chore(pre-commit): remove unnecessary exclude rule for changelog... (#2452)
* chore(pre-commit): remove unnecessary exclude rule for changelog and deprecated docs
* docs(changelog): reformat code blocks for consistency and clarity
* docs(changelog): reformat and align code blocks for consistent indentation and readability
* chore(pre-commit): update mdformat hooks to include gfm and frontmatter extensions
* chore(pre-commit): split mdformat hook into gfm and mkdocs variants
* docs(changelog): fix nested code fences breaking mdformat-mkdocs
* fix(pre_commit): 🎨 auto format pre-commit hooks
---------
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Jirka Borovec
GitHub
parent
156ee33740
commit
bcbe8de1e4
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|
@ -11,14 +11,14 @@ Please read and adhere to our [Code of Conduct](https://supervision.roboflow.com
|
|||
## Table of Contents
|
||||
|
||||
- [Contribution Guidelines](#contribution-guidelines)
|
||||
- [Contributing Features](#contributing-features)
|
||||
- [API Design Principles](#api-design-principles)
|
||||
- [Contributing Features](#contributing-features)
|
||||
- [API Design Principles](#api-design-principles)
|
||||
- [How to Contribute Changes](#how-to-contribute-changes)
|
||||
- [Installation for Contributors](#installation-for-contributors)
|
||||
- [Code Style and Quality](#code-style-and-quality)
|
||||
- [Pre-commit tool](#pre-commit-tool)
|
||||
- [Docstrings](#docstrings)
|
||||
- [Type checking](#type-checking)
|
||||
- [Pre-commit tool](#pre-commit-tool)
|
||||
- [Docstrings](#docstrings)
|
||||
- [Type checking](#type-checking)
|
||||
- [Documentation](#documentation)
|
||||
- [Cookbooks](#cookbooks)
|
||||
- [Tests](#tests)
|
||||
|
|
@ -142,63 +142,63 @@ Before starting your work on the project, set up your development environment:
|
|||
|
||||
1. **Clone your fork of the project:**
|
||||
|
||||
**Option A: Recommended for most contributors (shallow clone of develop branch):**
|
||||
**Option A: Recommended for most contributors (shallow clone of develop branch):**
|
||||
|
||||
```bash
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||||
git clone --depth 1 -b develop https://github.com/YOUR_USERNAME/supervision.git
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cd supervision
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||||
```
|
||||
```bash
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||||
git clone --depth 1 -b develop https://github.com/YOUR_USERNAME/supervision.git
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cd supervision
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||||
```
|
||||
|
||||
Replace `YOUR_USERNAME` with your GitHub username.
|
||||
Replace `YOUR_USERNAME` with your GitHub username.
|
||||
|
||||
> **Note**: Using `--depth 1` creates a shallow clone with minimal history and `-b develop` ensures you start with the development branch. This significantly reduces download size while providing everything needed to contribute.
|
||||
> **Note**: Using `--depth 1` creates a shallow clone with minimal history and `-b develop` ensures you start with the development branch. This significantly reduces download size while providing everything needed to contribute.
|
||||
|
||||
**Option B: Full repository clone (if you need complete history):**
|
||||
**Option B: Full repository clone (if you need complete history):**
|
||||
|
||||
```bash
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||||
git clone https://github.com/YOUR_USERNAME/supervision.git
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cd supervision
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git checkout develop
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```
|
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```bash
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git clone https://github.com/YOUR_USERNAME/supervision.git
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cd supervision
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git checkout develop
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||||
```
|
||||
|
||||
2. **Set up the upstream remote:**
|
||||
|
||||
```bash
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||||
git remote add upstream https://github.com/roboflow/supervision.git
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git fetch upstream
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```
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```bash
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git remote add upstream https://github.com/roboflow/supervision.git
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git fetch upstream
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||||
```
|
||||
|
||||
3. **Create and activate a virtual environment:**
|
||||
|
||||
**On Linux/macOS:**
|
||||
**On Linux/macOS:**
|
||||
|
||||
```bash
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||||
python3 -m venv .venv
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||||
source .venv/bin/activate
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||||
```
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||||
```bash
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python3 -m venv .venv
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source .venv/bin/activate
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```
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||||
|
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**On Windows:**
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**On Windows:**
|
||||
|
||||
```cmd
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python -m venv .venv
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.venv\Scripts\activate
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```
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```cmd
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python -m venv .venv
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.venv\Scripts\activate
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```
|
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|
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4. **Install `uv`:**
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Follow the instructions on the [uv installation page](https://docs.astral.sh/uv/getting-started/installation/).
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Follow the instructions on the [uv installation page](https://docs.astral.sh/uv/getting-started/installation/).
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|
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5. **Install project dependencies:**
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|
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```bash
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uv pip install -r pyproject.toml --group dev --group docs --extra metrics
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```
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```bash
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uv pip install -r pyproject.toml --group dev --group docs --extra metrics
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```
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|
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6. **Verify the setup:**
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|
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```bash
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uv run pytest
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```
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```bash
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uv run pytest
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```
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||||
|
||||
## 🎨 Code Style and Quality
|
||||
|
||||
|
|
@ -212,27 +212,27 @@ To run the pre-commit tool, follow these steps:
|
|||
|
||||
1. **Install pre-commit** (already included if you followed the installation steps above):
|
||||
|
||||
```bash
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uv sync --group dev
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||||
```
|
||||
```bash
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uv sync --group dev
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||||
```
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||||
|
||||
2. **Navigate to the project's root directory** (if not already there).
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||||
|
||||
3. **Run pre-commit checks**:
|
||||
|
||||
```bash
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uv run pre-commit run --all-files
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||||
```
|
||||
```bash
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||||
uv run pre-commit run --all-files
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||||
```
|
||||
|
||||
This will execute the pre-commit hooks configured for this project. If any issues are found, the pre-commit tool will provide feedback on how to resolve them. Make the necessary changes and re-run the command until all issues are resolved.
|
||||
This will execute the pre-commit hooks configured for this project. If any issues are found, the pre-commit tool will provide feedback on how to resolve them. Make the necessary changes and re-run the command until all issues are resolved.
|
||||
|
||||
4. **Install pre-commit as a git hook** (optional but recommended):
|
||||
|
||||
```bash
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||||
uv run pre-commit install
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||||
```
|
||||
```bash
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||||
uv run pre-commit install
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||||
```
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||||
|
||||
This will automatically run pre-commit checks every time you make a `git commit`.
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||||
This will automatically run pre-commit checks every time you make a `git commit`.
|
||||
|
||||
### Docstrings
|
||||
|
||||
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@ -284,15 +284,15 @@ To run the documentation locally:
|
|||
|
||||
1. **Install documentation dependencies** (if not already installed):
|
||||
|
||||
```bash
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uv sync --group docs
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||||
```
|
||||
```bash
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uv sync --group docs
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||||
```
|
||||
|
||||
2. **Start the documentation server**:
|
||||
|
||||
```bash
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uv run mkdocs serve
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||||
```
|
||||
```bash
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uv run mkdocs serve
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||||
```
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||||
|
||||
3. **Access the documentation** at `http://127.0.0.1:8000` in your browser.
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
This file provides context-aware guidance for GitHub Copilot when working in the Supervision repository.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 📚 Repository Overview
|
||||
|
||||
|
|
@ -12,7 +12,7 @@ This file provides context-aware guidance for GitHub Copilot when working in the
|
|||
- **Key Dependencies**: NumPy, OpenCV, SciPy
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||||
- **License**: MIT
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🏗️ Project Structure
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ supervision/
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└── examples/ # Usage examples
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||||
```
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🔧 Development Commands
|
||||
|
||||
|
|
@ -61,7 +61,7 @@ uv run pytest --cov=supervision
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|||
uv run mkdocs serve
|
||||
```
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 💻 Code Conventions
|
||||
|
||||
|
|
@ -77,8 +77,8 @@ uv run mkdocs serve
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|||
- **Linting**: Enforced by `ruff-check` (pre-commit)
|
||||
- **Type Hints**: Required on all new code
|
||||
- **Docstrings**: Required using [Google Python style](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods)
|
||||
- Must include usage examples with primitive values
|
||||
- Serve as runnable documentation
|
||||
- Must include usage examples with primitive values
|
||||
- Serve as runnable documentation
|
||||
|
||||
### Performance
|
||||
|
||||
|
|
@ -92,7 +92,7 @@ uv run mkdocs serve
|
|||
- Maintain backward compatibility unless explicitly breaking
|
||||
- Prefer functional utilities over complex classes
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🧪 Testing Requirements
|
||||
|
||||
|
|
@ -103,7 +103,7 @@ All new features must include:
|
|||
- Clear test names describing what they validate
|
||||
- Proper assertions (not just "no exception raised")
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 📝 Documentation Requirements
|
||||
|
||||
|
|
@ -114,7 +114,7 @@ For new public functions/classes:
|
|||
- Entry in appropriate `docs/*.md` file
|
||||
- Reference in `mkdocs.yml` navigation
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🔍 Pull Request Reviews
|
||||
|
||||
|
|
@ -129,7 +129,7 @@ Quick checklist:
|
|||
- Score code quality, testing, docs (n/5 scale)
|
||||
- Use inline comments + GitHub suggestion format
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🌿 Branching & Commits
|
||||
|
||||
|
|
@ -137,7 +137,7 @@ Quick checklist:
|
|||
- Use **conventional commits**: `feat:`, `fix:`, `docs:`, `refactor:`, `perf:`, `test:`, `chore:`
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||||
- All PRs target `develop` branch
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 🎯 Context-Aware Behavior
|
||||
|
||||
|
|
|
|||
|
|
@ -68,11 +68,21 @@ repos:
|
|||
rev: 1.0.0
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||||
hooks:
|
||||
- id: mdformat
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||||
name: mdformat (gfm)
|
||||
exclude: ^docs/
|
||||
additional_dependencies:
|
||||
- "mdformat-frontmatter"
|
||||
- "mdformat-gfm"
|
||||
- "mdformat-ruff"
|
||||
args: ["--number", "--wrap=no"]
|
||||
- id: mdformat
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||||
name: mdformat (mkdocs)
|
||||
files: ^docs/
|
||||
additional_dependencies:
|
||||
- "mdformat-frontmatter"
|
||||
- "mdformat-mkdocs[recommended]>=2.1.0"
|
||||
- "mdformat-ruff"
|
||||
args: ["--number", "--wrap=no"]
|
||||
exclude: ^(docs/changelog\.md|docs/deprecated\.md)$
|
||||
|
||||
- repo: https://github.com/pre-commit/mirrors-mypy
|
||||
rev: v2.3.0
|
||||
|
|
|
|||
16
AGENTS.md
16
AGENTS.md
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
Behave like a senior contributor: precise, efficient, maintainable. When this file and [CONTRIBUTING.md](.github/CONTRIBUTING.md) conflict, **CONTRIBUTING.md wins**.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 1. Before You Code
|
||||
|
||||
|
|
@ -11,7 +11,7 @@ Behave like a senior contributor: precise, efficient, maintainable. When this fi
|
|||
- Check whether the feature already exists under a different name.
|
||||
- Confirm alignment with `src/supervision/` architecture.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 2. Repository Architecture
|
||||
|
||||
|
|
@ -44,7 +44,7 @@ src/supervision/
|
|||
- **Vectorized throughout** — NumPy arrays, no Python loops in hot paths. Never write `for det in detections`.
|
||||
- **Lazy-import heavy deps** — `torch`, `transformers`, `ultralytics` must be imported inside the function that needs them, never at module top level.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 3. Agent-Critical Rules
|
||||
|
||||
|
|
@ -71,13 +71,13 @@ These supplement [CONTRIBUTING.md](.github/CONTRIBUTING.md) — covering gaps or
|
|||
|
||||
For branching, commit, code style, and API design conventions see [CONTRIBUTING.md](.github/CONTRIBUTING.md).
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 4. Deprecated Module Aliases
|
||||
|
||||
`supervision.keypoint` deprecated since `0.27.0`, removed in `0.31.0`. Always import from `supervision.key_points`, not `supervision.keypoint`.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 5. Deprecating APIs
|
||||
|
||||
|
|
@ -93,7 +93,7 @@ Always name the version introduced and the removal version:
|
|||
warn_deprecated("'foo' deprecated in `0.29.0`, removed in `0.32.0`. Use 'bar'.")
|
||||
```
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 6. Implementing Features
|
||||
|
||||
|
|
@ -122,7 +122,7 @@ def from_myframework(cls, result) -> "Detections":
|
|||
|
||||
VLM connectors go in `detection/vlm.py`, not `core.py`.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 7. Bugs & Refactoring
|
||||
|
||||
|
|
@ -130,7 +130,7 @@ VLM connectors go in `detection/vlm.py`, not `core.py`.
|
|||
|
||||
**Refactoring**: preserve behavior and API; reduce duplication; avoid sweeping changes unless requested; apply §5 deprecation when removing public API.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## 8. Before You Commit
|
||||
|
||||
|
|
|
|||
156
README.md
156
README.md
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|
@ -30,9 +30,9 @@
|
|||
- [👋 Hello](#-hello)
|
||||
- [💻 Install](#-install)
|
||||
- [🔥 Quickstart](#-quickstart)
|
||||
- [Models](#models)
|
||||
- [Annotators](#annotators)
|
||||
- [Datasets](#datasets)
|
||||
- [Models](#models)
|
||||
- [Annotators](#annotators)
|
||||
- [Datasets](#datasets)
|
||||
- [🎬 Tutorials](#-tutorials)
|
||||
- [💜 Built with Supervision](#-built-with-supervision)
|
||||
- [📚 Documentation](#-documentation)
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||||
|
|
@ -80,21 +80,21 @@ len(detections)
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|||
|
||||
- inference
|
||||
|
||||
Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
|
||||
Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
|
||||
|
||||
```python
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import supervision as sv
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from PIL import Image
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from inference import get_model
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```python
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import supervision as sv
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from PIL import Image
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from inference import get_model
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image = Image.open("path/to/image.jpg")
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model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY")
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result = model.infer(image)[0]
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detections = sv.Detections.from_inference(result)
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image = Image.open("path/to/image.jpg")
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model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY")
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result = model.infer(image)[0]
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detections = sv.Detections.from_inference(result)
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||||
len(detections)
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# 5
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||||
```
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||||
len(detections)
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||||
# 5
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||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
|
@ -145,88 +145,88 @@ for path, image, annotation in ds:
|
|||
|
||||
- load
|
||||
|
||||
```python
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||||
dataset = sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
)
|
||||
```python
|
||||
dataset = sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
)
|
||||
|
||||
dataset = sv.DetectionDataset.from_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
dataset = sv.DetectionDataset.from_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
|
||||
dataset = sv.DetectionDataset.from_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...,
|
||||
)
|
||||
```
|
||||
dataset = sv.DetectionDataset.from_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...,
|
||||
)
|
||||
```
|
||||
|
||||
- split
|
||||
|
||||
```python
|
||||
train_dataset, test_dataset = dataset.split(split_ratio=0.7)
|
||||
test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
|
||||
```python
|
||||
train_dataset, test_dataset = dataset.split(split_ratio=0.7)
|
||||
test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
|
||||
|
||||
len(train_dataset), len(test_dataset), len(valid_dataset)
|
||||
# (700, 150, 150)
|
||||
```
|
||||
len(train_dataset), len(test_dataset), len(valid_dataset)
|
||||
# (700, 150, 150)
|
||||
```
|
||||
|
||||
- merge
|
||||
|
||||
```python
|
||||
ds_1 = sv.DetectionDataset(...)
|
||||
len(ds_1)
|
||||
# 100
|
||||
ds_1.classes
|
||||
# ['dog', 'person']
|
||||
```python
|
||||
ds_1 = sv.DetectionDataset(...)
|
||||
len(ds_1)
|
||||
# 100
|
||||
ds_1.classes
|
||||
# ['dog', 'person']
|
||||
|
||||
ds_2 = sv.DetectionDataset(...)
|
||||
len(ds_2)
|
||||
# 200
|
||||
ds_2.classes
|
||||
# ['cat']
|
||||
ds_2 = sv.DetectionDataset(...)
|
||||
len(ds_2)
|
||||
# 200
|
||||
ds_2.classes
|
||||
# ['cat']
|
||||
|
||||
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
|
||||
len(ds_merged)
|
||||
# 300
|
||||
ds_merged.classes
|
||||
# ['cat', 'dog', 'person']
|
||||
```
|
||||
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
|
||||
len(ds_merged)
|
||||
# 300
|
||||
ds_merged.classes
|
||||
# ['cat', 'dog', 'person']
|
||||
```
|
||||
|
||||
- save
|
||||
|
||||
```python
|
||||
dataset.as_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
)
|
||||
```python
|
||||
dataset.as_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
)
|
||||
|
||||
dataset.as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
dataset.as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
|
||||
dataset.as_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...,
|
||||
)
|
||||
```
|
||||
dataset.as_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...,
|
||||
)
|
||||
```
|
||||
|
||||
- convert
|
||||
|
||||
```python
|
||||
sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
).as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
```
|
||||
```python
|
||||
sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...,
|
||||
).as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
|
|
|||
1263
docs/changelog.md
1263
docs/changelog.md
File diff suppressed because it is too large
Load Diff
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
This example benchmarks `CompactMask`, a new mask representation introduced in `supervision` that replaces dense `(N, H, W)` boolean arrays with a crop-scoped Run-Length Encoding (RLE). The benchmark demonstrates full API compatibility, massive memory savings, and order-of-magnitude annotation speedups — with no change to your existing `Detections` code.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## The Problem
|
||||
|
||||
|
|
@ -16,7 +16,7 @@ For a 4K image with 1 000 detected objects:
|
|||
|
||||
At this scale, typical pipelines crash with `MemoryError` before a single frame is annotated. Aerial imagery, satellite tiles, and high-density crowd scenes all hit this wall.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## The Solution — Crop-RLE Storage
|
||||
|
||||
|
|
@ -95,7 +95,7 @@ Crop RLE's `.crop()` method powers the `MaskAnnotator` optimisation — it never
|
|||
|
||||
At N=1 000 with 1 % overlap, bbox pre-filter reduces 499 500 candidate pairs to ~5 000 overlapping pairs — a ~2 000x reduction in pixel-level work.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Why Crop-RLE Was Chosen over Local Crop
|
||||
|
||||
|
|
@ -107,7 +107,7 @@ Both formats compress extremely well; the deciding factors for Crop-RLE are:
|
|||
|
||||
The main trade-off: crop-only decode is O(A) rather than O(1). For the common solid-fill segmentation mask this is negligible (\<0.1 ms per mask).
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Operation-by-Operation Speedup Analysis
|
||||
|
||||
|
|
@ -115,7 +115,7 @@ This section walks through every `Detections` operation that touches masks and s
|
|||
|
||||
At 50% fill on an FHD image each mask's bounding box covers a large portion of the frame, producing many RLE runs per row.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### Memory
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ Scaled to N=200: 200 x 4.7 KB = ~933 KB of RLE data, plus `_crop_shapes` (1.6 KB
|
|||
|
||||
At 5% fill with 8-vertex polygons, the ratio reaches 10 000x–20 000x because crops are tiny and RLEs are extremely short. The benchmark's 4K-200-5%-v8 scenario measures 21 786x (theory) / ~6 000x (malloc). The SAT-200-5%-v8 scenario reaches 62 968x theoretical.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `.area`
|
||||
|
||||
|
|
@ -179,7 +179,7 @@ At FHD-200-50%-v600, dense `.area` takes 84.66 ms; compact takes 0.48 ms — a *
|
|||
| No (H, W) allocation per mask | latency |
|
||||
| **Combined** | **~1 000x** |
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `filter` / `__getitem__` (boolean index)
|
||||
|
||||
|
|
@ -212,7 +212,7 @@ At FHD-200-50%-v600, dense `filter` takes 14.56 ms; compact takes 0.03 ms — a
|
|||
| Allocation | new `(K, H, W)` array | new `CompactMask` shell (~trivial) |
|
||||
| **Speedup** | | **hundreds to tens of thousands x** |
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `annotate` (`MaskAnnotator`)
|
||||
|
||||
|
|
@ -246,7 +246,7 @@ colored_mask[y1 : y1 + crop_h, x1 : x1 + crop_w][crop_m] = color.as_bgr()
|
|||
| x N masks | compounds |
|
||||
| **Combined** | **~26 – 400x** |
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### IoU (`mask_iou_batch` / `compact_mask_iou_batch`)
|
||||
|
||||
|
|
@ -315,7 +315,7 @@ At FHD-200-50%-v600, dense IoU takes 23 915 ms; compact takes 51.58 ms — a **4
|
|||
|
||||
At 20% fill the gaps close — more pairs overlap, larger crops — speedup drops toward the lower end of the range.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### NMS (`mask_non_max_suppression`)
|
||||
|
||||
|
|
@ -339,7 +339,7 @@ All three IoU optimisations apply to the compact path:
|
|||
|
||||
At FHD-200-50%-v600, dense NMS takes 5 231 ms; compact takes 48.15 ms — a **481x speedup**. Dense IoU/NMS is skipped for scenarios above 1 GB (4K-200 and SAT-200 tiers); compact NMS still runs on those.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `merge` (`Detections.merge`)
|
||||
|
||||
|
|
@ -387,7 +387,7 @@ if len(self.xyxy) > 0:
|
|||
|
||||
This O(1) check avoids the O(N x H x W) dense materialisation that previously dominated compact merge time.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `offset` / `with_offset` (`InferenceSlicer` tile stitching)
|
||||
|
||||
|
|
@ -425,7 +425,7 @@ At FHD-200-50%-v600, dense offset takes 42.30 ms; compact takes 0.02 ms — a **
|
|||
|
||||
In the `InferenceSlicer` pipeline the canvas is always expanded by the tile offset, so no crop ever overflows — the fast path is always taken. Clipping only activates for objects that genuinely straddle the image boundary.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### `centroids` (`calculate_masks_centroids`)
|
||||
|
||||
|
|
@ -460,7 +460,7 @@ At FHD-200-50%-v600, dense centroids takes 1 133.68 ms; compact takes 60.39 ms
|
|||
| No global `np.indices((H, W))` allocation | saves large float64 |
|
||||
| **Combined (N=200)** | **~19 – 1 000x** |
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
### Summary
|
||||
|
||||
|
|
@ -480,7 +480,7 @@ Measured speedups at the **FHD-200-50%-v600** operating point (dense fill, compl
|
|||
|
||||
All speedups are larger at sparser fill fractions and larger resolutions. At SAT-200-20%-v128, `.area` reaches 1 204x and `merge` reaches 89 046x. At the sparsest scenarios (5% fill, 8-vertex polygons), memory ratios exceed 60 000x.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Drop-In Compatibility
|
||||
|
||||
|
|
@ -517,7 +517,7 @@ Supported indexing patterns:
|
|||
| `mask[slice]` | New `CompactMask` |
|
||||
| `np.asarray(mask)` | Dense `(N, H, W)` bool array |
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Benchmark
|
||||
|
||||
|
|
@ -626,7 +626,7 @@ Dense timing is skipped automatically when the dense IoU/NMS array would exceed
|
|||
|
||||
All non-skipped scenarios pass: pixel-perfect annotation, exact area, lossless `to_dense()` roundtrip.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Use-Cases
|
||||
|
||||
|
|
@ -636,7 +636,7 @@ All non-skipped scenarios pass: pixel-perfect annotation, exact area, lossless `
|
|||
- **Long-running tracking** — accumulated `Detections` across many frames stay in kilobytes rather than gigabytes.
|
||||
- **`InferenceSlicer`** — `with_offset()` adjusts crop origins directly when stitching tile results; no dense materialisation needed.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Limitations
|
||||
|
||||
|
|
@ -644,7 +644,7 @@ All non-skipped scenarios pass: pixel-perfect annotation, exact area, lossless `
|
|||
- RLE format is **column-major (F-order), crop-scoped** — pixel-scan order matches COCO / pycocotools, but crop scope differs from full-image scope. Use `.to_dense()` to materialize a full-image dense mask, then encode that mask to COCO RLE before passing it to pycocotools.
|
||||
- `from_dense()` requires the input `(N, H, W)` array to fit in memory. For truly OOM-scale data, build `CompactMask` per-detection directly from model output crops rather than from a pre-allocated dense stack.
|
||||
|
||||
---
|
||||
______________________________________________________________________
|
||||
|
||||
## Files
|
||||
|
||||
|
|
|
|||
|
|
@ -12,61 +12,61 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/count_people_in_zone
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/count_people_in_zone
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `traffic_analysis.pt` and `traffic_analysis.mov` files
|
||||
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path` (optional): The path to the YOLO model's weights file. Defaults to `"yolov8x.pt"` if not specified.
|
||||
- `--source_weights_path` (optional): The path to the YOLO model's weights file. Defaults to `"yolov8x.pt"` if not specified.
|
||||
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone configurations. This file defines the polygonal areas in the video where objects will be counted.
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone configurations. This file defines the polygonal areas in the video where objects will be counted.
|
||||
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not provided, the processed video will be displayed in real-time.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not provided, the processed video will be displayed in real-time.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is `0.7`.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is `0.7`.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"yolov8x-1280"`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"yolov8x-1280"`.
|
||||
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone configurations. This file defines the polygonal areas in the video where objects will be counted.
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone configurations. This file defines the polygonal areas in the video where objects will be counted.
|
||||
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not provided, the processed video will be displayed in real-time.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not provided, the processed video will be displayed in real-time.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is `0.7`.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is `0.7`.
|
||||
|
||||
## 📌 zone configuration
|
||||
|
||||
|
|
@ -79,24 +79,24 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
|
|||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
|
|
|
|||
|
|
@ -8,23 +8,23 @@ This script performs heatmap and tracking analysis using YOLOv8, an object-detec
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/heatmap_and_track
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/heatmap_and_track
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
|
|
|
|||
|
|
@ -14,29 +14,29 @@ https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/speed_estimation
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/speed_estimation
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `vehicles.mp4` file
|
||||
|
||||
```bash
|
||||
python video_downloader.py
|
||||
```
|
||||
```bash
|
||||
python video_downloader.py
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
|
|
@ -58,34 +58,34 @@ https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-
|
|||
|
||||
- yolo-nas
|
||||
|
||||
```bash
|
||||
python yolo_nas_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python yolo_nas_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
|
|
|
|||
|
|
@ -12,23 +12,23 @@ https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/time_in_zone
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/time_in_zone
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
The three RTSP `*_stream_example.py` scripts display frames from an `InferencePipeline` callback running on a worker thread, so they use OpenCV HighGUI instead of `sv.ImageWindow`. Install `opencv-python` and keep only one OpenCV wheel installed to run those scripts. The file and naive-stream examples use `sv.ImageWindow`, which works regardless of which OpenCV wheel (or none) is installed.
|
||||
|
||||
|
|
|
|||
|
|
@ -8,71 +8,71 @@ This script provides functionality for processing videos using YOLOv8 for object
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/tracking
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/tracking
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.
|
||||
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model identifies objects in the video. Default is `0.3`. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model identifies objects in the video. Default is `0.3`. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to `0.7`. This parameter helps in differentiating between distinct objects, especially in crowded scenes.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to `0.7`. This parameter helps in differentiating between distinct objects, especially in crowded scenes.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"yolov8x-1280"`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"yolov8x-1280"`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.
|
||||
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model identifies objects in the video. Default is `0.3`. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model identifies objects in the video. Default is `0.3`. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to `0.7`. This parameter helps in differentiating between distinct objects, especially in crowded scenes.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to `0.7`. This parameter helps in differentiating between distinct objects, especially in crowded scenes.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path yolov8s.pt \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path yolov8s.pt \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
|
|
|
|||
|
|
@ -10,81 +10,81 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/traffic_analysis
|
||||
```
|
||||
```bash
|
||||
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/traffic_analysis
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `traffic_analysis.pt` and `traffic_analysis.mov` files
|
||||
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
- `--source_video_path`: Required. The path to the source video file that will be analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not specified, the processed video will be displayed in real-time without being saved.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not specified, the processed video will be displayed in real-time without being saved.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`. This determines how confident the model should be to recognize an object in the video.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`. This determines how confident the model should be to recognize an object in the video.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key) to acquire your `API KEY`.
|
||||
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"vehicle-count-in-drone-video/6"`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default value is `"vehicle-count-in-drone-video/6"`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
- `--source_video_path`: Required. The path to the source video file that will be analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not specified, the processed video will be displayed in real-time without being saved.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations. If not specified, the processed video will be displayed in real-time without being saved.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`. This determines how confident the model should be to recognize an object in the video.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`. This determines how confident the model should be to recognize an object in the video.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path data/traffic_analysis.pt \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path data/traffic_analysis.pt \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key "ROBOFLOW_API_KEY" \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue