Add VOC grayscale masks test and documentation (cvat-ai#1576)

* Add a test for unpainted masks
* Update format documentation

cvat/apps/dataset_manager/formats/README.md

@@ -322,6 +322,26 @@ Uploaded file: a zip archive of the following structure:
       └── image2.png
   ```
 
+It is also possible to import grayscale (1-channel) PNG masks.
+For grayscale masks provide a list of labels with the number of lines equal
+to the maximum color index on images. The lines must be in the right order
+so that line index is equal to the color index. Lines can have arbitrary,
+but different, colors. If there are gaps in the used color
+indices in the annotations, they must be filled with arbitrary dummy labels.
+Example:
+```
+q:0,128,0:: # color index 0
+aeroplane:10,10,128:: # color index 1
+_dummy2:2,2,2:: # filler for color index 2
+_dummy3:3,3,3:: # filler for color index 3
+boat:108,0,100:: # color index 3
+...
+_dummy198:198,198,198:: # filler for color index 198
+_dummy199:199,199,199:: # filler for color index 199
+...
+the last label:12,28,0:: # color index 200
+```
+
 - supported shapes: Polygons
 
 #### How to create a task from Pascal VOC dataset

datumaro/tests/test_voc_format.py

@@ -444,6 +444,40 @@ def __iter__(self):
                 VocSegmentationConverter(label_map='voc'), test_dir,
                 target_dataset=DstExtractor())
 
+    def test_can_save_voc_segm_unpainted(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_save_and_load(TestExtractor(),
+                VocSegmentationConverter(label_map='voc', apply_colormap=False),
+                test_dir, target_dataset=DstExtractor())
+
     def test_can_save_voc_segm_with_many_instances(self):
         def bit(x, y, shape):
             mask = np.zeros(shape)