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Chore CI by adding checks for links and references (#2113)

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* Chore CI by adding checks for links and references
* Potential fix for code scanning alert no. 6: Workflow does not contain permissions
* Fix markdown image formatting in metrics documentation
* Update all `docs` links to use absolute URLs and fix broken references in documentation and workflows.
* Fix broken `albumentations` links in dataset processing documentation
* lycheeVersion: v0.22.0
* Refactor CI link check workflow to use lychee config file for cleaner argument management
* add TODOs for future file type support
* Fix header formatting for example plots across metric files

---------

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Jirka BorovecCopilot Autofix powered by AICopilot
 authored and GitHub committed 2026-02-02 17:01:19 +01:00
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commit a5b51a11e5
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.codecov.yml
-3
View File
@@ -16,16 +16,13 @@ coverage:
round: nearest # how coverage is rounded: down/up/nearest
range: 40...100 # custom range of coverage colors from red -> yellow -> green
status:
# https://codecov.readme.io/v1.0/docs/commit-status
project:
default:
informational: true
target: 95% # specify the target coverage for each commit status
threshold: 10% # allow this little decrease on project
# https://github.com/codecov/support/wiki/Filtering-Branches
# branches: master
if_ci_failed: error
# https://github.com/codecov/support/wiki/Patch-Status
patch:
default:
informational: true
.github/lychee.toml
+11
View File
@@ -0,0 +1,11 @@
verbose = "info"
no_progress = true
include_verbatim = true
retry_wait_time = 3
max_concurrency = 10
exclude = [
"https://github.com/YOUR_USERNAME/supervision.git", # hint for forking contributors
"http://127.0.0.1:8000", # hint for local docs server
"https://sam2.metademolab.com/", # returns 403 Forbidden
]
.github/workflows/ci-check-links.yml
+37
View File
@@ -0,0 +1,37 @@
name: Check links & references
permissions:
contents: read
on:
push:
branches: [main, develop]
pull_request:
schedule:
# Run once a week on Sundays
- cron: "0 9 * * 0"
concurrency:
group: ci-check-links-${{ github.ref }}
cancel-in-progress: true
jobs:
links-check:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v6
- name: Link Checker
uses: lycheeverse/lychee-action@v2
with:
lycheeVersion: v0.22.0
args: |
--config .github/lychee.toml
--header "x-ratelimit-bypass: ${{ secrets.UNIVERSE_RATELIMIT_BYPASS_KEY }}"
'./**/*.md'
# TODO: enable also following file types
# './**/*.toml'
# './**/*.yml'
# './**/*.yaml'
# './**/*.py'
fail: true
docs/changelog.md
+98 -98
View File
@@ -32,7 +32,7 @@
- Changed: [`sv.denormalize_boxes`](https://supervision.roboflow.com/0.27.0/detection/utils/boxes/#supervision.detection.utils.boxes.denormalize_boxes) now supports batch conversion of bounding boxes. The function accepts arrays of shape `(N, 4)` and returns a batch of absolute pixel coordinates.
- Changed [#1917](https://github.com/roboflow/supervision/pull/1917): [`sv.LabelAnnotator`](https://supervision.roboflow.com/develop/0.27.0/annotators/#supervision.annotators.core.LabelAnnotator) and [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/develop/0.27.0/annotators/#supervision.annotators.core.LabelAnnotator) now accept `text_offset=(x, y)` to shift the label relative to `text_position`. Works with smart label position and line wrapping.
- Changed [#1917](https://github.com/roboflow/supervision/pull/1917): [`sv.LabelAnnotator`](https://supervision.roboflow.com/0.27.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) and [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.27.0/detection/annotators/#supervision.annotators.core.RichLabelAnnotator) now accept `text_offset=(x, y)` to shift the label relative to `text_position`. Works with smart label position and line wrapping.
!!! failure "Removed"
Removed the deprecated `overlap_ratio_wh` argument from `sv.InferenceSlicer`. Use the pixel based `overlap_wh` argument to control slice overlap.
@@ -198,7 +198,7 @@
# [1908.01, 1346.67, 2585.99, 2024.11]])
```
- Changed [#1709](https://github.com/roboflow/supervision/pull/1790): [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) now supports parsing bounding boxes from responses generated by [Qwen-2.5 VL](https://github.com/QwenLM/Qwen2.5-VL).
- Changed [#1709](https://github.com/roboflow/supervision/pull/1709): [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.26.0/detection/core/#supervision.detection.core.Detections.from_vlm) now supports parsing bounding boxes from responses generated by [Qwen-2.5 VL](https://github.com/QwenLM/Qwen2.5-VL).
```python
import supervision as sv
@@ -901,9 +901,9 @@ annotated_image = label_annotator.annotate(
### 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.
- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.KeyPoints`](https://supervision.roboflow.com/0.20.0/keypoint/core/#supervision.keypoint.core.KeyPoints) to provide initial support for pose estimation and broader keypoint detection models.
- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.EdgeAnnotator`](/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator) and [`sv.VertexAnnotator`](/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.VertexAnnotator) to enable rendering of results from keypoint detection models.
- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.EdgeAnnotator`](https://supervision.roboflow.com/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator) and [`sv.VertexAnnotator`](https://supervision.roboflow.com/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.VertexAnnotator) to enable rendering of results from keypoint detection models.
```python
import cv2
@@ -920,17 +920,17 @@ edge_annotators = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
annotated_image = edge_annotators.annotate(image.copy(), keypoints)
```
- Changed [#1037](https://github.com/roboflow/supervision/pull/1037): [`sv.LabelAnnotator`](/0.20.0/annotators/#supervision.annotators.core.LabelAnnotator) by adding an additional `corner_radius` argument that allows for rounding the corners of the bounding box.
- Changed [#1037](https://github.com/roboflow/supervision/pull/1037): [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) by adding an additional `corner_radius` argument that allows for rounding the corners of the bounding box.
- Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
- Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](https://supervision.roboflow.com/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
!!! failure "Deprecated"
The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
- Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
- Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](https://supervision.roboflow.com/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
- Changed [#1069](https://github.com/roboflow/supervision/pull/1069): [`sv.Detections.from_transformers`](/0.20.0/detection/core/#supervision.detection.core.Detections.from_transformers) by adding support for Transformers segmentation models and extract class names values.
- Changed [#1069](https://github.com/roboflow/supervision/pull/1069): [`sv.Detections.from_transformers`](https://supervision.roboflow.com/0.20.0/detection/core/#supervision.detection.core.Detections.from_transformers) by adding support for Transformers segmentation models and extract class names values.
```python
import torch
@@ -962,11 +962,11 @@ annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
```
- Fixed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack.update_with_detections`](/0.20.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack.update_with_detections) which was removing segmentation masks while tracking. Now, `ByteTrack` can be used alongside segmentation models.
- Fixed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack.update_with_detections`](https://supervision.roboflow.com/0.20.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack.update_with_detections) which was removing segmentation masks while tracking. Now, `ByteTrack` can be used alongside segmentation models.
### 0.19.0 <small>March 15, 2024</small>
- Added [#818](https://github.com/roboflow/supervision/pull/818): [`sv.CSVSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) allowing for the straightforward saving of image, video, or stream inference results in a `.csv` file.
- Added [#818](https://github.com/roboflow/supervision/pull/818): [`sv.CSVSink`](https://supervision.roboflow.com/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) allowing for the straightforward saving of image, video, or stream inference results in a `.csv` file.
```python
import supervision as sv
@@ -983,7 +983,7 @@ with csv_sink:
csv_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
```
- Added [#819](https://github.com/roboflow/supervision/pull/819): [`sv.JSONSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink) allowing for the straightforward saving of image, video, or stream inference results in a `.json` file.
- Added [#819](https://github.com/roboflow/supervision/pull/819): [`sv.JSONSink`](https://supervision.roboflow.com/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink) allowing for the straightforward saving of image, video, or stream inference results in a `.json` file.
```python
import supervision as sv
@@ -1000,11 +1000,11 @@ with json_sink:
json_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
```
- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_iou_batch`](/0.19.0/detection/utils/#supervision.detection.utils.mask_iou_batch) allowing to compute Intersection over Union (IoU) of two sets of masks.
- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_iou_batch`](https://supervision.roboflow.com/0.19.0/detection/utils/#supervision.detection.utils.mask_iou_batch) allowing to compute Intersection over Union (IoU) of two sets of masks.
- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_non_max_suppression`](/0.19.0/detection/utils/#supervision.detection.utils.mask_non_max_suppression) allowing to perform Non-Maximum Suppression (NMS) on segmentation predictions.
- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_non_max_suppression`](https://supervision.roboflow.com/0.19.0/detection/utils/#supervision.detection.utils.mask_non_max_suppression) allowing to perform Non-Maximum Suppression (NMS) on segmentation predictions.
- Added [#888](https://github.com/roboflow/supervision/pull/888): [`sv.CropAnnotator`](/0.19.0/annotators/#supervision.annotators.core.CropAnnotator) allowing users to annotate the scene with scaled-up crops of detections.
- Added [#888](https://github.com/roboflow/supervision/pull/888): [`sv.CropAnnotator`](https://supervision.roboflow.com/0.19.0/annotators/#supervision.annotators.core.CropAnnotator) allowing users to annotate the scene with scaled-up crops of detections.
```python
import cv2
@@ -1024,17 +1024,17 @@ annotated_frame = crop_annotator.annotate(
)
```
- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](/0.19.0/trackers/#supervision.tracker.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](https://supervision.roboflow.com/0.19.0/trackers/#supervision.tracker.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
- Changed [#802](https://github.com/roboflow/supervision/pull/802): [`sv.LineZoneAnnotator`](/0.19.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) allowing to hide in/out count using `display_in_count` and `display_out_count` properties.
- Changed [#802](https://github.com/roboflow/supervision/pull/802): [`sv.LineZoneAnnotator`](https://supervision.roboflow.com/0.19.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) allowing to hide in/out count using `display_in_count` and `display_out_count` properties.
- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](https://supervision.roboflow.com/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
!!! failure "Deprecated"
The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
- Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
- Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](https://supervision.roboflow.com/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
!!! failure "Deprecated"
@@ -1042,11 +1042,11 @@ annotated_frame = crop_annotator.annotate(
- Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
- Fixed [#944](https://github.com/roboflow/supervision/pull/944): [`sv.DetectionsSmoother`](/0.19.0/detection/tools/smoother/#supervision.detection.tools.smoother.DetectionsSmoother) removing `tracking_id` from `sv.Detections`.
- Fixed [#944](https://github.com/roboflow/supervision/pull/944): [`sv.DetectionsSmoother`](https://supervision.roboflow.com/0.19.0/detection/tools/smoother/#supervision.detection.tools.smoother.DetectionsSmoother) removing `tracking_id` from `sv.Detections`.
### 0.18.0 <small>January 25, 2024</small>
- Added [#720](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](/0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
- Added [#720](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](https://supervision.roboflow.com/0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
```python
>>> import supervision as sv
@@ -1061,9 +1061,9 @@ annotated_frame = crop_annotator.annotate(
... )
```
- Added [#702](https://github.com/roboflow/supervision/pull/702): [`sv.RoundBoxAnnotator`](/0.18.0/annotators/#roundboxannotator) allowing to annotate images and videos with rounded corners bounding boxes.
- Added [#702](https://github.com/roboflow/supervision/pull/702): [`sv.RoundBoxAnnotator`](https://supervision.roboflow.com/0.18.0/annotators/#roundboxannotator) allowing to annotate images and videos with rounded corners bounding boxes.
- Added [#770](https://github.com/roboflow/supervision/pull/770): [`sv.OrientedBoxAnnotator`](/0.18.0/annotators/#orientedboxannotator) allowing to annotate images and videos with OBB (Oriented Bounding Boxes).
- Added [#770](https://github.com/roboflow/supervision/pull/770): [`sv.OrientedBoxAnnotator`](https://supervision.roboflow.com/0.18.0/annotators/#orientedboxannotator) allowing to annotate images and videos with OBB (Oriented Bounding Boxes).
```python
import cv2
@@ -1083,9 +1083,9 @@ annotated_frame = oriented_box_annotator.annotate(
)
```
- Added [#696](https://github.com/roboflow/supervision/pull/696): [`sv.DetectionsSmoother`](/0.18.0/detection/tools/smoother/#detection-smoother) allowing for smoothing detections over multiple frames in video tracking.
- Added [#696](https://github.com/roboflow/supervision/pull/696): [`sv.DetectionsSmoother`](https://supervision.roboflow.com/0.18.0/detection/tools/smoother/#detection-smoother) allowing for smoothing detections over multiple frames in video tracking.
- Added [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.from_matplotlib`](/0.18.0/draw/color/#supervision.draw.color.ColorPalette.from_matplotlib) allowing users to create a `sv.ColorPalette` instance from a Matplotlib color palette.
- Added [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.from_matplotlib`](https://supervision.roboflow.com/0.18.0/draw/color/#supervision.draw.color.ColorPalette.from_matplotlib) allowing users to create a `sv.ColorPalette` instance from a Matplotlib color palette.
```python
>>> import supervision as sv
@@ -1094,33 +1094,33 @@ annotated_frame = oriented_box_annotator.annotate(
ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
```
- Changed [#770](https://github.com/roboflow/supervision/pull/770): [`sv.Detections.from_ultralytics`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) adding support for OBB (Oriented Bounding Boxes).
- Changed [#770](https://github.com/roboflow/supervision/pull/770): [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/0.18.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) adding support for OBB (Oriented Bounding Boxes).
- Changed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) to now accept a list of specific box anchors that must cross the line for a detection to be counted. This update marks a significant improvement from the previous requirement, where all four box corners were necessary. Users can now specify a single anchor, such as `sv.Position.BOTTOM_CENTER`, or any other combination of anchors defined as `List[sv.Position]`.
- Changed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](https://supervision.roboflow.com/0.18.0/detection/tools/line_zone/#linezone) to now accept a list of specific box anchors that must cross the line for a detection to be counted. This update marks a significant improvement from the previous requirement, where all four box corners were necessary. Users can now specify a single anchor, such as `sv.Position.BOTTOM_CENTER`, or any other combination of anchors defined as `List[sv.Position]`.
- Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
- Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](https://supervision.roboflow.com/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](https://supervision.roboflow.com/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
!!! failure "Deprecated"
`sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
- Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
- Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](https://supervision.roboflow.com/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
- Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
- Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](https://supervision.roboflow.com/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
!!! failure "Deprecated"
`Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
- Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
- Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](https://supervision.roboflow.com/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
### 0.17.0 <small>December 06, 2023</small>
- Added [#633](https://github.com/roboflow/supervision/pull/633): [`sv.PixelateAnnotator`](/0.17.0/annotators/#supervision.annotators.core.PixelateAnnotator) allowing to pixelate objects on images and videos.
- Added [#633](https://github.com/roboflow/supervision/pull/633): [`sv.PixelateAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.PixelateAnnotator) allowing to pixelate objects on images and videos.
- Added [#652](https://github.com/roboflow/supervision/pull/652): [`sv.TriangleAnnotator`](/0.17.0/annotators/#supervision.annotators.core.TriangleAnnotator) allowing to annotate images and videos with triangle markers.
- Added [#652](https://github.com/roboflow/supervision/pull/652): [`sv.TriangleAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.TriangleAnnotator) allowing to annotate images and videos with triangle markers.
- Added [#602](https://github.com/roboflow/supervision/pull/602): [`sv.PolygonAnnotator`](/0.17.0/annotators/#supervision.annotators.core.PolygonAnnotator) allowing to annotate images and videos with segmentation mask outline.
- Added [#602](https://github.com/roboflow/supervision/pull/602): [`sv.PolygonAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.PolygonAnnotator) allowing to annotate images and videos with segmentation mask outline.
```python
>>> import supervision as sv
@@ -1135,7 +1135,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
... )
```
- Added [#476](https://github.com/roboflow/supervision/pull/476): [`sv.assets`](/0.18.0/assets/) allowing download of video files that you can use in your demos.
- Added [#476](https://github.com/roboflow/supervision/pull/476): [`sv.assets`](https://supervision.roboflow.com/0.18.0/assets/) allowing download of video files that you can use in your demos.
```python
>>> from supervision.assets import download_assets, VideoAssets
@@ -1143,35 +1143,35 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
"vehicles.mp4"
```
- Added [#605](https://github.com/roboflow/supervision/pull/605): [`Position.CENTER_OF_MASS`](/0.17.0/geometry/core/#position) allowing to place labels in center of mass of segmentation masks.
- Added [#605](https://github.com/roboflow/supervision/pull/605): [`Position.CENTER_OF_MASS`](https://supervision.roboflow.com/0.17.0/geometry/core/#position) allowing to place labels in center of mass of segmentation masks.
- Added [#651](https://github.com/roboflow/supervision/pull/651): [`sv.scale_boxes`](/0.17.0/detection/utils/#supervision.detection.utils.scale_boxes) allowing to scale [`sv.Detections.xyxy`](/0.17.0/detection/core/#supervision.detection.core.Detections) values.
- Added [#651](https://github.com/roboflow/supervision/pull/651): [`sv.scale_boxes`](https://supervision.roboflow.com/0.17.0/detection/utils/#supervision.detection.utils.scale_boxes) allowing to scale [`sv.Detections.xyxy`](https://supervision.roboflow.com/0.17.0/detection/core/#supervision.detection.core.Detections) values.
- Added [#637](https://github.com/roboflow/supervision/pull/637): [`sv.calculate_dynamic_text_scale`](/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_text_scale) and [`sv.calculate_dynamic_line_thickness`](/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_line_thickness) allowing text scale and line thickness to match image resolution.
- Added [#637](https://github.com/roboflow/supervision/pull/637): [`sv.calculate_dynamic_text_scale`](https://supervision.roboflow.com/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_text_scale) and [`sv.calculate_dynamic_line_thickness`](https://supervision.roboflow.com/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_line_thickness) allowing text scale and line thickness to match image resolution.
- Added [#620](https://github.com/roboflow/supervision/pull/620): [`sv.Color.as_hex`](/0.17.0/draw/color/#supervision.draw.color.Color.as_hex) allowing to extract color value in HEX format.
- Added [#620](https://github.com/roboflow/supervision/pull/620): [`sv.Color.as_hex`](https://supervision.roboflow.com/0.17.0/draw/color/#supervision.draw.color.Color.as_hex) allowing to extract color value in HEX format.
- Added [#572](https://github.com/roboflow/supervision/pull/572): [`sv.Classifications.from_timm`](/0.17.0/classification/core/#supervision.classification.core.Classifications.from_timm) allowing to load classification result from [timm](https://huggingface.co/docs/hub/timm) models.
- Added [#572](https://github.com/roboflow/supervision/pull/572): [`sv.Classifications.from_timm`](https://supervision.roboflow.com/0.17.0/classification/core/#supervision.classification.core.Classifications.from_timm) allowing to load classification result from [timm](https://huggingface.co/docs/hub/timm) models.
- Added [#478](https://github.com/roboflow/supervision/pull/478): [`sv.Classifications.from_clip`](/0.17.0/classification/core/#supervision.classification.core.Classifications.from_clip) allowing to load classification result from [clip](https://github.com/openai/clip) model.
- Added [#478](https://github.com/roboflow/supervision/pull/478): [`sv.Classifications.from_clip`](https://supervision.roboflow.com/0.17.0/classification/core/#supervision.classification.core.Classifications.from_clip) allowing to load classification result from [clip](https://github.com/openai/clip) model.
- Added [#571](https://github.com/roboflow/supervision/pull/571): [`sv.Detections.from_azure_analyze_image`](/0.17.0/detection/core/#supervision.detection.core.Detections.from_azure_analyze_image) allowing to load detection results from [Azure Image Analysis](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40).
- Added [#571](https://github.com/roboflow/supervision/pull/571): [`sv.Detections.from_azure_analyze_image`](https://supervision.roboflow.com/0.17.0/detection/core/#supervision.detection.core.Detections.from_azure_analyze_image) allowing to load detection results from [Azure Image Analysis](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40).
- Changed [#646](https://github.com/roboflow/supervision/pull/646): `sv.BoxMaskAnnotator` renaming it to [`sv.ColorAnnotator`](/0.17.0/annotators/#supervision.annotators.core.ColorAnnotator).
- Changed [#646](https://github.com/roboflow/supervision/pull/646): `sv.BoxMaskAnnotator` renaming it to [`sv.ColorAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.ColorAnnotator).
- Changed [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](/0.17.0/annotators/#supervision.annotators.core.MaskAnnotator) to make it **5x faster**.
- Changed [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.MaskAnnotator) to make it **5x faster**.
- Fixed [#584](https://github.com/roboflow/supervision/pull/584): [`sv.DetectionDataset.from_yolo`](/0.17.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to ignore empty lines in annotation files.
- Fixed [#584](https://github.com/roboflow/supervision/pull/584): [`sv.DetectionDataset.from_yolo`](https://supervision.roboflow.com/0.17.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to ignore empty lines in annotation files.
- Fixed [#555](https://github.com/roboflow/supervision/pull/555): [`sv.BlurAnnotator`](/0.17.0/annotators/#supervision.annotators.core.BlurAnnotator) to trim negative coordinates before bluring detections.
- Fixed [#555](https://github.com/roboflow/supervision/pull/555): [`sv.BlurAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.BlurAnnotator) to trim negative coordinates before bluring detections.
- Fixed [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](/0.17.0/annotators/#supervision.annotators.core.TraceAnnotator) to respect trace position.
- Fixed [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](https://supervision.roboflow.com/0.17.0/annotators/#supervision.annotators.core.TraceAnnotator) to respect trace position.
### 0.16.0 <small>October 19, 2023</small>
- Added [#422](https://github.com/roboflow/supervision/pull/422): [`sv.BoxMaskAnnotator`](/0.16.0/annotators/#supervision.annotators.core.BoxMaskAnnotator) allowing to annotate images and videos with mox masks.
- Added [#422](https://github.com/roboflow/supervision/pull/422): [`sv.BoxMaskAnnotator`](https://supervision.roboflow.com/0.16.0/annotators/#supervision.annotators.core.BoxMaskAnnotator) allowing to annotate images and videos with mox masks.
- Added [#433](https://github.com/roboflow/supervision/pull/433): [`sv.HaloAnnotator`](/0.16.0/annotators/#supervision.annotators.core.HaloAnnotator) allowing to annotate images and videos with halo effect.
- Added [#433](https://github.com/roboflow/supervision/pull/433): [`sv.HaloAnnotator`](https://supervision.roboflow.com/0.16.0/annotators/#supervision.annotators.core.HaloAnnotator) allowing to annotate images and videos with halo effect.
```python
>>> import supervision as sv
@@ -1186,45 +1186,45 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
... )
```
- Added [#466](https://github.com/roboflow/supervision/pull/466): [`sv.HeatMapAnnotator`](/0.16.0/annotators/#supervision.annotators.core.HeatMapAnnotator) allowing to annotate videos with heat maps.
- Added [#466](https://github.com/roboflow/supervision/pull/466): [`sv.HeatMapAnnotator`](https://supervision.roboflow.com/0.16.0/annotators/#supervision.annotators.core.HeatMapAnnotator) allowing to annotate videos with heat maps.
- Added [#492](https://github.com/roboflow/supervision/pull/492): [`sv.DotAnnotator`](/0.16.0/annotators/#supervision.annotators.core.DotAnnotator) allowing to annotate images and videos with dots.
- Added [#492](https://github.com/roboflow/supervision/pull/492): [`sv.DotAnnotator`](https://supervision.roboflow.com/0.16.0/annotators/#supervision.annotators.core.DotAnnotator) allowing to annotate images and videos with dots.
- Added [#449](https://github.com/roboflow/supervision/pull/449): [`sv.draw_image`](/0.16.0/draw/utils/#supervision.draw.utils.draw_image) allowing to draw an image onto a given scene with specified opacity and dimensions.
- Added [#449](https://github.com/roboflow/supervision/pull/449): [`sv.draw_image`](https://supervision.roboflow.com/0.16.0/draw/utils/#supervision.draw.utils.draw_image) allowing to draw an image onto a given scene with specified opacity and dimensions.
- Added [#280](https://github.com/roboflow/supervision/pull/280): [`sv.FPSMonitor`](/0.16.0/utils/video/#supervision.utils.video.FPSMonitor) for monitoring frames per second (FPS) to benchmark latency.
- Added [#280](https://github.com/roboflow/supervision/pull/280): [`sv.FPSMonitor`](https://supervision.roboflow.com/0.16.0/utils/video/#supervision.utils.video.FPSMonitor) for monitoring frames per second (FPS) to benchmark latency.
- Added [#454](https://github.com/roboflow/supervision/pull/454): 🤗 Hugging Face Annotators [space](https://huggingface.co/spaces/Roboflow/Annotators).
- Changed [#482](https://github.com/roboflow/supervision/pull/482): [`sv.LineZone.trigger`](/0.16.0/detection/tools/line_zone/#supervision.detection.line_counter.LineZone.trigger) now return `Tuple[np.ndarray, np.ndarray]`. The first array indicates which detections have crossed the line from outside to inside. The second array indicates which detections have crossed the line from inside to outside.
- Changed [#482](https://github.com/roboflow/supervision/pull/482): [`sv.LineZone.trigger`](https://supervision.roboflow.com/0.16.0/detection/tools/line_zone/#supervision.detection.line_counter.LineZone.trigger) now return `Tuple[np.ndarray, np.ndarray]`. The first array indicates which detections have crossed the line from outside to inside. The second array indicates which detections have crossed the line from inside to outside.
- Changed [#465](https://github.com/roboflow/supervision/pull/465): Annotator argument name from `color_map: str` to `color_lookup: ColorLookup` enum to increase type safety.
- Changed [#426](https://github.com/roboflow/supervision/pull/426): [`sv.MaskAnnotator`](/0.16.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing 2x faster annotation.
- Changed [#426](https://github.com/roboflow/supervision/pull/426): [`sv.MaskAnnotator`](https://supervision.roboflow.com/0.16.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing 2x faster annotation.
- Fixed [#477](https://github.com/roboflow/supervision/pull/477): Poetry env definition allowing proper local installation.
- Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
- Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](https://supervision.roboflow.com/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
!!! failure "Deprecated"
`sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
`sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](https://supervision.roboflow.com/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
### 0.15.0 <small>October 5, 2023</small>
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](/0.15.0/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoxCornerAnnotator `](/0.15.0/annotators/#supervision.annotators.core.BoxCornerAnnotator) allowing to annotate images and videos with just bounding box corners.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoxCornerAnnotator `](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.BoxCornerAnnotator) allowing to annotate images and videos with just bounding box corners.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.MaskAnnotator`](/0.15.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing to annotate images and videos with segmentation masks.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.MaskAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing to annotate images and videos with segmentation masks.
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.EllipseAnnotator`](/0.15.0/annotators/#supervision.annotators.core.EllipseAnnotator) allowing to annotate images and videos with ellipses (sports game style).
- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.EllipseAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.EllipseAnnotator) allowing to annotate images and videos with ellipses (sports game style).
- Added [#386](https://github.com/roboflow/supervision/pull/386): [`sv.CircleAnnotator`](/0.15.0/annotators/#supervision.annotators.core.CircleAnnotator) allowing to annotate images and videos with circles.
- Added [#386](https://github.com/roboflow/supervision/pull/386): [`sv.CircleAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.CircleAnnotator) allowing to annotate images and videos with circles.
- Added [#354](https://github.com/roboflow/supervision/pull/354): [`sv.TraceAnnotator`](/0.15.0/annotators/#supervision.annotators.core.TraceAnnotator) allowing to draw path of moving objects on videos.
- Added [#354](https://github.com/roboflow/supervision/pull/354): [`sv.TraceAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.TraceAnnotator) allowing to draw path of moving objects on videos.
- Added [#405](https://github.com/roboflow/supervision/pull/405): [`sv.BlurAnnotator`](/0.15.0/annotators/#supervision.annotators.core.BlurAnnotator) allowing to blur objects on images and videos.
- Added [#405](https://github.com/roboflow/supervision/pull/405): [`sv.BlurAnnotator`](https://supervision.roboflow.com/0.15.0/annotators/#supervision.annotators.core.BlurAnnotator) allowing to blur objects on images and videos.
```python
>>> import supervision as sv
@@ -1241,17 +1241,17 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
- Added [#354](https://github.com/roboflow/supervision/pull/354): Supervision usage [example](https://github.com/roboflow/supervision/tree/develop/examples/traffic_analysis). You can now learn how to perform traffic flow analysis with Supervision.
- Changed [#399](https://github.com/roboflow/supervision/pull/399): [`sv.Detections.from_roboflow`](/0.15.0/detection/core/#supervision.detection.core.Detections.from_roboflow) now does not require `class_list` to be specified. The `class_id` value can be extracted directly from the [inference](https://github.com/roboflow/inference) response.
- Changed [#399](https://github.com/roboflow/supervision/pull/399): [`sv.Detections.from_roboflow`](https://supervision.roboflow.com/0.15.0/detection/core/#supervision.detection.core.Detections.from_roboflow) now does not require `class_list` to be specified. The `class_id` value can be extracted directly from the [inference](https://github.com/roboflow/inference) response.
- Changed [#381](https://github.com/roboflow/supervision/pull/381): [`sv.VideoSink`](/0.15.0/utils/video/#videosink) now allows to customize the output codec.
- Changed [#381](https://github.com/roboflow/supervision/pull/381): [`sv.VideoSink`](https://supervision.roboflow.com/0.15.0/utils/video/#videosink) now allows to customize the output codec.
- Changed [#361](https://github.com/roboflow/supervision/pull/361): [`sv.InferenceSlicer`](/0.15.0/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can now operate in multithreading mode.
- Changed [#361](https://github.com/roboflow/supervision/pull/361): [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.15.0/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can now operate in multithreading mode.
- Fixed [#348](https://github.com/roboflow/supervision/pull/348): [`sv.Detections.from_deepsparse`](/0.15.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to allow processing empty [deepsparse](https://github.com/neuralmagic/deepsparse) result object.
- Fixed [#348](https://github.com/roboflow/supervision/pull/348): [`sv.Detections.from_deepsparse`](https://supervision.roboflow.com/0.15.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to allow processing empty [deepsparse](https://github.com/neuralmagic/deepsparse) result object.
### 0.14.0 <small>August 31, 2023</small>
- Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](/0.14.0/detection/tools/inference_slicer).
- Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.14.0/detection/tools/inference_slicer).
```python
>>> import cv2
@@ -1270,23 +1270,23 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
>>> detections = slicer(image)
```
- Added [#297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.
- Added [#297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](https://supervision.roboflow.com/0.14.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.
- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](https://supervision.roboflow.com/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
!!! failure "Deprecated"
[sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
[sv.Detections.from_yolov8](https://supervision.roboflow.com/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](https://supervision.roboflow.com/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
- Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
- Changed [#296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](/0.14.0/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](/0.14.0/dataset/core/#supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.
- Changed [#296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.14.0/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](https://supervision.roboflow.com/0.14.0/dataset/core/#supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.
- Fixed [#300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.
- Fixed [#300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](https://supervision.roboflow.com/0.14.0/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.
### 0.13.0 <small>August 8, 2023</small>
- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](/0.13.0/metrics/detection/#meanaverageprecision).
- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.13.0/metrics/detection/#meanaverageprecision).
```python
>>> import supervision as sv
@@ -1308,17 +1308,17 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
0.433
```
- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](/0.13.0/tracker/core/#bytetrack).
- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](https://supervision.roboflow.com/0.13.0/tracker/core/#bytetrack).
- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
!!! failure "Deprecated"
[`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
[`sv.Detections.from_yolov8`](https://supervision.roboflow.com/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](https://supervision.roboflow.com/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](/0.13.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).
- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](https://supervision.roboflow.com/0.13.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).
### 0.12.0 <small>July 24, 2023</small>
@@ -1326,7 +1326,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
- Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).
- Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](https://supervision.roboflow.com/0.12.0/metrics/detection/#confusionmatrix).
```python
>>> import supervision as sv
@@ -1353,23 +1353,23 @@ array([
])
```
- Added [#173](https://github.com/roboflow/supervision/pull/173): [`Detections.from_mmdetection`](/0.12.0/detection/core/#supervision.detection.core.Detections.from_mmdetection) to enable seamless integration with [MMDetection](https://github.com/open-mmlab/mmdetection) framework.
- Added [#173](https://github.com/roboflow/supervision/pull/173): [`Detections.from_mmdetection`](https://supervision.roboflow.com/0.12.0/detection/core/#supervision.detection.core.Detections.from_mmdetection) to enable seamless integration with [MMDetection](https://github.com/open-mmlab/mmdetection) framework.
- Added [#130](https://github.com/roboflow/supervision/issues/130): ability to [install](https://supervision.roboflow.com/) package in `headless` or `desktop` mode.
- Changed [#180](https://github.com/roboflow/supervision/pull/180): packing method from `setup.py` to `pyproject.toml`.
- Fixed [#188](https://github.com/roboflow/supervision/issues/188): [`sv.DetectionDataset.from_cooc`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) can't be loaded when there are images without annotations.
- Fixed [#188](https://github.com/roboflow/supervision/issues/188): [`sv.DetectionDataset.from_cooc`](https://supervision.roboflow.com/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) can't be loaded when there are images without annotations.
- Fixed [#226](https://github.com/roboflow/supervision/issues/226): [`sv.DetectionDataset.from_yolo`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_yolo) can't load background instances.
- Fixed [#226](https://github.com/roboflow/supervision/issues/226): [`sv.DetectionDataset.from_yolo`](https://supervision.roboflow.com/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_yolo) can't load background instances.
### 0.11.1 <small>June 29, 2023</small>
- Fixed [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
- Fixed [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](https://supervision.roboflow.com/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
### 0.11.0 <small>June 28, 2023</small>
- Added [#150](https://github.com/roboflow/supervision/pull/150): ability to load and save [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) in COCO format using [`as_coco`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_coco) and [`from_coco`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) methods.
- Added [#150](https://github.com/roboflow/supervision/pull/150): ability to load and save [`sv.DetectionDataset`](https://supervision.roboflow.com/0.11.0/dataset/core/#detectiondataset) in COCO format using [`as_coco`](https://supervision.roboflow.com/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_coco) and [`from_coco`](https://supervision.roboflow.com/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) methods.
```python
>>> import supervision as sv
@@ -1385,7 +1385,7 @@ array([
... )
```
- Added [#158](https://github.com/roboflow/supervision/pull/158): ability to merge multiple [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) together using [`merge`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.merge) method.
- Added [#158](https://github.com/roboflow/supervision/pull/158): ability to merge multiple [`sv.DetectionDataset`](https://supervision.roboflow.com/0.11.0/dataset/core/#detectiondataset) together using [`merge`](https://supervision.roboflow.com/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.merge) method.
```python
>>> import supervision as sv
@@ -1409,13 +1409,13 @@ array([
['cat', 'dog', 'person']
```
- Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.
- Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](https://supervision.roboflow.com/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.
- Fixed [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.
### 0.10.0 <small>June 14, 2023</small>
- Added [#125](https://github.com/roboflow/supervision/pull/125): ability to load and save [`sv.ClassificationDataset`](/0.10.0/dataset/core/#classificationdataset) in a folder structure format.
- Added [#125](https://github.com/roboflow/supervision/pull/125): ability to load and save [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.10.0/dataset/core/#classificationdataset) in a folder structure format.
```python
>>> import supervision as sv
@@ -1429,9 +1429,9 @@ array([
... )
```
- Added [#125](https://github.com/roboflow/supervision/pull/125): support for [`sv.ClassificationDataset.split`](/0.10.0/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.
- Added [#125](https://github.com/roboflow/supervision/pull/125): support for [`sv.ClassificationDataset.split`](https://supervision.roboflow.com/0.10.0/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.
- Added [#110](https://github.com/roboflow/supervision/pull/110): ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](/0.10.0/detection/core/#supervision.detection.core.Detections.from_roboflow).
- Added [#110](https://github.com/roboflow/supervision/pull/110): ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](https://supervision.roboflow.com/0.10.0/detection/core/#supervision.detection.core.Detections.from_roboflow).
- Added [commit hash](https://github.com/roboflow/supervision/commit/d000292eb2f2342544e0947b65528082e60fb8d6): Supervision Quickstart [notebook](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) where you can learn more about Detection, Dataset and Video APIs.
@@ -1439,7 +1439,7 @@ array([
### 0.9.0 <small>June 7, 2023</small>
- Added [#118](https://github.com/roboflow/supervision/pull/118): ability to select [`sv.Detections`](/0.9.0/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.
- Added [#118](https://github.com/roboflow/supervision/pull/118): ability to select [`sv.Detections`](https://supervision.roboflow.com/0.9.0/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.
```python
>>> import supervision as sv
@@ -1453,11 +1453,11 @@ array([
2
```
- Added [#101](https://github.com/roboflow/supervision/pull/101): ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](/0.8.0/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
- Added [#101](https://github.com/roboflow/supervision/pull/101): ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](https://supervision.roboflow.com/0.8.0/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](/0.9.0/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](https://supervision.roboflow.com/0.9.0/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
- Added [#120](https://github.com/roboflow/supervision/pull/120): ability to conveniently save multiple images into directory using [`sv.ImageSink`](/0.9.0/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.
- Added [#120](https://github.com/roboflow/supervision/pull/120): ability to conveniently save multiple images into directory using [`sv.ImageSink`](https://supervision.roboflow.com/0.9.0/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.
```python
>>> import supervision as sv
@@ -1467,12 +1467,12 @@ array([
... sink.save_image(image=image)
```
- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](/0.8.0/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.
- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](https://supervision.roboflow.com/0.8.0/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.
### 0.8.0 <small>May 17, 2023</small>
- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](/0.8.0/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
- Added [#100](https://github.com/roboflow/supervision/pull/100): ability to save datasets in YOLO format using [`DetectionDataset.as_yolo`](/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_yolo).
- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](https://supervision.roboflow.com/0.8.0/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
- Added [#100](https://github.com/roboflow/supervision/pull/100): ability to save datasets in YOLO format using [`DetectionDataset.as_yolo`](https://supervision.roboflow.com/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_yolo).
```python
>>> import roboflow
@@ -1496,7 +1496,7 @@ array([
['dog', 'person']
```
- Added [#102](https://github.com/roboflow/supervision/pull/103): support for [`DetectionDataset.split`](/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.split) allowing to divide `DetectionDataset` into two parts.
- Added [#103](https://github.com/roboflow/supervision/pull/103): support for [`DetectionDataset.split`](https://supervision.roboflow.com/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.split) allowing to divide `DetectionDataset` into two parts.
```python
>>> import supervision as sv
docs/deprecated.md
+1 -1
View File
@@ -39,6 +39,6 @@ These features are phased out due to better alternatives or potential issues in
- The method `sv.Color.red()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.RED` instead.
- The method `sv.Color.green()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.GREEN` instead.
- The method `sv.Color.blue()` was removed as of `supervision-0.22.0`. Use the constant `sv.Color.BLUE` instead.
- The method `sv.ColorPalette.default()` was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
- The method `sv.ColorPalette.default()` was removed as of `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](utils/draw.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
- `sv.BoxAnnotator` was removed as of `supervision-0.22.0`, however `sv.BoundingBoxAnnotator` was immediately renamed to `sv.BoxAnnotator`. Use [`BoxAnnotator`](detection/annotators.md/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead of the old `sv.BoxAnnotator`.
- The method `sv.FPSMonitor.__call__` was removed as of `supervision-0.22.0`. Use the attribute [`sv.FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
docs/detection/annotators.md
+11 -22
View File
@@ -233,8 +233,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
mask-annotator-example-purple.png){ align=center width="800" }
![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/mask-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -255,8 +254,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![polygon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
polygon-annotator-example-purple.png){ align=center width="800" }
![polygon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/polygon-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -284,8 +282,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
label-annotator-example-purple.png){ align=center width="800" }
![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -316,8 +313,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
label-annotator-example-purple.png){ align=center width="800" }
![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -344,8 +340,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![icon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
icon-annotator-example.png){ align=center width="800" }
![icon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/icon-annotator-example.png){ align=center width="800" }
</div>
@@ -365,8 +360,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
```
<div class="result" markdown>
![crop-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
crop-annotator-example.png){ align=center width="800" }
![crop-annotator-example](https://media.roboflow.com/supervision-annotator-examples/crop-annotator-example.png){ align=center width="800" }
</div>
-->
@@ -388,8 +382,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
blur-annotator-example-purple.png){ align=center width="800" }
![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/blur-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -410,8 +403,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
pixelate-annotator-example-10.png){ align=center width="800" }
![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/pixelate-annotator-example-10.png){ align=center width="800" }
</div>
@@ -442,8 +434,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
trace-annotator-example-purple.png){ align=center width="800" }
![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/trace-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -472,8 +463,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![heat-map-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
heat-map-annotator-example-purple.png){ align=center width="800" }
![heat-map-annotator-example](https://media.roboflow.com/supervision-annotator-examples/heat-map-annotator-example-purple.png){ align=center width="800" }
</div>
@@ -517,8 +507,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<div class="result" markdown>
![comparison-annotator-example](https://media.roboflow.com/supervision-annotator-examples/
comparison-annotator-example.png){ align=center width="800" }
![comparison-annotator-example](https://media.roboflow.com/supervision-annotator-examples/comparison-annotator-example.png){ align=center width="800" }
</div>
docs/how_to/detect_and_annotate.md
+12 -12
View File
@@ -65,7 +65,7 @@ model.
Now that we have predictions from a model, we can load them into Supervision.
=== "Inference"
We can do so using the [`sv.Detections.from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
We can do so using the [`sv.Detections.from_inference`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 8" }
import cv2
@@ -79,7 +79,7 @@ Now that we have predictions from a model, we can load them into Supervision.
```
=== "Ultralytics"
We can do so using the [`sv.Detections.from_ultralytics`](/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
We can do so using the [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 8" }
import cv2
@@ -93,7 +93,7 @@ Now that we have predictions from a model, we can load them into Supervision.
```
=== "Transformers"
We can do so using the [`sv.Detections.from_transformers`](/latest/detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
We can do so using the [`sv.Detections.from_transformers`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 19-21" }
import torch
@@ -121,15 +121,15 @@ Now that we have predictions from a model, we can load them into Supervision.
You can load predictions from other computer vision frameworks and libraries using:
- [`from_deepsparse`](/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
- [`from_detectron2`](/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
- [`from_mmdetection`](/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
- [`from_sam`](/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
- [`from_yolo_nas`](/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
- [`from_deepsparse`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
- [`from_detectron2`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
- [`from_mmdetection`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
- [`from_sam`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
- [`from_yolo_nas`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
## Annotate Image with Detections
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes.
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes.
=== "Inference"
```{ .py hl_lines="10-16" }
@@ -208,7 +208,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
## Display Custom Labels
By default, [`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
By default, [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
will label each detection with its `class_name` (if possible) or `class_id`. You can
override this behavior by passing a list of custom `labels` to the `annotate` method.
@@ -308,9 +308,9 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
## Annotate Image with Segmentations
If you are running the segmentation model
[`sv.MaskAnnotator`](/latest/detection/annotators/#supervision.annotators.core.MaskAnnotator)
[`sv.MaskAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.MaskAnnotator)
is a drop-in replacement for
[`sv.BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator)
[`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator)
that will allow you to draw masks instead of boxes.
=== "Inference"
docs/how_to/detect_small_objects.md
+3 -3
View File
@@ -8,7 +8,7 @@ This guide shows how to detect small objects
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).
[`InferenceSlicer`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
<video controls>
<source src="https://media.roboflow.com/supervision_detect_small_objects_example.mp4" type="video/mp4">
@@ -148,7 +148,7 @@ is less effective for ultra-high-resolution images (4K and above).
## Inference Slicer
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
[`InferenceSlicer`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
processes high-resolution images by dividing them into smaller segments, detecting
objects within each, and aggregating the results.
@@ -258,7 +258,7 @@ objects within each, and aggregating the results.
## Small Object Segmentation
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
[`InferenceSlicer`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
=== "Inference"
```{ .py hl_lines="6 16 19-20" }
docs/how_to/process_datasets.md
+4 -4
View File
@@ -399,12 +399,12 @@ pip install augmentation
```
Albumentations provides a flexible and powerful API for image augmentation. The core of
the library is the [`Compose`](https://albumentations.ai/docs/api_reference/full_reference/?h=compose#albumentations.core.composition.Compose)
the library is the [`Compose`](https://albumentations.ai/docs/api-reference/albumentations/core/composition/#Compose)
class, which allows you to chain multiple image transformations together. Each
transformation is defined using a dedicated class, such as
[`HorizontalFlip`](https://albumentations.ai/docs/api_reference/full_reference/?h=horizontalflip#albumentations.augmentations.geometric.transforms.HorizontalFlip),
[`RandomBrightnessContrast`](https://albumentations.ai/docs/api_reference/full_reference/?h=horizontalflip#albumentations.augmentations.transforms.RandomBrightnessContrast),
or [`Perspective`](https://albumentations.ai/docs/api_reference/full_reference/?h=horizontalflip#albumentations.augmentations.geometric.transforms.Perspective).
[`HorizontalFlip`](https://albumentations.ai/docs/api-reference/albumentations/augmentations/geometric/flip/#HorizontalFlip),
[`RandomBrightnessContrast`](https://albumentations.ai/docs/api-reference/albumentations/augmentations/pixel/transforms/#RandomBrightnessContrast),
or [`Perspective`](https://albumentations.ai/docs/api-reference/albumentations/augmentations/geometric/transforms/#Perspective).
```python
import albumentations as A
docs/how_to/save_detections.md
+9 -9
View File
@@ -9,14 +9,14 @@ processing. This guide demonstrates how to perform video inference using the
[Inference](https://github.com/roboflow/inference),
[Ultralytics](https://github.com/ultralytics/ultralytics) or
[Transformers](https://github.com/huggingface/transformers) packages and save their results with
[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) and
[`sv.JSONSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
[`sv.CSVSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) and
[`sv.JSONSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
## Run Detection
First, you'll need to obtain predictions from your object detection or segmentation
model. You can learn more on this topic in our
[How to Detect and Annotate](/latest/how_to/detect_and_annotate.md) guide.
[How to Detect and Annotate](https://supervision.roboflow.com/latest/how_to/detect_and_annotate/) guide.
=== "Inference"
```python
@@ -74,9 +74,9 @@ model. You can learn more on this topic in our
## Save Detections as CSV
To save detections to a `.CSV` file, open our
[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
[`sv.CSVSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
and then pass the
[`sv.Detections`](/latest/detection/core/#supervision.detection.core.Detections)
[`sv.Detections`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections)
object resulting from the inference to it. Its fields are parsed and saved on disk.
=== "Inference"
@@ -147,8 +147,8 @@ object resulting from the inference to it. Its fields are parsed and saved on di
## Custom Fields
Besides regular fields in
[`sv.Detections`](/latest/detection/core/#supervision.detection.core.Detections),
[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
[`sv.Detections`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections),
[`sv.CSVSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
also allows you to add custom information to each row, which can be passed via the
`custom_data` dictionary. Let's utilize this feature to save information about the
frame index from which the detections originate.
@@ -222,9 +222,9 @@ frame index from which the detections originate.
If you prefer to save the result in a `.JSON` file instead of a `.CSV` file, all you
need to do is replace
[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
[`sv.CSVSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
with
[`sv.JSONSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
[`sv.JSONSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
=== "Inference"
```{ .py hl_lines="7" }
docs/how_to/track_objects.md
+13 -13
View File
@@ -5,7 +5,7 @@ comments: true
# Track Objects
Leverage Supervision's advanced capabilities for enhancing your video analysis by
seamlessly [tracking](/latest/trackers/) objects recognized by
seamlessly [tracking](https://supervision.roboflow.com/latest/trackers/) objects recognized by
a multitude of object detection, segmentation and keypoint models. This comprehensive guide will
take you through the steps to perform inference using the YOLOv8 model via either the
[Inference](https://github.com/roboflow/inference) or
@@ -17,7 +17,7 @@ for a deeper analysis.
To make it easier for you to follow our tutorial download the video we will use as an
example. You can do this using
[`supervision[assets]`](/latest/assets/) extension.
[`supervision[assets]`](https://supervision.roboflow.com/latest/assets/) extension.
```python
from supervision.assets import download_assets, VideoAssets
@@ -34,7 +34,7 @@ download_assets(VideoAssets.PEOPLE_WALKING)
First, you'll need to obtain predictions from your object detection or segmentation
model. In this tutorial, we are using the YOLOv8 model as an example. However,
Supervision is versatile and compatible with various models. Check this
[link](/latest/how_to/detect_and_annotate/#load-predictions-into-supervision)
[link](https://supervision.roboflow.com/latest/how_to/detect_and_annotate/#load-predictions-into-supervision)
for guidance on how to plug in other models.
We will define a `callback` function, which will process each frame of the video
@@ -98,7 +98,7 @@ it will be modified to include tracking, labeling, and trace annotations.
After running inference and obtaining predictions, the next step is to track the
detected objects throughout the video. Utilizing Supervision’s
[`sv.ByteTrack`](/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack)
[`sv.ByteTrack`](https://supervision.roboflow.com/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack)
functionality, each detected object is assigned a unique tracker ID,
enabling the continuous following of the object's motion path across different frames.
@@ -154,7 +154,7 @@ enabling the continuous following of the object's motion path across different f
Annotating the video with tracking IDs helps in distinguishing and following each object
distinctly. With the
[`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
[`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator)
in Supervision, we can overlay the tracker IDs and class labels on the detected objects,
offering a clear visual representation of each object's class and unique identifier.
@@ -236,7 +236,7 @@ offering a clear visual representation of each object's class and unique identif
Adding traces to the video involves overlaying the historical paths of the detected
objects. This feature, powered by the
[`sv.TraceAnnotator`](/latest/detection/annotators/#supervision.annotators.core.TraceAnnotator),
[`sv.TraceAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.TraceAnnotator),
allows for visualizing the trajectories of objects, helping in understanding the
movement patterns and interactions between objects in the video.
@@ -325,7 +325,7 @@ movement patterns and interactions between objects in the video.
Models aren't limited to object detection and segmentation. Keypoint detection allows for detailed analysis of body joints and connections, especially valuable for applications like human pose estimation. This section introduces keypoint tracking. We'll walk through the steps of annotating keypoints, converting them into bounding box detections compatible with `ByteTrack`, and applying detection smoothing for enhanced stability.
To make it easier for you to follow our tutorial, let's download the video we will use as an
example. You can do this using [`supervision[assets]`](/latest/assets/) extension.
example. You can do this using [`supervision[assets]`](https://supervision.roboflow.com/latest/assets/) extension.
```python
from supervision.assets import download_assets, VideoAssets
@@ -340,11 +340,11 @@ download_assets(VideoAssets.SKIING)
### Keypoint Detection
First, you'll need to obtain predictions from your keypoint detection model. In this tutorial, we are using the YOLOv8 model as an example. However,
Supervision is versatile and compatible with various models. Check this [link](/latest/keypoint/core/) for guidance on how to plug in other models.
Supervision is versatile and compatible with various models. Check this [link](https://supervision.roboflow.com/latest/keypoint/core/) for guidance on how to plug in other models.
We will define a `callback` function, which will process each frame of the video by obtaining model predictions and then annotating the frame based on these predictions.
Let's immediately visualize the results with our [`EdgeAnnotator`](/latest/keypoint/annotators/#supervision.key_points.annotators.EdgeAnnotator) and [`VertexAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.key_points.annotators.VertexAnnotator).
Let's immediately visualize the results with our [`EdgeAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.key_points.annotators.EdgeAnnotator) and [`VertexAnnotator`](https://supervision.roboflow.com/latest/keypoint/annotators/#supervision.key_points.annotators.VertexAnnotator).
=== "Ultralytics"
@@ -407,9 +407,9 @@ Let's immediately visualize the results with our [`EdgeAnnotator`](/latest/keypo
### Convert to Detections
Keypoint tracking is currently supported via the conversion of `KeyPoints` to `Detections`. This is achieved with the [`KeyPoints.as_detections()`](/latest/keypoint/core/#supervision.key_points.core.KeyPoints.as_detections) function.
Keypoint tracking is currently supported via the conversion of `KeyPoints` to `Detections`. This is achieved with the [`KeyPoints.as_detections()`](https://supervision.roboflow.com/latest/keypoint/core/#supervision.key_points.core.KeyPoints.as_detections) function.
Let's convert to detections and visualize the results with our [`BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator).
Let's convert to detections and visualize the results with our [`BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator).
!!! tip
@@ -484,7 +484,7 @@ Let's convert to detections and visualize the results with our [`BoxAnnotator`](
### Keypoint Tracking
Now that we have a `Detections` object, we can track it throughout the video. Utilizing Supervision’s [`sv.ByteTrack`](/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) functionality, each detected object is assigned a unique tracker ID, enabling the continuous following of the object's motion path across different frames. We shall visualize the result with `TraceAnnotator`.
Now that we have a `Detections` object, we can track it throughout the video. Utilizing Supervision's [`sv.ByteTrack`](https://supervision.roboflow.com/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) functionality, each detected object is assigned a unique tracker ID, enabling the continuous following of the object's motion path across different frames. We shall visualize the result with `TraceAnnotator`.
=== "Ultralytics"
@@ -567,7 +567,7 @@ Now that we have a `Detections` object, we can track it throughout the video. Ut
### Bonus: Smoothing
We could stop here as we have successfully tracked the object detected by the keypoint model. However, we can further enhance the stability of the boxes by applying [`DetectionsSmoother`](/latest/detection/tools/smoother/). This tool helps in stabilizing the boxes by smoothing the bounding box coordinates across frames. It is very simple to use:
We could stop here as we have successfully tracked the object detected by the keypoint model. However, we can further enhance the stability of the boxes by applying [`DetectionsSmoother`](https://supervision.roboflow.com/latest/detection/tools/smoother/). This tool helps in stabilizing the boxes by smoothing the bounding box coordinates across frames. It is very simple to use:
=== "Ultralytics"
examples/README.md
+1 -1
View File
@@ -2,7 +2,7 @@
Here, you'll find end-to-end examples that show how to solve common computer vision problems using Supervision.
For more information and examples, visit our [documentation](https://supervision.roboflow.com/develop/annotators/) and explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/) and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/). Join our [Discord](https://discord.com/invite/GbfgXGJ8Bk) and meet other Supervision power users!
For more information and examples, visit our [documentation](https://supervision.roboflow.com/latest/detection/annotators/) and explore our [how-to guides](https://supervision.roboflow.com/latest/how_to/detect_and_annotate/) and [cookbooks](https://supervision.roboflow.com/latest/cookbooks/). Join our [Discord](https://discord.com/invite/GbfgXGJ8Bk) and meet other Supervision power users!
- [tracking](./tracking) by [@SkalskiP](https://github.com/SkalskiP)
- [count people in zone](./count_people_in_zone) by [@SkalskiP](https://github.com/SkalskiP)
supervision/metrics/f1_score.py
+6 -6
View File
@@ -54,9 +54,9 @@ class F1Score(Metric):
>>> round(float(f1_result.f1_50), 2)
1.0
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png
){ align=center width="800" }
"""
def __init__(
@@ -573,9 +573,9 @@ class F1ScoreResult:
"""
Plot the F1 results.
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png
){ align=center width="800" }
"""
labels = ["F1@50", "F1@75"]
supervision/metrics/mean_average_precision.py
+6 -6
View File
@@ -149,9 +149,9 @@ class MeanAveragePrecisionResult:
"""
Plot the mAP results.
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png
){ align=center width="800" }
"""
labels = ["mAP@50:95", "mAP@50", "mAP@75"]
@@ -1164,9 +1164,9 @@ class MeanAveragePrecision(Metric):
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png
){ align=center width="800" }
"""
def __init__(
supervision/metrics/mean_average_recall.py
+6 -6
View File
@@ -56,9 +56,9 @@ class MeanAverageRecall(Metric):
>>> round(float(mar_result.mAR_at_100), 2)
1.0
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png
){ align=center width="800" }
"""
def __init__(
@@ -612,9 +612,9 @@ class MeanAverageRecallResult:
"""
Plot the Mean Average Recall results.
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png
){ align=center width="800" }
"""
labels = ["mAR @ 1", "mAR @ 10", "mAR @ 100"]
values = [self.mAR_at_1, self.mAR_at_10, self.mAR_at_100]
supervision/metrics/precision.py
+6 -6
View File
@@ -57,9 +57,9 @@ class Precision(Metric):
>>> round(float(precision_result.precision_at_50), 2)
1.0
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png
){ align=center width="800" }
"""
def __init__(
@@ -578,9 +578,9 @@ class PrecisionResult:
"""
Plot the precision results.
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png
){ align=center width="800" }
"""
labels = ["Precision@50", "Precision@75"]
supervision/metrics/recall.py
+6 -6
View File
@@ -57,9 +57,9 @@ class Recall(Metric):
>>> round(float(recall_result.recall_at_50), 2)
1.0
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png
){ align=center width="800" }
"""
def __init__(
@@ -576,9 +576,9 @@ class RecallResult:
"""
Plot the recall results.
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png\
){ align=center width="800" }
![example_plot](
https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png
){ align=center width="800" }
"""
labels = ["Recall@50", "Recall@75"]