supervision-0.13.0 release
`supervision-0.13.0` release
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@@ -24,7 +24,7 @@ Before you contribute a new feature, consider submitting an Issue to discuss the
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## How to Contribute Changes
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First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to this repository.
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First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to `develop` branch of this repository.
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When creating new functions, please ensure you have the following:
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@@ -32,6 +32,7 @@ When creating new functions, please ensure you have the following:
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2. Unit tests for the function.
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3. Examples in the documentation for the function.
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4. Created an entry in our docs to autogenerate the documentation for the function.
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5. Please share google colab with minimal code to test new feature or reproduce PR whenever it is possible. Please ensure that google colab can be accessed without any issue.
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All pull requests will be reviewed by the maintainers of the project. We will provide feedback and ask for changes if necessary.
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@@ -48,4 +49,4 @@ So far, **there is no types checking with mypy**. See [issue](https://github.com
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## 🧪 tests
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[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
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[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
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@@ -31,7 +31,7 @@ Pip install the supervision package in a
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pip install supervision[desktop]
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```
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Read more about desktop, headless and local installation in our [guide](https://roboflow.github.io/supervision/).
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Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
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## 🔥 quickstart
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@@ -52,7 +52,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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<details close>
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<summary>👉 more detections utils</summary>
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- Easily switch inference pipeline between supported object detection / instance segmentation models
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- Easily switch inference pipeline between supported object detection/instance segmentation models
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```python
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>>> import supervision as sv
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@@ -107,7 +107,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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<details close>
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<summary>👉 more dataset utils</summary>
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- Load object detection / instance segmentation datasets in one of supported formats
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- Load object detection/instance segmentation datasets in one of the supported formats
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```python
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>>> dataset = sv.DetectionDataset.from_yolo(
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@@ -138,7 +138,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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[ 20.154999, 347.825 , 416.125 , 915.895 ]], dtype=float32)
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```
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- Split dataset for training, testing and validation
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- Split dataset for training, testing, and validation
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```python
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>>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
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@@ -148,7 +148,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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(700, 150, 150)
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```
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- Merge multiple datasets together
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- Merge multiple datasets
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```python
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>>> ds_1 = sv.DetectionDataset(...)
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@@ -170,7 +170,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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['cat', 'dog', 'person']
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```
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- Save object detection / instance segmentation datasets in one of supported formats
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- Save object detection/instance segmentation datasets in one of the supported formats
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```python
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>>> dataset.as_yolo(
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@@ -203,7 +203,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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... )
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```
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- Load classification datasets in one of supported formats
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- Load classification datasets in one of the supported formats
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```python
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>>> cs = sv.ClassificationDataset.from_folder_structure(
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@@ -211,7 +211,7 @@ Read more about desktop, headless and local installation in our [guide](https://
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... )
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```
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- Save classification datasets in one of supported formats
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- Save classification datasets in one of the supported formats
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```python
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>>> cs.as_folder_structure(
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@@ -245,6 +245,30 @@ array([
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])
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```
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<details close>
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<summary>👉 more metrics</summary>
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- Mean average precision (mAP) for object detection tasks.
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```python
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>>> import supervision as sv
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>>> dataset = sv.DetectionDataset.from_yolo(...)
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>>> def callback(image: np.ndarray) -> sv.Detections:
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... ...
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>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
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... dataset = dataset,
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... callback = callback
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... )
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>>> mean_average_precision.map50_95
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0.433
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```
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</details>
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## 🎬 tutorials
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<p align="left">
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@@ -1,3 +1,39 @@
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### 0.13.0 <small>August 8, 2023</small>
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- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](https://roboflow.github.io/supervision/metrics/detection/#meanaverageprecision).
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```python
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>>> import supervision as sv
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>>> from ultralytics import YOLO
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>>> dataset = sv.DetectionDataset.from_yolo(...)
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>>> model = YOLO(...)
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>>> def callback(image: np.ndarray) -> sv.Detections:
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... result = model(image)[0]
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... return sv.Detections.from_yolov8(result)
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>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
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... dataset = dataset,
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... callback = callback
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... )
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>>> mean_average_precision.map50_95
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0.433
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```
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- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](https://roboflow.github.io/supervision/tracker/core/#bytetrack).
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- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](https://roboflow.github.io/supervision/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.
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!!! warning
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[`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
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- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
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- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).
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### 0.12.0 <small>July 24, 2023</small>
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!!! warning
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@@ -6,3 +6,7 @@
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## ConfusionMatrix
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:::supervision.metrics.detection.ConfusionMatrix
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## MeanAveragePrecision
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:::supervision.metrics.detection.MeanAveragePrecision
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@@ -0,0 +1,4 @@
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## ByteTrack
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:::supervision.tracker.byte_tracker.core.ByteTrack
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@@ -37,6 +37,8 @@ nav:
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- Utils: detection/utils.md
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- Tools:
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- Polygon Zone: detection/tools/polygon_zone.md
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- Trackers:
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- Core: tracker/core.md
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- Dataset:
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- Core: dataset/core.md
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- Metrics:
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Generated
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@@ -2156,54 +2156,83 @@ files = [
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[[package]]
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name = "pillow"
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version = "8.4.0"
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version = "9.5.0"
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description = "Python Imaging Library (Fork)"
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optional = false
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python-versions = ">=3.6"
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python-versions = ">=3.7"
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[package.extras]
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tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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[[package]]
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optional = false
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[package.dependencies]
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||||
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||||
[package.extras]
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||||
dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
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||||
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
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||||
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
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||||
[[package]]
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@@ -3443,4 +3510,4 @@ desktop = ["opencv-python"]
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[metadata]
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lock-version = "2.0"
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python-versions = ">=3.8,<3.12.0"
|
||||
content-hash = "be27f05c8857580f327c9d6b89216524c9b8cacf662a24b1d5d4147e4a194f81"
|
||||
content-hash = "fcefb11e72c40fa7defc13f4a9cb1fe24b9d3d171b64bb54fc263d8add9ef40f"
|
||||
+5
-3
@@ -1,10 +1,11 @@
|
||||
[tool.poetry]
|
||||
name = "supervision"
|
||||
version = "0.12.0"
|
||||
version = "0.13.0"
|
||||
description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
|
||||
authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
|
||||
maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
|
||||
readme = "README.md"
|
||||
license = "MIT"
|
||||
packages = [{include = "supervision"}]
|
||||
homepage = "https://github.com/roboflow/supervision"
|
||||
repository = "https://github.com/roboflow/supervision"
|
||||
@@ -14,7 +15,7 @@ keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "YO
|
||||
classifiers=[
|
||||
'Intended Audience :: Developers',
|
||||
'Intended Audience :: Science/Research',
|
||||
'License :: OSI Approved :: BSD License',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.8',
|
||||
'Programming Language :: Python :: 3.9',
|
||||
@@ -37,9 +38,10 @@ python = ">=3.8,<3.12.0"
|
||||
numpy = "^1.20.0"
|
||||
matplotlib = "^3.7.1"
|
||||
pyyaml = "^6.0"
|
||||
pillow = "^8.4.0"
|
||||
pillow = "^9.4.0"
|
||||
opencv-python = { version = "^4.8.0.74", optional = true }
|
||||
opencv-python-headless = "^4.8.0.74"
|
||||
scipy = "^1.9.0"
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
|
||||
@@ -30,7 +30,8 @@ from supervision.draw.color import Color, ColorPalette
|
||||
from supervision.draw.utils import draw_filled_rectangle, draw_polygon, draw_text
|
||||
from supervision.geometry.core import Point, Position, Rect
|
||||
from supervision.geometry.utils import get_polygon_center
|
||||
from supervision.metrics.detection import ConfusionMatrix
|
||||
from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
|
||||
from supervision.tracker.byte_tracker.core import ByteTrack
|
||||
from supervision.utils.file import list_files_with_extensions
|
||||
from supervision.utils.image import ImageSink, crop
|
||||
from supervision.utils.notebook import plot_image, plot_images_grid
|
||||
|
||||
+11
-29
@@ -31,7 +31,6 @@ from supervision.dataset.utils import (
|
||||
train_test_split,
|
||||
)
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.utils.file import list_files_with_extensions
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -197,7 +196,10 @@ class DetectionDataset(BaseDataset):
|
||||
|
||||
@classmethod
|
||||
def from_pascal_voc(
|
||||
cls, images_directory_path: str, annotations_directory_path: str
|
||||
cls,
|
||||
images_directory_path: str,
|
||||
annotations_directory_path: str,
|
||||
force_masks: bool = False,
|
||||
) -> DetectionDataset:
|
||||
"""
|
||||
Creates a Dataset instance from PASCAL VOC formatted data.
|
||||
@@ -205,6 +207,7 @@ class DetectionDataset(BaseDataset):
|
||||
Args:
|
||||
images_directory_path (str): The path to the directory containing the images.
|
||||
annotations_directory_path (str): The path to the directory containing the PASCAL VOC XML annotations.
|
||||
force_masks (bool, optional): If True, forces masks to be loaded for all annotations, regardless of whether they are present.
|
||||
|
||||
Returns:
|
||||
DetectionDataset: A DetectionDataset instance containing the loaded images and annotations.
|
||||
@@ -222,7 +225,7 @@ class DetectionDataset(BaseDataset):
|
||||
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
|
||||
>>> dataset = project.version(PROJECT_VERSION).download("voc")
|
||||
|
||||
>>> ds = sv.DetectionDataset.from_yolo(
|
||||
>>> ds = sv.DetectionDataset.from_pascal_voc(
|
||||
... images_directory_path=f"{dataset.location}/train/images",
|
||||
... annotations_directory_path=f"{dataset.location}/train/labels"
|
||||
... )
|
||||
@@ -231,34 +234,13 @@ class DetectionDataset(BaseDataset):
|
||||
['dog', 'person']
|
||||
```
|
||||
"""
|
||||
image_paths = list_files_with_extensions(
|
||||
directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
|
||||
)
|
||||
annotation_paths = list_files_with_extensions(
|
||||
directory=annotations_directory_path, extensions=["xml"]
|
||||
|
||||
classes, images, annotations = load_pascal_voc_annotations(
|
||||
images_directory_path=images_directory_path,
|
||||
annotations_directory_path=annotations_directory_path,
|
||||
force_masks=force_masks,
|
||||
)
|
||||
|
||||
raw_annotations: List[Tuple[str, Detections, List[str]]] = [
|
||||
load_pascal_voc_annotations(annotation_path=str(annotation_path))
|
||||
for annotation_path in annotation_paths
|
||||
]
|
||||
|
||||
classes = []
|
||||
for annotation in raw_annotations:
|
||||
classes.extend(annotation[2])
|
||||
classes = list(set(classes))
|
||||
|
||||
for annotation in raw_annotations:
|
||||
class_id = [classes.index(class_name) for class_name in annotation[2]]
|
||||
annotation[1].class_id = np.array(class_id)
|
||||
|
||||
images = {
|
||||
image_path.name: cv2.imread(str(image_path)) for image_path in image_paths
|
||||
}
|
||||
|
||||
annotations = {
|
||||
image_name: detections for image_name, detections, _ in raw_annotations
|
||||
}
|
||||
return DetectionDataset(classes=classes, images=images, annotations=annotations)
|
||||
|
||||
@classmethod
|
||||
|
||||
@@ -124,7 +124,7 @@ def detections_to_coco_annotations(
|
||||
"category_id": int(class_id),
|
||||
"bbox": [xyxy[0], xyxy[1], box_width, box_height],
|
||||
"area": box_width * box_height,
|
||||
"segmentation": polygon,
|
||||
"segmentation": [polygon] if polygon else [],
|
||||
"iscrowd": 0,
|
||||
}
|
||||
coco_annotations.append(coco_annotation)
|
||||
|
||||
@@ -1,12 +1,16 @@
|
||||
from typing import List, Optional, Tuple
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from xml.dom.minidom import parseString
|
||||
from xml.etree.ElementTree import Element, SubElement, parse, tostring
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from supervision.dataset.utils import approximate_mask_with_polygons
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import polygon_to_xyxy
|
||||
from supervision.detection.utils import polygon_to_mask, polygon_to_xyxy
|
||||
from supervision.utils.file import list_files_with_extensions
|
||||
|
||||
|
||||
def object_to_pascal_voc(
|
||||
@@ -120,24 +124,91 @@ def detections_to_pascal_voc(
|
||||
|
||||
|
||||
def load_pascal_voc_annotations(
|
||||
annotation_path: str,
|
||||
) -> Tuple[str, Detections, List[str]]:
|
||||
images_directory_path: str,
|
||||
annotations_directory_path: str,
|
||||
force_masks: bool = False,
|
||||
) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
|
||||
"""
|
||||
Loads PASCAL VOC XML annotations and returns the image name, a Detections instance, and a list of class names.
|
||||
Loads PASCAL VOC annotations and returns class names, images, and their corresponding detections.
|
||||
|
||||
Args:
|
||||
annotation_path (str): The path to the PASCAL VOC XML annotations file.
|
||||
images_directory_path (str): The path to the directory containing the images.
|
||||
annotations_directory_path (str): The path to the directory containing the PASCAL VOC annotation files.
|
||||
force_masks (bool, optional): If True, forces masks to be loaded for all annotations, regardless of whether they are present.
|
||||
|
||||
Returns:
|
||||
Tuple[str, Detections, List[str]]: A tuple containing the image name, a Detections instance, and a list of class names of objects in the detections.
|
||||
Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]: A tuple containing a list of class names, a dictionary with image names as keys and images as values, and a dictionary with image names as keys and corresponding Detections instances as values.
|
||||
"""
|
||||
tree = parse(annotation_path)
|
||||
root = tree.getroot()
|
||||
|
||||
image_name = root.find("filename").text
|
||||
image_paths = list_files_with_extensions(
|
||||
directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
|
||||
)
|
||||
|
||||
classes = []
|
||||
images = {}
|
||||
annotations = {}
|
||||
|
||||
for image_path in image_paths:
|
||||
image_name = Path(image_path).stem
|
||||
image = cv2.imread(str(image_path))
|
||||
|
||||
annotation_path = os.path.join(annotations_directory_path, f"{image_name}.xml")
|
||||
if not os.path.exists(annotation_path):
|
||||
images[image_path.name] = image
|
||||
annotations[image_path.name] = Detections.empty()
|
||||
continue
|
||||
|
||||
tree = parse(annotation_path)
|
||||
root = tree.getroot()
|
||||
|
||||
resolution_wh = (image.shape[1], image.shape[0])
|
||||
annotation, classes = detections_from_xml_obj(
|
||||
root, classes, resolution_wh, force_masks
|
||||
)
|
||||
|
||||
images[image_path.name] = image
|
||||
annotations[image_path.name] = annotation
|
||||
|
||||
return classes, images, annotations
|
||||
|
||||
|
||||
def detections_from_xml_obj(
|
||||
root: Element, classes: List[str], resolution_wh, force_masks: bool = False
|
||||
) -> Tuple[Detections, List[str]]:
|
||||
"""
|
||||
Converts an XML object in Pascal VOC format to a Detections object.
|
||||
Expected XML format:
|
||||
<annotation>
|
||||
...
|
||||
<object>
|
||||
<name>dog</name>
|
||||
<bndbox>
|
||||
<xmin>48</xmin>
|
||||
<ymin>240</ymin>
|
||||
<xmax>195</xmax>
|
||||
<ymax>371</ymax>
|
||||
</bndbox>
|
||||
<polygon>
|
||||
<x1>48</x1>
|
||||
<y1>240</y1>
|
||||
<x2>195</x2>
|
||||
<y2>240</y2>
|
||||
<x3>195</x3>
|
||||
<y3>371</y3>
|
||||
<x4>48</x4>
|
||||
<y4>371</y4>
|
||||
</polygon>
|
||||
</object>
|
||||
</annotation>
|
||||
|
||||
Returns:
|
||||
Tuple[Detections, List[str]]: A tuple containing a Detections object and an updated list of class names, extended with the class names from the XML object.
|
||||
"""
|
||||
xyxy = []
|
||||
class_names = []
|
||||
masks = []
|
||||
with_masks = False
|
||||
extended_classes = classes[:]
|
||||
for obj in root.findall("object"):
|
||||
class_name = obj.find("name").text
|
||||
class_names.append(class_name)
|
||||
@@ -150,7 +221,40 @@ def load_pascal_voc_annotations(
|
||||
|
||||
xyxy.append([x1, y1, x2, y2])
|
||||
|
||||
xyxy = np.array(xyxy)
|
||||
detections = Detections(xyxy=xyxy)
|
||||
with_masks = obj.find("polygon") is not None
|
||||
with_masks = force_masks if force_masks else with_masks
|
||||
|
||||
return image_name, detections, class_names
|
||||
for polygon in obj.findall("polygon"):
|
||||
polygon_points = parse_polygon_points(polygon)
|
||||
|
||||
mask_from_polygon = polygon_to_mask(
|
||||
polygon=np.array(polygon_points),
|
||||
resolution_wh=resolution_wh,
|
||||
)
|
||||
masks.append(mask_from_polygon)
|
||||
|
||||
xyxy = np.array(xyxy) if len(xyxy) > 0 else np.empty((0, 4))
|
||||
for k in set(class_names):
|
||||
if k not in extended_classes:
|
||||
extended_classes.append(k)
|
||||
class_id = np.array(
|
||||
[extended_classes.index(class_name) for class_name in class_names]
|
||||
)
|
||||
|
||||
if with_masks:
|
||||
annotation = Detections(
|
||||
xyxy=xyxy, mask=np.array(masks).astype(bool), class_id=class_id
|
||||
)
|
||||
else:
|
||||
annotation = Detections(xyxy=xyxy, class_id=class_id)
|
||||
return annotation, extended_classes
|
||||
|
||||
|
||||
def parse_polygon_points(polygon: Element) -> List[List[int]]:
|
||||
polygon_points = []
|
||||
coords = polygon.findall(".//*")
|
||||
for i in range(0, len(coords), 2):
|
||||
x = int(coords[i].text)
|
||||
y = int(coords[i + 1].text)
|
||||
polygon_points.append([x, y])
|
||||
return polygon_points
|
||||
@@ -126,9 +126,6 @@ def load_yolo_annotations(
|
||||
image_paths = list_files_with_extensions(
|
||||
directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
|
||||
)
|
||||
annotation_paths = list_files_with_extensions(
|
||||
directory=annotations_directory_path, extensions=["txt"]
|
||||
)
|
||||
|
||||
classes = _extract_class_names(file_path=data_yaml_path)
|
||||
images = {}
|
||||
|
||||
@@ -7,12 +7,13 @@ import cv2
|
||||
import numpy as np
|
||||
|
||||
from supervision.detection.utils import (
|
||||
extract_yolov8_masks,
|
||||
extract_ultralytics_masks,
|
||||
non_max_suppression,
|
||||
process_roboflow_result,
|
||||
xywh_to_xyxy,
|
||||
)
|
||||
from supervision.geometry.core import Position
|
||||
from supervision.utils.internal import deprecated
|
||||
|
||||
|
||||
def _validate_xyxy(xyxy: Any, n: int) -> None:
|
||||
@@ -26,7 +27,7 @@ def _validate_mask(mask: Any, n: int) -> None:
|
||||
isinstance(mask, np.ndarray) and len(mask.shape) == 3 and mask.shape[0] == n
|
||||
)
|
||||
if not is_valid:
|
||||
raise ValueError("mask must be 3d np.ndarray with (n, W, H) shape")
|
||||
raise ValueError("mask must be 3d np.ndarray with (n, H, W) shape")
|
||||
|
||||
|
||||
def _validate_class_id(class_id: Any, n: int) -> None:
|
||||
@@ -59,7 +60,7 @@ class Detections:
|
||||
Data class containing information about the detections in a video frame.
|
||||
Attributes:
|
||||
xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes coordinates in format `[x1, y1, x2, y2]`
|
||||
mask: (Optional[np.ndarray]): An array of shape `(n, W, H)` containing the segmentation masks.
|
||||
mask: (Optional[np.ndarray]): An array of shape `(n, H, W)` containing the segmentation masks.
|
||||
confidence (Optional[np.ndarray]): An array of shape `(n,)` containing the confidence scores of the detections.
|
||||
class_id (Optional[np.ndarray]): An array of shape `(n,)` containing the class ids of the detections.
|
||||
tracker_id (Optional[np.ndarray]): An array of shape `(n,)` containing the tracker ids of the detections.
|
||||
@@ -170,6 +171,9 @@ class Detections:
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@deprecated(
|
||||
"This method is deprecated and removed in 0.15.0 release. Use sv.Detections.from_ultralytics() instead."
|
||||
)
|
||||
def from_yolov8(cls, yolov8_results) -> Detections:
|
||||
"""
|
||||
Creates a Detections instance from a [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
|
||||
@@ -196,7 +200,47 @@ class Detections:
|
||||
xyxy=yolov8_results.boxes.xyxy.cpu().numpy(),
|
||||
confidence=yolov8_results.boxes.conf.cpu().numpy(),
|
||||
class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int),
|
||||
mask=extract_yolov8_masks(yolov8_results),
|
||||
mask=extract_ultralytics_masks(yolov8_results),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_ultralytics(cls, ultralytics_results) -> Detections:
|
||||
"""
|
||||
Creates a Detections instance from a [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
|
||||
|
||||
Args:
|
||||
ultralytics_results (ultralytics.yolo.engine.results.Results): The output Results instance from YOLOv8
|
||||
|
||||
Returns:
|
||||
Detections: A new Detections object.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> import cv2
|
||||
>>> from ultralytics import YOLO, FastSAM, SAM, RTDETR
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
|
||||
>>> model = YOLO('yolov8s.pt')
|
||||
>>> model = SAM('sam_b.pt')
|
||||
>>> model = SAM('mobile_sam.pt')
|
||||
>>> model = FastSAM('FastSAM-s.pt')
|
||||
>>> model = RTDETR('rtdetr-l.pt')
|
||||
>>> # model inferences
|
||||
>>> result = model(image)[0]
|
||||
>>> # if tracker is enabled
|
||||
>>> result = model.track(image)[0]
|
||||
>>> detections = sv.Detections.from_ultralytics(result)
|
||||
```
|
||||
"""
|
||||
return cls(
|
||||
xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
|
||||
confidence=ultralytics_results.boxes.conf.cpu().numpy(),
|
||||
class_id=ultralytics_results.boxes.cls.cpu().numpy().astype(int),
|
||||
mask=extract_ultralytics_masks(ultralytics_results),
|
||||
tracker_id=ultralytics_results.boxes.id.int().cpu().numpy()
|
||||
if ultralytics_results.boxes.id is not None
|
||||
else None,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@@ -235,10 +279,10 @@ class Detections:
|
||||
Also supported for [mmyolo](https://github.com/open-mmlab/mmyolo)
|
||||
|
||||
Args:
|
||||
mmdet_results (mmdet.structures.DetDataSample): The output Results instance from MMDetection
|
||||
mmdet_results (mmdet.structures.DetDataSample): The output Results instance from MMDetection
|
||||
|
||||
Returns:
|
||||
Detections: A new Detections object.
|
||||
Detections: A new Detections object.
|
||||
|
||||
Example:
|
||||
```python
|
||||
@@ -383,6 +427,42 @@ class Detections:
|
||||
|
||||
return Detections(xyxy=xywh_to_xyxy(boxes_xywh=xywh), mask=mask)
|
||||
|
||||
@classmethod
|
||||
def from_paddledet(cls, paddledet_result) -> Detections:
|
||||
"""
|
||||
Creates a Detections instance from [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) inference result.
|
||||
|
||||
Args:
|
||||
paddledet_result (List[dict]): The output Results instance from SAM
|
||||
|
||||
Returns:
|
||||
Detections: A new Detections object.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
>>> import paddle
|
||||
>>> from ppdet.engine import Trainer
|
||||
>>> from ppdet.core.workspace import load_config
|
||||
|
||||
>>> weights = (...)
|
||||
>>> config = (...)
|
||||
|
||||
>>> cfg = load_config(config)
|
||||
>>> trainer = Trainer(cfg, mode='test')
|
||||
>>> trainer.load_weights(weights)
|
||||
|
||||
>>> paddledet_result = trainer.predict([images])[0]
|
||||
|
||||
>>> detections = sv.Detections.from_paddledet(paddledet_result=paddledet_result)
|
||||
```
|
||||
"""
|
||||
return cls(
|
||||
xyxy=paddledet_result["bbox"][:, 2:6],
|
||||
confidence=paddledet_result["bbox"][:, 1],
|
||||
class_id=paddledet_result["bbox"][:, 0].astype(int),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def empty(cls) -> Detections:
|
||||
"""
|
||||
|
||||
@@ -17,7 +17,7 @@ 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), dtype=np.uint8)
|
||||
mask = np.zeros((height, width))
|
||||
cv2.fillPoly(mask, [polygon], color=1)
|
||||
return mask
|
||||
|
||||
@@ -260,7 +260,7 @@ def approximate_polygon(
|
||||
return np.squeeze(approximated_points, axis=1)
|
||||
|
||||
|
||||
def extract_yolov8_masks(yolov8_results) -> Optional[np.ndarray]:
|
||||
def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:
|
||||
if not yolov8_results.masks:
|
||||
return None
|
||||
|
||||
@@ -288,7 +288,10 @@ def extract_yolov8_masks(yolov8_results) -> Optional[np.ndarray]:
|
||||
for i in range(masks.shape[0]):
|
||||
mask = masks[i]
|
||||
mask = mask[top:bottom, left:right]
|
||||
mask = cv2.resize(mask, (orig_shape[1], orig_shape[0]))
|
||||
|
||||
if mask.shape != orig_shape:
|
||||
mask = cv2.resize(mask, (orig_shape[1], orig_shape[0]))
|
||||
|
||||
mask_maps.append(mask)
|
||||
|
||||
return np.asarray(mask_maps, dtype=bool)
|
||||
|
||||
@@ -12,6 +12,59 @@ from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import box_iou_batch
|
||||
|
||||
|
||||
def detections_to_tensor(
|
||||
detections: Detections, with_confidence: bool = False
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Convert Supervision Detections to numpy tensors for further computation
|
||||
Args:
|
||||
detections (sv.Detections): Detections/Targets in the format of sv.Detections
|
||||
with_confidence (bool): Whether to include confidence in the tensor
|
||||
Returns:
|
||||
(np.ndarray): Detections as numpy tensors as in (xyxy, class_id, confidence) order
|
||||
"""
|
||||
if detections.class_id is None:
|
||||
raise ValueError(
|
||||
"ConfusionMatrix can only be calculated for Detections with class_id"
|
||||
)
|
||||
|
||||
arrays_to_concat = [detections.xyxy, np.expand_dims(detections.class_id, 1)]
|
||||
|
||||
if with_confidence:
|
||||
if detections.confidence is None:
|
||||
raise ValueError(
|
||||
"ConfusionMatrix can only be calculated for Detections with confidence"
|
||||
)
|
||||
arrays_to_concat.append(np.expand_dims(detections.confidence, 1))
|
||||
|
||||
return np.concatenate(arrays_to_concat, axis=1)
|
||||
|
||||
|
||||
def validate_input_tensors(predictions: List[np.ndarray], targets: List[np.ndarray]):
|
||||
"""
|
||||
Checks for shape consistency of input tensors.
|
||||
"""
|
||||
if len(predictions) != len(targets):
|
||||
raise ValueError(
|
||||
f"Number of predictions ({len(predictions)}) and targets ({len(targets)}) must be equal."
|
||||
)
|
||||
if len(predictions) > 0:
|
||||
if not isinstance(predictions[0], np.ndarray) or not isinstance(
|
||||
targets[0], np.ndarray
|
||||
):
|
||||
raise ValueError(
|
||||
f"Predictions and targets must be lists of numpy arrays. Got {type(predictions[0])} and {type(targets[0])} instead."
|
||||
)
|
||||
if predictions[0].shape[1] != 6:
|
||||
raise ValueError(
|
||||
f"Predictions must have shape (N, 6). Got {predictions[0].shape} instead."
|
||||
)
|
||||
if targets[0].shape[1] != 5:
|
||||
raise ValueError(
|
||||
f"Targets must have shape (N, 5). Got {targets[0].shape} instead."
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConfusionMatrix:
|
||||
"""
|
||||
@@ -85,11 +138,9 @@ class ConfusionMatrix:
|
||||
target_tensors = []
|
||||
for prediction, target in zip(predictions, targets):
|
||||
prediction_tensors.append(
|
||||
ConfusionMatrix.detections_to_tensor(prediction, with_confidence=True)
|
||||
)
|
||||
target_tensors.append(
|
||||
ConfusionMatrix.detections_to_tensor(target, with_confidence=False)
|
||||
detections_to_tensor(prediction, with_confidence=True)
|
||||
)
|
||||
target_tensors.append(detections_to_tensor(target, with_confidence=False))
|
||||
return cls.from_tensors(
|
||||
predictions=prediction_tensors,
|
||||
targets=target_tensors,
|
||||
@@ -98,26 +149,6 @@ class ConfusionMatrix:
|
||||
iou_threshold=iou_threshold,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def detections_to_tensor(
|
||||
detections: Detections, with_confidence: bool = False
|
||||
) -> np.ndarray:
|
||||
if detections.class_id is None:
|
||||
raise ValueError(
|
||||
"ConfusionMatrix can only be calculated for Detections with class_id"
|
||||
)
|
||||
|
||||
arrays_to_concat = [detections.xyxy, np.expand_dims(detections.class_id, 1)]
|
||||
|
||||
if with_confidence:
|
||||
if detections.confidence is None:
|
||||
raise ValueError(
|
||||
"ConfusionMatrix can only be calculated for Detections with confidence"
|
||||
)
|
||||
arrays_to_concat.append(np.expand_dims(detections.confidence, 1))
|
||||
|
||||
return np.concatenate(arrays_to_concat, axis=1)
|
||||
|
||||
@classmethod
|
||||
def from_tensors(
|
||||
cls,
|
||||
@@ -184,7 +215,7 @@ class ConfusionMatrix:
|
||||
])
|
||||
```
|
||||
"""
|
||||
cls._validate_input_tensors(predictions, targets)
|
||||
validate_input_tensors(predictions, targets)
|
||||
|
||||
num_classes = len(classes)
|
||||
matrix = np.zeros((num_classes + 1, num_classes + 1))
|
||||
@@ -203,33 +234,6 @@ class ConfusionMatrix:
|
||||
iou_threshold=iou_threshold,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _validate_input_tensors(
|
||||
cls, predictions: List[np.ndarray], targets: List[np.ndarray]
|
||||
):
|
||||
"""
|
||||
Checks for shape consistency of input tensors.
|
||||
"""
|
||||
if len(predictions) != len(targets):
|
||||
raise ValueError(
|
||||
f"Number of predictions ({len(predictions)}) and targets ({len(targets)}) must be equal."
|
||||
)
|
||||
if len(predictions) > 0:
|
||||
if not isinstance(predictions[0], np.ndarray) or not isinstance(
|
||||
targets[0], np.ndarray
|
||||
):
|
||||
raise ValueError(
|
||||
f"Predictions and targets must be lists of numpy arrays. Got {type(predictions[0])} and {type(targets[0])} instead."
|
||||
)
|
||||
if predictions[0].shape[1] != 6:
|
||||
raise ValueError(
|
||||
f"Predictions must have shape (N, 6). Got {predictions[0].shape} instead."
|
||||
)
|
||||
if targets[0].shape[1] != 5:
|
||||
raise ValueError(
|
||||
f"Targets must have shape (N, 5). Got {targets[0].shape} instead."
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def evaluate_detection_batch(
|
||||
predictions: np.ndarray,
|
||||
@@ -242,8 +246,8 @@ class ConfusionMatrix:
|
||||
Calculate confusion matrix for a batch of detections for a single image.
|
||||
|
||||
Args:
|
||||
predictions (List[np.ndarray]): Each element of the list describes a single image and has `shape = (M, 6)` where `M` is the number of detected objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class, conf)` format.
|
||||
targets (List[np.ndarray]): Each element of the list describes a single image and has `shape = (N, 5)` where `N` is the number of ground-truth objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class)` format.
|
||||
predictions (np.ndarray): Batch prediction. Describes a single image and has `shape = (M, 6)` where `M` is the number of detected objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class, conf)` format.
|
||||
targets (np.ndarray): Batch target labels. Describes a single image and has `shape = (N, 5)` where `N` is the number of ground-truth objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class)` format.
|
||||
num_classes (int): Number of classes.
|
||||
conf_threshold (float): Detection confidence threshold between `0` and `1`. Detections with lower confidence will be excluded.
|
||||
iou_threshold (float): Detection iou threshold between `0` and `1`. Detections with lower iou will be classified as `FP`.
|
||||
@@ -319,7 +323,7 @@ class ConfusionMatrix:
|
||||
iou_threshold: float = 0.5,
|
||||
) -> ConfusionMatrix:
|
||||
"""
|
||||
Create confusion matrix from dataset and callback function.
|
||||
Calculate confusion matrix from dataset and callback function.
|
||||
|
||||
Args:
|
||||
dataset (DetectionDataset): Object detection dataset used for evaluation.
|
||||
@@ -456,3 +460,333 @@ class ConfusionMatrix:
|
||||
save_path, dpi=250, facecolor=fig.get_facecolor(), transparent=True
|
||||
)
|
||||
return fig
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class MeanAveragePrecision:
|
||||
"""
|
||||
Mean Average Precision for object detection tasks.
|
||||
|
||||
Attributes:
|
||||
map50_95 (float): Mean Average Precision (mAP) calculated over IoU thresholds ranging from `0.50` to `0.95` with a step size of `0.05`.
|
||||
map50 (float): Mean Average Precision (mAP) calculated specifically at an IoU threshold of `0.50`.
|
||||
map75 (float): Mean Average Precision (mAP) calculated specifically at an IoU threshold of `0.75`.
|
||||
per_class_ap50_95 (np.ndarray): Average Precision (AP) values calculated over IoU thresholds ranging from `0.50` to `0.95` with a step size of `0.05`, provided for each individual class.
|
||||
"""
|
||||
|
||||
map50_95: float
|
||||
map50: float
|
||||
map75: float
|
||||
per_class_ap50_95: np.ndarray
|
||||
|
||||
@classmethod
|
||||
def from_detections(
|
||||
cls,
|
||||
predictions: List[Detections],
|
||||
targets: List[Detections],
|
||||
) -> MeanAveragePrecision:
|
||||
"""
|
||||
Calculate mean average precision based on predicted and ground-truth detections.
|
||||
|
||||
Args:
|
||||
targets (List[Detections]): Detections objects from ground-truth.
|
||||
predictions (List[Detections]): Detections objects predicted by the model.
|
||||
Returns:
|
||||
MeanAveragePrecision: New instance of ConfusionMatrix.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> targets = [
|
||||
... sv.Detections(...),
|
||||
... sv.Detections(...)
|
||||
... ]
|
||||
|
||||
>>> predictions = [
|
||||
... sv.Detections(...),
|
||||
... sv.Detections(...)
|
||||
... ]
|
||||
|
||||
>>> mean_average_precision = sv.MeanAveragePrecision.from_detections(
|
||||
... predictions=predictions,
|
||||
... targets=target,
|
||||
... )
|
||||
|
||||
>>> mean_average_precison.map50_95
|
||||
0.2899
|
||||
```
|
||||
"""
|
||||
prediction_tensors = []
|
||||
target_tensors = []
|
||||
for prediction, target in zip(predictions, targets):
|
||||
prediction_tensors.append(
|
||||
detections_to_tensor(prediction, with_confidence=True)
|
||||
)
|
||||
target_tensors.append(detections_to_tensor(target, with_confidence=False))
|
||||
return cls.from_tensors(
|
||||
predictions=prediction_tensors,
|
||||
targets=target_tensors,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def benchmark(
|
||||
cls,
|
||||
dataset: DetectionDataset,
|
||||
callback: Callable[[np.ndarray], Detections],
|
||||
) -> MeanAveragePrecision:
|
||||
"""
|
||||
Calculate mean average precision from dataset and callback function.
|
||||
|
||||
Args:
|
||||
dataset (DetectionDataset): Object detection dataset used for evaluation.
|
||||
callback (Callable[[np.ndarray], Detections]): Function that takes an image as input and returns Detections object.
|
||||
Returns:
|
||||
MeanAveragePrecision: New instance of MeanAveragePrecision.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
>>> from ultralytics import YOLO
|
||||
|
||||
>>> dataset = sv.DetectionDataset.from_yolo(...)
|
||||
|
||||
>>> model = YOLO(...)
|
||||
>>> def callback(image: np.ndarray) -> sv.Detections:
|
||||
... result = model(image)[0]
|
||||
... return sv.Detections.from_yolov8(result)
|
||||
|
||||
>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
|
||||
... dataset = dataset,
|
||||
... callback = callback
|
||||
... )
|
||||
|
||||
>>> mean_average_precision.map50_95
|
||||
0.433
|
||||
```
|
||||
"""
|
||||
predictions, targets = [], []
|
||||
for img_name, img in dataset.images.items():
|
||||
predictions_batch = callback(img)
|
||||
predictions.append(predictions_batch)
|
||||
targets_batch = dataset.annotations[img_name]
|
||||
targets.append(targets_batch)
|
||||
return cls.from_detections(
|
||||
predictions=predictions,
|
||||
targets=targets,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_tensors(
|
||||
cls,
|
||||
predictions: List[np.ndarray],
|
||||
targets: List[np.ndarray],
|
||||
) -> MeanAveragePrecision:
|
||||
"""
|
||||
Calculate Mean Average Precision based on predicted and ground-truth detections at different threshold.
|
||||
|
||||
Args:
|
||||
predictions (List[np.ndarray]): Each element of the list describes a single image and has `shape = (M, 6)` where `M` is the number of detected objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class, conf)` format.
|
||||
targets (List[np.ndarray]): Each element of the list describes a single image and has `shape = (N, 5)` where `N` is the number of ground-truth objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class)` format.
|
||||
Returns:
|
||||
MeanAveragePrecision: New instance of MeanAveragePrecision.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> targets = (
|
||||
... [
|
||||
... array(
|
||||
... [
|
||||
... [0.0, 0.0, 3.0, 3.0, 1],
|
||||
... [2.0, 2.0, 5.0, 5.0, 1],
|
||||
... [6.0, 1.0, 8.0, 3.0, 2],
|
||||
... ]
|
||||
... ),
|
||||
... array([1.0, 1.0, 2.0, 2.0, 2]),
|
||||
... ]
|
||||
... )
|
||||
|
||||
>>> predictions = [
|
||||
... array(
|
||||
... [
|
||||
... [0.0, 0.0, 3.0, 3.0, 1, 0.9],
|
||||
... [0.1, 0.1, 3.0, 3.0, 0, 0.9],
|
||||
... [6.0, 1.0, 8.0, 3.0, 1, 0.8],
|
||||
... [1.0, 6.0, 2.0, 7.0, 1, 0.8],
|
||||
... ]
|
||||
... ),
|
||||
... array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
|
||||
... ]
|
||||
|
||||
>>> mean_average_precison = sv.MeanAveragePrecision.from_tensors(
|
||||
... predictions=predictions,
|
||||
... targets=targets,
|
||||
... )
|
||||
|
||||
>>> mean_average_precison.map50_95
|
||||
0.2899
|
||||
```
|
||||
"""
|
||||
validate_input_tensors(predictions, targets)
|
||||
iou_thresholds = np.linspace(0.5, 0.95, 10)
|
||||
stats = []
|
||||
|
||||
# Gather matching stats for predictions and targets
|
||||
for true_objs, predicted_objs in zip(targets, predictions):
|
||||
if predicted_objs.shape[0] == 0:
|
||||
if true_objs.shape[0]:
|
||||
stats.append(
|
||||
(
|
||||
np.zeros((0, iou_thresholds.size), dtype=bool),
|
||||
*np.zeros((2, 0)),
|
||||
true_objs[:, 4],
|
||||
)
|
||||
)
|
||||
continue
|
||||
|
||||
if true_objs.shape[0]:
|
||||
matches = cls._match_detection_batch(
|
||||
predicted_objs, true_objs, iou_thresholds
|
||||
)
|
||||
stats.append(
|
||||
(
|
||||
matches,
|
||||
predicted_objs[:, 5],
|
||||
predicted_objs[:, 4],
|
||||
true_objs[:, 4],
|
||||
)
|
||||
)
|
||||
|
||||
# Compute average precisions if any matches exist
|
||||
if stats:
|
||||
concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
|
||||
average_precisions = cls._average_precisions_per_class(*concatenated_stats)
|
||||
map50 = average_precisions[:, 0].mean()
|
||||
map75 = average_precisions[:, 5].mean()
|
||||
map50_95 = average_precisions.mean()
|
||||
else:
|
||||
map50, map75, map50_95 = 0, 0, 0
|
||||
average_precisions = []
|
||||
|
||||
return cls(
|
||||
map50_95=map50_95,
|
||||
map50=map50,
|
||||
map75=map75,
|
||||
per_class_ap50_95=average_precisions,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def compute_average_precision(recall: np.ndarray, precision: np.ndarray) -> float:
|
||||
"""
|
||||
Compute the average precision using 101-point interpolation (COCO), given the recall and precision curves.
|
||||
|
||||
Args:
|
||||
recall (np.ndarray): The recall curve.
|
||||
precision (np.ndarray): The precision curve.
|
||||
|
||||
Returns:
|
||||
float: Average precision.
|
||||
"""
|
||||
extended_recall = np.concatenate(([0.0], recall, [1.0]))
|
||||
extended_precision = np.concatenate(([1.0], precision, [0.0]))
|
||||
max_accumulated_precision = np.flip(
|
||||
np.maximum.accumulate(np.flip(extended_precision))
|
||||
)
|
||||
interpolated_recall_levels = np.linspace(0, 1, 101)
|
||||
interpolated_precision = np.interp(
|
||||
interpolated_recall_levels, extended_recall, max_accumulated_precision
|
||||
)
|
||||
average_precision = np.trapz(interpolated_precision, interpolated_recall_levels)
|
||||
return average_precision
|
||||
|
||||
@staticmethod
|
||||
def _match_detection_batch(
|
||||
predictions: np.ndarray, targets: np.ndarray, iou_thresholds: np.ndarray
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Match predictions with target labels based on IoU levels.
|
||||
|
||||
Args:
|
||||
predictions (np.ndarray): Batch prediction. Describes a single image and has `shape = (M, 6)` where `M` is the number of detected objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class, conf)` format.
|
||||
targets (np.ndarray): Batch target labels. Describes a single image and has `shape = (N, 5)` where `N` is the number of ground-truth objects. Each row is expected to be in `(x_min, y_min, x_max, y_max, class)` format.
|
||||
iou_thresholds (np.ndarray): Array contains different IoU thresholds.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Matched prediction with target labels result.
|
||||
"""
|
||||
num_predictions, num_iou_levels = predictions.shape[0], iou_thresholds.shape[0]
|
||||
correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
|
||||
iou = box_iou_batch(targets[:, :4], predictions[:, :4])
|
||||
correct_class = targets[:, 4:5] == predictions[:, 4]
|
||||
|
||||
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 _average_precisions_per_class(
|
||||
matches: np.ndarray,
|
||||
prediction_confidence: np.ndarray,
|
||||
prediction_class_ids: np.ndarray,
|
||||
true_class_ids: np.ndarray,
|
||||
eps: float = 1e-16,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Compute the average precision, given the recall and precision curves.
|
||||
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
|
||||
|
||||
Args:
|
||||
matches (np.ndarray): True positives.
|
||||
prediction_confidence (np.ndarray): Objectness value from 0-1.
|
||||
prediction_class_ids (np.ndarray): Predicted object classes.
|
||||
true_class_ids (np.ndarray): True object classes.
|
||||
eps (float, optional): Small value to prevent division by zero.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Average precision for different IoU levels.
|
||||
"""
|
||||
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)
|
||||
num_classes = unique_classes.shape[0]
|
||||
|
||||
average_precisions = np.zeros((num_classes, matches.shape[1]))
|
||||
|
||||
for class_idx, class_id in enumerate(unique_classes):
|
||||
is_class = prediction_class_ids == class_id
|
||||
total_true = class_counts[class_idx]
|
||||
total_prediction = is_class.sum()
|
||||
|
||||
if total_prediction == 0 or total_true == 0:
|
||||
continue
|
||||
|
||||
false_positives = (1 - matches[is_class]).cumsum(0)
|
||||
true_positives = matches[is_class].cumsum(0)
|
||||
recall = true_positives / (total_true + eps)
|
||||
precision = true_positives / (true_positives + false_positives)
|
||||
|
||||
for iou_level_idx in range(matches.shape[1]):
|
||||
average_precisions[
|
||||
class_idx, iou_level_idx
|
||||
] = MeanAveragePrecision.compute_average_precision(
|
||||
recall[:, iou_level_idx], precision[:, iou_level_idx]
|
||||
)
|
||||
|
||||
return average_precisions
|
||||
Whitespace-only changes.
Whitespace-only changes.
@@ -0,0 +1,55 @@
|
||||
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
|
||||
|
||||
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
|
||||
@@ -0,0 +1,466 @@
|
||||
from typing import List, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from supervision.detection.core import Detections
|
||||
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()
|
||||
|
||||
def __init__(self, tlwh, score, class_ids):
|
||||
# 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
|
||||
|
||||
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 = np.asarray([st.mean.copy() for st in stracks])
|
||||
multi_covariance = np.asarray([st.covariance for st in stracks])
|
||||
for i, st in enumerate(stracks):
|
||||
if st.state != TrackState.Tracked:
|
||||
multi_mean[i][7] = 0
|
||||
multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(
|
||||
multi_mean, 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.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
|
||||
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.is_activated = True
|
||||
self.frame_id = frame_id
|
||||
if new_id:
|
||||
self.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
|
||||
self.is_activated = True
|
||||
|
||||
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 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.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],
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class ByteTrack:
|
||||
"""
|
||||
Initialize the ByteTrack object.
|
||||
|
||||
Parameters:
|
||||
track_thresh (float, optional): Detection confidence threshold for track activation.
|
||||
track_buffer (int, optional): Number of frames to buffer when a track is lost.
|
||||
match_thresh (float, optional): Threshold for matching tracks with detections.
|
||||
frame_rate (int, optional): The frame rate of the video.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
track_thresh: float = 0.25,
|
||||
track_buffer: int = 30,
|
||||
match_thresh: float = 0.8,
|
||||
frame_rate: int = 30,
|
||||
):
|
||||
self.track_thresh = track_thresh
|
||||
self.match_thresh = match_thresh
|
||||
|
||||
self.frame_id = 0
|
||||
self.det_thresh = self.track_thresh + 0.1
|
||||
self.max_time_lost = int(frame_rate / 30.0 * track_buffer)
|
||||
self.kalman_filter = KalmanFilter()
|
||||
|
||||
self.tracked_tracks: List[STrack] = []
|
||||
self.lost_tracks: List[STrack] = []
|
||||
self.removed_tracks: List[STrack] = []
|
||||
|
||||
def update_with_detections(self, detections: Detections) -> Detections:
|
||||
"""
|
||||
Updates the tracker with the provided detections and returns the updated detection results.
|
||||
|
||||
Parameters:
|
||||
detections: The new detections to update with.
|
||||
Returns:
|
||||
Detection: The updated detection results that now include tracking IDs.
|
||||
Example:
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
>>> from ultralytics import YOLO
|
||||
|
||||
>>> model = YOLO(...)
|
||||
>>> byte_tracker = sv.ByteTrack()
|
||||
>>> annotator = sv.BoxAnnotator()
|
||||
|
||||
>>> def callback(frame: np.ndarray, index: int) -> np.ndarray:
|
||||
... results = model(frame)[0]
|
||||
... detections = sv.Detections.from_yolov8(results)
|
||||
... detections = byte_tracker.update_from_detections(detections=detections)
|
||||
... labels = [
|
||||
... f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
|
||||
... for _, _, confidence, class_id, tracker_id
|
||||
... in detections
|
||||
... ]
|
||||
... return annotator.annotate(scene=frame.copy(), detections=detections, labels=labels)
|
||||
|
||||
>>> sv.process_video(
|
||||
... source_path='...',
|
||||
... target_path='...',
|
||||
... callback=callback
|
||||
... )
|
||||
```
|
||||
"""
|
||||
|
||||
tracks = self.update_with_tensors(
|
||||
tensors=detections2boxes(detections=detections)
|
||||
)
|
||||
detections = Detections.empty()
|
||||
if len(tracks) > 0:
|
||||
detections.xyxy = np.array(
|
||||
[track.tlbr for track in tracks], dtype=np.float32
|
||||
)
|
||||
detections.class_id = np.array(
|
||||
[int(t.class_ids) for t in tracks], dtype=int
|
||||
)
|
||||
detections.tracker_id = np.array(
|
||||
[int(t.track_id) for t in tracks], dtype=int
|
||||
)
|
||||
detections.confidence = np.array(
|
||||
[t.score for t in tracks], dtype=np.float32
|
||||
)
|
||||
|
||||
return detections
|
||||
|
||||
def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
|
||||
"""
|
||||
Updates the tracker with the provided tensors and returns the updated tracks.
|
||||
|
||||
Parameters:
|
||||
tensors: The new tensors to update with.
|
||||
|
||||
Returns:
|
||||
List[STrack]: Updated tracks.
|
||||
"""
|
||||
self.frame_id += 1
|
||||
activated_starcks = []
|
||||
refind_stracks = []
|
||||
lost_stracks = []
|
||||
removed_stracks = []
|
||||
|
||||
class_ids = tensors[:, 5]
|
||||
scores = tensors[:, 4]
|
||||
bboxes = tensors[:, :4]
|
||||
|
||||
remain_inds = scores > self.track_thresh
|
||||
inds_low = scores > 0.1
|
||||
inds_high = scores < self.track_thresh
|
||||
|
||||
inds_second = np.logical_and(inds_low, inds_high)
|
||||
dets_second = bboxes[inds_second]
|
||||
dets = bboxes[remain_inds]
|
||||
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)
|
||||
for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
|
||||
]
|
||||
else:
|
||||
detections = []
|
||||
|
||||
""" Add newly detected tracklets to tracked_stracks"""
|
||||
unconfirmed = []
|
||||
tracked_stracks = [] # type: list[STrack]
|
||||
for track in self.tracked_tracks:
|
||||
if not track.is_activated:
|
||||
unconfirmed.append(track)
|
||||
else:
|
||||
tracked_stracks.append(track)
|
||||
|
||||
""" 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)
|
||||
dists = matching.iou_distance(strack_pool, detections)
|
||||
|
||||
dists = matching.fuse_score(dists, detections)
|
||||
matches, u_track, u_detection = matching.linear_assignment(
|
||||
dists, thresh=self.match_thresh
|
||||
)
|
||||
|
||||
for itracked, idet in matches:
|
||||
track = strack_pool[itracked]
|
||||
det = detections[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
""" Step 3: Second association, with low score detection boxes"""
|
||||
# association the untrack to the low score detections
|
||||
if len(dets_second) > 0:
|
||||
"""Detections"""
|
||||
detections_second = [
|
||||
STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
|
||||
for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
|
||||
]
|
||||
else:
|
||||
detections_second = []
|
||||
r_tracked_stracks = [
|
||||
strack_pool[i]
|
||||
for i in u_track
|
||||
if strack_pool[i].state == TrackState.Tracked
|
||||
]
|
||||
dists = matching.iou_distance(r_tracked_stracks, detections_second)
|
||||
matches, u_track, u_detection_second = matching.linear_assignment(
|
||||
dists, thresh=0.5
|
||||
)
|
||||
for itracked, idet in matches:
|
||||
track = r_tracked_stracks[itracked]
|
||||
det = detections_second[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(det, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
for it in u_track:
|
||||
track = r_tracked_stracks[it]
|
||||
if not track.state == TrackState.Lost:
|
||||
track.mark_lost()
|
||||
lost_stracks.append(track)
|
||||
|
||||
"""Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
|
||||
detections = [detections[i] for i in u_detection]
|
||||
dists = matching.iou_distance(unconfirmed, detections)
|
||||
|
||||
dists = matching.fuse_score(dists, detections)
|
||||
matches, u_unconfirmed, u_detection = matching.linear_assignment(
|
||||
dists, thresh=0.7
|
||||
)
|
||||
for itracked, idet in matches:
|
||||
unconfirmed[itracked].update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(unconfirmed[itracked])
|
||||
for it in u_unconfirmed:
|
||||
track = unconfirmed[it]
|
||||
track.mark_removed()
|
||||
removed_stracks.append(track)
|
||||
|
||||
""" Step 4: Init new stracks"""
|
||||
for inew in u_detection:
|
||||
track = detections[inew]
|
||||
if track.score < self.det_thresh:
|
||||
continue
|
||||
track.activate(self.kalman_filter, self.frame_id)
|
||||
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()
|
||||
removed_stracks.append(track)
|
||||
|
||||
self.tracked_tracks = [
|
||||
t for t in self.tracked_tracks if t.state == TrackState.Tracked
|
||||
]
|
||||
self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
|
||||
self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
|
||||
self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
|
||||
self.lost_tracks.extend(lost_stracks)
|
||||
self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
|
||||
self.removed_tracks.extend(removed_stracks)
|
||||
self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
|
||||
self.tracked_tracks, self.lost_tracks
|
||||
)
|
||||
output_stracks = [track for track in self.tracked_tracks if track.is_activated]
|
||||
|
||||
return output_stracks
|
||||
|
||||
|
||||
def joint_tracks(
|
||||
track_list_a: List[STrack], track_list_b: List[STrack]
|
||||
) -> List[STrack]:
|
||||
"""
|
||||
Joins two lists of tracks, ensuring that the resulting list does not
|
||||
contain tracks with duplicate track_id values.
|
||||
|
||||
Parameters:
|
||||
track_list_a: First list of tracks (with track_id attribute).
|
||||
track_list_b: Second list of tracks (with track_id attribute).
|
||||
|
||||
Returns:
|
||||
Combined list of tracks from track_list_a and track_list_b without duplicate track_id values.
|
||||
"""
|
||||
seen_track_ids = set()
|
||||
result = []
|
||||
|
||||
for track in track_list_a + track_list_b:
|
||||
if track.track_id not in seen_track_ids:
|
||||
seen_track_ids.add(track.track_id)
|
||||
result.append(track)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:
|
||||
"""
|
||||
Returns a list of tracks from track_list_a after removing any tracks
|
||||
that share the same track_id with tracks in track_list_b.
|
||||
|
||||
Parameters:
|
||||
track_list_a: List of tracks (with track_id attribute).
|
||||
track_list_b: List of tracks (with track_id attribute) to be subtracted from track_list_a.
|
||||
Returns:
|
||||
List of remaining tracks from track_list_a after subtraction.
|
||||
"""
|
||||
tracks = {track.track_id: track for track in track_list_a}
|
||||
track_ids_b = {track.track_id for track in track_list_b}
|
||||
|
||||
for track_id in track_ids_b:
|
||||
tracks.pop(track_id, None)
|
||||
|
||||
return list(tracks.values())
|
||||
|
||||
|
||||
def remove_duplicate_tracks(tracks_a: List, tracks_b: List) -> Tuple[List, List]:
|
||||
pairwise_distance = matching.iou_distance(tracks_a, tracks_b)
|
||||
matching_pairs = np.where(pairwise_distance < 0.15)
|
||||
|
||||
duplicates_a, duplicates_b = set(), set()
|
||||
for track_index_a, track_index_b in zip(*matching_pairs):
|
||||
time_a = tracks_a[track_index_a].frame_id - tracks_a[track_index_a].start_frame
|
||||
time_b = tracks_b[track_index_b].frame_id - tracks_b[track_index_b].start_frame
|
||||
if time_a > time_b:
|
||||
duplicates_b.add(track_index_b)
|
||||
else:
|
||||
duplicates_a.add(track_index_a)
|
||||
|
||||
result_a = [
|
||||
track for index, track in enumerate(tracks_a) if index not in duplicates_a
|
||||
]
|
||||
result_b = [
|
||||
track for index, track in enumerate(tracks_b) if index not in duplicates_b
|
||||
]
|
||||
|
||||
return result_a, result_b
|
||||
@@ -0,0 +1,190 @@
|
||||
from typing import Tuple
|
||||
|
||||
import numpy as np
|
||||
import scipy.linalg
|
||||
|
||||
|
||||
class KalmanFilter:
|
||||
"""
|
||||
A simple Kalman filter for tracking bounding boxes in image space.
|
||||
|
||||
The 8-dimensional state space
|
||||
|
||||
x, y, a, h, vx, vy, va, vh
|
||||
|
||||
contains the bounding box center position (x, y), aspect ratio a, height h,
|
||||
and their respective velocities.
|
||||
|
||||
Object motion follows a constant velocity model. The bounding box location
|
||||
(x, y, a, h) is taken as direct observation of the state space (linear
|
||||
observation model).
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
ndim, dt = 4, 1.0
|
||||
|
||||
self._motion_mat = np.eye(2 * ndim, 2 * ndim)
|
||||
for i in range(ndim):
|
||||
self._motion_mat[i, ndim + i] = dt
|
||||
self._update_mat = np.eye(ndim, 2 * ndim)
|
||||
self._std_weight_position = 1.0 / 20
|
||||
self._std_weight_velocity = 1.0 / 160
|
||||
|
||||
def initiate(self, measurement: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Create track from an unassociated measurement.
|
||||
|
||||
Args:
|
||||
measurement (ndarray): Bounding box coordinates (x, y, a, h) with center position (x, y), aspect ratio a, and height h.
|
||||
|
||||
Returns:
|
||||
Tuple[ndarray, ndarray]: Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track. Unobserved velocities are initialized to 0 mean.
|
||||
"""
|
||||
mean_pos = measurement
|
||||
mean_vel = np.zeros_like(mean_pos)
|
||||
mean = np.r_[mean_pos, mean_vel]
|
||||
|
||||
std = [
|
||||
2 * self._std_weight_position * measurement[3],
|
||||
2 * self._std_weight_position * measurement[3],
|
||||
1e-2,
|
||||
2 * self._std_weight_position * measurement[3],
|
||||
10 * self._std_weight_velocity * measurement[3],
|
||||
10 * self._std_weight_velocity * measurement[3],
|
||||
1e-5,
|
||||
10 * self._std_weight_velocity * measurement[3],
|
||||
]
|
||||
covariance = np.diag(np.square(std))
|
||||
return mean, covariance
|
||||
|
||||
def predict(
|
||||
self, mean: np.ndarray, covariance: np.ndarray
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Run Kalman filter prediction step.
|
||||
|
||||
Args:
|
||||
mean (ndarray): The 8 dimensional mean vector of the object state at the previous time step.
|
||||
covariance (ndarray): The 8x8 dimensional covariance matrix of the object state at the previous time step.
|
||||
|
||||
Returns:
|
||||
Tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
|
||||
"""
|
||||
std_pos = [
|
||||
self._std_weight_position * mean[3],
|
||||
self._std_weight_position * mean[3],
|
||||
1e-2,
|
||||
self._std_weight_position * mean[3],
|
||||
]
|
||||
std_vel = [
|
||||
self._std_weight_velocity * mean[3],
|
||||
self._std_weight_velocity * mean[3],
|
||||
1e-5,
|
||||
self._std_weight_velocity * mean[3],
|
||||
]
|
||||
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
|
||||
|
||||
mean = np.dot(mean, self._motion_mat.T)
|
||||
covariance = (
|
||||
np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T))
|
||||
+ motion_cov
|
||||
)
|
||||
|
||||
return mean, covariance
|
||||
|
||||
def project(
|
||||
self, mean: np.ndarray, covariance: np.ndarray
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Project state distribution to measurement space.
|
||||
|
||||
Args:
|
||||
mean (ndarray): The state's mean vector (8 dimensional array).
|
||||
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
|
||||
|
||||
Returns:
|
||||
Tuple[ndarray, ndarray]: Returns the projected mean and covariance matrix of the given state estimate.
|
||||
"""
|
||||
std = [
|
||||
self._std_weight_position * mean[3],
|
||||
self._std_weight_position * mean[3],
|
||||
1e-1,
|
||||
self._std_weight_position * mean[3],
|
||||
]
|
||||
innovation_cov = np.diag(np.square(std))
|
||||
|
||||
mean = np.dot(self._update_mat, mean)
|
||||
covariance = np.linalg.multi_dot(
|
||||
(self._update_mat, covariance, self._update_mat.T)
|
||||
)
|
||||
return mean, covariance + innovation_cov
|
||||
|
||||
def multi_predict(
|
||||
self, mean: np.ndarray, covariance: np.ndarray
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Run Kalman filter prediction step (Vectorized version).
|
||||
|
||||
Args:
|
||||
mean (ndarray): The Nx8 dimensional mean matrix of the object states at the previous time step.
|
||||
covariance (ndarray): The Nx8x8 dimensional covariance matrices of the object states at the previous time step.
|
||||
|
||||
Returns:
|
||||
Tuple[ndarray, ndarray]: Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
|
||||
"""
|
||||
std_pos = [
|
||||
self._std_weight_position * mean[:, 3],
|
||||
self._std_weight_position * mean[:, 3],
|
||||
1e-2 * np.ones_like(mean[:, 3]),
|
||||
self._std_weight_position * mean[:, 3],
|
||||
]
|
||||
std_vel = [
|
||||
self._std_weight_velocity * mean[:, 3],
|
||||
self._std_weight_velocity * mean[:, 3],
|
||||
1e-5 * np.ones_like(mean[:, 3]),
|
||||
self._std_weight_velocity * mean[:, 3],
|
||||
]
|
||||
sqr = np.square(np.r_[std_pos, std_vel]).T
|
||||
|
||||
motion_cov = []
|
||||
for i in range(len(mean)):
|
||||
motion_cov.append(np.diag(sqr[i]))
|
||||
motion_cov = np.asarray(motion_cov)
|
||||
|
||||
mean = np.dot(mean, self._motion_mat.T)
|
||||
left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
|
||||
covariance = np.dot(left, self._motion_mat.T) + motion_cov
|
||||
|
||||
return mean, covariance
|
||||
|
||||
def update(
|
||||
self, mean: np.ndarray, covariance: np.ndarray, measurement: np.ndarray
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Run Kalman filter correction step.
|
||||
|
||||
Args:
|
||||
mean (ndarray): The predicted state's mean vector (8 dimensional).
|
||||
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
|
||||
measurement (ndarray): The 4-dimensional measurement vector (x, y, a, h), where (x, y) is the center position, a the aspect ratio, and h the height of the bounding box.
|
||||
|
||||
Returns:
|
||||
Tuple[ndarray, ndarray]: Returns the measurement-corrected state distribution.
|
||||
"""
|
||||
projected_mean, projected_cov = self.project(mean, covariance)
|
||||
|
||||
chol_factor, lower = scipy.linalg.cho_factor(
|
||||
projected_cov, lower=True, check_finite=False
|
||||
)
|
||||
kalman_gain = scipy.linalg.cho_solve(
|
||||
(chol_factor, lower),
|
||||
np.dot(covariance, self._update_mat.T).T,
|
||||
check_finite=False,
|
||||
).T
|
||||
innovation = measurement - projected_mean
|
||||
|
||||
new_mean = mean + np.dot(innovation, kalman_gain.T)
|
||||
new_covariance = covariance - np.linalg.multi_dot(
|
||||
(kalman_gain, projected_cov, kalman_gain.T)
|
||||
)
|
||||
return new_mean, new_covariance
|
||||
@@ -0,0 +1,64 @@
|
||||
from typing import List, Tuple
|
||||
|
||||
import numpy as np
|
||||
from scipy.optimize import linear_sum_assignment
|
||||
|
||||
from supervision.detection.utils import box_iou_batch
|
||||
|
||||
|
||||
def indices_to_matches(
|
||||
cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
|
||||
) -> Tuple[np.ndarray, tuple, tuple]:
|
||||
matched_cost = cost_matrix[tuple(zip(*indices))]
|
||||
matched_mask = matched_cost <= thresh
|
||||
|
||||
matches = indices[matched_mask]
|
||||
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
|
||||
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
|
||||
return matches, unmatched_a, unmatched_b
|
||||
|
||||
|
||||
def linear_assignment(
|
||||
cost_matrix: np.ndarray, thresh: float
|
||||
) -> [np.ndarray, Tuple[int], Tuple[int, int]]:
|
||||
if cost_matrix.size == 0:
|
||||
return (
|
||||
np.empty((0, 2), dtype=int),
|
||||
tuple(range(cost_matrix.shape[0])),
|
||||
tuple(range(cost_matrix.shape[1])),
|
||||
)
|
||||
|
||||
cost_matrix[cost_matrix > thresh] = thresh + 1e-4
|
||||
row_ind, col_ind = linear_sum_assignment(cost_matrix)
|
||||
indices = np.column_stack((row_ind, col_ind))
|
||||
|
||||
return indices_to_matches(cost_matrix, indices, thresh)
|
||||
|
||||
|
||||
def iou_distance(atracks: List, btracks: List) -> np.ndarray:
|
||||
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
|
||||
len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
|
||||
):
|
||||
atlbrs = atracks
|
||||
btlbrs = btracks
|
||||
else:
|
||||
atlbrs = [track.tlbr for track in atracks]
|
||||
btlbrs = [track.tlbr for track in btracks]
|
||||
|
||||
_ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
|
||||
if _ious.size != 0:
|
||||
_ious = box_iou_batch(np.asarray(atlbrs), np.asarray(btlbrs))
|
||||
cost_matrix = 1 - _ious
|
||||
|
||||
return cost_matrix
|
||||
|
||||
|
||||
def fuse_score(cost_matrix: np.ndarray, detections: List) -> 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.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
|
||||
fuse_sim = iou_sim * det_scores
|
||||
fuse_cost = 1 - fuse_sim
|
||||
return fuse_cost
|
||||
@@ -163,15 +163,15 @@ def process_video(
|
||||
|
||||
Examples:
|
||||
```python
|
||||
>>> from supervision import process_video
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> def process_frame(scene: np.ndarray) -> np.ndarray:
|
||||
>>> def callback(scene: np.ndarray, index: int) -> np.ndarray:
|
||||
... ...
|
||||
|
||||
>>> process_video(
|
||||
... source_path='source_video.mp4',
|
||||
... target_path='target_video.mp4',
|
||||
... callback=process_frame
|
||||
... source_path='...',
|
||||
... target_path='...',
|
||||
... callback=callback
|
||||
... )
|
||||
```
|
||||
"""
|
||||
|
||||
@@ -0,0 +1,147 @@
|
||||
import xml.etree.ElementTree as ET
|
||||
from contextlib import ExitStack as DoesNotRaise
|
||||
from test.utils import mock_detections
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from supervision.dataset.formats.pascal_voc import (
|
||||
detections_from_xml_obj,
|
||||
object_to_pascal_voc,
|
||||
parse_polygon_points,
|
||||
)
|
||||
|
||||
|
||||
def are_xml_elements_equal(elem1, elem2):
|
||||
if (
|
||||
elem1.tag != elem2.tag
|
||||
or elem1.attrib != elem2.attrib
|
||||
or elem1.text != elem2.text
|
||||
or len(elem1) != len(elem2)
|
||||
):
|
||||
return False
|
||||
|
||||
for child1, child2 in zip(elem1, elem2):
|
||||
if not are_xml_elements_equal(child1, child2):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"xyxy, name, polygon, expected_result, exception",
|
||||
[
|
||||
(
|
||||
[0, 0, 10, 10],
|
||||
"test",
|
||||
None,
|
||||
ET.fromstring(
|
||||
"""<object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin><xmax>10</xmax><ymax>10</ymax></bndbox></object>"""
|
||||
),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
[0, 0, 10, 10],
|
||||
"test",
|
||||
[[0, 0], [10, 0], [10, 10], [0, 10]],
|
||||
ET.fromstring(
|
||||
"""<object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin><xmax>10</xmax><ymax>10</ymax></bndbox><polygon><x1>0</x1><y1>0</y1><x2>10</x2><y2>0</y2><x3>10</x3><y3>10</y3><x4>0</x4><y4>10</y4></polygon></object>"""
|
||||
),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_object_to_pascal_voc(
|
||||
xyxy: np.ndarray,
|
||||
name: str,
|
||||
polygon: Optional[np.ndarray],
|
||||
expected_result,
|
||||
exception: Exception,
|
||||
):
|
||||
with exception:
|
||||
result = object_to_pascal_voc(xyxy=xyxy, name=name, polygon=polygon)
|
||||
assert are_xml_elements_equal(result, expected_result)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"polygon_element, expected_result, exception",
|
||||
[
|
||||
(
|
||||
ET.fromstring(
|
||||
"""<polygon><x1>0</x1><y1>0</y1><x2>10</x2><y2>0</y2><x3>10</x3><y3>10</y3><x4>0</x4><y4>10</y4></polygon>"""
|
||||
),
|
||||
[[0, 0], [10, 0], [10, 10], [0, 10]],
|
||||
DoesNotRaise(),
|
||||
)
|
||||
],
|
||||
)
|
||||
def test_parse_polygon_points(
|
||||
polygon_element,
|
||||
expected_result: List[list],
|
||||
exception,
|
||||
):
|
||||
with exception:
|
||||
result = parse_polygon_points(polygon_element)
|
||||
assert result == expected_result
|
||||
|
||||
|
||||
ONE_CLASS_N_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin><xmax>10</xmax><ymax>10</ymax></bndbox></object><object><name>test</name><bndbox><xmin>10</xmin><ymin>10</ymin><xmax>20</xmax><ymax>20</ymax></bndbox></object></annotation>"""
|
||||
|
||||
|
||||
ONE_CLASS_ONE_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin><xmax>10</xmax><ymax>10</ymax></bndbox></object></annotation>"""
|
||||
|
||||
|
||||
N_CLASS_N_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin><xmax>10</xmax><ymax>10</ymax></bndbox></object><object><name>test</name><bndbox><xmin>20</xmin><ymin>30</ymin><xmax>30</xmax><ymax>40</ymax></bndbox></object><object><name>test2</name><bndbox><xmin>10</xmin><ymin>10</ymin><xmax>20</xmax><ymax>20</ymax></bndbox></object></annotation>"""
|
||||
|
||||
NO_DETECTIONS = """<annotation></annotation>"""
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"xml_string, classes, resolution_wh, force_masks, expected_result, exception",
|
||||
[
|
||||
(
|
||||
ONE_CLASS_ONE_BBOX,
|
||||
["test"],
|
||||
(100, 100),
|
||||
False,
|
||||
mock_detections(np.array([[0, 0, 10, 10]]), None, [0]),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
ONE_CLASS_N_BBOX,
|
||||
["test"],
|
||||
(100, 100),
|
||||
False,
|
||||
mock_detections(np.array([[0, 0, 10, 10], [10, 10, 20, 20]]), None, [0, 0]),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
N_CLASS_N_BBOX,
|
||||
["test", "test2"],
|
||||
(100, 100),
|
||||
False,
|
||||
mock_detections(
|
||||
np.array([[0, 0, 10, 10], [20, 30, 30, 40], [10, 10, 20, 20]]),
|
||||
None,
|
||||
[0, 0, 1],
|
||||
),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
NO_DETECTIONS,
|
||||
[],
|
||||
(100, 100),
|
||||
False,
|
||||
mock_detections(np.empty((0, 4)), None, []),
|
||||
DoesNotRaise(),
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_detections_from_xml_obj(
|
||||
xml_string, classes, resolution_wh, force_masks, expected_result, exception
|
||||
):
|
||||
with exception:
|
||||
root = ET.fromstring(xml_string)
|
||||
result, _ = detections_from_xml_obj(root, classes, resolution_wh, force_masks)
|
||||
assert result == expected_result
|
||||
@@ -5,8 +5,8 @@ import numpy as np
|
||||
import pytest
|
||||
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.metrics.detection import ConfusionMatrix
|
||||
from test.utils import mock_detections
|
||||
from supervision.metrics.detection import ConfusionMatrix, detections_to_tensor, MeanAveragePrecision
|
||||
from test.utils import mock_detections, assert_almost_equal
|
||||
|
||||
CLASSES = np.arange(80)
|
||||
NUM_CLASSES = len(CLASSES)
|
||||
@@ -167,7 +167,7 @@ def test_detections_to_tensor(
|
||||
exception: Exception
|
||||
):
|
||||
with exception:
|
||||
result = ConfusionMatrix.detections_to_tensor(
|
||||
result = detections_to_tensor(
|
||||
detections=detections,
|
||||
with_confidence=with_confidence
|
||||
)
|
||||
@@ -400,3 +400,43 @@ def test_drop_extra_matches(
|
||||
result = ConfusionMatrix._drop_extra_matches(matches)
|
||||
|
||||
assert np.array_equal(result, expected_result)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'recall, precision, expected_result, exception',
|
||||
[
|
||||
(
|
||||
np.array([1.0]),
|
||||
np.array([1.0]),
|
||||
1.0,
|
||||
DoesNotRaise()
|
||||
), # perfect recall and precision
|
||||
(
|
||||
np.array([0.0]),
|
||||
np.array([0.0]),
|
||||
0.0,
|
||||
DoesNotRaise()
|
||||
), # no recall and precision
|
||||
(
|
||||
np.array([0.0, 0.2, 0.2, 0.8, 0.8, 1.0]),
|
||||
np.array([0.7, 0.8, 0.4, 0.5, 0.1, 0.2]),
|
||||
0.5,
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
np.array([0.0, 0.5, 0.5, 1.0]),
|
||||
np.array([0.75, 0.75, 0.75, 0.75]),
|
||||
0.75,
|
||||
DoesNotRaise()
|
||||
)
|
||||
]
|
||||
)
|
||||
def test_compute_average_precision(
|
||||
recall: np.ndarray,
|
||||
precision: np.ndarray,
|
||||
expected_result: float,
|
||||
exception: Exception
|
||||
) -> None:
|
||||
with exception:
|
||||
result = MeanAveragePrecision.compute_average_precision(recall=recall, precision=precision)
|
||||
assert_almost_equal(result, expected_result, tolerance=0.01)
|
||||
@@ -21,3 +21,7 @@ def mock_detections(
|
||||
if tracker_id is None
|
||||
else np.array(tracker_id, dtype=int),
|
||||
)
|
||||
|
||||
|
||||
def assert_almost_equal(actual, expected, tolerance=1e-5):
|
||||
assert abs(actual - expected) < tolerance, f"Expected {expected}, but got {actual}."
|
||||
Reference in new issue
Block a user