Add Cityscapes format

cvat/apps/dataset_manager/formats/cityscapes.py

@@ -0,0 +1,47 @@
+# Copyright (C) 2021 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import os.path as osp
+from tempfile import TemporaryDirectory
+
+from datumaro.components.dataset import Dataset
+from datumaro.plugins.cityscapes_format import write_label_map
+from pyunpack import Archive
+
+from cvat.apps.dataset_manager.bindings import (GetCVATDataExtractor,
+                                                import_dm_annotations)
+from cvat.apps.dataset_manager.util import make_zip_archive
+
+from .registry import dm_env, exporter, importer
+from .utils import make_colormap
+
+
+@exporter(name='Cityscapes', ext='ZIP', version='1.0')
+def _export(dst_file, instance_data, save_images=False):
+    dataset = Dataset.from_extractors(GetCVATDataExtractor(
+        instance_data, include_images=save_images), env=dm_env)
+    dataset.transform('polygons_to_masks')
+    dataset.transform('boxes_to_masks')
+    dataset.transform('merge_instance_segments')
+    with TemporaryDirectory() as temp_dir:
+        dataset.export(temp_dir, 'cityscapes', save_images=save_images,
+            apply_colormap=True, label_map={label: info[0]
+                for label, info in make_colormap(instance_data).items()})
+
+        make_zip_archive(temp_dir, dst_file)
+
+@importer(name='Cityscapes', ext='ZIP', version='1.0')
+def _import(src_file, instance_data):
+    with TemporaryDirectory() as tmp_dir:
+        Archive(src_file.name).extractall(tmp_dir)
+
+        labelmap_file = osp.join(tmp_dir, 'label_colors.txt')
+        if not osp.isfile(labelmap_file):
+            colormap = {label: info[0]
+                for label, info in make_colormap(instance_data).items()}
+            write_label_map(labelmap_file, colormap)
+
+        dataset = Dataset.import_from(tmp_dir, 'cityscapes', env=dm_env)
+        dataset.transform('masks_to_polygons')
+        import_dm_annotations(dataset, instance_data)

cvat/apps/dataset_manager/formats/registry.py

@@ -121,4 +121,5 @@ def make_exporter(name):
 import cvat.apps.dataset_manager.formats.icdar
 import cvat.apps.dataset_manager.formats.velodynepoint
 import cvat.apps.dataset_manager.formats.pointcloud
+import cvat.apps.dataset_manager.formats.cityscapes
 

cvat/apps/dataset_manager/tests/assets/annotations.json

@@ -1168,5 +1168,23 @@
       }
     ],
     "tracks": []
+  },
+  "Cityscapes 1.0": {
+    "version": 0,
+    "tags": [],
+    "shapes": [
+      {
+        "type": "polygon",
+        "occluded": false,
+        "z_order": 0,
+        "points": [11.9, 21.5, 35.2, 21.9, 33.6, 31.9, 12.4, 30.47],
+        "frame": 0,
+        "label_id": null,
+        "group": 0,
+        "source": "manual",
+        "attributes": []
+      }
+    ],
+    "tracks": []
   }
 }

cvat/apps/dataset_manager/tests/assets/tasks.json

@@ -54,7 +54,7 @@
       }
     ]
   },
-  "icdar_localization_and_recognition": {
+  "ICDAR Localization 1.0": {
     "name": "icdar localization/recogntion task",
     "overlap": 0,
     "segment_size": 100,
@@ -74,7 +74,27 @@
       }
     ]
   },
-  "icdar_segmentation": {
+  "ICDAR Recognition 1.0": {
+    "name": "icdar localization/recogntion task",
+    "overlap": 0,
+    "segment_size": 100,
+    "owner_id": 1,
+    "assignee_id": 2,
+    "labels": [
+      {
+        "name": "icdar",
+        "attributes": [
+          {
+            "name": "text",
+            "mutable": false,
+            "input_type": "text",
+            "values": ["word_1", "word_2", "word_3"]
+          }
+        ]
+      }
+    ]
+  },
+  "ICDAR Segmentation 1.0": {
     "name": "icdar segmentation task",
     "overlap": 0,
     "segment_size": 100,
@@ -112,7 +132,7 @@
       }
     ]
   },
-  "market1501": {
+  "Market-1501 1.0": {
     "name": "market1501 task",
     "overlap": 0,
     "segment_size": 100,
@@ -144,6 +164,38 @@
       }
     ]
   },
+  "Cityscapes 1.0": {
+    "name": "cityscapes task",
+    "overlap": 0,
+    "segment_size": 100,
+    "owner_id": 1,
+    "assignee_id": 2,
+    "labels": [
+      {
+        "name": "car",
+        "color": "#2080c0",
+        "attributes": [
+          {
+            "name": "is_crowd",
+            "mutable": false,
+            "input_type": "checkbox",
+            "default_value": "false",
+            "values": ["false", "true"]
+          }
+        ]
+      },
+      {
+        "name": "person",
+        "color": "#c06060",
+        "attributes": []
+      },
+      {
+        "name": "background",
+        "color": "#000000",
+        "attributes": []
+      }
+    ]
+  },
   "wrong_checkbox_value": {
     "name": "wrong checkbox value task",
     "overlap": 0,

cvat/apps/dataset_manager/tests/test_formats.py

@@ -27,7 +27,7 @@
 from cvat.apps.engine.models import Task
 
 
-def generate_image_file(filename, size=(100, 50)):
+def generate_image_file(filename, size=(100, 100)):
     f = BytesIO()
     image = Image.new('RGB', size=size)
     image.save(f, 'jpeg')
@@ -295,8 +295,8 @@ def test_export_formats_query(self):
             'ICDAR Localization 1.0',
             'ICDAR Segmentation 1.0',
             'Kitti Raw Format 1.0',
-            'Sly Point Cloud Format 1.0'
-
+            'Sly Point Cloud Format 1.0',
+            'Cityscapes 1.0'
         })
 
     def test_import_formats_query(self):
@@ -323,6 +323,7 @@ def test_import_formats_query(self):
             'ICDAR Segmentation 1.0',
             'Kitti Raw Format 1.0',
             'Sly Point Cloud Format 1.0',
+            'Cityscapes 1.0',
             'Datumaro 1.0',
             'Datumaro 3D 1.0'
         })
@@ -371,6 +372,7 @@ def test_empty_images_are_exported(self):
             ('ICDAR Recognition 1.0', 'icdar_word_recognition'),
             ('ICDAR Localization 1.0', 'icdar_text_localization'),
             ('ICDAR Segmentation 1.0', 'icdar_text_segmentation'),
+            # ('Cityscapes 1.0', 'cityscapes'), does not support, empty annotations
         ]:
             with self.subTest(format=format_name):
                 if not dm.formats.registry.EXPORT_FORMATS[format_name].ENABLED:
@@ -701,6 +703,10 @@ def _generate_task(self, images, annotation_format, **overrides):
                         }
                     ]
                 },
+                {
+                    "name": "background",
+                    "attributes": [],
+                },
                 {"name": "person"}
             ]
 
@@ -837,7 +843,7 @@ def _generate_annotations(self, task, annotation_format):
                     {
                         "frame": 0,
                         "attributes": [],
-                        "points": [1.0, 2.1, 10.6, 53.22, 100, 300.222],
+                        "points": [1.0, 2.1, 10.6, 53.22, 90, 90.222],
                         "type": "polygon",
                         "occluded": False,
                         "outside": False
@@ -878,10 +884,10 @@ def _generate_annotations(self, task, annotation_format):
             elif annotation_format == "MOTS PNG 1.0":
                 tracks = [track_wo_attrs]
             else:
-                shapes = [rectangle_shape_wo_attrs,
+                shapes = [rectangle_shape_wo_attrs, \
                     rectangle_shape_with_attrs]
-                tags = tag_wo_attrs
-                tracks = track_wo_attrs
+                tags = [tag_wo_attrs]
+                tracks = [track_wo_attrs]
 
         annotations = {
             "version": 0,