @@ from unittest import TestCase
 
 from datumaro.components.project import Project
 from datumaro.components.extractor import Extractor, DatasetItem
-from datumaro.util.test_utils import TestDir
+from datumaro.util.test_utils import TestDir, compare_datasets
 from datumaro.util.image import save_image
 
 
@@ -22,27 +22,9 @@ class ImageDirFormatTest(TestCase):
             source_dataset = self.TestExtractor()
 
             for item in source_dataset:
-                save_image(osp.join(test_dir.path, '%s.jpg' % item.id),
+                save_image(osp.join(test_dir, '%s.jpg' % item.id), item.image)
-                    item.image)
 
-            project = Project.import_from(test_dir.path, 'image_dir')
+            project = Project.import_from(test_dir, 'image_dir')
             parsed_dataset = project.make_dataset()
 
-            self.assertListEqual(
+            compare_datasets(self, source_dataset, parsed_dataset)
-                sorted(source_dataset.subsets()),
-                sorted(parsed_dataset.subsets()),
-            )
-
-            self.assertEqual(len(source_dataset), len(parsed_dataset))
-
-            for subset_name in source_dataset.subsets():
-                source_subset = source_dataset.get_subset(subset_name)
-                parsed_subset = parsed_dataset.get_subset(subset_name)
-                self.assertEqual(len(source_subset), len(parsed_subset))
-                for idx, (item_a, item_b) in enumerate(
-                        zip(source_subset, parsed_subset)):
-                    self.assertEqual(item_a, item_b, str(idx))
-
-            self.assertEqual(
-                source_dataset.categories(),
-                parsed_dataset.categories())

datumaro/tests/test_images.py

@@ -15,7 +15,7 @@ class LazyImageTest(TestCase):
             image = np.ones((100, 100, 3), dtype=np.uint8)
             image = Image.fromarray(image).convert('RGB')
 
-            image_path = osp.join(test_dir.path, 'image.jpg')
+            image_path = osp.join(test_dir, 'image.jpg')
             image.save(image_path)
 
             caching_loader = lazy_image(image_path, cache=None)

datumaro/tests/test_masks.py

@@ -120,5 +120,19 @@ class ColormapOperationsTest(TestCase):
 
         actual = mask_tools.remap_mask(src, remap_fn)
 
+        self.assertTrue(np.array_equal(expected, actual),
+            '%s\nvs.\n%s' % (expected, actual))
+
+    def test_can_merge_masks(self):
+        masks = [
+            np.array([0, 2, 4, 0, 0, 1]),
+            np.array([0, 1, 1, 0, 2, 0]),
+            np.array([0, 0, 2, 3, 0, 0]),
+        ]
+        expected = \
+            np.array([0, 1, 2, 3, 2, 1])
+
+        actual = mask_tools.merge_masks(masks)
+
         self.assertTrue(np.array_equal(expected, actual),
             '%s\nvs.\n%s' % (expected, actual))

datumaro/tests/test_project.py

@@ -9,8 +9,7 @@ from datumaro.components.project import Source, Model
 from datumaro.components.launcher import Launcher, InferenceWrapper
 from datumaro.components.converter import Converter
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    LabelObject, MaskObject, PointsObject, PolygonObject,
+    Label, Mask, Points, Polygon, PolyLine, Bbox, Caption,
-    PolyLineObject, BboxObject, CaptionObject,
 )
 from datumaro.components.config import Config, DefaultConfig, SchemaBuilder
 from datumaro.components.dataset_filter import \
@@ -26,7 +25,7 @@ class ProjectTest(TestCase):
         })
 
         with TestDir() as test_dir:
-            project_path = test_dir.path
+            project_path = test_dir
             Project.generate(project_path, src_config)
 
             self.assertTrue(osp.isdir(project_path))
@@ -80,9 +79,9 @@ class ProjectTest(TestCase):
         project.add_source(source_name, origin)
 
         with TestDir() as test_dir:
-            project.save(test_dir.path)
+            project.save(test_dir)
 
-            loaded = Project.load(test_dir.path)
+            loaded = Project.load(test_dir)
             loaded = loaded.get_source(source_name)
             self.assertEqual(origin, loaded)
 
@@ -114,19 +113,19 @@ class ProjectTest(TestCase):
         project.add_model(model_name, saved)
 
         with TestDir() as test_dir:
-            project.save(test_dir.path)
+            project.save(test_dir)
 
-            loaded = Project.load(test_dir.path)
+            loaded = Project.load(test_dir)
             loaded = loaded.get_model(model_name)
             self.assertEqual(saved, loaded)
 
     def test_can_have_project_source(self):
         with TestDir() as test_dir:
-            Project.generate(test_dir.path)
+            Project.generate(test_dir)
 
             project2 = Project()
             project2.add_source('project1', {
-                'url': test_dir.path,
+                'url': test_dir,
             })
             dataset = project2.make_dataset()
 
@@ -141,7 +140,7 @@ class ProjectTest(TestCase):
         class TestLauncher(Launcher):
             def launch(self, inputs):
                 for i, inp in enumerate(inputs):
-                    yield [ LabelObject(attributes={'idx': i, 'data': inp}) ]
+                    yield [ Label(attributes={'idx': i, 'data': inp}) ]
 
         model_name = 'model'
         launcher_name = 'custom_launcher'
@@ -167,12 +166,12 @@ class ProjectTest(TestCase):
             def __iter__(self):
                 for i in range(2):
                     yield DatasetItem(id=i, subset='train', image=i,
-                        annotations=[ LabelObject(i) ])
+                        annotations=[ Label(i) ])
 
         class TestLauncher(Launcher):
             def launch(self, inputs):
                 for inp in inputs:
-                    yield [ LabelObject(inp) ]
+                    yield [ Label(inp) ]
 
         class TestConverter(Converter):
             def __call__(self, extractor, save_dir):
@@ -194,7 +193,7 @@ class ProjectTest(TestCase):
                         label = int(f.readline().strip())
                         assert subset == 'train'
                         yield DatasetItem(id=index, subset=subset,
-                            annotations=[ LabelObject(label) ])
+                            annotations=[ Label(label) ])
 
         model_name = 'model'
         launcher_name = 'custom_launcher'
@@ -208,10 +207,10 @@ class ProjectTest(TestCase):
         project.add_source('source', { 'format': extractor_name })
 
         with TestDir() as test_dir:
-            project.make_dataset().apply_model(model_name=model_name,
+            project.make_dataset().apply_model(model=model_name,
-                save_dir=test_dir.path)
+                save_dir=test_dir)
 
-            result = Project.load(test_dir.path)
+            result = Project.load(test_dir)
             result.env.extractors.register(extractor_name, TestExtractorDst)
             it = iter(result.make_dataset())
             item1 = next(it)
@@ -266,9 +265,9 @@ class ProjectTest(TestCase):
             src_dataset = src_project.make_dataset()
             item = DatasetItem(id=1)
             src_dataset.put(item)
-            src_dataset.save(test_dir.path)
+            src_dataset.save(test_dir)
 
-            loaded_project = Project.load(test_dir.path)
+            loaded_project = Project.load(test_dir)
             loaded_dataset = loaded_project.make_dataset()
 
             self.assertEqual(list(src_dataset), list(loaded_dataset))
@@ -285,12 +284,12 @@ class ProjectTest(TestCase):
     def test_project_compound_child_can_be_modified_recursively(self):
         with TestDir() as test_dir:
             child1 = Project({
-                'project_dir': osp.join(test_dir.path, 'child1'),
+                'project_dir': osp.join(test_dir, 'child1'),
             })
             child1.save()
 
             child2 = Project({
-                'project_dir': osp.join(test_dir.path, 'child2'),
+                'project_dir': osp.join(test_dir, 'child2'),
             })
             child2.save()
 
@@ -316,15 +315,15 @@ class ProjectTest(TestCase):
         class TestExtractor1(Extractor):
             def __iter__(self):
                 yield DatasetItem(id=1, subset='train', annotations=[
-                    LabelObject(2, id=3),
+                    Label(2, id=3),
-                    LabelObject(3, attributes={ 'x': 1 }),
+                    Label(3, attributes={ 'x': 1 }),
                 ])
 
         class TestExtractor2(Extractor):
             def __iter__(self):
                 yield DatasetItem(id=1, subset='train', annotations=[
-                    LabelObject(3, attributes={ 'x': 1 }),
+                    Label(3, attributes={ 'x': 1 }),
-                    LabelObject(4, id=4),
+                    Label(4, id=4),
                 ])
 
         project = Project()
@@ -346,17 +345,17 @@ class DatasetFilterTest(TestCase):
         item = DatasetItem(id=1, subset='subset', path=['a', 'b'],
             image=np.ones((5, 4, 3)),
             annotations=[
-                LabelObject(0, attributes={'a1': 1, 'a2': '2'}, id=1, group=2),
+                Label(0, attributes={'a1': 1, 'a2': '2'}, id=1, group=2),
-                CaptionObject('hello', id=1),
+                Caption('hello', id=1),
-                CaptionObject('world', group=5),
+                Caption('world', group=5),
-                LabelObject(2, id=3, attributes={ 'x': 1, 'y': '2' }),
+                Label(2, id=3, attributes={ 'x': 1, 'y': '2' }),
-                BboxObject(1, 2, 3, 4, label=4, id=4, attributes={ 'a': 1.0 }),
+                Bbox(1, 2, 3, 4, label=4, id=4, attributes={ 'a': 1.0 }),
-                BboxObject(5, 6, 7, 8, id=5, group=5),
+                Bbox(5, 6, 7, 8, id=5, group=5),
-                PointsObject([1, 2, 2, 0, 1, 1], label=0, id=5),
+                Points([1, 2, 2, 0, 1, 1], label=0, id=5),
-                MaskObject(id=5, image=np.ones((3, 2))),
+                Mask(id=5, image=np.ones((3, 2))),
-                MaskObject(label=3, id=5, image=np.ones((2, 3))),
+                Mask(label=3, id=5, image=np.ones((2, 3))),
-                PolyLineObject([1, 2, 3, 4, 5, 6, 7, 8], id=11),
+                PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11),
-                PolygonObject([1, 2, 3, 4, 5, 6, 7, 8]),
+                Polygon([1, 2, 3, 4, 5, 6, 7, 8]),
             ]
         )
 
@@ -381,12 +380,12 @@ class DatasetFilterTest(TestCase):
                 return iter([
                     DatasetItem(id=0),
                     DatasetItem(id=1, annotations=[
-                        LabelObject(0),
+                        Label(0),
-                        LabelObject(1),
+                        Label(1),
                     ]),
                     DatasetItem(id=2, annotations=[
-                        LabelObject(0),
+                        Label(0),
-                        LabelObject(2),
+                        Label(2),
                     ]),
                 ])
 
@@ -395,10 +394,10 @@ class DatasetFilterTest(TestCase):
                 return iter([
                     DatasetItem(id=0),
                     DatasetItem(id=1, annotations=[
-                        LabelObject(0),
+                        Label(0),
                     ]),
                     DatasetItem(id=2, annotations=[
-                        LabelObject(0),
+                        Label(0),
                     ]),
                 ])
 
@@ -415,12 +414,12 @@ class DatasetFilterTest(TestCase):
                 return iter([
                     DatasetItem(id=0),
                     DatasetItem(id=1, annotations=[
-                        LabelObject(0),
+                        Label(0),
-                        LabelObject(1),
+                        Label(1),
                     ]),
                     DatasetItem(id=2, annotations=[
-                        LabelObject(0),
+                        Label(0),
-                        LabelObject(2),
+                        Label(2),
                     ]),
                 ])
 
@@ -428,7 +427,7 @@ class DatasetFilterTest(TestCase):
             def __iter__(self):
                 return iter([
                     DatasetItem(id=2, annotations=[
-                        LabelObject(2),
+                        Label(2),
                     ]),
                 ])
 

datumaro/tests/test_tfrecord_format.py

@@ -2,43 +2,29 @@ import numpy as np
 
 from unittest import TestCase
 
-from datumaro.components.project import Project
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, BboxObject, LabelCategories
+    AnnotationType, Bbox, LabelCategories
 )
-from datumaro.components.extractors.tfrecord import DetectionApiExtractor
+from datumaro.plugins.tf_detection_api_format.importer import TfDetectionApiImporter
-from datumaro.components.converters.tfrecord import DetectionApiConverter
+from datumaro.plugins.tf_detection_api_format.extractor import TfDetectionApiExtractor
-from datumaro.util import find
+from datumaro.plugins.tf_detection_api_format.converter import TfDetectionApiConverter
-from datumaro.util.test_utils import TestDir
+from datumaro.util.test_utils import TestDir, compare_datasets
 
 
 class TfrecordConverterTest(TestCase):
-    def _test_can_save_and_load(self, source_dataset, converter, test_dir,
+    def _test_save_and_load(self, source_dataset, converter, test_dir,
-            importer_params=None):
+            target_dataset=None, importer_args=None):
-        converter(source_dataset, test_dir.path)
+        converter(source_dataset, test_dir)
-
+
-        if not importer_params:
+        if importer_args is None:
-            importer_params = {}
+            importer_args = {}
-        project = Project.import_from(test_dir.path, 'tf_detection_api',
+        parsed_dataset = TfDetectionApiImporter()(test_dir, **importer_args) \
-            **importer_params)
+            .make_dataset()
-        parsed_dataset = project.make_dataset()
+
-
+        if target_dataset is None:
-        self.assertListEqual(
+            target_dataset = source_dataset
-            sorted(source_dataset.subsets()),
+
-            sorted(parsed_dataset.subsets()),
+        compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
-        )
-
-        self.assertEqual(len(source_dataset), len(parsed_dataset))
-
-        for item_a in source_dataset:
-            item_b = find(parsed_dataset, lambda x: x.id == item_a.id)
-            self.assertFalse(item_b is None)
-            self.assertEqual(len(item_a.annotations), len(item_b.annotations))
-            for ann_a in item_a.annotations:
-                ann_b = find(item_b.annotations, lambda x: \
-                    x.id == ann_a.id and \
-                    x.type == ann_a.type and x.group == ann_a.group)
-                self.assertEqual(ann_a, ann_b, 'id: ' + str(ann_a.id))
 
     def test_can_save_bboxes(self):
         class TestExtractor(Extractor):
@@ -47,16 +33,16 @@ class TfrecordConverterTest(TestCase):
                     DatasetItem(id=1, subset='train',
                         image=np.ones((16, 16, 3)),
                         annotations=[
-                            BboxObject(0, 4, 4, 8, label=2, id=0),
+                            Bbox(0, 4, 4, 8, label=2, id=0),
-                            BboxObject(0, 4, 4, 4, label=3, id=1),
+                            Bbox(0, 4, 4, 4, label=3, id=1),
-                            BboxObject(2, 4, 4, 4, id=2),
+                            Bbox(2, 4, 4, 4, id=2),
                         ]
                     ),
 
                     DatasetItem(id=2, subset='val',
                         image=np.ones((8, 8, 3)),
                         annotations=[
-                            BboxObject(1, 2, 4, 2, label=3, id=0),
+                            Bbox(1, 2, 4, 2, label=3, id=0),
                         ]
                     ),
 
@@ -74,8 +60,8 @@ class TfrecordConverterTest(TestCase):
                 }
 
         with TestDir() as test_dir:
-            self._test_can_save_and_load(
+            self._test_save_and_load(
-                TestExtractor(), DetectionApiConverter(save_images=True),
+                TestExtractor(), TfDetectionApiConverter(save_images=True),
                 test_dir)
 
     def test_can_save_dataset_with_no_subsets(self):
@@ -85,15 +71,15 @@ class TfrecordConverterTest(TestCase):
                     DatasetItem(id=1,
                         image=np.ones((16, 16, 3)),
                         annotations=[
-                            BboxObject(2, 1, 4, 4, label=2, id=0),
+                            Bbox(2, 1, 4, 4, label=2, id=0),
-                            BboxObject(4, 2, 8, 4, label=3, id=1),
+                            Bbox(4, 2, 8, 4, label=3, id=1),
                         ]
                     ),
 
                     DatasetItem(id=2,
                         image=np.ones((8, 8, 3)) * 2,
                         annotations=[
-                            BboxObject(4, 4, 4, 4, label=3, id=0),
+                            Bbox(4, 4, 4, 4, label=3, id=0),
                         ]
                     ),
 
@@ -111,8 +97,8 @@ class TfrecordConverterTest(TestCase):
                 }
 
         with TestDir() as test_dir:
-            self._test_can_save_and_load(
+            self._test_save_and_load(
-                TestExtractor(), DetectionApiConverter(save_images=True),
+                TestExtractor(), TfDetectionApiConverter(save_images=True),
                 test_dir)
 
     def test_labelmap_parsing(self):
@@ -137,6 +123,6 @@ class TfrecordConverterTest(TestCase):
             'qw3': 6,
             'qw4': 7,
         }
-        parsed = DetectionApiExtractor._parse_labelmap(text)
+        parsed = TfDetectionApiExtractor._parse_labelmap(text)
 
         self.assertEqual(expected, parsed)

datumaro/tests/test_transforms.py

@@ -0,0 +1,188 @@
+import numpy as np
+
+from unittest import TestCase
+
+from datumaro.components.extractor import (Extractor, DatasetItem,
+    Mask, Polygon
+)
+from datumaro.util.test_utils import compare_datasets
+import datumaro.plugins.transforms as transforms
+
+
+class TransformsTest(TestCase):
+    def test_reindex(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=10),
+                    DatasetItem(id=10, subset='train'),
+                    DatasetItem(id='a', subset='val'),
+                ])
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=5),
+                    DatasetItem(id=6, subset='train'),
+                    DatasetItem(id=7, subset='val'),
+                ])
+
+        actual = transforms.Reindex(SrcExtractor(), start=5)
+        compare_datasets(self, DstExtractor(), actual)
+
+    def test_mask_to_polygons(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                items = [
+                    DatasetItem(id=1, image=np.zeros((5, 10, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
+                                    [0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
+                                    [0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                                ]),
+                            ),
+                        ]
+                    ),
+                ]
+                return iter(items)
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 10, 3)),
+                        annotations=[
+                            Polygon([3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5]),
+                            Polygon([5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5]),
+                        ]
+                    ),
+                ])
+
+        actual = transforms.MasksToPolygons(SrcExtractor())
+        compare_datasets(self, DstExtractor(), actual)
+
+    def test_polygons_to_masks(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 10, 3)),
+                        annotations=[
+                            Polygon([0, 0, 4, 0, 4, 4]),
+                            Polygon([5, 0, 9, 0, 5, 5]),
+                        ]
+                    ),
+                ])
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 10, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 0, 0, 0, 0, 1, 1, 1, 1, 0],
+                                    [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+                                    [0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                                ]),
+                            ),
+                            Mask(np.array([
+                                    [0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+                                    [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
+                                    [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                                ]),
+                            ),
+                        ]
+                    ),
+                ])
+
+        actual = transforms.PolygonsToMasks(SrcExtractor())
+        compare_datasets(self, DstExtractor(), actual)
+
+    def test_crop_covered_segments(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 5, 3)),
+                        annotations=[
+                            # The mask is partially covered by the polygon
+                            Mask(np.array([
+                                    [0, 0, 1, 1, 1],
+                                    [0, 0, 1, 1, 1],
+                                    [1, 1, 1, 1, 1],
+                                    [1, 1, 1, 0, 0],
+                                    [1, 1, 1, 0, 0]],
+                                ),
+                                z_order=0),
+                            Polygon([1, 1, 4, 1, 4, 4, 1, 4],
+                                z_order=1),
+                        ]
+                    ),
+                ])
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 5, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 0, 1, 1, 1],
+                                    [0, 0, 0, 0, 1],
+                                    [1, 0, 0, 0, 1],
+                                    [1, 0, 0, 0, 0],
+                                    [1, 1, 1, 0, 0]],
+                                ),
+                                z_order=0),
+                            Polygon([1, 1, 4, 1, 4, 4, 1, 4],
+                                z_order=1),
+                        ]
+                    ),
+                ])
+
+        actual = transforms.CropCoveredSegments(SrcExtractor())
+        compare_datasets(self, DstExtractor(), actual)
+
+    def test_merge_instance_segments(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 5, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 0, 1, 1, 1],
+                                    [0, 0, 0, 0, 1],
+                                    [1, 0, 0, 0, 1],
+                                    [1, 0, 0, 0, 0],
+                                    [1, 1, 1, 0, 0]],
+                                ),
+                                z_order=0),
+                            Polygon([1, 1, 4, 1, 4, 4, 1, 4],
+                                z_order=1),
+                        ]
+                    ),
+                ])
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, image=np.zeros((5, 5, 3)),
+                        annotations=[
+                            Mask(np.array([
+                                    [0, 0, 1, 1, 1],
+                                    [0, 1, 1, 1, 1],
+                                    [1, 1, 1, 1, 1],
+                                    [1, 1, 1, 1, 0],
+                                    [1, 1, 1, 0, 0]],
+                                ),
+                                z_order=0),
+                        ]
+                    ),
+                ])
+
+        actual = transforms.MergeInstanceSegments(SrcExtractor(),
+            include_polygons=True)
+        compare_datasets(self, DstExtractor(), actual)

datumaro/tests/test_voc_format.py

@@ -8,17 +8,17 @@ import shutil
 from unittest import TestCase
 
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, BboxObject, LabelCategories,
+    AnnotationType, Label, Bbox, Mask, LabelCategories,
 )
-import datumaro.components.formats.voc as VOC
+import datumaro.plugins.voc_format.format as VOC
-from datumaro.components.extractors.voc import (
+from datumaro.plugins.voc_format.extractor import (
     VocClassificationExtractor,
     VocDetectionExtractor,
     VocSegmentationExtractor,
     VocLayoutExtractor,
     VocActionExtractor,
 )
-from datumaro.components.converters.voc import (
+from datumaro.plugins.voc_format.converter import (
     VocConverter,
     VocClassificationConverter,
     VocDetectionConverter,
@@ -26,10 +26,9 @@ from datumaro.components.converters.voc import (
     VocActionConverter,
     VocSegmentationConverter,
 )
-from datumaro.components.importers.voc import VocImporter
+from datumaro.plugins.voc_format.importer import VocImporter
 from datumaro.components.project import Project
-from datumaro.util import find
+from datumaro.util.test_utils import TestDir, compare_datasets
-from datumaro.util.test_utils import TestDir
 
 
 class VocTest(TestCase):
@@ -121,7 +120,7 @@ def generate_dummy_voc(path):
         ET.SubElement(root_elem, 'segmented').text = '1'
 
         obj1_elem = ET.SubElement(root_elem, 'object')
-        ET.SubElement(obj1_elem, 'name').text = VOC.VocLabel(1).name
+        ET.SubElement(obj1_elem, 'name').text = 'cat'
         ET.SubElement(obj1_elem, 'pose').text = VOC.VocPose(1).name
         ET.SubElement(obj1_elem, 'truncated').text = '1'
         ET.SubElement(obj1_elem, 'difficult').text = '0'
@@ -132,7 +131,7 @@ def generate_dummy_voc(path):
         ET.SubElement(obj1bb_elem, 'ymax').text = '4'
 
         obj2_elem = ET.SubElement(root_elem, 'object')
-        ET.SubElement(obj2_elem, 'name').text = VOC.VocLabel.person.name
+        ET.SubElement(obj2_elem, 'name').text = 'person'
         obj2bb_elem = ET.SubElement(obj2_elem, 'bndbox')
         ET.SubElement(obj2bb_elem, 'xmin').text = '4'
         ET.SubElement(obj2bb_elem, 'ymin').text = '5'
@@ -157,9 +156,10 @@ def generate_dummy_voc(path):
     subset = subsets[subset_name]
     for item in subset:
         cv2.imwrite(osp.join(segm_dir, item + '.png'),
-            np.ones([10, 20, 3]) * VOC.VocColormap[2])
+            np.tile(VOC.VocColormap[2][::-1], (5, 10, 1))
+        )
         cv2.imwrite(osp.join(inst_dir, item + '.png'),
-            np.ones([10, 20, 3]) * VOC.VocColormap[2])
+            np.tile(1, (5, 10, 1)))
 
     # Test images
     subset_name = 'test'
@@ -170,338 +170,440 @@ def generate_dummy_voc(path):
 
     return subsets
 
-class VocExtractorTest(TestCase):
+class TestExtractorBase(Extractor):
-    def test_can_load_voc_cls(self):
+    _categories = VOC.make_voc_categories()
-        with TestDir() as test_dir:
-            generated_subsets = generate_dummy_voc(test_dir.path)
-
-            extractor = VocClassificationExtractor(test_dir.path)
 
-            self.assertEqual(len(generated_subsets), len(extractor.subsets()))
+    def _label(self, voc_label):
+        return self.categories()[AnnotationType.label].find(voc_label)[0]
 
-            subset_name = 'train'
+    def categories(self):
-            generated_subset = generated_subsets[subset_name]
+        return self._categories
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
 
-                item = find(parsed_subset, lambda x: x.id == id_)
+class VocExtractorTest(TestCase):
-                self.assertFalse(item is None)
+    def test_can_load_voc_cls(self):
-
+        class DstExtractor(TestExtractorBase):
-                count = 0
+            def __iter__(self):
-                for label in VOC.VocLabel:
+                return iter([
-                    if label.value % 2 == 1:
+                    DatasetItem(id='2007_000001', subset='train',
-                        count += 1
+                        annotations=[
-                        ann = find(item.annotations,
+                            Label(self._label(l.name))
-                            lambda x: x.type == AnnotationType.label and \
+                            for l in VOC.VocLabel if l.value % 2 == 1
-                                get_label(extractor, x.label) == label.name)
+                        ]
-                        self.assertFalse(ann is None)
+                    ),
-                self.assertEqual(count, len(item.annotations))
+
-
+                    DatasetItem(id='2007_000002', subset='test')
-            subset_name = 'test'
+                ])
-            generated_subset = generated_subsets[subset_name]
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
-
-                item = find(parsed_subset, lambda x: x.id == id_)
-                self.assertFalse(item is None)
 
-                self.assertEqual(0, len(item.annotations))
+        with TestDir() as test_dir:
+            generate_dummy_voc(test_dir)
+            parsed_dataset = VocClassificationExtractor(test_dir)
+            compare_datasets(self, DstExtractor(), parsed_dataset)
 
     def test_can_load_voc_det(self):
-        with TestDir() as test_dir:
+        class DstExtractor(TestExtractorBase):
-            generated_subsets = generate_dummy_voc(test_dir.path)
+            def __iter__(self):
-
+                return iter([
-            extractor = VocDetectionExtractor(test_dir.path)
+                    DatasetItem(id='2007_000001', subset='train',
-
+                        annotations=[
-            self.assertEqual(len(generated_subsets), len(extractor.subsets()))
+                            Bbox(1, 2, 2, 2, label=self._label('cat'),
-
+                                attributes={
-            subset_name = 'train'
+                                    'pose': VOC.VocPose(1).name,
-            generated_subset = generated_subsets[subset_name]
+                                    'truncated': True,
-            for id_ in generated_subset:
+                                    'difficult': False,
-                parsed_subset = extractor.get_subset(subset_name)
+                                    'occluded': False,
-                self.assertEqual(len(generated_subset), len(parsed_subset))
+                                },
-
+                                id=1,
-                item = find(parsed_subset, lambda x: x.id == id_)
+                            ),
-                self.assertFalse(item is None)
+                            Bbox(4, 5, 2, 2, label=self._label('person'),
-
+                                attributes={
-                obj1 = find(item.annotations,
+                                    'truncated': False,
-                    lambda x: x.type == AnnotationType.bbox and \
+                                    'difficult': False,
-                        get_label(extractor, x.label) == VOC.VocLabel(1).name)
+                                    'occluded': False,
-                self.assertFalse(obj1 is None)
+                                    **{
-                self.assertListEqual([1, 2, 2, 2], obj1.get_bbox())
+                                        a.name: a.value % 2 == 1
-                self.assertDictEqual(
+                                        for a in VOC.VocAction
-                    {
+                                    }
-                        'pose': VOC.VocPose(1).name,
+                                },
-                        'truncated': True,
+                                id=2, group=2,
-                        'occluded': False,
+                                # TODO: Actions and group should be excluded
-                        'difficult': False,
+                                # as soon as correct merge is implemented
-                    },
+                            ),
-                    obj1.attributes)
+                        ]
-
+                    ),
-                obj2 = find(item.annotations,
+
-                    lambda x: x.type == AnnotationType.bbox and \
+                    DatasetItem(id='2007_000002', subset='test')
-                        get_label(extractor, x.label) == VOC.VocLabel.person.name)
+                ])
-                self.assertFalse(obj2 is None)
-                self.assertListEqual([4, 5, 2, 2], obj2.get_bbox())
-
-                self.assertEqual(2, len(item.annotations))
-
-            subset_name = 'test'
-            generated_subset = generated_subsets[subset_name]
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
-
-                item = find(parsed_subset, lambda x: x.id == id_)
-                self.assertFalse(item is None)
-
-                self.assertEqual(0, len(item.annotations))
 
-    def test_can_load_voc_segm(self):
         with TestDir() as test_dir:
-            generated_subsets = generate_dummy_voc(test_dir.path)
+            generate_dummy_voc(test_dir)
-
+            parsed_dataset = VocDetectionExtractor(test_dir)
-            extractor = VocSegmentationExtractor(test_dir.path)
+            compare_datasets(self, DstExtractor(), parsed_dataset)
-
-            self.assertEqual(len(generated_subsets), len(extractor.subsets()))
-
-            subset_name = 'train'
-            generated_subset = generated_subsets[subset_name]
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
-
-                item = find(parsed_subset, lambda x: x.id == id_)
-                self.assertFalse(item is None)
-
-                cls_mask = find(item.annotations,
-                    lambda x: x.type == AnnotationType.mask and \
-                        x.attributes.get('class') == True)
-                self.assertFalse(cls_mask is None)
-                self.assertFalse(cls_mask.image is None)
 
-                inst_mask = find(item.annotations,
+    def test_can_load_voc_segm(self):
-                    lambda x: x.type == AnnotationType.mask and \
+        class DstExtractor(TestExtractorBase):
-                        x.attributes.get('instances') == True)
+            def __iter__(self):
-                self.assertFalse(inst_mask is None)
+                return iter([
-                self.assertFalse(inst_mask.image is None)
+                    DatasetItem(id='2007_000001', subset='train',
-
+                        annotations=[
-                self.assertEqual(2, len(item.annotations))
+                            Mask(image=np.ones([5, 10]),
-
+                                label=self._label(VOC.VocLabel(2).name),
-            subset_name = 'test'
+                                group=1,
-            generated_subset = generated_subsets[subset_name]
+                            ),
-            for id_ in generated_subset:
+                        ]
-                parsed_subset = extractor.get_subset(subset_name)
+                    ),
-                self.assertEqual(len(generated_subset), len(parsed_subset))
+
-
+                    DatasetItem(id='2007_000002', subset='test')
-                item = find(parsed_subset, lambda x: x.id == id_)
+                ])
-                self.assertFalse(item is None)
-
-                self.assertEqual(0, len(item.annotations))
 
-    def test_can_load_voc_layout(self):
         with TestDir() as test_dir:
-            generated_subsets = generate_dummy_voc(test_dir.path)
+            generate_dummy_voc(test_dir)
-
+            parsed_dataset = VocSegmentationExtractor(test_dir)
-            extractor = VocLayoutExtractor(test_dir.path)
+            compare_datasets(self, DstExtractor(), parsed_dataset)
-
-            self.assertEqual(len(generated_subsets), len(extractor.subsets()))
-
-            subset_name = 'train'
-            generated_subset = generated_subsets[subset_name]
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
-
-                item = find(parsed_subset, lambda x: x.id == id_)
-                self.assertFalse(item is None)
-
-                obj2 = find(item.annotations,
-                    lambda x: x.type == AnnotationType.bbox and \
-                        get_label(extractor, x.label) == VOC.VocLabel.person.name)
-                self.assertFalse(obj2 is None)
-                self.assertListEqual([4, 5, 2, 2], obj2.get_bbox())
-
-                obj2head = find(item.annotations,
-                    lambda x: x.type == AnnotationType.bbox and \
-                        get_label(extractor, x.label) == VOC.VocBodyPart(1).name)
-                self.assertTrue(obj2.id == obj2head.group)
-                self.assertListEqual([5.5, 6, 2, 2], obj2head.get_bbox())
 
-                self.assertEqual(2, len(item.annotations))
+    def test_can_load_voc_layout(self):
-
+        class DstExtractor(TestExtractorBase):
-            subset_name = 'test'
+            def __iter__(self):
-            generated_subset = generated_subsets[subset_name]
+                return iter([
-            for id_ in generated_subset:
+                    DatasetItem(id='2007_000001', subset='train',
-                parsed_subset = extractor.get_subset(subset_name)
+                        annotations=[
-                self.assertEqual(len(generated_subset), len(parsed_subset))
+                            Bbox(4, 5, 2, 2, label=self._label('person'),
-
+                                attributes={
-                item = find(parsed_subset, lambda x: x.id == id_)
+                                    'truncated': False,
-                self.assertFalse(item is None)
+                                    'difficult': False,
-
+                                    'occluded': False,
-                self.assertEqual(0, len(item.annotations))
+                                    **{
+                                        a.name: a.value % 2 == 1
+                                        for a in VOC.VocAction
+                                    }
+                                },
+                                id=2, group=2,
+                                # TODO: Actions should be excluded
+                                # as soon as correct merge is implemented
+                            ),
+                            Bbox(5.5, 6, 2, 2, label=self._label(
+                                    VOC.VocBodyPart(1).name),
+                                group=2
+                            )
+                        ]
+                    ),
+
+                    DatasetItem(id='2007_000002', subset='test')
+                ])
 
-    def test_can_load_voc_action(self):
         with TestDir() as test_dir:
-            generated_subsets = generate_dummy_voc(test_dir.path)
+            generate_dummy_voc(test_dir)
-
+            parsed_dataset = VocLayoutExtractor(test_dir)
-            extractor = VocActionExtractor(test_dir.path)
+            compare_datasets(self, DstExtractor(), parsed_dataset)
-
-            self.assertEqual(len(generated_subsets), len(extractor.subsets()))
-
-            subset_name = 'train'
-            generated_subset = generated_subsets[subset_name]
-            for id_ in generated_subset:
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
 
-                item = find(parsed_subset, lambda x: x.id == id_)
+    def test_can_load_voc_action(self):
-                self.assertFalse(item is None)
+        class DstExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id='2007_000001', subset='train',
+                        annotations=[
+                            Bbox(4, 5, 2, 2, label=self._label('person'),
+                                attributes={
+                                    'truncated': False,
+                                    'difficult': False,
+                                    'occluded': False,
+                                    **{
+                                        a.name: a.value % 2 == 1
+                                        for a in VOC.VocAction
+                                    }
+                                    # TODO: group should be excluded
+                                    # as soon as correct merge is implemented
+                                },
+                                id=2, group=2,
+                            ),
+                        ]
+                    ),
+
+                    DatasetItem(id='2007_000002', subset='test')
+                ])
 
-                obj2 = find(item.annotations,
+        with TestDir() as test_dir:
-                    lambda x: x.type == AnnotationType.bbox and \
+            generate_dummy_voc(test_dir)
-                        get_label(extractor, x.label) == VOC.VocLabel.person.name)
+            parsed_dataset = VocActionExtractor(test_dir)
-                self.assertFalse(obj2 is None)
+            compare_datasets(self, DstExtractor(), parsed_dataset)
-                self.assertListEqual([4, 5, 2, 2], obj2.get_bbox())
 
-                for action in VOC.VocAction:
+class VocConverterTest(TestCase):
-                    attr = obj2.attributes[action.name]
+    def _test_save_and_load(self, source_dataset, converter, test_dir,
-                    self.assertEqual(attr, action.value % 2)
+            target_dataset=None, importer_args=None):
+        converter(source_dataset, test_dir)
 
-            subset_name = 'test'
+        if importer_args is None:
-            generated_subset = generated_subsets[subset_name]
+            importer_args = {}
-            for id_ in generated_subset:
+        parsed_dataset = VocImporter()(test_dir, **importer_args).make_dataset()
-                parsed_subset = extractor.get_subset(subset_name)
-                self.assertEqual(len(generated_subset), len(parsed_subset))
 
-                item = find(parsed_subset, lambda x: x.id == id_)
+        if target_dataset is None:
-                self.assertFalse(item is None)
+            target_dataset = source_dataset
 
-                self.assertEqual(0, len(item.annotations))
+        compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
 
-class VocConverterTest(TestCase):
+    def test_can_save_voc_cls(self):
-    def _test_can_save_voc(self, src_extractor, converter, test_dir,
+        class TestExtractor(TestExtractorBase):
-            target_extractor=None):
+            def __iter__(self):
-        converter(src_extractor, test_dir)
+                return iter([
+                    DatasetItem(id=0, subset='a', annotations=[
+                        Label(1),
+                        Label(2),
+                        Label(3),
+                    ]),
 
-        result_extractor = VocImporter()(test_dir).make_dataset()
+                    DatasetItem(id=1, subset='b', annotations=[
-        if target_extractor is None:
+                        Label(4),
-            target_extractor = src_extractor
+                    ]),
+                ])
 
-        if AnnotationType.label in target_extractor.categories():
+        with TestDir() as test_dir:
-            self.assertEqual(
+            self._test_save_and_load(TestExtractor(),
-                target_extractor.categories()[AnnotationType.label].items,
+                VocClassificationConverter(label_map='voc'), test_dir)
-                result_extractor.categories()[AnnotationType.label].items)
-        if AnnotationType.mask in target_extractor.categories():
-            self.assertEqual(
-                target_extractor.categories()[AnnotationType.mask].colormap,
-                result_extractor.categories()[AnnotationType.mask].colormap)
 
-        self.assertEqual(len(target_extractor), len(result_extractor))
+    def test_can_save_voc_det(self):
-        for item_a, item_b in zip(target_extractor, result_extractor):
+        class TestExtractor(TestExtractorBase):
-            self.assertEqual(item_a.id, item_b.id)
+            def __iter__(self):
-            self.assertEqual(len(item_a.annotations), len(item_b.annotations))
+                return iter([
-            for ann_a, ann_b in zip(item_a.annotations, item_b.annotations):
+                    DatasetItem(id=1, subset='a', annotations=[
-                self.assertEqual(ann_a.type, ann_b.type)
+                        Bbox(2, 3, 4, 5, label=2,
+                            attributes={ 'occluded': True }
+                        ),
+                        Bbox(2, 3, 4, 5, label=3,
+                            attributes={ 'truncated': True },
+                        ),
+                    ]),
 
-    def _test_can_save_voc_dummy(self, extractor_type, converter, test_dir):
+                    DatasetItem(id=2, subset='b', annotations=[
-        dummy_dir = osp.join(test_dir, 'dummy')
+                        Bbox(5, 4, 6, 5, label=3,
-        generate_dummy_voc(dummy_dir)
+                            attributes={ 'difficult': True },
-        gen_extractor = extractor_type(dummy_dir)
+                        ),
+                    ]),
+                ])
 
-        self._test_can_save_voc(gen_extractor, converter,
+        class DstExtractor(TestExtractorBase):
-            osp.join(test_dir, 'converted'))
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        Bbox(2, 3, 4, 5, label=2, id=1,
+                            attributes={
+                                'truncated': False,
+                                'difficult': False,
+                                'occluded': True,
+                            }
+                        ),
+                        Bbox(2, 3, 4, 5, label=3, id=2,
+                            attributes={
+                                'truncated': True,
+                                'difficult': False,
+                                'occluded': False,
+                            },
+                        ),
+                    ]),
 
-    def test_can_save_voc_cls(self):
+                    DatasetItem(id=2, subset='b', annotations=[
-        with TestDir() as test_dir:
+                        Bbox(5, 4, 6, 5, label=3, id=1,
-            self._test_can_save_voc_dummy(
+                            attributes={
-                VocClassificationExtractor, VocClassificationConverter(label_map='voc'),
+                                'truncated': False,
-                test_dir.path)
+                                'difficult': True,
+                                'occluded': False,
+                            },
+                        ),
+                    ]),
+                ])
 
-    def test_can_save_voc_det(self):
         with TestDir() as test_dir:
-            self._test_can_save_voc_dummy(
+            self._test_save_and_load(TestExtractor(),
-                VocDetectionExtractor, VocDetectionConverter(label_map='voc'),
+                VocDetectionConverter(label_map='voc'), test_dir,
-                test_dir.path)
+                target_dataset=DstExtractor())
 
     def test_can_save_voc_segm(self):
+        class TestExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        # overlapping masks, the first should be truncated
+                        # the second and third are different instances
+                        Mask(image=np.array([[0, 1, 1, 1, 0]]), label=4,
+                            z_order=1),
+                        Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
+                            z_order=2),
+                        Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
+                            z_order=2),
+                    ]),
+                ])
+
+        class DstExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        Mask(image=np.array([[0, 0, 1, 0, 0]]), label=4,
+                            group=1),
+                        Mask(image=np.array([[1, 1, 0, 0, 0]]), label=3,
+                            group=2),
+                        Mask(image=np.array([[0, 0, 0, 1, 0]]), label=3,
+                            group=3),
+                    ]),
+                ])
+
         with TestDir() as test_dir:
-            self._test_can_save_voc_dummy(
+            self._test_save_and_load(TestExtractor(),
-                VocSegmentationExtractor, VocSegmentationConverter(label_map='voc'),
+                VocSegmentationConverter(label_map='voc'), test_dir,
-                test_dir.path)
+                target_dataset=DstExtractor())
 
     def test_can_save_voc_layout(self):
+        class TestExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        Bbox(2, 3, 4, 5, label=2, id=1, group=1,
+                            attributes={
+                                'pose': VOC.VocPose(1).name,
+                                'truncated': True,
+                                'difficult': False,
+                                'occluded': False,
+                            }
+                        ),
+                        Bbox(2, 3, 1, 1, label=self._label(
+                            VOC.VocBodyPart(1).name), group=1),
+                        Bbox(5, 4, 3, 2, label=self._label(
+                            VOC.VocBodyPart(2).name), group=1),
+                    ]),
+                ])
+
         with TestDir() as test_dir:
-            self._test_can_save_voc_dummy(
+            self._test_save_and_load(TestExtractor(),
-                VocLayoutExtractor, VocLayoutConverter(label_map='voc'),
+                VocLayoutConverter(label_map='voc'), test_dir)
-                test_dir.path)
 
     def test_can_save_voc_action(self):
+        class TestExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        Bbox(2, 3, 4, 5, label=2,
+                            attributes={
+                                'truncated': True,
+                                VOC.VocAction(1).name: True,
+                                VOC.VocAction(2).name: True,
+                            }
+                        ),
+                        Bbox(5, 4, 3, 2, label=self._label('person'),
+                            attributes={
+                                'truncated': True,
+                                VOC.VocAction(1).name: True,
+                                VOC.VocAction(2).name: True,
+                            }
+                        ),
+                    ]),
+                ])
+
+        class DstExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', annotations=[
+                        Bbox(2, 3, 4, 5, label=2, id=1,
+                            attributes={
+                                'truncated': True,
+                                'difficult': False,
+                                'occluded': False,
+                                # no attributes here in the label categories
+                            }
+                        ),
+                        Bbox(5, 4, 3, 2, label=self._label('person'), id=2,
+                            attributes={
+                                'truncated': True,
+                                'difficult': False,
+                                'occluded': False,
+                                VOC.VocAction(1).name: True,
+                                VOC.VocAction(2).name: True,
+                                **{
+                                    a.name: False for a in VOC.VocAction
+                                        if a.value not in {1, 2}
+                                }
+                            }
+                        ),
+                    ]),
+                ])
+
         with TestDir() as test_dir:
-            self._test_can_save_voc_dummy(
+            self._test_save_and_load(TestExtractor(),
-                VocActionExtractor, VocActionConverter(label_map='voc'),
+                VocActionConverter(label_map='voc'), test_dir,
-                test_dir.path)
+                target_dataset=DstExtractor())
 
     def test_can_save_dataset_with_no_subsets(self):
-        class TestExtractor(Extractor):
+        class TestExtractor(TestExtractorBase):
             def __iter__(self):
                 return iter([
                     DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=2, id=1),
+                        Label(2),
-                        BboxObject(2, 3, 4, 5, label=3, id=2),
+                        Label(3),
                     ]),
 
                     DatasetItem(id=2, annotations=[
-                        BboxObject(5, 4, 6, 5, label=3, id=1),
+                        Label(3),
                     ]),
                 ])
 
-            def categories(self):
+        with TestDir() as test_dir:
-                return VOC.make_voc_categories()
+            self._test_save_and_load(TestExtractor(),
+                VocConverter(label_map='voc'), test_dir)
+
+    def test_can_save_dataset_with_images(self):
+        class TestExtractor(TestExtractorBase):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, subset='a', image=np.ones([4, 5, 3])),
+                    DatasetItem(id=2, subset='a', image=np.ones([5, 4, 3])),
+
+                    DatasetItem(id=3, subset='b', image=np.ones([2, 6, 3])),
+                ])
 
         with TestDir() as test_dir:
-            self._test_can_save_voc(TestExtractor(), VocConverter(label_map='voc'),
+            self._test_save_and_load(TestExtractor(),
-                test_dir.path)
+                VocConverter(label_map='voc', save_images=True), test_dir)
 
     def test_dataset_with_voc_labelmap(self):
-        class SrcExtractor(Extractor):
+        class SrcExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=0, id=1),
+                    Bbox(2, 3, 4, 5, label=self._label('cat'), id=1),
-                        BboxObject(1, 2, 3, 4, label=1, id=2),
+                    Bbox(1, 2, 3, 4, label=self._label('non_voc_label'), id=2),
-                    ])
+                ])
 
             def categories(self):
                 label_cat = LabelCategories()
-                label_cat.add(VOC.VocLabel(1).name)
+                label_cat.add(VOC.VocLabel.cat.name)
                 label_cat.add('non_voc_label')
                 return {
                     AnnotationType.label: label_cat,
                 }
 
-        class DstExtractor(Extractor):
+        class DstExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=0, id=1),
+                    # drop non voc label
-                    ])
+                    Bbox(2, 3, 4, 5, label=self._label('cat'), id=1,
+                        attributes={
+                            'truncated': False,
+                            'difficult': False,
+                            'occluded': False,
+                        }
+                    ),
+                ])
 
             def categories(self):
                 return VOC.make_voc_categories()
 
         with TestDir() as test_dir:
-            self._test_can_save_voc(
+            self._test_save_and_load(
                 SrcExtractor(), VocConverter(label_map='voc'),
-                test_dir.path, target_extractor=DstExtractor())
+                test_dir, target_dataset=DstExtractor())
 
     def test_dataset_with_guessed_labelmap(self):
-        class SrcExtractor(Extractor):
+        class SrcExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=0, id=1),
+                    Bbox(2, 3, 4, 5, label=0, id=1),
-                        BboxObject(1, 2, 3, 4, label=1, id=2),
+                    Bbox(1, 2, 3, 4, label=1, id=2),
-                    ])
+                ])
 
             def categories(self):
                 label_cat = LabelCategories()
@@ -511,14 +613,25 @@ class VocConverterTest(TestCase):
                     AnnotationType.label: label_cat,
                 }
 
-        class DstExtractor(Extractor):
+        class DstExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=0, id=1),
+                    Bbox(2, 3, 4, 5, label=self._label(VOC.VocLabel(1).name), id=1,
-                        BboxObject(1, 2, 3, 4,
+                        attributes={
-                            label=self.categories()[AnnotationType.label] \
+                            'truncated': False,
-                                .find('non_voc_label')[0], id=2),
+                            'difficult': False,
-                    ])
+                            'occluded': False,
+                        }
+                    ),
+                    Bbox(1, 2, 3, 4,
+                        label=self._label('non_voc_label'), id=2,
+                        attributes={
+                            'truncated': False,
+                            'difficult': False,
+                            'occluded': False,
+                        }
+                    ),
+                ])
 
             def categories(self):
                 label_map = VOC.make_voc_label_map()
@@ -528,20 +641,20 @@ class VocConverterTest(TestCase):
                 return VOC.make_voc_categories(label_map)
 
         with TestDir() as test_dir:
-            self._test_can_save_voc(
+            self._test_save_and_load(
                 SrcExtractor(), VocConverter(label_map='guess'),
-                test_dir.path, target_extractor=DstExtractor())
+                test_dir, target_dataset=DstExtractor())
 
     def test_dataset_with_fixed_labelmap(self):
-        class SrcExtractor(Extractor):
+        class SrcExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(2, 3, 4, 5, label=0, id=1),
+                    Bbox(2, 3, 4, 5, label=0, id=1),
-                        BboxObject(1, 2, 3, 4, label=1, id=2, group=2,
+                    Bbox(1, 2, 3, 4, label=1, id=2, group=2,
-                            attributes={'act1': True}),
+                        attributes={'act1': True}),
-                        BboxObject(2, 3, 4, 5, label=2, id=3, group=2),
+                    Bbox(2, 3, 4, 5, label=2, id=3, group=2),
-                        BboxObject(2, 3, 4, 6, label=3, id=4, group=2),
+                    Bbox(2, 3, 4, 6, label=3, id=4, group=2),
-                    ])
+                ])
 
             def categories(self):
                 label_cat = LabelCategories()
@@ -557,30 +670,36 @@ class VocConverterTest(TestCase):
             'label': [None, ['label_part1', 'label_part2'], ['act1', 'act2']]
         }
 
-        class DstExtractor(Extractor):
+        class DstExtractor(TestExtractorBase):
             def __iter__(self):
                 yield DatasetItem(id=1, annotations=[
-                        BboxObject(1, 2, 3, 4, label=0, id=2, group=2,
+                    Bbox(1, 2, 3, 4, label=0, id=1, group=1,
-                            attributes={'act1': True, 'act2': False}),
+                        attributes={
-                        BboxObject(2, 3, 4, 5, label=1, id=3, group=2),
+                            'act1': True,
-                        BboxObject(2, 3, 4, 6, label=2, id=4, group=2),
+                            'act2': False,
-                    ])
+                            'truncated': False,
+                            'difficult': False,
+                            'occluded': False,
+                        }
+                    ),
+                    Bbox(2, 3, 4, 5, label=1, group=1),
+                    Bbox(2, 3, 4, 6, label=2, group=1),
+                ])
 
             def categories(self):
                 return VOC.make_voc_categories(label_map)
 
         with TestDir() as test_dir:
-            self._test_can_save_voc(
+            self._test_save_and_load(
                 SrcExtractor(), VocConverter(label_map=label_map),
-                test_dir.path, target_extractor=DstExtractor())
+                test_dir, target_dataset=DstExtractor())
 
-class VocImporterTest(TestCase):
+class VocImportTest(TestCase):
     def test_can_import(self):
         with TestDir() as test_dir:
-            dummy_dir = osp.join(test_dir.path, 'dummy')
+            subsets = generate_dummy_voc(test_dir)
-            subsets = generate_dummy_voc(dummy_dir)
 
-            dataset = Project.import_from(dummy_dir, 'voc').make_dataset()
+            dataset = Project.import_from(test_dir, 'voc').make_dataset()
 
             self.assertEqual(len(VOC.VocTask), len(dataset.sources))
             self.assertEqual(set(subsets), set(dataset.subsets()))
@@ -594,7 +713,7 @@ class VocFormatTest(TestCase):
         src_label_map['qq'] = [None, ['part1', 'part2'], ['act1', 'act2']]
 
         with TestDir() as test_dir:
-            file_path = osp.join(test_dir.path, 'test.txt')
+            file_path = osp.join(test_dir, 'test.txt')
 
             VOC.write_label_map(file_path, src_label_map)
             dst_label_map = VOC.parse_label_map(file_path)

datumaro/tests/test_yolo_format.py

@@ -3,11 +3,11 @@ import numpy as np
 from unittest import TestCase
 
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    AnnotationType, BboxObject, LabelCategories,
+    AnnotationType, Bbox, LabelCategories,
 )
-from datumaro.components.importers.yolo import YoloImporter
+from datumaro.plugins.yolo_format.importer import YoloImporter
-from datumaro.components.converters.yolo import YoloConverter
+from datumaro.plugins.yolo_format.converter import YoloConverter
-from datumaro.util.test_utils import TestDir
+from datumaro.util.test_utils import TestDir, compare_datasets
 
 
 class YoloFormatTest(TestCase):
@@ -17,22 +17,22 @@ class YoloFormatTest(TestCase):
                 return iter([
                     DatasetItem(id=1, subset='train', image=np.ones((8, 8, 3)),
                         annotations=[
-                            BboxObject(0, 2, 4, 2, label=2),
+                            Bbox(0, 2, 4, 2, label=2),
-                            BboxObject(0, 1, 2, 3, label=4),
+                            Bbox(0, 1, 2, 3, label=4),
                         ]),
                     DatasetItem(id=2, subset='train', image=np.ones((10, 10, 3)),
                         annotations=[
-                            BboxObject(0, 2, 4, 2, label=2),
+                            Bbox(0, 2, 4, 2, label=2),
-                            BboxObject(3, 3, 2, 3, label=4),
+                            Bbox(3, 3, 2, 3, label=4),
-                            BboxObject(2, 1, 2, 3, label=4),
+                            Bbox(2, 1, 2, 3, label=4),
                         ]),
 
                     DatasetItem(id=3, subset='valid', image=np.ones((8, 8, 3)),
                         annotations=[
-                            BboxObject(0, 1, 5, 2, label=2),
+                            Bbox(0, 1, 5, 2, label=2),
-                            BboxObject(0, 2, 3, 2, label=5),
+                            Bbox(0, 2, 3, 2, label=5),
-                            BboxObject(0, 2, 4, 2, label=6),
+                            Bbox(0, 2, 4, 2, label=6),
-                            BboxObject(0, 7, 3, 2, label=7),
+                            Bbox(0, 7, 3, 2, label=7),
                         ]),
                 ])
 
@@ -47,22 +47,7 @@ class YoloFormatTest(TestCase):
         with TestDir() as test_dir:
             source_dataset = TestExtractor()
 
-            YoloConverter(save_images=True)(source_dataset, test_dir.path)
+            YoloConverter(save_images=True)(source_dataset, test_dir)
-            parsed_dataset = YoloImporter()(test_dir.path).make_dataset()
+            parsed_dataset = YoloImporter()(test_dir).make_dataset()
 
-            self.assertListEqual(
+            compare_datasets(self, source_dataset, parsed_dataset)
-                sorted(source_dataset.subsets()),
-                sorted(parsed_dataset.subsets()),
-            )
-            self.assertEqual(len(source_dataset), len(parsed_dataset))
-            for subset_name in source_dataset.subsets():
-                source_subset = source_dataset.get_subset(subset_name)
-                parsed_subset = parsed_dataset.get_subset(subset_name)
-                for item_a, item_b in zip(source_subset, parsed_subset):
-                    self.assertEqual(len(item_a.annotations), len(item_b.annotations))
-                    for ann_a, ann_b in zip(item_a.annotations, item_b.annotations):
-                        self.assertEqual(ann_a.type, ann_b.type)
-                        self.assertAlmostEqual(ann_a.x, ann_b.x)
-                        self.assertAlmostEqual(ann_a.y, ann_b.y)
-                        self.assertAlmostEqual(ann_a.w, ann_b.w)
-                        self.assertAlmostEqual(ann_a.h, ann_b.h)