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Merge pull request #1258 from roboflow/develop 

`supervision-0.21.0` release
This commit is contained in:
Piotr Skalski 2024-06-06 08:11:08 +02:00 committed by GitHub
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2
.pre-commit-config.yaml
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@ -45,7 +45,7 @@ repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.4.1
rev: v0.4.7
hooks:
- id: ruff
args: [--fix, --exit-non-zero-on-fix]

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docs/changelog.md
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@ -1,3 +1,83 @@
### 0.21.0 <small>Jun 5, 2024</small>
- Added [#500](https://github.com/roboflow/supervision/pull/500): [`sv.Detections.with_nmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.with_nmm) to perform non-maximum merging on the current set of object detections.
- Added [#1221](https://github.com/roboflow/supervision/pull/1221): [`sv.Detections.from_lmm`](https://supervision.roboflow.com/develop/detection/core/#supervision.detection.core.Detections.from_lmm) allowing to parse Large Multimodal Model (LMM) text result into [`sv.Detections`](https://supervision.roboflow.com/develop/detection/core/) object. For now `from_lmm` supports only [PaliGemma](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb) result parsing.
```python
import supervision as sv
paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_lmm(
sv.LMM.PALIGEMMA,
paligemma_result,
resolution_wh=(1000, 1000),
classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])
detections.class_id
# array([0])
```
- Added [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.VertexLabelAnnotator`](https://supervision.roboflow.com/develop/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator.annotate) allowing to annotate every vertex of a keypoint skeleton with custom text and color.
```python
import supervision as sv
image = ...
key_points = sv.KeyPoints(...)
edge_annotator = sv.EdgeAnnotator(
color=sv.Color.GREEN,
thickness=5
)
annotated_frame = edge_annotator.annotate(
scene=image.copy(),
key_points=key_points
)
```
- Added [#1147](https://github.com/roboflow/supervision/pull/1147): [`sv.KeyPoints.from_inference`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints) from [Inference](https://github.com/roboflow/inference) result.
- Added [#1138](https://github.com/roboflow/supervision/pull/1138): [`sv.KeyPoints.from_yolo_nas`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints.from_yolo_nas) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/develop/keypoint/core/#supervision.keypoint.core.KeyPoints) from [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) result.
- Added [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.mask_to_rle`](https://supervision.roboflow.com/develop/datasets/utils/#supervision.dataset.utils.rle_to_mask) and [`sv.rle_to_mask`](https://supervision.roboflow.com/develop/datasets/utils/#supervision.dataset.utils.rle_to_mask) allowing for easy conversion between mask and rle formats.
- Changed [#1236](https://github.com/roboflow/supervision/pull/1236): [`sv.InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) allowing to select overlap filtering strategy (`NONE`, `NON_MAX_SUPPRESSION` and `NON_MAX_MERGE`).
- Changed [#1178](https://github.com/roboflow/supervision/pull/1178): [`sv.InferenceSlicer`](https://supervision.roboflow.com/develop/detection/tools/inference_slicer/) adding instance segmentation model support.
```python
import cv2
import numpy as np
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8x-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
def callback(image_slice: np.ndarray) -> sv.Detections:
results = model.infer(image_slice)[0]
return sv.Detections.from_inference(results)
slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)
mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
```
- Changed [#1228](https://github.com/roboflow/supervision/pull/1228): [`sv.LineZone`](https://supervision.roboflow.com/develop/detection/tools/line_zone/) making it 10-20 times faster, depending on the use case.
- Changed [#1163](https://github.com/roboflow/supervision/pull/1163): [`sv.DetectionDataset.from_coco`](https://supervision.roboflow.com/develop/datasets/core/#supervision.dataset.core.DetectionDataset.from_coco) and [`sv.DetectionDataset.as_coco`](https://supervision.roboflow.com/develop/datasets/core/#supervision.dataset.core.DetectionDataset.as_coco) adding support for run-length encoding (RLE) mask format.
### 0.20.0 <small>April 24, 2024</small>
- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.KeyPoints`](/0.20.0/keypoint/core/#supervision.keypoint.core.KeyPoints) to provide initial support for pose estimation and broader keypoint detection models.

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---
template: cookbooks.html
comments: true
status: new
hide:
- navigation
- toc

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---
comments: true
status: new
---
# Datasets

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---
comments: true
status: new
---
# Datasets Utils
<div class="md-typeset">
<h2><a href="#supervision.dataset.utils.rle_to_mask">rle_to_mask</a></h2>
</div>
:::supervision.dataset.utils.rle_to_mask
<div class="md-typeset">
<h2><a href="#supervision.dataset.utils.mask_to_rle">mask_to_rle</a></h2>
</div>
:::supervision.dataset.utils.mask_to_rle

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@ -1,6 +1,5 @@
---
comments: true
status: new
---
# Annotators
@ -285,6 +284,37 @@ status: new
</div>
=== "RichLabel"
```python
import supervision as sv
image = ...
detections = sv.Detections(...)
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence
in zip(detections['class_name'], detections.confidence)
]
rich_label_annotator = sv.RichLabelAnnotator(
font_path=".../font.ttf",
text_position=sv.Position.CENTER
)
annotated_frame = label_annotator.annotate(
scene=image.copy(),
detections=detections,
labels=labels
)
```
<div class="result" markdown>
![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
</div>
=== "Crop"
```python
@ -492,6 +522,12 @@ status: new
:::supervision.annotators.core.LabelAnnotator
<div class="md-typeset">
<h2><a href="#supervision.annotators.core.RichLabelAnnotator">RichLabelAnnotator</a></h2>
</div>
:::supervision.annotators.core.RichLabelAnnotator
<div class="md-typeset">
<h2><a href="#supervision.annotators.core.BlurAnnotator">BlurAnnotator</a></h2>
</div>

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@ -0,0 +1,30 @@
---
comments: true
status: new
---
# Double Detection Filter
<div class="md-typeset">
<h2><a href="#supervision.detection.overlap_filter.OverlapFilter">OverlapFilter</a></h2>
</div>
:::supervision.detection.overlap_filter.OverlapFilter
<div class="md-typeset">
<h2><a href="#supervision.detection.overlap_filter.box_non_max_suppression">box_non_max_suppression</a></h2>
</div>
:::supervision.detection.overlap_filter.box_non_max_suppression
<div class="md-typeset">
<h2><a href="#supervision.detection.overlap_filter.mask_non_max_suppression">mask_non_max_suppression</a></h2>
</div>
:::supervision.detection.overlap_filter.mask_non_max_suppression
<div class="md-typeset">
<h2><a href="#supervision.detection.overlap_filter.box_non_max_merge">box_non_max_merge</a></h2>
</div>
:::supervision.detection.overlap_filter.box_non_max_merge

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---
comments: true
status: new
---
# InferenceSlicer

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---
comments: true
status: new
---
<div class="md-typeset">

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---
comments: true
status: new
---
# Save Detections

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---
comments: true
status: new
---
# Detection Utils
@ -17,18 +16,6 @@ status: new
:::supervision.detection.utils.mask_iou_batch
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.box_non_max_suppression">box_non_max_suppression</a></h2>
</div>
:::supervision.detection.utils.box_non_max_suppression
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.mask_non_max_suppression">mask_non_max_suppression</a></h2>
</div>
:::supervision.detection.utils.mask_non_max_suppression
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.polygon_to_mask">polygon_to_mask</a></h2>
</div>
@ -65,8 +52,38 @@ status: new
:::supervision.detection.utils.move_boxes
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.move_masks">move_masks</a></h2>
</div>
:::supervision.detection.utils.move_masks
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.scale_boxes">scale_boxes</a></h2>
</div>
:::supervision.detection.utils.scale_boxes
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.clip_boxes">clip_boxes</a></h2>
</div>
:::supervision.detection.utils.clip_boxes
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.pad_boxes">pad_boxes</a></h2>
</div>
:::supervision.detection.utils.pad_boxes
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.contains_holes">contains_holes</a></h2>
</div>
:::supervision.detection.utils.contains_holes
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.contains_multiple_segments">contains_multiple_segments</a></h2>
</div>
:::supervision.detection.utils.contains_multiple_segments

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---
comments: true
status: new
---
# Detect and Annotate

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docs/how_to/detect_small_objects.md
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@ -6,7 +6,7 @@ status: new
# Detect Small Objects
This guide shows how to detect small objects
with the [Inference](https://github.com/roboflow/inference),
with the [Inference](https://github.com/roboflow/inference),
[Ultralytics](https://github.com/ultralytics/ultralytics) or
[Transformers](https://github.com/huggingface/transformers) packages using
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
@ -68,10 +68,10 @@ size relative to the image resolution.
import torch
import supervision as sv
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection
from transformers import DetrImageProcessor, DetrForSegmentation
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
inputs = processor(images=image, return_tensors="pt")
@ -79,8 +79,8 @@ size relative to the image resolution.
with torch.no_grad():
outputs = model(**inputs)
width, height = image.size
target_size = torch.tensor([[height, width]])
width, height = image_slice.size
target_size = torch.tensor([[width, height]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
detections = sv.Detections.from_transformers(results)
@ -175,7 +175,7 @@ objects within each, and aggregating the results.
def callback(image_slice: np.ndarray) -> sv.Detections:
results = model.infer(image_slice)[0]
detections = sv.Detections.from_inference(results)
return sv.Detections.from_inference(results)
slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)
@ -239,8 +239,8 @@ objects within each, and aggregating the results.
with torch.no_grad():
outputs = model(**inputs)
width, height = image.size
target_size = torch.tensor([[height, width]])
width, height = image_slice.size
target_size = torch.tensor([[width, height]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
return sv.Detections.from_transformers(results)
@ -264,3 +264,63 @@ objects within each, and aggregating the results.
```
![detection-with-inference-slicer](https://media.roboflow.com/supervision_detect_small_objects_example_3.png)
## Small Object Segmentation
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
=== "Inference"
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8x-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
def callback(image_slice: np.ndarray) -> sv.Detections:
results = model.infer(image_slice)[0]
return sv.Detections.from_inference(results)
slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)
mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
```
=== "Ultralytics"
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8x-seg.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]
return sv.Detections.from_ultralytics(result)
slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)
mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
```
![detection-with-inference-slicer](https://media.roboflow.com/supervision-docs/inference-slicer-segmentation-example.png)

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@ -1,6 +1,5 @@
---
comments: true
status: new
---
# Save Detections

41
docs/keypoint/annotators.md
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@ -13,7 +13,10 @@ status: new
image = ...
key_points = sv.KeyPoints(...)
vertex_annotator = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
vertex_annotator = sv.VertexAnnotator(
color=sv.Color.GREEN,
radius=10
)
annotated_frame = vertex_annotator.annotate(
scene=image.copy(),
key_points=key_points
@ -34,7 +37,10 @@ status: new
image = ...
key_points = sv.KeyPoints(...)
edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
edge_annotator = sv.EdgeAnnotator(
color=sv.Color.GREEN,
thickness=5
)
annotated_frame = edge_annotator.annotate(
scene=image.copy(),
key_points=key_points
@ -47,6 +53,31 @@ status: new
</div>
=== "VertexLabelAnnotator"
```python
import supervision as sv
image = ...
key_points = sv.KeyPoints(...)
vertex_label_annotator = sv.VertexLabelAnnotator(
color=sv.Color.GREEN,
text_color=sv.Color.BLACK,
border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
scene=image.copy(),
key_points=key_points
)
```
<div class="result" markdown>
![vertex-label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/vertex-label-annotator-example.png){ align=center width="800" }
</div>
<div class="md-typeset">
<h2><a href="#supervision.keypoint.annotators.VertexAnnotator">VertexAnnotator</a></h2>
</div>
@ -58,3 +89,9 @@ status: new
</div>
:::supervision.keypoint.annotators.EdgeAnnotator
<div class="md-typeset">
<h2><a href="#supervision.keypoint.annotators.VertexLabelAnnotator">VertexLabelAnnotator</a></h2>
</div>
:::supervision.keypoint.annotators.VertexLabelAnnotator

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---
comments: true
status: new
---
# ByteTrack

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docs/utils/draw.md
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@ -41,7 +41,7 @@ comments: true
:::supervision.draw.utils.draw_image
<div class="md-typeset">
<h2><a href="#supervision.draw.utils.calculate_optimal_font_scale">calculate_optimal_font_scale</a></h2>
<h2><a href="#supervision.draw.utils.calculate_optimal_text_scale">calculate_optimal_text_scale</a></h2>
</div>
:::supervision.draw.utils.calculate_optimal_text_scale

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@ -1,6 +1,5 @@
---
comments: true
status: new
---
# Image Utils
@ -12,7 +11,7 @@ status: new
:::supervision.utils.image.crop_image
<div class="md-typeset">
<h2><a href="#supervision.utils.image.scale_image">crop_image</a></h2>
<h2><a href="#supervision.utils.image.scale_image">scale_image</a></h2>
</div>
:::supervision.utils.image.scale_image

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docs/utils/iterables.md
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@ -1,6 +1,5 @@
---
comments: true
status: new
---
# Iterables Utils

6
examples/count_people_in_zone/inference_example.py
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@ -38,15 +38,15 @@ def initiate_annotators(
) -> Tuple[
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
]:
line_thickness = sv.calculate_dynamic_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=resolution_wh)
line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
zones = []
zone_annotators = []
box_annotators = []
for index, polygon in enumerate(polygons):
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
zone = sv.PolygonZone(polygon=polygon)
zone_annotator = sv.PolygonZoneAnnotator(
zone=zone,
color=COLORS.by_idx(index),

4
examples/count_people_in_zone/requirements.txt
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@ -1,5 +1,5 @@
gdown
inference
supervision==0.19.0
inference==0.9.17
supervision>=0.20.0
tqdm
ultralytics

6
examples/count_people_in_zone/ultralytics_example.py
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@ -36,15 +36,15 @@ def initiate_annotators(
) -> Tuple[
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
]:
line_thickness = sv.calculate_dynamic_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=resolution_wh)
line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
zones = []
zone_annotators = []
box_annotators = []
for index, polygon in enumerate(polygons):
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
zone = sv.PolygonZone(polygon=polygon)
zone_annotator = sv.PolygonZoneAnnotator(
zone=zone,
color=COLORS.by_idx(index),

8
examples/speed_estimation/inference_example.py
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@ -98,10 +98,10 @@ if __name__ == "__main__":
frame_rate=video_info.fps, track_thresh=args.confidence_threshold
)
thickness = sv.calculate_dynamic_line_thickness(
thickness = sv.calculate_optimal_line_thickness(
resolution_wh=video_info.resolution_wh
)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
label_annotator = sv.LabelAnnotator(
text_scale=text_scale,
@ -116,9 +116,7 @@ if __name__ == "__main__":
frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
polygon_zone = sv.PolygonZone(
polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
)
polygon_zone = sv.PolygonZone(polygon=SOURCE)
view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

6
examples/speed_estimation/requirements.txt
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@ -1,6 +1,6 @@
supervision==0.19.0
tqdm==4.66.1
supervision>=0.20.0
tqdm==4.66.3
requests
ultralytics==8.0.237
super-gradients==3.5.0
inference==0.9.8
inference==0.9.17

8
examples/speed_estimation/ultralytics_example.py
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@ -76,10 +76,10 @@ if __name__ == "__main__":
frame_rate=video_info.fps, track_thresh=args.confidence_threshold
)
thickness = sv.calculate_dynamic_line_thickness(
thickness = sv.calculate_optimal_line_thickness(
resolution_wh=video_info.resolution_wh
)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
label_annotator = sv.LabelAnnotator(
text_scale=text_scale,
@ -94,9 +94,7 @@ if __name__ == "__main__":
frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
polygon_zone = sv.PolygonZone(
polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
)
polygon_zone = sv.PolygonZone(polygon=SOURCE)
view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

8
examples/speed_estimation/yolo_nas_example.py
View File

@ -77,10 +77,10 @@ if __name__ == "__main__":
frame_rate=video_info.fps, track_thresh=args.confidence_threshold
)
thickness = sv.calculate_dynamic_line_thickness(
thickness = sv.calculate_optimal_line_thickness(
resolution_wh=video_info.resolution_wh
)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
label_annotator = sv.LabelAnnotator(
text_scale=text_scale,
@ -95,9 +95,7 @@ if __name__ == "__main__":
frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
polygon_zone = sv.PolygonZone(
polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
)
polygon_zone = sv.PolygonZone(polygon=SOURCE)
view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

14
examples/time_in_zone/README.md
View File

@ -103,7 +103,7 @@ python scripts/draw_zones.py \
```bash
python scripts/draw_zones.py \
--source_path "data/traffic/video.mp4" \
--zone_configuration_path "data/traffic/custom_config.json"
--zone_configuration_path "data/traffic/config.json"
```
https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-6ac1ad6ae5ac
@ -157,7 +157,7 @@ Script to run object detection on a video stream using the Roboflow Inference mo
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
```bash
python inference_file_example.py \
python inference_stream_example.py \
--zone_configuration_path "data/checkout/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--model_id "yolov8x-640" \
@ -167,7 +167,7 @@ python inference_file_example.py \
```
```bash
python inference_file_example.py \
python inference_stream_example.py \
--zone_configuration_path "data/traffic/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--model_id "yolov8x-640" \
@ -192,7 +192,7 @@ Script to run object detection on a video file using the Ultralytics YOLOv8 mode
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
```bash
python inference_file_example.py \
python ultralytics_file_example.py \
--zone_configuration_path "data/checkout/config.json" \
--source_video_path "data/checkout/video.mp4" \
--weights "yolov8x.pt" \
@ -203,7 +203,7 @@ python inference_file_example.py \
```
```bash
python inference_file_example.py \
python ultralytics_file_example.py \
--zone_configuration_path "data/traffic/config.json" \
--source_video_path "data/traffic/video.mp4" \
--weights "yolov8x.pt" \
@ -226,7 +226,7 @@ Script to run object detection on a video stream using the Ultralytics YOLOv8 mo
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
```bash
python inference_file_example.py \
python ultralytics_stream_example.py \
--zone_configuration_path "data/checkout/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--weights "yolov8x.pt" \
@ -237,7 +237,7 @@ python inference_file_example.py \
```
```bash
python inference_file_example.py \
python ultralytics_stream_example.py \
--zone_configuration_path "data/traffic/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--weights "yolov8x.pt" \

4
examples/time_in_zone/inference_file_example.py
View File

@ -29,14 +29,10 @@ def main(
video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
frames_generator = sv.get_video_frames_generator(source_video_path)
frame = next(frames_generator)
resolution_wh = frame.shape[1], frame.shape[0]
polygons = load_zones_config(file_path=zone_configuration_path)
zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in polygons

4
examples/time_in_zone/inference_naive_stream_example.py
View File

@ -29,14 +29,10 @@ def main(
frames_generator = get_stream_frames_generator(rtsp_url=rtsp_url)
fps_monitor = sv.FPSMonitor()
frame = next(frames_generator)
resolution_wh = frame.shape[1], frame.shape[0]
polygons = load_zones_config(file_path=zone_configuration_path)
zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in polygons

19
examples/time_in_zone/inference_stream_example.py
View File

@ -24,20 +24,15 @@ class CustomSink:
self.fps_monitor = sv.FPSMonitor()
self.polygons = load_zones_config(file_path=zone_configuration_path)
self.timers = [ClockBasedTimer() for _ in self.polygons]
self.zones = None
self.zones = [
sv.PolygonZone(
polygon=polygon,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in self.polygons
]
def on_prediction(self, result: dict, frame: VideoFrame) -> None:
if self.zones is None:
resolution_wh = frame.image.shape[1], frame.image.shape[0]
self.zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in self.polygons
]
self.fps_monitor.tick()
fps = self.fps_monitor.fps

4
examples/time_in_zone/requirements.txt
View File

@ -1,5 +1,5 @@
opencv-python
supervision
supervision>=0.20.0
ultralytics
inference
inference==0.9.17
pytube

4
examples/time_in_zone/ultralytics_file_example.py
View File

@ -30,14 +30,10 @@ def main(
video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
frames_generator = sv.get_video_frames_generator(source_video_path)
frame = next(frames_generator)
resolution_wh = frame.shape[1], frame.shape[0]
polygons = load_zones_config(file_path=zone_configuration_path)
zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in polygons

4
examples/time_in_zone/ultralytics_naive_stream_example.py
View File

@ -30,14 +30,10 @@ def main(
frames_generator = get_stream_frames_generator(rtsp_url=rtsp_url)
fps_monitor = sv.FPSMonitor()
frame = next(frames_generator)
resolution_wh = frame.shape[1], frame.shape[0]
polygons = load_zones_config(file_path=zone_configuration_path)
zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in polygons

19
examples/time_in_zone/ultralytics_stream_example.py
View File

@ -25,20 +25,15 @@ class CustomSink:
self.fps_monitor = sv.FPSMonitor()
self.polygons = load_zones_config(file_path=zone_configuration_path)
self.timers = [ClockBasedTimer() for _ in self.polygons]
self.zones = None
self.zones = [
sv.PolygonZone(
polygon=polygon,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in self.polygons
]
def on_prediction(self, detections: sv.Detections, frame: VideoFrame) -> None:
if self.zones is None:
resolution_wh = frame.image.shape[1], frame.image.shape[0]
self.zones = [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=resolution_wh,
triggering_anchors=(sv.Position.CENTER,),
)
for polygon in self.polygons
]
self.fps_monitor.tick()
fps = self.fps_monitor.fps

2
examples/tracking/requirements.txt
View File

@ -1,4 +1,4 @@
inference
inference==0.9.17
supervision==0.19.0
tqdm
ultralytics

12
examples/traffic_analysis/inference_example.py
View File

@ -1,6 +1,6 @@
import argparse
import os
from typing import Dict, Iterable, List, Set, Tuple
from typing import Dict, Iterable, List, Set
import cv2
import numpy as np
@ -60,13 +60,11 @@ class DetectionsManager:
def initiate_polygon_zones(
polygons: List[np.ndarray],
frame_resolution_wh: Tuple[int, int],
triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
) -> List[sv.PolygonZone]:
return [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=frame_resolution_wh,
triggering_anchors=triggering_anchors,
)
for polygon in polygons
@ -92,12 +90,8 @@ class VideoProcessor:
self.tracker = sv.ByteTrack()
self.video_info = sv.VideoInfo.from_video_path(source_video_path)
self.zones_in = initiate_polygon_zones(
ZONE_IN_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
)
self.zones_out = initiate_polygon_zones(
ZONE_OUT_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
)
self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])
self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
self.label_annotator = sv.LabelAnnotator(

4
examples/traffic_analysis/requirements.txt
View File

@ -1,5 +1,5 @@
gdown
inference
supervision>=0.19.0
inference==0.9.17
supervision>=0.20.0
tqdm
ultralytics

12
examples/traffic_analysis/ultralytics_example.py
View File

@ -1,5 +1,5 @@
import argparse
from typing import Dict, Iterable, List, Set, Tuple
from typing import Dict, Iterable, List, Set
import cv2
import numpy as np
@ -58,13 +58,11 @@ class DetectionsManager:
def initiate_polygon_zones(
polygons: List[np.ndarray],
frame_resolution_wh: Tuple[int, int],
triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
) -> List[sv.PolygonZone]:
return [
sv.PolygonZone(
polygon=polygon,
frame_resolution_wh=frame_resolution_wh,
triggering_anchors=triggering_anchors,
)
for polygon in polygons
@ -89,12 +87,8 @@ class VideoProcessor:
self.tracker = sv.ByteTrack()
self.video_info = sv.VideoInfo.from_video_path(source_video_path)
self.zones_in = initiate_polygon_zones(
ZONE_IN_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
)
self.zones_out = initiate_polygon_zones(
ZONE_OUT_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
)
self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])
self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
self.label_annotator = sv.LabelAnnotator(

15
mkdocs.yml
View File

@ -35,19 +35,20 @@ extra_css:
nav:
- Home: index.md
- How to:
- Supervision: index.md
- Learn:
- Detect and Annotate: how_to/detect_and_annotate.md
- Save Detections: how_to/save_detections.md
- Filter Detections: how_to/filter_detections.md
- Detect Small Objects: how_to/detect_small_objects.md
- Track Objects: how_to/track_objects.md
- Track Objects on Video: how_to/track_objects.md
- API:
- Reference - Code API:
- Detection and Segmentation:
- Core: detection/core.md
- Annotators: detection/annotators.md
- Metrics: detection/metrics.md
- Double Detection Filter: detection/double_detection_filter.md
- Utils: detection/utils.md
- Keypoint Detection:
- Core: keypoint/core.md
@ -61,7 +62,9 @@ nav:
- Detection Smoother: detection/tools/smoother.md
- Save Detections: detection/tools/save_detections.md
- Trackers: trackers.md
- Datasets: datasets.md
- Datasets:
- Core: datasets/core.md
- Utils: datasets/utils.md
- Utils:
- Video: utils/video.md
- Image: utils/image.md
@ -76,7 +79,7 @@ nav:
- Contributing: contributing.md
- Code of Conduct: code_of_conduct.md
- License: license.md
- Changelog:
- Release Notes:
- Changelog: changelog.md
- Deprecated: deprecated.md

300
poetry.lock generated
View File

@ -1253,21 +1253,21 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa
[[package]]
name = "ipywidgets"
version = "8.1.2"
version = "8.1.3"
description = "Jupyter interactive widgets"
optional = false
python-versions = ">=3.7"
files = [
{file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
{file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
{file = "ipywidgets-8.1.3-py3-none-any.whl", hash = "sha256:efafd18f7a142248f7cb0ba890a68b96abd4d6e88ddbda483c9130d12667eaf2"},
{file = "ipywidgets-8.1.3.tar.gz", hash = "sha256:f5f9eeaae082b1823ce9eac2575272952f40d748893972956dc09700a6392d9c"},
]
[package.dependencies]
comm = ">=0.1.3"
ipython = ">=6.1.0"
jupyterlab-widgets = ">=3.0.10,<3.1.0"
jupyterlab-widgets = ">=3.0.11,<3.1.0"
traitlets = ">=4.3.1"
widgetsnbextension = ">=4.0.10,<4.1.0"
widgetsnbextension = ">=4.0.11,<4.1.0"
[package.extras]
test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@ -1340,13 +1340,13 @@ trio = ["async_generator", "trio"]
[[package]]
name = "jinja2"
version = "3.1.3"
version = "3.1.4"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
files = [
{file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
{file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
{file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
{file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
]
[package.dependencies]
@ -1566,13 +1566,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
[[package]]
name = "jupyterlab"
version = "4.1.2"
version = "4.2.0"
description = "JupyterLab computational environment"
optional = false
python-versions = ">=3.8"
files = [
{file = "jupyterlab-4.1.2-py3-none-any.whl", hash = "sha256:aa88193f03cf4d3555f6712f04d74112b5eb85edd7d222c588c7603a26d33c5b"},
{file = "jupyterlab-4.1.2.tar.gz", hash = "sha256:5d6348b3ed4085181499f621b7dfb6eb0b1f57f3586857aadfc8e3bf4c4885f9"},
{file = "jupyterlab-4.2.0-py3-none-any.whl", hash = "sha256:0dfe9278e25a145362289c555d9beb505697d269c10e99909766af7c440ad3cc"},
{file = "jupyterlab-4.2.0.tar.gz", hash = "sha256:356e9205a6a2ab689c47c8fe4919dba6c076e376d03f26baadc05748c2435dd5"},
]
[package.dependencies]
@ -1580,23 +1580,24 @@ async-lru = ">=1.0.0"
httpx = ">=0.25.0"
importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
ipykernel = "*"
ipykernel = ">=6.5.0"
jinja2 = ">=3.0.3"
jupyter-core = "*"
jupyter-lsp = ">=2.0.0"
jupyter-server = ">=2.4.0,<3"
jupyterlab-server = ">=2.19.0,<3"
jupyterlab-server = ">=2.27.1,<3"
notebook-shim = ">=0.2"
packaging = "*"
tomli = {version = "*", markers = "python_version < \"3.11\""}
tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
tornado = ">=6.2.0"
traitlets = "*"
[package.extras]
dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
upgrade-extension = ["copier (>=8,<10)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"]
[[package]]
name = "jupyterlab-pygments"
@ -1611,13 +1612,13 @@ files = [
[[package]]
name = "jupyterlab-server"
version = "2.25.3"
version = "2.27.1"
description = "A set of server components for JupyterLab and JupyterLab like applications."
optional = false
python-versions = ">=3.8"
files = [
{file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
{file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
{file = "jupyterlab_server-2.27.1-py3-none-any.whl", hash = "sha256:f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75"},
{file = "jupyterlab_server-2.27.1.tar.gz", hash = "sha256:097b5ac709b676c7284ac9c5e373f11930a561f52cd5a86e4fc7e5a9c8a8631d"},
]
[package.dependencies]
@ -1633,28 +1634,28 @@ requests = ">=2.31"
[package.extras]
docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
[[package]]
name = "jupyterlab-widgets"
version = "3.0.10"
version = "3.0.11"
description = "Jupyter interactive widgets for JupyterLab"
optional = false
python-versions = ">=3.7"
files = [
{file = "jupyterlab_widgets-3.0.10-py3-none-any.whl", hash = "sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64"},
{file = "jupyterlab_widgets-3.0.10.tar.gz", hash = "sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0"},
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]
[[package]]
name = "jupytext"
version = "1.16.1"
version = "1.16.2"
description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
optional = false
python-versions = ">=3.8"
files = [
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toml = "*"
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pyyaml-env-tag = "*"
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@ -4219,13 +4227,13 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
[[package]]
name = "widgetsnbextension"
version = "4.0.10"
version = "4.0.11"
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
files = [
{file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
{file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
{file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
{file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
]
[[package]]
@ -4250,4 +4258,4 @@ desktop = ["opencv-python"]
[metadata]
lock-version = "2.0"
python-versions = "^3.8"
content-hash = "56ddae6824a9f28c9954badd4c642c57f687b099c6169f97fa0372e294500c17"
content-hash = "e3d79f6c93041323b04c7b45e93bb3c4198b21889044004af8a0485a6145a207"

8
pyproject.toml
View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "supervision"
version = "0.20.0"
version = "0.21.0"
description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@ -42,8 +42,8 @@ pyyaml = ">=5.3"
defusedxml = "^0.7.1"
opencv-python = { version = ">=4.5.5.64", optional = true }
opencv-python-headless = ">=4.5.5.64"
requests = { version = ">=2.26.0,<=2.31.0", optional = true }
tqdm = { version = ">=4.62.3,<=4.66.2", optional = true }
requests = { version = ">=2.26.0,<=2.32.3", optional = true }
tqdm = { version = ">=4.62.3,<=4.66.4", optional = true }
pillow = ">=9.4"
[tool.poetry.extras]
@ -67,7 +67,7 @@ nbconvert = "^7.14.2"
[tool.poetry.group.docs.dependencies]
mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
mkdocstrings = {extras = ["python"], version = ">=0.20,<0.26"}
mike = "^2.0.0"
# For Documentation Development use Python 3.10 or above
# Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support

22
supervision/__init__.py
View File

@ -23,6 +23,7 @@ from supervision.annotators.core import (
PercentageBarAnnotator,
PixelateAnnotator,
PolygonAnnotator,
RichLabelAnnotator,
RoundBoxAnnotator,
TraceAnnotator,
TriangleAnnotator,
@ -34,9 +35,17 @@ from supervision.dataset.core import (
ClassificationDataset,
DetectionDataset,
)
from supervision.dataset.utils import mask_to_rle, rle_to_mask
from supervision.detection.annotate import BoxAnnotator
from supervision.detection.core import Detections
from supervision.detection.line_zone import LineZone, LineZoneAnnotator
from supervision.detection.lmm import LMM
from supervision.detection.overlap_filter import (
OverlapFilter,
box_non_max_merge,
box_non_max_suppression,
mask_non_max_suppression,
)
from supervision.detection.tools.csv_sink import CSVSink
from supervision.detection.tools.inference_slicer import InferenceSlicer
from supervision.detection.tools.json_sink import JSONSink
@ -44,14 +53,17 @@ from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnn
from supervision.detection.tools.smoother import DetectionsSmoother
from supervision.detection.utils import (
box_iou_batch,
box_non_max_suppression,
calculate_masks_centroids,
clip_boxes,
contains_holes,
contains_multiple_segments,
filter_polygons_by_area,
mask_iou_batch,
mask_non_max_suppression,
mask_to_polygons,
mask_to_xyxy,
move_boxes,
move_masks,
pad_boxes,
polygon_to_mask,
polygon_to_xyxy,
scale_boxes,
@ -69,7 +81,11 @@ from supervision.draw.utils import (
)
from supervision.geometry.core import Point, Position, Rect
from supervision.geometry.utils import get_polygon_center
from supervision.keypoint.annotators import EdgeAnnotator, VertexAnnotator
from supervision.keypoint.annotators import (
EdgeAnnotator,
VertexAnnotator,
VertexLabelAnnotator,
)
from supervision.keypoint.core import KeyPoints
from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
from supervision.tracker.byte_tracker.core import ByteTrack

230
supervision/annotators/core.py
View File

@ -3,9 +3,15 @@ from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from supervision.annotators.base import BaseAnnotator, ImageType
from supervision.annotators.utils import ColorLookup, Trace, resolve_color
from supervision.annotators.utils import (
ColorLookup,
Trace,
resolve_color,
resolve_text_background_xyxy,
)
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
@ -936,59 +942,6 @@ class LabelAnnotator:
self.text_anchor: Position = text_position
self.color_lookup: ColorLookup = color_lookup
@staticmethod
def resolve_text_background_xyxy(
center_coordinates: Tuple[int, int],
text_wh: Tuple[int, int],
position: Position,
) -> Tuple[int, int, int, int]:
center_x, center_y = center_coordinates
text_w, text_h = text_wh
if position == Position.TOP_LEFT:
return center_x, center_y - text_h, center_x + text_w, center_y
elif position == Position.TOP_RIGHT:
return center_x - text_w, center_y - text_h, center_x, center_y
elif position == Position.TOP_CENTER:
return (
center_x - text_w // 2,
center_y - text_h,
center_x + text_w // 2,
center_y,
)
elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
return (
center_x - text_w // 2,
center_y - text_h // 2,
center_x + text_w // 2,
center_y + text_h // 2,
)
elif position == Position.BOTTOM_LEFT:
return center_x, center_y, center_x + text_w, center_y + text_h
elif position == Position.BOTTOM_RIGHT:
return center_x - text_w, center_y, center_x, center_y + text_h
elif position == Position.BOTTOM_CENTER:
return (
center_x - text_w // 2,
center_y,
center_x + text_w // 2,
center_y + text_h,
)
elif position == Position.CENTER_LEFT:
return (
center_x - text_w,
center_y - text_h // 2,
center_x,
center_y + text_h // 2,
)
elif position == Position.CENTER_RIGHT:
return (
center_x,
center_y - text_h // 2,
center_x + text_w,
center_y + text_h // 2,
)
@convert_for_annotation_method
def annotate(
self,
@ -1056,9 +1009,11 @@ class LabelAnnotator:
color=self.color,
detections=detections,
detection_idx=detection_idx,
color_lookup=self.color_lookup
if custom_color_lookup is None
else custom_color_lookup,
color_lookup=(
self.color_lookup
if custom_color_lookup is None
else custom_color_lookup
),
)
if labels is not None:
@ -1078,7 +1033,7 @@ class LabelAnnotator:
)[0]
text_w_padded = text_w + 2 * self.text_padding
text_h_padded = text_h + 2 * self.text_padding
text_background_xyxy = self.resolve_text_background_xyxy(
text_background_xyxy = resolve_text_background_xyxy(
center_coordinates=tuple(center_coordinates),
text_wh=(text_w_padded, text_h_padded),
position=self.text_anchor,
@ -1148,6 +1103,165 @@ class LabelAnnotator:
return scene
class RichLabelAnnotator:
"""
A class for annotating labels on an image using provided detections,
with support for Unicode characters by using a custom font.
"""
def __init__(
self,
color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
text_color: Color = Color.WHITE,
font_path: str = None,
font_size: int = 10,
text_padding: int = 10,
text_position: Position = Position.TOP_LEFT,
color_lookup: ColorLookup = ColorLookup.CLASS,
border_radius: int = 0,
):
"""
Args:
color (Union[Color, ColorPalette]): The color or color palette to use for
annotating the text background.
text_color (Color): The color to use for the text.
font_path (str): Path to the font file (e.g., ".ttf" or ".otf") to use for
rendering text. If `None`, the default PIL font will be used.
font_size (int): Font size for the text.
text_padding (int): Padding around the text within its background box.
text_position (Position): Position of the text relative to the detection.
Possible values are defined in the `Position` enum.
color_lookup (ColorLookup): Strategy for mapping colors to annotations.
Options are `INDEX`, `CLASS`, `TRACK`.
border_radius (int): The radius to apply round edges. If the selected
value is higher than the lower dimension, width or height, is clipped.
"""
self.color = color
self.text_color = text_color
self.text_padding = text_padding
self.text_anchor = text_position
self.color_lookup = color_lookup
self.border_radius = border_radius
if font_path is not None:
try:
self.font = ImageFont.truetype(font_path, font_size)
except OSError:
print(f"Font path '{font_path}' not found. Using PIL's default font.")
self.font = ImageFont.load_default(size=font_size)
else:
self.font = ImageFont.load_default(size=font_size)
def annotate(
self,
scene: ImageType,
detections: Detections,
labels: List[str] = None,
custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
"""
Annotates the given scene with labels based on the provided
detections, with support for Unicode characters.
Args:
scene (ImageType): The image where labels will be drawn.
`ImageType` is a flexible type, accepting either `numpy.ndarray`
or `PIL.Image.Image`.
detections (Detections): Object detections to annotate.
labels (List[str]): Optional. Custom labels for each detection.
custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
Allows to override the default color mapping strategy.
Returns:
The annotated image, matching the type of `scene` (`numpy.ndarray`
or `PIL.Image.Image`)
Example:
```python
import supervision as sv
image = ...
detections = sv.Detections(...)
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence
in zip(detections['class_name'], detections.confidence)
]
rich_label_annotator = sv.RichLabelAnnotator(font_path="path/to/font.ttf")
annotated_frame = label_annotator.annotate(
scene=image.copy(),
detections=detections,
labels=labels
)
```
"""
if isinstance(scene, np.ndarray):
scene = Image.fromarray(cv2.cvtColor(scene, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(scene)
anchors_coordinates = detections.get_anchors_coordinates(
anchor=self.text_anchor
).astype(int)
if labels is not None and len(labels) != len(detections):
raise ValueError(
f"The number of labels provided ({len(labels)}) does not match the "
f"number of detections ({len(detections)}). Each detection should have "
f"a corresponding label. This discrepancy can occur if the labels and "
f"detections are not aligned or if an incorrect number of labels has "
f"been provided. Please ensure that the labels array has the same "
f"length as the Detections object."
)
for detection_idx, center_coordinates in enumerate(anchors_coordinates):
color = resolve_color(
color=self.color,
detections=detections,
detection_idx=detection_idx,
color_lookup=(
self.color_lookup
if custom_color_lookup is None
else custom_color_lookup
),
)
if labels is not None:
text = labels[detection_idx]
elif detections[CLASS_NAME_DATA_FIELD] is not None:
text = detections[CLASS_NAME_DATA_FIELD][detection_idx]
elif detections.class_id is not None:
text = str(detections.class_id[detection_idx])
else:
text = str(detection_idx)
left, top, right, bottom = draw.textbbox((0, 0), text, font=self.font)
text_width = right - left
text_height = bottom - top
text_w_padded = text_width + 2 * self.text_padding
text_h_padded = text_height + 2 * self.text_padding
text_background_xyxy = resolve_text_background_xyxy(
center_coordinates=tuple(center_coordinates),
text_wh=(text_w_padded, text_h_padded),
position=self.text_anchor,
)
text_x = text_background_xyxy[0] + self.text_padding - left
text_y = text_background_xyxy[1] + self.text_padding - top
draw.rounded_rectangle(
text_background_xyxy,
radius=self.border_radius,
fill=color.as_rgb(),
outline=None,
)
draw.text(
xy=(text_x, text_y),
text=text,
font=self.font,
fill=self.text_color.as_rgb(),
)
return scene
class BlurAnnotator(BaseAnnotator):
"""
A class for blurring regions in an image using provided detections.

63
supervision/annotators/utils.py
View File

@ -1,5 +1,5 @@
from enum import Enum
from typing import Optional, Union
from typing import Optional, Tuple, Union
import numpy as np
@ -34,14 +34,14 @@ def resolve_color_idx(
) -> int:
if detection_idx >= len(detections):
raise ValueError(
f"Detection index {detection_idx}"
f"Detection index {detection_idx} "
f"is out of bounds for detections of length {len(detections)}"
)
if isinstance(color_lookup, np.ndarray):
if len(color_lookup) != len(detections):
raise ValueError(
f"Length of color lookup {len(color_lookup)}"
f"Length of color lookup {len(color_lookup)} "
f"does not match length of detections {len(detections)}"
)
return color_lookup[detection_idx]
@ -50,19 +50,72 @@ def resolve_color_idx(
elif color_lookup == ColorLookup.CLASS:
if detections.class_id is None:
raise ValueError(
"Could not resolve color by class because"
"Could not resolve color by class because "
"Detections do not have class_id"
)
return detections.class_id[detection_idx]
elif color_lookup == ColorLookup.TRACK:
if detections.tracker_id is None:
raise ValueError(
"Could not resolve color by track because"
"Could not resolve color by track because "
"Detections do not have tracker_id"
)
return detections.tracker_id[detection_idx]
def resolve_text_background_xyxy(
center_coordinates: Tuple[int, int],
text_wh: Tuple[int, int],
position: Position,
) -> Tuple[int, int, int, int]:
center_x, center_y = center_coordinates
text_w, text_h = text_wh
if position == Position.TOP_LEFT:
return center_x, center_y - text_h, center_x + text_w, center_y
elif position == Position.TOP_RIGHT:
return center_x - text_w, center_y - text_h, center_x, center_y
elif position == Position.TOP_CENTER:
return (
center_x - text_w // 2,
center_y - text_h,
center_x + text_w // 2,
center_y,
)
elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
return (
center_x - text_w // 2,
center_y - text_h // 2,
center_x + text_w // 2,
center_y + text_h // 2,
)
elif position == Position.BOTTOM_LEFT:
return center_x, center_y, center_x + text_w, center_y + text_h
elif position == Position.BOTTOM_RIGHT:
return center_x - text_w, center_y, center_x, center_y + text_h
elif position == Position.BOTTOM_CENTER:
return (
center_x - text_w // 2,
center_y,
center_x + text_w // 2,
center_y + text_h,
)
elif position == Position.CENTER_LEFT:
return (
center_x - text_w,
center_y - text_h // 2,
center_x,
center_y + text_h // 2,
)
elif position == Position.CENTER_RIGHT:
return (
center_x,
center_y - text_h // 2,
center_x + text_w,
center_y + text_h // 2,
)
def get_color_by_index(color: Union[Color, ColorPalette], idx: int) -> Color:
if isinstance(color, ColorPalette):
return color.by_idx(idx)

34
supervision/dataset/core.py
View File

@ -116,13 +116,12 @@ class DetectionDataset(BaseDataset):
Tuple[DetectionDataset, DetectionDataset]: A tuple containing
the training and testing datasets.
Example:
Examples:
```python
import supervision as sv
ds = sv.DetectionDataset(...)
train_ds, test_ds = ds.split(split_ratio=0.7,
random_state=42, shuffle=True)
train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)
```
@ -229,7 +228,7 @@ class DetectionDataset(BaseDataset):
DetectionDataset: A DetectionDataset instance containing
the loaded images and annotations.
Example:
Examples:
```python
import roboflow
from roboflow import Roboflow
@ -286,7 +285,7 @@ class DetectionDataset(BaseDataset):
DetectionDataset: A DetectionDataset instance
containing the loaded images and annotations.
Example:
Examples:
```python
import roboflow
from roboflow import Roboflow
@ -391,7 +390,7 @@ class DetectionDataset(BaseDataset):
DetectionDataset: A DetectionDataset instance containing
the loaded images and annotations.
Example:
Examples:
```python
import roboflow
from roboflow import Roboflow
@ -431,6 +430,20 @@ class DetectionDataset(BaseDataset):
Exports the dataset to COCO format. This method saves the
images and their corresponding annotations in COCO format.
!!! tip
The format of the mask is determined automatically based on its structure:
- If a mask contains multiple disconnected components or holes, it will be
saved using the Run-Length Encoding (RLE) format for efficient storage and
processing.
- If a mask consists of a single, contiguous region without any holes, it
will be encoded as a polygon, preserving the outline of the object.
This automatic selection ensures that the masks are stored in the most
appropriate and space-efficient format, complying with COCO dataset
standards.
Args:
images_directory_path (Optional[str]): The path to the directory
where the images should be saved.
@ -482,7 +495,7 @@ class DetectionDataset(BaseDataset):
(DetectionDataset): A single `DetectionDataset` object containing
the merged data from the input list.
Example:
Examples:
```python
import supervision as sv
@ -567,13 +580,12 @@ class ClassificationDataset(BaseDataset):
Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
the training and testing datasets.
Example:
Examples:
```python
import supervision as sv
cd = sv.ClassificationDataset(...)
train_cd,test_cd = cd.split(split_ratio=0.7,
random_state=42,shuffle=True)
train_cd,test_cd = cd.split(split_ratio=0.7, random_state=42,shuffle=True)
len(train_cd), len(test_cd)
# (700, 300)
```
@ -635,7 +647,7 @@ class ClassificationDataset(BaseDataset):
Returns:
ClassificationDataset: The dataset.
Example:
Examples:
```python
import roboflow
from roboflow import Roboflow

73
supervision/dataset/formats/coco.py
View File

@ -5,13 +5,20 @@ from typing import Dict, List, Tuple
import cv2
import numpy as np
import numpy.typing as npt
from supervision.dataset.utils import (
approximate_mask_with_polygons,
map_detections_class_id,
mask_to_rle,
rle_to_mask,
)
from supervision.detection.core import Detections
from supervision.detection.utils import polygon_to_mask
from supervision.detection.utils import (
contains_holes,
contains_multiple_segments,
polygon_to_mask,
)
from supervision.utils.file import read_json_file, save_json_file
@ -57,13 +64,24 @@ def group_coco_annotations_by_image_id(
return annotations
def _polygons_to_masks(
polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
) -> np.ndarray:
def coco_annotations_to_masks(
image_annotations: List[dict], resolution_wh: Tuple[int, int]
) -> npt.NDArray[np.bool_]:
return np.array(
[
polygon_to_mask(polygon=polygon, resolution_wh=resolution_wh)
for polygon in polygons
rle_to_mask(
rle=np.array(image_annotation["segmentation"]["counts"]),
resolution_wh=resolution_wh,
)
if image_annotation["iscrowd"]
else polygon_to_mask(
polygon=np.reshape(
np.asarray(image_annotation["segmentation"], dtype=np.int32),
(-1, 2),
),
resolution_wh=resolution_wh,
)
for image_annotation in image_annotations
],
dtype=bool,
)
@ -83,13 +101,9 @@ def coco_annotations_to_detections(
xyxy[:, 2:4] += xyxy[:, 0:2]
if with_masks:
polygons = [
np.reshape(
np.asarray(image_annotation["segmentation"], dtype=np.int32), (-1, 2)
)
for image_annotation in image_annotations
]
mask = _polygons_to_masks(polygons=polygons, resolution_wh=resolution_wh)
mask = coco_annotations_to_masks(
image_annotations=image_annotations, resolution_wh=resolution_wh
)
return Detections(
class_id=np.asarray(class_ids, dtype=int), xyxy=xyxy, mask=mask
)
@ -108,24 +122,35 @@ def detections_to_coco_annotations(
coco_annotations = []
for xyxy, mask, _, class_id, _, _ in detections:
box_width, box_height = xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]
polygon = []
segmentation = []
iscrowd = 0
if mask is not None:
polygon = list(
approximate_mask_with_polygons(
mask=mask,
min_image_area_percentage=min_image_area_percentage,
max_image_area_percentage=max_image_area_percentage,
approximation_percentage=approximation_percentage,
)[0].flatten()
)
iscrowd = contains_holes(mask=mask) or contains_multiple_segments(mask=mask)
if iscrowd:
segmentation = {
"counts": mask_to_rle(mask=mask),
"size": list(mask.shape[:2]),
}
else:
segmentation = [
list(
approximate_mask_with_polygons(
mask=mask,
min_image_area_percentage=min_image_area_percentage,
max_image_area_percentage=max_image_area_percentage,
approximation_percentage=approximation_percentage,
)[0].flatten()
)
]
coco_annotation = {
"id": annotation_id,
"image_id": image_id,
"category_id": int(class_id),
"bbox": [xyxy[0], xyxy[1], box_width, box_height],
"area": box_width * box_height,
"segmentation": [polygon] if polygon else [],
"iscrowd": 0,
"segmentation": segmentation,
"iscrowd": iscrowd,
}
coco_annotations.append(coco_annotation)
annotation_id += 1

123
supervision/dataset/utils.py
View File

@ -2,10 +2,11 @@ import copy
import os
import random
from pathlib import Path
from typing import Dict, List, Optional, Tuple, TypeVar
from typing import Dict, List, Optional, Tuple, TypeVar, Union
import cv2
import numpy as np
import numpy.typing as npt
from supervision.detection.core import Detections
from supervision.detection.utils import (
@ -129,3 +130,123 @@ def train_test_split(
split_index = int(len(data) * train_ratio)
return data[:split_index], data[split_index:]
def rle_to_mask(
rle: Union[npt.NDArray[np.int_], List[int]], resolution_wh: Tuple[int, int]
) -> npt.NDArray[np.bool_]:
"""
Converts run-length encoding (RLE) to a binary mask.
Args:
rle (Union[npt.NDArray[np.int_], List[int]]): The 1D RLE array, the format
used in the COCO dataset (column-wise encoding, values of an array with
even indices represent the number of pixels assigned as background,
values of an array with odd indices represent the number of pixels
assigned as foreground object).
resolution_wh (Tuple[int, int]): The width (w) and height (h)
of the desired binary mask.
Returns:
The generated 2D Boolean mask of shape `(h, w)`, where the foreground object is
marked with `True`'s and the rest is filled with `False`'s.
Raises:
AssertionError: If the sum of pixels encoded in RLE differs from the
number of pixels in the expected mask (computed based on resolution_wh).
Examples:
```python
import supervision as sv
sv.rle_to_mask([5, 2, 2, 2, 5], (4, 4))
# array([
# [False, False, False, False],
# [False, True, True, False],
# [False, True, True, False],
# [False, False, False, False],
# ])
```
"""
if isinstance(rle, list):
rle = np.array(rle, dtype=int)
width, height = resolution_wh
assert width * height == np.sum(rle), (
"the sum of the number of pixels in the RLE must be the same "
"as the number of pixels in the expected mask"
)
zero_one_values = np.zeros(shape=(rle.size, 1), dtype=np.uint8)
zero_one_values[1::2] = 1
decoded_rle = np.repeat(zero_one_values, rle, axis=0)
decoded_rle = np.append(
decoded_rle, np.zeros(width * height - len(decoded_rle), dtype=np.uint8)
)
return decoded_rle.reshape((height, width), order="F")
def mask_to_rle(mask: npt.NDArray[np.bool_]) -> List[int]:
"""
Converts a binary mask into a run-length encoding (RLE).
Args:
mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
object and `False` indicates background.
Returns:
The run-length encoded mask. Values of a list with even indices
represent the number of pixels assigned as background (`False`), values
of a list with odd indices represent the number of pixels assigned
as foreground object (`True`).
Raises:
AssertionError: If input mask is not 2D or is empty.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([
[True, True, True, True],
[True, True, True, True],
[True, True, True, True],
[True, True, True, True],
])
sv.mask_to_rle(mask)
# [0, 16]
mask = np.array([
[False, False, False, False],
[False, True, True, False],
[False, True, True, False],
[False, False, False, False],
])
sv.mask_to_rle(mask)
# [5, 2, 2, 2, 5]
```
![mask_to_rle](https://media.roboflow.com/supervision-docs/mask-to-rle.png){ align=center width="800" }
""" # noqa E501 // docs
assert mask.ndim == 2, "Input mask must be 2D"
assert mask.size != 0, "Input mask cannot be empty"
on_value_change_indices = np.where(
mask.ravel(order="F") != np.roll(mask.ravel(order="F"), 1)
)[0]
on_value_change_indices = np.append(on_value_change_indices, mask.size)
# need to add 0 at the beginning when the same value is in the first and
# last element of the flattened mask
if on_value_change_indices[0] != 0:
on_value_change_indices = np.insert(on_value_change_indices, 0, 0)
rle = np.diff(on_value_change_indices)
if mask[0][0] == 1:
rle = np.insert(rle, 0, 0)
return list(rle)

277
supervision/detection/core.py
View File

@ -7,20 +7,25 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
import numpy as np
from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
from supervision.detection.utils import (
from supervision.detection.lmm import LMM, from_paligemma, validate_lmm_and_kwargs
from supervision.detection.overlap_filter import (
box_non_max_merge,
box_non_max_suppression,
mask_non_max_suppression,
)
from supervision.detection.utils import (
box_iou_batch,
calculate_masks_centroids,
extract_ultralytics_masks,
get_data_item,
is_data_equal,
mask_non_max_suppression,
mask_to_xyxy,
merge_data,
process_roboflow_result,
xywh_to_xyxy,
)
from supervision.geometry.core import Position
from supervision.utils.internal import deprecated
from supervision.utils.internal import deprecated, get_instance_variables
from supervision.validators import validate_detections_fields
@ -240,7 +245,7 @@ class Detections:
Class names values can be accessed using `detections["class_name"]`.
""" # noqa: E501 // docs
if "obb" in ultralytics_results and ultralytics_results.obb is not None:
if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
class_names = np.array([ultralytics_results.names[i] for i in class_id])
oriented_box_coordinates = ultralytics_results.obb.xyxyxyxy.cpu().numpy()
@ -418,6 +423,9 @@ class Detections:
xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
confidence=mmdet_results.pred_instances.scores.cpu().numpy(),
class_id=mmdet_results.pred_instances.labels.cpu().numpy().astype(int),
mask=mmdet_results.pred_instances.masks.cpu().numpy()
if "masks" in mmdet_results.pred_instances
else None,
)
@classmethod
@ -802,6 +810,52 @@ class Detections:
class_id=paddledet_result["bbox"][:, 0].astype(int),
)
@classmethod
def from_lmm(cls, lmm: Union[LMM, str], result: str, **kwargs) -> Detections:
"""
Creates a Detections object from the given result string based on the specified
Large Multimodal Model (LMM).
Args:
lmm (Union[LMM, str]): The type of LMM (Large Multimodal Model) to use.
result (str): The result string containing the detection data.
**kwargs: Additional keyword arguments required by the specified LMM.
Returns:
Detections: A new Detections object.
Raises:
ValueError: If the LMM is invalid, required arguments are missing, or
disallowed arguments are provided.
ValueError: If the specified LMM is not supported.
Examples:
```python
import supervision as sv
paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_lmm(
sv.LMM.PALIGEMMA,
paligemma_result,
resolution_wh=(1000, 1000),
classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])
detections.class_id
# array([0])
```
"""
lmm = validate_lmm_and_kwargs(lmm, kwargs)
if lmm == LMM.PALIGEMMA:
xyxy, class_id, class_name = from_paligemma(result, **kwargs)
data = {CLASS_NAME_DATA_FIELD: class_name}
return cls(xyxy=xyxy, class_id=class_id, data=data)
raise ValueError(f"Unsupported LMM: {lmm}")
@classmethod
def empty(cls) -> Detections:
"""
@ -824,6 +878,14 @@ class Detections:
class_id=np.array([], dtype=int),
)
def is_empty(self) -> bool:
"""
Returns `True` if the `Detections` object is considered empty.
"""
empty_detections = Detections.empty()
empty_detections.data = self.data
return self == empty_detections
@classmethod
def merge(cls, detections_list: List[Detections]) -> Detections:
"""
@ -831,9 +893,14 @@ class Detections:
This method takes a list of Detections objects and combines their
respective fields (`xyxy`, `mask`, `confidence`, `class_id`, and `tracker_id`)
into a single Detections object. If all elements in a field are not
`None`, the corresponding field will be stacked.
Otherwise, the field will be set to `None`.
into a single Detections object.
For example, if merging Detections with 3 and 4 detected objects, this method
will return a Detections with 7 objects (7 entries in `xyxy`, `mask`, etc).
!!! Note
When merging, empty `Detections` objects are ignored.
Args:
detections_list (List[Detections]): A list of Detections objects to merge.
@ -873,6 +940,10 @@ class Detections:
array([0.1, 0.2, 0.3])
```
"""
detections_list = [
detections for detections in detections_list if not detections.is_empty()
]
if len(detections_list) == 0:
return Detections.empty()
@ -1147,3 +1218,195 @@ class Detections:
)
return self[indices]
def with_nmm(
self, threshold: float = 0.5, class_agnostic: bool = False
) -> Detections:
"""
Perform non-maximum merging on the current set of object detections.
Args:
threshold (float, optional): The intersection-over-union threshold
to use for non-maximum merging. Defaults to 0.5.
class_agnostic (bool, optional): Whether to perform class-agnostic
non-maximum merging. If True, the class_id of each detection
will be ignored. Defaults to False.
Returns:
Detections: A new Detections object containing the subset of detections
after non-maximum merging.
Raises:
AssertionError: If `confidence` is None or `class_id` is None and
class_agnostic is False.
![non-max-merging](https://media.roboflow.com/supervision-docs/non-max-merging.png){ align=center width="800" }
""" # noqa: E501 // docs
if len(self) == 0:
return self
assert (
self.confidence is not None
), "Detections confidence must be given for NMM to be executed."
if class_agnostic:
predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
else:
assert self.class_id is not None, (
"Detections class_id must be given for NMM to be executed. If you"
" intended to perform class agnostic NMM set class_agnostic=True."
)
predictions = np.hstack(
(
self.xyxy,
self.confidence.reshape(-1, 1),
self.class_id.reshape(-1, 1),
)
)
merge_groups = box_non_max_merge(
predictions=predictions, iou_threshold=threshold
)
result = []
for merge_group in merge_groups:
unmerged_detections = [self[i] for i in merge_group]
merged_detections = merge_inner_detections_objects(
unmerged_detections, threshold
)
result.append(merged_detections)
return Detections.merge(result)
def merge_inner_detection_object_pair(
detections_1: Detections, detections_2: Detections
) -> Detections:
"""
Merges two Detections object into a single Detections object.
Assumes each Detections contains exactly one object.
A `winning` detection is determined based on the confidence score of the two
input detections. This winning detection is then used to specify which
`class_id`, `tracker_id`, and `data` to include in the merged Detections object.
The resulting `confidence` of the merged object is calculated by the weighted
contribution of ea detection to the merged object.
The bounding boxes and masks of the two input detections are merged into a
single bounding box and mask, respectively.
Args:
detections_1 (Detections):
The first Detections object
detections_2 (Detections):
The second Detections object
Returns:
Detections: A new Detections object, with merged attributes.
Raises:
ValueError: If the input Detections objects do not have exactly 1 detected
object.
Example:
```python
import cv2
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id="yolov8s-640")
result = model.infer(image)[0]
detections = sv.Detections.from_inference(result)
merged_detections = merge_object_detection_pair(
detections[0], detections[1])
```
"""
if len(detections_1) != 1 or len(detections_2) != 1:
raise ValueError("Both Detections should have exactly 1 detected object.")
validate_fields_both_defined_or_none(detections_1, detections_2)
xyxy_1 = detections_1.xyxy[0]
xyxy_2 = detections_2.xyxy[0]
if detections_1.confidence is None and detections_2.confidence is None:
merged_confidence = None
else:
detection_1_area = (xyxy_1[2] - xyxy_1[0]) * (xyxy_1[3] - xyxy_1[1])
detections_2_area = (xyxy_2[2] - xyxy_2[0]) * (xyxy_2[3] - xyxy_2[1])
merged_confidence = (
detection_1_area * detections_1.confidence[0]
+ detections_2_area * detections_2.confidence[0]
) / (detection_1_area + detections_2_area)
merged_confidence = np.array([merged_confidence])
merged_x1, merged_y1 = np.minimum(xyxy_1[:2], xyxy_2[:2])
merged_x2, merged_y2 = np.maximum(xyxy_1[2:], xyxy_2[2:])
merged_xyxy = np.array([[merged_x1, merged_y1, merged_x2, merged_y2]])
if detections_1.mask is None and detections_2.mask is None:
merged_mask = None
else:
merged_mask = np.logical_or(detections_1.mask, detections_2.mask)
if detections_1.confidence is None and detections_2.confidence is None:
winning_detection = detections_1
elif detections_1.confidence[0] >= detections_2.confidence[0]:
winning_detection = detections_1
else:
winning_detection = detections_2
return Detections(
xyxy=merged_xyxy,
mask=merged_mask,
confidence=merged_confidence,
class_id=winning_detection.class_id,
tracker_id=winning_detection.tracker_id,
data=winning_detection.data,
)
def merge_inner_detections_objects(
detections: List[Detections], threshold=0.5
) -> Detections:
"""
Given N detections each of length 1 (exactly one object inside), combine them into a
single detection object of length 1. The contained inner object will be the merged
result of all the input detections.
For example, this lets you merge N boxes into one big box, N masks into one mask,
etc.
"""
detections_1 = detections[0]
for detections_2 in detections[1:]:
box_iou = box_iou_batch(detections_1.xyxy, detections_2.xyxy)[0]
if box_iou < threshold:
break
detections_1 = merge_inner_detection_object_pair(detections_1, detections_2)
return detections_1
def validate_fields_both_defined_or_none(
detections_1: Detections, detections_2: Detections
) -> None:
"""
Verify that for each optional field in the Detections, both instances either have
the field set to None or both have it set to non-None values.
`data` field is ignored.
Raises:
ValueError: If one field is None and the other is not, for any of the fields.
"""
attributes = get_instance_variables(detections_1)
for attribute in attributes:
value_1 = getattr(detections_1, attribute)
value_2 = getattr(detections_2, attribute)
if (value_1 is None) != (value_2 is None):
raise ValueError(
f"Field '{attribute}' should be consistently None or not None in both "
"Detections."
)

46
supervision/detection/line_zone.py
View File

@ -1,12 +1,15 @@
import warnings
from typing import Dict, Iterable, Optional, Tuple
import cv2
import numpy as np
from supervision.detection.core import Detections
from supervision.detection.utils import cross_product
from supervision.draw.color import Color
from supervision.draw.utils import draw_text
from supervision.geometry.core import Point, Position, Vector
from supervision.utils.internal import SupervisionWarnings
class LineZone:
@ -81,6 +84,8 @@ class LineZone:
self.in_count: int = 0
self.out_count: int = 0
self.triggering_anchors = triggering_anchors
if not list(self.triggering_anchors):
raise ValueError("Triggering anchors cannot be empty.")
@staticmethod
def calculate_region_of_interest_limits(vector: Vector) -> Tuple[Vector, Vector]:
@ -140,6 +145,15 @@ class LineZone:
if len(detections) == 0:
return crossed_in, crossed_out
if detections.tracker_id is None:
warnings.warn(
"Line zone counting skipped. LineZone requires tracker_id. Refer to "
"https://supervision.roboflow.com/latest/trackers for more "
"information.",
category=SupervisionWarnings,
)
return crossed_in, crossed_out
all_anchors = np.array(
[
detections.get_anchors_coordinates(anchor)
@ -147,31 +161,23 @@ class LineZone:
]
)
cross_products_1 = cross_product(all_anchors, self.limits[0])
cross_products_2 = cross_product(all_anchors, self.limits[1])
in_limits = (cross_products_1 > 0) == (cross_products_2 > 0)
in_limits = np.all(in_limits, axis=0)
triggers = cross_product(all_anchors, self.vector) < 0
has_any_left_trigger = np.any(triggers, axis=0)
has_any_right_trigger = np.any(~triggers, axis=0)
is_uniformly_triggered = ~(has_any_left_trigger & has_any_right_trigger)
for i, tracker_id in enumerate(detections.tracker_id):
if tracker_id is None:
if not in_limits[i]:
continue
box_anchors = [Point(x=x, y=y) for x, y in all_anchors[:, i, :]]
in_limits = all(
[
self.is_point_in_limits(point=anchor, limits=self.limits)
for anchor in box_anchors
]
)
if not in_limits:
if not is_uniformly_triggered[i]:
continue
triggers = [
self.vector.cross_product(point=anchor) < 0 for anchor in box_anchors
]
if len(set(triggers)) == 2:
continue
tracker_state = triggers[0]
tracker_state = has_any_left_trigger[i]
if tracker_id not in self.tracker_state:
self.tracker_state[tracker_id] = tracker_state
continue

59
supervision/detection/lmm.py Normal file
View File

@ -0,0 +1,59 @@
import re
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
class LMM(Enum):
PALIGEMMA = "paligemma"
REQUIRED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh"]}
ALLOWED_ARGUMENTS: Dict[LMM, List[str]] = {LMM.PALIGEMMA: ["resolution_wh", "classes"]}
def validate_lmm_and_kwargs(lmm: Union[LMM, str], kwargs: Dict[str, Any]) -> LMM:
if isinstance(lmm, str):
try:
lmm = LMM(lmm.lower())
except ValueError:
raise ValueError(
f"Invalid lmm value: {lmm}. Must be one of {[e.value for e in LMM]}"
)
required_args = REQUIRED_ARGUMENTS.get(lmm, [])
for arg in required_args:
if arg not in kwargs:
raise ValueError(f"Missing required argument: {arg}")
allowed_args = ALLOWED_ARGUMENTS.get(lmm, [])
for arg in kwargs:
if arg not in allowed_args:
raise ValueError(f"Argument {arg} is not allowed for {lmm.name}")
return lmm
def from_paligemma(
result: str, resolution_wh: Tuple[int, int], classes: Optional[List[str]] = None
) -> Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]:
w, h = resolution_wh
pattern = re.compile(
r"(?<!<loc\d{4}>)<loc(\d{4})><loc(\d{4})><loc(\d{4})><loc(\d{4})> ([\w\s\-]+)"
)
matches = pattern.findall(result)
matches = np.array(matches) if matches else np.empty((0, 5))
xyxy, class_name = matches[:, [1, 0, 3, 2]], matches[:, 4]
xyxy = xyxy.astype(int) / 1024 * np.array([w, h, w, h])
class_name = np.char.strip(class_name.astype(str))
class_id = None
if classes is not None:
mask = np.array([name in classes for name in class_name]).astype(bool)
xyxy, class_name = xyxy[mask], class_name[mask]
class_id = np.array([classes.index(name) for name in class_name])
return xyxy, class_id, class_name

263
supervision/detection/overlap_filter.py Normal file
View File

@ -0,0 +1,263 @@
from enum import Enum
from typing import List, Union
import numpy as np
import numpy.typing as npt
from supervision.detection.utils import box_iou_batch, mask_iou_batch
def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
"""
Resize all masks in the array to have a maximum dimension of max_dimension,
maintaining aspect ratio.
Args:
masks (np.ndarray): 3D array of binary masks with shape (N, H, W).
max_dimension (int): The maximum dimension for the resized masks.
Returns:
np.ndarray: Array of resized masks.
"""
max_height = np.max(masks.shape[1])
max_width = np.max(masks.shape[2])
scale = min(max_dimension / max_height, max_dimension / max_width)
new_height = int(scale * max_height)
new_width = int(scale * max_width)
x = np.linspace(0, max_width - 1, new_width).astype(int)
y = np.linspace(0, max_height - 1, new_height).astype(int)
xv, yv = np.meshgrid(x, y)
resized_masks = masks[:, yv, xv]
resized_masks = resized_masks.reshape(masks.shape[0], new_height, new_width)
return resized_masks
def mask_non_max_suppression(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float = 0.5,
mask_dimension: int = 640,
) -> np.ndarray:
"""
Perform Non-Maximum Suppression (NMS) on segmentation predictions.
Args:
predictions (np.ndarray): A 2D array of object detection predictions in
the format of `(x_min, y_min, x_max, y_max, score)`
or `(x_min, y_min, x_max, y_max, score, class)`. Shape: `(N, 5)` or
`(N, 6)`, where N is the number of predictions.
masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
dimensions of each mask.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression.
mask_dimension (int, optional): The dimension to which the masks should be
resized before computing IOU values. Defaults to 640.
Returns:
np.ndarray: A boolean array indicating which predictions to keep after
non-maximum suppression.
Raises:
AssertionError: If `iou_threshold` is not within the closed
range from `0` to `1`.
"""
assert 0 <= iou_threshold <= 1, (
"Value of `iou_threshold` must be in the closed range from 0 to 1, "
f"{iou_threshold} given."
)
rows, columns = predictions.shape
if columns == 5:
predictions = np.c_[predictions, np.zeros(rows)]
sort_index = predictions[:, 4].argsort()[::-1]
predictions = predictions[sort_index]
masks = masks[sort_index]
masks_resized = resize_masks(masks, mask_dimension)
ious = mask_iou_batch(masks_resized, masks_resized)
categories = predictions[:, 5]
keep = np.ones(rows, dtype=bool)
for i in range(rows):
if keep[i]:
condition = (ious[i] > iou_threshold) & (categories[i] == categories)
keep[i + 1 :] = np.where(condition[i + 1 :], False, keep[i + 1 :])
return keep[sort_index.argsort()]
def box_non_max_suppression(
predictions: np.ndarray, iou_threshold: float = 0.5
) -> np.ndarray:
"""
Perform Non-Maximum Suppression (NMS) on object detection predictions.
Args:
predictions (np.ndarray): An array of object detection predictions in
the format of `(x_min, y_min, x_max, y_max, score)`
or `(x_min, y_min, x_max, y_max, score, class)`.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression.
Returns:
np.ndarray: A boolean array indicating which predictions to keep after n
on-maximum suppression.
Raises:
AssertionError: If `iou_threshold` is not within the
closed range from `0` to `1`.
"""
assert 0 <= iou_threshold <= 1, (
"Value of `iou_threshold` must be in the closed range from 0 to 1, "
f"{iou_threshold} given."
)
rows, columns = predictions.shape
# add column #5 - category filled with zeros for agnostic nms
if columns == 5:
predictions = np.c_[predictions, np.zeros(rows)]
# sort predictions column #4 - score
sort_index = np.flip(predictions[:, 4].argsort())
predictions = predictions[sort_index]
boxes = predictions[:, :4]
categories = predictions[:, 5]
ious = box_iou_batch(boxes, boxes)
ious = ious - np.eye(rows)
keep = np.ones(rows, dtype=bool)
for index, (iou, category) in enumerate(zip(ious, categories)):
if not keep[index]:
continue
# drop detections with iou > iou_threshold and
# same category as current detections
condition = (iou > iou_threshold) & (categories == category)
keep = keep & ~condition
return keep[sort_index.argsort()]
def group_overlapping_boxes(
predictions: npt.NDArray[np.float64], iou_threshold: float = 0.5
) -> List[List[int]]:
"""
Apply greedy version of non-maximum merging to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
predictions (npt.NDArray[np.float64]): An array of shape `(n, 5)` containing
the bounding boxes coordinates in format `[x1, y1, x2, y2]`
and the confidence scores.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression. Defaults to 0.5.
Returns:
List[List[int]]: Groups of prediction indices be merged.
Each group may have 1 or more elements.
"""
merge_groups: List[List[int]] = []
scores = predictions[:, 4]
order = scores.argsort()
while len(order) > 0:
idx = int(order[-1])
order = order[:-1]
if len(order) == 0:
merge_groups.append([idx])
break
merge_candidate = np.expand_dims(predictions[idx], axis=0)
ious = box_iou_batch(predictions[order][:, :4], merge_candidate[:, :4])
ious = ious.flatten()
above_threshold = ious >= iou_threshold
merge_group = [idx] + np.flip(order[above_threshold]).tolist()
merge_groups.append(merge_group)
order = order[~above_threshold]
return merge_groups
def box_non_max_merge(
predictions: npt.NDArray[np.float64],
iou_threshold: float = 0.5,
) -> List[List[int]]:
"""
Apply greedy version of non-maximum merging per category to avoid detecting
too many overlapping bounding boxes for a given object.
Args:
predictions (npt.NDArray[np.float64]): An array of shape `(n, 5)` or `(n, 6)`
containing the bounding boxes coordinates in format `[x1, y1, x2, y2]`,
the confidence scores and class_ids. Omit class_id column to allow
detections of different classes to be merged.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression. Defaults to 0.5.
Returns:
List[List[int]]: Groups of prediction indices be merged.
Each group may have 1 or more elements.
"""
if predictions.shape[1] == 5:
return group_overlapping_boxes(predictions, iou_threshold)
category_ids = predictions[:, 5]
merge_groups = []
for category_id in np.unique(category_ids):
curr_indices = np.where(category_ids == category_id)[0]
merge_class_groups = group_overlapping_boxes(
predictions[curr_indices], iou_threshold
)
for merge_class_group in merge_class_groups:
merge_groups.append(curr_indices[merge_class_group].tolist())
for merge_group in merge_groups:
if len(merge_group) == 0:
raise ValueError(
f"Empty group detected when non-max-merging "
f"detections: {merge_groups}"
)
return merge_groups
class OverlapFilter(Enum):
"""
Enum specifying the strategy for filtering overlapping detections.
Attributes:
NONE: Do not filter detections based on overlap.
NON_MAX_SUPPRESSION: Filter detections using non-max suppression. This means,
detections that overlap by more than a set threshold will be discarded,
except for the one with the highest confidence.
NON_MAX_MERGE: Merge detections with non-max merging. This means,
detections that overlap by more than a set threshold will be merged
into a single detection.
"""
NONE = "none"
NON_MAX_SUPPRESSION = "non_max_suppression"
NON_MAX_MERGE = "non_max_merge"
def validate_overlap_filter(
strategy: Union[OverlapFilter, str],
) -> OverlapFilter:
if isinstance(strategy, str):
try:
strategy = OverlapFilter(strategy.lower())
except ValueError:
raise ValueError(
f"Invalid strategy value: {strategy}. Must be one of "
f"{[e.value for e in OverlapFilter]}"
)
return strategy

67
supervision/detection/tools/inference_slicer.py
View File

@ -1,23 +1,42 @@
import warnings
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Callable, Optional, Tuple
from typing import Callable, Optional, Tuple, Union
import numpy as np
from supervision.detection.core import Detections
from supervision.detection.utils import move_boxes
from supervision.detection.overlap_filter import OverlapFilter, validate_overlap_filter
from supervision.detection.utils import move_boxes, move_masks
from supervision.utils.image import crop_image
from supervision.utils.internal import SupervisionWarnings
def move_detections(detections: Detections, offset: np.array) -> Detections:
def move_detections(
detections: Detections,
offset: np.ndarray,
resolution_wh: Optional[Tuple[int, int]] = None,
) -> Detections:
"""
Args:
detections (sv.Detections): Detections object to be moved.
offset (np.array): An array of shape `(2,)` containing offset values in format
offset (np.ndarray): An array of shape `(2,)` containing offset values in format
is `[dx, dy]`.
resolution_wh (Tuple[int, int]): The width and height of the desired mask
resolution. Required for segmentation detections.
Returns:
(sv.Detections) repositioned Detections object.
"""
detections.xyxy = move_boxes(xyxy=detections.xyxy, offset=offset)
if detections.mask is not None:
if resolution_wh is None:
raise ValueError(
"Resolution width and height are required for moving segmentation "
"detections. This should be the same as (width, height) of image shape."
)
detections.mask = move_masks(
masks=detections.mask, offset=offset, resolution_wh=resolution_wh
)
return detections
@ -34,8 +53,10 @@ class InferenceSlicer:
`(width, height)`.
overlap_ratio_wh (Tuple[float, float]): Overlap ratio between consecutive
slices in the format `(width_ratio, height_ratio)`.
iou_threshold (Optional[float]): Intersection over Union (IoU) threshold
used for non-max suppression.
overlap_filter_strategy (Union[OverlapFilter, str]): Strategy for
filtering or merging overlapping detections in slices.
iou_threshold (float): Intersection over Union (IoU) threshold
used when filtering by overlap.
callback (Callable): A function that performs inference on a given image
slice and returns detections.
thread_workers (int): Number of threads for parallel execution.
@ -52,12 +73,18 @@ class InferenceSlicer:
callback: Callable[[np.ndarray], Detections],
slice_wh: Tuple[int, int] = (320, 320),
overlap_ratio_wh: Tuple[float, float] = (0.2, 0.2),
iou_threshold: Optional[float] = 0.5,
overlap_filter_strategy: Union[
OverlapFilter, str
] = OverlapFilter.NON_MAX_SUPPRESSION,
iou_threshold: float = 0.5,
thread_workers: int = 1,
):
overlap_filter_strategy = validate_overlap_filter(overlap_filter_strategy)
self.slice_wh = slice_wh
self.overlap_ratio_wh = overlap_ratio_wh
self.iou_threshold = iou_threshold
self.overlap_filter_strategy = overlap_filter_strategy
self.callback = callback
self.thread_workers = thread_workers
@ -88,7 +115,10 @@ class InferenceSlicer:
result = model(image_slice)[0]
return sv.Detections.from_ultralytics(result)
slicer = sv.InferenceSlicer(callback = callback)
slicer = sv.InferenceSlicer(
callback=callback,
overlap_filter_strategy=sv.OverlapFilter.NON_MAX_SUPPRESSION,
)
detections = slicer(image)
```
@ -108,9 +138,19 @@ class InferenceSlicer:
for future in as_completed(futures):
detections_list.append(future.result())
return Detections.merge(detections_list=detections_list).with_nms(
threshold=self.iou_threshold
)
merged = Detections.merge(detections_list=detections_list)
if self.overlap_filter_strategy == OverlapFilter.NONE:
return merged
elif self.overlap_filter_strategy == OverlapFilter.NON_MAX_SUPPRESSION:
return merged.with_nms(threshold=self.iou_threshold)
elif self.overlap_filter_strategy == OverlapFilter.NON_MAX_MERGE:
return merged.with_nmm(threshold=self.iou_threshold)
else:
warnings.warn(
f"Invalid overlap filter strategy: {self.overlap_filter_strategy}",
category=SupervisionWarnings,
)
return merged
def _run_callback(self, image, offset) -> Detections:
"""
@ -126,7 +166,10 @@ class InferenceSlicer:
"""
image_slice = crop_image(image=image, xyxy=offset)
detections = self.callback(image_slice)
detections = move_detections(detections=detections, offset=offset[:2])
resolution_wh = (image.shape[1], image.shape[0])
detections = move_detections(
detections=detections, offset=offset[:2], resolution_wh=resolution_wh
)
return detections

2
supervision/detection/tools/polygon_zone.py
View File

@ -54,6 +54,8 @@ class PolygonZone:
self.polygon = polygon.astype(int)
self.triggering_anchors = triggering_anchors
if not list(self.triggering_anchors):
raise ValueError("Triggering anchors cannot be empty.")
self.current_count = 0

10
supervision/detection/tools/smoother.py
View File

@ -1,3 +1,4 @@
import warnings
from collections import defaultdict, deque
from copy import deepcopy
from typing import Optional
@ -5,6 +6,7 @@ from typing import Optional
import numpy as np
from supervision.detection.core import Detections
from supervision.utils.internal import SupervisionWarnings
class DetectionsSmoother:
@ -70,16 +72,16 @@ class DetectionsSmoother:
"""
if detections.tracker_id is None:
print(
warnings.warn(
"Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
"https://supervision.roboflow.com/latest/trackers for more information."
"https://supervision.roboflow.com/latest/trackers for more "
"information.",
category=SupervisionWarnings,
)
return detections
for detection_idx in range(len(detections)):
tracker_id = detections.tracker_id[detection_idx]
if tracker_id is None:
continue
self.tracks[tracker_id].append(detections[detection_idx])

449
supervision/detection/utils.py
View File

@ -3,8 +3,10 @@ from typing import Dict, List, Optional, Tuple, Union
import cv2
import numpy as np
import numpy.typing as npt
from supervision.config import CLASS_NAME_DATA_FIELD
from supervision.geometry.core import Vector
MIN_POLYGON_POINT_COUNT = 3
@ -56,7 +58,9 @@ def box_iou_batch(boxes_true: np.ndarray, boxes_detection: np.ndarray) -> np.nda
bottom_right = np.minimum(boxes_true[:, None, 2:], boxes_detection[:, 2:])
area_inter = np.prod(np.clip(bottom_right - top_left, a_min=0, a_max=None), 2)
return area_inter / (area_true[:, None] + area_detection - area_inter)
ious = area_inter / (area_true[:, None] + area_detection - area_inter)
ious = np.nan_to_num(ious)
return ious
def _mask_iou_batch_split(
@ -136,144 +140,6 @@ def mask_iou_batch(
return np.vstack(ious)
def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
"""
Resize all masks in the array to have a maximum dimension of max_dimension,
maintaining aspect ratio.
Args:
masks (np.ndarray): 3D array of binary masks with shape (N, H, W).
max_dimension (int): The maximum dimension for the resized masks.
Returns:
np.ndarray: Array of resized masks.
"""
max_height = np.max(masks.shape[1])
max_width = np.max(masks.shape[2])
scale = min(max_dimension / max_height, max_dimension / max_width)
new_height = int(scale * max_height)
new_width = int(scale * max_width)
x = np.linspace(0, max_width - 1, new_width).astype(int)
y = np.linspace(0, max_height - 1, new_height).astype(int)
xv, yv = np.meshgrid(x, y)
resized_masks = masks[:, yv, xv]
resized_masks = resized_masks.reshape(masks.shape[0], new_height, new_width)
return resized_masks
def mask_non_max_suppression(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float = 0.5,
mask_dimension: int = 640,
) -> np.ndarray:
"""
Perform Non-Maximum Suppression (NMS) on segmentation predictions.
Args:
predictions (np.ndarray): A 2D array of object detection predictions in
the format of `(x_min, y_min, x_max, y_max, score)`
or `(x_min, y_min, x_max, y_max, score, class)`. Shape: `(N, 5)` or
`(N, 6)`, where N is the number of predictions.
masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
dimensions of each mask.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression.
mask_dimension (int, optional): The dimension to which the masks should be
resized before computing IOU values. Defaults to 640.
Returns:
np.ndarray: A boolean array indicating which predictions to keep after
non-maximum suppression.
Raises:
AssertionError: If `iou_threshold` is not within the closed
range from `0` to `1`.
"""
assert 0 <= iou_threshold <= 1, (
"Value of `iou_threshold` must be in the closed range from 0 to 1, "
f"{iou_threshold} given."
)
rows, columns = predictions.shape
if columns == 5:
predictions = np.c_[predictions, np.zeros(rows)]
sort_index = predictions[:, 4].argsort()[::-1]
predictions = predictions[sort_index]
masks = masks[sort_index]
masks_resized = resize_masks(masks, mask_dimension)
ious = mask_iou_batch(masks_resized, masks_resized)
categories = predictions[:, 5]
keep = np.ones(rows, dtype=bool)
for i in range(rows):
if keep[i]:
condition = (ious[i] > iou_threshold) & (categories[i] == categories)
keep[i + 1 :] = np.where(condition[i + 1 :], False, keep[i + 1 :])
return keep[sort_index.argsort()]
def box_non_max_suppression(
predictions: np.ndarray, iou_threshold: float = 0.5
) -> np.ndarray:
"""
Perform Non-Maximum Suppression (NMS) on object detection predictions.
Args:
predictions (np.ndarray): An array of object detection predictions in
the format of `(x_min, y_min, x_max, y_max, score)`
or `(x_min, y_min, x_max, y_max, score, class)`.
iou_threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression.
Returns:
np.ndarray: A boolean array indicating which predictions to keep after n
on-maximum suppression.
Raises:
AssertionError: If `iou_threshold` is not within the
closed range from `0` to `1`.
"""
assert 0 <= iou_threshold <= 1, (
"Value of `iou_threshold` must be in the closed range from 0 to 1, "
f"{iou_threshold} given."
)
rows, columns = predictions.shape
# add column #5 - category filled with zeros for agnostic nms
if columns == 5:
predictions = np.c_[predictions, np.zeros(rows)]
# sort predictions column #4 - score
sort_index = np.flip(predictions[:, 4].argsort())
predictions = predictions[sort_index]
boxes = predictions[:, :4]
categories = predictions[:, 5]
ious = box_iou_batch(boxes, boxes)
ious = ious - np.eye(rows)
keep = np.ones(rows, dtype=bool)
for index, (iou, category) in enumerate(zip(ious, categories)):
if not keep[index]:
continue
# drop detections with iou > iou_threshold and
# same category as current detections
condition = (iou > iou_threshold) & (categories == category)
keep = keep & ~condition
return keep[sort_index.argsort()]
def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
"""
Clips bounding boxes coordinates to fit within the frame resolution.
@ -289,6 +155,25 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
np.ndarray: A numpy array of shape `(N, 4)` where each row
corresponds to a bounding box with coordinates clipped to fit
within the frame resolution.
Examples:
```python
import numpy as np
import supervision as sv
xyxy = np.array([
[10, 20, 300, 200],
[15, 25, 350, 450],
[-10, -20, 30, 40]
])
sv.clip_boxes(xyxy=xyxy, resolution_wh=(320, 240))
# array([
# [ 10, 20, 300, 200],
# [ 15, 25, 320, 240],
# [ 0, 0, 30, 40]
# ])
```
"""
result = np.copy(xyxy)
width, height = resolution_wh
@ -297,6 +182,52 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
return result
def pad_boxes(xyxy: np.ndarray, px: int, py: Optional[int] = None) -> np.ndarray:
"""
Pads bounding boxes coordinates with a constant padding.
Args:
xyxy (np.ndarray): A numpy array of shape `(N, 4)` where each
row corresponds to a bounding box in the format
`(x_min, y_min, x_max, y_max)`.
px (int): The padding value to be added to both the left and right sides of
each bounding box.
py (Optional[int]): The padding value to be added to both the top and bottom
sides of each bounding box. If not provided, `px` will be used for both
dimensions.
Returns:
np.ndarray: A numpy array of shape `(N, 4)` where each row corresponds to a
bounding box with coordinates padded according to the provided padding
values.
Examples:
```python
import numpy as np
import supervision as sv
xyxy = np.array([
[10, 20, 30, 40],
[15, 25, 35, 45]
])
sv.pad_boxes(xyxy=xyxy, px=5, py=10)
# array([
# [ 5, 10, 35, 50],
# [10, 15, 40, 55]
# ])
```
"""
if py is None:
py = px
result = xyxy.copy()
result[:, [0, 1]] -= [px, py]
result[:, [2, 3]] += [px, py]
return result
def xywh_to_xyxy(boxes_xywh: np.ndarray) -> np.ndarray:
xyxy = boxes_xywh.copy()
xyxy[:, 2] = boxes_xywh[:, 0] + boxes_xywh[:, 2]
@ -317,7 +248,7 @@ def mask_to_xyxy(masks: np.ndarray) -> np.ndarray:
`(x_min, y_min, x_max, y_max)` for each mask
"""
n = masks.shape[0]
bboxes = np.zeros((n, 4), dtype=int)
xyxy = np.zeros((n, 4), dtype=int)
for i, mask in enumerate(masks):
rows, cols = np.where(mask)
@ -325,9 +256,9 @@ def mask_to_xyxy(masks: np.ndarray) -> np.ndarray:
if len(rows) > 0 and len(cols) > 0:
x_min, x_max = np.min(cols), np.max(cols)
y_min, y_max = np.min(rows), np.max(rows)
bboxes[i, :] = [x_min, y_min, x_max, y_max]
xyxy[i, :] = [x_min, y_min, x_max, y_max]
return bboxes
return xyxy
def mask_to_polygons(mask: np.ndarray) -> List[np.ndarray]:
@ -500,7 +431,7 @@ def process_roboflow_result(
np.ndarray,
Optional[np.ndarray],
Optional[np.ndarray],
Dict[str, List[np.ndarray]],
Dict[str, Union[List[np.ndarray], np.ndarray]],
]:
if not roboflow_result["predictions"]:
return (
@ -563,59 +494,103 @@ def process_roboflow_result(
return xyxy, confidence, class_id, masks, tracker_id, data
def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
def move_boxes(
xyxy: npt.NDArray[np.float64], offset: npt.NDArray[np.int32]
) -> npt.NDArray[np.float64]:
"""
Parameters:
xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
coordinates in format `[x1, y1, x2, y2]`
xyxy (npt.NDArray[np.float64]): An array of shape `(n, 4)` containing the
bounding boxes coordinates in format `[x1, y1, x2, y2]`
offset (np.array): An array of shape `(2,)` containing offset values in format
is `[dx, dy]`.
Returns:
np.ndarray: Repositioned bounding boxes.
npt.NDArray[np.float64]: Repositioned bounding boxes.
Example:
Examples:
```python
import numpy as np
import supervision as sv
boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
xyxy = np.array([
[10, 10, 20, 20],
[30, 30, 40, 40]
])
offset = np.array([5, 5])
moved_box = sv.move_boxes(boxes, offset)
print(moved_box)
# np.array([
sv.move_boxes(xyxy=xyxy, offset=offset)
# array([
# [15, 15, 25, 25],
# [35, 35, 45, 45]
# [35, 35, 45, 45]
# ])
```
"""
return xyxy + np.hstack([offset, offset])
def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
def move_masks(
masks: npt.NDArray[np.bool_],
offset: npt.NDArray[np.int32],
resolution_wh: Tuple[int, int],
) -> npt.NDArray[np.bool_]:
"""
Offset the masks in an array by the specified (x, y) amount.
Args:
masks (npt.NDArray[np.bool_]): A 3D array of binary masks corresponding to the
predictions. Shape: `(N, H, W)`, where N is the number of predictions, and
H, W are the dimensions of each mask.
offset (npt.NDArray[np.int32]): An array of shape `(2,)` containing non-negative
int values `[dx, dy]`.
resolution_wh (Tuple[int, int]): The width and height of the desired mask
resolution.
Returns:
(npt.NDArray[np.bool_]) repositioned masks, optionally padded to the specified
shape.
"""
if offset[0] < 0 or offset[1] < 0:
raise ValueError(f"Offset values must be non-negative integers. Got: {offset}")
mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
mask_array[
:,
offset[1] : masks.shape[1] + offset[1],
offset[0] : masks.shape[2] + offset[0],
] = masks
return mask_array
def scale_boxes(
xyxy: npt.NDArray[np.float64], factor: float
) -> npt.NDArray[np.float64]:
"""
Scale the dimensions of bounding boxes.
Parameters:
xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
coordinates in format `[x1, y1, x2, y2]`
xyxy (npt.NDArray[np.float64]): An array of shape `(n, 4)` containing the
bounding boxes coordinates in format `[x1, y1, x2, y2]`
factor (float): A float value representing the factor by which the box
dimensions are scaled. A factor greater than 1 enlarges the boxes, while a
factor less than 1 shrinks them.
Returns:
np.ndarray: Scaled bounding boxes.
npt.NDArray[np.float64]: Scaled bounding boxes.
Example:
Examples:
```python
import numpy as np
import supervision as sv
boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
factor = 1.5
scaled_bb = sv.scale_boxes(boxes, factor)
print(scaled_bb)
# np.array([
xyxy = np.array([
[10, 10, 20, 20],
[30, 30, 40, 40]
])
sv.scale_boxes(xyxy=xyxy, factor=1.5)
# array([
# [ 7.5, 7.5, 22.5, 22.5],
# [27.5, 27.5, 42.5, 42.5]
# ])
@ -672,17 +647,19 @@ def is_data_equal(data_a: Dict[str, np.ndarray], data_b: Dict[str, np.ndarray])
def merge_data(
data_list: List[Dict[str, Union[np.ndarray, List]]],
) -> Dict[str, Union[np.ndarray, List]]:
data_list: List[Dict[str, Union[npt.NDArray[np.generic], List]]],
) -> Dict[str, Union[npt.NDArray[np.generic], List]]:
"""
Merges the data payloads of a list of Detections instances.
Args:
data_list: The data payloads of the instances.
data_list: The data payloads of the Detections instances. Each data payload
is a dictionary with the same keys, and the values are either lists or
npt.NDArray[np.generic].
Returns:
A single data payload containing the merged data, preserving the original data
types (list or np.ndarray).
types (list or npt.NDArray[np.generic]).
Raises:
ValueError: If data values within a single object have different lengths or if
@ -703,9 +680,8 @@ def merge_data(
)
merged_data = {key: [] for key in all_keys_sets[0]}
for data in data_list:
for key in merged_data:
for key in data:
merged_data[key].append(data[key])
for key in merged_data:
@ -766,3 +742,138 @@ def get_data_item(
raise TypeError(f"Unsupported data type for key '{key}': {type(value)}")
return subset_data
def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
"""
Checks if the binary mask contains holes (background pixels fully enclosed by
foreground pixels).
Args:
mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
object and `False` indicates background.
Returns:
True if holes are detected, False otherwise.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_holes(mask=mask)
# True
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_holes(mask=mask)
# False
```
![contains_holes](https://media.roboflow.com/supervision-docs/contains-holes.png){ align=center width="800" }
""" # noqa E501 // docs
mask_uint8 = mask.astype(np.uint8)
_, hierarchy = cv2.findContours(mask_uint8, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
if hierarchy is not None:
parent_contour_index = 3
for h in hierarchy[0]:
if h[parent_contour_index] != -1:
return True
return False
def contains_multiple_segments(
mask: npt.NDArray[np.bool_], connectivity: int = 4
) -> bool:
"""
Checks if the binary mask contains multiple unconnected foreground segments.
Args:
mask (npt.NDArray[np.bool_]): 2D binary mask where `True` indicates foreground
object and `False` indicates background.
connectivity (int) : Default: 4 is 4-way connectivity, which means that
foreground pixels are the part of the same segment/component
if their edges touch.
Alternatively: 8 for 8-way connectivity, when foreground pixels are
connected by their edges or corners touch.
Returns:
True when the mask contains multiple not connected components, False otherwise.
Raises:
ValueError: If connectivity(int) parameter value is not 4 or 8.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 1],
[0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 0, 0]
]).astype(bool)
sv.contains_multiple_segments(mask=mask, connectivity=4)
# True
mask = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_multiple_segments(mask=mask, connectivity=4)
# False
```
![contains_multiple_segments](https://media.roboflow.com/supervision-docs/contains-multiple-segments.png){ align=center width="800" }
""" # noqa E501 // docs
if connectivity != 4 and connectivity != 8:
raise ValueError(
"Incorrect connectivity value. Possible connectivity values: 4 or 8."
)
mask_uint8 = mask.astype(np.uint8)
labels = np.zeros_like(mask_uint8, dtype=np.int32)
number_of_labels, _ = cv2.connectedComponents(
mask_uint8, labels, connectivity=connectivity
)
return number_of_labels > 2
def cross_product(anchors: np.ndarray, vector: Vector) -> np.ndarray:
"""
Get array of cross products of each anchor with a vector.
Args:
anchors: Array of anchors of shape (number of anchors, detections, 2)
vector: Vector to calculate cross product with
Returns:
Array of cross products of shape (number of anchors, detections)
"""
vector_at_zero = np.array(
[vector.end.x - vector.start.x, vector.end.y - vector.start.y]
)
vector_start = np.array([vector.start.x, vector.start.y])
return np.cross(vector_at_zero, anchors - vector_start)

52
supervision/draw/utils.py
View File

@ -81,6 +81,58 @@ def draw_filled_rectangle(scene: np.ndarray, rect: Rect, color: Color) -> np.nda
return scene
def draw_rounded_rectangle(
scene: np.ndarray,
rect: Rect,
color: Color,
border_radius: int,
) -> np.ndarray:
"""
Draws a rounded rectangle on an image.
Parameters:
scene (np.ndarray): The image on which the rounded rectangle will be drawn.
rect (Rect): The rectangle to be drawn.
color (Color): The color of the rounded rectangle.
border_radius (int): The radius of the corner rounding.
Returns:
np.ndarray: The image with the rounded rectangle drawn on it.
"""
x1, y1, x2, y2 = rect.as_xyxy_int_tuple()
width, height = x2 - x1, y2 - y1
border_radius = min(border_radius, min(width, height) // 2)
rectangle_coordinates = [
((x1 + border_radius, y1), (x2 - border_radius, y2)),
((x1, y1 + border_radius), (x2, y2 - border_radius)),
]
circle_centers = [
(x1 + border_radius, y1 + border_radius),
(x2 - border_radius, y1 + border_radius),
(x1 + border_radius, y2 - border_radius),
(x2 - border_radius, y2 - border_radius),
]
for coordinates in rectangle_coordinates:
cv2.rectangle(
img=scene,
pt1=coordinates[0],
pt2=coordinates[1],
color=color.as_bgr(),
thickness=-1,
)
for center in circle_centers:
cv2.circle(
img=scene,
center=center,
radius=border_radius,
color=color.as_bgr(),
thickness=-1,
)
return scene
def draw_polygon(
scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
) -> np.ndarray:

13
supervision/geometry/core.py
View File

@ -98,6 +98,11 @@ class Rect:
width: float
height: float
@classmethod
def from_xyxy(cls, xyxy: Tuple[float, float, float, float]) -> Rect:
x1, y1, x2, y2 = xyxy
return cls(x=x1, y=y1, width=x2 - x1, height=y2 - y1)
@property
def top_left(self) -> Point:
return Point(x=self.x, y=self.y)
@ -113,3 +118,11 @@ class Rect:
width=self.width + 2 * padding,
height=self.height + 2 * padding,
)
def as_xyxy_int_tuple(self) -> Tuple[int, int, int, int]:
return (
int(self.x),
int(self.y),
int(self.x + self.width),
int(self.y + self.height),
)

253
supervision/keypoint/annotators.py
View File

@ -1,12 +1,14 @@
from abc import ABC, abstractmethod
from logging import warn
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
from supervision import Rect, pad_boxes
from supervision.annotators.base import ImageType
from supervision.draw.color import Color
from supervision.draw.utils import draw_rounded_rectangle
from supervision.keypoint.core import KeyPoints
from supervision.keypoint.skeletons import SKELETONS_BY_VERTEX_COUNT
from supervision.utils.conversion import convert_for_annotation_method
@ -46,8 +48,8 @@ class VertexAnnotator(BaseKeyPointAnnotator):
points. It draws circles at each key point location.
Args:
scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
is a flexible type, accepting either `numpy.ndarray` or
scene (ImageType): The image where skeleton vertices will be drawn.
`ImageType` is a flexible type, accepting either `numpy.ndarray` or
`PIL.Image.Image`.
key_points (KeyPoints): A collection of key points where each key point
consists of x and y coordinates.
@ -63,7 +65,10 @@ class VertexAnnotator(BaseKeyPointAnnotator):
image = ...
key_points = sv.KeyPoints(...)
vertex_annotator = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
vertex_annotator = sv.VertexAnnotator(
color=sv.Color.GREEN,
radius=10
)
annotated_frame = vertex_annotator.annotate(
scene=image.copy(),
key_points=key_points
@ -119,7 +124,7 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
edges.
Args:
scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
scene (ImageType): The image where skeleton edges will be drawn. `ImageType`
is a flexible type, accepting either `numpy.ndarray` or
`PIL.Image.Image`.
key_points (KeyPoints): A collection of key points where each key point
@ -137,7 +142,10 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
image = ...
key_points = sv.KeyPoints(...)
edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
edge_annotator = sv.EdgeAnnotator(
color=sv.Color.GREEN,
thickness=5
)
annotated_frame = edge_annotator.annotate(
scene=image.copy(),
key_points=key_points
@ -175,3 +183,236 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
)
return scene
class VertexLabelAnnotator:
"""
A class that draws labels of skeleton vertices on images. It uses specified key
points to determine the locations where the vertices should be drawn.
"""
def __init__(
self,
color: Union[Color, List[Color]] = Color.ROBOFLOW,
text_color: Color = Color.WHITE,
text_scale: float = 0.5,
text_thickness: int = 1,
text_padding: int = 10,
border_radius: int = 0,
):
"""
Args:
color (Union[Color, List[Color]], optional): The color to use for each
keypoint label. If a list is provided, the colors will be used in order
for each keypoint.
text_color (Color, optional): The color to use for the labels.
text_scale (float, optional): The scale of the text.
text_thickness (int, optional): The thickness of the text.
text_padding (int, optional): The padding around the text.
border_radius (int, optional): The radius of the rounded corners of the
boxes. Set to a high value to produce circles.
"""
self.border_radius: int = border_radius
self.color: Union[Color, List[Color]] = color
self.text_color: Color = text_color
self.text_scale: float = text_scale
self.text_thickness: int = text_thickness
self.text_padding: int = text_padding
def annotate(
self, scene: ImageType, key_points: KeyPoints, labels: List[str] = None
) -> ImageType:
"""
A class that draws labels of skeleton vertices on images. It uses specified key
points to determine the locations where the vertices should be drawn.
Args:
scene (ImageType): The image where vertex labels will be drawn. `ImageType`
is a flexible type, accepting either `numpy.ndarray` or
`PIL.Image.Image`.
key_points (KeyPoints): A collection of key points where each key point
consists of x and y coordinates.
labels (List[str], optional): A list of labels to be displayed on the
annotated image. If not provided, keypoint indices will be used.
Returns:
The annotated image, matching the type of `scene` (`numpy.ndarray`
or `PIL.Image.Image`)
Example:
```python
import supervision as sv
image = ...
key_points = sv.KeyPoints(...)
vertex_label_annotator = sv.VertexLabelAnnotator(
color=sv.Color.GREEN,
text_color=sv.Color.BLACK,
border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
scene=image.copy(),
key_points=key_points
)
```
![vertex-label-annotator-example](https://media.roboflow.com/
supervision-annotator-examples/vertex-label-annotator-example.png)
!!! tip
`VertexLabelAnnotator` allows to customize the color of each keypoint label
values.
Example:
```python
import supervision as sv
image = ...
key_points = sv.KeyPoints(...)
LABELS = [
"nose", "left eye", "right eye", "left ear",
"right ear", "left shoulder", "right shoulder", "left elbow",
"right elbow", "left wrist", "right wrist", "left hip",
"right hip", "left knee", "right knee", "left ankle",
"right ankle"
]
COLORS = [
"#FF6347", "#FF6347", "#FF6347", "#FF6347",
"#FF6347", "#FF1493", "#00FF00", "#FF1493",
"#00FF00", "#FF1493", "#00FF00", "#FFD700",
"#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
"#00BFFF"
]
COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]
vertex_label_annotator = sv.VertexLabelAnnotator(
color=COLORS,
text_color=sv.Color.BLACK,
border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
scene=image.copy(),
key_points=key_points,
labels=labels
)
```
![vertex-label-annotator-custom-example](https://media.roboflow.com/
supervision-annotator-examples/vertex-label-annotator-custom-example.png)
"""
font = cv2.FONT_HERSHEY_SIMPLEX
skeletons_count, points_count, _ = key_points.xy.shape
if skeletons_count == 0:
return scene
anchors = key_points.xy.reshape(points_count * skeletons_count, 2).astype(int)
mask = np.all(anchors != 0, axis=1)
if not np.any(mask):
return scene
colors = self.preprocess_and_validate_colors(
colors=self.color,
points_count=points_count,
skeletons_count=skeletons_count,
)
labels = self.preprocess_and_validate_labels(
labels=labels, points_count=points_count, skeletons_count=skeletons_count
)
anchors = anchors[mask]
colors = colors[mask]
labels = labels[mask]
xyxy = np.array(
[
self.get_text_bounding_box(
text=label,
font=font,
text_scale=self.text_scale,
text_thickness=self.text_thickness,
center_coordinates=tuple(anchor),
)
for anchor, label in zip(anchors, labels)
]
)
xyxy_padded = pad_boxes(xyxy=xyxy, px=self.text_padding)
for text, color, box, box_padded in zip(labels, colors, xyxy, xyxy_padded):
draw_rounded_rectangle(
scene=scene,
rect=Rect.from_xyxy(box_padded),
color=color,
border_radius=self.border_radius,
)
cv2.putText(
img=scene,
text=text,
org=(box[0], box[1] + self.text_padding),
fontFace=font,
fontScale=self.text_scale,
color=self.text_color.as_rgb(),
thickness=self.text_thickness,
lineType=cv2.LINE_AA,
)
return scene
@staticmethod
def get_text_bounding_box(
text: str,
font: int,
text_scale: float,
text_thickness: int,
center_coordinates: Tuple[int, int],
) -> Tuple[int, int, int, int]:
text_w, text_h = cv2.getTextSize(
text=text,
fontFace=font,
fontScale=text_scale,
thickness=text_thickness,
)[0]
center_x, center_y = center_coordinates
return (
center_x - text_w // 2,
center_y - text_h // 2,
center_x + text_w // 2,
center_y + text_h // 2,
)
@staticmethod
def preprocess_and_validate_labels(
labels: Optional[List[str]], points_count: int, skeletons_count: int
) -> np.array:
if labels and len(labels) != points_count:
raise ValueError(
f"Number of labels ({len(labels)}) must match number of key points "
f"({points_count})."
)
if labels is None:
labels = [str(i) for i in range(points_count)]
return np.array(labels * skeletons_count)
@staticmethod
def preprocess_and_validate_colors(
colors: Optional[Union[Color, List[Color]]],
points_count: int,
skeletons_count: int,
) -> np.array:
if isinstance(colors, list) and len(colors) != points_count:
raise ValueError(
f"Number of colors ({len(colors)}) must match number of key points "
f"({points_count})."
)
return (
np.array(colors * skeletons_count)
if isinstance(colors, list)
else np.array([colors] * points_count * skeletons_count)
)

148
supervision/keypoint/core.py
View File

@ -1,5 +1,6 @@
from __future__ import annotations
from contextlib import suppress
from dataclasses import dataclass, field
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
@ -100,10 +101,94 @@ class KeyPoints:
]
)
@classmethod
def from_inference(cls, inference_result: Union[dict, Any]) -> KeyPoints:
"""
Create a `sv.KeyPoints` object from the [Roboflow](https://roboflow.com/)
API inference result or the [Inference](https://inference.roboflow.com/)
package results. When a keypoint detection model is used, this method
extracts the keypoint coordinates, class IDs, confidences, and class names.
Args:
inference_result (dict, any): The result from the
Roboflow API or Inference package containing predictions with keypoints.
Returns:
(KeyPoints): A KeyPoints object containing the keypoint coordinates,
class IDs, and confidences of each keypoint.
Example:
```python
import cv2
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)
result = model.infer(image)[0]
key_points = sv.KeyPoints.from_inference(result)
```
```python
import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient
image = cv2.imread(<SOURCE_IMAGE_PATH>)
client = InferenceHTTPClient(
api_url="https://detect.roboflow.com",
api_key=<ROBOFLOW_API_KEY>
)
result = client.infer(image, model_id=<POSE_MODEL_ID>)
key_points = sv.KeyPoints.from_inference(result)
```
"""
if isinstance(inference_result, list):
raise ValueError(
"from_inference() operates on a single result at a time."
"You can retrieve it like so: inference_result = model.infer(image)[0]"
)
# Unpack the result if received from inference.get_model,
# rather than inference_sdk.InferenceHTTPClient
with suppress(AttributeError):
inference_result = inference_result.dict(exclude_none=True, by_alias=True)
if not inference_result.get("predictions"):
return cls.empty()
xy = []
confidence = []
class_id = []
class_names = []
for prediction in inference_result["predictions"]:
prediction_xy = []
prediction_confidence = []
for keypoint in prediction["keypoints"]:
prediction_xy.append([keypoint["x"], keypoint["y"]])
prediction_confidence.append(keypoint["confidence"])
xy.append(prediction_xy)
confidence.append(prediction_confidence)
class_id.append(prediction["class_id"])
class_names.append(prediction["class"])
data = {CLASS_NAME_DATA_FIELD: np.array(class_names)}
return cls(
xy=np.array(xy, dtype=np.float32),
confidence=np.array(confidence, dtype=np.float32),
class_id=np.array(class_id, dtype=int),
data=data,
)
@classmethod
def from_ultralytics(cls, ultralytics_results) -> KeyPoints:
"""
Creates a Keypoints instance from a
Creates a KeyPoints instance from a
[YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
Args:
@ -111,7 +196,7 @@ class KeyPoints:
The output Results instance from YOLOv8
Returns:
KeyPoints: A new Keypoints object.
KeyPoints: A new KeyPoints object.
Example:
```python
@ -136,9 +221,66 @@ class KeyPoints:
data = {CLASS_NAME_DATA_FIELD: class_names}
return cls(xy, class_id, confidence, data)
@classmethod
def from_yolo_nas(cls, yolo_nas_results) -> KeyPoints:
"""
Create a KeyPoints instance from a YOLO NAS results.
Args:
yolo_nas_results (ImagePoseEstimationPrediction):
The output object from YOLO NAS.
Returns:
KeyPoints: A new KeyPoints object.
Example:
```python
import cv2
import torch
import supervision as sv
import super_gradients
image = cv2.imread(<SOURCE_IMAGE_PATH>)
device = "cuda" if torch.cuda.is_available() else "cpu"
yolo_nas = super_gradients.training.models.get(
"yolo_nas_pose_s", pretrained_weights="coco_pose").to(device)
results = yolo_nas.predict(image, conf=0.1)
keypoints = sv.KeyPoints.from_yolo_nas(results)
```
"""
if len(yolo_nas_results.prediction.poses) == 0:
return cls.empty()
xy = yolo_nas_results.prediction.poses[:, :, :2]
confidence = yolo_nas_results.prediction.poses[:, :, 2]
# yolo_nas_results treats params differently.
# prediction.labels may not exist, whereas class_names might be None
if hasattr(yolo_nas_results.prediction, "labels"):
class_id = yolo_nas_results.prediction.labels # np.array[int]
else:
class_id = None
data = {}
if class_id is not None and yolo_nas_results.class_names is not None:
class_names = []
for c_id in class_id:
name = yolo_nas_results.class_names[c_id] # tuple[str]
class_names.append(name)
data[CLASS_NAME_DATA_FIELD] = class_names
return cls(
xy=xy,
confidence=confidence,
class_id=class_id,
data=data,
)
def __getitem__(
self, index: Union[int, slice, List[int], np.ndarray, str]
) -> Union["KeyPoints", List, np.ndarray, None]:
) -> Union[KeyPoints, List, np.ndarray, None]:
"""
Get a subset of the KeyPoints object or access an item from its data field.

75
supervision/tracker/byte_tracker/core.py
View File

@ -12,8 +12,9 @@ from supervision.utils.internal import deprecated_parameter
class STrack(BaseTrack):
shared_kalman = KalmanFilter()
_external_count = 0
def __init__(self, tlwh, score, class_ids):
def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float32)
self.kalman_filter = None
@ -24,6 +25,10 @@ class STrack(BaseTrack):
self.class_ids = class_ids
self.tracklet_len = 0
self.external_track_id = -1
self.minimum_consecutive_frames = minimum_consecutive_frames
def predict(self):
mean_state = self.mean.copy()
if self.state != TrackState.Tracked:
@ -53,7 +58,7 @@ class STrack(BaseTrack):
def activate(self, kalman_filter, frame_id):
"""Start a new tracklet"""
self.kalman_filter = kalman_filter
self.track_id = self.next_id()
self.internal_track_id = self.next_id()
self.mean, self.covariance = self.kalman_filter.initiate(
self.tlwh_to_xyah(self._tlwh)
)
@ -62,6 +67,10 @@ class STrack(BaseTrack):
self.state = TrackState.Tracked
if frame_id == 1:
self.is_activated = True
if self.minimum_consecutive_frames == 1:
self.external_track_id = self.next_external_id()
self.frame_id = frame_id
self.start_frame = frame_id
@ -71,10 +80,10 @@ class STrack(BaseTrack):
)
self.tracklet_len = 0
self.state = TrackState.Tracked
self.is_activated = True
self.frame_id = frame_id
if new_id:
self.track_id = self.next_id()
self.internal_track_id = self.next_id()
self.score = new_track.score
def update(self, new_track, frame_id):
@ -93,7 +102,10 @@ class STrack(BaseTrack):
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
)
self.state = TrackState.Tracked
self.is_activated = True
if self.tracklet_len == self.minimum_consecutive_frames:
self.is_activated = True
if self.external_track_id == -1:
self.external_track_id = self.next_external_id()
self.score = new_track.score
@ -131,6 +143,15 @@ class STrack(BaseTrack):
def to_xyah(self):
return self.tlwh_to_xyah(self.tlwh)
@staticmethod
def next_external_id():
STrack._external_count += 1
return STrack._external_count
@staticmethod
def reset_external_counter():
STrack._external_count = 0
@staticmethod
def tlbr_to_tlwh(tlbr):
ret = np.asarray(tlbr).copy()
@ -144,7 +165,9 @@ class STrack(BaseTrack):
return ret
def __repr__(self):
return "OT_{}_({}-{})".format(self.track_id, self.start_frame, self.end_frame)
return "OT_{}_({}-{})".format(
self.internal_track_id, self.start_frame, self.end_frame
)
def detections2boxes(detections: Detections) -> np.ndarray:
@ -186,6 +209,10 @@ class ByteTrack:
Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
Decreasing it improves completeness but risks false positives and drift.
frame_rate (int, optional): The frame rate of the video.
minimum_consecutive_frames (int, optional): Number of consecutive frames that an object must
be tracked before it is considered a 'valid' track.
Increasing minimum_consecutive_frames prevents the creation of accidental tracks from
false detection or double detection, but risks missing shorter tracks.
""" # noqa: E501 // docs
@deprecated_parameter(
@ -218,6 +245,7 @@ class ByteTrack:
lost_track_buffer: int = 30,
minimum_matching_threshold: float = 0.8,
frame_rate: int = 30,
minimum_consecutive_frames: int = 1,
):
self.track_activation_threshold = track_activation_threshold
self.minimum_matching_threshold = minimum_matching_threshold
@ -225,6 +253,7 @@ class ByteTrack:
self.frame_id = 0
self.det_thresh = self.track_activation_threshold + 0.1
self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
self.minimum_consecutive_frames = minimum_consecutive_frames
self.kalman_filter = KalmanFilter()
self.tracked_tracks: List[STrack] = []
@ -285,11 +314,14 @@ class ByteTrack:
matches, _, _ = matching.linear_assignment(iou_costs, 0.5)
detections.tracker_id = np.full(len(detections), -1, dtype=int)
for i_detection, i_track in matches:
detections.tracker_id[i_detection] = int(tracks[i_track].track_id)
detections.tracker_id[i_detection] = int(
tracks[i_track].external_track_id
)
return detections[detections.tracker_id != -1]
else:
detections = Detections.empty()
detections.tracker_id = np.array([], dtype=int)
return detections
@ -308,6 +340,7 @@ class ByteTrack:
self.lost_tracks: List[STrack] = []
self.removed_tracks: List[STrack] = []
BaseTrack.reset_counter()
STrack.reset_external_counter()
def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
"""
@ -345,7 +378,7 @@ class ByteTrack:
if len(dets) > 0:
"""Detections"""
detections = [
STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
]
else:
@ -387,7 +420,7 @@ class ByteTrack:
if len(dets_second) > 0:
"""Detections"""
detections_second = [
STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
]
else:
@ -454,7 +487,7 @@ class ByteTrack:
self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
self.lost_tracks.extend(lost_stracks)
self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
self.removed_tracks.extend(removed_stracks)
self.removed_tracks = removed_stracks
self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
self.tracked_tracks, self.lost_tracks
)
@ -468,22 +501,22 @@ def joint_tracks(
) -> List[STrack]:
"""
Joins two lists of tracks, ensuring that the resulting list does not
contain tracks with duplicate track_id values.
contain tracks with duplicate internal_track_id values.
Parameters:
track_list_a: First list of tracks (with track_id attribute).
track_list_b: Second list of tracks (with track_id attribute).
track_list_a: First list of tracks (with internal_track_id attribute).
track_list_b: Second list of tracks (with internal_track_id attribute).
Returns:
Combined list of tracks from track_list_a and track_list_b
without duplicate track_id values.
without duplicate internal_track_id values.
"""
seen_track_ids = set()
result = []
for track in track_list_a + track_list_b:
if track.track_id not in seen_track_ids:
seen_track_ids.add(track.track_id)
if track.internal_track_id not in seen_track_ids:
seen_track_ids.add(track.internal_track_id)
result.append(track)
return result
@ -492,17 +525,17 @@ def joint_tracks(
def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
"""
Returns a list of tracks from track_list_a after removing any tracks
that share the same track_id with tracks in track_list_b.
that share the same internal_track_id with tracks in track_list_b.
Parameters:
track_list_a: List of tracks (with track_id attribute).
track_list_b: List of tracks (with track_id attribute) to
track_list_a: List of tracks (with internal_track_id attribute).
track_list_b: List of tracks (with internal_track_id attribute) to
be subtracted from track_list_a.
Returns:
List of remaining tracks from track_list_a after subtraction.
"""
tracks = {track.track_id: track for track in track_list_a}
track_ids_b = {track.track_id for track in track_list_b}
tracks = {track.internal_track_id: track for track in track_list_a}
track_ids_b = {track.internal_track_id for track in track_list_b}
for track_id in track_ids_b:
tracks.pop(track_id, None)

42
supervision/utils/internal.py
View File

@ -1,7 +1,8 @@
import functools
import inspect
import os
import warnings
from typing import Callable
from typing import Any, Callable, Set
class SupervisionWarnings(Warning):
@ -141,3 +142,42 @@ class classproperty(property):
The result of calling the function stored in 'fget' with 'owner_cls'.
"""
return self.fget(owner_cls)
def get_instance_variables(instance: Any, include_properties=False) -> Set[str]:
"""
Get the public variables of a class instance.
Args:
instance (Any): The instance of a class
include_properties (bool): Whether to include properties in the result
Usage:
```python
detections = Detections(xyxy=np.array([1,2,3,4]))
variables = get_class_variables(detections)
# ["xyxy", "mask", "confidence", ..., "data"]
```
"""
if isinstance(instance, type):
raise ValueError("Only class instances are supported, not classes.")
fields = set(
(
name
for name, val in inspect.getmembers(instance)
if not callable(val) and not name.startswith("_")
)
)
if not include_properties:
properties = set(
(
name
for name, val in inspect.getmembers(instance.__class__)
if isinstance(val, property)
)
)
fields -= properties
return fields

364
test/dataset/formats/test_coco.py
View File

@ -1,5 +1,5 @@
from contextlib import ExitStack as DoesNotRaise
from typing import Dict, List, Tuple
from typing import Dict, List, Tuple, Union
import numpy as np
import pytest
@ -10,24 +10,30 @@ from supervision.dataset.formats.coco import (
classes_to_coco_categories,
coco_annotations_to_detections,
coco_categories_to_classes,
detections_to_coco_annotations,
group_coco_annotations_by_image_id,
)
def mock_cock_coco_annotation(
def mock_coco_annotation(
annotation_id: int = 0,
image_id: int = 0,
category_id: int = 0,
bbox: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
area: float = 0.0,
segmentation: Union[List[list], Dict] = None,
iscrowd: bool = False,
) -> dict:
if not segmentation:
segmentation = []
return {
"id": annotation_id,
"image_id": image_id,
"category_id": category_id,
"bbox": list(bbox),
"area": area,
"iscrowd": 0,
"segmentation": segmentation,
"iscrowd": int(iscrowd),
}
@ -101,74 +107,46 @@ def test_classes_to_coco_categories_and_back_to_classes(
[
([], {}, DoesNotRaise()), # empty coco annotations
(
[mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
{
0: [
mock_cock_coco_annotation(
annotation_id=0, image_id=0, category_id=0
)
]
},
[mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
{0: [mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)]},
DoesNotRaise(),
), # single coco annotation
(
[
mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
mock_cock_coco_annotation(annotation_id=1, image_id=1, category_id=0),
mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
mock_coco_annotation(annotation_id=1, image_id=1, category_id=0),
],
{
0: [
mock_cock_coco_annotation(
annotation_id=0, image_id=0, category_id=0
)
],
1: [
mock_cock_coco_annotation(
annotation_id=1, image_id=1, category_id=0
)
],
0: [mock_coco_annotation(annotation_id=0, image_id=0, category_id=0)],
1: [mock_coco_annotation(annotation_id=1, image_id=1, category_id=0)],
},
DoesNotRaise(),
), # two coco annotations
(
[
mock_cock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
mock_cock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
mock_cock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
mock_cock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
mock_cock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
mock_cock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
mock_cock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
mock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
mock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
mock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
mock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
mock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
mock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
],
{
0: [
mock_cock_coco_annotation(
annotation_id=0, image_id=0, category_id=0
),
mock_coco_annotation(annotation_id=0, image_id=0, category_id=0),
],
1: [
mock_cock_coco_annotation(
annotation_id=1, image_id=1, category_id=1
),
mock_cock_coco_annotation(
annotation_id=2, image_id=1, category_id=2
),
mock_coco_annotation(annotation_id=1, image_id=1, category_id=1),
mock_coco_annotation(annotation_id=2, image_id=1, category_id=2),
],
2: [
mock_cock_coco_annotation(
annotation_id=3, image_id=2, category_id=3
),
mock_coco_annotation(annotation_id=3, image_id=2, category_id=3),
],
3: [
mock_cock_coco_annotation(
annotation_id=4, image_id=3, category_id=1
),
mock_cock_coco_annotation(
annotation_id=5, image_id=3, category_id=2
),
mock_cock_coco_annotation(
annotation_id=5, image_id=3, category_id=3
),
mock_coco_annotation(annotation_id=4, image_id=3, category_id=1),
mock_coco_annotation(annotation_id=5, image_id=3, category_id=2),
mock_coco_annotation(annotation_id=5, image_id=3, category_id=3),
],
},
DoesNotRaise(),
@ -195,7 +173,7 @@ def test_group_coco_annotations_by_image_id(
), # empty image annotations
(
[
mock_cock_coco_annotation(
mock_coco_annotation(
category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
)
],
@ -209,10 +187,10 @@ def test_group_coco_annotations_by_image_id(
), # single image annotations
(
[
mock_cock_coco_annotation(
mock_coco_annotation(
category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
),
mock_cock_coco_annotation(
mock_coco_annotation(
category_id=0, bbox=(100, 100, 100, 100), area=100 * 100
),
],
@ -226,6 +204,156 @@ def test_group_coco_annotations_by_image_id(
),
DoesNotRaise(),
), # two image annotations
(
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
)
],
(5, 5),
True,
Detections(
xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
[
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
]
]
),
),
DoesNotRaise(),
), # single image annotations with mask as polygon
(
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation={
"size": [5, 5],
"counts": [0, 15, 2, 3, 2, 3],
},
iscrowd=True,
)
],
(5, 5),
True,
Detections(
xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
[
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
]
]
),
),
DoesNotRaise(),
), # single image annotations with mask, RLE segmentation mask
(
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
),
mock_coco_annotation(
category_id=0,
bbox=(3, 0, 2, 2),
area=2 * 2,
segmentation={
"size": [5, 5],
"counts": [15, 2, 3, 2, 3],
},
iscrowd=True,
),
],
(5, 5),
True,
Detections(
xyxy=np.array([[0, 0, 5, 5], [3, 0, 5, 2]], dtype=np.float32),
class_id=np.array([0, 0], dtype=int),
mask=np.array(
[
[
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
],
[
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
]
),
),
DoesNotRaise(),
), # two image annotations with mask, one mask as polygon ans second as RLE
(
[
mock_coco_annotation(
category_id=0,
bbox=(3, 0, 2, 2),
area=2 * 2,
segmentation={
"size": [5, 5],
"counts": [15, 2, 3, 2, 3],
},
iscrowd=True,
),
mock_coco_annotation(
category_id=1,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
),
],
(5, 5),
True,
Detections(
xyxy=np.array([[3, 0, 5, 2], [0, 0, 5, 5]], dtype=np.float32),
class_id=np.array([0, 1], dtype=int),
mask=np.array(
[
[
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
[
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
],
]
),
),
DoesNotRaise(),
), # two image annotations with mask, first mask as RLE and second as polygon
],
)
def test_coco_annotations_to_detections(
@ -301,3 +429,131 @@ def test_build_coco_class_index_mapping(
coco_categories=coco_categories, target_classes=target_classes
)
assert result == expected_result
@pytest.mark.parametrize(
"detections, image_id, annotation_id, expected_result, exception",
[
(
Detections(
xyxy=np.array([[0, 0, 100, 100]], dtype=np.float32),
class_id=np.array([0], dtype=int),
),
0,
0,
[
mock_coco_annotation(
category_id=0, bbox=(0, 0, 100, 100), area=100 * 100
)
],
DoesNotRaise(),
), # no segmentation mask
(
Detections(
xyxy=np.array([[0, 0, 4, 5]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
[
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 0],
]
]
),
),
0,
0,
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 4, 5),
area=4 * 5,
segmentation=[[0, 0, 0, 4, 3, 4, 3, 0]],
)
],
DoesNotRaise(),
), # segmentation mask in single component,no holes in mask,
# expects polygon mask
(
Detections(
xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
[
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1],
]
]
),
),
0,
0,
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation={
"size": [5, 5],
"counts": [0, 3, 2, 3, 2, 3, 5, 2, 3, 2],
},
iscrowd=True,
)
],
DoesNotRaise(),
), # segmentation mask with 2 components, no holes in mask, expects RLE mask
(
Detections(
xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
[
[0, 1, 1, 1, 1],
[0, 1, 1, 1, 1],
[1, 1, 0, 0, 1],
[1, 1, 0, 0, 1],
[1, 1, 1, 1, 1],
]
]
),
),
0,
0,
[
mock_coco_annotation(
category_id=0,
bbox=(0, 0, 5, 5),
area=5 * 5,
segmentation={
"size": [5, 5],
"counts": [2, 10, 2, 3, 2, 6],
},
iscrowd=True,
)
],
DoesNotRaise(),
), # seg mask in single component, with holes in mask, expects RLE mask
],
)
def test_detections_to_coco_annotations(
detections: Detections,
image_id: int,
annotation_id: int,
expected_result: List[Dict],
exception: Exception,
) -> None:
with exception:
result, _ = detections_to_coco_annotations(
detections=detections,
image_id=image_id,
annotation_id=annotation_id,
)
assert result == expected_result

132
test/dataset/test_utils.py
View File

@ -2,13 +2,17 @@ from contextlib import ExitStack as DoesNotRaise
from test.test_utils import mock_detections
from typing import Dict, List, Optional, Tuple, TypeVar
import numpy as np
import numpy.typing as npt
import pytest
from supervision import Detections
from supervision.dataset.utils import (
build_class_index_mapping,
map_detections_class_id,
mask_to_rle,
merge_class_lists,
rle_to_mask,
train_test_split,
)
@ -229,3 +233,131 @@ def test_map_detections_class_id(
source_to_target_mapping=source_to_target_mapping, detections=detections
)
assert result == expected_result
@pytest.mark.parametrize(
"mask, expected_rle, exception",
[
(
np.zeros((3, 3)).astype(bool),
[9],
DoesNotRaise(),
), # mask with background only (mask with only False values)
(
np.ones((3, 3)).astype(bool),
[0, 9],
DoesNotRaise(),
), # mask with foreground only (mask with only True values)
(
np.array(
[
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
]
).astype(bool),
[6, 3, 2, 1, 1, 1, 2, 3, 6],
DoesNotRaise(),
), # mask where foreground object has hole
(
np.array(
[
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
]
).astype(bool),
[0, 5, 5, 5, 5, 5],
DoesNotRaise(),
), # mask where foreground consists of 3 separate components
(
np.array([[[]]]).astype(bool),
None,
pytest.raises(AssertionError),
), # raises AssertionError because mask dimentionality is not 2D
(
np.array([[]]).astype(bool),
None,
pytest.raises(AssertionError),
), # raises AssertionError because mask is empty
],
)
def test_mask_to_rle(
mask: npt.NDArray[np.bool_], expected_rle: List[int], exception: Exception
) -> None:
with exception:
result = mask_to_rle(mask=mask)
assert result == expected_rle
@pytest.mark.parametrize(
"rle, resolution_wh, expected_mask, exception",
[
(
np.array([9]),
[3, 3],
np.zeros((3, 3)).astype(bool),
DoesNotRaise(),
), # mask with background only (mask with only False values); rle as array
(
[9],
[3, 3],
np.zeros((3, 3)).astype(bool),
DoesNotRaise(),
), # mask with background only (mask with only False values); rle as list
(
np.array([0, 9]),
[3, 3],
np.ones((3, 3)).astype(bool),
DoesNotRaise(),
), # mask with foreground only (mask with only True values)
(
np.array([6, 3, 2, 1, 1, 1, 2, 3, 6]),
[5, 5],
np.array(
[
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
]
).astype(bool),
DoesNotRaise(),
), # mask where foreground object has hole
(
np.array([0, 5, 5, 5, 5, 5]),
[5, 5],
np.array(
[
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
[1, 0, 1, 0, 1],
]
).astype(bool),
DoesNotRaise(),
), # mask where foreground consists of 3 separate components
(
np.array([0, 5, 5, 5, 5, 5]),
[2, 2],
None,
pytest.raises(AssertionError),
), # raises AssertionError because number of pixels in RLE does not match
# number of pixels in expected mask (width x height).
],
)
def test_rle_to_mask(
rle: npt.NDArray[np.int_],
resolution_wh: Tuple[int, int],
expected_mask: npt.NDArray[np.bool_],
exception: Exception,
) -> None:
with exception:
result = rle_to_mask(rle=rle, resolution_wh=resolution_wh)
assert np.all(result == expected_mask)

332
test/detection/test_core.py
View File

@ -5,7 +5,7 @@ from typing import List, Optional, Union
import numpy as np
import pytest
from supervision.detection.core import Detections
from supervision.detection.core import Detections, merge_inner_detection_object_pair
from supervision.geometry.core import Position
PREDICTIONS = np.array(
@ -30,6 +30,84 @@ DETECTIONS = Detections(
)
# Merge test
TEST_MASK = np.zeros((1000, 1000), dtype=bool)
TEST_MASK[300:351, 200:251] = True
TEST_DET_1 = Detections(
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40], [50, 50, 60, 60]]),
mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK]),
confidence=np.array([0.1, 0.2, 0.3]),
class_id=np.array([1, 2, 3]),
tracker_id=np.array([1, 2, 3]),
data={
"some_key": [1, 2, 3],
"other_key": [["1", "2"], ["3", "4"], ["5", "6"]],
},
)
TEST_DET_2 = Detections(
xyxy=np.array([[70, 70, 80, 80], [90, 90, 100, 100]]),
mask=np.array([TEST_MASK, TEST_MASK]),
confidence=np.array([0.4, 0.5]),
class_id=np.array([4, 5]),
tracker_id=np.array([4, 5]),
data={
"some_key": [4, 5],
"other_key": [["7", "8"], ["9", "10"]],
},
)
TEST_DET_1_2 = Detections(
xyxy=np.array(
[
[10, 10, 20, 20],
[30, 30, 40, 40],
[50, 50, 60, 60],
[70, 70, 80, 80],
[90, 90, 100, 100],
]
),
mask=np.array([TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK, TEST_MASK]),
confidence=np.array([0.1, 0.2, 0.3, 0.4, 0.5]),
class_id=np.array([1, 2, 3, 4, 5]),
tracker_id=np.array([1, 2, 3, 4, 5]),
data={
"some_key": [1, 2, 3, 4, 5],
"other_key": [["1", "2"], ["3", "4"], ["5", "6"], ["7", "8"], ["9", "10"]],
},
)
TEST_DET_ZERO_LENGTH = Detections(
xyxy=np.empty((0, 4), dtype=np.float32),
mask=np.empty((0, *TEST_MASK.shape), dtype=bool),
confidence=np.empty((0,)),
class_id=np.empty((0,)),
tracker_id=np.empty((0,)),
data={
"some_key": [],
"other_key": [],
},
)
TEST_DET_NONE = Detections(
xyxy=np.empty((0, 4), dtype=np.float32),
)
TEST_DET_DIFFERENT_FIELDS = Detections(
xyxy=np.array([[88, 88, 99, 99]]),
mask=np.array([np.logical_not(TEST_MASK)]),
confidence=None,
class_id=None,
tracker_id=np.array([9]),
data={"some_key": [9], "other_key": [["11", "12"]]},
)
TEST_DET_DIFFERENT_DATA = Detections(
xyxy=np.array([[88, 88, 99, 99]]),
mask=np.array([np.logical_not(TEST_MASK)]),
confidence=np.array([0.9]),
class_id=np.array([9]),
tracker_id=np.array([9]),
data={
"never_seen_key": [9],
},
)
@pytest.mark.parametrize(
"detections, index, expected_result, exception",
[
@ -148,52 +226,73 @@ def test_getitem(
DoesNotRaise(),
), # single empty detections
(
[mock_detections(xyxy=[[10, 10, 20, 20]])],
mock_detections(xyxy=[[10, 10, 20, 20]]),
[Detections.empty(), Detections.empty()],
Detections.empty(),
DoesNotRaise(),
), # single detection with xyxy field
), # two empty detections
(
[TEST_DET_1],
TEST_DET_1,
DoesNotRaise(),
), # single detection with fields
(
[TEST_DET_NONE],
TEST_DET_NONE,
DoesNotRaise(),
), # Single weakly-defined detection
(
[TEST_DET_1, TEST_DET_2],
TEST_DET_1_2,
DoesNotRaise(),
), # Fields with same keys
(
[TEST_DET_1, Detections.empty()],
TEST_DET_1,
DoesNotRaise(),
), # single detection with fields
(
[
mock_detections(xyxy=[[10, 10, 20, 20]]),
mock_detections(xyxy=np.empty((0, 4), dtype=np.float32)),
TEST_DET_1,
TEST_DET_ZERO_LENGTH,
],
mock_detections(xyxy=[[10, 10, 20, 20]]),
TEST_DET_1,
DoesNotRaise(),
), # single detection with xyxy field + empty detection
), # Single detection and empty-array fields
(
[
mock_detections(xyxy=[[10, 10, 20, 20]]),
mock_detections(xyxy=[[20, 20, 30, 30]]),
TEST_DET_1,
TEST_DET_NONE,
],
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
DoesNotRaise(),
), # two detections with xyxy field
(
[
mock_detections(xyxy=[[10, 10, 20, 20]], class_id=[0]),
mock_detections(xyxy=[[20, 20, 30, 30]]),
],
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
None,
pytest.raises(ValueError),
), # detection with xyxy, class_id fields + detection with xyxy field
), # Empty detection, but not Detections.empty()
# Errors: Non-zero-length differently defined keys & data
(
[TEST_DET_1, TEST_DET_DIFFERENT_FIELDS],
None,
pytest.raises(ValueError),
), # Non-empty detections with different fields
(
[TEST_DET_1, TEST_DET_DIFFERENT_DATA],
None,
pytest.raises(ValueError),
), # Non-empty detections with different data keys
(
[
mock_detections(xyxy=[[10, 10, 20, 20]], class_id=[0]),
mock_detections(xyxy=[[20, 20, 30, 30]], class_id=[1]),
],
mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], class_id=[0, 1]),
DoesNotRaise(),
), # two detections with xyxy, class_id fields
(
[
mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
mock_detections(xyxy=[[20, 20, 30, 30]], data={"test": [2]}),
mock_detections(
xyxy=[[10, 10, 20, 20]],
class_id=[1],
mask=[np.zeros((4, 4), dtype=bool)],
),
Detections.empty(),
],
mock_detections(
xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], data={"test": [1, 2]}
xyxy=[[10, 10, 20, 20]],
class_id=[1],
mask=[np.zeros((4, 4), dtype=bool)],
),
DoesNotRaise(),
), # two detections with xyxy, data fields
), # Segmentation + Empty
],
)
def test_merge(
@ -337,3 +436,172 @@ def test_equal(
detections_a: Detections, detections_b: Detections, expected_result: bool
) -> None:
assert (detections_a == detections_b) == expected_result
@pytest.mark.parametrize(
"detection_1, detection_2, expected_result, exception",
[
(
mock_detections(
xyxy=[[10, 10, 30, 30]],
),
mock_detections(
xyxy=[[10, 10, 30, 30]],
),
mock_detections(
xyxy=[[10, 10, 30, 30]],
),
DoesNotRaise(),
), # Merge with self
(
mock_detections(
xyxy=[[10, 10, 30, 30]],
),
Detections.empty(),
None,
pytest.raises(ValueError),
), # merge with empty: error
(
mock_detections(
xyxy=[[10, 10, 30, 30]],
),
mock_detections(
xyxy=[[10, 10, 30, 30], [40, 40, 60, 60]],
),
None,
pytest.raises(ValueError),
), # merge with 2+ objects: error
(
mock_detections(
xyxy=[[10, 10, 30, 30]],
confidence=[0.1],
class_id=[1],
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
tracker_id=[1],
data={"key_1": [1]},
),
mock_detections(
xyxy=[[20, 20, 40, 40]],
confidence=[0.1],
class_id=[2],
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[2],
data={"key_2": [2]},
),
mock_detections(
xyxy=[[10, 10, 40, 40]],
confidence=[0.1],
class_id=[1],
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[1],
data={"key_1": [1]},
),
DoesNotRaise(),
), # Same confidence - merge box & mask, tie-break to detection_1
(
mock_detections(
xyxy=[[0, 0, 20, 20]],
confidence=[0.1],
class_id=[1],
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
tracker_id=[1],
data={"key_1": [1]},
),
mock_detections(
xyxy=[[10, 10, 50, 50]],
confidence=[0.2],
class_id=[2],
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[2],
data={"key_2": [2]},
),
mock_detections(
xyxy=[[0, 0, 50, 50]],
confidence=[(1 * 0.1 + 4 * 0.2) / 5],
class_id=[2],
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[2],
data={"key_2": [2]},
),
DoesNotRaise(),
), # Different confidence, different area
(
mock_detections(
xyxy=[[10, 10, 30, 30]],
confidence=None,
class_id=[1],
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
tracker_id=[1],
data={"key_1": [1]},
),
mock_detections(
xyxy=[[20, 20, 40, 40]],
confidence=None,
class_id=[2],
mask=[np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[2],
data={"key_2": [2]},
),
mock_detections(
xyxy=[[10, 10, 40, 40]],
confidence=None,
class_id=[1],
mask=[np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]], dtype=bool)],
tracker_id=[1],
data={"key_1": [1]},
),
DoesNotRaise(),
), # No confidence at all
(
mock_detections(
xyxy=[[0, 0, 20, 20]],
confidence=None,
),
mock_detections(
xyxy=[[10, 10, 30, 30]],
confidence=[0.2],
),
None,
pytest.raises(ValueError),
), # confidence: None + [x]
(
mock_detections(
xyxy=[[0, 0, 20, 20]],
mask=[np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=bool)],
),
mock_detections(
xyxy=[[10, 10, 30, 30]],
mask=None,
),
None,
pytest.raises(ValueError),
), # mask: None + [x]
(
mock_detections(xyxy=[[0, 0, 20, 20]], tracker_id=[1]),
mock_detections(
xyxy=[[10, 10, 30, 30]],
tracker_id=None,
),
None,
pytest.raises(ValueError),
), # tracker_id: None + []
(
mock_detections(xyxy=[[0, 0, 20, 20]], class_id=[1]),
mock_detections(
xyxy=[[10, 10, 30, 30]],
class_id=None,
),
None,
pytest.raises(ValueError),
), # class_id: None + []
],
)
def test_merge_inner_detection_object_pair(
detection_1: Detections,
detection_2: Detections,
expected_result: Optional[Detections],
exception: Exception,
):
with exception:
result = merge_inner_detection_object_pair(detection_1, detection_2)
assert result == expected_result

411
test/detection/test_line_counter.py
View File

@ -1,10 +1,11 @@
from contextlib import ExitStack as DoesNotRaise
from typing import Optional, Tuple
from test.test_utils import mock_detections
from typing import List, Optional, Tuple
import pytest
from supervision import LineZone
from supervision.geometry.core import Point, Vector
from supervision.geometry.core import Point, Position, Vector
@pytest.mark.parametrize(
@ -70,3 +71,409 @@ def test_calculate_region_of_interest_limits(
with exception:
result = LineZone.calculate_region_of_interest_limits(vector=vector)
assert result == expected_result
@pytest.mark.parametrize(
"vector, xyxy_sequence, expected_crossed_in, expected_crossed_out",
[
( # Vertical line, simple crossing
Vector(Point(0, 0), Point(0, 10)),
[
[4, 4, 6, 6],
[4 - 10, 4, 6 - 10, 6],
[4, 4, 6, 6],
[4 - 10, 4, 6 - 10, 6],
],
[False, False, True, False],
[False, True, False, True],
),
( # Vertical line reversed, simple crossing
Vector(Point(0, 10), Point(0, 0)),
[
[4, 4, 6, 6],
[4 - 10, 4, 6 - 10, 6],
[4, 4, 6, 6],
[4 - 10, 4, 6 - 10, 6],
],
[False, True, False, True],
[False, False, True, False],
),
( # Horizontal line, simple crossing
Vector(Point(0, 0), Point(10, 0)),
[
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
],
[False, True, False, True],
[False, False, True, False],
),
( # Horizontal line reversed, simple crossing
Vector(Point(10, 0), Point(0, 0)),
[
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
],
[False, False, True, False],
[False, True, False, True],
),
( # Diagonal line, simple crossing
Vector(Point(5, 0), Point(0, 5)),
[
[0, 0, 2, 2],
[0 + 10, 0 + 10, 2 + 10, 2 + 10],
[0, 0, 2, 2],
[0 + 10, 0 + 10, 2 + 10, 2 + 10],
],
[False, True, False, True],
[False, False, True, False],
),
( # Crossing beside - right side
Vector(Point(0, 0), Point(10, 0)),
[
[20, 4, 24, 6],
[20, 4 - 10, 24, 6 - 10],
[20, 4, 24, 6],
[20, 4 - 10, 24, 6 - 10],
],
[False, False, False, False],
[False, False, False, False],
),
( # Horizontal line, simple crossing, far away
Vector(Point(0, 0), Point(10, 0)),
[
[4, 1e32, 6, 1e32 + 2],
[4, -1e32, 6, -1e32 + 2],
[4, 1e32, 6, 1e32 + 2],
[4, -1e32, 6, -1e32 + 2],
],
[False, True, False, True],
[False, False, True, False],
),
( # Crossing beside - left side
Vector(Point(0, 0), Point(10, 0)),
[
[-20, 4, -24, 6],
[-20, 4 - 10, -24, 6 - 10],
[-20, 4, -24, 6],
[-20, 4 - 10, -24, 6 - 10],
],
[False, False, False, False],
[False, False, False, False],
),
( # Move above
Vector(Point(0, 0), Point(10, 0)),
[
[-4, 4, -2, 6],
[-4 + 20, 4, -2 + 20, 6],
[-4, 4, -2, 6],
[-4 + 20, 4, -2 + 20, 6],
],
[False, False, False, False],
[False, False, False, False],
),
( # Move below
Vector(Point(0, 0), Point(10, 0)),
[
[-4, -6, -2, -4],
[-4 + 20, -6, -2 + 20, -4],
[-4, -6, -2, -4],
[-4 + 20, -6, -2 + 20, -4],
],
[False, False, False, False],
[False, False, False, False],
),
( # Move into line partway
Vector(Point(0, 0), Point(10, 0)),
[
[4, 4, 6, 6],
[4 + 5, 4, 6 + 5, 6],
[4, 4, 6, 6],
[4 + 5, 4, 6 + 5, 6],
],
[False, False, False, False],
[False, False, False, False],
),
( # V-shaped crossing from outside limits - not supported.
Vector(Point(0, 0), Point(10, 0)),
[[-3, 6, -1, 8], [4, -6, 6, -4], [11, 6, 13, 8]],
[False, False, False],
[False, False, False],
),
( # Diagonal movement, from within limits to outside - not supported
Vector(Point(0, 0), Point(10, 0)),
[[4, 1, 6, 3], [11, 1 - 20, 13, 3 - 20]],
[False, False],
[False, False],
),
( # Diagonal movement, from outside limits to within - not supported
Vector(Point(0, 0), Point(10, 0)),
[
[11, 21, 13, 23],
[4, -3, 6, -1],
],
[False, False],
[False, False],
),
( # Diagonal crossing, from outside to outside limits - not supported.
Vector(Point(0, 0), Point(10, 0)),
[
[-4, 4, -2, 8],
[-4 + 16, -4, -2 + 16, -6],
[-4, 4, -2, 8],
[-4 + 16, -4, -2 + 16, -6],
],
[False, False, False, False],
[False, False, False, False],
),
],
)
def test_line_zone_one_detection_default_anchors(
vector: Vector,
xyxy_sequence: List[List[float]],
expected_crossed_in: List[bool],
expected_crossed_out: List[bool],
) -> None:
line_zone = LineZone(start=vector.start, end=vector.end)
crossed_in_list = []
crossed_out_list = []
for i, bbox in enumerate(xyxy_sequence):
detections = mock_detections(
xyxy=[bbox],
tracker_id=[0],
)
crossed_in, crossed_out = line_zone.trigger(detections)
crossed_in_list.append(crossed_in[0])
crossed_out_list.append(crossed_out[0])
assert (
crossed_in_list == expected_crossed_in
), f"expected {expected_crossed_in}, got {crossed_in_list}"
assert (
crossed_out_list == expected_crossed_out
), f"expected {expected_crossed_out}, got {crossed_out_list}"
@pytest.mark.parametrize(
"vector, xyxy_sequence, triggering_anchors, expected_crossed_in, "
"expected_crossed_out",
[
( # Scrape line, left side, corner anchors
Vector(Point(0, 0), Point(10, 0)),
[
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
],
[
Position.TOP_LEFT,
Position.BOTTOM_LEFT,
Position.TOP_RIGHT,
Position.BOTTOM_RIGHT,
],
[False, False, False, False],
[False, False, False, False],
),
( # Scrape line, left side, right anchors
Vector(Point(0, 0), Point(10, 0)),
[
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
],
[Position.TOP_RIGHT, Position.BOTTOM_RIGHT],
[False, True, False, True],
[False, False, True, False],
),
( # Scrape line, left side, center anchor (along line point)
Vector(Point(0, 0), Point(10, 0)),
[
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
[-2, 4, 2, 6],
[-2, 4 - 10, 2, 6 - 10],
],
[Position.CENTER],
[False, True, False, True],
[False, False, True, False],
),
( # Scrape line, right side, corner anchors
Vector(Point(0, 0), Point(10, 0)),
[
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
],
[
Position.TOP_LEFT,
Position.BOTTOM_LEFT,
Position.TOP_RIGHT,
Position.BOTTOM_RIGHT,
],
[False, False, False, False],
[False, False, False, False],
),
( # Scrape line, right side, left anchors
Vector(Point(0, 0), Point(10, 0)),
[
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
],
[Position.TOP_LEFT, Position.BOTTOM_LEFT],
[False, True, False, True],
[False, False, True, False],
),
( # Scrape line, right side, center anchor (along line point)
Vector(Point(0, 0), Point(10, 0)),
[
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
[8, 4, 12, 6],
[8, 4 - 10, 12, 6 - 10],
],
[Position.CENTER],
[False, True, False, True],
[False, False, True, False],
),
( # Simple crossing, one anchor
Vector(Point(0, 0), Point(10, 0)),
[
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
],
[Position.CENTER],
[False, True, False, True],
[False, False, True, False],
),
( # Simple crossing, all box anchors
Vector(Point(0, 0), Point(10, 0)),
[
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
[4, 4, 6, 6],
[4, 4 - 10, 6, 6 - 10],
],
[
Position.CENTER,
Position.CENTER_LEFT,
Position.CENTER_RIGHT,
Position.TOP_CENTER,
Position.TOP_LEFT,
Position.TOP_RIGHT,
Position.BOTTOM_LEFT,
Position.BOTTOM_CENTER,
Position.BOTTOM_RIGHT,
],
[False, True, False, True],
[False, False, True, False],
),
],
)
def test_line_zone_one_detection(
vector: Vector,
xyxy_sequence: List[List[float]],
triggering_anchors: List[Position],
expected_crossed_in: List[bool],
expected_crossed_out: List[bool],
) -> None:
line_zone = LineZone(
start=vector.start, end=vector.end, triggering_anchors=triggering_anchors
)
crossed_in_list = []
crossed_out_list = []
for i, bbox in enumerate(xyxy_sequence):
detections = mock_detections(
xyxy=[bbox],
tracker_id=[0],
)
crossed_in, crossed_out = line_zone.trigger(detections)
crossed_in_list.append(crossed_in[0])
crossed_out_list.append(crossed_out[0])
assert (
crossed_in_list == expected_crossed_in
), f"expected {expected_crossed_in}, got {crossed_in_list}"
assert (
crossed_out_list == expected_crossed_out
), f"expected {expected_crossed_out}, got {crossed_out_list}"
@pytest.mark.parametrize(
"vector, xyxy_sequence, anchors, expected_crossed_in, "
"expected_crossed_out, exception",
[
( # One stays, one crosses
Vector(Point(0, 0), Point(10, 0)),
[
[[4, 4, 6, 6], [4, 4, 6, 6]],
[[4, 4, 6, 6], [4, 4 - 10, 6, 6 - 10]],
[[4, 4, 6, 6], [4, 4, 6, 6]],
[[4, 4, 6, 6], [4, 4 - 10, 6, 6 - 10]],
],
[
Position.TOP_LEFT,
Position.TOP_RIGHT,
Position.BOTTOM_LEFT,
Position.BOTTOM_RIGHT,
],
[[False, False], [False, True], [False, False], [False, True]],
[[False, False], [False, False], [False, True], [False, False]],
DoesNotRaise(),
),
( # Both cross at the same time
Vector(Point(0, 0), Point(10, 0)),
[
[[4, 4, 6, 6], [4, 4, 6, 6]],
[[4, 4 - 10, 6, 6 - 10], [4, 4 - 10, 6, 6 - 10]],
[[4, 4, 6, 6], [4, 4, 6, 6]],
[[4, 4 - 10, 6, 6 - 10], [4, 4 - 10, 6, 6 - 10]],
],
[
Position.TOP_LEFT,
Position.TOP_RIGHT,
Position.BOTTOM_LEFT,
Position.BOTTOM_RIGHT,
],
[[False, False], [True, True], [False, False], [True, True]],
[[False, False], [False, False], [True, True], [False, False]],
DoesNotRaise(),
),
],
)
def test_line_zone_multiple_detections(
vector: Vector,
xyxy_sequence: List[List[List[float]]],
anchors: List[Position],
expected_crossed_in: List[List[bool]],
expected_crossed_out: List[List[bool]],
exception: Exception,
) -> None:
with exception:
line_zone = LineZone(
start=vector.start, end=vector.end, triggering_anchors=anchors
)
crossed_in_list = []
crossed_out_list = []
for bboxes in xyxy_sequence:
detections = mock_detections(
xyxy=bboxes,
tracker_id=[i for i in range(0, len(bboxes))],
)
crossed_in, crossed_out = line_zone.trigger(detections)
crossed_in_list.append(list(crossed_in))
crossed_out_list.append(list(crossed_out))
assert crossed_in_list == expected_crossed_in
assert crossed_out_list == expected_crossed_out

151
test/detection/test_lmm.py Normal file
View File

@ -0,0 +1,151 @@
from typing import List, Optional, Tuple
import numpy as np
import pytest
from supervision.detection.lmm import from_paligemma
@pytest.mark.parametrize(
"result, resolution_wh, classes, expected_results",
[
(
"",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # empty response
(
"",
(1000, 1000),
["cat", "dog"],
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # empty response with classes
(
"\n",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # new line response
(
"the quick brown fox jumps over the lazy dog.",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with no location
(
"<loc0256><loc0768><loc0768> cat",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with missing location
(
"<loc0256><loc0256><loc0768><loc0768><loc0768> cat",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with extra location
(
"<loc0256><loc0256><loc0768><loc0768>",
(1000, 1000),
None,
(np.empty((0, 4)), None, np.empty(0).astype(str)),
), # response with no class
(
"<loc0256><loc0256><loc0768><loc0768> catt",
(1000, 1000),
["cat", "dog"],
(np.empty((0, 4)), np.empty(0), np.empty(0).astype(str)),
), # response with invalid class
(
"<loc0256><loc0256><loc0768><loc0768> cat",
(1000, 1000),
None,
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(["cat"]).astype(str),
),
), # correct response; no classes
(
"<loc0256><loc0256><loc0768><loc0768> black cat",
(1000, 1000),
None,
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(["black cat"]).astype(np.dtype("U")),
),
), # correct response; class name with space; no classes
(
"<loc0256><loc0256><loc0768><loc0768> black-cat",
(1000, 1000),
None,
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(["black-cat"]).astype(np.dtype("U")),
),
), # correct response; class name with hyphen; no classes
(
"<loc0256><loc0256><loc0768><loc0768> black_cat",
(1000, 1000),
None,
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
None,
np.array(["black_cat"]).astype(np.dtype("U")),
),
), # correct response; class name with underscore; no classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ;",
(1000, 1000),
["cat", "dog"],
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(["cat"]).astype(str),
),
), # correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768> dog", # noqa: E501
(1000, 1000),
["cat", "dog"],
(
np.array([[250.0, 250.0, 750.0, 750.0], [250.0, 250.0, 750.0, 750.0]]),
np.array([0, 1]),
np.array(["cat", "dog"]).astype(np.dtype("U")),
),
), # correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768> cat", # noqa: E501
(1000, 1000),
["cat", "dog"],
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(["cat"]).astype(str),
),
), # partially correct response; with classes
(
"<loc0256><loc0256><loc0768><loc0768> cat ; <loc0256><loc0256><loc0768><loc0768><loc0768> cat", # noqa: E501
(1000, 1000),
["cat", "dog"],
(
np.array([[250.0, 250.0, 750.0, 750.0]]),
np.array([0]),
np.array(["cat"]).astype(str),
),
), # partially correct response; with classes
],
)
def test_from_paligemma(
result: str,
resolution_wh: Tuple[int, int],
classes: Optional[List[str]],
expected_results: Tuple[np.ndarray, Optional[np.ndarray], np.ndarray],
) -> None:
result = from_paligemma(result=result, resolution_wh=resolution_wh, classes=classes)
np.testing.assert_array_equal(result[0], expected_results[0])
np.testing.assert_array_equal(result[1], expected_results[1])
np.testing.assert_array_equal(result[2], expected_results[2])

449
test/detection/test_overlap_filter.py Normal file
View File

@ -0,0 +1,449 @@
from contextlib import ExitStack as DoesNotRaise
from typing import List, Optional
import numpy as np
import pytest
from supervision.detection.overlap_filter import (
box_non_max_suppression,
group_overlapping_boxes,
mask_non_max_suppression,
)
@pytest.mark.parametrize(
"predictions, iou_threshold, expected_result, exception",
[
(
np.empty(shape=(0, 5), dtype=float),
0.5,
[],
DoesNotRaise(),
),
(
np.array([[0, 0, 10, 10, 1.0]]),
0.5,
[[0]],
DoesNotRaise(),
),
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # High overlap, tie-break to second det
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 0.99]]),
0.5,
[[0, 1]],
DoesNotRaise(),
), # High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 0.99], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # (test symmetry) High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 0.90], [0, 0, 9, 9, 1.0]]),
0.5,
[[1, 0]],
DoesNotRaise(),
), # (test symmetry) High overlap, merge to high confidence
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
1.0,
[[1], [0]],
DoesNotRaise(),
), # High IOU required
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0]]),
0.0,
[[1, 0]],
DoesNotRaise(),
), # No IOU required
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 5, 5, 0.9]]),
0.25,
[[0, 1]],
DoesNotRaise(),
), # Below IOU requirement
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 5, 5, 0.9]]),
0.26,
[[0], [1]],
DoesNotRaise(),
), # Above IOU requirement
(
np.array([[0, 0, 10, 10, 1.0], [0, 0, 9, 9, 1.0], [0, 0, 8, 8, 1.0]]),
0.5,
[[2, 1, 0]],
DoesNotRaise(),
), # 3 boxes
(
np.array(
[
[0, 0, 10, 10, 1.0],
[0, 0, 9, 9, 1.0],
[5, 5, 10, 10, 1.0],
[6, 6, 10, 10, 1.0],
[9, 9, 10, 10, 1.0],
]
),
0.5,
[[4], [3, 2], [1, 0]],
DoesNotRaise(),
), # 5 boxes, 2 merges, 1 separate
(
np.array(
[
[0, 0, 2, 1, 1.0],
[1, 0, 3, 1, 1.0],
[2, 0, 4, 1, 1.0],
[3, 0, 5, 1, 1.0],
[4, 0, 6, 1, 1.0],
]
),
0.33,
[[4, 3], [2, 1], [0]],
DoesNotRaise(),
), # sequential merge, half overlap
(
np.array(
[
[0, 0, 2, 1, 0.9],
[1, 0, 3, 1, 0.9],
[2, 0, 4, 1, 1.0],
[3, 0, 5, 1, 0.9],
[4, 0, 6, 1, 0.9],
]
),
0.33,
[[2, 3, 1], [4], [0]],
DoesNotRaise(),
), # confidence
],
)
def test_group_overlapping_boxes(
predictions: np.ndarray,
iou_threshold: float,
expected_result: List[List[int]],
exception: Exception,
) -> None:
with exception:
result = group_overlapping_boxes(
predictions=predictions, iou_threshold=iou_threshold
)
assert result == expected_result
@pytest.mark.parametrize(
"predictions, iou_threshold, expected_result, exception",
[
(
np.empty(shape=(0, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8, 0]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8],
[15.0, 15.0, 40.0, 40.0, 0.9],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with no category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 1],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two boxes with different category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 0],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8],
[5.0, 5.0, 35.0, 45.0, 0.9],
[10.0, 10.0, 40.0, 50.0, 0.85],
]
),
0.5,
np.array([False, True, False]),
DoesNotRaise(),
), # three boxes with no category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 1],
[10.0, 10.0, 40.0, 50.0, 0.85, 2],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 0],
[10.0, 10.0, 40.0, 50.0, 0.85, 1],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three boxes with different category
],
)
def test_box_non_max_suppression(
predictions: np.ndarray,
iou_threshold: float,
expected_result: Optional[np.ndarray],
exception: Exception,
) -> None:
with exception:
result = box_non_max_suppression(
predictions=predictions, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)
@pytest.mark.parametrize(
"predictions, masks, iou_threshold, expected_result, exception",
[
(
np.empty((0, 6)),
np.empty((0, 5, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # empty predictions and masks
(
np.array([[0, 0, 0, 0, 0.8]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with no category
(
np.array([[0, 0, 0, 0, 0.8, 0]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks non-overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
np.array([False, True]),
DoesNotRaise(),
), # two masks partially overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks partially overlapping with different category
(
np.array(
[
[0, 0, 0, 0, 0.8],
[0, 0, 0, 0, 0.85],
[0, 0, 0, 0, 0.9],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three masks with no category
(
np.array(
[
[0, 0, 0, 0, 0.8, 0],
[0, 0, 0, 0, 0.85, 1],
[0, 0, 0, 0, 0.9, 2],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three masks with different category
],
)
def test_mask_non_max_suppression(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float,
expected_result: Optional[np.ndarray],
exception: Exception,
) -> None:
with exception:
result = mask_non_max_suppression(
predictions=predictions, masks=masks, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)

16
test/detection/test_polygonzone.py
View File

@ -92,3 +92,19 @@ def test_polygon_zone_trigger(
with exception:
in_zone = polygon_zone.trigger(detections)
assert np.all(in_zone == expected_results)
@pytest.mark.parametrize(
"polygon, triggering_anchors, exception",
[
(POLYGON, [sv.Position.CENTER], DoesNotRaise()),
(
POLYGON,
[],
pytest.raises(ValueError),
),
],
)
def test_polygon_zone_initialization(polygon, triggering_anchors, exception):
with exception:
sv.PolygonZone(polygon, FRAME_RESOLUTION, triggering_anchors=triggering_anchors)

515
test/detection/test_utils.py
View File

@ -2,16 +2,17 @@ from contextlib import ExitStack as DoesNotRaise
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
import pytest
from supervision.config import CLASS_NAME_DATA_FIELD
from supervision.detection.utils import (
box_non_max_suppression,
calculate_masks_centroids,
clip_boxes,
contains_holes,
contains_multiple_segments,
filter_polygons_by_area,
get_data_item,
mask_non_max_suppression,
merge_data,
move_boxes,
process_roboflow_result,
@ -22,317 +23,6 @@ TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
TEST_MASK[:, 300:351, 200:251] = True
@pytest.mark.parametrize(
"predictions, iou_threshold, expected_result, exception",
[
(
np.empty(shape=(0, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with no category
(
np.array([[10.0, 10.0, 40.0, 40.0, 0.8, 0]]),
0.5,
np.array([True]),
DoesNotRaise(),
), # single box with category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8],
[15.0, 15.0, 40.0, 40.0, 0.9],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with no category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 1],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two boxes with different category
(
np.array(
[
[10.0, 10.0, 40.0, 40.0, 0.8, 0],
[15.0, 15.0, 40.0, 40.0, 0.9, 0],
]
),
0.5,
np.array([False, True]),
DoesNotRaise(),
), # two boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8],
[5.0, 5.0, 35.0, 45.0, 0.9],
[10.0, 10.0, 40.0, 50.0, 0.85],
]
),
0.5,
np.array([False, True, False]),
DoesNotRaise(),
), # three boxes with no category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 1],
[10.0, 10.0, 40.0, 50.0, 0.85, 2],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three boxes with same category
(
np.array(
[
[0.0, 0.0, 30.0, 40.0, 0.8, 0],
[5.0, 5.0, 35.0, 45.0, 0.9, 0],
[10.0, 10.0, 40.0, 50.0, 0.85, 1],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three boxes with different category
],
)
def test_box_non_max_suppression(
predictions: np.ndarray,
iou_threshold: float,
expected_result: Optional[np.ndarray],
exception: Exception,
) -> None:
with exception:
result = box_non_max_suppression(
predictions=predictions, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)
@pytest.mark.parametrize(
"predictions, masks, iou_threshold, expected_result, exception",
[
(
np.empty((0, 6)),
np.empty((0, 5, 5)),
0.5,
np.array([]),
DoesNotRaise(),
), # empty predictions and masks
(
np.array([[0, 0, 0, 0, 0.8]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with no category
(
np.array([[0, 0, 0, 0, 0.8, 0]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
]
]
),
0.5,
np.array([True]),
DoesNotRaise(),
), # single mask with category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks non-overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.4,
np.array([False, True]),
DoesNotRaise(),
), # two masks partially overlapping with no category
(
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, True, True, True, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, True, True, True],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True]),
DoesNotRaise(),
), # two masks partially overlapping with different category
(
np.array(
[
[0, 0, 0, 0, 0.8],
[0, 0, 0, 0, 0.85],
[0, 0, 0, 0, 0.9],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, False, False, True, True],
[False, False, False, True, True],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([False, True, True]),
DoesNotRaise(),
), # three masks with no category
(
np.array(
[
[0, 0, 0, 0, 0.8, 0],
[0, 0, 0, 0, 0.85, 1],
[0, 0, 0, 0, 0.9, 2],
]
),
np.array(
[
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
],
[
[False, False, False, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
],
]
),
0.5,
np.array([True, True, True]),
DoesNotRaise(),
), # three masks with different category
],
)
def test_mask_non_max_suppression(
predictions: np.ndarray,
masks: np.ndarray,
iou_threshold: float,
expected_result: Optional[np.ndarray],
exception: Exception,
) -> None:
with exception:
result = mask_non_max_suppression(
predictions=predictions, masks=masks, iou_threshold=iou_threshold
)
assert np.array_equal(result, expected_result)
@pytest.mark.parametrize(
"xyxy, resolution_wh, expected_result",
[
@ -911,6 +601,14 @@ def test_calculate_masks_centroids(
{"test_1": []},
DoesNotRaise(),
), # single data dict with a single field name and empty list values
(
[
{"test_1": []},
{"test_1": []},
],
{"test_1": []},
DoesNotRaise(),
), # two data dicts with the same field name and empty list values
(
[
{"test_1": np.array([])},
@ -918,6 +616,14 @@ def test_calculate_masks_centroids(
{"test_1": np.array([])},
DoesNotRaise(),
), # single data dict with a single field name and empty np.array values
(
[
{"test_1": np.array([])},
{"test_1": np.array([])},
],
{"test_1": np.array([])},
DoesNotRaise(),
), # two data dicts with the same field name and empty np.array values
(
[
{"test_1": [1, 2, 3]},
@ -932,7 +638,7 @@ def test_calculate_masks_centroids(
],
{"test_1": [3, 2, 1]},
DoesNotRaise(),
), # two data dicts with the same field name and empty and list values
), # two data dicts with the same field name; one of with empty list as value
(
[
{"test_1": [1, 2, 3]},
@ -1012,6 +718,49 @@ def test_calculate_masks_centroids(
None,
pytest.raises(ValueError),
), # two data dicts with the same field name and different length arrays values
(
[{}, {"test_1": [1, 2, 3]}],
None,
pytest.raises(ValueError),
), # two data dicts; one empty and one non-empty dict
(
[{"test_1": [], "test_2": []}, {"test_1": [1, 2, 3], "test_2": [1, 2, 3]}],
{"test_1": [1, 2, 3], "test_2": [1, 2, 3]},
DoesNotRaise(),
), # two data dicts; one empty and one non-empty dict; same keys
(
[{"test_1": []}, {"test_1": [1, 2, 3], "test_2": [4, 5, 6]}],
None,
pytest.raises(ValueError),
), # two data dicts; one empty and one non-empty dict; different keys
(
[
{
"test_1": [1, 2, 3],
"test_2": [4, 5, 6],
"test_3": [7, 8, 9],
},
{"test_1": [1, 2, 3], "test_2": [4, 5, 6]},
],
None,
pytest.raises(ValueError),
), # two data dicts; one with three keys, one with two keys
(
[
{"test_1": [1, 2, 3]},
{"test_1": [1, 2, 3], "test_2": [1, 2, 3]},
],
None,
pytest.raises(ValueError),
), # some keys missing in one dict
(
[
{"test_1": [1, 2, 3], "test_2": ["a", "b"]},
{"test_1": [4, 5], "test_2": ["c", "d", "e"]},
],
None,
pytest.raises(ValueError),
), # different value lengths for the same key
],
)
def test_merge_data(
@ -1021,6 +770,9 @@ def test_merge_data(
):
with exception:
result = merge_data(data_list=data_list)
if expected_result is None:
assert False, f"Expected an error, but got result {result}"
for key in result:
if isinstance(result[key], np.ndarray):
assert np.array_equal(
@ -1203,3 +955,138 @@ def test_get_data_item(
assert (
result[key] == expected_result[key]
), f"Mismatch in non-array data for key {key}"
@pytest.mark.parametrize(
"mask, expected_result, exception",
[
(
np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 1, 0]]).astype(
bool
),
False,
DoesNotRaise(),
), # foreground object in one continuous piece
(
np.array([[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 0]]).astype(
bool
),
False,
DoesNotRaise(),
), # foreground object in 2 seperate elements
(
np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
bool
),
False,
DoesNotRaise(),
), # no foreground pixels in mask
(
np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]).astype(
bool
),
False,
DoesNotRaise(),
), # only foreground pixels in mask
(
np.array([[1, 1, 1, 0], [1, 0, 1, 0], [1, 1, 1, 0], [0, 0, 0, 0]]).astype(
bool
),
True,
DoesNotRaise(),
), # foreground object has 1 hole
(
np.array([[1, 1, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1], [0, 1, 1, 1]]).astype(
bool
),
True,
DoesNotRaise(),
), # foreground object has 2 holes
],
)
def test_contains_holes(
mask: npt.NDArray[np.bool_], expected_result: bool, exception: Exception
) -> None:
with exception:
result = contains_holes(mask)
assert result == expected_result
@pytest.mark.parametrize(
"mask, connectivity, expected_result, exception",
[
(
np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 1, 0]]).astype(
bool
),
4,
False,
DoesNotRaise(),
), # foreground object in one continuous piece
(
np.array([[1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 0]]).astype(
bool
),
4,
True,
DoesNotRaise(),
), # foreground object in 2 seperate elements
(
np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]).astype(
bool
),
4,
False,
DoesNotRaise(),
), # no foreground pixels in mask
(
np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]).astype(
bool
),
4,
False,
DoesNotRaise(),
), # only foreground pixels in mask
(
np.array([[1, 1, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1], [0, 1, 1, 1]]).astype(
bool
),
4,
False,
DoesNotRaise(),
), # foreground object has 2 holes, but is in single piece
(
np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
bool
),
4,
True,
DoesNotRaise(),
), # foreground object in 2 elements with respect to 4-way connectivity
(
np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
bool
),
8,
False,
DoesNotRaise(),
), # foreground object in single piece with respect to 8-way connectivity
(
np.array([[1, 1, 0, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 0, 1, 1]]).astype(
bool
),
5,
None,
pytest.raises(ValueError),
), # Incorrect connectivity parameter value, raises ValueError
],
)
def test_contains_multiple_segments(
mask: npt.NDArray[np.bool_],
connectivity: int,
expected_result: bool,
exception: Exception,
) -> None:
with exception:
result = contains_multiple_segments(mask=mask, connectivity=connectivity)
assert result == expected_result

193
test/utils/test_internal.py Normal file
View File

@ -0,0 +1,193 @@
from contextlib import ExitStack as DoesNotRaise
from dataclasses import dataclass, field
from typing import Any, Set
import numpy as np
import pytest
from supervision.detection.core import Detections
from supervision.utils.internal import get_instance_variables
class MockClass:
def __init__(self):
self.public = 0
self._protected = 1
self.__private = 2
def public_method(self):
pass
def _protected_method(self):
pass
def __private_method(self):
pass
@property
def public_property(self):
return 0
@property
def _protected_property(self):
return 1
@property
def __private_property(self):
return 2
@dataclass
class MockDataclass:
public: int = 0
_protected: int = 1
__private: int = 2
public_field: int = field(default=0)
_protected_field: int = field(default=1)
__private_field: int = field(default=2)
public_field_with_factory: dict = field(default_factory=dict)
_protected_field_with_factory: dict = field(default_factory=dict)
__private_field_with_factory: dict = field(default_factory=dict)
def public_method(self):
pass
def _protected_method(self):
pass
def __private_method(self):
pass
@property
def public_property(self):
return 0
@property
def _protected_property(self):
return 1
@property
def __private_property(self):
return 2
@pytest.mark.parametrize(
"input_instance, include_properties, expected, exception",
[
(
MockClass,
False,
None,
pytest.raises(ValueError),
),
(
MockClass(),
False,
{"public"},
DoesNotRaise(),
),
(
MockClass(),
True,
{"public", "public_property"},
DoesNotRaise(),
),
(
MockDataclass(),
False,
{"public", "public_field", "public_field_with_factory"},
DoesNotRaise(),
),
(
MockDataclass(),
True,
{"public", "public_field", "public_field_with_factory", "public_property"},
DoesNotRaise(),
),
(
Detections,
False,
None,
pytest.raises(ValueError),
),
(
Detections,
True,
None,
pytest.raises(ValueError),
),
(
Detections.empty(),
False,
{"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
DoesNotRaise(),
),
(
Detections.empty(),
True,
{
"xyxy",
"class_id",
"confidence",
"mask",
"tracker_id",
"data",
"area",
"box_area",
},
DoesNotRaise(),
),
(
Detections(xyxy=np.array([[1, 2, 3, 4]])),
False,
{
"xyxy",
"class_id",
"confidence",
"mask",
"tracker_id",
"data",
},
DoesNotRaise(),
),
(
Detections(
xyxy=np.array([[1, 2, 3, 4], [5, 6, 7, 8]]),
class_id=np.array([1, 2]),
confidence=np.array([0.1, 0.2]),
mask=np.array([[[1]], [[2]]]),
tracker_id=np.array([1, 2]),
data={"key_1": [1, 2], "key_2": [3, 4]},
),
False,
{
"xyxy",
"class_id",
"confidence",
"mask",
"tracker_id",
"data",
},
DoesNotRaise(),
),
(
Detections.empty(),
False,
{"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
DoesNotRaise(),
),
],
)
def test_get_instance_variables(
input_instance: Any,
include_properties: bool,
expected: Set[str],
exception: Exception,
) -> None:
with exception:
result = get_instance_variables(
input_instance, include_properties=include_properties
)
assert result == expected