AC: add Mapillary dataset support (openvinotoolkit#1735)

tools/accuracy_checker/accuracy_checker/annotation_converters/README.md

@@ -133,6 +133,10 @@ Accuracy Checker supports following list of annotation converters and specific f
   * `images_suffix` - suffix for image file names (Optional, default `_leftImg8bit`).
   * `use_full_label_map` - allows to use full label map with 33 classes instead train label map with 18 classes (Optional, default `False`).
   * `dataset_meta_file` - path path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in [Customizing dataset meta](#customizing-dataset-meta) section.
+* `mapillary_20` - converts Mapillary dataset contained 20 classes to `SegmentationAnnotation`.
+  * `data_dir` - path to dataset root folder. Relative paths to images and masks directory determine as `imgs` and `masks` respectively. In way when images and masks are located in non default directories, you can use parameters described below.
+  * `images_dir` - path to images folder.
+  * `mask_dir` - path to ground truth mask folder.
 * `vgg_face` - converts VGG Face 2 dataset for facial landmarks regression task to `FacialLandmarksAnnotation`.
   * `landmarks_csv_file` - path to csv file with coordinates of landmarks points.
   * `bbox_csv_file` - path to cvs file which contains bounding box coordinates for faces (optional parameter).

tools/accuracy_checker/accuracy_checker/annotation_converters/__init__.py

@@ -89,6 +89,7 @@
 from .place_recognition import PlaceRecognitionDatasetConverter
 from .cluttered_mnist import ClutteredMNISTConverter
 from .mpii import MPIIDatasetConverter
+from .mapillary_20 import Mapillary20Converter
 
 __all__ = [
     'BaseFormatConverter',
@@ -117,6 +118,7 @@
     'MSCocoSingleKeypointsConverter',
     'MSCocoDetectionConverter',
     'CityscapesConverter',
+    'Mapillary20Converter',
     'MovieLensConverter',
     'BratsConverter',
     'BratsNumpyConverter',

tools/accuracy_checker/accuracy_checker/annotation_converters/mapillary_20.py

@@ -0,0 +1,90 @@
+"""
+Copyright (c) 2018-2020 Intel Corporation
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from ..config import PathField
+from ..representation import SegmentationAnnotation
+from ..representation.segmentation_representation import GTMaskLoader
+from ..utils import get_path
+from .format_converter import BaseFormatConverter, ConverterReturn
+
+
+class Mapillary20Converter(BaseFormatConverter):
+    __provider__ = 'mapillary_20'
+    annotation_types = (SegmentationAnnotation, )
+
+    label_map = {
+        0: 'Road',
+        1: 'Sidewalk',
+        2: 'Building',
+        3: 'Wall',
+        4: 'Fence',
+        5: 'Pole',
+        6: 'Traffic Light',
+        7: 'Traffic Sign',
+        8: 'Vegetation',
+        9: 'Terrain',
+        10: 'Sky',
+        11: 'Person',
+        12: 'Rider',
+        13: 'Car',
+        14: 'Truck',
+        15: 'Bus',
+        16: 'Train',
+        17: 'Motorcycle',
+        18: 'Bicycle',
+        19: 'Ego-Vehicle'
+    }
+
+    @classmethod
+    def parameters(cls):
+        parameters = super().parameters()
+        parameters.update({
+            'data_dir': PathField(
+                is_directory=True,
+                description="Path to dataset root folder. Relative paths to images and masks directory "
+                            "determine as imgs and masks respectively. "
+                            "In way when images and masks are located in non default directories, "
+                            "you can use parameters described below."
+            ),
+            'images_dir': PathField(
+                optional=True, is_directory=True, check_exists=False,
+                default='imgs', description="Path to images folder."
+            ),
+            'mask_dir': PathField(
+                optional=True, is_directory=True, check_exists=False,
+                default='masks', description="Path to ground truth mask folder."
+            )
+        })
+
+        return parameters
+
+    def configure(self):
+        data_dir = self.get_value_from_config('data_dir')
+        image_folder = self.get_value_from_config('images_dir')
+        mask_folder = self.get_value_from_config('mask_dir')
+        if data_dir:
+            image_folder = data_dir / image_folder
+            mask_folder = data_dir / mask_folder
+        self.images_dir = get_path(image_folder, is_directory=True)
+        self.mask_dir = get_path(mask_folder, is_directory=True)
+
+    def convert(self, *args, **kwargs):
+        annotations = []
+        for file_in_dir in self.images_dir.iterdir():
+            annotation = SegmentationAnnotation(file_in_dir.name, file_in_dir.name, mask_loader=GTMaskLoader.PILLOW)
+            annotations.append(annotation)
+
+        return ConverterReturn(annotations, {'label_map': self.label_map}, None)

tools/accuracy_checker/dataset_definitions.yml

@@ -204,6 +204,13 @@ datasets:
     annotation: voc2012_segmentation.pickle
     dataset_meta: voc2012_segmentation.json
 
+  - name: mapillary_20
+    annotation_conversion:
+      converter: mapillary_20
+      data_dir: Mapillary_20
+    annotation: mapillary_20.pickle
+    dataset_meta: mapillary_20.json
+
   - name: wider
     data_source: WIDER_val/images
     annotation_conversion: