Merge branch 'develop' into feature/remove-false-line-counts
This commit is contained in:
LinasKo
commit
ee3aa9f08d
|
|
@ -0,0 +1,37 @@
|
|||
name: 🔧 Poetry Check and Installation Test Workflow
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- 'poetry.lock'
|
||||
- 'pyproject.toml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'poetry.lock'
|
||||
- 'pyproject.toml'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
poetry-tests:
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
os: [ubuntu-latest, windows-latest, macos-latest]
|
||||
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
|
||||
runs-on: ${{ matrix.os }}
|
||||
steps:
|
||||
- name: 📥 Checkout the repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: 🐍 Set up Python ${{ matrix.python-version }}
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
|
||||
- name: 📦 Install the base dependencies
|
||||
run: python -m pip install --upgrade poetry
|
||||
|
||||
- name: 🔍 Check the correctness of the project config
|
||||
run: poetry check
|
||||
|
||||
- name: 🚀 Do Install the package Test
|
||||
run: poetry install
|
||||
|
|
@ -9,7 +9,7 @@ jobs:
|
|||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
|
||||
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
|
||||
steps:
|
||||
- name: 🛎️ Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
|
|
|||
|
|
@ -1,18 +0,0 @@
|
|||
name: Welcome WorkFlow
|
||||
|
||||
on:
|
||||
issues:
|
||||
types: [opened]
|
||||
pull_request_target:
|
||||
types: [opened]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
name: 👋 Welcome
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/first-interaction@v1.3.0
|
||||
with:
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
|
||||
pr-message: "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."
|
||||
|
|
@ -7,7 +7,7 @@ ci:
|
|||
|
||||
repos:
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v4.6.0
|
||||
rev: v5.0.0
|
||||
hooks:
|
||||
- id: trailing-whitespace
|
||||
exclude: test/.*\.py
|
||||
|
|
@ -32,7 +32,7 @@ repos:
|
|||
additional_dependencies: ["bandit[toml]"]
|
||||
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.6.8
|
||||
rev: v0.7.2
|
||||
hooks:
|
||||
- id: ruff
|
||||
args: [--fix, --exit-non-zero-on-fix]
|
||||
|
|
|
|||
|
|
@ -34,8 +34,6 @@
|
|||
|
||||
**We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
|
||||
|
||||
[](https://github.com/orgs/roboflow/projects)
|
||||
|
||||
## 💻 install
|
||||
|
||||
Pip install the supervision package in a
|
||||
|
|
|
|||
|
|
@ -1,5 +1,144 @@
|
|||
# CHANGELOG
|
||||
|
||||
### 0.24.0 <small>Oct 4, 2024</small>
|
||||
|
||||
- Added [F1 score](https://supervision.roboflow.com/0.24.0/metrics/f1_score/#supervision.metrics.f1_score.F1Score) as a new metric for detection and segmentation. [#1521](https://github.com/roboflow/supervision/pull/1521)
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import F1Score
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
f1_metric = F1Score()
|
||||
f1_result = f1_metric.update(predictions, targets).compute()
|
||||
|
||||
print(f1_result)
|
||||
print(f1_result.f1_50)
|
||||
print(f1_result.small_objects.f1_50)
|
||||
```
|
||||
|
||||
- Added new cookbook: [Small Object Detection with SAHI](https://supervision.roboflow.com/0.24.0/notebooks/small-object-detection-with-sahi/). This cookbook provides a detailed guide on using [`InferenceSlicer`](https://supervision.roboflow.com/0.24.0/detection/tools/inference_slicer/) for small object detection. [#1483](https://github.com/roboflow/supervision/pull/1483)
|
||||
|
||||
- Added an [Embedded Workflow](https://roboflow.com/workflows), which allows you to [preview annotators](https://supervision.roboflow.com/0.24.0/detection/annotators/). [#1533](https://github.com/roboflow/supervision/pull/1533)
|
||||
|
||||
- Enhanced [`LineZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotator), allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) labels. [#854](https://github.com/roboflow/supervision/pull/854)
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
import cv2
|
||||
|
||||
image = cv2.imread("<SOURCE_IMAGE_PATH>")
|
||||
|
||||
line_zone = sv.LineZone(
|
||||
start=sv.Point(0, 100),
|
||||
end=sv.Point(50, 200)
|
||||
)
|
||||
line_zone_annotator = sv.LineZoneAnnotator(
|
||||
text_orient_to_line=True,
|
||||
display_text_box=False,
|
||||
text_centered=False
|
||||
)
|
||||
|
||||
annotated_frame = line_zone_annotator.annotate(
|
||||
frame=image.copy(), line_counter=line_zone
|
||||
)
|
||||
|
||||
sv.plot_image(frame)
|
||||
```
|
||||
|
||||
- Added per-class counting capabilities to [`LineZone`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) and introduced [`LineZoneAnnotatorMulticlass`](https://supervision.roboflow.com/0.24.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZoneAnnotatorMulticlass) for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. [#1555](https://github.com/roboflow/supervision/pull/1555)
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
import cv2
|
||||
|
||||
image = cv2.imread("<SOURCE_IMAGE_PATH>")
|
||||
|
||||
line_zone = sv.LineZone(
|
||||
start=sv.Point(0, 100),
|
||||
end=sv.Point(50, 200)
|
||||
)
|
||||
line_zone_annotator = sv.LineZoneAnnotatorMulticlass()
|
||||
|
||||
frame = line_zone_annotator.annotate(
|
||||
frame=frame, line_zones=[line_zone]
|
||||
)
|
||||
|
||||
sv.plot_image(frame)
|
||||
```
|
||||
|
||||
- Added [`from_easyocr`](https://supervision.roboflow.com/0.24.0/detection/core/#supervision.detection.core.Detections.from_easyocr), allowing integration of OCR results into the supervision framework. [EasyOCR](https://github.com/JaidedAI/EasyOCR) is an open-source optical character recognition (OCR) library that can read text from images. [#1515](https://github.com/roboflow/supervision/pull/1515)
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
import easyocr
|
||||
import cv2
|
||||
|
||||
image = cv2.imread("<SOURCE_IMAGE_PATH>")
|
||||
|
||||
reader = easyocr.Reader(["en"])
|
||||
result = reader.readtext("<SOURCE_IMAGE_PATH>", paragraph=True)
|
||||
detections = sv.Detections.from_easyocr(result)
|
||||
|
||||
box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
|
||||
label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
|
||||
|
||||
annotated_image = image.copy()
|
||||
annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections)
|
||||
annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
|
||||
|
||||
sv.plot_image(annotated_image)
|
||||
```
|
||||
|
||||
- Added [`oriented_box_iou_batch`](https://supervision.roboflow.com/0.24.0/detection/utils/#supervision.detection.utils.oriented_box_iou_batch) function to `detection.utils`. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). [#1502](https://github.com/roboflow/supervision/pull/1502)
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
||||
boxes_true = np.array([[[1, 0], [0, 1], [3, 4], [4, 3]]])
|
||||
boxes_detection = np.array([[[1, 1], [2, 0], [4, 2], [3, 3]]])
|
||||
ious = sv.oriented_box_iou_batch(boxes_true, boxes_detection)
|
||||
print("IoU between true and detected boxes:", ious)
|
||||
```
|
||||
|
||||
- Extended [`PolygonZoneAnnotator`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZoneAnnotator) to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. [#1527](https://github.com/roboflow/supervision/pull/1527)
|
||||
|
||||
```python
|
||||
import cv2
|
||||
from ncnn.model_zoo import get_model
|
||||
import supervision as sv
|
||||
|
||||
image = cv2.imread("<SOURCE_IMAGE_PATH>")
|
||||
model = get_model(
|
||||
"yolov8s",
|
||||
target_size=640,
|
||||
prob_threshold=0.5,
|
||||
nms_threshold=0.45,
|
||||
num_threads=4,
|
||||
use_gpu=True,
|
||||
)
|
||||
result = model(image)
|
||||
detections = sv.Detections.from_ncnn(result)
|
||||
```
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
The `frame_resolution_wh` parameter in [`PolygonZone`](https://supervision.roboflow.com/0.24.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
|
||||
|
||||
- Supervision now depends on `opencv-python` rather than `opencv-python-headless`. [#1530](https://github.com/roboflow/supervision/pull/1530)
|
||||
|
||||
- Fixed the COCO 101 point Average Precision algorithm to correctly interpolate precision, providing a more precise calculation of average precision without averaging out intermediate values. [#1500](https://github.com/roboflow/supervision/pull/1500)
|
||||
|
||||
- Resolved miscellaneous issues highlighted when building documentation. This mostly includes whitespace adjustments and type inconsistencies. Updated documentation for clarity and fixed formatting issues. Added explicit version for `mkdocstrings-python`. [#1549](https://github.com/roboflow/supervision/pull/1549)
|
||||
|
||||
- Enabled and fixed Ruff rules for code formatting, including changes like avoiding unnecessary iterable allocations and using Optional for default mutable arguments. [#1526](https://github.com/roboflow/supervision/pull/1526)
|
||||
|
||||
### 0.23.0 <small>Aug 28, 2024</small>
|
||||
|
||||
- Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
|
||||
|
|
@ -117,15 +256,19 @@ for frame in sv.get_video_frames_generator(
|
|||
- Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
|
||||
|
||||
### 0.22.0 <small>Jul 12, 2024</small>
|
||||
|
|
@ -133,9 +276,11 @@ for frame in sv.get_video_frames_generator(
|
|||
- Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
|
||||
|
||||
```python
|
||||
|
|
@ -192,7 +337,7 @@ annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detecti
|
|||
```
|
||||
|
||||
- Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
|
||||
- Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
|
||||
- Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -230,9 +375,11 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
|
|||
- Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
|
||||
|
||||
- Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
|
||||
|
|
@ -242,27 +389,35 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
|
|||
- Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.red()` has been removed due to deprecation. Use `color.RED` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
|
||||
|
||||
### 0.21.0 <small>Jun 5, 2024</small>
|
||||
|
|
@ -371,6 +526,7 @@ annotated_image = edge_annotators.annotate(image.copy(), keypoints)
|
|||
- 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`.
|
||||
|
||||
!!! 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.
|
||||
|
|
@ -476,11 +632,13 @@ annotated_frame = crop_annotator.annotate(
|
|||
- 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.
|
||||
|
||||
!!! 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.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
|
||||
|
||||
- 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.
|
||||
|
|
@ -544,6 +702,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- 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`).
|
||||
|
||||
!!! 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.
|
||||
|
|
@ -551,6 +710,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- 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).
|
||||
|
||||
!!! 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.
|
||||
|
|
@ -648,6 +808,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- 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.
|
||||
|
||||
!!! 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).
|
||||
|
||||
### 0.15.0 <small>October 5, 2023</small>
|
||||
|
|
@ -715,6 +876,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- 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.
|
||||
|
||||
!!! 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.
|
||||
|
||||
- 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.
|
||||
|
|
@ -752,6 +914,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- 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.
|
||||
|
||||
!!! 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.
|
||||
|
||||
- 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.
|
||||
|
|
@ -761,6 +924,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
### 0.12.0 <small>July 24, 2023</small>
|
||||
|
||||
!!! failure "Python 3.7. Support Terminated"
|
||||
|
||||
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).
|
||||
|
|
|
|||
|
|
@ -7,8 +7,6 @@ status: deprecated
|
|||
|
||||
These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **five subsequent releases**, providing time for users to transition to updated methods.
|
||||
|
||||
- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) will be removed in `supervision-0.24.0`.
|
||||
|
||||
- Constructing [`DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
|
||||
|
||||
- The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
|
||||
|
|
@ -21,10 +19,15 @@ These features are phased out due to better alternatives or potential issues in
|
|||
|
||||
# Removed
|
||||
|
||||
### 0.24.0
|
||||
|
||||
- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) has been removed.
|
||||
- Supervision installation methods `"headless"` and `"desktop"` were removed, as they are no longer needed. `pip install supervision[headless]` will install the base library and harmlessly warn of non-existent extras.
|
||||
|
||||
### 0.23.0
|
||||
|
||||
- The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
|
||||
- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors ` instead.
|
||||
- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
|
||||
|
||||
### 0.22.0
|
||||
|
||||
|
|
|
|||
|
|
@ -5,9 +5,7 @@ status: new
|
|||
|
||||
# Annotators
|
||||
|
||||
Supervision provides a variety of annotators to annotate detections on images and videos. You can try them out below, with a [Workflow](https://roboflow.com/workflows) that runs [Microsoft's COCO](https://cocodataset.org/#home) dataset through a Instance Segmentation model and annotates the detections using supervision's annotators.
|
||||
|
||||
<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
|
||||
Annotators accept detections and apply box or mask visualizations to the detections. Annotators have many available styles.
|
||||
|
||||
=== "Box"
|
||||
|
||||
|
|
@ -485,6 +483,13 @@ Supervision provides a variety of annotators to annotate detections on images an
|
|||
|
||||
</div>
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2>Try Supervision Annotators on your own image</h2>
|
||||
Visualize annotators on images with COCO classes such as people, vehicles, animals, household items.
|
||||
</div>
|
||||
|
||||
<div style="height: 400px; width: 100%; border-radius: 8px; overflow: hidden;"><iframe src="https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiNDdtd2xuWW16S25VNWtOYUZjMG8iLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiRVJNUFBZY3FQMmZWWjB1NkRpNXZaYXJDdlZPMiIsImlhdCI6MTcyNjgzOTM2N30.gj2F6SnmmURAScJe4PTC1raUXsAK5mZyrUIGIJ44NhM?hideToolbar=true&hideHeader=true&defaultVisual=true" loading="lazy" title="Roboflow Workflow for Supervision Annotators" style="width: 100%; height: 100%; min-height: 400px; border: none;"></iframe></div>
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.annotators.core.BoxAnnotator">BoxAnnotator</a></h2>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
<div class="md-typeset">
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
<div class="md-typeset">
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Detection Utils
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Annotators
|
||||
|
|
|
|||
|
|
@ -0,0 +1,20 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Common Values
|
||||
|
||||
This page contains supplementary values, types and enums that metrics use.
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.core.MetricTarget">MetricTarget</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.core.MetricTarget
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.core.AveragingMethod">AveragingMethod</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.core.AveragingMethod
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Precision
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.precision.Precision">Precision</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.precision.Precision
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.precision.PrecisionResult">PrecisionResult</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.precision.PrecisionResult
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Recall
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.recall.Recall">Recall</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.recall.Recall
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.metrics.recall.RecallResult">RecallResult</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.metrics.recall.RecallResult
|
||||
|
|
@ -15,7 +15,7 @@
|
|||
"\n",
|
||||
"This cookbook shows how to use [Slicing Aided Hyper Inference (SAHI) ](https://arxiv.org/abs/2202.06934) for small object detection with `supervision`.\n",
|
||||
"\n",
|
||||
"![\"Small Object Detection\"](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/animation.gif \"Small Object Detection\")\n",
|
||||
"![\"Small Object Detection\"](https://media.roboflow.com/supervision/cookbooks/sahi/animation.gif \"Small Object Detection\")\n",
|
||||
"\n",
|
||||
"Click the Open in Colab button to run the cookbook on Google Colab.\n",
|
||||
"\n",
|
||||
|
|
@ -70,7 +70,7 @@
|
|||
"\n",
|
||||
"Detecting people (or their heads) is a common problem that has been addressed by many researchers in the past. In this project, we\u2019ll use an open-source public dataset and a fine-tuned model to perform inference on images.\n",
|
||||
"\n",
|
||||
"![Roboflow Universe](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
|
||||
"![Roboflow Universe](https://media.roboflow.com/supervision/cookbooks/sahi/roboflow_universe.png \"Open source model for counting people's heads\")\n",
|
||||
"\n",
|
||||
"Some details about the project [\"people_counterv0 Computer Vision Project\"](https://universe.roboflow.com/sit-cx0ng/people_counterv0):\n",
|
||||
"\n",
|
||||
|
|
@ -782,9 +782,9 @@
|
|||
"\n",
|
||||
"| Example| Observations |\n",
|
||||
"|----|----|\n",
|
||||
"| ![Overlapping](https://github.com/ediardo/notebooks/blob/main/sahi/overlapping_1.png?raw=true \"Overlapping\") | False Negative, Incomplete bbox |\n",
|
||||
"| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
|
||||
"| ![Overlapping](https://raw.githubusercontent.com/ediardo/notebooks/main/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
|
||||
"| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_1.png \"Overlapping\") | False Negative, Incomplete bbox |\n",
|
||||
"| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_2.png \"Overlapping\")| Double detection, Incomplete bbox|\n",
|
||||
"| ![Overlapping](https://media.roboflow.com/supervision/cookbooks/sahi/overlapping_3.png \"Overlapping\")| Incomplete bounding box|\n",
|
||||
"\n",
|
||||
"## Improving Object Detection Near Boundaries with Overlapping\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Image Utils
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
---
|
||||
comments: true
|
||||
status: new
|
||||
---
|
||||
|
||||
# Video Utils
|
||||
|
|
|
|||
|
|
@ -66,7 +66,10 @@ nav:
|
|||
- Utils: datasets/utils.md
|
||||
- Metrics:
|
||||
- mAP: metrics/mean_average_precision.md
|
||||
- Precision: metrics/precision.md
|
||||
- Recall: metrics/recall.md
|
||||
- F1 Score: metrics/f1_score.md
|
||||
- Common Values: metrics/common_values.md
|
||||
- Legacy Metrics: detection/metrics.md
|
||||
- Utils:
|
||||
- Video: utils/video.md
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "supervision"
|
||||
version = "0.24.0rc1"
|
||||
version = "0.24.0"
|
||||
description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
|
||||
authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
|
||||
maintainers = [
|
||||
|
|
@ -9,7 +9,7 @@ maintainers = [
|
|||
]
|
||||
readme = "README.md"
|
||||
license = "MIT"
|
||||
packages = [{ include = "supervision" }]
|
||||
packages = [{ include = "supervision" }, { include = "supervision/py.typed" }]
|
||||
homepage = "https://github.com/roboflow/supervision"
|
||||
repository = "https://github.com/roboflow/supervision"
|
||||
documentation = "https://supervision.roboflow.com/latest/"
|
||||
|
|
@ -46,17 +46,32 @@ python = "^3.8"
|
|||
numpy = [
|
||||
{ version = ">=1.21.2,<1.23.3", python = "<=3.10" },
|
||||
{ version = ">=1.23.3", python = ">3.10" },
|
||||
{ version = ">=2.1.0", python = ">=3.13" },
|
||||
]
|
||||
scipy = [
|
||||
{ version = "1.10.0", python = "<3.9" },
|
||||
{ version = "^1.10.0", python = ">=3.9" },
|
||||
{ version = ">=1.14.1", python = ">=3.13" },
|
||||
|
||||
]
|
||||
|
||||
# Matplotlib sub-dependency
|
||||
# The 'contourpy' package is required by Matplotlib for contour plotting.
|
||||
# We need to ensure compatibility with both Python 3.8 and Python 3.13.
|
||||
#
|
||||
# For Python 3.8 and above, we use version 1.0.7 or higher, as it is the lowest major version that supports Python 3.8.
|
||||
# For Python 3.13 and above, we use version 1.3.0 or higher, as it is the first version that explicitly supports Python 3.13.
|
||||
contourpy = [
|
||||
{ version = ">=1.0.7", python = ">=3.8" },
|
||||
{ version = ">=1.3.0", python = ">=3.13" },
|
||||
]
|
||||
|
||||
matplotlib = ">=3.6.0"
|
||||
pyyaml = ">=5.3"
|
||||
defusedxml = "^0.7.1"
|
||||
pillow = ">=9.4"
|
||||
requests = { version = ">=2.26.0,<=2.32.3", optional = true }
|
||||
tqdm = { version = ">=4.62.3,<=4.66.5", optional = true }
|
||||
tqdm = { version = ">=4.62.3,<=4.66.6", optional = true }
|
||||
# pandas: picked lowest major version that supports Python 3.8
|
||||
pandas = { version = ">=2.0.0", optional = true }
|
||||
pandas-stubs = { version = ">=2.0.0.230412", optional = true }
|
||||
|
|
@ -92,7 +107,7 @@ mike = "^2.0.0"
|
|||
# For Documentation Development use Python 3.10 or above
|
||||
# Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
|
||||
mkdocs-jupyter = "^0.24.3"
|
||||
mkdocs-git-committers-plugin-2 = "^2.2.3"
|
||||
mkdocs-git-committers-plugin-2 = "^2.4.1"
|
||||
mkdocs-git-revision-date-localized-plugin = "^1.2.4"
|
||||
|
||||
[tool.poetry.group.typecheck]
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
|
|||
|
||||
for class_list in class_lists:
|
||||
for class_name in class_list:
|
||||
unique_classes.add(class_name.lower())
|
||||
unique_classes.add(class_name)
|
||||
|
||||
return sorted(list(unique_classes))
|
||||
|
||||
|
|
|
|||
|
|
@ -32,8 +32,10 @@ from supervision.detection.utils import (
|
|||
extract_ultralytics_masks,
|
||||
get_data_item,
|
||||
is_data_equal,
|
||||
is_metadata_equal,
|
||||
mask_to_xyxy,
|
||||
merge_data,
|
||||
merge_metadata,
|
||||
process_roboflow_result,
|
||||
xywh_to_xyxy,
|
||||
)
|
||||
|
|
@ -125,6 +127,9 @@ class Detections:
|
|||
data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
|
||||
data where each key is a string representing the data type, and the value
|
||||
is either a NumPy array or a list of corresponding data.
|
||||
metadata (Dict[str, Any]): A dictionary containing collection-level metadata
|
||||
that applies to the entire set of detections. This may include information such
|
||||
as the video name, camera parameters, timestamp, or other global metadata.
|
||||
""" # noqa: E501 // docs
|
||||
|
||||
xyxy: np.ndarray
|
||||
|
|
@ -133,6 +138,7 @@ class Detections:
|
|||
class_id: Optional[np.ndarray] = None
|
||||
tracker_id: Optional[np.ndarray] = None
|
||||
data: Dict[str, Union[np.ndarray, List]] = field(default_factory=dict)
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def __post_init__(self):
|
||||
validate_detections_fields(
|
||||
|
|
@ -185,6 +191,7 @@ class Detections:
|
|||
np.array_equal(self.confidence, other.confidence),
|
||||
np.array_equal(self.tracker_id, other.tracker_id),
|
||||
is_data_equal(self.data, other.data),
|
||||
is_metadata_equal(self.metadata, other.metadata),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
@ -985,6 +992,7 @@ class Detections:
|
|||
"""
|
||||
empty_detections = Detections.empty()
|
||||
empty_detections.data = self.data
|
||||
empty_detections.metadata = self.metadata
|
||||
return self == empty_detections
|
||||
|
||||
@classmethod
|
||||
|
|
@ -1078,6 +1086,9 @@ class Detections:
|
|||
|
||||
data = merge_data([d.data for d in detections_list])
|
||||
|
||||
metadata_list = [detections.metadata for detections in detections_list]
|
||||
metadata = merge_metadata(metadata_list)
|
||||
|
||||
return cls(
|
||||
xyxy=xyxy,
|
||||
mask=mask,
|
||||
|
|
@ -1085,6 +1096,7 @@ class Detections:
|
|||
class_id=class_id,
|
||||
tracker_id=tracker_id,
|
||||
data=data,
|
||||
metadata=metadata,
|
||||
)
|
||||
|
||||
def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
|
||||
|
|
@ -1198,6 +1210,7 @@ class Detections:
|
|||
class_id=self.class_id[index] if self.class_id is not None else None,
|
||||
tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
|
||||
data=get_data_item(self.data, index),
|
||||
metadata=self.metadata,
|
||||
)
|
||||
|
||||
def __setitem__(self, key: str, value: Union[np.ndarray, List]):
|
||||
|
|
@ -1459,6 +1472,8 @@ def merge_inner_detection_object_pair(
|
|||
else:
|
||||
winning_detection = detections_2
|
||||
|
||||
metadata = merge_metadata([detections_1.metadata, detections_2.metadata])
|
||||
|
||||
return Detections(
|
||||
xyxy=merged_xyxy,
|
||||
mask=merged_mask,
|
||||
|
|
@ -1466,6 +1481,7 @@ def merge_inner_detection_object_pair(
|
|||
class_id=winning_detection.class_id,
|
||||
tracker_id=winning_detection.tracker_id,
|
||||
data=winning_detection.data,
|
||||
metadata=metadata,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -771,6 +771,19 @@ class LineZoneAnnotatorMulticlass:
|
|||
line_zones: List[LineZone],
|
||||
line_zone_labels: Optional[List[str]] = None,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Draws a table with the number of objects of each class that crossed each line.
|
||||
|
||||
Attributes:
|
||||
frame (np.ndarray): The image on which the table will be drawn.
|
||||
line_zones (List[LineZone]): The line zones to be annotated.
|
||||
line_zone_labels (Optional[List[str]]): The labels, one for each
|
||||
line zone. If not provided, the default labels will be used.
|
||||
|
||||
Returns:
|
||||
(np.ndarray): The image with the table drawn on it.
|
||||
|
||||
"""
|
||||
if line_zone_labels is None:
|
||||
line_zone_labels = [f"Line {i + 1}:" for i in range(len(line_zones))]
|
||||
if len(line_zones) != len(line_zone_labels):
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import warnings
|
||||
from dataclasses import replace
|
||||
from typing import Iterable, Optional, Tuple
|
||||
from typing import Iterable, Optional
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -12,13 +11,18 @@ from supervision.draw.color import Color
|
|||
from supervision.draw.utils import draw_filled_polygon, draw_polygon, draw_text
|
||||
from supervision.geometry.core import Position
|
||||
from supervision.geometry.utils import get_polygon_center
|
||||
from supervision.utils.internal import SupervisionWarnings
|
||||
|
||||
|
||||
class PolygonZone:
|
||||
"""
|
||||
A class for defining a polygon-shaped zone within a frame for detecting objects.
|
||||
|
||||
!!! warning
|
||||
|
||||
LineZone uses the `tracker_id`. Read
|
||||
[here](/latest/trackers/) to learn how to plug
|
||||
tracking into your inference pipeline.
|
||||
|
||||
Attributes:
|
||||
polygon (np.ndarray): A polygon represented by a numpy array of shape
|
||||
`(N, 2)`, containing the `x`, `y` coordinates of the points.
|
||||
|
|
@ -28,22 +32,35 @@ class PolygonZone:
|
|||
(default: (sv.Position.BOTTOM_CENTER,)).
|
||||
current_count (int): The current count of detected objects within the zone
|
||||
mask (np.ndarray): The 2D bool mask for the polygon zone
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from ultralytics import YOLO
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
image = cv2.imread(<SOURCE_IMAGE_PATH>)
|
||||
model = YOLO("yolo11s")
|
||||
tracker = sv.ByteTrack()
|
||||
|
||||
polygon = np.array([[100, 200], [200, 100], [300, 200], [200, 300]])
|
||||
polygon_zone = sv.PolygonZone(polygon=polygon)
|
||||
|
||||
result = model.infer(image)[0]
|
||||
detections = sv.Detections.from_ultralytics(result)
|
||||
detections = tracker.update_with_detections(detections)
|
||||
|
||||
is_detections_in_zone = polygon_zone.trigger(detections)
|
||||
print(polygon_zone.current_count)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
polygon: npt.NDArray[np.int64],
|
||||
frame_resolution_wh: Optional[Tuple[int, int]] = None,
|
||||
triggering_anchors: Iterable[Position] = (Position.BOTTOM_CENTER,),
|
||||
):
|
||||
if frame_resolution_wh is not None:
|
||||
warnings.warn(
|
||||
"The `frame_resolution_wh` parameter is no longer required and will be "
|
||||
"dropped in version supervision-0.24.0. The mask resolution is now "
|
||||
"calculated automatically based on the polygon coordinates.",
|
||||
category=SupervisionWarnings,
|
||||
)
|
||||
|
||||
self.polygon = polygon.astype(int)
|
||||
self.triggering_anchors = triggering_anchors
|
||||
if not list(self.triggering_anchors):
|
||||
|
|
@ -99,7 +116,7 @@ class PolygonZoneAnnotator:
|
|||
|
||||
Attributes:
|
||||
zone (PolygonZone): The polygon zone to be annotated
|
||||
color (Color): The color to draw the polygon lines
|
||||
color (Color): The color to draw the polygon lines, default is white
|
||||
thickness (int): The thickness of the polygon lines, default is 2
|
||||
text_color (Color): The color of the text on the polygon, default is black
|
||||
text_scale (float): The scale of the text on the polygon, default is 0.5
|
||||
|
|
@ -115,7 +132,7 @@ class PolygonZoneAnnotator:
|
|||
def __init__(
|
||||
self,
|
||||
zone: PolygonZone,
|
||||
color: Color,
|
||||
color: Color = Color.WHITE,
|
||||
thickness: int = 2,
|
||||
text_color: Color = Color.BLACK,
|
||||
text_scale: float = 0.5,
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from itertools import chain
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -23,10 +23,9 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
|
|||
np.ndarray: The generated 2D mask, where the polygon is marked with
|
||||
`1`'s and the rest is filled with `0`'s.
|
||||
"""
|
||||
width, height = resolution_wh
|
||||
mask = np.zeros((height, width))
|
||||
|
||||
cv2.fillPoly(mask, [polygon], color=1)
|
||||
width, height = map(int, resolution_wh)
|
||||
mask = np.zeros((height, width), dtype=np.uint8)
|
||||
cv2.fillPoly(mask, [polygon.astype(np.int32)], color=1)
|
||||
return mask
|
||||
|
||||
|
||||
|
|
@ -163,9 +162,9 @@ def oriented_box_iou_batch(
|
|||
boxes_true = boxes_true.reshape(-1, 4, 2)
|
||||
boxes_detection = boxes_detection.reshape(-1, 4, 2)
|
||||
|
||||
max_height = max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1
|
||||
max_height = int(max(boxes_true[:, :, 0].max(), boxes_detection[:, :, 0].max()) + 1)
|
||||
# adding 1 because we are 0-indexed
|
||||
max_width = max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1
|
||||
max_width = int(max(boxes_true[:, :, 1].max(), boxes_detection[:, :, 1].max()) + 1)
|
||||
|
||||
mask_true = np.zeros((boxes_true.shape[0], max_height, max_width))
|
||||
for i, box_true in enumerate(boxes_true):
|
||||
|
|
@ -808,12 +807,36 @@ def is_data_equal(data_a: Dict[str, np.ndarray], data_b: Dict[str, np.ndarray])
|
|||
)
|
||||
|
||||
|
||||
def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) -> bool:
|
||||
"""
|
||||
Compares the metadata payloads of two Detections instances.
|
||||
|
||||
Args:
|
||||
metadata_a, metadata_b: The metadata payloads of the instances.
|
||||
|
||||
Returns:
|
||||
True if the metadata payloads are equal, False otherwise.
|
||||
"""
|
||||
return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
|
||||
np.array_equal(metadata_a[key], metadata_b[key])
|
||||
if (
|
||||
isinstance(metadata_a[key], np.ndarray)
|
||||
and isinstance(metadata_b[key], np.ndarray)
|
||||
)
|
||||
else metadata_a[key] == metadata_b[key]
|
||||
for key in metadata_a
|
||||
)
|
||||
|
||||
|
||||
def merge_data(
|
||||
data_list: List[Dict[str, Union[npt.NDArray[np.generic], List]]],
|
||||
) -> Dict[str, Union[npt.NDArray[np.generic], List]]:
|
||||
"""
|
||||
Merges the data payloads of a list of Detections instances.
|
||||
|
||||
Warning: Assumes that empty detections were filtered-out before passing data to
|
||||
this function.
|
||||
|
||||
Args:
|
||||
data_list: The data payloads of the Detections instances. Each data payload
|
||||
is a dictionary with the same keys, and the values are either lists or
|
||||
|
|
@ -866,6 +889,45 @@ def merge_data(
|
|||
return merged_data
|
||||
|
||||
|
||||
def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Merge metadata from a list of metadata dictionaries.
|
||||
|
||||
This function combines the metadata dictionaries. If a key appears in more than one
|
||||
dictionary, the values must be identical for the merge to succeed.
|
||||
|
||||
Warning: Assumes that empty detections were filtered-out before passing metadata to
|
||||
this function.
|
||||
|
||||
Args:
|
||||
metadata_list (List[Dict[str, Any]]): A list of metadata dictionaries to merge.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: A single merged metadata dictionary.
|
||||
|
||||
Raises:
|
||||
ValueError: If there are conflicting values for the same key or if
|
||||
dictionaries have different keys.
|
||||
"""
|
||||
if not metadata_list:
|
||||
return {}
|
||||
|
||||
all_keys_sets = [set(metadata.keys()) for metadata in metadata_list]
|
||||
if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
|
||||
raise ValueError("All metadata dictionaries must have the same keys to merge.")
|
||||
|
||||
merged_metadata: Dict[str, Any] = {}
|
||||
for metadata in metadata_list:
|
||||
for key, value in metadata.items():
|
||||
if key in merged_metadata:
|
||||
if merged_metadata[key] != value:
|
||||
raise ValueError(f"Conflicting metadata for key: '{key}'.")
|
||||
else:
|
||||
merged_metadata[key] = value
|
||||
|
||||
return merged_metadata
|
||||
|
||||
|
||||
def get_data_item(
|
||||
data: Dict[str, Union[np.ndarray, List]],
|
||||
index: Union[int, slice, List[int], np.ndarray],
|
||||
|
|
|
|||
|
|
@ -9,7 +9,11 @@ from supervision.geometry.core import Point, Rect
|
|||
|
||||
|
||||
def draw_line(
|
||||
scene: np.ndarray, start: Point, end: Point, color: Color, thickness: int = 2
|
||||
scene: np.ndarray,
|
||||
start: Point,
|
||||
end: Point,
|
||||
color: Color = Color.ROBOFLOW,
|
||||
thickness: int = 2,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Draws a line on a given scene.
|
||||
|
|
@ -18,7 +22,7 @@ def draw_line(
|
|||
scene (np.ndarray): The scene on which the line will be drawn
|
||||
start (Point): The starting point of the line
|
||||
end (Point): The end point of the line
|
||||
color (Color): The color of the line
|
||||
color (Color): The color of the line, defaults to Color.ROBOFLOW
|
||||
thickness (int): The thickness of the line
|
||||
|
||||
Returns:
|
||||
|
|
@ -35,7 +39,7 @@ def draw_line(
|
|||
|
||||
|
||||
def draw_rectangle(
|
||||
scene: np.ndarray, rect: Rect, color: Color, thickness: int = 2
|
||||
scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, thickness: int = 2
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Draws a rectangle on an image.
|
||||
|
|
@ -60,7 +64,7 @@ def draw_rectangle(
|
|||
|
||||
|
||||
def draw_filled_rectangle(
|
||||
scene: np.ndarray, rect: Rect, color: Color, opacity: float = 1
|
||||
scene: np.ndarray, rect: Rect, color: Color = Color.ROBOFLOW, opacity: float = 1
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Draws a filled rectangle on an image.
|
||||
|
|
@ -151,14 +155,17 @@ def draw_rounded_rectangle(
|
|||
|
||||
|
||||
def draw_polygon(
|
||||
scene: np.ndarray, polygon: np.ndarray, color: Color, thickness: int = 2
|
||||
scene: np.ndarray,
|
||||
polygon: np.ndarray,
|
||||
color: Color = Color.ROBOFLOW,
|
||||
thickness: int = 2,
|
||||
) -> np.ndarray:
|
||||
"""Draw a polygon on a scene.
|
||||
|
||||
Parameters:
|
||||
scene (np.ndarray): The scene to draw the polygon on.
|
||||
polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
|
||||
color (Color): The color of the polygon.
|
||||
color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
|
||||
thickness (int): The thickness of the polygon lines, by default 2.
|
||||
|
||||
Returns:
|
||||
|
|
@ -171,14 +178,17 @@ def draw_polygon(
|
|||
|
||||
|
||||
def draw_filled_polygon(
|
||||
scene: np.ndarray, polygon: np.ndarray, color: Color, opacity: float = 1
|
||||
scene: np.ndarray,
|
||||
polygon: np.ndarray,
|
||||
color: Color = Color.ROBOFLOW,
|
||||
opacity: float = 1,
|
||||
) -> np.ndarray:
|
||||
"""Draw a filled polygon on a scene.
|
||||
|
||||
Parameters:
|
||||
scene (np.ndarray): The scene to draw the polygon on.
|
||||
polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
|
||||
color (Color): The color of the polygon.
|
||||
color (Color): The color of the polygon. Defaults to Color.ROBOFLOW.
|
||||
opacity (float): The opacity of polygon when drawn on the scene.
|
||||
|
||||
Returns:
|
||||
|
|
|
|||
|
|
@ -16,6 +16,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
|
|||
Point: The center of the polygon, represented as a
|
||||
Point object with x and y attributes.
|
||||
|
||||
Raises:
|
||||
ValueError: If the polygon has no vertices.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import numpy as np
|
||||
|
|
@ -30,6 +33,9 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
|
|||
# This is one of the 3 candidate algorithms considered for centroid calculation.
|
||||
# For a more detailed discussion, see PR #1084 and commit eb33176
|
||||
|
||||
if len(polygon) == 0:
|
||||
raise ValueError("Polygon must have at least one vertex.")
|
||||
|
||||
shift_polygon = np.roll(polygon, -1, axis=0)
|
||||
signed_areas = np.cross(polygon, shift_polygon) / 2
|
||||
if signed_areas.sum() == 0:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
from supervision.metrics.core import (
|
||||
CLASS_ID_NONE,
|
||||
AveragingMethod,
|
||||
Metric,
|
||||
MetricTarget,
|
||||
|
|
@ -9,6 +8,8 @@ from supervision.metrics.mean_average_precision import (
|
|||
MeanAveragePrecision,
|
||||
MeanAveragePrecisionResult,
|
||||
)
|
||||
from supervision.metrics.precision import Precision, PrecisionResult
|
||||
from supervision.metrics.recall import Recall, RecallResult
|
||||
from supervision.metrics.utils.object_size import (
|
||||
ObjectSizeCategory,
|
||||
get_detection_size_category,
|
||||
|
|
|
|||
|
|
@ -4,9 +4,6 @@ from abc import ABC, abstractmethod
|
|||
from enum import Enum
|
||||
from typing import Any
|
||||
|
||||
CLASS_ID_NONE = -1
|
||||
"""Used by metrics module as class ID, when none is present"""
|
||||
|
||||
|
||||
class Metric(ABC):
|
||||
"""
|
||||
|
|
@ -40,9 +37,10 @@ class MetricTarget(Enum):
|
|||
"""
|
||||
Specifies what type of detection is used to compute the metric.
|
||||
|
||||
* BOXES: xyxy bounding boxes
|
||||
* MASKS: Binary masks
|
||||
* ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
|
||||
Attributes:
|
||||
BOXES: xyxy bounding boxes
|
||||
MASKS: Binary masks
|
||||
ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
|
||||
"""
|
||||
|
||||
BOXES = "boxes"
|
||||
|
|
@ -57,15 +55,16 @@ class AveragingMethod(Enum):
|
|||
Suppose, before returning the final result, a metric is computed for each class.
|
||||
How do you combine those to get the final number?
|
||||
|
||||
* MACRO: Calculate the metric for each class and average the results. The simplest
|
||||
averaging method, but it does not take class imbalance into account.
|
||||
* MICRO: Calculate the metric globally by counting the total true positives, false
|
||||
positives, and false negatives. Micro averaging is useful when you want to give
|
||||
more importance to classes with more samples. It's also more appropriate if you
|
||||
have an imbalance in the number of instances per class.
|
||||
* WEIGHTED: Calculate the metric for each class and average the results, weighted by
|
||||
the number of true instances of each class. Use weighted averaging if you want
|
||||
to take class imbalance into account.
|
||||
Attributes:
|
||||
MACRO: Calculate the metric for each class and average the results. The simplest
|
||||
averaging method, but it does not take class imbalance into account.
|
||||
MICRO: Calculate the metric globally by counting the total true positives, false
|
||||
positives, and false negatives. Micro averaging is useful when you want to
|
||||
give more importance to classes with more samples. It's also more
|
||||
appropriate if you have an imbalance in the number of instances per class.
|
||||
WEIGHTED: Calculate the metric for each class and average the results, weighted
|
||||
by the number of true instances of each class. Use weighted averaging if
|
||||
you want to take class imbalance into account.
|
||||
"""
|
||||
|
||||
MACRO = "macro"
|
||||
|
|
|
|||
|
|
@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
|
|||
|
||||
from supervision.config import ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import box_iou_batch, mask_iou_batch
|
||||
from supervision.detection.utils import (
|
||||
box_iou_batch,
|
||||
mask_iou_batch,
|
||||
oriented_box_iou_batch,
|
||||
)
|
||||
from supervision.draw.color import LEGACY_COLOR_PALETTE
|
||||
from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
|
||||
from supervision.metrics.utils.object_size import (
|
||||
|
|
@ -23,23 +27,55 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
class F1Score(Metric):
|
||||
"""
|
||||
F1 Score is a metric used to evaluate object detection models. It is the harmonic
|
||||
mean of precision and recall, calculated at different IoU thresholds.
|
||||
|
||||
In simple terms, F1 Score is a measure of a model's balance between precision and
|
||||
recall (accuracy and completeness), calculated as:
|
||||
|
||||
`F1 = 2 * (precision * recall) / (precision + recall)`
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import F1Score
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
f1_metric = F1Score()
|
||||
f1_result = f1_metric.update(predictions, targets).compute()
|
||||
|
||||
print(f1_result)
|
||||
print(f1_result.f1_50)
|
||||
print(f1_result.small_objects.f1_50)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric_target: MetricTarget = MetricTarget.BOXES,
|
||||
averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
|
||||
):
|
||||
self._metric_target = metric_target
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
raise NotImplementedError(
|
||||
"F1 score is not implemented for oriented bounding boxes."
|
||||
)
|
||||
"""
|
||||
Initialize the F1Score metric.
|
||||
|
||||
Args:
|
||||
metric_target (MetricTarget): The type of detection data to use.
|
||||
averaging_method (AveragingMethod): The averaging method used to compute the
|
||||
F1 scores. Determines how the F1 scores are aggregated across classes.
|
||||
"""
|
||||
self._metric_target = metric_target
|
||||
self.averaging_method = averaging_method
|
||||
|
||||
self._predictions_list: List[Detections] = []
|
||||
self._targets_list: List[Detections] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Reset the metric to its initial state, clearing all stored data.
|
||||
"""
|
||||
self._predictions_list = []
|
||||
self._targets_list = []
|
||||
|
||||
|
|
@ -48,6 +84,16 @@ class F1Score(Metric):
|
|||
predictions: Union[Detections, List[Detections]],
|
||||
targets: Union[Detections, List[Detections]],
|
||||
) -> F1Score:
|
||||
"""
|
||||
Add new predictions and targets to the metric, but do not compute the result.
|
||||
|
||||
Args:
|
||||
predictions (Union[Detections, List[Detections]]): The predicted detections.
|
||||
targets (Union[Detections, List[Detections]]): The target detections.
|
||||
|
||||
Returns:
|
||||
(F1Score): The updated metric instance.
|
||||
"""
|
||||
if not isinstance(predictions, list):
|
||||
predictions = [predictions]
|
||||
if not isinstance(targets, list):
|
||||
|
|
@ -65,6 +111,13 @@ class F1Score(Metric):
|
|||
return self
|
||||
|
||||
def compute(self) -> F1ScoreResult:
|
||||
"""
|
||||
Calculate the F1 score metric based on the stored predictions and ground-truth
|
||||
data, at different IoU thresholds.
|
||||
|
||||
Returns:
|
||||
(F1ScoreResult): The F1 score metric result.
|
||||
"""
|
||||
result = self._compute(self._predictions_list, self._targets_list)
|
||||
|
||||
small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
|
||||
|
|
@ -112,8 +165,12 @@ class F1Score(Metric):
|
|||
iou = box_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.MASKS:
|
||||
iou = mask_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
iou = oriented_box_iou_batch(
|
||||
target_contents, prediction_contents
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
raise ValueError(
|
||||
"Unsupported metric target for IoU calculation"
|
||||
)
|
||||
|
||||
|
|
@ -312,12 +369,22 @@ class F1Score(Metric):
|
|||
return (
|
||||
detections.mask
|
||||
if detections.mask is not None
|
||||
else np.empty((0, 0, 0), dtype=bool)
|
||||
else self._make_empty_content()
|
||||
)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
|
||||
return np.ndarray(obb, dtype=np.float32)
|
||||
return np.empty((0, 8), dtype=np.float32)
|
||||
obb = detections.data.get(ORIENTED_BOX_COORDINATES)
|
||||
if obb is not None and len(obb) > 0:
|
||||
return np.array(obb, dtype=np.float32)
|
||||
return self._make_empty_content()
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _make_empty_content(self) -> np.ndarray:
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
return np.empty((0, 4), dtype=np.float32)
|
||||
if self._metric_target == MetricTarget.MASKS:
|
||||
return np.empty((0, 0, 0), dtype=bool)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
return np.empty((0, 4, 2), dtype=np.float32)
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _filter_detections_by_size(
|
||||
|
|
@ -373,7 +440,6 @@ class F1ScoreResult:
|
|||
The results of the F1 score metric calculation.
|
||||
|
||||
Defaults to `0` if no detections or targets were provided.
|
||||
Provides a custom `__str__` method for pretty printing.
|
||||
|
||||
Attributes:
|
||||
metric_target (MetricTarget): the type of data used for the metric -
|
||||
|
|
|
|||
|
|
@ -9,7 +9,11 @@ from matplotlib import pyplot as plt
|
|||
|
||||
from supervision.config import ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import box_iou_batch, mask_iou_batch
|
||||
from supervision.detection.utils import (
|
||||
box_iou_batch,
|
||||
mask_iou_batch,
|
||||
oriented_box_iou_batch,
|
||||
)
|
||||
from supervision.draw.color import LEGACY_COLOR_PALETTE
|
||||
from supervision.metrics.core import Metric, MetricTarget
|
||||
from supervision.metrics.utils.object_size import (
|
||||
|
|
@ -23,6 +27,27 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
class MeanAveragePrecision(Metric):
|
||||
"""
|
||||
Mean Average Precision (mAP) is a metric used to evaluate object detection models.
|
||||
It is the average of the precision-recall curves at different IoU thresholds.
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import MeanAveragePrecision
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
map_metric = MeanAveragePrecision()
|
||||
map_result = map_metric.update(predictions, targets).compute()
|
||||
|
||||
print(map_result)
|
||||
print(map_result.map50_95)
|
||||
map_result.plot()
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric_target: MetricTarget = MetricTarget.BOXES,
|
||||
|
|
@ -36,17 +61,15 @@ class MeanAveragePrecision(Metric):
|
|||
class_agnostic (bool): Whether to treat all data as a single class.
|
||||
"""
|
||||
self._metric_target = metric_target
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
raise NotImplementedError(
|
||||
"Mean Average Precision is not implemented for oriented bounding boxes."
|
||||
)
|
||||
|
||||
self._class_agnostic = class_agnostic
|
||||
|
||||
self._predictions_list: List[Detections] = []
|
||||
self._targets_list: List[Detections] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Reset the metric to its initial state, clearing all stored data.
|
||||
"""
|
||||
self._predictions_list = []
|
||||
self._targets_list = []
|
||||
|
||||
|
|
@ -76,6 +99,15 @@ class MeanAveragePrecision(Metric):
|
|||
f" targets ({len(targets)}) during the update must be the same."
|
||||
)
|
||||
|
||||
if self._class_agnostic:
|
||||
predictions = deepcopy(predictions)
|
||||
targets = deepcopy(targets)
|
||||
|
||||
for prediction in predictions:
|
||||
prediction.class_id[:] = -1
|
||||
for target in targets:
|
||||
target.class_id[:] = -1
|
||||
|
||||
self._predictions_list.extend(predictions)
|
||||
self._targets_list.extend(targets)
|
||||
|
||||
|
|
@ -86,26 +118,10 @@ class MeanAveragePrecision(Metric):
|
|||
) -> MeanAveragePrecisionResult:
|
||||
"""
|
||||
Calculate Mean Average Precision based on predicted and ground-truth
|
||||
detections at different thresholds.
|
||||
detections at different thresholds.
|
||||
|
||||
Returns:
|
||||
(MeanAveragePrecisionResult): New instance of MeanAveragePrecision.
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import MeanAveragePrecision
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
map_metric = MeanAveragePrecision()
|
||||
map_result = map_metric.update(predictions, targets).compute()
|
||||
|
||||
print(map_result)
|
||||
print(map_result.map50_95)
|
||||
map_result.plot()
|
||||
```
|
||||
(MeanAveragePrecisionResult): The Mean Average Precision result.
|
||||
"""
|
||||
result = self._compute(self._predictions_list, self._targets_list)
|
||||
|
||||
|
|
@ -172,14 +188,19 @@ class MeanAveragePrecision(Metric):
|
|||
iou = box_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.MASKS:
|
||||
iou = mask_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
iou = oriented_box_iou_batch(
|
||||
target_contents, prediction_contents
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
raise ValueError(
|
||||
"Unsupported metric target for IoU calculation"
|
||||
)
|
||||
|
||||
matches = self._match_detection_batch(
|
||||
predictions.class_id, targets.class_id, iou, iou_thresholds
|
||||
)
|
||||
|
||||
stats.append(
|
||||
(
|
||||
matches,
|
||||
|
|
@ -203,6 +224,7 @@ class MeanAveragePrecision(Metric):
|
|||
|
||||
return MeanAveragePrecisionResult(
|
||||
metric_target=self._metric_target,
|
||||
is_class_agnostic=self._class_agnostic,
|
||||
mAP_scores=mAP_scores,
|
||||
iou_thresholds=iou_thresholds,
|
||||
matched_classes=unique_classes,
|
||||
|
|
@ -230,7 +252,7 @@ class MeanAveragePrecision(Metric):
|
|||
for r, p in zip(recall[::-1], precision[::-1]):
|
||||
precision_levels[recall_levels <= r] = p
|
||||
|
||||
average_precision = (1 / 100 * precision_levels).sum()
|
||||
average_precision = (1 / 101 * precision_levels).sum()
|
||||
return average_precision
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -332,8 +354,9 @@ class MeanAveragePrecision(Metric):
|
|||
else self._make_empty_content()
|
||||
)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
|
||||
return np.ndarray(obb, dtype=np.float32)
|
||||
obb = detections.data.get(ORIENTED_BOX_COORDINATES)
|
||||
if obb is not None and len(obb) > 0:
|
||||
return np.array(obb, dtype=np.float32)
|
||||
return self._make_empty_content()
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
|
|
@ -343,7 +366,7 @@ class MeanAveragePrecision(Metric):
|
|||
if self._metric_target == MetricTarget.MASKS:
|
||||
return np.empty((0, 0, 0), dtype=bool)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
return np.empty((0, 8), dtype=np.float32)
|
||||
return np.empty((0, 4, 2), dtype=np.float32)
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _filter_detections_by_size(
|
||||
|
|
@ -383,6 +406,8 @@ class MeanAveragePrecisionResult:
|
|||
Attributes:
|
||||
metric_target (MetricTarget): the type of data used for the metric -
|
||||
boxes, masks or oriented bounding boxes.
|
||||
class_agnostic (bool): When computing class-agnostic results, class ID
|
||||
is set to `-1`.
|
||||
mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
|
||||
mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
|
||||
mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
|
||||
|
|
@ -402,6 +427,7 @@ class MeanAveragePrecisionResult:
|
|||
"""
|
||||
|
||||
metric_target: MetricTarget
|
||||
is_class_agnostic: bool
|
||||
|
||||
@property
|
||||
def map50_95(self) -> float:
|
||||
|
|
@ -436,6 +462,7 @@ class MeanAveragePrecisionResult:
|
|||
out_str = (
|
||||
f"{self.__class__.__name__}:\n"
|
||||
f"Metric target: {self.metric_target}\n"
|
||||
f"Class agnostic: {self.is_class_agnostic}\n"
|
||||
f"mAP @ 50:95: {self.map50_95:.4f}\n"
|
||||
f"mAP @ 50: {self.map50:.4f}\n"
|
||||
f"mAP @ 75: {self.map75:.4f}\n"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,616 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from copy import deepcopy
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from supervision.config import ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import (
|
||||
box_iou_batch,
|
||||
mask_iou_batch,
|
||||
oriented_box_iou_batch,
|
||||
)
|
||||
from supervision.draw.color import LEGACY_COLOR_PALETTE
|
||||
from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
|
||||
from supervision.metrics.utils.object_size import (
|
||||
ObjectSizeCategory,
|
||||
get_detection_size_category,
|
||||
)
|
||||
from supervision.metrics.utils.utils import ensure_pandas_installed
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class Precision(Metric):
|
||||
"""
|
||||
Precision is a metric used to evaluate object detection models. It is the ratio of
|
||||
true positive detections to the total number of predicted detections. We calculate
|
||||
it at different IoU thresholds.
|
||||
|
||||
In simple terms, Precision is a measure of a model's accuracy, calculated as:
|
||||
|
||||
`Precision = TP / (TP + FP)`
|
||||
|
||||
Here, `TP` is the number of true positives (correct detections), and `FP` is the
|
||||
number of false positive detections (detected, but incorrectly).
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import Precision
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
precision_metric = Precision()
|
||||
precision_result = precision_metric.update(predictions, targets).compute()
|
||||
|
||||
print(precision_result)
|
||||
print(precision_result.precision_at_50)
|
||||
print(precision_result.small_objects.precision_at_50)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric_target: MetricTarget = MetricTarget.BOXES,
|
||||
averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
|
||||
):
|
||||
"""
|
||||
Initialize the Precision metric.
|
||||
|
||||
Args:
|
||||
metric_target (MetricTarget): The type of detection data to use.
|
||||
averaging_method (AveragingMethod): The averaging method used to compute the
|
||||
precision. Determines how the precision is aggregated across classes.
|
||||
"""
|
||||
self._metric_target = metric_target
|
||||
self.averaging_method = averaging_method
|
||||
|
||||
self._predictions_list: List[Detections] = []
|
||||
self._targets_list: List[Detections] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Reset the metric to its initial state, clearing all stored data.
|
||||
"""
|
||||
self._predictions_list = []
|
||||
self._targets_list = []
|
||||
|
||||
def update(
|
||||
self,
|
||||
predictions: Union[Detections, List[Detections]],
|
||||
targets: Union[Detections, List[Detections]],
|
||||
) -> Precision:
|
||||
"""
|
||||
Add new predictions and targets to the metric, but do not compute the result.
|
||||
|
||||
Args:
|
||||
predictions (Union[Detections, List[Detections]]): The predicted detections.
|
||||
targets (Union[Detections, List[Detections]]): The target detections.
|
||||
|
||||
Returns:
|
||||
(Precision): The updated metric instance.
|
||||
"""
|
||||
if not isinstance(predictions, list):
|
||||
predictions = [predictions]
|
||||
if not isinstance(targets, list):
|
||||
targets = [targets]
|
||||
|
||||
if len(predictions) != len(targets):
|
||||
raise ValueError(
|
||||
f"The number of predictions ({len(predictions)}) and"
|
||||
f" targets ({len(targets)}) during the update must be the same."
|
||||
)
|
||||
|
||||
self._predictions_list.extend(predictions)
|
||||
self._targets_list.extend(targets)
|
||||
|
||||
return self
|
||||
|
||||
def compute(self) -> PrecisionResult:
|
||||
"""
|
||||
Calculate the precision metric based on the stored predictions and ground-truth
|
||||
data, at different IoU thresholds.
|
||||
|
||||
Returns:
|
||||
(PrecisionResult): The precision metric result.
|
||||
"""
|
||||
result = self._compute(self._predictions_list, self._targets_list)
|
||||
|
||||
small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
|
||||
)
|
||||
result.small_objects = self._compute(small_predictions, small_targets)
|
||||
|
||||
medium_predictions, medium_targets = (
|
||||
self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
|
||||
)
|
||||
)
|
||||
result.medium_objects = self._compute(medium_predictions, medium_targets)
|
||||
|
||||
large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
|
||||
)
|
||||
result.large_objects = self._compute(large_predictions, large_targets)
|
||||
|
||||
return result
|
||||
|
||||
def _compute(
|
||||
self, predictions_list: List[Detections], targets_list: List[Detections]
|
||||
) -> PrecisionResult:
|
||||
iou_thresholds = np.linspace(0.5, 0.95, 10)
|
||||
stats = []
|
||||
|
||||
for predictions, targets in zip(predictions_list, targets_list):
|
||||
prediction_contents = self._detections_content(predictions)
|
||||
target_contents = self._detections_content(targets)
|
||||
|
||||
if len(targets) > 0:
|
||||
if len(predictions) == 0:
|
||||
stats.append(
|
||||
(
|
||||
np.zeros((0, iou_thresholds.size), dtype=bool),
|
||||
np.zeros((0,), dtype=np.float32),
|
||||
np.zeros((0,), dtype=int),
|
||||
targets.class_id,
|
||||
)
|
||||
)
|
||||
|
||||
else:
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
iou = box_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.MASKS:
|
||||
iou = mask_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
iou = oriented_box_iou_batch(
|
||||
target_contents, prediction_contents
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported metric target for IoU calculation"
|
||||
)
|
||||
|
||||
matches = self._match_detection_batch(
|
||||
predictions.class_id, targets.class_id, iou, iou_thresholds
|
||||
)
|
||||
stats.append(
|
||||
(
|
||||
matches,
|
||||
predictions.confidence,
|
||||
predictions.class_id,
|
||||
targets.class_id,
|
||||
)
|
||||
)
|
||||
|
||||
if not stats:
|
||||
return PrecisionResult(
|
||||
metric_target=self._metric_target,
|
||||
averaging_method=self.averaging_method,
|
||||
precision_scores=np.zeros(iou_thresholds.shape[0]),
|
||||
precision_per_class=np.zeros((0, iou_thresholds.shape[0])),
|
||||
iou_thresholds=iou_thresholds,
|
||||
matched_classes=np.array([], dtype=int),
|
||||
small_objects=None,
|
||||
medium_objects=None,
|
||||
large_objects=None,
|
||||
)
|
||||
|
||||
concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
|
||||
precision_scores, precision_per_class, unique_classes = (
|
||||
self._compute_precision_for_classes(*concatenated_stats)
|
||||
)
|
||||
|
||||
return PrecisionResult(
|
||||
metric_target=self._metric_target,
|
||||
averaging_method=self.averaging_method,
|
||||
precision_scores=precision_scores,
|
||||
precision_per_class=precision_per_class,
|
||||
iou_thresholds=iou_thresholds,
|
||||
matched_classes=unique_classes,
|
||||
small_objects=None,
|
||||
medium_objects=None,
|
||||
large_objects=None,
|
||||
)
|
||||
|
||||
def _compute_precision_for_classes(
|
||||
self,
|
||||
matches: np.ndarray,
|
||||
prediction_confidence: np.ndarray,
|
||||
prediction_class_ids: np.ndarray,
|
||||
true_class_ids: np.ndarray,
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
sorted_indices = np.argsort(-prediction_confidence)
|
||||
matches = matches[sorted_indices]
|
||||
prediction_class_ids = prediction_class_ids[sorted_indices]
|
||||
unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
|
||||
|
||||
# Shape: PxTh,P,C,C -> CxThx3
|
||||
confusion_matrix = self._compute_confusion_matrix(
|
||||
matches, prediction_class_ids, unique_classes, class_counts
|
||||
)
|
||||
|
||||
# Shape: CxThx3 -> CxTh
|
||||
precision_per_class = self._compute_precision(confusion_matrix)
|
||||
|
||||
# Shape: CxTh -> Th
|
||||
if self.averaging_method == AveragingMethod.MACRO:
|
||||
precision_scores = np.mean(precision_per_class, axis=0)
|
||||
elif self.averaging_method == AveragingMethod.MICRO:
|
||||
confusion_matrix_merged = confusion_matrix.sum(0)
|
||||
precision_scores = self._compute_precision(confusion_matrix_merged)
|
||||
elif self.averaging_method == AveragingMethod.WEIGHTED:
|
||||
class_counts = class_counts.astype(np.float32)
|
||||
precision_scores = np.average(
|
||||
precision_per_class, axis=0, weights=class_counts
|
||||
)
|
||||
|
||||
return precision_scores, precision_per_class, unique_classes
|
||||
|
||||
@staticmethod
|
||||
def _match_detection_batch(
|
||||
predictions_classes: np.ndarray,
|
||||
target_classes: np.ndarray,
|
||||
iou: np.ndarray,
|
||||
iou_thresholds: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
num_predictions, num_iou_levels = (
|
||||
predictions_classes.shape[0],
|
||||
iou_thresholds.shape[0],
|
||||
)
|
||||
correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
|
||||
correct_class = target_classes[:, None] == predictions_classes
|
||||
|
||||
for i, iou_level in enumerate(iou_thresholds):
|
||||
matched_indices = np.where((iou >= iou_level) & correct_class)
|
||||
|
||||
if matched_indices[0].shape[0]:
|
||||
combined_indices = np.stack(matched_indices, axis=1)
|
||||
iou_values = iou[matched_indices][:, None]
|
||||
matches = np.hstack([combined_indices, iou_values])
|
||||
|
||||
if matched_indices[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
|
||||
correct[matches[:, 1].astype(int), i] = True
|
||||
|
||||
return correct
|
||||
|
||||
@staticmethod
|
||||
def _compute_confusion_matrix(
|
||||
sorted_matches: np.ndarray,
|
||||
sorted_prediction_class_ids: np.ndarray,
|
||||
unique_classes: np.ndarray,
|
||||
class_counts: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Compute the confusion matrix for each class and IoU threshold.
|
||||
|
||||
Assumes the matches and prediction_class_ids are sorted by confidence
|
||||
in descending order.
|
||||
|
||||
Arguments:
|
||||
sorted_matches: np.ndarray, bool, shape (P, Th), that is True
|
||||
if the prediction is a true positive at the given IoU threshold.
|
||||
sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
|
||||
the class id for each prediction.
|
||||
unique_classes: np.ndarray, int, shape (C,), containing the unique
|
||||
class ids.
|
||||
class_counts: np.ndarray, int, shape (C,), containing the number
|
||||
of true instances for each class.
|
||||
|
||||
Returns:
|
||||
np.ndarray, shape (C, Th, 3), containing the true positives, false
|
||||
positives, and false negatives for each class and IoU threshold.
|
||||
"""
|
||||
|
||||
num_thresholds = sorted_matches.shape[1]
|
||||
num_classes = unique_classes.shape[0]
|
||||
|
||||
confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
|
||||
for class_idx, class_id in enumerate(unique_classes):
|
||||
is_class = sorted_prediction_class_ids == class_id
|
||||
num_true = class_counts[class_idx]
|
||||
num_predictions = is_class.sum()
|
||||
|
||||
if num_predictions == 0:
|
||||
true_positives = np.zeros(num_thresholds)
|
||||
false_positives = np.zeros(num_thresholds)
|
||||
false_negatives = np.full(num_thresholds, num_true)
|
||||
elif num_true == 0:
|
||||
true_positives = np.zeros(num_thresholds)
|
||||
false_positives = np.full(num_thresholds, num_predictions)
|
||||
false_negatives = np.zeros(num_thresholds)
|
||||
else:
|
||||
true_positives = sorted_matches[is_class].sum(0)
|
||||
false_positives = (1 - sorted_matches[is_class]).sum(0)
|
||||
false_negatives = num_true - true_positives
|
||||
confusion_matrix[class_idx] = np.stack(
|
||||
[true_positives, false_positives, false_negatives], axis=1
|
||||
)
|
||||
|
||||
return confusion_matrix
|
||||
|
||||
@staticmethod
|
||||
def _compute_precision(confusion_matrix: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Broadcastable function, computing the precision from the confusion matrix.
|
||||
|
||||
Arguments:
|
||||
confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
|
||||
contains the true positives, false positives, and false negatives.
|
||||
|
||||
Returns:
|
||||
np.ndarray, shape (N, ...), containing the precision for each element.
|
||||
"""
|
||||
if not confusion_matrix.shape[-1] == 3:
|
||||
raise ValueError(
|
||||
f"Confusion matrix must have shape (..., 3), got "
|
||||
f"{confusion_matrix.shape}"
|
||||
)
|
||||
true_positives = confusion_matrix[..., 0]
|
||||
false_positives = confusion_matrix[..., 1]
|
||||
|
||||
denominator = true_positives + false_positives
|
||||
precision = np.where(denominator == 0, 0, true_positives / denominator)
|
||||
|
||||
return precision
|
||||
|
||||
def _detections_content(self, detections: Detections) -> np.ndarray:
|
||||
"""Return boxes, masks or oriented bounding boxes from detections."""
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
return detections.xyxy
|
||||
if self._metric_target == MetricTarget.MASKS:
|
||||
return (
|
||||
detections.mask
|
||||
if detections.mask is not None
|
||||
else self._make_empty_content()
|
||||
)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
obb = detections.data.get(ORIENTED_BOX_COORDINATES)
|
||||
if obb is not None and len(obb) > 0:
|
||||
return np.array(obb, dtype=np.float32)
|
||||
return self._make_empty_content()
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _make_empty_content(self) -> np.ndarray:
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
return np.empty((0, 4), dtype=np.float32)
|
||||
if self._metric_target == MetricTarget.MASKS:
|
||||
return np.empty((0, 0, 0), dtype=bool)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
return np.empty((0, 4, 2), dtype=np.float32)
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _filter_detections_by_size(
|
||||
self, detections: Detections, size_category: ObjectSizeCategory
|
||||
) -> Detections:
|
||||
"""Return a copy of detections with contents filtered by object size."""
|
||||
new_detections = deepcopy(detections)
|
||||
if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
|
||||
return new_detections
|
||||
|
||||
sizes = get_detection_size_category(new_detections, self._metric_target)
|
||||
size_mask = sizes == size_category.value
|
||||
|
||||
new_detections.xyxy = new_detections.xyxy[size_mask]
|
||||
if new_detections.mask is not None:
|
||||
new_detections.mask = new_detections.mask[size_mask]
|
||||
if new_detections.class_id is not None:
|
||||
new_detections.class_id = new_detections.class_id[size_mask]
|
||||
if new_detections.confidence is not None:
|
||||
new_detections.confidence = new_detections.confidence[size_mask]
|
||||
if new_detections.tracker_id is not None:
|
||||
new_detections.tracker_id = new_detections.tracker_id[size_mask]
|
||||
if new_detections.data is not None:
|
||||
for key, value in new_detections.data.items():
|
||||
new_detections.data[key] = np.array(value)[size_mask]
|
||||
|
||||
return new_detections
|
||||
|
||||
def _filter_predictions_and_targets_by_size(
|
||||
self,
|
||||
predictions_list: List[Detections],
|
||||
targets_list: List[Detections],
|
||||
size_category: ObjectSizeCategory,
|
||||
) -> Tuple[List[Detections], List[Detections]]:
|
||||
"""
|
||||
Filter predictions and targets by object size category.
|
||||
"""
|
||||
new_predictions_list = []
|
||||
new_targets_list = []
|
||||
for predictions, targets in zip(predictions_list, targets_list):
|
||||
new_predictions_list.append(
|
||||
self._filter_detections_by_size(predictions, size_category)
|
||||
)
|
||||
new_targets_list.append(
|
||||
self._filter_detections_by_size(targets, size_category)
|
||||
)
|
||||
return new_predictions_list, new_targets_list
|
||||
|
||||
|
||||
@dataclass
|
||||
class PrecisionResult:
|
||||
"""
|
||||
The results of the precision metric calculation.
|
||||
|
||||
Defaults to `0` if no detections or targets were provided.
|
||||
|
||||
Attributes:
|
||||
metric_target (MetricTarget): the type of data used for the metric -
|
||||
boxes, masks or oriented bounding boxes.
|
||||
averaging_method (AveragingMethod): the averaging method used to compute the
|
||||
precision. Determines how the precision is aggregated across classes.
|
||||
precision_at_50 (float): the precision at IoU threshold of `0.5`.
|
||||
precision_at_75 (float): the precision at IoU threshold of `0.75`.
|
||||
precision_scores (np.ndarray): the precision scores at each IoU threshold.
|
||||
Shape: `(num_iou_thresholds,)`
|
||||
precision_per_class (np.ndarray): the precision scores per class and
|
||||
IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)`
|
||||
iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
|
||||
matched_classes (np.ndarray): the class IDs of all matched classes.
|
||||
Corresponds to the rows of `precision_per_class`.
|
||||
small_objects (Optional[PrecisionResult]): the Precision metric results
|
||||
for small objects.
|
||||
medium_objects (Optional[PrecisionResult]): the Precision metric results
|
||||
for medium objects.
|
||||
large_objects (Optional[PrecisionResult]): the Precision metric results
|
||||
for large objects.
|
||||
"""
|
||||
|
||||
metric_target: MetricTarget
|
||||
averaging_method: AveragingMethod
|
||||
|
||||
@property
|
||||
def precision_at_50(self) -> float:
|
||||
return self.precision_scores[0]
|
||||
|
||||
@property
|
||||
def precision_at_75(self) -> float:
|
||||
return self.precision_scores[5]
|
||||
|
||||
precision_scores: np.ndarray
|
||||
precision_per_class: np.ndarray
|
||||
iou_thresholds: np.ndarray
|
||||
matched_classes: np.ndarray
|
||||
|
||||
small_objects: Optional[PrecisionResult]
|
||||
medium_objects: Optional[PrecisionResult]
|
||||
large_objects: Optional[PrecisionResult]
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""
|
||||
Format as a pretty string.
|
||||
|
||||
Example:
|
||||
```python
|
||||
print(precision_result)
|
||||
```
|
||||
"""
|
||||
out_str = (
|
||||
f"{self.__class__.__name__}:\n"
|
||||
f"Metric target: {self.metric_target}\n"
|
||||
f"Averaging method: {self.averaging_method}\n"
|
||||
f"P @ 50: {self.precision_at_50:.4f}\n"
|
||||
f"P @ 75: {self.precision_at_75:.4f}\n"
|
||||
f"P @ thresh: {self.precision_scores}\n"
|
||||
f"IoU thresh: {self.iou_thresholds}\n"
|
||||
f"Precision per class:\n"
|
||||
)
|
||||
if self.precision_per_class.size == 0:
|
||||
out_str += " No results\n"
|
||||
for class_id, precision_of_class in zip(
|
||||
self.matched_classes, self.precision_per_class
|
||||
):
|
||||
out_str += f" {class_id}: {precision_of_class}\n"
|
||||
|
||||
indent = " "
|
||||
if self.small_objects is not None:
|
||||
indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nSmall objects:\n{indented}"
|
||||
if self.medium_objects is not None:
|
||||
indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nMedium objects:\n{indented}"
|
||||
if self.large_objects is not None:
|
||||
indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nLarge objects:\n{indented}"
|
||||
|
||||
return out_str
|
||||
|
||||
def to_pandas(self) -> "pd.DataFrame":
|
||||
"""
|
||||
Convert the result to a pandas DataFrame.
|
||||
|
||||
Returns:
|
||||
(pd.DataFrame): The result as a DataFrame.
|
||||
"""
|
||||
ensure_pandas_installed()
|
||||
import pandas as pd
|
||||
|
||||
pandas_data = {
|
||||
"P@50": self.precision_at_50,
|
||||
"P@75": self.precision_at_75,
|
||||
}
|
||||
|
||||
if self.small_objects is not None:
|
||||
small_objects_df = self.small_objects.to_pandas()
|
||||
for key, value in small_objects_df.items():
|
||||
pandas_data[f"small_objects_{key}"] = value
|
||||
if self.medium_objects is not None:
|
||||
medium_objects_df = self.medium_objects.to_pandas()
|
||||
for key, value in medium_objects_df.items():
|
||||
pandas_data[f"medium_objects_{key}"] = value
|
||||
if self.large_objects is not None:
|
||||
large_objects_df = self.large_objects.to_pandas()
|
||||
for key, value in large_objects_df.items():
|
||||
pandas_data[f"large_objects_{key}"] = value
|
||||
|
||||
return pd.DataFrame(pandas_data, index=[0])
|
||||
|
||||
def plot(self):
|
||||
"""
|
||||
Plot the precision results.
|
||||
"""
|
||||
|
||||
labels = ["Precision@50", "Precision@75"]
|
||||
values = [self.precision_at_50, self.precision_at_75]
|
||||
colors = [LEGACY_COLOR_PALETTE[0]] * 2
|
||||
|
||||
if self.small_objects is not None:
|
||||
small_objects = self.small_objects
|
||||
labels += ["Small: P@50", "Small: P@75"]
|
||||
values += [small_objects.precision_at_50, small_objects.precision_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[3]] * 2
|
||||
|
||||
if self.medium_objects is not None:
|
||||
medium_objects = self.medium_objects
|
||||
labels += ["Medium: P@50", "Medium: P@75"]
|
||||
values += [medium_objects.precision_at_50, medium_objects.precision_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[2]] * 2
|
||||
|
||||
if self.large_objects is not None:
|
||||
large_objects = self.large_objects
|
||||
labels += ["Large: P@50", "Large: P@75"]
|
||||
values += [large_objects.precision_at_50, large_objects.precision_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[4]] * 2
|
||||
|
||||
plt.rcParams["font.family"] = "monospace"
|
||||
|
||||
_, ax = plt.subplots(figsize=(10, 6))
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_ylabel("Value", fontweight="bold")
|
||||
title = (
|
||||
f"Precision, by Object Size"
|
||||
f"\n(target: {self.metric_target.value},"
|
||||
f" averaging: {self.averaging_method.value})"
|
||||
)
|
||||
ax.set_title(title, fontweight="bold")
|
||||
|
||||
x_positions = range(len(labels))
|
||||
bars = ax.bar(x_positions, values, color=colors, align="center")
|
||||
|
||||
ax.set_xticks(x_positions)
|
||||
ax.set_xticklabels(labels, rotation=45, ha="right")
|
||||
|
||||
for bar in bars:
|
||||
y_value = bar.get_height()
|
||||
ax.text(
|
||||
bar.get_x() + bar.get_width() / 2,
|
||||
y_value + 0.02,
|
||||
f"{y_value:.2f}",
|
||||
ha="center",
|
||||
va="bottom",
|
||||
)
|
||||
|
||||
plt.rcParams["font.family"] = "sans-serif"
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,614 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from copy import deepcopy
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from supervision.config import ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import (
|
||||
box_iou_batch,
|
||||
mask_iou_batch,
|
||||
oriented_box_iou_batch,
|
||||
)
|
||||
from supervision.draw.color import LEGACY_COLOR_PALETTE
|
||||
from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
|
||||
from supervision.metrics.utils.object_size import (
|
||||
ObjectSizeCategory,
|
||||
get_detection_size_category,
|
||||
)
|
||||
from supervision.metrics.utils.utils import ensure_pandas_installed
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class Recall(Metric):
|
||||
"""
|
||||
Recall is a metric used to evaluate object detection models. It is the ratio of
|
||||
true positive detections to the total number of ground truth instances. We calculate
|
||||
it at different IoU thresholds.
|
||||
|
||||
In simple terms, Recall is a measure of a model's completeness, calculated as:
|
||||
|
||||
`Recall = TP / (TP + FN)`
|
||||
|
||||
Here, `TP` is the number of true positives (correct detections), and `FN` is the
|
||||
number of false negatives (missed detections).
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
from supervision.metrics import Recall
|
||||
|
||||
predictions = sv.Detections(...)
|
||||
targets = sv.Detections(...)
|
||||
|
||||
recall_metric = Recall()
|
||||
recall_result = recall_metric.update(predictions, targets).compute()
|
||||
|
||||
print(recall_result)
|
||||
print(recall_result.recall_at_50)
|
||||
print(recall_result.small_objects.recall_at_50)
|
||||
```
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric_target: MetricTarget = MetricTarget.BOXES,
|
||||
averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
|
||||
):
|
||||
"""
|
||||
Initialize the Recall metric.
|
||||
|
||||
Args:
|
||||
metric_target (MetricTarget): The type of detection data to use.
|
||||
averaging_method (AveragingMethod): The averaging method used to compute the
|
||||
recall. Determines how the recall is aggregated across classes.
|
||||
"""
|
||||
self._metric_target = metric_target
|
||||
self.averaging_method = averaging_method
|
||||
|
||||
self._predictions_list: List[Detections] = []
|
||||
self._targets_list: List[Detections] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Reset the metric to its initial state, clearing all stored data.
|
||||
"""
|
||||
self._predictions_list = []
|
||||
self._targets_list = []
|
||||
|
||||
def update(
|
||||
self,
|
||||
predictions: Union[Detections, List[Detections]],
|
||||
targets: Union[Detections, List[Detections]],
|
||||
) -> Recall:
|
||||
"""
|
||||
Add new predictions and targets to the metric, but do not compute the result.
|
||||
|
||||
Args:
|
||||
predictions (Union[Detections, List[Detections]]): The predicted detections.
|
||||
targets (Union[Detections, List[Detections]]): The target detections.
|
||||
|
||||
Returns:
|
||||
(Recall): The updated metric instance.
|
||||
"""
|
||||
if not isinstance(predictions, list):
|
||||
predictions = [predictions]
|
||||
if not isinstance(targets, list):
|
||||
targets = [targets]
|
||||
|
||||
if len(predictions) != len(targets):
|
||||
raise ValueError(
|
||||
f"The number of predictions ({len(predictions)}) and"
|
||||
f" targets ({len(targets)}) during the update must be the same."
|
||||
)
|
||||
|
||||
self._predictions_list.extend(predictions)
|
||||
self._targets_list.extend(targets)
|
||||
|
||||
return self
|
||||
|
||||
def compute(self) -> RecallResult:
|
||||
"""
|
||||
Calculate the precision metric based on the stored predictions and ground-truth
|
||||
data, at different IoU thresholds.
|
||||
|
||||
Returns:
|
||||
(RecallResult): The precision metric result.
|
||||
"""
|
||||
result = self._compute(self._predictions_list, self._targets_list)
|
||||
|
||||
small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
|
||||
)
|
||||
result.small_objects = self._compute(small_predictions, small_targets)
|
||||
|
||||
medium_predictions, medium_targets = (
|
||||
self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
|
||||
)
|
||||
)
|
||||
result.medium_objects = self._compute(medium_predictions, medium_targets)
|
||||
|
||||
large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
|
||||
self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
|
||||
)
|
||||
result.large_objects = self._compute(large_predictions, large_targets)
|
||||
|
||||
return result
|
||||
|
||||
def _compute(
|
||||
self, predictions_list: List[Detections], targets_list: List[Detections]
|
||||
) -> RecallResult:
|
||||
iou_thresholds = np.linspace(0.5, 0.95, 10)
|
||||
stats = []
|
||||
|
||||
for predictions, targets in zip(predictions_list, targets_list):
|
||||
prediction_contents = self._detections_content(predictions)
|
||||
target_contents = self._detections_content(targets)
|
||||
|
||||
if len(targets) > 0:
|
||||
if len(predictions) == 0:
|
||||
stats.append(
|
||||
(
|
||||
np.zeros((0, iou_thresholds.size), dtype=bool),
|
||||
np.zeros((0,), dtype=np.float32),
|
||||
np.zeros((0,), dtype=int),
|
||||
targets.class_id,
|
||||
)
|
||||
)
|
||||
|
||||
else:
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
iou = box_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.MASKS:
|
||||
iou = mask_iou_batch(target_contents, prediction_contents)
|
||||
elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
iou = oriented_box_iou_batch(
|
||||
target_contents, prediction_contents
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported metric target for IoU calculation"
|
||||
)
|
||||
|
||||
matches = self._match_detection_batch(
|
||||
predictions.class_id, targets.class_id, iou, iou_thresholds
|
||||
)
|
||||
stats.append(
|
||||
(
|
||||
matches,
|
||||
predictions.confidence,
|
||||
predictions.class_id,
|
||||
targets.class_id,
|
||||
)
|
||||
)
|
||||
|
||||
if not stats:
|
||||
return RecallResult(
|
||||
metric_target=self._metric_target,
|
||||
averaging_method=self.averaging_method,
|
||||
recall_scores=np.zeros(iou_thresholds.shape[0]),
|
||||
recall_per_class=np.zeros((0, iou_thresholds.shape[0])),
|
||||
iou_thresholds=iou_thresholds,
|
||||
matched_classes=np.array([], dtype=int),
|
||||
small_objects=None,
|
||||
medium_objects=None,
|
||||
large_objects=None,
|
||||
)
|
||||
|
||||
concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
|
||||
recall_scores, recall_per_class, unique_classes = (
|
||||
self._compute_recall_for_classes(*concatenated_stats)
|
||||
)
|
||||
|
||||
return RecallResult(
|
||||
metric_target=self._metric_target,
|
||||
averaging_method=self.averaging_method,
|
||||
recall_scores=recall_scores,
|
||||
recall_per_class=recall_per_class,
|
||||
iou_thresholds=iou_thresholds,
|
||||
matched_classes=unique_classes,
|
||||
small_objects=None,
|
||||
medium_objects=None,
|
||||
large_objects=None,
|
||||
)
|
||||
|
||||
def _compute_recall_for_classes(
|
||||
self,
|
||||
matches: np.ndarray,
|
||||
prediction_confidence: np.ndarray,
|
||||
prediction_class_ids: np.ndarray,
|
||||
true_class_ids: np.ndarray,
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
sorted_indices = np.argsort(-prediction_confidence)
|
||||
matches = matches[sorted_indices]
|
||||
prediction_class_ids = prediction_class_ids[sorted_indices]
|
||||
unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)
|
||||
|
||||
# Shape: PxTh,P,C,C -> CxThx3
|
||||
confusion_matrix = self._compute_confusion_matrix(
|
||||
matches, prediction_class_ids, unique_classes, class_counts
|
||||
)
|
||||
|
||||
# Shape: CxThx3 -> CxTh
|
||||
recall_per_class = self._compute_recall(confusion_matrix)
|
||||
|
||||
# Shape: CxTh -> Th
|
||||
if self.averaging_method == AveragingMethod.MACRO:
|
||||
recall_scores = np.mean(recall_per_class, axis=0)
|
||||
elif self.averaging_method == AveragingMethod.MICRO:
|
||||
confusion_matrix_merged = confusion_matrix.sum(0)
|
||||
recall_scores = self._compute_recall(confusion_matrix_merged)
|
||||
elif self.averaging_method == AveragingMethod.WEIGHTED:
|
||||
class_counts = class_counts.astype(np.float32)
|
||||
recall_scores = np.average(recall_per_class, axis=0, weights=class_counts)
|
||||
|
||||
return recall_scores, recall_per_class, unique_classes
|
||||
|
||||
@staticmethod
|
||||
def _match_detection_batch(
|
||||
predictions_classes: np.ndarray,
|
||||
target_classes: np.ndarray,
|
||||
iou: np.ndarray,
|
||||
iou_thresholds: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
num_predictions, num_iou_levels = (
|
||||
predictions_classes.shape[0],
|
||||
iou_thresholds.shape[0],
|
||||
)
|
||||
correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
|
||||
correct_class = target_classes[:, None] == predictions_classes
|
||||
|
||||
for i, iou_level in enumerate(iou_thresholds):
|
||||
matched_indices = np.where((iou >= iou_level) & correct_class)
|
||||
|
||||
if matched_indices[0].shape[0]:
|
||||
combined_indices = np.stack(matched_indices, axis=1)
|
||||
iou_values = iou[matched_indices][:, None]
|
||||
matches = np.hstack([combined_indices, iou_values])
|
||||
|
||||
if matched_indices[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
|
||||
correct[matches[:, 1].astype(int), i] = True
|
||||
|
||||
return correct
|
||||
|
||||
@staticmethod
|
||||
def _compute_confusion_matrix(
|
||||
sorted_matches: np.ndarray,
|
||||
sorted_prediction_class_ids: np.ndarray,
|
||||
unique_classes: np.ndarray,
|
||||
class_counts: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Compute the confusion matrix for each class and IoU threshold.
|
||||
|
||||
Assumes the matches and prediction_class_ids are sorted by confidence
|
||||
in descending order.
|
||||
|
||||
Arguments:
|
||||
sorted_matches: np.ndarray, bool, shape (P, Th), that is True
|
||||
if the prediction is a true positive at the given IoU threshold.
|
||||
sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
|
||||
the class id for each prediction.
|
||||
unique_classes: np.ndarray, int, shape (C,), containing the unique
|
||||
class ids.
|
||||
class_counts: np.ndarray, int, shape (C,), containing the number
|
||||
of true instances for each class.
|
||||
|
||||
Returns:
|
||||
np.ndarray, shape (C, Th, 3), containing the true positives, false
|
||||
positives, and false negatives for each class and IoU threshold.
|
||||
"""
|
||||
|
||||
num_thresholds = sorted_matches.shape[1]
|
||||
num_classes = unique_classes.shape[0]
|
||||
|
||||
confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
|
||||
for class_idx, class_id in enumerate(unique_classes):
|
||||
is_class = sorted_prediction_class_ids == class_id
|
||||
num_true = class_counts[class_idx]
|
||||
num_predictions = is_class.sum()
|
||||
|
||||
if num_predictions == 0:
|
||||
true_positives = np.zeros(num_thresholds)
|
||||
false_positives = np.zeros(num_thresholds)
|
||||
false_negatives = np.full(num_thresholds, num_true)
|
||||
elif num_true == 0:
|
||||
true_positives = np.zeros(num_thresholds)
|
||||
false_positives = np.full(num_thresholds, num_predictions)
|
||||
false_negatives = np.zeros(num_thresholds)
|
||||
else:
|
||||
true_positives = sorted_matches[is_class].sum(0)
|
||||
false_positives = (1 - sorted_matches[is_class]).sum(0)
|
||||
false_negatives = num_true - true_positives
|
||||
confusion_matrix[class_idx] = np.stack(
|
||||
[true_positives, false_positives, false_negatives], axis=1
|
||||
)
|
||||
|
||||
return confusion_matrix
|
||||
|
||||
@staticmethod
|
||||
def _compute_recall(confusion_matrix: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Broadcastable function, computing the recall from the confusion matrix.
|
||||
|
||||
Arguments:
|
||||
confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
|
||||
contains the true positives, false positives, and false negatives.
|
||||
|
||||
Returns:
|
||||
np.ndarray, shape (N, ...), containing the recall for each element.
|
||||
"""
|
||||
if not confusion_matrix.shape[-1] == 3:
|
||||
raise ValueError(
|
||||
f"Confusion matrix must have shape (..., 3), got "
|
||||
f"{confusion_matrix.shape}"
|
||||
)
|
||||
true_positives = confusion_matrix[..., 0]
|
||||
false_negatives = confusion_matrix[..., 2]
|
||||
|
||||
denominator = true_positives + false_negatives
|
||||
recall = np.where(denominator == 0, 0, true_positives / denominator)
|
||||
|
||||
return recall
|
||||
|
||||
def _detections_content(self, detections: Detections) -> np.ndarray:
|
||||
"""Return boxes, masks or oriented bounding boxes from detections."""
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
return detections.xyxy
|
||||
if self._metric_target == MetricTarget.MASKS:
|
||||
return (
|
||||
detections.mask
|
||||
if detections.mask is not None
|
||||
else self._make_empty_content()
|
||||
)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
obb = detections.data.get(ORIENTED_BOX_COORDINATES)
|
||||
if obb is not None and len(obb) > 0:
|
||||
return np.array(obb, dtype=np.float32)
|
||||
return self._make_empty_content()
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _make_empty_content(self) -> np.ndarray:
|
||||
if self._metric_target == MetricTarget.BOXES:
|
||||
return np.empty((0, 4), dtype=np.float32)
|
||||
if self._metric_target == MetricTarget.MASKS:
|
||||
return np.empty((0, 0, 0), dtype=bool)
|
||||
if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
|
||||
return np.empty((0, 4, 2), dtype=np.float32)
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _filter_detections_by_size(
|
||||
self, detections: Detections, size_category: ObjectSizeCategory
|
||||
) -> Detections:
|
||||
"""Return a copy of detections with contents filtered by object size."""
|
||||
new_detections = deepcopy(detections)
|
||||
if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
|
||||
return new_detections
|
||||
|
||||
sizes = get_detection_size_category(new_detections, self._metric_target)
|
||||
size_mask = sizes == size_category.value
|
||||
|
||||
new_detections.xyxy = new_detections.xyxy[size_mask]
|
||||
if new_detections.mask is not None:
|
||||
new_detections.mask = new_detections.mask[size_mask]
|
||||
if new_detections.class_id is not None:
|
||||
new_detections.class_id = new_detections.class_id[size_mask]
|
||||
if new_detections.confidence is not None:
|
||||
new_detections.confidence = new_detections.confidence[size_mask]
|
||||
if new_detections.tracker_id is not None:
|
||||
new_detections.tracker_id = new_detections.tracker_id[size_mask]
|
||||
if new_detections.data is not None:
|
||||
for key, value in new_detections.data.items():
|
||||
new_detections.data[key] = np.array(value)[size_mask]
|
||||
|
||||
return new_detections
|
||||
|
||||
def _filter_predictions_and_targets_by_size(
|
||||
self,
|
||||
predictions_list: List[Detections],
|
||||
targets_list: List[Detections],
|
||||
size_category: ObjectSizeCategory,
|
||||
) -> Tuple[List[Detections], List[Detections]]:
|
||||
"""
|
||||
Filter predictions and targets by object size category.
|
||||
"""
|
||||
new_predictions_list = []
|
||||
new_targets_list = []
|
||||
for predictions, targets in zip(predictions_list, targets_list):
|
||||
new_predictions_list.append(
|
||||
self._filter_detections_by_size(predictions, size_category)
|
||||
)
|
||||
new_targets_list.append(
|
||||
self._filter_detections_by_size(targets, size_category)
|
||||
)
|
||||
return new_predictions_list, new_targets_list
|
||||
|
||||
|
||||
@dataclass
|
||||
class RecallResult:
|
||||
"""
|
||||
The results of the recall metric calculation.
|
||||
|
||||
Defaults to `0` if no detections or targets were provided.
|
||||
|
||||
Attributes:
|
||||
metric_target (MetricTarget): the type of data used for the metric -
|
||||
boxes, masks or oriented bounding boxes.
|
||||
averaging_method (AveragingMethod): the averaging method used to compute the
|
||||
recall. Determines how the recall is aggregated across classes.
|
||||
recall_at_50 (float): the recall at IoU threshold of `0.5`.
|
||||
recall_at_75 (float): the recall at IoU threshold of `0.75`.
|
||||
recall_scores (np.ndarray): the recall scores at each IoU threshold.
|
||||
Shape: `(num_iou_thresholds,)`
|
||||
recall_per_class (np.ndarray): the recall scores per class and IoU threshold.
|
||||
Shape: `(num_target_classes, num_iou_thresholds)`
|
||||
iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
|
||||
matched_classes (np.ndarray): the class IDs of all matched classes.
|
||||
Corresponds to the rows of `recall_per_class`.
|
||||
small_objects (Optional[RecallResult]): the Recall metric results
|
||||
for small objects.
|
||||
medium_objects (Optional[RecallResult]): the Recall metric results
|
||||
for medium objects.
|
||||
large_objects (Optional[RecallResult]): the Recall metric results
|
||||
for large objects.
|
||||
"""
|
||||
|
||||
metric_target: MetricTarget
|
||||
averaging_method: AveragingMethod
|
||||
|
||||
@property
|
||||
def recall_at_50(self) -> float:
|
||||
return self.recall_scores[0]
|
||||
|
||||
@property
|
||||
def recall_at_75(self) -> float:
|
||||
return self.recall_scores[5]
|
||||
|
||||
recall_scores: np.ndarray
|
||||
recall_per_class: np.ndarray
|
||||
iou_thresholds: np.ndarray
|
||||
matched_classes: np.ndarray
|
||||
|
||||
small_objects: Optional[RecallResult]
|
||||
medium_objects: Optional[RecallResult]
|
||||
large_objects: Optional[RecallResult]
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""
|
||||
Format as a pretty string.
|
||||
|
||||
Example:
|
||||
```python
|
||||
print(recall_result)
|
||||
```
|
||||
"""
|
||||
out_str = (
|
||||
f"{self.__class__.__name__}:\n"
|
||||
f"Metric target: {self.metric_target}\n"
|
||||
f"Averaging method: {self.averaging_method}\n"
|
||||
f"R @ 50: {self.recall_at_50:.4f}\n"
|
||||
f"R @ 75: {self.recall_at_75:.4f}\n"
|
||||
f"R @ thresh: {self.recall_scores}\n"
|
||||
f"IoU thresh: {self.iou_thresholds}\n"
|
||||
f"Recall per class:\n"
|
||||
)
|
||||
if self.recall_per_class.size == 0:
|
||||
out_str += " No results\n"
|
||||
for class_id, recall_of_class in zip(
|
||||
self.matched_classes, self.recall_per_class
|
||||
):
|
||||
out_str += f" {class_id}: {recall_of_class}\n"
|
||||
|
||||
indent = " "
|
||||
if self.small_objects is not None:
|
||||
indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nSmall objects:\n{indented}"
|
||||
if self.medium_objects is not None:
|
||||
indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nMedium objects:\n{indented}"
|
||||
if self.large_objects is not None:
|
||||
indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
|
||||
out_str += f"\nLarge objects:\n{indented}"
|
||||
|
||||
return out_str
|
||||
|
||||
def to_pandas(self) -> "pd.DataFrame":
|
||||
"""
|
||||
Convert the result to a pandas DataFrame.
|
||||
|
||||
Returns:
|
||||
(pd.DataFrame): The result as a DataFrame.
|
||||
"""
|
||||
ensure_pandas_installed()
|
||||
import pandas as pd
|
||||
|
||||
pandas_data = {
|
||||
"R@50": self.recall_at_50,
|
||||
"R@75": self.recall_at_75,
|
||||
}
|
||||
|
||||
if self.small_objects is not None:
|
||||
small_objects_df = self.small_objects.to_pandas()
|
||||
for key, value in small_objects_df.items():
|
||||
pandas_data[f"small_objects_{key}"] = value
|
||||
if self.medium_objects is not None:
|
||||
medium_objects_df = self.medium_objects.to_pandas()
|
||||
for key, value in medium_objects_df.items():
|
||||
pandas_data[f"medium_objects_{key}"] = value
|
||||
if self.large_objects is not None:
|
||||
large_objects_df = self.large_objects.to_pandas()
|
||||
for key, value in large_objects_df.items():
|
||||
pandas_data[f"large_objects_{key}"] = value
|
||||
|
||||
return pd.DataFrame(pandas_data, index=[0])
|
||||
|
||||
def plot(self):
|
||||
"""
|
||||
Plot the recall results.
|
||||
"""
|
||||
|
||||
labels = ["Recall@50", "Recall@75"]
|
||||
values = [self.recall_at_50, self.recall_at_75]
|
||||
colors = [LEGACY_COLOR_PALETTE[0]] * 2
|
||||
|
||||
if self.small_objects is not None:
|
||||
small_objects = self.small_objects
|
||||
labels += ["Small: R@50", "Small: R@75"]
|
||||
values += [small_objects.recall_at_50, small_objects.recall_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[3]] * 2
|
||||
|
||||
if self.medium_objects is not None:
|
||||
medium_objects = self.medium_objects
|
||||
labels += ["Medium: R@50", "Medium: R@75"]
|
||||
values += [medium_objects.recall_at_50, medium_objects.recall_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[2]] * 2
|
||||
|
||||
if self.large_objects is not None:
|
||||
large_objects = self.large_objects
|
||||
labels += ["Large: R@50", "Large: R@75"]
|
||||
values += [large_objects.recall_at_50, large_objects.recall_at_75]
|
||||
colors += [LEGACY_COLOR_PALETTE[4]] * 2
|
||||
|
||||
plt.rcParams["font.family"] = "monospace"
|
||||
|
||||
_, ax = plt.subplots(figsize=(10, 6))
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_ylabel("Value", fontweight="bold")
|
||||
title = (
|
||||
f"Recall, by Object Size"
|
||||
f"\n(target: {self.metric_target.value},"
|
||||
f" averaging: {self.averaging_method.value})"
|
||||
)
|
||||
ax.set_title(title, fontweight="bold")
|
||||
|
||||
x_positions = range(len(labels))
|
||||
bars = ax.bar(x_positions, values, color=colors, align="center")
|
||||
|
||||
ax.set_xticks(x_positions)
|
||||
ax.set_xticklabels(labels, rotation=45, ha="right")
|
||||
|
||||
for bar in bars:
|
||||
y_value = bar.get_height()
|
||||
ax.text(
|
||||
bar.get_x() + bar.get_width() / 2,
|
||||
y_value + 0.02,
|
||||
f"{y_value:.2f}",
|
||||
ha="center",
|
||||
va="bottom",
|
||||
)
|
||||
|
||||
plt.rcParams["font.family"] = "sans-serif"
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
|
@ -1,63 +0,0 @@
|
|||
from collections import OrderedDict
|
||||
from enum import Enum
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class TrackState(Enum):
|
||||
New = 0
|
||||
Tracked = 1
|
||||
Lost = 2
|
||||
Removed = 3
|
||||
|
||||
|
||||
class BaseTrack:
|
||||
_count = 0
|
||||
|
||||
def __init__(self):
|
||||
self.track_id = 0
|
||||
self.is_activated = False
|
||||
self.state = TrackState.New
|
||||
|
||||
self.history = OrderedDict()
|
||||
self.features = []
|
||||
self.curr_feature = None
|
||||
self.score = 0
|
||||
self.start_frame = 0
|
||||
self.frame_id = 0
|
||||
self.time_since_update = 0
|
||||
|
||||
# multi-camera
|
||||
self.location = (np.inf, np.inf)
|
||||
|
||||
@property
|
||||
def end_frame(self) -> int:
|
||||
return self.frame_id
|
||||
|
||||
@staticmethod
|
||||
def next_id() -> int:
|
||||
BaseTrack._count += 1
|
||||
return BaseTrack._count
|
||||
|
||||
@staticmethod
|
||||
def reset_counter():
|
||||
BaseTrack._count = 0
|
||||
BaseTrack.track_id = 0
|
||||
BaseTrack.start_frame = 0
|
||||
BaseTrack.frame_id = 0
|
||||
BaseTrack.time_since_update = 0
|
||||
|
||||
def activate(self, *args):
|
||||
raise NotImplementedError
|
||||
|
||||
def predict(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def update(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def mark_lost(self):
|
||||
self.state = TrackState.Lost
|
||||
|
||||
def mark_removed(self):
|
||||
self.state = TrackState.Removed
|
||||
|
|
@ -5,186 +5,9 @@ import numpy as np
|
|||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import box_iou_batch
|
||||
from supervision.tracker.byte_tracker import matching
|
||||
from supervision.tracker.byte_tracker.basetrack import BaseTrack, TrackState
|
||||
from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
|
||||
|
||||
|
||||
class STrack(BaseTrack):
|
||||
shared_kalman = KalmanFilter()
|
||||
_external_count = 0
|
||||
|
||||
def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
|
||||
# wait activate
|
||||
self._tlwh = np.asarray(tlwh, dtype=np.float32)
|
||||
self.kalman_filter = None
|
||||
self.mean, self.covariance = None, None
|
||||
self.is_activated = False
|
||||
|
||||
self.score = score
|
||||
self.class_ids = class_ids
|
||||
self.tracklet_len = 0
|
||||
|
||||
self.external_track_id = -1
|
||||
|
||||
self.minimum_consecutive_frames = minimum_consecutive_frames
|
||||
|
||||
def predict(self):
|
||||
mean_state = self.mean.copy()
|
||||
if self.state != TrackState.Tracked:
|
||||
mean_state[7] = 0
|
||||
self.mean, self.covariance = self.kalman_filter.predict(
|
||||
mean_state, self.covariance
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def multi_predict(stracks):
|
||||
if len(stracks) > 0:
|
||||
multi_mean = []
|
||||
multi_covariance = []
|
||||
for i, st in enumerate(stracks):
|
||||
multi_mean.append(st.mean.copy())
|
||||
multi_covariance.append(st.covariance)
|
||||
if st.state != TrackState.Tracked:
|
||||
multi_mean[i][7] = 0
|
||||
|
||||
multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(
|
||||
np.asarray(multi_mean), np.asarray(multi_covariance)
|
||||
)
|
||||
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
|
||||
stracks[i].mean = mean
|
||||
stracks[i].covariance = cov
|
||||
|
||||
def activate(self, kalman_filter, frame_id):
|
||||
"""Start a new tracklet"""
|
||||
self.kalman_filter = kalman_filter
|
||||
self.internal_track_id = self.next_id()
|
||||
self.mean, self.covariance = self.kalman_filter.initiate(
|
||||
self.tlwh_to_xyah(self._tlwh)
|
||||
)
|
||||
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
if frame_id == 1:
|
||||
self.is_activated = True
|
||||
|
||||
if self.minimum_consecutive_frames == 1:
|
||||
self.external_track_id = self.next_external_id()
|
||||
|
||||
self.frame_id = frame_id
|
||||
self.start_frame = frame_id
|
||||
|
||||
def re_activate(self, new_track, frame_id, new_id=False):
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
||||
)
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
|
||||
self.frame_id = frame_id
|
||||
if new_id:
|
||||
self.internal_track_id = self.next_id()
|
||||
self.score = new_track.score
|
||||
|
||||
def update(self, new_track, frame_id):
|
||||
"""
|
||||
Update a matched track
|
||||
:type new_track: STrack
|
||||
:type frame_id: int
|
||||
:type update_feature: bool
|
||||
:return:
|
||||
"""
|
||||
self.frame_id = frame_id
|
||||
self.tracklet_len += 1
|
||||
|
||||
new_tlwh = new_track.tlwh
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
|
||||
)
|
||||
self.state = TrackState.Tracked
|
||||
if self.tracklet_len == self.minimum_consecutive_frames:
|
||||
self.is_activated = True
|
||||
if self.external_track_id == -1:
|
||||
self.external_track_id = self.next_external_id()
|
||||
|
||||
self.score = new_track.score
|
||||
|
||||
@property
|
||||
def tlwh(self):
|
||||
"""Get current position in bounding box format `(top left x, top left y,
|
||||
width, height)`.
|
||||
"""
|
||||
if self.mean is None:
|
||||
return self._tlwh.copy()
|
||||
ret = self.mean[:4].copy()
|
||||
ret[2] *= ret[3]
|
||||
ret[:2] -= ret[2:] / 2
|
||||
return ret
|
||||
|
||||
@property
|
||||
def tlbr(self):
|
||||
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||
`(top left, bottom right)`.
|
||||
"""
|
||||
ret = self.tlwh.copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
def tlwh_to_xyah(tlwh):
|
||||
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||
height)`, where the aspect ratio is `width / height`.
|
||||
"""
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[:2] += ret[2:] / 2
|
||||
ret[2] /= ret[3]
|
||||
return ret
|
||||
|
||||
def to_xyah(self):
|
||||
return self.tlwh_to_xyah(self.tlwh)
|
||||
|
||||
@staticmethod
|
||||
def next_external_id():
|
||||
STrack._external_count += 1
|
||||
return STrack._external_count
|
||||
|
||||
@staticmethod
|
||||
def reset_external_counter():
|
||||
STrack._external_count = 0
|
||||
|
||||
@staticmethod
|
||||
def tlbr_to_tlwh(tlbr):
|
||||
ret = np.asarray(tlbr).copy()
|
||||
ret[2:] -= ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
def tlwh_to_tlbr(tlwh):
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
def __repr__(self):
|
||||
return "OT_{}_({}-{})".format(
|
||||
self.internal_track_id, self.start_frame, self.end_frame
|
||||
)
|
||||
|
||||
|
||||
def detections2boxes(detections: Detections) -> np.ndarray:
|
||||
"""
|
||||
Convert Supervision Detections to numpy tensors for further computation.
|
||||
Args:
|
||||
detections (Detections): Detections/Targets in the format of sv.Detections.
|
||||
Returns:
|
||||
(np.ndarray): Detections as numpy tensors as in
|
||||
`(x_min, y_min, x_max, y_max, confidence, class_id)` order.
|
||||
"""
|
||||
return np.hstack(
|
||||
(
|
||||
detections.xyxy,
|
||||
detections.confidence[:, np.newaxis],
|
||||
detections.class_id[:, np.newaxis],
|
||||
)
|
||||
)
|
||||
from supervision.tracker.byte_tracker.single_object_track import STrack, TrackState
|
||||
from supervision.tracker.byte_tracker.utils import IdCounter
|
||||
|
||||
|
||||
class ByteTrack:
|
||||
|
|
@ -230,11 +53,17 @@ class ByteTrack:
|
|||
self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
|
||||
self.minimum_consecutive_frames = minimum_consecutive_frames
|
||||
self.kalman_filter = KalmanFilter()
|
||||
self.shared_kalman = KalmanFilter()
|
||||
|
||||
self.tracked_tracks: List[STrack] = []
|
||||
self.lost_tracks: List[STrack] = []
|
||||
self.removed_tracks: List[STrack] = []
|
||||
|
||||
# Warning, possible bug: If you also set internal_id to start at 1,
|
||||
# all traces will be connected across objects.
|
||||
self.internal_id_counter = IdCounter()
|
||||
self.external_id_counter = IdCounter(start_id=1)
|
||||
|
||||
def update_with_detections(self, detections: Detections) -> Detections:
|
||||
"""
|
||||
Updates the tracker with the provided detections and returns the updated
|
||||
|
|
@ -274,8 +103,12 @@ class ByteTrack:
|
|||
)
|
||||
```
|
||||
"""
|
||||
|
||||
tensors = detections2boxes(detections=detections)
|
||||
tensors = np.hstack(
|
||||
(
|
||||
detections.xyxy,
|
||||
detections.confidence[:, np.newaxis],
|
||||
)
|
||||
)
|
||||
tracks = self.update_with_tensors(tensors=tensors)
|
||||
|
||||
if len(tracks) > 0:
|
||||
|
|
@ -301,7 +134,7 @@ class ByteTrack:
|
|||
|
||||
return detections
|
||||
|
||||
def reset(self):
|
||||
def reset(self) -> None:
|
||||
"""
|
||||
Resets the internal state of the ByteTrack tracker.
|
||||
|
||||
|
|
@ -311,11 +144,11 @@ class ByteTrack:
|
|||
ensuring the tracker starts with a clean state for each new video.
|
||||
"""
|
||||
self.frame_id = 0
|
||||
self.tracked_tracks: List[STrack] = []
|
||||
self.lost_tracks: List[STrack] = []
|
||||
self.removed_tracks: List[STrack] = []
|
||||
BaseTrack.reset_counter()
|
||||
STrack.reset_external_counter()
|
||||
self.internal_id_counter.reset()
|
||||
self.external_id_counter.reset()
|
||||
self.tracked_tracks = []
|
||||
self.lost_tracks = []
|
||||
self.removed_tracks = []
|
||||
|
||||
def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
|
||||
"""
|
||||
|
|
@ -333,7 +166,6 @@ class ByteTrack:
|
|||
lost_stracks = []
|
||||
removed_stracks = []
|
||||
|
||||
class_ids = tensors[:, 5]
|
||||
scores = tensors[:, 4]
|
||||
bboxes = tensors[:, :4]
|
||||
|
||||
|
|
@ -347,14 +179,18 @@ class ByteTrack:
|
|||
scores_keep = scores[remain_inds]
|
||||
scores_second = scores[inds_second]
|
||||
|
||||
class_ids_keep = class_ids[remain_inds]
|
||||
class_ids_second = class_ids[inds_second]
|
||||
|
||||
if len(dets) > 0:
|
||||
"""Detections"""
|
||||
detections = [
|
||||
STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
|
||||
for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
|
||||
STrack(
|
||||
STrack.tlbr_to_tlwh(tlbr),
|
||||
score_keep,
|
||||
self.minimum_consecutive_frames,
|
||||
self.shared_kalman,
|
||||
self.internal_id_counter,
|
||||
self.external_id_counter,
|
||||
)
|
||||
for (tlbr, score_keep) in zip(dets, scores_keep)
|
||||
]
|
||||
else:
|
||||
detections = []
|
||||
|
|
@ -372,7 +208,7 @@ class ByteTrack:
|
|||
""" Step 2: First association, with high score detection boxes"""
|
||||
strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
|
||||
# Predict the current location with KF
|
||||
STrack.multi_predict(strack_pool)
|
||||
STrack.multi_predict(strack_pool, self.shared_kalman)
|
||||
dists = matching.iou_distance(strack_pool, detections)
|
||||
|
||||
dists = matching.fuse_score(dists, detections)
|
||||
|
|
@ -387,7 +223,7 @@ class ByteTrack:
|
|||
track.update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
track.re_activate(det, self.frame_id)
|
||||
refind_stracks.append(track)
|
||||
|
||||
""" Step 3: Second association, with low score detection boxes"""
|
||||
|
|
@ -395,8 +231,15 @@ class ByteTrack:
|
|||
if len(dets_second) > 0:
|
||||
"""Detections"""
|
||||
detections_second = [
|
||||
STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
|
||||
for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
|
||||
STrack(
|
||||
STrack.tlbr_to_tlwh(tlbr),
|
||||
score_second,
|
||||
self.minimum_consecutive_frames,
|
||||
self.shared_kalman,
|
||||
self.internal_id_counter,
|
||||
self.external_id_counter,
|
||||
)
|
||||
for (tlbr, score_second) in zip(dets_second, scores_second)
|
||||
]
|
||||
else:
|
||||
detections_second = []
|
||||
|
|
@ -416,13 +259,13 @@ class ByteTrack:
|
|||
track.update(det, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
track.re_activate(det, self.frame_id)
|
||||
refind_stracks.append(track)
|
||||
|
||||
for it in u_track:
|
||||
track = r_tracked_stracks[it]
|
||||
if not track.state == TrackState.Lost:
|
||||
track.mark_lost()
|
||||
track.state = TrackState.Lost
|
||||
lost_stracks.append(track)
|
||||
|
||||
"""Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
|
||||
|
|
@ -438,7 +281,7 @@ class ByteTrack:
|
|||
activated_starcks.append(unconfirmed[itracked])
|
||||
for it in u_unconfirmed:
|
||||
track = unconfirmed[it]
|
||||
track.mark_removed()
|
||||
track.state = TrackState.Removed
|
||||
removed_stracks.append(track)
|
||||
|
||||
""" Step 4: Init new stracks"""
|
||||
|
|
@ -450,8 +293,8 @@ class ByteTrack:
|
|||
activated_starcks.append(track)
|
||||
""" Step 5: Update state"""
|
||||
for track in self.lost_tracks:
|
||||
if self.frame_id - track.end_frame > self.max_time_lost:
|
||||
track.mark_removed()
|
||||
if self.frame_id - track.frame_id > self.max_time_lost:
|
||||
track.state = TrackState.Removed
|
||||
removed_stracks.append(track)
|
||||
|
||||
self.tracked_tracks = [
|
||||
|
|
@ -497,7 +340,7 @@ def joint_tracks(
|
|||
return result
|
||||
|
||||
|
||||
def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
|
||||
def sub_tracks(track_list_a: List[STrack], track_list_b: List[STrack]) -> List[int]:
|
||||
"""
|
||||
Returns a list of tracks from track_list_a after removing any tracks
|
||||
that share the same internal_track_id with tracks in track_list_b.
|
||||
|
|
@ -518,7 +361,9 @@ def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
|
|||
return list(tracks.values())
|
||||
|
||||
|
||||
def remove_duplicate_tracks(tracks_a: List, tracks_b: List) -> Tuple[List, List]:
|
||||
def remove_duplicate_tracks(
|
||||
tracks_a: List[STrack], tracks_b: List[STrack]
|
||||
) -> Tuple[List[STrack], List[STrack]]:
|
||||
pairwise_distance = matching.iou_distance(tracks_a, tracks_b)
|
||||
matching_pairs = np.where(pairwise_distance < 0.15)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,15 @@
|
|||
from typing import List, Tuple
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, List, Tuple
|
||||
|
||||
import numpy as np
|
||||
from scipy.optimize import linear_sum_assignment
|
||||
|
||||
from supervision.detection.utils import box_iou_batch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from supervision.tracker.byte_tracker.core import STrack
|
||||
|
||||
|
||||
def indices_to_matches(
|
||||
cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
|
||||
|
|
@ -20,7 +25,7 @@ def indices_to_matches(
|
|||
|
||||
def linear_assignment(
|
||||
cost_matrix: np.ndarray, thresh: float
|
||||
) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
|
||||
) -> Tuple[np.ndarray, Tuple[int], Tuple[int, int]]:
|
||||
if cost_matrix.size == 0:
|
||||
return (
|
||||
np.empty((0, 2), dtype=int),
|
||||
|
|
@ -35,7 +40,7 @@ def linear_assignment(
|
|||
return indices_to_matches(cost_matrix, indices, thresh)
|
||||
|
||||
|
||||
def iou_distance(atracks: List, btracks: List) -> np.ndarray:
|
||||
def iou_distance(atracks: List[STrack], btracks: List[STrack]) -> np.ndarray:
|
||||
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
|
||||
len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
|
||||
):
|
||||
|
|
@ -53,11 +58,11 @@ def iou_distance(atracks: List, btracks: List) -> np.ndarray:
|
|||
return cost_matrix
|
||||
|
||||
|
||||
def fuse_score(cost_matrix: np.ndarray, detections: List) -> np.ndarray:
|
||||
def fuse_score(cost_matrix: np.ndarray, stracks: List[STrack]) -> np.ndarray:
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
iou_sim = 1 - cost_matrix
|
||||
det_scores = np.array([det.score for det in detections])
|
||||
det_scores = np.array([strack.score for strack in stracks])
|
||||
det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
|
||||
fuse_sim = iou_sim * det_scores
|
||||
fuse_cost = 1 - fuse_sim
|
||||
|
|
|
|||
|
|
@ -0,0 +1,178 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from enum import Enum
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
from supervision.tracker.byte_tracker.kalman_filter import KalmanFilter
|
||||
from supervision.tracker.byte_tracker.utils import IdCounter
|
||||
|
||||
|
||||
class TrackState(Enum):
|
||||
New = 0
|
||||
Tracked = 1
|
||||
Lost = 2
|
||||
Removed = 3
|
||||
|
||||
|
||||
class STrack:
|
||||
def __init__(
|
||||
self,
|
||||
tlwh: npt.NDArray[np.float32],
|
||||
score: npt.NDArray[np.float32],
|
||||
minimum_consecutive_frames: int,
|
||||
shared_kalman: KalmanFilter,
|
||||
internal_id_counter: IdCounter,
|
||||
external_id_counter: IdCounter,
|
||||
):
|
||||
self.state = TrackState.New
|
||||
self.is_activated = False
|
||||
self.start_frame = 0
|
||||
self.frame_id = 0
|
||||
|
||||
self._tlwh = np.asarray(tlwh, dtype=np.float32)
|
||||
self.kalman_filter = None
|
||||
self.shared_kalman = shared_kalman
|
||||
self.mean, self.covariance = None, None
|
||||
self.is_activated = False
|
||||
|
||||
self.score = score
|
||||
self.tracklet_len = 0
|
||||
|
||||
self.minimum_consecutive_frames = minimum_consecutive_frames
|
||||
|
||||
self.internal_id_counter = internal_id_counter
|
||||
self.external_id_counter = external_id_counter
|
||||
self.internal_track_id = self.internal_id_counter.NO_ID
|
||||
self.external_track_id = self.external_id_counter.NO_ID
|
||||
|
||||
def predict(self) -> None:
|
||||
mean_state = self.mean.copy()
|
||||
if self.state != TrackState.Tracked:
|
||||
mean_state[7] = 0
|
||||
self.mean, self.covariance = self.kalman_filter.predict(
|
||||
mean_state, self.covariance
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def multi_predict(stracks: List[STrack], shared_kalman: KalmanFilter) -> None:
|
||||
if len(stracks) > 0:
|
||||
multi_mean = []
|
||||
multi_covariance = []
|
||||
for i, st in enumerate(stracks):
|
||||
multi_mean.append(st.mean.copy())
|
||||
multi_covariance.append(st.covariance)
|
||||
if st.state != TrackState.Tracked:
|
||||
multi_mean[i][7] = 0
|
||||
|
||||
multi_mean, multi_covariance = shared_kalman.multi_predict(
|
||||
np.asarray(multi_mean), np.asarray(multi_covariance)
|
||||
)
|
||||
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
|
||||
stracks[i].mean = mean
|
||||
stracks[i].covariance = cov
|
||||
|
||||
def activate(self, kalman_filter: KalmanFilter, frame_id: int) -> None:
|
||||
"""Start a new tracklet"""
|
||||
self.kalman_filter = kalman_filter
|
||||
self.internal_track_id = self.internal_id_counter.new_id()
|
||||
self.mean, self.covariance = self.kalman_filter.initiate(
|
||||
self.tlwh_to_xyah(self._tlwh)
|
||||
)
|
||||
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
if frame_id == 1:
|
||||
self.is_activated = True
|
||||
|
||||
if self.minimum_consecutive_frames == 1:
|
||||
self.external_track_id = self.external_id_counter.new_id()
|
||||
|
||||
self.frame_id = frame_id
|
||||
self.start_frame = frame_id
|
||||
|
||||
def re_activate(self, new_track: STrack, frame_id: int) -> None:
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
||||
)
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
|
||||
self.frame_id = frame_id
|
||||
self.score = new_track.score
|
||||
|
||||
def update(self, new_track: STrack, frame_id: int) -> None:
|
||||
"""
|
||||
Update a matched track
|
||||
:type new_track: STrack
|
||||
:type frame_id: int
|
||||
:type update_feature: bool
|
||||
:return:
|
||||
"""
|
||||
self.frame_id = frame_id
|
||||
self.tracklet_len += 1
|
||||
|
||||
new_tlwh = new_track.tlwh
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)
|
||||
)
|
||||
self.state = TrackState.Tracked
|
||||
if self.tracklet_len == self.minimum_consecutive_frames:
|
||||
self.is_activated = True
|
||||
if self.external_track_id == self.external_id_counter.NO_ID:
|
||||
self.external_track_id = self.external_id_counter.new_id()
|
||||
|
||||
self.score = new_track.score
|
||||
|
||||
@property
|
||||
def tlwh(self) -> npt.NDArray[np.float32]:
|
||||
"""Get current position in bounding box format `(top left x, top left y,
|
||||
width, height)`.
|
||||
"""
|
||||
if self.mean is None:
|
||||
return self._tlwh.copy()
|
||||
ret = self.mean[:4].copy()
|
||||
ret[2] *= ret[3]
|
||||
ret[:2] -= ret[2:] / 2
|
||||
return ret
|
||||
|
||||
@property
|
||||
def tlbr(self) -> npt.NDArray[np.float32]:
|
||||
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||
`(top left, bottom right)`.
|
||||
"""
|
||||
ret = self.tlwh.copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
def tlwh_to_xyah(tlwh) -> npt.NDArray[np.float32]:
|
||||
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||
height)`, where the aspect ratio is `width / height`.
|
||||
"""
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[:2] += ret[2:] / 2
|
||||
ret[2] /= ret[3]
|
||||
return ret
|
||||
|
||||
def to_xyah(self) -> npt.NDArray[np.float32]:
|
||||
return self.tlwh_to_xyah(self.tlwh)
|
||||
|
||||
@staticmethod
|
||||
def tlbr_to_tlwh(tlbr) -> npt.NDArray[np.float32]:
|
||||
ret = np.asarray(tlbr).copy()
|
||||
ret[2:] -= ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
def tlwh_to_tlbr(tlwh) -> npt.NDArray[np.float32]:
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return "OT_{}_({}-{})".format(
|
||||
self.internal_track_id, self.start_frame, self.frame_id
|
||||
)
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
class IdCounter:
|
||||
def __init__(self, start_id: int = 0):
|
||||
self.start_id = start_id
|
||||
if self.start_id <= self.NO_ID:
|
||||
raise ValueError(f"start_id must be greater than {self.NO_ID}")
|
||||
self.reset()
|
||||
|
||||
def reset(self) -> None:
|
||||
self._id = self.start_id
|
||||
|
||||
def new_id(self) -> int:
|
||||
returned_id = self._id
|
||||
self._id += 1
|
||||
return returned_id
|
||||
|
||||
@property
|
||||
def NO_ID(self) -> int:
|
||||
return -1
|
||||
|
|
@ -65,8 +65,9 @@ class VideoSink:
|
|||
|
||||
Attributes:
|
||||
target_path (str): The path to the output file where the video will be saved.
|
||||
video_info (VideoInfo): Information about the video resolution, fps,
|
||||
and total frame count.
|
||||
video_info (Optional[VideoInfo]): Information about the output video resolution,
|
||||
fps, and total frame count. If not provided, the information will be inferred
|
||||
from the video path.
|
||||
codec (str): FOURCC code for video format
|
||||
|
||||
Example:
|
||||
|
|
@ -82,8 +83,16 @@ class VideoSink:
|
|||
```
|
||||
""" # noqa: E501 // docs
|
||||
|
||||
def __init__(self, target_path: str, video_info: VideoInfo, codec: str = "mp4v"):
|
||||
def __init__(
|
||||
self,
|
||||
target_path: str,
|
||||
video_info: Optional[VideoInfo] = None,
|
||||
codec: str = "mp4v",
|
||||
):
|
||||
self.target_path = target_path
|
||||
|
||||
if video_info is None:
|
||||
video_info = VideoInfo.from_video_path(target_path)
|
||||
self.video_info = video_info
|
||||
self.__codec = codec
|
||||
self.__writer = None
|
||||
|
|
|
|||
|
|
@ -0,0 +1,40 @@
|
|||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import supervision as sv
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"detections, expected_results",
|
||||
[
|
||||
(
|
||||
[
|
||||
sv.Detections(
|
||||
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
||||
class_id=np.array([1, 1]),
|
||||
confidence=np.array([1, 1]),
|
||||
),
|
||||
sv.Detections(
|
||||
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
||||
class_id=np.array([1, 1]),
|
||||
confidence=np.array([1, 1]),
|
||||
),
|
||||
],
|
||||
sv.Detections(
|
||||
xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]),
|
||||
class_id=np.array([1, 1]),
|
||||
confidence=np.array([1, 1]),
|
||||
tracker_id=np.array([1, 2]),
|
||||
),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_byte_tracker(
|
||||
detections: List[sv.Detections],
|
||||
expected_results: sv.Detections,
|
||||
) -> None:
|
||||
byte_tracker = sv.ByteTrack()
|
||||
tracked_detections = [byte_tracker.update_with_detections(d) for d in detections]
|
||||
assert tracked_detections[-1] == expected_results
|
||||
|
|
@ -121,7 +121,15 @@ class MockDataclass:
|
|||
(
|
||||
Detections.empty(),
|
||||
False,
|
||||
{"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
|
||||
{
|
||||
"xyxy",
|
||||
"class_id",
|
||||
"confidence",
|
||||
"mask",
|
||||
"tracker_id",
|
||||
"data",
|
||||
"metadata",
|
||||
},
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
|
|
@ -134,6 +142,7 @@ class MockDataclass:
|
|||
"mask",
|
||||
"tracker_id",
|
||||
"data",
|
||||
"metadata",
|
||||
"area",
|
||||
"box_area",
|
||||
},
|
||||
|
|
@ -149,6 +158,7 @@ class MockDataclass:
|
|||
"mask",
|
||||
"tracker_id",
|
||||
"data",
|
||||
"metadata",
|
||||
},
|
||||
DoesNotRaise(),
|
||||
),
|
||||
|
|
@ -169,13 +179,22 @@ class MockDataclass:
|
|||
"mask",
|
||||
"tracker_id",
|
||||
"data",
|
||||
"metadata",
|
||||
},
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
False,
|
||||
{"xyxy", "class_id", "confidence", "mask", "tracker_id", "data"},
|
||||
{
|
||||
"xyxy",
|
||||
"class_id",
|
||||
"confidence",
|
||||
"mask",
|
||||
"tracker_id",
|
||||
"data",
|
||||
"metadata",
|
||||
},
|
||||
DoesNotRaise(),
|
||||
),
|
||||
],
|
||||
|
|
|
|||
Loading…
Reference in New Issue