Add CamVid format (#2559)

* Update Datumaro to 0.1.4

* Add CamVid format

* Add CamVid to documentation

* Update changelog.

* Added information about ImageNet and CamVid into README.md
Co-authored-by: NNikita Manovich <nikita.manovich@intel.com>

CHANGELOG.md

@@ -10,11 +10,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - Manual review pipeline: issues/comments/workspace (<https://github.com/openvinotoolkit/cvat/pull/2357>)
-- Added basic projects implementation (<https://github.com/openvinotoolkit/cvat/pull/2255>)
-- Added documentation on how to mount cloud starage(AWS S3 bucket, Azure container, Google Drive) as FUSE (<https://github.com/openvinotoolkit/cvat/pull/2377>)
-- Added ability to work with share files without copying inside (<https://github.com/openvinotoolkit/cvat/pull/2377>)
+- Basic projects implementation (<https://github.com/openvinotoolkit/cvat/pull/2255>)
+- Documentation on how to mount cloud starage(AWS S3 bucket, Azure container, Google Drive) as FUSE (<https://github.com/openvinotoolkit/cvat/pull/2377>)
+- Ability to work with share files without copying inside (<https://github.com/openvinotoolkit/cvat/pull/2377>)
 - Tooltips in label selectors (<https://github.com/openvinotoolkit/cvat/pull/2509>)
 - Page redirect after login using `next` query parameter (<https://github.com/openvinotoolkit/cvat/pull/2527>)
+- [ImageNet](http://www.image-net.org) format support (<https://github.com/openvinotoolkit/cvat/pull/2376>)
+- [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) format support (<https://github.com/openvinotoolkit/cvat/pull/2559>)
 
 ### Changed
 

README.md

@@ -40,11 +40,13 @@ annotation team. Try it online [cvat.org](https://cvat.org).
 
 ## Supported annotation formats
 
-Format selection is possible after clicking on the Upload annotation
-and Dump annotation buttons.
-[Datumaro](https://github.com/openvinotoolkit/datumaro) dataset
-framework allows additional dataset transformations
-via its command line tool and Python library.
+Format selection is possible after clicking on the Upload annotation and Dump
+annotation buttons. [Datumaro](https://github.com/openvinotoolkit/datumaro)
+dataset framework allows additional dataset transformations via its command
+line tool and Python library.
+
+For more information about supported formats look at the
+[documentation](cvat/apps/dataset_manager/formats/README.md#formats).
 
 | Annotation format                                                             | Import | Export |
 | ----------------------------------------------------------------------------- | ------ | ------ |
@@ -58,6 +60,8 @@ via its command line tool and Python library.
 | [TFrecord](https://www.tensorflow.org/tutorials/load_data/tf_records)         | X      | X      |
 | [MOT](https://motchallenge.net/)                                              | X      | X      |
 | [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0)                        | X      | X      |
+| [ImageNet](http://www.image-net.org)                                          | X      | X      |
+| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)          | X      | X      |
 
 ## Deep learning models for automatic labeling
 

cvat/apps/dataset_manager/formats/README.md

@@ -19,6 +19,7 @@
   - [YOLO](#yolo)
   - [TF detection API](#tfrecord)
   - [ImageNet](#imagenet)
+  - [CamVid](#camvid)
 
 ## How to add a new annotation format support<a id="how-to-add"></a>
 
@@ -835,3 +836,41 @@ taskname.zip/
 Uploaded file: a zip archive of the structure above
 
 - supported annotations: Labels
+
+### [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)<a id="camvid" />
+
+#### CamVid Dumper
+
+Downloaded file: a zip archive of the following structure:
+
+```bash
+taskname.zip/
+├── labelmap.txt # optional, required for non-CamVid labels
+└── <any_subset_name>/
+    ├── image1.png
+    └── image2.png
+└── <any_subset_name>annot/
+    ├── image1.png
+    └── image2.png
+└── <any_subset_name>.txt
+
+# labelmap.txt
+# color (RGB) label
+0 0 0 Void
+64 128 64 Animal
+192 0 128 Archway
+0 128 192 Bicyclist
+0 128 64 Bridge
+```
+
+Mask is a `png` image with 1 or 3 channels where each pixel
+has own color which corresponds to a label.
+`(0, 0, 0)` is used for background by default.
+
+- supported annotations: Rectangles, Polygons
+
+#### CamVid Loader
+
+Uploaded file: a zip archive of the structure above
+
+- supported annotations: Polygons

cvat/apps/dataset_manager/formats/camvid.py

+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+from tempfile import TemporaryDirectory
+
+from datumaro.components.project import Dataset
+from pyunpack import Archive
+
+from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
+    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='CamVid', ext='ZIP', version='1.0')
+def _export(dst_file, task_data, save_images=False):
+    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
+    envt = dm_env.transforms
+    extractor = extractor.transform(envt.get('polygons_to_masks'))
+    extractor = extractor.transform(envt.get('boxes_to_masks'))
+    extractor = extractor.transform(envt.get('merge_instance_segments'))
+    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    label_map = make_colormap(task_data)
+    with TemporaryDirectory() as temp_dir:
+        dm_env.converters.get('camvid').convert(extractor,
+            save_dir=temp_dir, save_images=save_images, apply_colormap=True,
+            label_map={label: label_map[label][0] for label in label_map})
+
+        make_zip_archive(temp_dir, dst_file)
+
+@importer(name='CamVid', ext='ZIP', version='1.0')
+def _import(src_file, task_data):
+    with TemporaryDirectory() as tmp_dir:
+        Archive(src_file.name).extractall(tmp_dir)
+
+        dataset = dm_env.make_importer('camvid')(tmp_dir).make_dataset()
+        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
+        dataset = dataset.transform(masks_to_polygons)
+        import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/registry.py

@@ -91,4 +91,5 @@ import cvat.apps.dataset_manager.formats.mots
 import cvat.apps.dataset_manager.formats.pascal_voc
 import cvat.apps.dataset_manager.formats.tfrecord
 import cvat.apps.dataset_manager.formats.yolo
-import cvat.apps.dataset_manager.formats.imagenet
\ No newline at end of file
+import cvat.apps.dataset_manager.formats.imagenet
+import cvat.apps.dataset_manager.formats.camvid
\ No newline at end of file

cvat/apps/dataset_manager/tests/test_formats.py

@@ -270,6 +270,7 @@ class TaskExportTest(_DbTestBase):
             'TFRecord 1.0',
             'YOLO 1.1',
             'ImageNet 1.0',
+            'CamVid 1.0',
         })
 
     def test_import_formats_query(self):
@@ -287,6 +288,7 @@ class TaskExportTest(_DbTestBase):
             'TFRecord 1.0',
             'YOLO 1.1',
             'ImageNet 1.0',
+            'CamVid 1.0',
         })
 
     def test_exports(self):
@@ -323,6 +325,7 @@ class TaskExportTest(_DbTestBase):
             ('TFRecord 1.0', 'tf_detection_api'),
             ('YOLO 1.1', 'yolo'),
             ('ImageNet 1.0', 'imagenet_txt'),
+            ('CamVid 1.0', 'camvid'),
         ]:
             with self.subTest(format=format_name):
                 if not dm.formats.registry.EXPORT_FORMATS[format_name].ENABLED:

cvat/apps/engine/tests/test_rest_api.py

@@ -3751,6 +3751,10 @@ class TaskAnnotationAPITestCase(JobAnnotationAPITestCase):
             elif annotation_format == "ImageNet 1.0":
                 annotations["tags"] = tags_wo_attrs
 
+            elif annotation_format == "CamVid 1.0":
+                annotations["shapes"] = rectangle_shapes_wo_attrs \
+                                      + polygon_shapes_wo_attrs
+
             else:
                 raise Exception("Unknown format {}".format(annotation_format))
 
@@ -3847,7 +3851,7 @@ class TaskAnnotationAPITestCase(JobAnnotationAPITestCase):
                 self.assertEqual(response.status_code, HTTP_201_CREATED)
 
                 # 7. check annotation
-                if import_format in {"Segmentation mask 1.1", "MOTS PNG 1.0"}:
+                if import_format in {"Segmentation mask 1.1", "MOTS PNG 1.0", "CamVid 1.0"}:
                     continue # can't really predict the result to check
                 response = self._get_api_v1_tasks_id_annotations(task["id"], annotator)
                 self.assertEqual(response.status_code, HTTP_200_OK)