[CVAT] Draw roi point along with bbox in skeleton tasks

packages/examples/cvat/exchange-oracle/src/core/tasks/skeletons_from_boxes.py

@@ -23,6 +23,9 @@ class RoiInfo:
     bbox_y: int
     bbox_label: int
 
+    point_x: int
+    point_y: int
+
     # RoI is centered on the bbox center
     # Coordinates can be out of image boundaries.
     # In this case RoI includes extra margins to be centered on bbox center
@@ -117,7 +120,10 @@ def parse_skeleton_bbox_mapping(self, skeleton_bbox_mapping_data: bytes) -> Skel
         return {int(k): int(v) for k, v in parse_json(skeleton_bbox_mapping_data).items()}
 
     def parse_roi_info(self, rois_info_data: bytes) -> RoiInfos:
-        return [RoiInfo(**roi_info) for roi_info in parse_json(rois_info_data)]
+        return [
+            RoiInfo(**{"point_x": 0, "point_y": 0, **roi_info})
+            for roi_info in parse_json(rois_info_data)
+        ]
 
     def parse_roi_filenames(self, roi_filenames_data: bytes) -> RoiFilenames:
         return {int(k): v for k, v in parse_json(roi_filenames_data).items()}

packages/examples/cvat/exchange-oracle/src/handlers/job_creation.py

@@ -20,7 +20,7 @@
 import datumaro as dm
 import numpy as np
 from datumaro.util import filter_dict, take_by
-from datumaro.util.annotation_util import BboxCoords, bbox_iou
+from datumaro.util.annotation_util import BboxCoords, bbox_iou, find_instances
 from datumaro.util.image import IMAGE_EXTENSIONS, decode_image, encode_image
 
 import src.core.tasks.boxes_from_points as boxes_from_points_task
@@ -1709,13 +1709,18 @@ def __init__(self, manifest: TaskManifest, escrow_address: str, chain_id: int) -
         )
         "Minimum absolute ROI size, (w, h)"
 
-        self.boxes_format = "coco_instances"
+        self.boxes_format = "coco_person_keypoints"
 
         self.embed_bbox_in_roi_image = True
         "Put a bbox into the extracted skeleton RoI images"
 
         self.embed_tile_border = True
 
+        self.embedded_point_radius = 15
+        self.min_embedded_point_radius_percent = 0.005
+        self.max_embedded_point_radius_percent = 0.01
+        self.embedded_point_color = (0, 255, 255)
+
         self.roi_embedded_bbox_color = (0, 255, 255)  # BGR
         self.roi_background_color = (245, 240, 242)  # BGR - CVAT background color
 
@@ -1729,6 +1734,9 @@ def __init__(self, manifest: TaskManifest, escrow_address: str, chain_id: int) -
         GT annotations or samples for successful job launch
         """
 
+        self.gt_id_attribute = "object_id"
+        "An additional way to match GT skeletons with input boxes"
+
         # TODO: probably, need to also add an absolute number of minimum GT RoIs per class
 
     def _download_input_data(self):
@@ -1948,7 +1956,7 @@ def _validate_boxes_filenames(self):
                 )
             )
 
-    def _validate_boxes_annotations(self):
+    def _validate_boxes_annotations(self):  # noqa: PLR0912
         # Convert possible polygons and masks into boxes
         self._boxes_dataset.transform(InstanceSegmentsToBbox)
         self._boxes_dataset.init_cache()
@@ -1962,22 +1970,87 @@ def _validate_boxes_annotations(self):
             # Could fail on this as well
             image_h, image_w = sample.media_as(dm.Image).size
 
-            sample_boxes = [a for a in sample.annotations if isinstance(a, dm.Bbox)]
-            valid_boxes = []
-            for bbox in sample_boxes:
-                if not (
-                    (0 <= int(bbox.x) < int(bbox.x + bbox.w) <= image_w)
-                    and (0 <= int(bbox.y) < int(bbox.y + bbox.h) <= image_h)
-                ):
+            valid_instances: list[tuple[dm.Bbox, dm.Points]] = []
+            instances = find_instances(
+                [a for a in sample.annotations if isinstance(a, dm.Bbox | dm.Skeleton)]
+            )
+            for instance_anns in instances:
+                if len(instance_anns) != 2:
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - unexpected group size ({})".format(
+                            sample.id,
+                            instance_anns[0].id,
+                            label_cat[instance_anns[0].label].name,
+                            len(instance_anns),
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
+                bbox = next((a for a in instance_anns if isinstance(a, dm.Bbox)), None)
+                if not bbox:
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - no matching bbox".format(
+                            sample.id, instance_anns[0].id, label_cat[instance_anns[0].label].name
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
+                skeleton = next((a for a in instance_anns if isinstance(a, dm.Skeleton)), None)
+                if not skeleton:
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - no matching skeleton".format(
+                            sample.id, instance_anns[0].id, label_cat[instance_anns[0].label].name
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
+                if len(skeleton.elements) != 1 or len(skeleton.elements[0].points) != 2:
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - invalid skeleton points".format(
+                            sample.id, skeleton.id, label_cat[skeleton.label].name
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
+                point = skeleton.elements[0]
+                if not is_point_in_bbox(point.points[0], point.points[1], (0, 0, image_w, image_h)):
                     excluded_boxes_info.add_message(
-                        "Sample '{}': bbox #{} ({}) skipped - invalid coordinates".format(
+                        "Sample '{}': object #{} ({}) skipped - invalid point coordinates".format(
+                            sample.id, skeleton.id, label_cat[skeleton.label].name
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
+                if not is_point_in_bbox(int(bbox.x), int(bbox.y), (0, 0, image_w, image_h)):
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - invalid bbox coordinates".format(
                             sample.id, bbox.id, label_cat[bbox.label].name
                         ),
                         sample_id=sample.id,
                         sample_subset=sample.subset,
                     )
                     continue
 
+                if not is_point_in_bbox(point.points[0], point.points[1], bbox):
+                    excluded_boxes_info.add_message(
+                        "Sample '{}': object #{} ({}) skipped - point is outside the bbox".format(
+                            sample.id, skeleton.id, label_cat[skeleton.label].name
+                        ),
+                        sample_id=sample.id,
+                        sample_subset=sample.subset,
+                    )
+                    continue
+
                 if bbox.id in visited_ids:
                     excluded_boxes_info.add_message(
                         "Sample '{}': bbox #{} ({}) skipped - repeated annotation id {}".format(
@@ -1988,14 +2061,18 @@ def _validate_boxes_annotations(self):
                     )
                     continue
 
-                valid_boxes.append(bbox)
+                valid_instances.append(
+                    (bbox, point.wrap(group=bbox.group, id=bbox.id, attributes=bbox.attributes))
+                )
                 visited_ids.add(bbox.id)
 
-            excluded_boxes_info.excluded_count += len(sample_boxes) - len(valid_boxes)
-            excluded_boxes_info.total_count += len(sample_boxes)
+            excluded_boxes_info.excluded_count += len(instances) - len(valid_instances)
+            excluded_boxes_info.total_count += len(instances)
 
-            if len(valid_boxes) != len(sample.annotations):
-                self._boxes_dataset.put(sample.wrap(annotations=valid_boxes))
+            if len(valid_instances) != len(sample.annotations):
+                self._boxes_dataset.put(
+                    sample.wrap(annotations=list(chain.from_iterable(valid_instances)))
+                )
 
         if excluded_boxes_info.excluded_count > ceil(
             excluded_boxes_info.total_count * self.max_discarded_threshold
@@ -2066,8 +2143,14 @@ def _find_unambiguous_matches(
             input_boxes: list[dm.Bbox],
             gt_skeletons: list[dm.Skeleton],
             *,
+            input_points: list[dm.Points],
             gt_annotations: list[dm.Annotation],
         ) -> list[tuple[dm.Bbox, dm.Skeleton]]:
+            bbox_point_mapping: dict[int, dm.Points] = {
+                bbox.id: next(p for p in input_points if p.group == bbox.group)
+                for bbox in input_boxes
+            }
+
             matches = [
                 [
                     (input_bbox.label == gt_skeleton.label)
@@ -2077,6 +2160,18 @@ def _find_unambiguous_matches(
                             self._get_skeleton_bbox(gt_skeleton, gt_annotations),
                         )
                     )
+                    and (input_point := bbox_point_mapping[input_bbox.id])
+                    and is_point_in_bbox(
+                        input_point.points[0],
+                        input_point.points[1],
+                        self._get_skeleton_bbox(gt_skeleton, gt_annotations),
+                    )
+                    and (
+                        # a way to customize matching if the default method is too rough
+                        not (bbox_id := input_bbox.attributes.get(self.gt_id_attribute))
+                        or not (skeleton_id := gt_skeleton.attributes.get(self.gt_id_attribute))
+                        or bbox_id == skeleton_id
+                    )
                     for gt_skeleton in gt_skeletons
                 ]
                 for input_bbox in input_boxes
@@ -2167,10 +2262,11 @@ def _find_good_gt_skeletons(
             input_boxes: list[dm.Bbox],
             gt_skeletons: list[dm.Skeleton],
             *,
+            input_points: list[dm.Points],
             gt_annotations: list[dm.Annotation],
         ) -> list[dm.Skeleton]:
             matches = _find_unambiguous_matches(
-                input_boxes, gt_skeletons, gt_annotations=gt_annotations
+                input_boxes, gt_skeletons, input_points=input_points, gt_annotations=gt_annotations
             )
 
             matched_skeletons = []
@@ -2221,13 +2317,18 @@ def _find_good_gt_skeletons(
 
             gt_skeletons = [a for a in gt_sample.annotations if isinstance(a, dm.Skeleton)]
             input_boxes = [a for a in boxes_sample.annotations if isinstance(a, dm.Bbox)]
+            input_points = [a for a in boxes_sample.annotations if isinstance(a, dm.Points)]
+            assert len(input_boxes) == len(input_points)
 
             # Samples without boxes are allowed, so we just skip them without an error
             if not gt_skeletons:
                 continue
 
             matched_skeletons = _find_good_gt_skeletons(
-                input_boxes, gt_skeletons, gt_annotations=gt_sample.annotations
+                input_boxes,
+                gt_skeletons,
+                input_points=input_points,
+                gt_annotations=gt_sample.annotations,
             )
             if not matched_skeletons:
                 continue
@@ -2294,9 +2395,10 @@ def _prepare_roi_infos(self):
 
         rois: list[skeletons_from_boxes_task.RoiInfo] = []
         for sample in self._boxes_dataset:
-            for bbox in sample.annotations:
-                if not isinstance(bbox, dm.Bbox):
-                    continue
+            instances = find_instances(sample.annotations)
+            for instance_anns in instances:
+                bbox = next(a for a in instance_anns if isinstance(a, dm.Bbox))
+                point = next(a for a in instance_anns if isinstance(a, dm.Points))
 
                 # RoI is centered on bbox center
                 original_bbox_cx = int(bbox.x + bbox.w / 2)
@@ -2320,6 +2422,8 @@ def _prepare_roi_infos(self):
                         bbox_label=bbox.label,
                         bbox_x=new_bbox_x,
                         bbox_y=new_bbox_y,
+                        point_x=point.points[0] - roi_x,
+                        point_y=point.points[1] - roi_y,
                         roi_x=roi_x,
                         roi_y=roi_y,
                         roi_w=roi_w,
@@ -2511,6 +2615,32 @@ def _draw_roi_bbox(self, roi_image: np.ndarray, bbox: dm.Bbox) -> np.ndarray:
             cv2.LINE_4,
         )
 
+    def _draw_roi_point(self, roi_image: np.ndarray, point: tuple[float, float]) -> np.ndarray:
+        roi_r = (roi_image.shape[0] ** 2 + roi_image.shape[1] ** 2) ** 0.5 / 2
+        radius = int(
+            min(
+                self.max_embedded_point_radius_percent * roi_r,
+                max(self.embedded_point_radius, self.min_embedded_point_radius_percent * roi_r),
+            )
+        )
+
+        roi_image = cv2.circle(
+            roi_image,
+            tuple(map(int, (point[0], point[1]))),
+            radius + 1,
+            (255, 255, 255),
+            -1,
+            cv2.LINE_4,
+        )
+        return cv2.circle(
+            roi_image,
+            tuple(map(int, (point[0], point[1]))),
+            radius,
+            self.embedded_point_color,
+            -1,
+            cv2.LINE_4,
+        )
+
     def _extract_and_upload_rois(self):
         assert self._roi_filenames is not _unset
         assert self._roi_infos is not _unset
@@ -2564,6 +2694,9 @@ def process_file(filename: str, image_pixels: np.ndarray):
 
                 if self.embed_bbox_in_roi_image:
                     roi_pixels = self._draw_roi_bbox(roi_pixels, bbox_by_id[roi_info.bbox_id])
+                    roi_pixels = self._draw_roi_point(
+                        roi_pixels, (roi_info.point_x, roi_info.point_y)
+                    )
 
                 filename = self._roi_filenames[roi_info.bbox_id]
                 roi_bytes = encode_image(roi_pixels, os.path.splitext(filename)[-1])

packages/examples/cvat/exchange-oracle/src/utils/annotations.py

@@ -8,7 +8,7 @@
 import datumaro as dm
 import numpy as np
 from datumaro.util import filter_dict, mask_tools
-from datumaro.util.annotation_util import find_group_leader, find_instances, max_bbox
+from datumaro.util.annotation_util import BboxCoords, find_group_leader, find_instances, max_bbox
 from defusedxml import ElementTree
 
 
@@ -343,8 +343,12 @@ def transform_item(self, item):
         return item.wrap(annotations=annotations)
 
 
-def is_point_in_bbox(px: float, py: float, bbox: dm.Bbox) -> bool:
-    return (bbox.x <= px <= bbox.x + bbox.w) and (bbox.y <= py <= bbox.y + bbox.h)
+def is_point_in_bbox(px: float, py: float, bbox: dm.Bbox | BboxCoords) -> bool:
+    if isinstance(bbox, dm.Bbox):
+        bbox = bbox.get_bbox()
+
+    x, y, w, h = bbox
+    return (x <= px <= x + w) and (y <= py <= y + h)
 
 
 class InstanceSegmentsToBbox(dm.ItemTransform):