diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index d48f8020..55d09c21 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,4 +1,4 @@ # These owners will be the default owners for everything in # the repo. They will be requested for review when someone # opens a pull request. -* @soumik12345 @SkalskiP @onuralpszr +* @SkalskiP diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 30c0adb1..794e6b13 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -4,15 +4,17 @@ updates: - package-ecosystem: "github-actions" directory: "/" schedule: - interval: "weekly" + interval: "monthly" commit-message: - prefix: ⬆️ - target-branch: "main" + prefix: πŸ”„ + groups: + github-actions: + patterns: + - "*" # Python - package-ecosystem: "uv" directory: "/" schedule: - interval: "daily" + interval: "weekly" commit-message: prefix: ⬆️ - target-branch: "main" diff --git a/.github/workflows/build-package.yml b/.github/workflows/build-package.yml new file mode 100644 index 00000000..f0be15ed --- /dev/null +++ b/.github/workflows/build-package.yml @@ -0,0 +1,37 @@ +name: Build Package + +permissions: + contents: read + +on: + workflow_call: + inputs: + python-version: + required: false + type: string + default: "3.10" + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: πŸ“₯ Checkout the repository + uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 + + - name: 🐍 Install uv and set Python version ${{ inputs.python-version }} + uses: astral-sh/setup-uv@61cb8a9741eeb8a550a1b8544337180c0fc8476b # v7.2.0 + with: + python-version: ${{ inputs.python-version }} + activate-environment: true + + - name: πŸ—οΈ Build source and wheel distributions + run: | + uv pip install -r pyproject.toml --group build + uv build + uv run twine check --strict dist/* + + - name: πŸ“€ Upload distribution artifacts + uses: actions/upload-artifact@v6 + with: + name: dist + path: dist/ diff --git a/.github/workflows/test-doc.yml b/.github/workflows/ci-build-docs.yml similarity index 68% rename from .github/workflows/test-doc.yml rename to .github/workflows/ci-build-docs.yml index b2e953c8..cc5a646a 100644 --- a/.github/workflows/test-doc.yml +++ b/.github/workflows/ci-build-docs.yml @@ -1,4 +1,4 @@ -name: πŸ§ͺ Docs Test WorkFlow πŸ“š +name: Docs Test WorkFlow πŸ“š on: pull_request: @@ -14,23 +14,20 @@ jobs: name: Test docs build runs-on: ubuntu-latest timeout-minutes: 10 - strategy: - matrix: - python-version: ["3.10"] steps: - name: πŸ“₯ Checkout the repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: 🐍 Install uv and set Python ${{ matrix.python-version }} + - name: 🐍 Install uv and set Python uses: astral-sh/setup-uv@61cb8a9741eeb8a550a1b8544337180c0fc8476b # v7.2.0 with: - python-version: ${{ matrix.python-version }} + python-version: "3.10" activate-environment: true - name: πŸ—οΈ Install dependencies - run: uv pip install -r pyproject.toml --group docs --python-version ${{ matrix.python-version }} + run: uv pip install -r pyproject.toml --group docs - name: πŸ§ͺ Test Docs Build run: uv run mkdocs build --verbose diff --git a/.github/workflows/uv-test.yml b/.github/workflows/ci-tests.yml similarity index 70% rename from .github/workflows/uv-test.yml rename to .github/workflows/ci-tests.yml index ee0d1a37..10e3fca5 100644 --- a/.github/workflows/uv-test.yml +++ b/.github/workflows/ci-tests.yml @@ -1,4 +1,4 @@ -name: πŸ”§ Pytest/Test Workflow +name: Pytest/Test Workflow on: pull_request: @@ -12,7 +12,7 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + python-version: ["3.10", "3.11", "3.12", "3.13"] runs-on: ${{ matrix.os }} steps: @@ -28,10 +28,16 @@ jobs: prune-cache: ${{ matrix.os != 'windows-latest' }} - name: πŸš€ Install Packages - run: uv pip install -r pyproject.toml --group dev --group docs --extra cpu --extra reid + run: uv pip install -r pyproject.toml --group dev - name: πŸ§ͺ Run the Import test run: uv run python -c "import trackers" - name: πŸ§ͺ Run the Test - run: uv run pytest + shell: bash + run: | + if [ -d test ] && [ "$(find test -type f \( -name 'test_*.py' -o -name '*_test.py' \) | wc -l)" -gt 0 ]; then + uv run pytest + else + echo "No tests found. Skipping pytest." + fi diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml index cc41edab..c2a4a31e 100644 --- a/.github/workflows/publish-docs.yml +++ b/.github/workflows/publish-docs.yml @@ -1,9 +1,9 @@ -name: Build and Publish Docs +name: Docs/Build and Publish on: push: branches: - - main + - develop workflow_dispatch: release: types: [published] @@ -20,52 +20,41 @@ permissions: pull-requests: write # Required for PR comments jobs: - deploy: + docs-build-deploy: name: Publish Docs runs-on: ubuntu-latest timeout-minutes: 10 - strategy: - matrix: - python-version: ["3.10"] steps: - name: πŸ“₯ Checkout the repository uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: 🐍 Install uv and set Python ${{ matrix.python-version }} + - name: 🐍 Install uv and set Python uses: astral-sh/setup-uv@61cb8a9741eeb8a550a1b8544337180c0fc8476b # v7.2.0 with: - python-version: ${{ matrix.python-version }} + python-version: "3.10" activate-environment: true - - name: πŸ”‘ Create GitHub App token (mkdocs) - id: mkdocs_token - uses: actions/create-github-app-token@29824e69f54612133e76f7eaac726eef6c875baf # v2.2.1 - with: - app-id: ${{ secrets.MKDOCS_APP_ID }} - private-key: ${{ secrets.MKDOCS_PEM }} - owner: roboflow - repositories: mkdocs-material-insiders - - name: πŸ—οΈ Install dependencies - run: | - uv pip install -r pyproject.toml --group docs - # Install mkdocs-material-insiders using the GitHub App token - uv pip install "git+https://roboflow:${{ steps.mkdocs_token.outputs.token }}@github.com/roboflow/mkdocs-material-insiders.git@9.5.49-insiders-4.53.14#egg=mkdocs-material[imaging]" + run: uv pip install -r pyproject.toml --group docs - name: βš™οΈ Configure git for github-actions run: | - git config --global user.name "github-actions[bot]" - git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" + git config --global user.name "${{ github.actor }}" + git config --global user.email "${{ github.actor }}@users.noreply.github.com" - name: πŸš€ Deploy Development Docs - if: (github.event_name == 'push' && github.ref == 'refs/heads/main') || github.event_name == 'workflow_dispatch' + if: (github.event_name == 'push' && github.ref == 'refs/heads/develop') || github.event_name == 'workflow_dispatch' + env: + MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.GITHUB_TOKEN }} run: | - MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} uv run mike deploy --push develop + uv run mike deploy --push develop - name: πŸš€ Deploy Release Docs if: github.event_name == 'release' && github.event.action == 'published' + env: + MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.GITHUB_TOKEN }} run: | latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`) - MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} uv run mike deploy --push --update-aliases $latest_tag latest + uv run mike deploy --push --update-aliases $latest_tag latest diff --git a/.github/workflows/publish-pre-release.yml b/.github/workflows/publish-pre-release.yml index 7c38fc23..8e1ab143 100644 --- a/.github/workflows/publish-pre-release.yml +++ b/.github/workflows/publish-pre-release.yml @@ -3,22 +3,27 @@ name: Publish Trackers Pre-Releases to PyPI on: push: tags: - - "[0-9]+.[0-9]+[0-9]+.[0-9]+[0-9]+a[0-9]" - - "[0-9]+.[0-9]+[0-9]+.[0-9]+[0-9]+b[0-9]" - - "[0-9]+.[0-9]+[0-9]+.[0-9]+[0-9]+rc[0-9]" - "[0-9]+.[0-9]+[0-9]+.[0-9]+a[0-9]" - "[0-9]+.[0-9]+[0-9]+.[0-9]+b[0-9]" - "[0-9]+.[0-9]+[0-9]+.[0-9]+rc[0-9]" - - "[0-9]+.[0-9]+.[0-9]+a[0-9]" - - "[0-9]+.[0-9]+.[0-9]+b[0-9]" - - "[0-9]+.[0-9]+.[0-9]+rc[0-9]" workflow_dispatch: + pull_request: + branches: [main, develop] + paths: + - '.github/workflows/build-package.yml' + - '.github/workflows/publish-pre-release.yml' permissions: {} # Explicitly remove all permissions by default jobs: + build: + permissions: + contents: read + uses: ./.github/workflows/build-package.yml + publish-pre-release: name: Publish Pre-release Package + needs: build runs-on: ubuntu-latest environment: name: test @@ -27,26 +32,17 @@ jobs: permissions: id-token: write # Required for PyPI publishing contents: read # Required for checkout - strategy: - matrix: - python-version: ["3.10"] steps: - - name: πŸ“₯ Checkout the repository - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - - - name: 🐍 Install uv and set Python version ${{ matrix.python-version }} - uses: astral-sh/setup-uv@61cb8a9741eeb8a550a1b8544337180c0fc8476b # v7.2.0 + - name: πŸ“₯ Download distribution artifacts + uses: actions/download-artifact@v7 with: - python-version: ${{ matrix.python-version }} - activate-environment: true - - - name: πŸ—οΈ Build source and wheel distributions - run: | - uv pip install -r pyproject.toml --group build - uv build - uv run twine check --strict dist/* + name: dist + path: dist/ + - name: List distribution artifacts + run: ls -lh dist/ - name: πŸš€ Publish to PyPi + if: github.event_name != 'pull_request' uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0 with: attestations: true diff --git a/.github/workflows/publish-release.yml b/.github/workflows/publish-release.yml index 0e0be89b..cb43a4d0 100644 --- a/.github/workflows/publish-release.yml +++ b/.github/workflows/publish-release.yml @@ -3,16 +3,25 @@ name: Publish Trackers Releases to PyPI on: push: tags: - - "[0-9]+.[0-9]+[0-9]+.[0-9]+[0-9]" - "[0-9]+.[0-9]+[0-9]+.[0-9]" - - "[0-9]+.[0-9]+.[0-9]" workflow_dispatch: + pull_request: + branches: [main, develop] + paths: + - '.github/workflows/build-package.yml' + - '.github/workflows/publish-release.yml' permissions: {} # Explicitly remove all permissions by default jobs: + build: + permissions: + contents: read + uses: ./.github/workflows/build-package.yml + publish-release: name: Publish Release Package + needs: build runs-on: ubuntu-latest environment: name: release @@ -21,26 +30,17 @@ jobs: permissions: id-token: write # Required for PyPI publishing contents: read # Required for checkout - strategy: - matrix: - python-version: ["3.10"] steps: - - name: πŸ“₯ Checkout the repository - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - - - name: 🐍 Install uv and set Python version ${{ matrix.python-version }} - uses: astral-sh/setup-uv@61cb8a9741eeb8a550a1b8544337180c0fc8476b # v7.2.0 + - name: πŸ“₯ Download distribution artifacts + uses: actions/download-artifact@v7 with: - python-version: ${{ matrix.python-version }} - activate-environment: true - - - name: πŸ—οΈ Build source and wheel distributions - run: | - uv pip install -r pyproject.toml --group build - uv build - uv run twine check --strict dist/* + name: dist + path: dist/ + - name: List distribution artifacts + run: ls -lh dist/ - name: πŸš€ Publish to PyPi + if: github.event_name != 'pull_request' uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0 with: attestations: true diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e8d8e448..b51a8805 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,10 +7,9 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 + rev: v6.0.0 hooks: - id: trailing-whitespace - exclude: test/.*\.py - id: check-executables-have-shebangs - id: check-toml - id: check-case-conflict @@ -18,27 +17,19 @@ repos: - id: detect-private-key - id: pretty-format-json args: ['--autofix', '--no-sort-keys', '--indent=4'] - exclude: /.*\.ipynb - id: end-of-file-fixer - id: mixed-line-ending - - repo: https://github.com/PyCQA/bandit - rev: '1.8.6' - hooks: - - id: bandit - args: ["-c", "pyproject.toml"] - additional_dependencies: ["bandit[toml]"] - - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.2 - hooks: - - id: ruff - args: [--fix, --exit-non-zero-on-fix] - - id: ruff-format - types_or: [ python, pyi, jupyter] + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.14 + hooks: + - id: ruff-check + args: [--fix] + - id: ruff-format + types_or: [python, pyi, jupyter] - - repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v1.16.1' - hooks: - - id: mypy - additional_dependencies: [numpy,types-aiofiles] + - repo: https://github.com/pre-commit/mirrors-mypy + rev: 'v1.19.1' + hooks: + - id: mypy + additional_dependencies: [numpy,types-aiofiles] diff --git a/README.md b/README.md index f5fd794f..6ef840fd 100644 --- a/README.md +++ b/README.md @@ -1,82 +1,21 @@ -
-

trackers

- trackers logo - [![version](https://badge.fury.io/py/trackers.svg)](https://badge.fury.io/py/trackers) [![downloads](https://img.shields.io/pypi/dm/trackers)](https://pypistats.org/packages/trackers) [![license](https://img.shields.io/badge/license-Apache%202.0-blue)](https://github.com/roboflow/trackers/blob/main/LICENSE.md) [![python-version](https://img.shields.io/pypi/pyversions/trackers)](https://badge.fury.io/py/trackers) - -[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VT_FYIe3kborhWrfKKBqqfR0EjQeQNiO?usp=sharing) [![discord](https://img.shields.io/discord/1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk) -
- -## Hello - -`trackers` is a unified library offering clean room re-implementations of leading multi-object tracking algorithms. Its modular design allows you to easily swap trackers and integrate them with object detectors from various libraries like `inference`, `ultralytics`, or `transformers`.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TrackerPaperMOTAYearStatusColab
SORTarXiv74.62016βœ…colab
DeepSORTarXiv75.42017βœ…colab
ByteTrackarXiv77.82021🚧🚧
OC-SORTarXiv75.92022🚧🚧
BoT-SORTarXiv77.82022🚧🚧
+

trackers

+ trackers logo
+Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms released under the permissive Apache 2.0 license. You combine them with any detection model you already use. + https://github.com/user-attachments/assets/eef9b00a-cfe4-40f7-a495-954550e3ef1f -## Installation +## Install -Pip install the `trackers` package in a [**Python>=3.9**](https://www.python.org/) environment. +You can install and use `trackers` in a [**Python>=3.10**](https://www.python.org/) environment. For detailed installation instructions, including installing from source and setting up a local development environment, check out our [install](https://trackers.roboflow.com/develop/learn/install/) page. ```bash pip install trackers @@ -90,91 +29,185 @@ pip install trackers By installing `trackers` from source, you can explore the most recent features and enhancements that have not yet been officially released. Please note that these updates are still in development and may not be as stable as the latest published release. ```bash -pip install git+https://github.com/roboflow/trackers.git +pip install https://github.com/roboflow/trackers/archive/refs/heads/develop.zip ``` +## Tracking Algorithms + +Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms. The package currently supports [SORT](https://arxiv.org/abs/1602.00763) and [ByteTrack](https://arxiv.org/abs/2110.06864). [OC-SORT](https://arxiv.org/abs/2203.14360) support is coming soon. For full results, see the [benchmarks](https://trackers.roboflow.com/develop/learn/benchmarks/) page. + +| Algorithm | Trackers API | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | +|:-------------:|:-------------------------------------------------------------------------------:|:----------:|:-----------:|:----------:|:--------------:|:--------------:| +| SORT | [`SORTTracker`](https://trackers.roboflow.com/develop/trackers/sort/) | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | +| ByteTrack | [`ByteTrackTracker`](https://trackers.roboflow.com/develop/trackers/bytetrack/) | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | +| OC-SORT | `OCSORTTracker` | β€” | β€” | β€” | β€” | β€” | + ## Quickstart -With a modular design, `trackers` lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use `SORTTracker` with various detectors: +With a modular design, Trackers lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use ByteTrack with various detectors. These examples use OpenCV for decoding and display. Replace `` with your input. + +```python +import cv2 +import supervision as sv +from rfdetr import RFDETRMedium +from trackers import ByteTrack + +tracker = ByteTrack() +model = RFDETRMedium() + +box_annotator = sv.BoxAnnotator() +label_annotator = sv.LabelAnnotator() + +video_capture = cv2.VideoCapture("") +if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + +while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + +video_capture.release() +cv2.destroyAllWindows() +``` + +
+run with Inference + +
```python +import cv2 import supervision as sv -from trackers import SORTTracker from inference import get_model +from trackers import ByteTrack + +tracker = ByteTrack() +model = get_model(model_id="rfdetr-medium") + +box_annotator = sv.BoxAnnotator() +label_annotator = sv.LabelAnnotator() -tracker = SORTTracker() -model = get_model(model_id="yolov11m-640") -annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) +video_capture = cv2.VideoCapture("") +if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") -def callback(frame, _): - result = model.infer(frame)[0] +while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + result = model.infer(frame_rgb)[0] detections = sv.Detections.from_inference(result) detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) -sv.process_video( - source_path="", - target_path="", - callback=callback, -) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Inference + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + +video_capture.release() +cv2.destroyAllWindows() ``` +
-run with ultralytics +run with Ultralytics
```python +import cv2 import supervision as sv -from trackers import SORTTracker from ultralytics import YOLO +from trackers import ByteTrack + +tracker = ByteTrack() +model = YOLO("yolo26m.pt") + +box_annotator = sv.BoxAnnotator() +label_annotator = sv.LabelAnnotator() -tracker = SORTTracker() -model = YOLO("yolo11m.pt") -annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) +video_capture = cv2.VideoCapture("") +if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") -def callback(frame, _): - result = model(frame)[0] +while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + result = model(frame_rgb)[0] detections = sv.Detections.from_ultralytics(result) detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) -sv.process_video( - source_path="", - target_path="", - callback=callback, -) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Ultralytics + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + +video_capture.release() +cv2.destroyAllWindows() ```
-run with transformers +run with Transformers
```python import torch +import cv2 import supervision as sv -from trackers import SORTTracker -from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor +from trackers import ByteTrack +from transformers import RTDetrImageProcessor, RTDetrV2ForObjectDetection -tracker = SORTTracker() -image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd") +tracker = ByteTrack() +processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd") model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd") -annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) -def callback(frame, _): - inputs = image_processor(images=frame, return_tensors="pt") +box_annotator = sv.BoxAnnotator() +label_annotator = sv.LabelAnnotator() + +video_capture = cv2.VideoCapture("") +if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + +while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + inputs = processor(images=frame_rgb, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) - h, w, _ = frame.shape - results = image_processor.post_process_object_detection( + h, w = frame_bgr.shape[:2] + results = processor.post_process_object_detection( outputs, - target_sizes=torch.tensor([(h, w)]), + target_sizes=torch.tensor([[h, w]]), threshold=0.5 )[0] @@ -182,15 +215,17 @@ def callback(frame, _): transformers_results=results, id2label=model.config.id2label ) - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) -sv.process_video( - source_path="", - target_path="", - callback=callback, -) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Transformers + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + +video_capture.release() +cv2.destroyAllWindows() ```
diff --git a/docs/index.md b/docs/index.md index 5375e94d..8aee49df 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,225 +6,221 @@ comments: true Trackers Logo -
- -version license python-version - -
- -colab discord - -`trackers` is a unified library offering clean room re-implementations of leading multi-object tracking algorithms. Its modular design allows you to easily swap trackers and integrate them with object detectors from various libraries like `inference`, `ultralytics`, or `transformers`. +Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms released under the permissive Apache 2.0 license. You combine them with any detection model you already use. -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TrackerPaperMOTAYearStatusColab
SORTarXiv74.62016βœ…colab
DeepSORTarXiv75.42017βœ…colab
ByteTrackarXiv77.82021🚧🚧
OC-SORTarXiv75.92022🚧🚧
BoT-SORTarXiv77.82022🚧🚧
-
+## Install -# Installation +You can install and use `trackers` in a [**Python>=3.10**](https://www.python.org/) environment. For detailed installation instructions, including installing from source and setting up a local development environment, check out our [install](learn/install.md) page. -You can install `trackers` in a [**Python>=3.9**](https://www.python.org/) environment. - -!!! example "Basic Installation" +!!! example "Installation" + [![version](https://badge.fury.io/py/trackers.svg)](https://badge.fury.io/py/trackers) + [![downloads](https://img.shields.io/pypi/dm/trackers)](https://pypistats.org/packages/trackers) + [![license](https://img.shields.io/badge/license-Apache%202.0-blue)](https://github.com/roboflow/trackers/blob/main/LICENSE.md) + [![python-version](https://img.shields.io/pypi/pyversions/trackers)](https://badge.fury.io/py/trackers) === "pip" ```bash pip install trackers ``` - === "poetry" - ```bash - poetry add trackers - ``` - === "uv" ```bash uv pip install trackers ``` -!!! example "Hardware Acceleration" +## Tutorials - === "CPU" - ```bash - pip install "trackers[cpu]" - ``` +
- === "CUDA 11.8" - ```bash - pip install "trackers[cu118]" - ``` +- **How to Track Objects with SORT** - === "CUDA 12.4" - ```bash - pip install "trackers[cu124]" - ``` + --- - === "CUDA 12.6" - ```bash - pip install "trackers[cu126]" - ``` + [![](https://storage.googleapis.com/com-roboflow-marketing/trackers/assets/sort-sample.png)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-sort-tracker.ipynb) - === "ROCm 6.1" - ```bash - pip install "trackers[rocm61]" - ``` + End-to-end example showing how to run RF-DETR detection with the SORT tracker. - === "ROCm 6.2.4" - ```bash - pip install "trackers[rocm624]" - ``` + [:simple-googlecolab: Run Google Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-sort-tracker.ipynb) + +- **How to Track Objects with ByteTrack** + + --- + + [![](https://storage.googleapis.com/com-roboflow-marketing/trackers/assets/bytetrack-sample.png)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) + + End-to-end example showing how to run RF-DETR detection with the ByteTrack tracker. + + [:simple-googlecolab: Run Google Colab](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) + +
-# Quickstart -With a modular design, `trackers` lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use `SORTTracker` with various detectors: +## Tracking Algorithms -=== "inference" +Trackers gives you clean, modular re-implementations of leading multi-object tracking algorithms. The package currently supports [SORT](https://arxiv.org/abs/1602.00763) and [ByteTrack](https://arxiv.org/abs/2110.06864). [OC-SORT](https://arxiv.org/abs/2203.14360) support is coming soon. For full results, see the [benchmarks](learn/benchmarks.md) page. - ```python hl_lines="2 5 12" +| Algorithm | Trackers API | MOT17 HOTA | MOT17 IDF1 | MOT17 MOTA | SportsMOT HOTA | SoccerNet HOTA | +|:-----------:|:-------------------------------------------:|:----------:|:----------:|:----------:|:--------------:|:--------------:| +| SORT | [`SORTTracker`](trackers/sort.md) | 58.4 | 69.9 | 67.2 | 70.9 | 81.6 | +| ByteTrack | [`ByteTrackTracker`](trackers/bytetrack.md) | **60.1** | **73.2** | **74.1** | **73.0** | **84.0** | +| OC-SORT | `OCSORTTracker` | β€” | β€” | β€” | β€” | β€” | + +## Quickstart + +With a modular design, Trackers lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use ByteTrack with various detectors. These examples use OpenCV for decoding and display. Replace `` with your input. + +=== "RF-DETR" + + ```python + import cv2 import supervision as sv - from trackers import SORTTracker - from inference import get_model + from rfdetr import RFDETRMedium + from trackers import ByteTrack - tracker = SORTTracker() - model = get_model(model_id="yolov11m-640") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) + tracker = ByteTrack() + model = RFDETRMedium() - def callback(frame, _): - result = model.infer(frame)[0] - detections = sv.Detections.from_inference(result) + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() ``` -=== "rf-detr" +=== "Inference" - ```python hl_lines="2 5 11" + ```python + import cv2 import supervision as sv - from trackers import SORTTracker - from rfdetr import RFDETRBase + from inference import get_model + from trackers import ByteTrack + + tracker = ByteTrack() + model = get_model(model_id="rfdetr-medium") + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") - tracker = SORTTracker() - model = RFDETRBase() - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) + while True: + success, frame_bgr = video_capture.read() + if not success: + break - def callback(frame, _): - detections = model.predict(frame) + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + result = model.infer(frame_rgb)[0] + detections = sv.Detections.from_inference(result) detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Inference + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() ``` -=== "ultralytics" +=== "Ultralytics" - ```python hl_lines="2 5 12" + ```python + import cv2 import supervision as sv - from trackers import SORTTracker from ultralytics import YOLO + from trackers import ByteTrack + + tracker = ByteTrack() + model = YOLO("yolo26m.pt") - tracker = SORTTracker() - model = YOLO("yolo11m.pt") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() - def callback(frame, _): - result = model(frame)[0] + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + result = model(frame_rgb)[0] detections = sv.Detections.from_ultralytics(result) detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Ultralytics + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() ``` -=== "transformers" +=== "Transformers" - ```python hl_lines="3 6 28" + ```python import torch + import cv2 import supervision as sv - from trackers import SORTTracker - from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor + from trackers import ByteTrack + from transformers import RTDetrImageProcessor, RTDetrV2ForObjectDetection - tracker = SORTTracker() + tracker = ByteTrack() processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd") model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - def callback(frame, _): - inputs = processor(images=frame, return_tensors="pt") + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + inputs = processor(images=frame_rgb, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) - h, w, _ = frame.shape + h, w = frame_bgr.shape[:2] results = processor.post_process_object_detection( outputs, - target_sizes=torch.tensor([(h, w)]), + target_sizes=torch.tensor([[h, w]]), threshold=0.5 )[0] @@ -232,13 +228,15 @@ With a modular design, `trackers` lets you combine object detectors from differe transformers_results=results, id2label=model.config.id2label ) - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("Transformers + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() ``` diff --git a/docs/learn/benchmarks.md b/docs/learn/benchmarks.md new file mode 100644 index 00000000..f3be55e7 --- /dev/null +++ b/docs/learn/benchmarks.md @@ -0,0 +1,62 @@ +# Benchmarks + +This page presents performance results of the trackers available in the `trackers` Python package on standard multiple object tracking (MOT) benchmarks. + +The goal of this page is not only to report scores, but also to help you interpret them for real world projects. + +## Datasets + +### MOT17 + +Classic pedestrian MOT benchmark based on MOT16 sequences but with significantly improved, higher-accuracy ground truth annotations. Contains crowded street scenes captured from static and slowly moving cameras, featuring frequent occlusions, moderate density (up to ~30–40 pedestrians), and partial camera motion in some sequences. Strong emphasis on handling short- to medium-term occlusions and re-acquiring identities after crossings or background clutter. + +| Tracker | HOTA | IDF1 | MOTA | +|------------|----------|----------|----------| +| SORT | 58.4 | 69.9 | 67.2 | +| ByteTrack | **60.1** | **73.2** | **74.1** | + +### SportsMOT + +Large-scale MOT dataset targeting sports analysis, containing 240 video clips (basketball, football/soccer, volleyball) collected from professional matches (Olympics, NCAA, NBA). Characterized by fast and highly variable player motion, rapid camera panning/zooming, complex backgrounds, and players with very similar appearance (uniforms within team). Only players on the court/field are annotated (excluding referees, coaches, spectators), making it particularly challenging for association under speed and visual similarity. + +| Tracker | HOTA | IDF1 | MOTA | +|-----------|----------|----------|----------| +| SORT | 70.9 | 68.9 | 95.7 | +| ByteTrack | **73.0** | **72.5** | **96.4** | + +### SoccerNet-tracking + +Specialized soccer MOT dataset derived from SoccerNet-tracking broadcast videos, containing 200 short 30-second clips + one full 45-minute half-time sequence. Focuses on professional soccer matches with main camera view, including players (both teams), goalkeepers, referees, and the ball; features long sequences with occlusions by players, fast directional changes, and non-linear trajectories. Particularly useful for evaluating long-term association and robustness to crowded penalty areas or midfield clusters. + +| Tracker | HOTA | IDF1 | MOTA | +|------------|----------|----------|----------| +| SORT | 81.6 | 76.2 | 95.1 | +| ByteTrack | **84.0** | **78.1** | **97.8** | + +## Metrics + +### HOTA (Higher Order Tracking Accuracy) + +HOTA is a balanced tracking metric that evaluates detection quality, localization accuracy, and identity association at the same time. The score is computed over a range of IoU thresholds, from loose to strict, which helps capture both easy and hard matching cases. Detection and association scores are then combined into a single value, giving a more complete view of tracker behavior than metrics focused on one aspect only. The metric is computed after processing the full video sequence and gives limited penalty when the same object is represented by several shorter tracks instead of one continuous track. + +Use HOTA when you need an overall assessment of tracking quality and want to compare systems under realistic conditions where both detection errors and association errors matter. + +### IDF1 (Identification F1 Score) + +IDF1 measures how well a tracker keeps the same ID for the same object over time. It matches predicted IDs to ground truth IDs across the whole video and scores identity precision and recall. The metric reacts strongly to ID switches and lost identities over long periods. Detection coverage has little effect on the score if the remaining IDs stay consistent. + +Use IDF1 when stable track IDs matter more than full object coverage, for example trajectory analysis, player tracking, or any logic built on long lived identities. + +### MOTA (Multi-Object Tracking Accuracy) + +MOTA sums three types of errors. Missed objects, false detections, and ID switches. The score reflects how often a tracker makes basic mistakes frame to frame. Missed detections and false positives dominate the result. The metric does not account for box accuracy and does not reflect long term identity consistency. + +Use MOTA when detection coverage represents the main objective and identity stability plays a secondary role. + +## Reproducibility + +All reported numbers were obtained using the official TrackEval library. We used YOLOX detections for MOT17 (provided by the ByteTrack authors), YOLOX detections trained on SportsMOT (provided by the SportsMOT dataset authors), and dataset-provided oracle detections (perfect detections) for SoccerNet-tracking. + +We are currently working on releasing dedicated benchmarking utilities in the `trackers` package that will allow users to easily reproduce these exact numbers, run evaluation on custom trackers using the same protocol, and benchmark their own models on these datasets with standardized splits and settings. + +Stay tuned for the upcoming release of the benchmarking module. diff --git a/docs/learn/install.md b/docs/learn/install.md new file mode 100644 index 00000000..325f9e71 --- /dev/null +++ b/docs/learn/install.md @@ -0,0 +1,87 @@ +# Installation + +Welcome to Trackers! This guide will help you install and set up Trackers for your projects. Whether you're a developer looking to contribute or an end-user ready to start using Trackers, we've got you covered. + +## Installation Methods + +Trackers supports several installation methods. [Python 3.10](https://devguide.python.org/versions/) or higher is required. Choose the option which best fits your workflow. + +!!! example "Installation" + + === "pip (recommended)" + The easiest way to install Trackers is using `pip`. This method is recommended for most users. + + ```bash + pip install trackers + ``` + + === "uv" + If you are using `uv`, you can install Trackers using the following command. + + ```bash + uv pip install trackers + ``` + + For uv-managed projects, add Trackers as a project dependency. + + ```bash + uv add trackers + ``` + + === "Source Archive" + To install the latest development version of Trackers from source without cloning the full repository, run the command below. + + ```bash + pip install https://github.com/roboflow/trackers/archive/refs/heads/develop.zip + ``` + +## Dev Environment + +If you plan to contribute to Trackers or modify the codebase locally, set up a local development environment using the steps below. We recommend using an isolated environment to avoid dependency conflicts. + +!!! example "Development Setup" + + === "virtualenv" + ```bash + # Clone repository + git clone --depth 1 -b develop https://github.com/roboflow/trackers.git + cd trackers + + # Create virtual environment + python3.10 -m venv venv + + # Activate environment + source venv/bin/activate + + # Upgrade pip + pip install --upgrade pip + + # Install in editable mode + pip install -e "." + ``` + + === "uv" + ```bash + # Clone repository + git clone --depth 1 -b develop https://github.com/roboflow/trackers.git + cd trackers + + # Pin Python version + uv python pin 3.10 + + # Sync environment and install dependencies + uv sync + + # Install in editable mode with extras + uv pip install -e . --all-extras + ``` + +## Troubleshooting + +Installation issues usually fall into a few common categories. + +- **Permission Issues**. Package install fails due to missing write access to system Python paths. Fix by running `pip install --user trackers` or by using an isolated environment. +- **Dependency Conflicts**. Errors appear after installing other Python packages. Resolve by installing Trackers inside a fresh virtual environment or a clean `uv` project. +- **Python Version**. Installation fails or runtime errors appear when using older Python releases. Trackers requires Python 3.10 or higher. + +If problems persist, open an issue on the [GitHub repository](https://github.com/roboflow/trackers). diff --git a/docs/trackers/bytetrack.md b/docs/trackers/bytetrack.md new file mode 100644 index 00000000..caaaa07d --- /dev/null +++ b/docs/trackers/bytetrack.md @@ -0,0 +1,141 @@ +--- +comments: true +--- + +# ByteTrack + +## Overview + +ByteTrack builds on the same Kalman filter plus Hungarian algorithm framework as SORT but changes the data association strategy to use almost every detection box regardless of confidence score. It runs a two-stage matching: first match high-confidence detections to tracks, then match low-confidence detections to any unmatched tracks using IoU. This reduces missed tracks and fragmentation for occluded or weak detections while retaining simplicity and high frame rates. ByteTrack has set state-of-the-art results on standard MOT benchmarks with real-time performance, because it recovers valid low-score detections instead of discarding them. + +## Benchmarks + +For comparisons with other trackers, plus full details on the datasets and evaluation metrics used, see the [benchmarks](../learn/benchmarks.md) page. + +| Dataset | HOTA | IDF1 | MOTA | +|:---------:|:----:|:----:|:----:| +| MOT17 | 60.1 | 73.2 | 74.1 | +| SportsMOT | 73.0 | 72.5 | 96.4 | +| SoccerNet | 84.0 | 78.1 | 97.8 | + +## Run on video, webcam, or RTSP stream + +These examples use OpenCV for decoding and display. Replace ``, ``, and `` with your inputs. `` is usually 0 for the default camera. + +=== "Video" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import ByteTrackTracker + + tracker = ByteTrackTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "Webcam" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import ByteTrackTracker + + tracker = ByteTrackTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open webcam") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "RTSP" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import ByteTrackTracker + + tracker = ByteTrackTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open RTSP stream") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + ByteTrack", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +## API + +::: trackers.core.bytetrack.tracker.ByteTrackTracker diff --git a/docs/trackers/core/deepsort/tracker.md b/docs/trackers/core/deepsort/tracker.md deleted file mode 100644 index 8fc2d027..00000000 --- a/docs/trackers/core/deepsort/tracker.md +++ /dev/null @@ -1,166 +0,0 @@ ---- -comments: true ---- - -# DeepSORT - -[![arXiv](https://img.shields.io/badge/arXiv-1703.07402-b31b1b.svg)](https://arxiv.org/abs/1703.07402) -[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-deepsort-tracker.ipynb) - -## Overview - -DeepSORT extends the original [SORT](../sort/tracker.md) algorithm by integrating appearance information through a deep association metric. While maintaining the core Kalman filtering and Hungarian algorithm components from SORT, DeepSORT adds a convolutional neural network (CNN) trained on large-scale person re-identification datasets to extract appearance features from detected objects. This integration allows the tracker to maintain object identities through longer periods of occlusion, effectively reducing identity switches compared to the original SORT. DeepSORT operates with a dual-metric approach, combining motion information (Mahalanobis distance) with appearance similarity (cosine distance in feature space) to improve data association decisions. It also introduces a matching cascade that prioritizes recently seen tracks, enhancing robustness during occlusions. Most of the computational complexity is offloaded to an offline pre-training stage, allowing the online tracking component to run efficiently at approximately 20Hz, making it suitable for real-time applications while achieving competitive tracking performance with significantly improved identity preservation. - - -## Examples - -=== "inference" - - ```python hl_lines="2 5-6 13" - import supervision as sv - from trackers import DeepSORTTracker, ReIDModel - from inference import get_model - - reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - tracker = DeepSORTTracker(reid_model=reid_model) - model = get_model(model_id="yolov11m-640") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - result = model.infer(frame)[0] - detections = sv.Detections.from_inference(result) - detections = tracker.update(detections, frame) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "rf-detr" - - ```python hl_lines="2 5-6 12" - import supervision as sv - from trackers import DeepSORTTracker, ReIDModel - from rfdetr import RFDETRBase - - reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - tracker = DeepSORTTracker(reid_model=reid_model) - model = RFDETRBase() - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - detections = model.predict(frame) - detections = tracker.update(detections, frame) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "ultralytics" - - ```python hl_lines="2 5-6 13" - import supervision as sv - from trackers import DeepSORTTracker, ReIDModel - from ultralytics import YOLO - - reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - tracker = DeepSORTTracker(reid_model=reid_model) - model = YOLO("yolo11m.pt") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - result = model(frame)[0] - detections = sv.Detections.from_ultralytics(result) - detections = tracker.update(detections, frame) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "transformers" - - ```python hl_lines="3 6-7 29" - import torch - import supervision as sv - from trackers import DeepSORTTracker, ReIDModel - from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor - - reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - tracker = DeepSORTTracker(reid_model=reid_model) - processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd") - model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - inputs = processor(images=frame, return_tensors="pt") - with torch.no_grad(): - outputs = model(**inputs) - - h, w, _ = frame.shape - results = processor.post_process_object_detection( - outputs, - target_sizes=torch.tensor([(h, w)]), - threshold=0.5 - )[0] - - detections = sv.Detections.from_transformers( - transformers_results=results, - id2label=model.config.id2label - ) - - detections = tracker.update(detections, frame) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -## API - -!!! example "Install DeepSORT" - - === "CPU" - ```bash - pip install "trackers[reid,cpu]" - ``` - - === "CUDA 11.8" - ```bash - pip install "trackers[reid,cu118]" - ``` - - === "CUDA 12.4" - ```bash - pip install "trackers[reid,cu124]" - ``` - - === "CUDA 12.6" - ```bash - pip install "trackers[reid,cu126]" - ``` - - === "ROCm 6.1" - ```bash - pip install "trackers[reid,rocm61]" - ``` - - === "ROCm 6.2.4" - ```bash - pip install "trackers[reid,rocm624]" - ``` - -::: trackers.core.deepsort.tracker.DeepSORTTracker diff --git a/docs/trackers/core/reid/reid.md b/docs/trackers/core/reid/reid.md deleted file mode 100644 index db9358e0..00000000 --- a/docs/trackers/core/reid/reid.md +++ /dev/null @@ -1,234 +0,0 @@ ---- -comments: true ---- - -# Re-Identification (ReID) - -Re-identification (ReID) enables object tracking systems to recognize the same object or identity across different framesβ€”even when occlusion, appearance changes, or re-entries occur. This is essential for robust, long-term multi-object tracking. - -## Installation - -To use ReID features in the trackers library, install the package with the appropriate dependencies for your hardware: - -!!! example "Install trackers with ReID support" - - === "CPU" - ```bash - pip install "trackers[reid,cpu]" - ``` - - === "CUDA 11.8" - ```bash - pip install "trackers[reid,cu118]" - ``` - - === "CUDA 12.4" - ```bash - pip install "trackers[reid,cu124]" - ``` - - === "CUDA 12.6" - ```bash - pip install "trackers[reid,cu126]" - ``` - - === "ROCm 6.1" - ```bash - pip install "trackers[reid,rocm61]" - ``` - - === "ROCm 6.2.4" - ```bash - pip install "trackers[reid,rocm624]" - ``` - -## ReIDModel - -The `ReIDModel` class provides a flexible interface to extract appearance features from object detections, which can be used by trackers to associate identities across frames. - -### Loading a ReIDModel - -You can initialize a `ReIDModel` from any supported pretrained model in the [`timm`](https://huggingface.co/docs/timm/en/index) library using the `from_timm` method. - -```python -from trackers import ReIDModel - -reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") -``` - -### Supported Models - -The `ReIDModel` supports all models available in the timm library. You can list available models using: - -```python -import timm -print(timm.list_models()) -``` - -### Extracting Embeddings - -To extract embeddings (feature vectors) from detected objects in an image frame, use the `extract_features` method. It crops each detected bounding box from the frame, applies necessary transforms, and passes the crops through the backbone model: - -```python -import cv2 -import supervision as sv -from trackers import ReIDModel -from inference import get_model - -reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") -model = get_model(model_id="yolov11m-640") - -image = cv2.imread("") - -result = model.infer(image)[0] -detections = sv.Detections.from_inference(result) -features = reid_model.extract_features(image, detections) -``` - -## Tracking Integration - -ReID models are integrated into trackers like DeepSORT to improve identity association by providing appearance features alongside motion cues. - -```python -import supervision as sv -from trackers import DeepSORTTracker, ReIDModel -from inference import get_model - -reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") -tracker = DeepSORTTracker(reid_model=reid_model) -model = get_model(model_id="yolov11m-640") -annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - -def callback(frame, _): - result = model.infer(frame)[0] - detections = sv.Detections.from_inference(result) - detections = tracker.update(detections, frame) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - -sv.process_video( - source_path="", - target_path="", - callback=callback, -) -``` - -This setup extracts appearance embeddings for detected objects and uses them in the tracker to maintain consistent IDs across frames. - -## Training - -You can train a custom ReID model using the `TripletsDataset` class, which provides triplets of anchor, positive, and negative samples for metric learning. - -Fine-tuning a pre-trained ReID model or training one from scratch can be beneficial when: - -- Your target domain (specific camera angles, lighting, object appearances) differs significantly from the data the pre-trained model was exposed to. - -- You have a custom dataset featuring unique identities or appearance variations not covered by generic models. - -- You aim to boost performance for specific tracking scenarios where general models might underperform. This allows the model to learn features more specific to your data. - -### Dataset Structure - -Prepare your dataset with the following directory structure, where each subfolder represents a unique identity: - -```rext -root/ -β”œβ”€β”€ identity_1/ -β”‚ β”œβ”€β”€ image_1.png -β”‚ β”œβ”€β”€ image_2.png -β”‚ └── image_3.png -β”œβ”€β”€ identity_2/ -β”‚ β”œβ”€β”€ image_1.png -β”‚ β”œβ”€β”€ image_2.png -β”‚ └── image_3.png -β”œβ”€β”€ identity_3/ -β”‚ β”œβ”€β”€ image_1.png -β”‚ β”œβ”€β”€ image_2.png -β”‚ └── image_3.png -... -``` - -Each folder contains images of the same object or person under different conditions. - -```python -from torch.utils.data import DataLoader -from trackers.core.reid.dataset.base import TripletsDataset -from trackers import ReIDModel - -train_dataset = TripletsDataset.from_image_directories( - root_directory="", -) - -train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) - -reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - -reid_model.train( - train_loader, - epochs=10, - projection_dimension=len(train_dataset), - freeze_backbone=True, - learning_rate=5e-4, - weight_decay=1e-2, - checkpoint_interval=5, -) -``` - -## Metrics and Monitoring - -During training, the model monitors metrics such as triplet loss and triplet accuracy to evaluate embedding quality. - -- Triplet Loss: Encourages embeddings of the same identity to be close and different identities to be far apart. - -- Triplet Accuracy: Measures how often the model correctly ranks positive samples closer than negatives. - -You can enable logging to various backends (matplotlib, TensorBoard, Weights & Biases) during training for real-time monitoring: - -```python -from torch.utils.data import DataLoader -from trackers.core.reid.dataset.base import TripletsDataset -from trackers import ReIDModel - -train_dataset = TripletsDataset.from_image_directories( - root_directory="", -) - -train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) - -reid_model = ReIDModel.from_timm("resnetv2_50.a1h_in1k") - -reid_model.train( - train_loader, - epochs=10, - projection_dimension=len(train_dataset), - freeze_backbone=True, - learning_rate=5e-4, - weight_decay=1e-2, - checkpoint_interval=5, - log_to_matplotlib=True, - log_to_tensorboard=True, - log_to_wandb=True, -) -``` - -To use the logging capabilities for Matplotlib, TensorBoard, or Weights & Biases, you might need to install additional dependencies. - -```bash -pip install "trackers[metrics]" -``` - -## Resuming from Checkpoints - -You can load custom-trained weights or resume training from a checkpoint: - -```python -from trackers import ReIDModel - -reid_model = ReIDModel.from_timm("") -``` - -## API - - -::: trackers.core.reid.model.ReIDModel - -::: trackers.core.reid.dataset.base.TripletsDataset diff --git a/docs/trackers/core/sort/tracker.md b/docs/trackers/core/sort/tracker.md deleted file mode 100644 index d00457c4..00000000 --- a/docs/trackers/core/sort/tracker.md +++ /dev/null @@ -1,129 +0,0 @@ ---- -comments: true ---- - -# SORT - -[![arXiv](https://img.shields.io/badge/arXiv-1602.00763-b31b1b.svg)](https://arxiv.org/abs/1602.00763) -[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-sort-tracker.ipynb) - -## Overview - -SORT (Simple Online and Realtime Tracking) is a lean, tracking-by-detection method that combines a Kalman filter for motion prediction with the Hungarian algorithm for data association. It uses object detectionsβ€”commonly from a high-performing CNN-based detectorβ€”as its input, updating each tracked object’s bounding box based on linear velocity estimates. Because SORT relies on minimal appearance modeling (only bounding box geometry is used), it is extremely fast and can run comfortably at hundreds of frames per second. This speed and simplicity make it well suited for real-time applications in robotics or surveillance, where rapid, approximate solutions are essential. However, its reliance on frame-to-frame matching makes SORT susceptible to ID switches and less robust during long occlusions, since there is no built-in re-identification module. - -## Examples - -=== "inference" - - ```python hl_lines="2 5 12" - import supervision as sv - from trackers import SORTTracker - from inference import get_model - - tracker = SORTTracker() - model = get_model(model_id="yolov11m-640") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - result = model.infer(frame)[0] - detections = sv.Detections.from_inference(result) - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "rf-detr" - - ```python hl_lines="2 5 11" - import supervision as sv - from trackers import SORTTracker - from rfdetr import RFDETRBase - - tracker = SORTTracker() - model = RFDETRBase() - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - detections = model.predict(frame) - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "ultralytics" - - ```python hl_lines="2 5 12" - import supervision as sv - from trackers import SORTTracker - from ultralytics import YOLO - - tracker = SORTTracker() - model = YOLO("yolo11m.pt") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - result = model(frame)[0] - detections = sv.Detections.from_ultralytics(result) - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -=== "transformers" - - ```python hl_lines="3 6 28" - import torch - import supervision as sv - from trackers import SORTTracker - from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor - - tracker = SORTTracker() - processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd") - model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd") - annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER) - - def callback(frame, _): - inputs = processor(images=frame, return_tensors="pt") - with torch.no_grad(): - outputs = model(**inputs) - - h, w, _ = frame.shape - results = processor.post_process_object_detection( - outputs, - target_sizes=torch.tensor([(h, w)]), - threshold=0.5 - )[0] - - detections = sv.Detections.from_transformers( - transformers_results=results, - id2label=model.config.id2label - ) - - detections = tracker.update(detections) - return annotator.annotate(frame, detections, labels=detections.tracker_id) - - sv.process_video( - source_path="", - target_path="", - callback=callback, - ) - ``` - -## API - -::: trackers.core.sort.tracker.SORTTracker diff --git a/docs/trackers/sort.md b/docs/trackers/sort.md new file mode 100644 index 00000000..2a9271a2 --- /dev/null +++ b/docs/trackers/sort.md @@ -0,0 +1,141 @@ +--- +comments: true +--- + +# SORT + +## Overview + +SORT is a classic online, tracking-by-detection method that predicts object motion with a Kalman filter and matches predicted tracks to detections using the Hungarian algorithm based on Intersection over Union (IoU). The tracker uses only geometric cues from bounding boxes, without appearance features, so it runs extremely fast and scales to hundreds of frames per second on typical hardware. Detections from a strong CNN detector feed SORT, which updates each track’s state via a constant velocity motion model and prunes stale tracks. Because SORT lacks explicit re-identification or appearance cues, it can suffer identity switches and fragmented tracks under long occlusions or heavy crowding. + +## Benchmarks + +For comparisons with other trackers, plus full details on the datasets and evaluation metrics used, see the [benchmarks](../learn/benchmarks.md) page. + +| Dataset | HOTA | IDF1 | MOTA | +|:---------:|:----:|:----:|:----:| +| MOT17 | 58.4 | 69.9 | 67.2 | +| SportsMOT | 70.9 | 68.9 | 95.7 | +| SoccerNet | 81.6 | 76.2 | 95.1 | + +## Run on video, webcam, or RTSP stream + +These examples use OpenCV for decoding and display. Replace ``, ``, and `` with your inputs. `` is usually 0 for the default camera. + +=== "Video" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import SORTTracker + + tracker = SORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open video source") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "Webcam" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import SORTTracker + + tracker = SORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open webcam") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +=== "RTSP" + + ```python + import cv2 + import supervision as sv + from rfdetr import RFDETRMedium + from trackers import SORTTracker + + tracker = SORTTracker() + model = RFDETRMedium() + + box_annotator = sv.BoxAnnotator() + label_annotator = sv.LabelAnnotator() + + video_capture = cv2.VideoCapture("") + if not video_capture.isOpened(): + raise RuntimeError("Failed to open RTSP stream") + + while True: + success, frame_bgr = video_capture.read() + if not success: + break + + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + detections = model.predict(frame_rgb) + detections = tracker.update(detections) + + annotated_frame = box_annotator.annotate(frame_bgr, detections) + annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id) + + cv2.imshow("RF-DETR + SORT", annotated_frame) + if cv2.waitKey(1) & 0xFF == ord("q"): + break + + video_capture.release() + cv2.destroyAllWindows() + ``` + +## API + +::: trackers.core.sort.tracker.SORTTracker diff --git a/mkdocs.yml b/mkdocs.yml index ad896311..764ae865 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -46,6 +46,13 @@ extra_css: - stylesheets/rf.css markdown_extensions: + # Adds support for card grids + - attr_list + - md_in_html + # Enables the use of icons and emojis + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg # Code syntax highlighting with line numbers and anchors - pymdownx.highlight: # Adds anchors to line numbers @@ -87,12 +94,16 @@ plugins: docstring_style: google # Controls the order of members (by source order in this case) members_order: source - # Controls whether to sort members alphabetically - sort_members: false + + extra: + # Controls whether to sort members alphabetically + sort_members: false nav: - Home: index.md + - Learn: + - Install: learn/install.md + - Benchmarks: learn/benchmarks.md - Trackers: - - SORT: trackers/core/sort/tracker.md - - DeepSORT: trackers/core/deepsort/tracker.md - - ReID: trackers/core/reid/reid.md + - SORT: trackers/sort.md + - ByteTrack: trackers/bytetrack.md diff --git a/pyproject.toml b/pyproject.toml index 0ad6bafe..9fc24887 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,14 +1,16 @@ [project] name = "trackers" -version = "2.0.2rc0" +version = "2.1.0" description = "A unified library for object tracking featuring clean room re-implementations of leading multi-object tracking algorithms" readme = "README.md" +maintainers = [ + { name = "Piotr Skalski", email = "piotr@roboflow.com" }, +] authors = [ - {name = "Piotr Skalski", email = "piotr.skalski92@gmail.com"}, - {name = "Soumik Rakshit", email = "soumik@roboflow.com"}, + { name = "Roboflow et al.", email = "develop@roboflow.com" }, ] license = {text = "Apache License 2.0"} -requires-python = ">=3.9" +requires-python = ">=3.10" classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", @@ -16,7 +18,6 @@ classifiers = [ "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", @@ -31,83 +32,28 @@ classifiers = [ "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", ] -keywords = ["tracking","mot","sort","deepsort", "machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "DETR", "YOLO", "Roboflow"] +keywords = ["tracking", "mot", "sort", "bytetrack", "machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "DETR", "YOLO", "Roboflow"] dependencies = [ - "aiofiles>=24.1.0", - "aiohttp>=3.11.16", "numpy>=2.0.2", "supervision>=0.26.1", - "tqdm>=4.67.1", - "validators>=0.34.0", "scipy>=1.13.1", ] -[project.optional-dependencies] - -cpu = [ - "torch>=2.6.0", - "torchvision>=0.21.0", -] - -cu126 = [ - "torch>=2.6.0", - "torchvision>=0.21.0", -] - -cu124 = [ - "torch>=2.6.0", - "torchvision>=0.21.0", -] - -cu118 = [ - "torch>=2.6.0", - "torchvision>=0.21.0", -] - -rocm61 = [ - "torch>=2.6.0", - "torchvision>=0.21.0", - "pytorch-triton-rocm>=2.0.0", -] - -rocm624 = [ - "torch>=2.6.0", - "torchvision>=0.21.0", - "pytorch-triton-rocm>=2.0.0", -] - -reid = [ - "safetensors>=0.5.3", - "timm>=1.0.15", -] - -metrics = [ - "matplotlib>=3.9.4", - "tensorboard>=2.19.0", - "wandb>=0.19.11", -] - [dependency-groups] dev = [ "uv>=0.4.20", "pytest>=8.3.3", "ruff>=0.6.9", - "bandit>=1.8.3", "mypy>=1.15.0", "pre-commit>=4.2.0", ] - - docs = [ - "jupyter>=1.1.1", "mkdocs>=1.6.1", "mkdocs-glightbox>=0.4.0", - "mkdocs-jupyter>=0.25.1", "mkdocs-material>=9.6.11", "mkdocs-minify-plugin>=0.8.0", "mkdocstrings-python>=1.10.9, <2.0.0", # todo: breaking changes in 2.x - "mkdocstrings-python>=1.16.10", "mike>=2.1.3", ] @@ -123,74 +69,6 @@ mypy-types = [ "types-tqdm>=4.67.0.20250417", ] - -[tool.uv] - -conflicts = [ - [ - { extra = "cpu" }, - { extra = "cu118" }, - { extra = "cu124" }, - { extra = "cu126" }, - { extra = "rocm61" }, - { extra = "rocm624" }, - ], -] - -[tool.uv.sources] -torch = [ - { index = "pytorch-cpu", extra = "cpu" }, - { index = "pytorch-cu118", extra = "cu118", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-cu124", extra = "cu124", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-cu126", extra = "cu126", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-rocm61", extra = "rocm61", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-rocm624", extra = "rocm624", marker = "sys_platform != 'darwin'"}, -] -torchvision = [ - { index = "pytorch-cpu", extra = "cpu" }, - { index = "pytorch-cu118", extra = "cu118", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-cu124", extra = "cu124", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-cu126", extra = "cu126", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-rocm61", extra = "rocm61", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-rocm624", extra = "rocm624", marker = "sys_platform != 'darwin'"}, -] - -pytorch-triton-rocm = [ - { index = "pytorch-rocm61", extra = "rocm61", marker = "sys_platform != 'darwin'"}, - { index = "pytorch-rocm624", extra = "rocm624", marker = "sys_platform != 'darwin'"}, -] - -[[tool.uv.index]] -name = "pytorch-cpu" -url = "https://download.pytorch.org/whl/cpu" -explicit = true - -[[tool.uv.index]] -name = "pytorch-cu118" -url = "https://download.pytorch.org/whl/cu118" -explicit = true - -[[tool.uv.index]] -name = "pytorch-cu124" -url = "https://download.pytorch.org/whl/cu124" -explicit = true - -[[tool.uv.index]] -name = "pytorch-cu126" -url = "https://download.pytorch.org/whl/cu126" -explicit = true - -[[tool.uv.index]] -name = "pytorch-rocm61" -url = "https://download.pytorch.org/whl/rocm6.1" -explicit = true - -[[tool.uv.index]] -name = "pytorch-rocm624" -url = "https://download.pytorch.org/whl/rocm6.2.4" -explicit = true - - [build-system] requires = ["hatchling"] build-backend = "hatchling.build" @@ -198,12 +76,8 @@ build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] packages = ["trackers"] -[tool.bandit] -target = ["test", "trackers"] -tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412"] - [tool.ruff] -target-version = "py39" +target-version = "py310" # Exclude a variety of commonly ignored directories. exclude = [ @@ -238,55 +112,17 @@ indent-width = 4 [tool.ruff.lint] # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. -select = ["E", "F", "I", "A", "Q", "W", "RUF"] -ignore = [] -# Allow autofix for all enabled rules (when `--fix`) is provided. -fixable = [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "I", - "N", - "Q", - "S", - "T", - "W", - "ANN", - "ARG", - "BLE", - "COM", - "DJ", - "DTZ", - "EM", - "ERA", - "EXE", - "FBT", - "ICN", - "INP", - "ISC", - "NPY", - "PD", - "PGH", - "PIE", - "PL", - "PT", - "PTH", - "PYI", - "RET", - "RSE", - "RUF", - "SIM", - "SLF", - "TCH", - "TID", - "TRY", - "UP", - "YTT", +select = [ + "E", # pycodestyle errors and warnings (https://docs.astral.sh/ruff/rules/#error-e) + "F", # Pyflakes (imports, unused variables, etc.) (https://docs.astral.sh/ruff/rules/#pyflakes-f) + "I", # isort (import sorting) (https://docs.astral.sh/ruff/rules/#isort-i) + "A", # flake8-builtins (no use of built-in variable names) (https://docs.astral.sh/ruff/rules/#flake8-builtins-a) + "Q", # flake8-quotes (consistent quote style) (https://docs.astral.sh/ruff/rules/#flake8-quotes-q) + "W", # pycodestyle warnings (https://docs.astral.sh/ruff/rules/#warning-w) + "RUF", # Ruff-specific rules (https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf) + "S", # security rules (https://docs.astral.sh/ruff/rules/#flake8-bandit-s) ] +ignore = [] unfixable = [] # Allow unused variables when underscore-prefixed. dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" @@ -302,6 +138,8 @@ convention = "google" [tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402", "F401"] +"test" = [] +"trackers" = [] [tool.ruff.lint.mccabe] # Flag errors (`C901`) whenever the complexity level exceeds 10. diff --git a/test/core/reid/dataset/__init__.py b/test/core/reid/dataset/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/test/core/reid/dataset/test_base.py b/test/core/reid/dataset/test_base.py deleted file mode 100644 index f58327a7..00000000 --- a/test/core/reid/dataset/test_base.py +++ /dev/null @@ -1,159 +0,0 @@ -from contextlib import ExitStack as DoesNotRaise - -import pytest - -from trackers.core.reid.dataset.base import TripletsDataset - - -@pytest.mark.parametrize( - "tracker_id_to_images, exception", - [ - ( - {"0111": []}, - pytest.raises(ValueError), - ), # Single tracker with no images - should raise ValueError - ( - {"0111": ["0111_00000000.jpg"]}, - pytest.raises(ValueError), - ), # Single tracker with one image - should raise ValueError - ( - {"0111": ["0111_00000000.jpg", "0111_00000001.jpg"]}, - pytest.raises(ValueError), - ), # Single tracker with multiple images - should raise ValueError - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg"], - }, - pytest.raises(ValueError), - ), # Two trackers but one has only one image - should raise ValueError - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - }, - DoesNotRaise(), - ), # Two trackers with multiple images - should not raise - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg"], - }, - DoesNotRaise(), - ), # Three trackers, one with fewer images - should validate dataset length - ], -) -def test_triplet_dataset_initialization(tracker_id_to_images, exception): - with exception: - _ = TripletsDataset(tracker_id_to_images) - - -@pytest.mark.parametrize( - "tracker_id_to_images, split_ratio, expected_train_size, expected_val_size, exception", # noqa: E501 - [ - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - }, - 0.5, - 1, - 1, - pytest.raises(ValueError), - ), # Split results in only 1 tracker in test set - should raise ValueError - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg", "0113_00000001.jpg"], - "0114": ["0114_00000000.jpg", "0114_00000001.jpg"], - "0115": ["0115_00000000.jpg", "0115_00000001.jpg"], - }, - 0.2, - 1, - 4, - pytest.raises(ValueError), - ), # Split results in only 1 tracker in test set - should raise ValueError - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg", "0113_00000001.jpg"], - "0114": ["0114_00000000.jpg", "0114_00000001.jpg"], - "0115": ["0115_00000000.jpg", "0115_00000001.jpg"], - }, - 0.8, - 4, - 1, - pytest.raises(ValueError), - ), # Split results in only 1 tracker in val set - should raise ValueError - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg", "0113_00000001.jpg"], - "0114": ["0114_00000000.jpg", "0114_00000001.jpg"], - "0115": ["0115_00000000.jpg", "0115_00000001.jpg"], - }, - 0.6, - 3, - 2, - DoesNotRaise(), - ), # Valid split with multiple trackers in both sets - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg", "0113_00000001.jpg"], - "0114": ["0114_00000000.jpg", "0114_00000001.jpg"], - }, - 0.5, - 2, - 2, - DoesNotRaise(), - ), # 50% train, 50% validation - valid - ], -) -def test_triplet_dataset_split( - tracker_id_to_images, split_ratio, expected_train_size, expected_val_size, exception -): - with exception: - dataset = TripletsDataset(tracker_id_to_images) - train_dataset, val_dataset = dataset.split(split_ratio=split_ratio) - - assert len(train_dataset) == expected_train_size, ( - f"Expected train dataset size {expected_train_size}, " - f"got {len(train_dataset)}" - ) - assert len(val_dataset) == expected_val_size, ( - f"Expected validation dataset size {expected_val_size}, " - f"got {len(val_dataset)}" - ) - - -@pytest.mark.parametrize( - "tracker_id_to_images, tracker_id, exception", - [ - ( - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - "0113": ["0113_00000000.jpg", "0113_00000001.jpg"], - }, - "0111", - DoesNotRaise(), - ), - ], -) -def test_get_triplet_image_paths(tracker_id_to_images, tracker_id, exception) -> None: - with exception: - dataset = TripletsDataset(tracker_id_to_images) - anchor_path, positive_path, negative_path = dataset._get_triplet_image_paths( - tracker_id - ) - - assert anchor_path in tracker_id_to_images[tracker_id] - assert positive_path in tracker_id_to_images[tracker_id] - assert negative_path not in tracker_id_to_images[tracker_id] - assert anchor_path != positive_path diff --git a/test/core/reid/dataset/test_market_1501.py b/test/core/reid/dataset/test_market_1501.py deleted file mode 100644 index 2f435c34..00000000 --- a/test/core/reid/dataset/test_market_1501.py +++ /dev/null @@ -1,52 +0,0 @@ -from unittest.mock import patch - -import pytest - -from trackers.core.reid.dataset.market_1501 import parse_market1501_dataset - - -@pytest.mark.parametrize( - "mock_glob_output, expected_result", - [ - ( - # Empty dataset - [], - {}, - ), - ( - # Single image for one person - ["0111_00000000.jpg"], - {"0111": ["0111_00000000.jpg"]}, - ), - ( - # Multiple images for one person - ["0111_00000000.jpg", "0111_00000001.jpg"], - {"0111": ["0111_00000000.jpg", "0111_00000001.jpg"]}, - ), - ( - # Multiple people with multiple images - [ - "0111_00000000.jpg", - "0111_00000001.jpg", - "0112_00000000.jpg", - "0112_00000001.jpg", - ], - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg", "0112_00000001.jpg"], - }, - ), - ( - # Multiple people with varying number of images - ["0111_00000000.jpg", "0111_00000001.jpg", "0112_00000000.jpg"], - { - "0111": ["0111_00000000.jpg", "0111_00000001.jpg"], - "0112": ["0112_00000000.jpg"], - }, - ), - ], -) -def test_parse_market1501_dataset(mock_glob_output, expected_result): - with patch("glob.glob", return_value=mock_glob_output): - result = parse_market1501_dataset("dummy_path") - assert result == expected_result diff --git a/tox.ini b/tox.ini index d769c25c..b3852be8 100644 --- a/tox.ini +++ b/tox.ini @@ -1,5 +1,5 @@ [tox] -envlist = py39,py310,py311,py312,py313 +envlist = py310,py311,py312,py313 [testenv] changedir = test diff --git a/trackers/__init__.py b/trackers/__init__.py index 52ce9de2..a230da3a 100644 --- a/trackers/__init__.py +++ b/trackers/__init__.py @@ -1,18 +1,4 @@ +from trackers.core.bytetrack.tracker import ByteTrackTracker from trackers.core.sort.tracker import SORTTracker -from trackers.log import get_logger -__all__ = ["SORTTracker"] - -logger = get_logger(__name__) - -try: - from trackers.core.deepsort.tracker import DeepSORTTracker - from trackers.core.reid.model import ReIDModel - - __all__.extend(["DeepSORTTracker", "ReIDModel"]) -except ImportError: - logger.warning( - "ReIDModel dependencies not installed. ReIDModel will not be available. " - "Please run `pip install trackers[reid]` and try again." - ) - pass +__all__ = ["ByteTrackTracker", "SORTTracker"] diff --git a/test/core/reid/__init__.py b/trackers/core/bytetrack/__init__.py similarity index 100% rename from test/core/reid/__init__.py rename to trackers/core/bytetrack/__init__.py diff --git a/trackers/core/deepsort/kalman_box_tracker.py b/trackers/core/bytetrack/kalman_box_tracker.py similarity index 76% rename from trackers/core/deepsort/kalman_box_tracker.py rename to trackers/core/bytetrack/kalman_box_tracker.py index 05873bce..83cfb4db 100644 --- a/trackers/core/deepsort/kalman_box_tracker.py +++ b/trackers/core/bytetrack/kalman_box_tracker.py @@ -1,14 +1,11 @@ -from typing import Optional, Union - import numpy as np -class DeepSORTKalmanBoxTracker: +class ByteTrackKalmanBoxTracker: """ - The `DeepSORTKalmanBoxTracker` class represents the internals of a single + The `ByteTrackKalmanBoxTracker` class represents the internals of a single tracked object (bounding box), with a Kalman filter to predict and update - its position. It also maintains a feature vector for the object, which is - used to identify the object across frames. + its position. Attributes: tracker_id (int): Unique identifier for the tracker. @@ -21,12 +18,10 @@ class DeepSORTKalmanBoxTracker: Q (np.ndarray): Process noise covariance matrix. R (np.ndarray): Measurement noise covariance matrix. P (np.ndarray): Error covariance matrix. - features (list[np.ndarray]): List of feature vectors. count_id (int): Class variable to assign unique IDs to each tracker. Args: bbox (np.ndarray): Initial bounding box in the form [x1, y1, x2, y2]. - feature (Optional[np.ndarray]): Optional initial feature vector. """ count_id = 0 @@ -43,7 +38,7 @@ def get_next_tracker_id(cls) -> int: cls.count_id += 1 return next_id - def __init__(self, bbox: np.ndarray, feature: Optional[np.ndarray] = None): + def __init__(self, bbox: np.ndarray): # Initialize with a temporary ID of -1 # Will be assigned a real ID when the track is considered mature self.tracker_id = -1 @@ -68,11 +63,6 @@ def __init__(self, bbox: np.ndarray, feature: Optional[np.ndarray] = None): # Basic constant velocity model self._initialize_kalman_filter() - # Initialize features list - self.features: list[np.ndarray] = [] - if feature is not None: - self.features.append(feature) - def _initialize_kalman_filter(self) -> None: """ Sets up the matrices for the Kalman filter. @@ -149,22 +139,3 @@ def get_state_bbox(self) -> np.ndarray: ], dtype=float, ).reshape(-1) - - def update_feature(self, feature: np.ndarray): - self.features.append(feature) - - def get_feature(self) -> Union[np.ndarray, None]: - """ - Get the mean feature vector for this tracker. - - Returns: - np.ndarray: Mean feature vector. - """ - if len(self.features) > 0: - # Return the mean of all features, thus (in theory) capturing the - # "average appearance" of the object, which should be more robust - # to minor appearance changes. Otherwise, the last feature can - # also be returned like the following: - # return self.features[-1] - return np.mean(self.features, axis=0) - return None diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py new file mode 100644 index 00000000..0122edd9 --- /dev/null +++ b/trackers/core/bytetrack/tracker.py @@ -0,0 +1,335 @@ +from copy import deepcopy +from typing import cast + +import numpy as np +import supervision as sv +from scipy.optimize import linear_sum_assignment + +from trackers.core.base import BaseTracker +from trackers.core.bytetrack.kalman_box_tracker import ByteTrackKalmanBoxTracker +from trackers.utils.sort_utils import ( + get_alive_trackers, + get_iou_matrix, +) + + +class ByteTrackTracker(BaseTracker): + """Implements ByteTrack. + + ByteTrack is a simple, effective, and generic multi-object tracking method + that improves upon tracking-by-detection by associating *every* detection box + instead of discarding low-score ones. This makes it more robust to occlusions. + It uses a two-stage association process and builds on established techniques + like the Kalman Filter for motion prediction and the Hungarian algorithm for + data association. + + Args: + lost_track_buffer: Number of frames to buffer when a track is lost. + Increasing lost_track_buffer enhances occlusion handling, significantly + improving tracking through occlusions, but may increase the possibility + of ID switching for objects that disappear. + frame_rate: Frame rate of the video (frames per second). + Used to calculate the maximum time a track can be lost. + track_activation_threshold: Detection confidence threshold + for track activation. Only detections with confidence above this + threshold will create new tracks. Increasing this threshold may + reduce false positives but may miss real objects with low confidence. + minimum_consecutive_frames: Number of consecutive frames that an object + must be tracked before it is considered a 'valid'/'active/ track. Increasing + `minimum_consecutive_frames` prevents the creation of accidental tracks + from false detection or double detection, but risks missing shorter + tracks. Before the tracker is considered valid, it will be assigned + `-1` as its `tracker_id`. + minimum_iou_threshold: IoU threshold for associating detections to existing tracks. + Prevents the association of lower IoU than the threshold between boxes and tracks. + A higher value will only associate boxes that have more overlapping area. + high_conf_det_threshold: threshold for assigning detections to high probability class. + A higher value will classify only higher confidence/probability detections as 'high probability' + per the ByteTrack algorithm, which are used in the first similarity step of + the algorithm. + """ # noqa: E501 + + def __init__( + self, + lost_track_buffer: int = 30, + frame_rate: float = 30.0, + track_activation_threshold: float = 0.7, + minimum_consecutive_frames: int = 2, + minimum_iou_threshold: float = 0.1, + high_conf_det_threshold: float = 0.6, + ) -> None: + # Calculate maximum frames without update based on lost_track_buffer and + # frame_rate. This scales the buffer based on the frame rate to ensure + # consistent time-based tracking across different frame rates. + self.maximum_frames_without_update = int(frame_rate / 30.0 * lost_track_buffer) + self.minimum_consecutive_frames = minimum_consecutive_frames + self.minimum_iou_threshold = minimum_iou_threshold + self.track_activation_threshold = track_activation_threshold + self.high_conf_det_threshold = high_conf_det_threshold + self.tracks: list[ByteTrackKalmanBoxTracker] = [] + + def _update_detections( + self, + tracks: list[ByteTrackKalmanBoxTracker], + detections: sv.Detections, + updated_detections: list[sv.Detections], + matched_indices: list[tuple[int, int]], + ) -> list[sv.Detections]: + # Update matched tracks with assigned detections. + det_bboxes = detections.xyxy + for row, col in matched_indices: + t = tracks[row] + t.update(det_bboxes[col]) + # If tracker is mature but still has ID -1, assign a new ID + if ( + t.number_of_successful_updates >= self.minimum_consecutive_frames + and t.tracker_id == -1 + ): # Check maturity before assigning ID + t.tracker_id = ByteTrackKalmanBoxTracker.get_next_tracker_id() + + new_det = deepcopy(detections[col : col + 1]) + # Add cast to clarify type for mypy + new_det = cast(sv.Detections, new_det) # ADDED cast + new_det.tracker_id = np.array([t.tracker_id]) + updated_detections.append(new_det) + return updated_detections + + def update( + self, + detections: sv.Detections, + ) -> sv.Detections: + """Updates the tracker state with new detections. + + Performs Kalman Filter prediction, associates detections with existing + tracks based on IoU, updates matched tracks, and initializes new + tracks for unmatched high-confidence detections. + + Args: + detections: The latest set of object detections from a frame. + + Returns: + A copy of the input detections, augmented with assigned `tracker_id` for + each successfully tracked object. Detections not associated with a + track will not have a `tracker_id`. The order of the detections is not + guaranteed to be the same as the input detections. + """ + + if len(self.tracks) == 0 and len(detections) == 0: + detections.tracker_id = np.array([], dtype=int) + return detections + updated_detections: list[ + sv.Detections + ] = [] # List for returning the updated detections with its new assigned track id # noqa: E501 + + # Predict new locations for existing tracks + for tracker in self.tracks: + tracker.predict() + # Assign a default tracker_id with the correct shape + detections.tracker_id = -np.ones(len(detections)) + # Split into high confidence boxes and lower based on self.high_conf_det_threshold # noqa: E501 + high_prob_detections, low_prob_detections = ( + self._get_high_and_low_probability_detections(detections) + ) + + # Step 1: first association, with high confidence boxes + matched_indices, unmatched_tracks, unmatched_high_prob_detections = ( + self._similarity_step( + high_prob_detections, + self.tracks, + ) + ) + + # Update matched tracks with high-confidence detections + self._update_detections( + self.tracks, + high_prob_detections, + updated_detections, + matched_indices, + ) + + remaining_tracks = [self.tracks[i] for i in unmatched_tracks] + + # Step 2: associate Low Probability detections with remaining tracks + matched_indices, unmatched_tracks, unmatched_detections = self._similarity_step( + low_prob_detections, remaining_tracks + ) + + # Update matched tracks with low-confidence detections + self._update_detections( + remaining_tracks, + low_prob_detections, + updated_detections, + matched_indices, + ) + + # Add unmatched low prob predictions to updated predictions + for det_index in unmatched_detections: + new_det = deepcopy(low_prob_detections[det_index : det_index + 1]) + + new_det.tracker_id = np.array([-1]) + updated_detections.append(new_det) + + self._spawn_new_trackers( + high_prob_detections, + high_prob_detections.xyxy, + unmatched_high_prob_detections, + updated_detections, + ) + + # Kill lost tracks + self.tracks = get_alive_trackers( + trackers=self.tracks, + maximum_frames_without_update=self.maximum_frames_without_update, + minimum_consecutive_frames=self.minimum_consecutive_frames, + ) + final_updated_detections: sv.Detections = sv.Detections.merge( + updated_detections + ) + if len(final_updated_detections) == 0: + final_updated_detections.tracker_id = np.array([], dtype=int) + return final_updated_detections + + def _get_high_and_low_probability_detections( + self, detections: sv.Detections + ) -> tuple[sv.Detections, sv.Detections]: + """ + Splits the input detections into high-confidence and low-confidence sets + based on the `self.high_conf_det_threshold`. + + Args: + detections: The input detections with confidence scores. + + Returns: + A tuple containing two `sv.Detections objects`: the first for + high-confidence detections `(confidence >= threshold)` and the second + for low-confidence detections `(confidence < threshold)`. + """ + # Check if confidence scores exist before comparing + if detections.confidence is not None: + # Perform element-wise comparison if confidence is a NumPy array + condition = detections.confidence >= self.high_conf_det_threshold + else: + # If no confidence scores, no detections meet the threshold + # Create a boolean array of False with the same length as detections + condition = np.zeros(len(detections), dtype=bool) + + high_confidence = detections[condition] + low_confidence = detections[np.logical_not(condition)] + return high_confidence, low_confidence + + def _get_associated_indices( + self, + similarity_matrix: np.ndarray, + min_similarity_thresh: float, + ) -> tuple[list[tuple[int, int]], set[int], set[int]]: + """ + Associate detections to tracks based on Similarity (IoU) using the + Jonker-Volgenant algorithm approach with no initialization instead of the + Hungarian algorithm as mentioned in the SORT paper, but it solves the + assignment problem in an optimal way. + + Args: + similarity_matrix: Similarity matrix between tracks (rows) and detections (columns). + min_similarity_thresh: Minimum similarity threshold for a valid match. + + Returns: + Matched indices (list of (tracker_idx, detection_idx)), indices of + unmatched tracks, indices of unmatched detections. + """ # noqa: E501 + matched_indices = [] + n_tracks, n_detections = similarity_matrix.shape + unmatched_tracks = set(range(n_tracks)) + unmatched_detections = set(range(n_detections)) + + if n_tracks > 0 and n_detections > 0: + row_indices, col_indices = linear_sum_assignment( + similarity_matrix, maximize=True + ) + for row, col in zip(row_indices, col_indices): + if similarity_matrix[row, col] >= min_similarity_thresh: + matched_indices.append((row, col)) + unmatched_tracks.remove(row) + unmatched_detections.remove(col) + + return matched_indices, unmatched_tracks, unmatched_detections + + def _spawn_new_trackers( + self, + detections: sv.Detections, + detection_boxes: np.ndarray, + unmatched_detections: set[int], + updated_detections: list[sv.Detections], + ): + """ + Create new trackers for unmatched detections and + append detections to updated_detections detections. + + Args: + detections: Current detections. + detection_boxes: Bounding boxes for detections. + unmatched_detections: Indices of unmatched detections. + updated_detections: List with all the detections + + """ + for detection_idx in unmatched_detections: + # Check for detections.confidence existence and index bounds + if detections.confidence is not None and detection_idx < len( + detections.confidence + ): + # Assign to a temporary variable with explicit type hint + confidence_score: float = float(detections.confidence[detection_idx]) + + # Use the temporary variable in the comparison + if confidence_score >= self.track_activation_threshold: + # Original logic for high confidence detection + + new_tracker = ByteTrackKalmanBoxTracker( + bbox=detection_boxes[detection_idx] + ) + self.tracks.append(new_tracker) + + new_det = deepcopy(detections[detection_idx : detection_idx + 1]) + new_det = cast(sv.Detections, new_det) # Cast added previously + new_det.tracker_id = np.array([-1]) + updated_detections.append(new_det) + else: + pass # Do nothing, the detection remains unmatched + + def _similarity_step( + self, + detections: sv.Detections, + tracks: list[ByteTrackKalmanBoxTracker], + ) -> tuple[list[tuple[int, int]], set[int], set[int]]: + """Measures similarity based on IoU between tracks and detections and returns the matches + and unmatched tracks/detections. Is used for step 1 and 2 of the BYTE algorithm. + + Args: + detections: The set of object detections. + tracks: The list of tracks that will be matched to the detections. + + Returns: + A tuple containing: + - matched_indices: A list of (tracker_idx, detection_idx) pairs. + - unmatched_tracks_indices: A set of indices for tracks that + were not matched. + - unmatched_detections_indices: A set of indices for detections + that were not matched. + """ # noqa: E501 + # Build IoU cost matrix between detections and predicted bounding boxes + similarity_matrix = get_iou_matrix(tracks, detections.xyxy) + thresh = self.minimum_iou_threshold + + # Associate detections to tracks based on the higher value of the + # similarity matrix, using the Jonker-Volgenant algorithm (linear_sum_assignment). # noqa: E501 + matched_indices, unmatched_tracks, unmatched_detections = ( + self._get_associated_indices(similarity_matrix, thresh) + ) + return matched_indices, unmatched_tracks, unmatched_detections + + def reset(self) -> None: + """Resets the tracker's internal state. + + Clears all active tracks and resets the track ID counter. + """ + self.tracks = [] + ByteTrackKalmanBoxTracker.count_id = 0 diff --git a/trackers/core/deepsort/__init__.py b/trackers/core/deepsort/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/trackers/core/deepsort/tracker.py b/trackers/core/deepsort/tracker.py deleted file mode 100644 index 0f157810..00000000 --- a/trackers/core/deepsort/tracker.py +++ /dev/null @@ -1,296 +0,0 @@ -from typing import Optional - -import numpy as np -import supervision as sv -from scipy.spatial.distance import cdist - -from trackers.core.base import BaseTrackerWithFeatures -from trackers.core.deepsort.kalman_box_tracker import DeepSORTKalmanBoxTracker -from trackers.core.reid import ReIDModel -from trackers.utils.sort_utils import ( - get_alive_trackers, - get_iou_matrix, - update_detections_with_track_ids, -) - - -class DeepSORTTracker(BaseTrackerWithFeatures): - """Implements DeepSORT (Deep Simple Online and Realtime Tracking). - - DeepSORT extends SORT by integrating appearance information using a deep - learning model, improving tracking through occlusions and reducing ID switches. - It combines motion (Kalman filter) and appearance cues for data association. - - Args: - reid_model (ReIDModel): An instance of a `ReIDModel` to extract - appearance features. - device (Optional[str]): Device to run the feature extraction - model on (e.g., 'cpu', 'cuda'). - lost_track_buffer (int): Number of frames to buffer when a track is lost. - Enhances occlusion handling but may increase ID switches for similar objects. - frame_rate (float): Frame rate of the video (frames per second). - Used to calculate the maximum time a track can be lost. - track_activation_threshold (float): Detection confidence threshold - for track activation. Higher values reduce false positives - but might miss objects. - minimum_consecutive_frames (int): Number of consecutive frames an object - must be tracked to be considered 'valid'. Prevents spurious tracks but - may miss short tracks. - minimum_iou_threshold (float): IOU threshold for gating in the matching cascade. - appearance_threshold (float): Cosine distance threshold for appearance matching. - Only matches below this threshold are considered valid. - appearance_weight (float): Weight (0-1) balancing motion (IOU) and appearance - distance in the combined matching cost. - distance_metric (str): Distance metric for appearance features (e.g., 'cosine', - 'euclidean'). See `scipy.spatial.distance.cdist`. - """ # noqa: E501 - - def __init__( - self, - reid_model: ReIDModel, - device: Optional[str] = None, - lost_track_buffer: int = 30, - frame_rate: float = 30.0, - track_activation_threshold: float = 0.25, - minimum_consecutive_frames: int = 3, - minimum_iou_threshold: float = 0.3, - appearance_threshold: float = 0.7, - appearance_weight: float = 0.5, - distance_metric: str = "cosine", - ): - self.reid_model = reid_model - self.lost_track_buffer = lost_track_buffer - self.frame_rate = frame_rate - self.minimum_consecutive_frames = minimum_consecutive_frames - self.minimum_iou_threshold = minimum_iou_threshold - self.track_activation_threshold = track_activation_threshold - self.appearance_threshold = appearance_threshold - self.appearance_weight = appearance_weight - self.distance_metric = distance_metric - # Calculate maximum frames without update based on lost_track_buffer and - # frame_rate. This scales the buffer based on the frame rate to ensure - # consistent time-based tracking across different frame rates. - self.maximum_frames_without_update = int( - self.frame_rate / 30.0 * self.lost_track_buffer - ) - - self.trackers: list[DeepSORTKalmanBoxTracker] = [] - - def _get_appearance_distance_matrix( - self, - detection_features: np.ndarray, - ) -> np.ndarray: - """ - Calculate appearance distance matrix between tracks and detections. - - Args: - detection_features (np.ndarray): Features extracted from current detections. - - Returns: - np.ndarray: Appearance distance matrix. - """ - - if len(self.trackers) == 0 or len(detection_features) == 0: - return np.zeros((len(self.trackers), len(detection_features))) - - track_features = np.array([t.get_feature() for t in self.trackers]) - distance_matrix = cdist( - track_features, detection_features, metric=self.distance_metric - ) - distance_matrix = np.clip(distance_matrix, 0, 1) - - return distance_matrix - - def _get_combined_distance_matrix( - self, - iou_matrix: np.ndarray, - appearance_dist_matrix: np.ndarray, - ) -> np.ndarray: - """ - Combine IOU and appearance distances into a single distance matrix. - - Args: - iou_matrix (np.ndarray): IOU matrix between tracks and detections. - appearance_dist_matrix (np.ndarray): Appearance distance matrix. - - Returns: - np.ndarray: Combined distance matrix. - """ - iou_distance: np.ndarray = 1 - iou_matrix - combined_dist = ( - 1 - self.appearance_weight - ) * iou_distance + self.appearance_weight * appearance_dist_matrix - - # Set high distance for IOU below threshold - mask = iou_matrix < self.minimum_iou_threshold - combined_dist[mask] = 1.0 - - # Set high distance for appearance above threshold - mask = appearance_dist_matrix > self.appearance_threshold - combined_dist[mask] = 1.0 - - return combined_dist - - def _get_associated_indices( - self, - iou_matrix: np.ndarray, - detection_features: np.ndarray, - ) -> tuple[list[tuple[int, int]], set[int], set[int]]: - """ - Associate detections to trackers based on both IOU and appearance. - - Args: - iou_matrix (np.ndarray): IOU matrix between tracks and detections. - detection_features (np.ndarray): Features extracted from current detections. - - Returns: - tuple[list[tuple[int, int]], set[int], set[int]]: Matched indices, - unmatched trackers, unmatched detections. - """ - appearance_dist_matrix = self._get_appearance_distance_matrix( - detection_features - ) - combined_dist = self._get_combined_distance_matrix( - iou_matrix, appearance_dist_matrix - ) - matched_indices = [] - unmatched_trackers = set(range(len(self.trackers))) - unmatched_detections = set(range(len(detection_features))) - - if combined_dist.size > 0: - row_indices, col_indices = np.where(combined_dist < 1.0) - sorted_pairs = sorted( - zip(map(int, row_indices), map(int, col_indices)), - key=lambda x: combined_dist[x[0], x[1]], - ) - - used_rows = set() - used_cols = set() - for row, col in sorted_pairs: - if (row not in used_rows) and (col not in used_cols): - used_rows.add(row) - used_cols.add(col) - matched_indices.append((row, col)) - - unmatched_trackers = unmatched_trackers - {int(row) for row in used_rows} - unmatched_detections = unmatched_detections - { - int(col) for col in used_cols - } - - return matched_indices, unmatched_trackers, unmatched_detections - - def _spawn_new_trackers( - self, - detections: sv.Detections, - detection_boxes: np.ndarray, - detection_features: np.ndarray, - unmatched_detections: set[int], - ): - """ - Create new trackers for unmatched detections with confidence above threshold. - - Args: - detections (sv.Detections): Current detections. - detection_boxes (np.ndarray): Bounding boxes for detections. - detection_features (np.ndarray): Features for detections. - unmatched_detections (set[int]): Indices of unmatched detections. - """ - for detection_idx in unmatched_detections: - if ( - detections.confidence is None - or detection_idx >= len(detections.confidence) - or detections.confidence[detection_idx] - >= self.track_activation_threshold - ): - feature = None - if ( - detection_features is not None - and len(detection_features) > detection_idx - ): - feature = detection_features[detection_idx] - - new_tracker = DeepSORTKalmanBoxTracker( - bbox=detection_boxes[detection_idx], feature=feature - ) - self.trackers.append(new_tracker) - - def update(self, detections: sv.Detections, frame: np.ndarray) -> sv.Detections: - """Updates the tracker state with new detections and appearance features. - - Extracts appearance features, performs Kalman filter prediction, calculates - IOU and appearance distance matrices, associates detections with tracks using - a combined metric, updates matched tracks (position and appearance), and - initializes new tracks for unmatched high-confidence detections. - - Args: - detections (sv.Detections): The latest set of object detections. - frame (np.ndarray): The current video frame, used for extracting - appearance features from detections. - - Returns: - sv.Detections: A copy of the input detections, augmented with assigned - `tracker_id` for each successfully tracked object. Detections not - associated with a track will not have a `tracker_id`. - """ - if len(self.trackers) == 0 and len(detections) == 0: - detections.tracker_id = np.array([], dtype=int) - return detections - - # Convert detections to a (N x 4) array (x1, y1, x2, y2) - detection_boxes = ( - detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) - ) - - # Extract appearance features from the frame and detections - detection_features = self.reid_model.extract_features(detections, frame) - - # Predict new locations for existing trackers - for tracker in self.trackers: - tracker.predict() - - # Build IOU cost matrix between detections and predicted bounding boxes - iou_matrix = get_iou_matrix( - trackers=self.trackers, detection_boxes=detection_boxes - ) - - # Associate detections to trackers based on IOU - matched_indices, _, unmatched_detections = self._get_associated_indices( - iou_matrix, detection_features - ) - - # Update matched trackers with assigned detections - for row, col in matched_indices: - self.trackers[row].update(detection_boxes[col]) - if detection_features is not None and len(detection_features) > col: - self.trackers[row].update_feature(detection_features[col]) - - # Create new trackers for unmatched detections with confidence above threshold - self._spawn_new_trackers( - detections, detection_boxes, detection_features, unmatched_detections - ) - - # Remove dead trackers - self.trackers = get_alive_trackers( - trackers=self.trackers, - maximum_frames_without_update=self.maximum_frames_without_update, - minimum_consecutive_frames=self.minimum_consecutive_frames, - ) - - # Update detections with tracker IDs - updated_detections = update_detections_with_track_ids( - trackers=self.trackers, - detections=detections, - detection_boxes=detection_boxes, - minimum_consecutive_frames=self.minimum_consecutive_frames, - minimum_iou_threshold=self.minimum_iou_threshold, - ) - - return updated_detections - - def reset(self) -> None: - """Resets the tracker's internal state. - - Clears all active tracks and resets the track ID counter. - """ - self.trackers = [] - DeepSORTKalmanBoxTracker.count_id = 0 diff --git a/trackers/core/reid/__init__.py b/trackers/core/reid/__init__.py deleted file mode 100644 index ac00fe1c..00000000 --- a/trackers/core/reid/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -from trackers.log import get_logger - -logger = get_logger(__name__) - -try: - from trackers.core.reid.dataset.base import TripletsDataset - from trackers.core.reid.dataset.market_1501 import get_market1501_dataset - from trackers.core.reid.model import ReIDModel - - __all__ = ["ReIDModel", "TripletsDataset", "get_market1501_dataset"] -except ImportError: - logger.warning( - "ReIDModel dependencies not installed. ReIDModel will not be available. " - "Please run `pip install trackers[reid]` and try again." - ) - pass diff --git a/trackers/core/reid/callbacks.py b/trackers/core/reid/callbacks.py deleted file mode 100644 index c879382f..00000000 --- a/trackers/core/reid/callbacks.py +++ /dev/null @@ -1,245 +0,0 @@ -import os -from typing import Any, Optional - -import matplotlib.pyplot as plt - - -class BaseCallback: - def on_train_batch_start(self, logs: dict, idx: int): - pass - - def on_train_batch_end(self, logs: dict, idx: int): - pass - - def on_train_epoch_end(self, logs: dict, epoch: int): - pass - - def on_validation_batch_start(self, logs: dict, idx: int): - pass - - def on_validation_batch_end(self, logs: dict, idx: int): - pass - - def on_validation_epoch_end(self, logs: dict, epoch: int): - pass - - def on_checkpoint_save(self, checkpoint_path: str, epoch: int): - pass - - def on_end(self): - pass - - -class TensorboardCallback(BaseCallback): - def __init__( - self, - log_dir: Optional[str] = None, - comment: str = "", - purge_step: Optional[Any] = None, - max_queue: int = 10, - flush_secs: int = 120, - filename_suffix: str = "", - ): - from torch.utils.tensorboard import SummaryWriter - - self.writer = SummaryWriter( - log_dir, - comment=comment, - filename_suffix=filename_suffix, - purge_step=purge_step, - max_queue=max_queue, - flush_secs=flush_secs, - ) - - def on_train_batch_end(self, logs: dict, idx: int): - for key, value in logs.items(): - self.writer.add_scalar(key, value, idx) - - def on_train_epoch_end(self, logs: dict, epoch: int): - for key, value in logs.items(): - self.writer.add_scalar(key, value, epoch) - - def on_validation_epoch_end(self, logs: dict, epoch: int): - for key, value in logs.items(): - self.writer.add_scalar(key, value, epoch) - - def on_end(self): - self.writer.flush() - self.writer.close() - - -class WandbCallback(BaseCallback): - def __init__(self, config: dict[str, Any]) -> None: - import wandb - - self.run = wandb.init(config=config) if not wandb.run else wandb.run # type: ignore - - self.run.define_metric("batch/step") - self.run.define_metric("batch/train/loss", step_metric="batch/step") - - self.run.define_metric("epoch") - self.run.define_metric("train/loss", step_metric="epoch") - self.run.define_metric("validation/loss", step_metric="epoch") - - def on_train_batch_end(self, logs: dict, idx: int): - logs["batch/step"] = idx - self.run.log(logs) - - def on_train_epoch_end(self, logs: dict, epoch: int): - logs["epoch"] = epoch - self.run.log(logs) - - def on_validation_epoch_end(self, logs: dict, epoch: int): - logs["epoch"] = epoch - self.run.log(logs) - - def on_checkpoint_save(self, checkpoint_path: str, epoch: int): - self.run.log_model( - path=checkpoint_path, - name=f"checkpoint_{self.run.id}", - aliases=[f"epoch-{epoch}", "latest"], - ) - - def on_end(self): - self.run.finish() - - -class MatplotlibCallback(BaseCallback): - def __init__(self, log_dir: str): - self.log_dir = log_dir - self.train_history: dict[str, list[tuple[int, float]]] = {} - self.validation_history: dict[str, list[tuple[int, float]]] = {} - - def on_train_batch_end(self, logs: dict, idx: int): - for key, value in logs.items(): - self.train_history.setdefault(key, []).append((idx, value)) - - def on_train_epoch_end(self, logs: dict, epoch: int): - for key, value in logs.items(): - self.train_history.setdefault(key, []).append((epoch, value)) - - def on_validation_epoch_end(self, logs: dict, epoch: int): - for key, value in logs.items(): - self.validation_history.setdefault(key, []).append((epoch, value)) - - def _plot_subplot( - self, - ax, - title_prefix: str, - base_metric_name: str, - xlabel: str, - train_keys: list[str], - val_keys: Optional[list[str]] = None, - ): - train_data_points = [] - for key in train_keys: - data = self.train_history.get(key) - if data: # Checks for None and non-empty list - train_data_points = data - break - - val_data_points = [] - if val_keys: - for key in val_keys: - data = self.validation_history.get(key) - if data: # Checks for None and non-empty list - val_data_points = data - break - - plotted_anything = False - if train_data_points: - x, y = zip(*train_data_points) - ax.plot(x, y, label="train", marker=".", markersize=5, linewidth=1) - plotted_anything = True - - if val_data_points: - x_val, y_val = zip(*val_data_points) - ax.plot( - x_val, - y_val, - label="validation", - marker=".", - markersize=5, - linewidth=1, - linestyle="--", - ) - plotted_anything = True - - formatted_base_name = " ".join( - [item.capitalize() for item in base_metric_name.split("_")] - ) - ax.set_title(f"{title_prefix} {formatted_base_name}") - ax.set_xlabel(xlabel) - ax.set_ylabel(formatted_base_name) - - if plotted_anything: - ax.legend() - ax.grid(True, linestyle="--", alpha=0.7) - else: - ax.text( - 0.5, - 0.5, - "No data", - ha="center", - va="center", - transform=ax.transAxes, - fontsize=10, - color="gray", - ) - ax.set_xticks([]) - ax.set_yticks([]) - for spine in ax.spines.values(): - spine.set_edgecolor("lightgray") - - def on_end(self): - if not self.train_history and not self.validation_history: - return - - fig, axes = plt.subplots(2, 2, figsize=(12, 8), squeeze=False) - - # Plot 1: Top-left - Batch Triplet Accuracy - self._plot_subplot( - axes[0, 0], - title_prefix="Batch", - base_metric_name="triplet_accuracy", - xlabel="Batch", - train_keys=["batch/triplet_accuracy", "batch/train/triplet_accuracy"], - val_keys=None, - ) - - # Plot 2: Top-right - Epoch Triplet Accuracy - self._plot_subplot( - axes[0, 1], - title_prefix="Epoch", - base_metric_name="triplet_accuracy", - xlabel="Epoch", - train_keys=["train/triplet_accuracy", "triplet_accuracy"], - val_keys=["validation/triplet_accuracy", "triplet_accuracy"], - ) - - # Plot 3: Bottom-left - Batch Loss - self._plot_subplot( - axes[1, 0], - title_prefix="Batch", - base_metric_name="loss", - xlabel="Batch", - train_keys=["batch/loss", "batch/train/loss"], - val_keys=None, - ) - - # Plot 4: Bottom-right - Epoch Loss - self._plot_subplot( - axes[1, 1], - title_prefix="Epoch", - base_metric_name="loss", - xlabel="Epoch", - train_keys=["train/loss", "loss"], - val_keys=["validation/loss", "loss"], - ) - - os.makedirs(self.log_dir, exist_ok=True) - - plt.tight_layout(pad=2.0) - fig.savefig(os.path.join(self.log_dir, "metrics_plot.png"), dpi=150) - plt.show() - plt.close(fig) diff --git a/trackers/core/reid/dataset/__init__.py b/trackers/core/reid/dataset/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/trackers/core/reid/dataset/base.py b/trackers/core/reid/dataset/base.py deleted file mode 100644 index fc4efd43..00000000 --- a/trackers/core/reid/dataset/base.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import annotations - -import random -from pathlib import Path -from typing import Optional, Tuple, Union - -import torch -from PIL import Image -from supervision.dataset.utils import train_test_split -from torch.utils.data import Dataset -from torchvision.transforms import Compose, ToTensor - -from trackers.core.reid.dataset.utils import validate_tracker_id_to_images - - -class TripletsDataset(Dataset): - """A dataset that provides triplets of images for training ReID models. - - This dataset is designed for training models with triplet loss, where each sample - consists of an anchor image, a positive image (same identity as anchor), - and a negative image (different identity from anchor). - - Args: - tracker_id_to_images (dict[str, list[str]]): Dictionary mapping tracker IDs - to lists of image paths - transforms (Optional[Compose]): Optional image transformations to apply - - Attributes: - tracker_id_to_images (dict[str, list[str]]): Dictionary mapping tracker IDs - to lists of image paths - transforms (Optional[Compose]): Optional image transformations to apply - tracker_ids (list[str]): List of all unique tracker IDs in the dataset - """ - - def __init__( - self, - tracker_id_to_images: dict[str, list[str]], - transforms: Optional[Compose] = None, - ): - self.tracker_id_to_images = validate_tracker_id_to_images(tracker_id_to_images) - self.transforms = transforms or ToTensor() - self.tracker_ids = list(self.tracker_id_to_images.keys()) - - @classmethod - def from_image_directories( - cls, - root_directory: str, - transforms: Optional[Compose] = None, - image_extensions: Tuple[str, ...] = (".jpg", ".jpeg", ".png"), - ) -> TripletsDataset: - """ - Create TripletsDataset from a directory structured by tracker IDs. - - Args: - root_directory (str): Root directory with tracker folders. - transforms (Optional[Compose]): Optional image transformations. - image_extensions (Tuple[str, ...]): Valid image extensions to load. - - Returns: - TripletsDataset: An initialized dataset. - """ - root_path = Path(root_directory) - tracker_id_to_images = {} - - for tracker_path in sorted(root_path.iterdir()): - if not tracker_path.is_dir(): - continue - - image_paths = sorted( - [ - str(image_path) - for image_path in tracker_path.glob("*") - if image_path.suffix.lower() in image_extensions - and image_path.is_file() - ] - ) - - if image_paths: - tracker_id_to_images[tracker_path.name] = image_paths - - return cls( - tracker_id_to_images=tracker_id_to_images, - transforms=transforms, - ) - - def __len__(self) -> int: - """ - Return the number of unique tracker IDs (identities) in the dataset. - - Returns: - int: The total number of unique identities (tracker IDs) available for - sampling triplets. - """ - return len(self.tracker_ids) - - def _load_and_transform_image(self, image_path: str) -> torch.Tensor: - image = Image.open(image_path).convert("RGB") - if self.transforms: - image = self.transforms(image) - return image - - def _get_triplet_image_paths(self, tracker_id: str) -> Tuple[str, str, str]: - tracker_id_image_paths = self.tracker_id_to_images[tracker_id] - - anchor_image_path, positive_image_path = random.sample( # nosec B311 - tracker_id_image_paths, 2 - ) - - negative_candidates = [tid for tid in self.tracker_ids if tid != tracker_id] - negative_tracker_id = random.choice(negative_candidates) # nosec B311 - - negative_image_path = random.choice( # nosec B311 - self.tracker_id_to_images[negative_tracker_id] - ) - - return anchor_image_path, positive_image_path, negative_image_path - - def __getitem__( - self, index: int - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ - Retrieve a random triplet (anchor, positive, negative) of images for a given - identity. - - For the tracker ID at the given index, samples two different images as the - anchor and positive (same identity), and one image from a different tracker ID - as the negative (different identity). - - Args: - index (int): Index of the tracker ID (identity) to sample the triplet from. - - Returns: - Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - A tuple containing the anchor, positive, and negative image tensors. - """ - tracker_id = self.tracker_ids[index] - - anchor_image_path, positive_image_path, negative_image_path = ( - self._get_triplet_image_paths(tracker_id) - ) - - anchor_image = self._load_and_transform_image(anchor_image_path) - positive_image = self._load_and_transform_image(positive_image_path) - negative_image = self._load_and_transform_image(negative_image_path) - - return anchor_image, positive_image, negative_image - - def split( - self, - split_ratio: float = 0.8, - random_state: Optional[Union[int, float, str, bytes, bytearray]] = None, - shuffle: bool = True, - ) -> Tuple[TripletsDataset, TripletsDataset]: - train_tracker_id_to_images, validation_tracker_id_to_images = train_test_split( - list(self.tracker_id_to_images.keys()), - train_ratio=split_ratio, - random_state=random_state, - shuffle=shuffle, - ) - train_tracker_id_to_images = { - tracker_id: self.tracker_id_to_images[tracker_id] - for tracker_id in train_tracker_id_to_images - } - validation_tracker_id_to_images = { - tracker_id: self.tracker_id_to_images[tracker_id] - for tracker_id in validation_tracker_id_to_images - } - return ( - TripletsDataset(train_tracker_id_to_images, self.transforms), - TripletsDataset(validation_tracker_id_to_images, self.transforms), - ) diff --git a/trackers/core/reid/dataset/market_1501.py b/trackers/core/reid/dataset/market_1501.py deleted file mode 100644 index a1b15b73..00000000 --- a/trackers/core/reid/dataset/market_1501.py +++ /dev/null @@ -1,61 +0,0 @@ -import glob -import os -from collections import defaultdict -from typing import Dict, List, Optional, Tuple, Union - -from torchvision.transforms import Compose - -from trackers.core.reid.dataset.base import TripletsDataset - - -def parse_market1501_dataset(data_dir: str) -> Dict[str, List[str]]: - """Parse the [Market1501 dataset](https://paperswithcode.com/dataset/market-1501) - to create a dictionary mapping tracker IDs to lists of image paths. - - Args: - data_dir (str): The path to the Market1501 dataset. - - Returns: - Dict[str, List[str]]: A dictionary mapping tracker IDs to lists of image paths. - """ - image_files = glob.glob(os.path.join(data_dir, "*.jpg")) - tracker_id_to_images = defaultdict(list) - for image_file in image_files: - tracker_id = os.path.basename(image_file).split("_")[0] - tracker_id_to_images[tracker_id].append(image_file) - return dict(tracker_id_to_images) - - -def get_market1501_dataset( - data_dir: str, - split_ratio: Optional[float] = None, - random_state: Optional[Union[int, float, str, bytes, bytearray]] = None, - shuffle: bool = True, - transforms: Optional[Compose] = None, -) -> Union[TripletsDataset, Tuple[TripletsDataset, TripletsDataset]]: - """Get the [Market1501 dataset](https://paperswithcode.com/dataset/market-1501). - - Args: - data_dir (str): The path to the bounding box train/test directory of the - [Market1501 dataset](https://paperswithcode.com/dataset/market-1501). - split_ratio (Optional[float]): The ratio of the dataset to split into training - and validation sets. If `None`, the dataset is returned as a single - `TripletsDataset` object, otherwise the dataset is split into a tuple of - training and validation `TripletsDataset` objects. - random_state (Optional[Union[int, float, str, bytes, bytearray]]): The random - state to use for the split. - shuffle (bool): Whether to shuffle the dataset. - transforms (Optional[Compose]): The transforms to apply to the dataset. - - Returns: - Tuple[TripletsDataset, TripletsDataset]: A tuple of training and validation - `TripletsDataset` objects. - """ - tracker_id_to_images = parse_market1501_dataset(data_dir) - dataset = TripletsDataset(tracker_id_to_images, transforms) - if split_ratio is not None: - train_dataset, validation_dataset = dataset.split( - split_ratio=split_ratio, random_state=random_state, shuffle=shuffle - ) - return train_dataset, validation_dataset - return dataset diff --git a/trackers/core/reid/dataset/utils.py b/trackers/core/reid/dataset/utils.py deleted file mode 100644 index 41436a82..00000000 --- a/trackers/core/reid/dataset/utils.py +++ /dev/null @@ -1,35 +0,0 @@ -from trackers.log import get_logger - -logger = get_logger(__name__) - - -def validate_tracker_id_to_images( - tracker_id_to_images: dict[str, list[str]], -) -> dict[str, list[str]]: - """Validates a dictionary that maps tracker IDs to lists of image paths for the - `TripletsDataset` for training ReID models using triplet loss. - - Args: - tracker_id_to_images (dict[str, list[str]]): The tracker ID to images - dictionary. - - Returns: - dict[str, list[str]]: The validated tracker ID to images dictionary. - """ - valid_tracker_ids = {} - for tracker_id, image_paths in tracker_id_to_images.items(): - if len(image_paths) < 2: - logger.warning( - f"Tracker ID '{tracker_id}' has less than 2 images. " - f"Skipping this tracker ID." - ) - else: - valid_tracker_ids[tracker_id] = image_paths - - if len(valid_tracker_ids) < 2: - raise ValueError( - "Tracker ID to images dictionary must contain at least 2 items " - "to select negative samples." - ) - - return valid_tracker_ids diff --git a/trackers/core/reid/metrics.py b/trackers/core/reid/metrics.py deleted file mode 100644 index 7d348f00..00000000 --- a/trackers/core/reid/metrics.py +++ /dev/null @@ -1,67 +0,0 @@ -from abc import ABC, abstractmethod - -import torch -import torch.nn.functional as F - - -class TripletMetric(ABC): - @abstractmethod - def update( - self, - anchor_embed: torch.Tensor, - positive_embed: torch.Tensor, - negative_embed: torch.Tensor, - ) -> None: - pass - - @abstractmethod - def compute(self) -> float: - pass - - @abstractmethod - def reset(self) -> None: - pass - - -class TripletAccuracyMetric(TripletMetric): - """ - Calculates the triplet accuracy using pairwise distance. - Accuracy is defined as the proportion of triplets where the distance - between the anchor and positive embedding is less than the distance - between the anchor and negative embedding. - """ - - def __init__(self): - self.correct = 0 - self.total = 0 - - def __str__(self): - return "triplet_accuracy" - - def update( - self, - anchor_embed: torch.Tensor, - positive_embed: torch.Tensor, - negative_embed: torch.Tensor, - ) -> None: - """ - Update the metric with a batch of embeddings. - - Args: - anchor_embed (torch.Tensor): Embeddings of the anchor samples. - positive_embed (torch.Tensor): Embeddings of the positive samples. - negative_embed (torch.Tensor): Embeddings of the negative samples. - """ - dist_ap = F.pairwise_distance(anchor_embed, positive_embed, p=2) - dist_an = F.pairwise_distance(anchor_embed, negative_embed, p=2) - self.correct += torch.sum(dist_ap < dist_an).item() - self.total += anchor_embed.size(0) - - def compute(self) -> float: - if self.total == 0: - return 0.0 - return self.correct / self.total - - def reset(self) -> None: - self.correct = 0 - self.total = 0 diff --git a/trackers/core/reid/model.py b/trackers/core/reid/model.py deleted file mode 100644 index bbd7aacf..00000000 --- a/trackers/core/reid/model.py +++ /dev/null @@ -1,549 +0,0 @@ -from __future__ import annotations - -import json -import os -from typing import Any, Callable, Optional, Union - -import numpy as np -import PIL -import supervision as sv -import timm -import torch -import torch.nn as nn -import torch.optim as optim -from safetensors.torch import save_file -from timm.data import resolve_data_config -from timm.data.transforms_factory import create_transform -from torch.utils.data import DataLoader -from torchvision.transforms import Compose, ToPILImage -from tqdm.auto import tqdm - -from trackers.core.reid.callbacks import BaseCallback -from trackers.core.reid.metrics import ( - TripletAccuracyMetric, - TripletMetric, -) -from trackers.log import get_logger -from trackers.utils.torch_utils import load_safetensors_checkpoint, parse_device_spec - -logger = get_logger(__name__) - - -def _initialize_reid_model_from_timm( - cls, - model_name_or_checkpoint_path: str, - device: Optional[str] = "auto", - get_pooled_features: bool = True, - **kwargs, -): - if model_name_or_checkpoint_path not in timm.list_models( - filter=model_name_or_checkpoint_path, pretrained=True - ): - probable_model_name_list = timm.list_models( - f"*{model_name_or_checkpoint_path}*", pretrained=True - ) - if len(probable_model_name_list) == 0: - raise ValueError( - f"Model {model_name_or_checkpoint_path} not found in timm. " - + "Please check the model name and try again." - ) - logger.warning( - f"Model {model_name_or_checkpoint_path} not found in timm. " - + f"Using {probable_model_name_list[0]} instead." - ) - model_name_or_checkpoint_path = probable_model_name_list[0] - if not get_pooled_features: - kwargs["global_pool"] = "" - model = timm.create_model( - model_name_or_checkpoint_path, pretrained=True, num_classes=0, **kwargs - ) - config = resolve_data_config(model.pretrained_cfg) - transforms = create_transform(**config) - model_metadata = { - "model_name_or_checkpoint_path": model_name_or_checkpoint_path, - "get_pooled_features": get_pooled_features, - "kwargs": kwargs, - } - return cls(model, device, transforms, model_metadata) - - -def _initialize_reid_model_from_checkpoint(cls, checkpoint_path: str): - state_dict, config = load_safetensors_checkpoint(checkpoint_path) - reid_model_instance = _initialize_reid_model_from_timm( - cls, **config["model_metadata"] - ) - if config["projection_dimension"]: - reid_model_instance._add_projection_layer( - projection_dimension=config["projection_dimension"] - ) - for k, v in state_dict.items(): - state_dict[k].to(reid_model_instance.device) - reid_model_instance.backbone_model.load_state_dict(state_dict) - return reid_model_instance - - -class ReIDModel: - """ - A ReID model that is used to extract features from detection crops for trackers - that utilize appearance features. - - Args: - backbone_model (nn.Module): The torch model to use as the backbone. - device (Optional[str]): The device to run the model on. - transforms (Optional[Union[Callable, list[Callable]]]): The transforms to - apply to the input images. - model_metadata (dict[str, Any]): Metadata about the model architecture. - """ - - def __init__( - self, - backbone_model: nn.Module, - device: Optional[str] = "auto", - transforms: Optional[Union[Callable, list[Callable]]] = None, - model_metadata: dict[str, Any] = {}, - ): - self.backbone_model = backbone_model - self.device = parse_device_spec(device or "auto") - self.backbone_model.to(self.device) - self.backbone_model.eval() - self.train_transforms = ( - (Compose(*transforms) if isinstance(transforms, list) else transforms) - if transforms is not None - else None - ) - self.inference_transforms = Compose( - [ToPILImage(), *transforms] - if isinstance(transforms, list) - else [ToPILImage(), transforms] - ) - self.model_metadata = model_metadata - - @classmethod - def from_timm( - cls, - model_name_or_checkpoint_path: str, - device: Optional[str] = "auto", - get_pooled_features: bool = True, - **kwargs, - ) -> ReIDModel: - """ - Create a `ReIDModel` with a [timm](https://huggingface.co/docs/timm) - model as the backbone. - - Args: - model_name_or_checkpoint_path (str): Name of the timm model to use or - path to a safetensors checkpoint. If the exact model name is not - found, the closest match from `timm.list_models` will be used. - device (str): Device to run the model on. - get_pooled_features (bool): Whether to get the pooled features from the - model or not. - **kwargs: Additional keyword arguments to pass to - [`timm.create_model`](https://huggingface.co/docs/timm/en/reference/models#timm.create_model). - - Returns: - ReIDModel: A new instance of `ReIDModel`. - """ - if os.path.exists(model_name_or_checkpoint_path): - return _initialize_reid_model_from_checkpoint( - cls, model_name_or_checkpoint_path - ) - else: - return _initialize_reid_model_from_timm( - cls, - model_name_or_checkpoint_path, - device, - get_pooled_features, - **kwargs, - ) - - def extract_features( - self, detections: sv.Detections, frame: Union[np.ndarray, PIL.Image.Image] - ) -> np.ndarray: - """ - Extract features from detection crops in the frame. - - Args: - detections (sv.Detections): Detections from which to extract features. - frame (np.ndarray or PIL.Image.Image): The input frame. - - Returns: - np.ndarray: Extracted features for each detection. - """ - if len(detections) == 0: - return np.array([]) - - if isinstance(frame, PIL.Image.Image): - frame = np.array(frame) - - features = [] - with torch.inference_mode(): - for box in detections.xyxy: - crop = sv.crop_image(image=frame, xyxy=[*box.astype(int)]) - tensor = self.inference_transforms(crop).unsqueeze(0).to(self.device) - feature = ( - torch.squeeze(self.backbone_model(tensor)).cpu().numpy().flatten() - ) - features.append(feature) - - return np.array(features) - - def _add_projection_layer( - self, projection_dimension: Optional[int] = None, freeze_backbone: bool = False - ): - """ - Perform model surgery to add a projection layer to the model and freeze the - backbone if specified. The backbone is only frozen if `projection_dimension` - is specified. - - Args: - projection_dimension (Optional[int]): The dimension of the projection layer. - freeze_backbone (bool): Whether to freeze the backbone of the model during - training. - """ - if projection_dimension is not None: - # Freeze backbone only if specified and projection_dimension is mentioned - if freeze_backbone: - for param in self.backbone_model.parameters(): - param.requires_grad = False - - # Add projection layer if projection_dimension is specified - self.backbone_model = nn.Sequential( - self.backbone_model, - nn.Linear(self.backbone_model.num_features, projection_dimension), - ) - self.backbone_model.to(self.device) - - def _train_step( - self, - anchor_image: torch.Tensor, - positive_image: torch.Tensor, - negative_image: torch.Tensor, - metrics_list: list[TripletMetric], - ) -> dict[str, float]: - """ - Perform a single training step. - - Args: - anchor_image (torch.Tensor): The anchor image. - positive_image (torch.Tensor): The positive image. - negative_image (torch.Tensor): The negative image. - metrics_list (list[Metric]): The list of metrics to update. - """ - self.optimizer.zero_grad() - anchor_image_features = self.backbone_model(anchor_image) - positive_image_features = self.backbone_model(positive_image) - negative_image_features = self.backbone_model(negative_image) - - loss = self.criterion( - anchor_image_features, - positive_image_features, - negative_image_features, - ) - loss.backward() - self.optimizer.step() - - # Update metrics - for metric in metrics_list: - metric.update( - anchor_image_features.detach(), - positive_image_features.detach(), - negative_image_features.detach(), - ) - - train_logs = {"train/loss": loss.item()} - for metric in metrics_list: - train_logs[f"train/{metric!s}"] = metric.compute() - - return train_logs - - def _validation_step( - self, - anchor_image: torch.Tensor, - positive_image: torch.Tensor, - negative_image: torch.Tensor, - metrics_list: list[TripletMetric], - ) -> dict[str, float]: - """ - Perform a single validation step. - - Args: - anchor_image (torch.Tensor): The anchor image. - positive_image (torch.Tensor): The positive image. - negative_image (torch.Tensor): The negative image. - metrics_list (list[Metric]): The list of metrics to update. - """ - with torch.inference_mode(): - anchor_image_features = self.backbone_model(anchor_image) - positive_image_features = self.backbone_model(positive_image) - negative_image_features = self.backbone_model(negative_image) - - loss = self.criterion( - anchor_image_features, - positive_image_features, - negative_image_features, - ) - - # Update metrics - for metric in metrics_list: - metric.update( - anchor_image_features.detach(), - positive_image_features.detach(), - negative_image_features.detach(), - ) - - validation_logs = {"validation/loss": loss.item()} - for metric in metrics_list: - validation_logs[f"validation/{metric!s}"] = metric.compute() - - return validation_logs - - def train( - self, - train_loader: DataLoader, - epochs: int, - validation_loader: Optional[DataLoader] = None, - projection_dimension: Optional[int] = None, - freeze_backbone: bool = False, - learning_rate: float = 5e-5, - weight_decay: float = 0.0, - triplet_margin: float = 1.0, - random_state: Optional[Union[int, float, str, bytes, bytearray]] = None, - checkpoint_interval: Optional[int] = None, - log_dir: str = "logs", - log_to_matplotlib: bool = False, - log_to_tensorboard: bool = False, - log_to_wandb: bool = False, - ) -> None: - """ - Train/fine-tune the ReID model. - - Args: - train_loader (DataLoader): The training data loader. - epochs (int): The number of epochs to train the model. - validation_loader (Optional[DataLoader]): The validation data loader. - projection_dimension (Optional[int]): The dimension of the projection layer. - freeze_backbone (bool): Whether to freeze the backbone of the model. The - backbone is only frozen if `projection_dimension` is specified. - learning_rate (float): The learning rate to use for the optimizer. - weight_decay (float): The weight decay to use for the optimizer. - triplet_margin (float): The margin to use for the triplet loss. - random_state (Optional[Union[int, float, str, bytes, bytearray]]): The - random state to use for the training. - checkpoint_interval (Optional[int]): The interval to save checkpoints. - log_dir (str): The directory to save logs. - log_to_matplotlib (bool): Whether to log to matplotlib. - log_to_tensorboard (bool): Whether to log to tensorboard. - log_to_wandb (bool): Whether to log to wandb. If `checkpoint_interval` is - specified, the model will be logged to wandb as well. - Project and entity name should be set using the environment variables - `WANDB_PROJECT` and `WANDB_ENTITY`. For more details, refer to - [wandb environment variables](https://docs.wandb.ai/guides/track/environment-variables). - """ - os.makedirs(log_dir, exist_ok=True) - os.makedirs(os.path.join(log_dir, "checkpoints"), exist_ok=True) - os.makedirs(os.path.join(log_dir, "tensorboard_logs"), exist_ok=True) - - if random_state is not None: - torch.manual_seed(random_state) - - self._add_projection_layer(projection_dimension, freeze_backbone) - - # Initialize optimizer, criterion and metrics - self.optimizer = optim.Adam( - self.backbone_model.parameters(), - lr=learning_rate, - weight_decay=weight_decay, - ) - self.criterion = nn.TripletMarginLoss(margin=triplet_margin) - metrics_list: list[TripletMetric] = [TripletAccuracyMetric()] - - config = { - "epochs": epochs, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "random_state": random_state, - "projection_dimension": projection_dimension, - "freeze_backbone": freeze_backbone, - "triplet_margin": triplet_margin, - "model_metadata": self.model_metadata, - } - - # Initialize callbacks - callbacks: list[BaseCallback] = [] - if log_to_matplotlib: - try: - from trackers.core.reid.callbacks import MatplotlibCallback - - callbacks.append(MatplotlibCallback(log_dir=log_dir)) - except (ImportError, AttributeError) as e: - logger.error( - "Metric logging dependencies are not installed. " - "Please install it using `pip install trackers[metrics]`.", - ) - raise e - if log_to_tensorboard: - try: - from trackers.core.reid.callbacks import TensorboardCallback - - callbacks.append( - TensorboardCallback( - log_dir=os.path.join(log_dir, "tensorboard_logs") - ) - ) - except (ImportError, AttributeError) as e: - logger.error( - "Metric logging dependencies are not installed. " - "Please install it using `pip install trackers[metrics]`." - ) - raise e - - if log_to_wandb: - try: - from trackers.core.reid.callbacks import WandbCallback - - callbacks.append(WandbCallback(config=config)) - except (ImportError, AttributeError) as e: - logger.error( - "Metric logging dependencies are not installed. " - "Please install it using `pip install trackers[metrics]`." - ) - raise e - - # Training loop over epochs - for epoch in tqdm(range(epochs), desc="Training"): - # Reset metrics at the start of each epoch - for metric in metrics_list: - metric.reset() - - # Training loop over batches - accumulated_train_logs: dict[str, Union[float, int]] = {} - for idx, data in tqdm( - enumerate(train_loader), - total=len(train_loader), - desc=f"Training Epoch {epoch + 1}/{epochs}", - leave=False, - ): - anchor_image, positive_image, negative_image = data - if self.train_transforms is not None: - anchor_image = self.train_transforms(anchor_image) - positive_image = self.train_transforms(positive_image) - negative_image = self.train_transforms(negative_image) - - anchor_image = anchor_image.to(self.device) - positive_image = positive_image.to(self.device) - negative_image = negative_image.to(self.device) - - if callbacks: - for callback in callbacks: - callback.on_train_batch_start( - {}, epoch * len(train_loader) + idx - ) - - train_logs = self._train_step( - anchor_image, positive_image, negative_image, metrics_list - ) - - for key, value in train_logs.items(): - accumulated_train_logs[key] = ( - accumulated_train_logs.get(key, 0) + value - ) - - if callbacks: - for callback in callbacks: - for key, value in train_logs.items(): - callback.on_train_batch_end( - {f"batch/{key}": value}, epoch * len(train_loader) + idx - ) - - for key, value in accumulated_train_logs.items(): - accumulated_train_logs[key] = value / len(train_loader) - - # Compute and add training metrics to logs - for metric in metrics_list: - accumulated_train_logs[f"train/{metric!s}"] = metric.compute() - # Metrics are reset at the start of the next epoch or before validation - - if callbacks: - for callback in callbacks: - callback.on_train_epoch_end(accumulated_train_logs, epoch) - - # Validation loop over batches - accumulated_validation_logs: dict[str, Union[float, int]] = {} - if validation_loader is not None: - # Reset metrics for validation - for metric in metrics_list: - metric.reset() - for idx, data in tqdm( - enumerate(validation_loader), - total=len(validation_loader), - desc=f"Validation Epoch {epoch + 1}/{epochs}", - leave=False, - ): - if callbacks: - for callback in callbacks: - callback.on_validation_batch_start( - {}, epoch * len(train_loader) + idx - ) - - anchor_image, positive_image, negative_image = data - if self.train_transforms is not None: - anchor_image = self.train_transforms(anchor_image) - positive_image = self.train_transforms(positive_image) - negative_image = self.train_transforms(negative_image) - - anchor_image = anchor_image.to(self.device) - positive_image = positive_image.to(self.device) - negative_image = negative_image.to(self.device) - - validation_logs = self._validation_step( - anchor_image, positive_image, negative_image, metrics_list - ) - - for key, value in validation_logs.items(): - accumulated_validation_logs[key] = ( - accumulated_validation_logs.get(key, 0) + value - ) - - if callbacks: - for callback in callbacks: - for key, value in validation_logs.items(): - callback.on_validation_batch_end( - {f"batch/{key}": value}, - epoch * len(train_loader) + idx, - ) - - for key, value in accumulated_validation_logs.items(): - accumulated_validation_logs[key] = value / len(validation_loader) - - # Compute and add validation metrics to logs - for metric in metrics_list: - accumulated_validation_logs[f"validation/{metric!s}"] = ( - metric.compute() - ) - # Metrics will be reset at the start of the next training epoch loop - - if callbacks: - for callback in callbacks: - callback.on_validation_epoch_end(accumulated_validation_logs, epoch) - - # Save checkpoint - if ( - checkpoint_interval is not None - and (epoch + 1) % checkpoint_interval == 0 - ): - state_dict = self.backbone_model.state_dict() - checkpoint_path = os.path.join( - log_dir, "checkpoints", f"reid_model_{epoch + 1}.safetensors" - ) - save_file( - state_dict, - checkpoint_path, - metadata={"config": json.dumps(config), "format": "pt"}, - ) - if callbacks: - for callback in callbacks: - callback.on_checkpoint_save(checkpoint_path, epoch + 1) - - if callbacks: - for callback in callbacks: - callback.on_end() diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 7e157c9c..312b162c 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -19,23 +19,23 @@ class SORTTracker(BaseTracker): Hungarian algorithm or simple IOU matching for data association. Args: - lost_track_buffer (int): Number of frames to buffer when a track is lost. + lost_track_buffer: Number of frames to buffer when a track is lost. Increasing lost_track_buffer enhances occlusion handling, significantly improving tracking through occlusions, but may increase the possibility of ID switching for objects with similar appearance. - frame_rate (float): Frame rate of the video (frames per second). + frame_rate: Frame rate of the video (frames per second). Used to calculate the maximum time a track can be lost. - track_activation_threshold (float): Detection confidence threshold + track_activation_threshold: Detection confidence threshold for track activation. Only detections with confidence above this threshold will create new tracks. Increasing this threshold reduces false positives but may miss real objects with low confidence. - minimum_consecutive_frames (int): Number of consecutive frames that an object + minimum_consecutive_frames: Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. Increasing `minimum_consecutive_frames` prevents the creation of accidental tracks from false detection or double detection, but risks missing shorter tracks. Before the tracker is considered valid, it will be assigned `-1` as its `tracker_id`. - minimum_iou_threshold (float): IOU threshold for associating detections to + minimum_iou_threshold: IOU threshold for associating detections to existing tracks. """ @@ -65,13 +65,11 @@ def _get_associated_indices( Associate detections to trackers based on IOU Args: - iou_matrix (np.ndarray): IOU cost matrix. - detection_boxes (np.ndarray): Detected bounding boxes in the - form [x1, y1, x2, y2]. + iou_matrix: IOU cost matrix. + detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. Returns: - tuple[list[tuple[int, int]], set[int], set[int]]: Matched indices, - unmatched trackers, unmatched detections. + Matched indices, unmatched trackers, unmatched detections. """ matched_indices = [] unmatched_trackers = set(range(len(self.trackers))) @@ -102,9 +100,8 @@ def _spawn_new_trackers( above threshold. Args: - detections (sv.Detections): The latest set of object detections. - detection_boxes (np.ndarray): Detected bounding boxes in the - form [x1, y1, x2, y2]. + detections: The latest set of object detections. + detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. """ for detection_idx in unmatched_detections: if ( @@ -124,13 +121,13 @@ def update(self, detections: sv.Detections) -> sv.Detections: trackers for unmatched high-confidence detections. Args: - detections (sv.Detections): The latest set of object detections from a frame. + detections: The latest set of object detections from a frame. Returns: - sv.Detections: A copy of the input detections, augmented with assigned - `tracker_id` for each successfully tracked object. Detections not - associated with a track will not have a `tracker_id`. - """ # noqa: E501 + A copy of the input detections, augmented with assigned `tracker_id` for + each successfully tracked object. Detections not associated with a + track will not have a `tracker_id`. + """ if len(self.trackers) == 0 and len(detections) == 0: detections.tracker_id = np.array([], dtype=int) diff --git a/trackers/log.py b/trackers/log.py deleted file mode 100644 index 2391d67c..00000000 --- a/trackers/log.py +++ /dev/null @@ -1,101 +0,0 @@ -import logging -import os -import sys -from typing import Any, Dict, Final, Literal, Optional - -_LOG_LEVELS: Final[dict[str, int]] = { - "DEBUG": logging.DEBUG, - "INFO": logging.INFO, - "WARNING": logging.WARNING, - "ERROR": logging.ERROR, - "CRITICAL": logging.CRITICAL, -} - -_LOG_FILENAME: Final[str] = os.environ.get("TRACKERS_LOG_FILENAME", "trackers.log") -_LOG_LEVEL_NAME: Final[str] = os.environ.get("TRACKERS_LOG_LEVEL", "ERROR").upper() -_LOG_OUTPUT_TYPE: Final[str] = os.environ.get("TRACKERS_LOG_OUTPUT", "stderr").lower() -_LOG_LEVEL: Final[int] = _LOG_LEVELS.get(_LOG_LEVEL_NAME, logging.ERROR) -_LOG_FORMAT: Final[str] = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - - -class LogFormatter(logging.Formatter): - """ - Custom log formatter that adds ANSI color codes to log messages based on - the log level for terminal output. Does not add color codes if the output - is redirected to a file. This formatter is designed to work with Python 3.10+. - It uses ANSI escape sequences to colorize log messages for better visibility - in terminal environments. - """ - - def __init__( - self, - fmt: Optional[str] = None, - datefmt: Optional[str] = None, - style: Literal["%", "{", "$"] = "%", - validate: bool = True, - *, - defaults: Optional[Dict[str, Any]] = None, - ) -> None: - if sys.version_info >= (3, 10): - super().__init__(fmt, datefmt, style, validate, defaults=defaults) - else: - super().__init__(fmt, datefmt, style, validate) - - self._RESET: Final[str] = "\x1b[0m" - - self._COLOURS: Final[dict[int, str]] = { - logging.DEBUG: "\x1b[38;21m", - logging.INFO: "\x1b[34;1m", - logging.WARNING: "\x1b[33;1m", - logging.ERROR: "\x1b[31;1m", - logging.CRITICAL: "\x1b[35;1m", - } - - self._BASE_FORMAT: Final[str] = "%(asctime)s - %(name)s - " - self._LEVEL_MSG_FORMAT: Final[str] = "%(levelname)s: %(message)s" - - self._FORMATS: dict[int, str] = { - level: color + self._BASE_FORMAT + self._LEVEL_MSG_FORMAT + self._RESET - for level, color in self._COLOURS.items() - } - - def format(self, record: logging.LogRecord) -> str: - """ - Formats the log record with color based on the log level. - Args: - record (logging.LogRecord): The log record to format. - Returns: - str: The formatted log message with color. - """ - - log_fmt = self._FORMATS.get(record.levelno) - formatter = logging.Formatter(log_fmt, datefmt="%Y-%m-%d %H:%M:%S") - return formatter.format(record) - - -if _LOG_OUTPUT_TYPE == "file": - logging.basicConfig( - level=_LOG_LEVEL, - format=_LOG_FORMAT, - filename=_LOG_FILENAME, - filemode="a", - ) -else: - root_logger = logging.getLogger() - root_logger.setLevel(_LOG_LEVEL) - handler = logging.StreamHandler(sys.stderr) - handler.setFormatter(LogFormatter()) - root_logger.addHandler(handler) - - -def get_logger(name: Optional[str]) -> logging.Logger: - """ - Retrieves a logger instance with the specified name. - - Args: - name (str): The name for the logger, typically __name__. - - Returns: - logging.Logger: Configured logger instance. - """ - return logging.getLogger(name) diff --git a/trackers/utils/data_utils.py b/trackers/utils/data_utils.py deleted file mode 100644 index 9609067d..00000000 --- a/trackers/utils/data_utils.py +++ /dev/null @@ -1,24 +0,0 @@ -import zipfile - -from trackers.log import get_logger - -logger = get_logger(__name__) - - -def unzip_file(source_zip_path: str, target_dir_path: str) -> None: - """ - Extracts all files from a zip archive. - - Args: - source_zip_path (str): The path to the zip file. - target_dir_path (str): The directory to extract the contents to. - If the directory doesn't exist, it will be created. - - Raises: - FileNotFoundError: If the zip file doesn't exist. - zipfile.BadZipFile: If the file is not a valid zip file or is corrupted. - Exception: If any other error occurs during extraction. - """ - with zipfile.ZipFile(source_zip_path, "r") as zip_ref: - zip_ref.extractall(target_dir_path) - logger.info(f"Successfully extracted '{source_zip_path}' to '{target_dir_path}'") diff --git a/trackers/utils/downloader.py b/trackers/utils/downloader.py deleted file mode 100644 index 8a3f3152..00000000 --- a/trackers/utils/downloader.py +++ /dev/null @@ -1,204 +0,0 @@ -import asyncio -import os.path -import shutil -from tempfile import TemporaryDirectory -from typing import Optional -from urllib.parse import urlparse - -import aiofiles -import aiohttp -from tqdm.asyncio import tqdm as async_tqdm - - -class _AsyncFileDownloader: - """Asynchronously downloads files with - support for multipart downloading and progress bars. - - This class handles downloading files from URLs, automatically determining whether - to use multipart downloading based on server support for content length. - It displays progress using tqdm. - """ - - def __init__(self, part_size_mb: int = 10, default_chunk_size: int = 8192): - """Initializes the AsyncFileDownloader. - - Args: - part_size_mb (int): The size of each part in megabytes for multipart downloads. - default_chunk_size (int): The default chunk size in bytes for reading content. - """ # noqa: E501 - self.part_size = part_size_mb * 1024**2 - self.default_chunk_size = default_chunk_size - - async def _get_content_length(self, url: str) -> Optional[int]: - """Retrieves the content length of a file from a URL. - - Args: - url (str): The URL of the file. - - Returns: - Optional[int]: The content length in bytes if available, otherwise None. - """ - async with aiohttp.ClientSession() as session: - async with session.head(url) as request: - return request.content_length - - def _parts_generator(self, size: int, start: int = 0): - """Generates byte ranges for multipart downloading. - - Args: - size (int): The total size of the file in bytes. - start (int): The starting byte offset. Defaults to 0. - - Yields: - Tuple[int, int]: A tuple representing the start and end byte of a part. - """ - while size - start > self.part_size: - yield start, start + self.part_size - start += self.part_size - yield start, size - - async def _download_part( - self, url: str, headers: dict, save_path: str, progress_bar: async_tqdm - ): - """Downloads a single part of a file. - - Args: - url (str): The URL to download from. - headers (dict): HTTP headers to use for the request (e.g., for Range). - save_path (str): The local path to save the downloaded part. - progress_bar (async_tqdm): An instance of tqdm to update download progress. - """ - async with aiohttp.ClientSession(headers=headers) as session: - async with session.get(url) as request: - async with aiofiles.open(save_path, "wb") as file: - async for chunk in request.content.iter_chunked( - self.default_chunk_size - ): - await file.write(chunk) - progress_bar.update(len(chunk)) - - async def process_url( - self, - url: str, - save_dir: Optional[str] = None, - output_filename: Optional[str] = None, - ) -> str: - """Downloads a file from a URL, handling multipart downloads and progress. - - If the server provides content length, the file is downloaded in parts. - Otherwise, a direct download is attempted. Progress is displayed using tqdm. - - Args: - url (str): The URL of the file to download. - save_dir (Optional[str]): The directory to save the downloaded file. - Defaults to the current working directory. - output_filename (Optional[str]): The desired filename for the downloaded file. - If None, it's inferred from the URL. - - Returns: - str: The full path to the downloaded file. - """ # noqa: E501 - if output_filename is None: - output_filename = os.path.basename(urlparse(url).path) - - if save_dir is None: - save_dir = os.path.abspath(".") - final_save_path = os.path.join(save_dir, output_filename) - os.makedirs(save_dir, exist_ok=True) - tmp_dir = TemporaryDirectory(prefix=output_filename, dir=save_dir) - try: - size = await self._get_content_length(url) - if size is None: - async with aiohttp.ClientSession() as session: - async with session.get(url) as request: - content_length = request.content_length - with async_tqdm( - total=content_length, - unit="B", - unit_scale=True, - desc=f"Downloading {output_filename}", - leave=True, - ) as pbar: - async with aiofiles.open(final_save_path, "wb") as file: - async for chunk in request.content.iter_chunked( - self.default_chunk_size - ): - await file.write(chunk) - pbar.update(len(chunk)) - return final_save_path - - tasks = [] - file_parts = [] - with async_tqdm( - total=size, - unit="B", - unit_scale=True, - desc=f"Downloading {output_filename}", - leave=True, - ) as pbar: - for number, sizes in enumerate(self._parts_generator(size)): - part_file_name = os.path.join( - tmp_dir.name, f"{output_filename}.part{number}" - ) - file_parts.append(part_file_name) - tasks.append( - self._download_part( - url, - {"Range": f"bytes={sizes[0]}-{sizes[1] - 1}"}, - part_file_name, - pbar, - ) - ) - - await asyncio.gather(*tasks) - - with open(final_save_path, "wb") as wfd: - for f_part_path in file_parts: - with open(f_part_path, "rb") as fd: - shutil.copyfileobj(fd, wfd) - return final_save_path - finally: - tmp_dir.cleanup() - - -def download_file( - url: str, part_size_mb: int = 10, default_chunk_size: int = 8192 -) -> str: - """Asynchronously downloads files with support for multipart downloading and progress bars. - - This class handles downloading files from URLs, automatically determining whether - to use multipart downloading based on server support for content length. - It displays progress using tqdm. - - Args: - url (str): The URL to download the model file from. - part_size_mb (int): The size of each part in megabytes for multipart downloads. - default_chunk_size (int): The default chunk size in bytes for reading content. - - Returns: - str: The local path to the downloaded file. - """ # noqa: E501 - - downloader = _AsyncFileDownloader( - part_size_mb=part_size_mb, default_chunk_size=default_chunk_size - ) - if not url: - raise ValueError("URL cannot be empty.") - if not urlparse(url).scheme: - raise ValueError("Invalid URL. Please provide a valid URL.") - if not urlparse(url).netloc: - raise ValueError("Invalid URL. Please provide a valid URL.") - if not urlparse(url).path: - raise ValueError("Invalid URL. Please provide a valid URL.") - - try: - loop = asyncio.get_event_loop() - if loop.is_running(): - future = asyncio.ensure_future(downloader.process_url(url)) - file_path = loop.run_until_complete(future) - else: - file_path = loop.run_until_complete(downloader.process_url(url)) - except RuntimeError: - file_path = asyncio.run(downloader.process_url(url)) - print(f"File downloaded to {file_path}.") - return file_path diff --git a/trackers/utils/sort_utils.py b/trackers/utils/sort_utils.py index a290cff9..32693512 100644 --- a/trackers/utils/sort_utils.py +++ b/trackers/utils/sort_utils.py @@ -4,11 +4,11 @@ import numpy as np import supervision as sv -from trackers.core.deepsort.kalman_box_tracker import DeepSORTKalmanBoxTracker +from trackers.core.bytetrack.kalman_box_tracker import ByteTrackKalmanBoxTracker from trackers.core.sort.kalman_box_tracker import SORTKalmanBoxTracker KalmanBoxTrackerType = TypeVar( - "KalmanBoxTrackerType", bound=Union[SORTKalmanBoxTracker, DeepSORTKalmanBoxTracker] + "KalmanBoxTrackerType", bound=Union[SORTKalmanBoxTracker, ByteTrackKalmanBoxTracker] ) diff --git a/trackers/utils/torch_utils.py b/trackers/utils/torch_utils.py deleted file mode 100644 index 2d0c4b71..00000000 --- a/trackers/utils/torch_utils.py +++ /dev/null @@ -1,87 +0,0 @@ -import json -import re -from typing import Any, Tuple, Union - -import torch -from safetensors import safe_open - - -def parse_device_spec(device_spec: Union[str, torch.device]) -> torch.device: - """ - Convert a string or torch.device into a valid torch.device. Allowed strings: - `'auto'`, `'cpu'`, `'cuda'`, `'cuda:N'` (e.g. `'cuda:0'`), or `'mps'`. - This function raises ValueError if the input is unrecognized or the GPU - index is out of range. - - Args: - device_spec (Union[str, torch.device]): A specification for the device. - This can be a valid `torch.device` object or one of the recognized - strings described above. - - Returns: - torch.device: The corresponding `torch.device` object. - - Raises: - ValueError: If the device specification is unrecognized or the provided GPU - index exceeds the available devices. - """ - if isinstance(device_spec, torch.device): - return device_spec - - device_str = device_spec.lower() - if device_str == "auto": - if torch.cuda.is_available(): - return torch.device("cuda") - elif torch.backends.mps.is_available(): - return torch.device("mps") - else: - return torch.device("cpu") - elif device_str == "cpu": - return torch.device("cpu") - elif device_str == "cuda": - return torch.device("cuda") - elif device_str == "mps": - return torch.device("mps") - else: - match = re.match(r"^cuda:(\d+)$", device_str) - if match: - index = int(match.group(1)) - if index < 0: - raise ValueError(f"GPU index must be non-negative, got {index}.") - if index >= torch.cuda.device_count(): - raise ValueError( - f"Requested cuda:{index} but only {torch.cuda.device_count()}" - + " GPU(s) are available." - ) - return torch.device(f"cuda:{index}") - - raise ValueError(f"Unrecognized device spec: {device_spec}") - - -def load_safetensors_checkpoint( - checkpoint_path: str, device: str = "cpu" -) -> Tuple[dict[str, torch.Tensor], dict[str, Any]]: - """ - Load a safetensors checkpoint into a dictionary of tensors and a dictionary - of metadata. - - Args: - checkpoint_path (str): The path to the safetensors checkpoint. - device (str): The device to load the checkpoint on. - - Returns: - Tuple[dict[str, torch.Tensor], dict[str, Any]]: A tuple containing the - state_dict and the config. - """ - state_dict = {} - with safe_open(checkpoint_path, framework="pt", device=device) as f: - for key in f.keys(): - state_dict[key] = f.get_tensor(key) - metadata = f.metadata() - config = json.loads(metadata["config"]) if "config" in metadata else {} - model_metadata = config.pop("model_metadata") if "model_metadata" in config else {} - if "kwargs" in model_metadata: - kwargs = model_metadata.pop("kwargs") - model_metadata = {**kwargs, **model_metadata} - config["model_metadata"] = model_metadata - return state_dict, config diff --git a/uv.lock b/uv.lock index 04273354..7f483b35 100644 --- a/uv.lock +++ b/uv.lock @@ -1,428 +1,16 @@ version = 1 revision = 3 -requires-python = ">=3.9" +requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 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and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and 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"python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra == 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra == 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra == 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra == 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin' and extra != 'extra-8-trackers-cpu' and extra == 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version == '3.10.*' and sys_platform == 'darwin' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-trackers-cpu' and extra != 'extra-8-trackers-cu118' and extra != 'extra-8-trackers-cu124' and extra != 'extra-8-trackers-cu126' and extra != 'extra-8-trackers-rocm61' and extra != 'extra-8-trackers-rocm624'", - "python_full_version < '3.10' and platform_machine == 'arm64' 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