diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index e1071121c..afe64ace0 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -28,26 +28,28 @@ Annotators accept detections and apply box or mask visualizations to the detecti
-=== "RoundBox"
+
=== "BoxCorner"
@@ -234,7 +236,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -255,7 +258,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -283,7 +287,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -314,7 +319,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -341,24 +347,32 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
-=== "Crop"
+
=== "Blur"
@@ -377,7 +391,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -398,7 +413,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -429,7 +445,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -458,7 +475,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- { align=center width="800" }
+ { align=center width="800" }
@@ -479,7 +497,31 @@ Annotators accept detections and apply box or mask visualizations to the detecti
- 
+ { align=center width="800" }
+
+
+
+=== "Comparison"
+
+ ```python
+ import supervision as sv
+
+ image = ...
+ detections_1 = sv.Detections(...)
+ detections_2 = sv.Detections(...)
+
+ comparison_annotator = sv.ComparisonAnnotator()
+ annotated_frame = comparison_annotator.annotate(
+ scene=image.copy(),
+ detections_1=detections_1,
+ detections_2=detections_2
+ )
+ ```
+
+
+
+ { align=center width="800" }
@@ -622,6 +664,12 @@ Annotators accept detections and apply box or mask visualizations to the detecti
:::supervision.annotators.core.BackgroundOverlayAnnotator
+
+
+:::supervision.annotators.core.ComparisonAnnotator
+
diff --git a/supervision/__init__.py b/supervision/__init__.py
index e2ed4bb75..2b2a0082d 100644
--- a/supervision/__init__.py
+++ b/supervision/__init__.py
@@ -14,6 +14,7 @@
BoxCornerAnnotator,
CircleAnnotator,
ColorAnnotator,
+ ComparisonAnnotator,
CropAnnotator,
DotAnnotator,
EllipseAnnotator,
@@ -76,6 +77,7 @@
scale_boxes,
xcycwh_to_xyxy,
xywh_to_xyxy,
+ xyxy_to_polygons,
)
from supervision.draw.color import Color, ColorPalette
from supervision.draw.utils import (
@@ -136,6 +138,7 @@
"ColorAnnotator",
"ColorLookup",
"ColorPalette",
+ "ComparisonAnnotator",
"ConfusionMatrix",
"CropAnnotator",
"DetectionDataset",
@@ -222,4 +225,5 @@
"scale_image",
"xcycwh_to_xyxy",
"xywh_to_xyxy",
+ "xyxy_to_polygons",
]
diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 02ab47d6f..a2b2ed565 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -16,10 +16,16 @@
)
from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
from supervision.detection.core import Detections
-from supervision.detection.utils import clip_boxes, mask_to_polygons, spread_out_boxes
+from supervision.detection.utils import (
+ clip_boxes,
+ mask_to_polygons,
+ polygon_to_mask,
+ spread_out_boxes,
+ xyxy_to_polygons,
+)
from supervision.draw.color import Color, ColorPalette
-from supervision.draw.utils import draw_polygon
-from supervision.geometry.core import Position
+from supervision.draw.utils import draw_polygon, draw_rounded_rectangle, draw_text
+from supervision.geometry.core import Point, Position, Rect
from supervision.utils.conversion import (
ensure_cv2_image_for_annotation,
ensure_pil_image_for_annotation,
@@ -2527,6 +2533,9 @@ def annotate(
detections=detections
)
```
+
+ 
"""
assert isinstance(scene, np.ndarray)
crops = [
@@ -2683,3 +2692,261 @@ def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
np.copyto(scene, colored_mask)
return scene
+
+
+class ComparisonAnnotator:
+ """
+ Highlights the differences between two sets of detections.
+ Useful for comparing results from two different models, or the difference
+ between a ground truth and a prediction.
+
+ If present, uses the oriented bounding box data.
+ Otherwise, if present, uses a mask.
+ Otherwise, uses the bounding box data.
+ """
+
+ def __init__(
+ self,
+ color_1: Color = Color.RED,
+ color_2: Color = Color.GREEN,
+ color_overlap: Color = Color.BLUE,
+ *,
+ opacity: float = 0.75,
+ label_1: str = "",
+ label_2: str = "",
+ label_overlap: str = "",
+ label_scale: float = 1.0,
+ ):
+ """
+ Args:
+ color_1 (Color): Color of areas only present in the first set of
+ detections.
+ color_2 (Color): Color of areas only present in the second set of
+ detections.
+ color_overlap (Color): Color of areas present in both sets of detections.
+ opacity (float): Annotator opacity, from `0` to `1`.
+ label_1 (str): Label for the first set of detections.
+ label_2 (str): Label for the second set of detections.
+ label_overlap (str): Label for areas present in both sets of detections.
+ label_scale (float): Controls how large the labels are.
+ """
+
+ self.color_1 = color_1
+ self.color_2 = color_2
+ self.color_overlap = color_overlap
+
+ self.opacity = opacity
+ self.label_1 = label_1
+ self.label_2 = label_2
+ self.label_overlap = label_overlap
+ self.label_scale = label_scale
+ self.text_thickness = int(self.label_scale + 1.2)
+
+ @ensure_cv2_image_for_annotation
+ def annotate(
+ self, scene: ImageType, detections_1: Detections, detections_2: Detections
+ ) -> ImageType:
+ """
+ Highlights the differences between two sets of detections.
+
+ Args:
+ scene (ImageType): The image where detections will be drawn.
+ `ImageType` is a flexible type, accepting either `numpy.ndarray`
+ or `PIL.Image.Image`.
+ detections_1 (Detections): The first set of detections or predictions.
+ detections_2 (Detections): The second set of detections to compare or
+ ground truth.
+
+ Returns:
+ The annotated image.
+
+ Example:
+ ```python
+ import supervision as sv
+
+ image = ...
+ detections_1 = sv.Detections(...)
+ detections_2 = sv.Detections(...)
+
+ comparison_annotator = sv.ComparisonAnnotator()
+ annotated_frame = comparison_annotator.annotate(
+ scene=image.copy(),
+ detections_1=detections_1,
+ detections_2=detections_2
+ )
+ ```
+
+ 
+ """
+ assert isinstance(scene, np.ndarray)
+ if detections_1.is_empty() and detections_2.is_empty():
+ return scene
+
+ use_obb = self._use_obb(detections_1, detections_2)
+ use_mask = self._use_mask(detections_1, detections_2)
+
+ if use_obb:
+ mask_1 = self._mask_from_obb(scene, detections_1)
+ mask_2 = self._mask_from_obb(scene, detections_2)
+
+ elif use_mask:
+ mask_1 = self._mask_from_mask(scene, detections_1)
+ mask_2 = self._mask_from_mask(scene, detections_2)
+
+ else:
+ mask_1 = self._mask_from_xyxy(scene, detections_1)
+ mask_2 = self._mask_from_xyxy(scene, detections_2)
+
+ mask_overlap = mask_1 & mask_2
+ mask_1 = mask_1 & ~mask_overlap
+ mask_2 = mask_2 & ~mask_overlap
+
+ color_layer = np.zeros_like(scene, dtype=np.uint8)
+ color_layer[mask_overlap] = self.color_overlap.as_bgr()
+ color_layer[mask_1] = self.color_1.as_bgr()
+ color_layer[mask_2] = self.color_2.as_bgr()
+
+ scene[mask_overlap] = (1 - self.opacity) * scene[
+ mask_overlap
+ ] + self.opacity * color_layer[mask_overlap]
+ scene[mask_1] = (1 - self.opacity) * scene[mask_1] + self.opacity * color_layer[
+ mask_1
+ ]
+ scene[mask_2] = (1 - self.opacity) * scene[mask_2] + self.opacity * color_layer[
+ mask_2
+ ]
+
+ self._draw_labels(scene)
+
+ return scene
+
+ @staticmethod
+ def _use_obb(detections_1: Detections, detections_2: Detections) -> bool:
+ assert not detections_1.is_empty() or not detections_2.is_empty()
+ is_obb_1 = ORIENTED_BOX_COORDINATES in detections_1.data
+ is_obb_2 = ORIENTED_BOX_COORDINATES in detections_2.data
+ return (
+ (is_obb_1 and is_obb_2)
+ or (is_obb_1 and detections_2.is_empty())
+ or (detections_1.is_empty() and is_obb_2)
+ )
+
+ @staticmethod
+ def _use_mask(detections_1: Detections, detections_2: Detections) -> bool:
+ assert not detections_1.is_empty() or not detections_2.is_empty()
+ is_mask_1 = detections_1.mask is not None
+ is_mask_2 = detections_2.mask is not None
+ return (
+ (is_mask_1 and is_mask_2)
+ or (is_mask_1 and detections_2.is_empty())
+ or (detections_1.is_empty() and is_mask_2)
+ )
+
+ @staticmethod
+ def _mask_from_xyxy(scene: np.ndarray, detections: Detections) -> np.ndarray:
+ mask = np.zeros(scene.shape[:2], dtype=np.bool_)
+ if detections.is_empty():
+ return mask
+
+ resolution_wh = scene.shape[1], scene.shape[0]
+ polygons = xyxy_to_polygons(detections.xyxy)
+
+ for polygon in polygons:
+ polygon_mask = polygon_to_mask(polygon, resolution_wh=resolution_wh)
+ mask |= polygon_mask.astype(np.bool_)
+ return mask
+
+ @staticmethod
+ def _mask_from_obb(scene: np.ndarray, detections: Detections) -> np.ndarray:
+ mask = np.zeros(scene.shape[:2], dtype=np.bool_)
+ if detections.is_empty():
+ return mask
+
+ resolution_wh = scene.shape[1], scene.shape[0]
+
+ for polygon in detections.data[ORIENTED_BOX_COORDINATES]:
+ polygon_mask = polygon_to_mask(polygon, resolution_wh=resolution_wh)
+ mask |= polygon_mask.astype(np.bool_)
+ return mask
+
+ @staticmethod
+ def _mask_from_mask(scene: np.ndarray, detections: Detections) -> np.ndarray:
+ mask = np.zeros(scene.shape[:2], dtype=np.bool_)
+ if detections.is_empty():
+ return mask
+ assert detections.mask is not None
+
+ for detections_mask in detections.mask:
+ mask |= detections_mask.astype(np.bool_)
+ return mask
+
+ def _draw_labels(self, scene: np.ndarray) -> None:
+ """
+ Draw the labels, explaining what each color represents, with automatically
+ computed positions.
+
+ Args:
+ scene (np.ndarray): The image where the labels will be drawn.
+ """
+ margin = int(50 * self.label_scale)
+ gap = int(40 * self.label_scale)
+ y0 = int(50 * self.label_scale)
+ height = int(50 * self.label_scale)
+
+ marker_size = int(20 * self.label_scale)
+ padding = int(10 * self.label_scale)
+ text_box_corner_radius = int(10 * self.label_scale)
+ marker_corner_radius = int(4 * self.label_scale)
+ text_scale = self.label_scale
+
+ label_color_pairs = [
+ (self.label_1, self.color_1),
+ (self.label_2, self.color_2),
+ (self.label_overlap, self.color_overlap),
+ ]
+
+ x0 = margin
+ for text, color in label_color_pairs:
+ if not text:
+ continue
+
+ (text_w, _) = cv2.getTextSize(
+ text=text,
+ fontFace=CV2_FONT,
+ fontScale=self.label_scale,
+ thickness=self.text_thickness,
+ )[0]
+
+ width = text_w + marker_size + padding * 4
+ center_x = x0 + width // 2
+ center_y = y0 + height // 2
+
+ draw_rounded_rectangle(
+ scene=scene,
+ rect=Rect(x=x0, y=y0, width=width, height=height),
+ color=Color.WHITE,
+ border_radius=text_box_corner_radius,
+ )
+
+ draw_rounded_rectangle(
+ scene=scene,
+ rect=Rect(
+ x=x0 + padding,
+ y=center_y - marker_size / 2,
+ width=marker_size,
+ height=marker_size,
+ ),
+ color=color,
+ border_radius=marker_corner_radius,
+ )
+
+ draw_text(
+ scene,
+ text,
+ text_anchor=Point(x=center_x + marker_size, y=center_y),
+ text_scale=text_scale,
+ text_thickness=self.text_thickness,
+ )
+
+ x0 += width + gap
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index f6bcd33bc..a2cbd87bd 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -11,6 +11,25 @@
MIN_POLYGON_POINT_COUNT = 3
+def xyxy_to_polygons(box: np.ndarray) -> np.ndarray:
+ """
+ Convert an array of boxes to an array of polygons.
+ Retains the input datatype.
+
+ Args:
+ box (np.ndarray): An array of boxes (N, 4), where each box is represented as a
+ list of four coordinates in the format `(x_min, y_min, x_max, y_max)`.
+
+ Returns:
+ np.ndarray: An array of polygons (N, 4, 2), where each polygon is
+ represented as a list of four coordinates in the format `(x, y)`.
+ """
+ polygon = np.zeros((box.shape[0], 4, 2), dtype=box.dtype)
+ polygon[:, :, 0] = box[:, [0, 2, 2, 0]]
+ polygon[:, :, 1] = box[:, [1, 1, 3, 3]]
+ return polygon
+
+
def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
"""Generate a mask from a polygon.