Improve mask import and export performance

changelog.d/20240619_143138_mzhiltso_mask_import_export_performance.md

@@ -0,0 +1,4 @@
+### Changed
+
+- Improved performance for mask import and export
+  (<https://github.com/cvat-ai/cvat/pull/8049>)

cvat/apps/dataset_manager/bindings.py

@@ -19,7 +19,6 @@
 from attrs.converters import to_bool
 import datumaro as dm
 import defusedxml.ElementTree as ET
-import numpy as np
 import rq
 from attr import attrib, attrs
 from datumaro.components.media import PointCloud
@@ -38,7 +37,7 @@
                                      Task)
 
 from .annotation import AnnotationIR, AnnotationManager, TrackManager
-from .formats.transformations import CVATRleToCOCORle, EllipsesToMasks
+from .formats.transformations import MaskConverter, EllipsesToMasks
 
 CVAT_INTERNAL_ATTRIBUTES = {'occluded', 'outside', 'keyframe', 'track_id', 'rotation'}
 
@@ -1815,7 +1814,7 @@ def _convert_shape(self,
                 "attributes": dm_attr,
             }), self.cvat_frame_anno.height, self.cvat_frame_anno.width)
         elif shape.type == ShapeType.MASK:
-            anno = CVATRleToCOCORle.convert_mask(SimpleNamespace(**{
+            anno = MaskConverter.cvat_rle_to_dm_rle(SimpleNamespace(**{
                 "points": shape.points,
                 "label": dm_label,
                 "z_order": shape.z_order,
@@ -2041,22 +2040,7 @@ def import_dm_annotations(dm_dataset: dm.Dataset, instance_data: Union[ProjectDa
                     if ann.type == dm.AnnotationType.cuboid_3d:
                         points = [*ann.position, *ann.rotation, *ann.scale, 0, 0, 0, 0, 0, 0, 0]
                     elif ann.type == dm.AnnotationType.mask:
-                        istrue = np.argwhere(ann.image == 1).transpose()
-                        top = int(istrue[0].min())
-                        left = int(istrue[1].min())
-                        bottom = int(istrue[0].max())
-                        right = int(istrue[1].max())
-                        points = ann.image[top:bottom + 1, left:right + 1]
-
-                        def reduce_fn(acc, v):
-                            if v == acc['val']:
-                                acc['res'][-1] += 1
-                            else:
-                                acc['val'] = v
-                                acc['res'].append(1)
-                            return acc
-                        points = reduce(reduce_fn, points.reshape(np.prod(points.shape)), { 'res': [0], 'val': False })['res']
-                        points.extend([int(left), int(top), int(right), int(bottom)])
+                        points = MaskConverter.dm_mask_to_cvat_rle(ann)
                     elif ann.type != dm.AnnotationType.skeleton:
                         points = ann.points
 

cvat/apps/dataset_manager/formats/transformations.py

@@ -1,4 +1,5 @@
 # Copyright (C) 2021-2022 Intel Corporation
+# Copyright (C) 2024 CVAT.ai Corporation
 #
 # SPDX-License-Identifier: MIT
 
@@ -8,10 +9,10 @@
 from itertools import chain
 from pycocotools import mask as mask_utils
 
-from datumaro.components.extractor import ItemTransform
-import datumaro.components.annotation as dm
+import datumaro as dm
 
-class RotatedBoxesToPolygons(ItemTransform):
+
+class RotatedBoxesToPolygons(dm.ItemTransform):
     def _rotate_point(self, p, angle, cx, cy):
         [x, y] = p
         rx = cx + math.cos(angle) * (x - cx) - math.sin(angle) * (y - cy)
@@ -36,28 +37,68 @@ def transform_item(self, item):
 
         return item.wrap(annotations=annotations)
 
-class CVATRleToCOCORle(ItemTransform):
+class MaskConverter:
     @staticmethod
-    def convert_mask(shape, img_h, img_w):
-        rle = shape.points[:-4]
-        left, top, right = list(math.trunc(v) for v in shape.points[-4:-1])
-        mat = np.zeros((img_h, img_w), dtype=np.uint8)
-        width = right - left + 1
-        value = 0
-        offset = 0
-        for rleCount in rle:
-            rleCount = math.trunc(rleCount)
-            while rleCount > 0:
-                x, y = offset % width, offset // width
-                mat[y + top][x + left] = value
-                rleCount -= 1
-                offset += 1
-            value = abs(value - 1)
+    def cvat_rle_to_dm_rle(shape, img_h: int, img_w: int) -> dm.RleMask:
+        "Converts a CVAT RLE to a Datumaro / COCO mask"
 
-        rle = mask_utils.encode(np.asfortranarray(mat))
-        return dm.RleMask(rle=rle, label=shape.label, z_order=shape.z_order,
+        # use COCO representation of CVAT RLE to avoid python loops
+        left, top, right, bottom = list(math.trunc(v) for v in shape.points[-4:])
+        h = bottom - top + 1
+        w = right - left + 1
+        cvat_as_coco_rle_uncompressed = {
+            "counts": shape.points[:-4],
+            "size": [w, h],
+        }
+        cvat_as_coco_rle_compressed = mask_utils.frPyObjects(
+            [cvat_as_coco_rle_uncompressed], h=h, w=w
+        )[0]
+
+        # expand the mask to the full image size
+        tight_mask = mask_utils.decode(cvat_as_coco_rle_compressed).transpose()
+        full_mask = np.zeros((img_h, img_w), dtype=np.uint8)
+        full_mask[top : bottom + 1, left : right + 1] = tight_mask
+
+        # obtain RLE
+        coco_rle = mask_utils.encode(np.asfortranarray(full_mask))
+        return dm.RleMask(rle=coco_rle, label=shape.label, z_order=shape.z_order,
             attributes=shape.attributes, group=shape.group)
 
+    @classmethod
+    def dm_mask_to_cvat_rle(cls, dm_mask: dm.Mask) -> list[int]:
+        "Converts a Datumaro mask to a CVAT RLE"
+
+        # get tight mask
+        x, y, w, h = dm_mask.get_bbox()
+        top = int(y)
+        left = int(x)
+        bottom = int(max(y, y + h - 1))
+        right = int(max(x, x + w - 1))
+        tight_binary_mask = dm_mask.image[top : bottom + 1, left : right + 1]
+
+        # obtain RLE
+        cvat_rle = cls.rle(tight_binary_mask.reshape(-1))
+        cvat_rle = cvat_rle.tolist()
+
+        # CVAT RLE starts from 0
+        if tight_binary_mask[0][0] != 0:
+            cvat_rle.insert(0, 0)
+
+        cvat_rle += [left, top, right, bottom]
+        return cvat_rle
+
+    @classmethod
+    def rle(cls, arr: np.ndarray) -> np.ndarray:
+        "Computes RLE for a flat array"
+        # adapted from https://stackoverflow.com/a/32681075
+
+        n = len(arr)
+        if n == 0:
+            return np.array([])
+
+        pairwise_unequal = arr[1:] != arr[:-1]
+        return np.diff(np.where(pairwise_unequal)[0], prepend=-1, append=n - 1)
+
 class EllipsesToMasks:
     @staticmethod
     def convert_ellipse(ellipse, img_h, img_w):