Added automatic clipping for normalized annotations

luxonis_ml/data/datasets/annotation.py

@@ -1,4 +1,5 @@
 import json
+import logging
 from abc import ABC, abstractmethod
 from datetime import datetime
 from typing import Any, ClassVar, Dict, List, Literal, Optional, Tuple, TypedDict, Union
@@ -20,6 +21,8 @@
 
 from ..utils.enums import LabelType
 
+logger = logging.getLogger(__name__)
+
 KeypointVisibility: TypeAlias = Literal[0, 1, 2]
 NormalizedFloat: TypeAlias = Annotated[float, Field(ge=0, le=1)]
 """C{NormalizedFloat} is a float that is restricted to the range [0, 1]."""
@@ -137,6 +140,25 @@ class BBoxAnnotation(Annotation):
 
  _label_type = LabelType.BOUNDINGBOX
 
+ @model_validator(mode="before")
+ @classmethod
+ def validate_values(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+ warn = False
+ for key in ["x", "y", "w", "h"]:
+ if values[key] < -2 or values[key] > 2:
+ raise ValueError(
+ "BBox annotation has value outside of automatic clipping range ([-2, 2]). "
+ "Values should be normalized based on image size to range [0, 1]."
+ )
+ if not (0 <= values[key] <= 1):
+ warn = True
+ values[key] = max(0, min(1, values[key]))
+ if warn:
+ logger.warning(
+ "BBox annotation has values outside of [0, 1] range. Clipping them to [0, 1]."
+ )
+ return values
+
  def to_numpy(self, class_mapping: Dict[str, int]) -> np.ndarray:
  class_ = class_mapping.get(self.class_, 0)
  return np.array([class_, self.x, self.y, self.w, self.h])
@@ -170,6 +192,33 @@ class KeypointAnnotation(Annotation):
 
  _label_type = LabelType.KEYPOINTS
 
+ @model_validator(mode="before")
+ @classmethod
+ def validate_values(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+ warn = False
+ for i, keypoint in enumerate(values["keypoints"]):
+ if (keypoint[0] < -2 or keypoint[0] > 2) or (
+ keypoint[1] < -2 or keypoint[1] > 2
+ ):
+ raise ValueError(
+ "Keypoint annotation has value outside of automatic clipping range ([-2, 2]). "
+ "Values should be normalized based on image size to range [0, 1]."
+ )
+ new_keypoint = list(keypoint)
+ if not (0 <= keypoint[0] <= 1):
+ new_keypoint[0] = max(0, min(1, keypoint[0]))
+ warn = True
+ if not (0 <= keypoint[1] <= 1):
+ new_keypoint[1] = max(0, min(1, keypoint[1]))
+ warn = True
+ values["keypoints"][i] = tuple(new_keypoint)
+
+ if warn:
+ logger.warning(
+ "Keypoint annotation has values outside of [0, 1] range. Clipping them to [0, 1]."
+ )
+ return values
+
  def to_numpy(self, class_mapping: Dict[str, int]) -> np.ndarray:
  class_ = class_mapping.get(self.class_, 0)
  kps = np.array(self.keypoints).reshape((-1, 3)).astype(np.float32)
@@ -340,6 +389,31 @@ class PolylineSegmentationAnnotation(SegmentationAnnotation):
 
  points: List[Tuple[NormalizedFloat, NormalizedFloat]] = Field(min_length=3)
 
+ @model_validator(mode="before")
+ @classmethod
+ def validate_values(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+ warn = False
+ for i, point in enumerate(values["points"]):
+ if (point[0] < -2 or point[0] > 2) or (point[1] < -2 or point[1] > 2):
+ raise ValueError(
+ "Polyline annotation has value outside of automatic clipping range ([-2, 2]). "
+ "Values should be normalized based on image size to range [0, 1]."
+ )
+ new_point = list(point)
+ if not (0 <= point[0] <= 1):
+ new_point[0] = max(0, min(1, point[0]))
+ warn = True
+ if not (0 <= point[1] <= 1):
+ new_point[1] = max(0, min(1, point[1]))
+ warn = True
+ values["points"][i] = tuple(new_point)
+
+ if warn:
+ logger.warning(
+ "Polyline annotation has values outside of [0, 1] range. Clipping them to [0, 1]."
+ )
+ return values
+
  def to_numpy(self, _: Dict[str, int], width: int, height: int) -> np.ndarray:
  polyline = [(round(x * width), round(y * height)) for x, y in self.points]
  mask = Image.new("L", (width, height), 0)