+ - **Notebooks** with free GPU:
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) 
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index e82aecc5a0..1ec1b9a93c 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -18,7 +18,7 @@ jobs:
name: Publish
runs-on: ubuntu-latest
permissions:
- id-token: write # for PyPI trusted publishing
+ id-token: write # for PyPI trusted publishing
steps:
- name: Checkout code
uses: actions/checkout@v4
@@ -41,49 +41,11 @@ jobs:
shell: python
run: |
import os
- import requests
- import toml
-
- # Load version and package name from pyproject.toml
- pyproject = toml.load('pyproject.toml')
- package_name = pyproject['project']['name']
- local_version = pyproject['project'].get('version', 'dynamic')
-
- # If version is dynamic, extract it from the specified file
- if local_version == 'dynamic':
- version_attr = pyproject['tool']['setuptools']['dynamic']['version']['attr']
- module_path, attr_name = version_attr.rsplit('.', 1)
- with open(f"{module_path.replace('.', '/')}/__init__.py") as f:
- local_version = next(line.split('=')[1].strip().strip("'\"") for line in f if line.startswith(attr_name))
-
- print(f"Local Version: {local_version}")
-
- # Get online version from PyPI
- response = requests.get(f"https://pypi.org/pypi/{package_name}/json")
- online_version = response.json()['info']['version'] if response.status_code == 200 else None
- print(f"Online Version: {online_version or 'Not Found'}")
-
- # Determine if a new version should be published
- publish = False
- if online_version:
- local_ver = tuple(map(int, local_version.split('.')))
- online_ver = tuple(map(int, online_version.split('.')))
- major_diff = local_ver[0] - online_ver[0]
- minor_diff = local_ver[1] - online_ver[1]
- patch_diff = local_ver[2] - online_ver[2]
-
- publish = (
- (major_diff == 0 and minor_diff == 0 and 0 < patch_diff <= 2) or
- (major_diff == 0 and minor_diff == 1 and local_ver[2] == 0) or
- (major_diff == 1 and local_ver[1] == 0 and local_ver[2] == 0)
- )
- else:
- publish = True # First release
-
+ from actions.utils import check_pypi_version
+ local_version, online_version, publish = check_pypi_version()
os.system(f'echo "increment={publish}" >> $GITHUB_OUTPUT')
os.system(f'echo "current_tag=v{local_version}" >> $GITHUB_OUTPUT')
os.system(f'echo "previous_tag=v{online_version}" >> $GITHUB_OUTPUT')
-
if publish:
print('Ready to publish new version to PyPI ✅.')
id: check_pypi
diff --git a/README.md b/README.md
index cce98e50b0..c70ed6a41b 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@
-
+
@@ -116,7 +116,7 @@ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more exam
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
-
+
All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@@ -207,7 +207,7 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
## 
+
+ 
-
- 
+
+ 
@@ -229,9 +229,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
-
+
@@ -116,7 +116,7 @@ path = model.export(format="onnx") # 返回导出模型的路径
YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/) 和 [姿态](https://docs.ultralytics.com/tasks/pose/) 模型在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上进行预训练，这些模型可在此处获得，此外还有在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的 YOLO11 [分类](https://docs.ultralytics.com/tasks/classify/) 模型。所有检测、分割和姿态模型均支持 [跟踪](https://docs.ultralytics.com/modes/track/) 模式。
-
+
所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时自动从最新的 Ultralytics [发布](https://github.com/ultralytics/assets/releases)下载。
@@ -207,7 +207,7 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
##
+
+
-
-
+
+
@@ -229,9 +229,9 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\"){kind=icon}
\n",
" ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
- " ![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ " \n",
"\n",
"Welcome to the Ultralytics Explorer API notebook! This notebook serves as the starting point for exploring the various resources available to help you get started with using Ultralytics to explore your datasets using with the power of semantic search. You can utilities out of the box that allow you to examine specific types of labels using vector search or even SQL queries.\n",
"\n",
diff --git a/docs/en/datasets/index.md b/docs/en/datasets/index.md
index 9d7a10ed7e..4b6bd6c968 100644
--- a/docs/en/datasets/index.md
+++ b/docs/en/datasets/index.md
@@ -46,7 +46,7 @@ Create [embeddings](https://www.ultralytics.com/glossary/embeddings) for your da
- [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
- [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
- [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
-- [Roboflow 100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
+- [RF100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
- [Brain-tumor](detect/brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
- [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
- [Signature](detect/signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
diff --git a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
index 5f6fceb781..8715483872 100644
--- a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
+++ b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
@@ -27,7 +27,7 @@ The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your
## Boost Raspberry Pi Model Performance with Coral Edge TPU
-Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [onnx](../integrations/onnx.md) or [openvino](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly.
+Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [ONNX](../integrations/onnx.md) or [OpenVINO](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly.
## Edge TPU on Raspberry Pi with TensorFlow Lite (New)⭐
@@ -85,7 +85,7 @@ After installing the runtime, you need to plug in your Coral Edge TPU into a USB
To use the Edge TPU, you need to convert your model into a compatible format. It is recommended that you run export on Google Colab, x86_64 Linux machine, using the official [Ultralytics Docker container](docker-quickstart.md), or using [Ultralytics HUB](../hub/quickstart.md), since the Edge TPU compiler is not available on ARM. See the [Export Mode](../modes/export.md) for the available arguments.
-!!! note "Exporting the model"
+!!! example "Exporting the model"
=== "Python"
@@ -105,13 +105,27 @@ To use the Edge TPU, you need to convert your model into a compatible format. It
yolo export model=path/to/model.pt format=edgetpu # Export an official model or custom model
```
-The exported model will be saved in the `
- 
-
+
diff --git a/docs/en/models/yolov5.md b/docs/en/models/yolov5.md
index 8ff1c36ec0..91c562a44e 100644
--- a/docs/en/models/yolov5.md
+++ b/docs/en/models/yolov5.md
@@ -4,7 +4,11 @@ description: Explore YOLOv5u, an advanced object detection model with optimized
keywords: YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-trained models, accuracy, speed, real-time detection
---
-# YOLOv5
+# Ultralytics YOLOv5
+
+!!! tip "Ultralytics YOLOv5 Publication"
+
+ Ultralytics has not published a formal research paper for YOLOv5 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com).
## Overview
diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md
index 036cd305a1..c8e4397d15 100644
--- a/docs/en/models/yolov8.md
+++ b/docs/en/models/yolov8.md
@@ -6,6 +6,10 @@ keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer
# Ultralytics YOLOv8
+!!! tip "Ultralytics YOLOv8 Publication"
+
+ Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com).
+
## Overview
YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
diff --git a/docs/en/yolov5/environments/aws_quickstart_tutorial.md b/docs/en/yolov5/environments/aws_quickstart_tutorial.md
index 0e5daf2fe9..387817dcc9 100644
--- a/docs/en/yolov5/environments/aws_quickstart_tutorial.md
+++ b/docs/en/yolov5/environments/aws_quickstart_tutorial.md
@@ -8,7 +8,7 @@ keywords: YOLOv5, AWS, Deep Learning, Machine Learning, AWS EC2, YOLOv5 setup, D
Setting up a high-performance deep learning environment can be daunting for newcomers, but fear not! 🛠️ With this guide, we'll walk you through the process of getting YOLOv5 up and running on an AWS Deep Learning instance. By leveraging the power of Amazon Web Services (AWS), even those new to [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) can get started quickly and cost-effectively. The AWS platform's scalability is perfect for both experimentation and production deployment.
-Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) , [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) 
, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) , [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md).
+You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) , [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md).
## Prerequisites
diff --git a/docs/en/yolov5/index.md b/docs/en/yolov5/index.md
index 17be5e24a0..0e0712997b 100644
--- a/docs/en/yolov5/index.md
+++ b/docs/en/yolov5/index.md
@@ -18,15 +18,15 @@ keywords: YOLOv5, Ultralytics, object detection, computer vision, deep learning,
-
+
-Welcome to the Ultralytics' YOLOv5🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" [object detection](https://www.ultralytics.com/glossary/object-detection) model, is designed to deliver high-speed, high-accuracy results in real-time.
+Welcome to the Ultralytics' YOLOv5🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
-Built on PyTorch, this powerful [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects. Let's get started!
+Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects. Let's get started!
@@ -54,7 +54,7 @@ Here's a compilation of comprehensive tutorials that will guide you through diff
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/hyperparameter_evolution.md b/docs/en/yolov5/tutorials/hyperparameter_evolution.md
index 3db460b126..9dff4ba3d7 100644
--- a/docs/en/yolov5/tutorials/hyperparameter_evolution.md
+++ b/docs/en/yolov5/tutorials/hyperparameter_evolution.md
@@ -153,7 +153,7 @@ We recommend a minimum of 300 generations of evolution for best results. Note th
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/model_ensembling.md b/docs/en/yolov5/tutorials/model_ensembling.md
index 814c896921..cc76cc0cda 100644
--- a/docs/en/yolov5/tutorials/model_ensembling.md
+++ b/docs/en/yolov5/tutorials/model_ensembling.md
@@ -134,7 +134,7 @@ Done. (0.223s)
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/model_export.md b/docs/en/yolov5/tutorials/model_export.md
index e5f0c73007..a3a945c1e1 100644
--- a/docs/en/yolov5/tutorials/model_export.md
+++ b/docs/en/yolov5/tutorials/model_export.md
@@ -234,7 +234,7 @@ YOLOv5 OpenVINO C++ inference examples:
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md b/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md
index 8bda8772e1..0adfb32c8a 100644
--- a/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md
+++ b/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md
@@ -97,7 +97,7 @@ In the results we can observe that we have achieved a **sparsity of 30%** in our
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/multi_gpu_training.md b/docs/en/yolov5/tutorials/multi_gpu_training.md
index 53f3a1c1fd..d61fab8327 100644
--- a/docs/en/yolov5/tutorials/multi_gpu_training.md
+++ b/docs/en/yolov5/tutorials/multi_gpu_training.md
@@ -173,7 +173,7 @@ If you went through all the above, feel free to raise an Issue by giving as much
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md
index 27e26f144f..0f464adf89 100644
--- a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md
+++ b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md
@@ -361,7 +361,7 @@ model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_paddle_mode
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md
index 55728f21e7..a6f700694c 100644
--- a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md
+++ b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md
@@ -60,7 +60,7 @@ The real world is messy and your model will invariably encounter situations your
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
@@ -102,4 +102,4 @@ Active learning is a machine learning strategy that iteratively improves a model
### How can I use Ultralytics environments for training YOLOv5 models on different platforms?
-Ultralytics provides ready-to-use environments with pre-installed dependencies like CUDA, CUDNN, Python, and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it easier to kickstart your training projects. These environments are available on various platforms such as Google Cloud, AWS, Azure, and Docker. You can also access free GPU notebooks via [Paperspace](https://bit.ly/yolov5-paperspace-notebook), [Google Colab](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb), and [Kaggle](https://www.kaggle.com/ultralytics/yolov5). For specific setup instructions, visit the [Supported Environments](#supported-environments) section of the documentation.
+Ultralytics provides ready-to-use environments with pre-installed dependencies like CUDA, CUDNN, Python, and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it easier to kickstart your training projects. These environments are available on various platforms such as Google Cloud, AWS, Azure, and Docker. You can also access free GPU notebooks via [Paperspace](https://bit.ly/yolov5-paperspace-notebook), [Google Colab](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb), and [Kaggle](https://www.kaggle.com/models/ultralytics/yolov5). For specific setup instructions, visit the [Supported Environments](#supported-environments) section of the documentation.
diff --git a/docs/en/yolov5/tutorials/test_time_augmentation.md b/docs/en/yolov5/tutorials/test_time_augmentation.md
index 336ad3f79b..30c53b7230 100644
--- a/docs/en/yolov5/tutorials/test_time_augmentation.md
+++ b/docs/en/yolov5/tutorials/test_time_augmentation.md
@@ -151,7 +151,7 @@ You can customize the TTA ops applied in the YOLOv5 `forward_augment()` method [
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md
index 8b465c5239..c6f9d6f269 100644
--- a/docs/en/yolov5/tutorials/train_custom_data.md
+++ b/docs/en/yolov5/tutorials/train_custom_data.md
@@ -77,7 +77,7 @@ Export in `YOLOv5 Pytorch` format, then copy the snippet into your training scri
### 2.1 Create `dataset.yaml`
-[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of [overfitting](https://www.ultralytics.com/glossary/overfitting). [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary:
+[COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of [overfitting](https://www.ultralytics.com/glossary/overfitting). [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary:
```yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@@ -145,7 +145,7 @@ python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt
💡 Always train from a local dataset. Mounted or network drives like Google Drive will be very slow.
-All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc. For more details see the Training section of our tutorial notebook.
+All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc. For more details see the Training section of our tutorial notebook.
## 5. Visualize
@@ -211,7 +211,7 @@ Once your model is trained you can use your best checkpoint `best.pt` to:
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md b/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md
index 9e689ad3eb..9a37ace165 100644
--- a/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md
+++ b/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md
@@ -141,7 +141,7 @@ Interestingly, the more modules are frozen the less GPU memory is required to tr
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
-- **Free GPU Notebooks**:
+- **Free GPU Notebooks**:
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml
index 0e0423c248..2e20921385 100644
--- a/docs/mkdocs_github_authors.yaml
+++ b/docs/mkdocs_github_authors.yaml
@@ -76,6 +76,9 @@
79740115+0xSynapse@users.noreply.github.com:
avatar: https://avatars.githubusercontent.com/u/79740115?v=4
username: 0xSynapse
+91465467+lalayants@users.noreply.github.com:
+ avatar: https://avatars.githubusercontent.com/u/91465467?v=4
+ username: lalayants
Francesco.mttl@gmail.com:
avatar: https://avatars.githubusercontent.com/u/3855193?v=4
username: ambitious-octopus
diff --git a/docs/overrides/javascript/extra.js b/docs/overrides/javascript/extra.js
index b106acdfe0..5029ff4893 100644
--- a/docs/overrides/javascript/extra.js
+++ b/docs/overrides/javascript/extra.js
@@ -1,71 +1,68 @@
-// Function that applies light/dark theme based on the user's preference
-const applyAutoTheme = () => {
- // Determine the user's preferred color scheme
- const prefersLight = window.matchMedia("(prefers-color-scheme: light)").matches;
- const prefersDark = window.matchMedia("(prefers-color-scheme: dark)").matches;
+// Apply theme based on user preference
+const applyTheme = (isDark) => {
+ document.body.setAttribute(
+ "data-md-color-scheme",
+ isDark ? "slate" : "default",
+ );
+ document.body.setAttribute(
+ "data-md-color-primary",
+ isDark ? "black" : "indigo",
+ );
+};
+
+// Check and apply auto theme
+const checkAutoTheme = () => {
+ const supportedLangCodes = [
+ "en",
+ "zh",
+ "ko",
+ "ja",
+ "ru",
+ "de",
+ "fr",
+ "es",
+ "pt",
+ "it",
+ "tr",
+ "vi",
+ "ar",
+ ];
+ const langCode = window.location.pathname.split("/")[1];
+ const localStorageKey = `${supportedLangCodes.includes(langCode) ? `/${langCode}` : ""}/.__palette`;
+ const palette = JSON.parse(localStorage.getItem(localStorageKey) || "{}");
- // Apply the appropriate attributes based on the user's preference
- if (prefersLight) {
- document.body.setAttribute("data-md-color-scheme", "default");
- document.body.setAttribute("data-md-color-primary", "indigo");
- } else if (prefersDark) {
- document.body.setAttribute("data-md-color-scheme", "slate");
- document.body.setAttribute("data-md-color-primary", "black");
+ if (palette.index === 0) {
+ applyTheme(window.matchMedia("(prefers-color-scheme: dark)").matches);
}
};
-// Function that checks and applies light/dark theme based on the user's preference (if auto theme is enabled)
-function checkAutoTheme() {
- // Array of supported language codes -> each language has its own palette (stored in local storage)
- const supportedLangCodes = ["en", "zh", "ko", "ja", "ru", "de", "fr", "es", "pt", "it", "tr", "vi", "nl"];
- // Get the URL path
- const path = window.location.pathname;
- // Extract the language code from the URL (assuming it's in the format /xx/...)
- const langCode = path.split("/")[1];
- // Check if the extracted language code is in the supported languages
- const isValidLangCode = supportedLangCodes.includes(langCode);
- // Construct the local storage key based on the language code if valid, otherwise default to the root key
- const localStorageKey = isValidLangCode ? `/${langCode}/.__palette` : "/.__palette";
- // Retrieve the palette from local storage using the constructed key
- const palette = localStorage.getItem(localStorageKey);
- if (palette) {
- // Check if the palette's index is 0 (auto theme)
- const paletteObj = JSON.parse(palette);
- if (paletteObj && paletteObj.index === 0) {
- applyAutoTheme();
- }
- }
-}
+// Event listeners for theme changes
+const mediaQueryList = window.matchMedia("(prefers-color-scheme: dark)");
+mediaQueryList.addListener(checkAutoTheme);
-// Run function when the script loads
+// Initial theme check
checkAutoTheme();
-// Re-run the function when the user's preference changes (when the user changes their system theme)
-window.matchMedia("(prefers-color-scheme: light)").addEventListener("change", checkAutoTheme);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", checkAutoTheme);
-
-// Re-run the function when the palette changes (e.g. user switched from dark theme to auto theme)
-// ! We can't use window.addEventListener("storage", checkAutoTheme) because it will NOT be triggered on the current tab
-// ! So we have to use the following workaround:
-// Get the palette input for auto theme
-var autoThemeInput = document.getElementById("__palette_1");
-if (autoThemeInput) {
- // Add a click event listener to the input
- autoThemeInput.addEventListener("click", function () {
- // Check if the auto theme is selected
- if (autoThemeInput.checked) {
- // Re-run the function after a short delay (to ensure that the palette has been updated)
- setTimeout(applyAutoTheme);
- }
+// Auto theme input listener
+document.addEventListener("DOMContentLoaded", () => {
+ const autoThemeInput = document.getElementById("__palette_1");
+ autoThemeInput?.addEventListener("click", () => {
+ if (autoThemeInput.checked) setTimeout(checkAutoTheme);
});
-}
+});
-// Add iframe navigation
-window.onhashchange = function() {
- window.parent.postMessage({
- type: 'navigation',
- hash: window.location.pathname + window.location.search + window.location.hash
- }, '*');
+// Iframe navigation
+window.onhashchange = () => {
+ window.parent.postMessage(
+ {
+ type: "navigation",
+ hash:
+ window.location.pathname +
+ window.location.search +
+ window.location.hash,
+ },
+ "*",
+ );
};
// Add Inkeep button
@@ -112,35 +109,35 @@ document.addEventListener("DOMContentLoaded", () => {
},
aiChatSettings: {
chatSubjectName: "Ultralytics",
- botAvatarSrcUrl: "https://storage.googleapis.com/organization-image-assets/ultralytics-botAvatarSrcUrl-1727908259285.png",
- botAvatarDarkSrcUrl: "https://storage.googleapis.com/organization-image-assets/ultralytics-botAvatarDarkSrcUrl-1727908258478.png",
+ botAvatarSrcUrl:
+ "https://storage.googleapis.com/organization-image-assets/ultralytics-botAvatarSrcUrl-1729379860806.svg",
quickQuestions: [
"What's new in Ultralytics YOLO11?",
"How can I get started with Ultralytics HUB?",
- "How does Ultralytics Enterprise Licensing work?"
+ "How does Ultralytics Enterprise Licensing work?",
],
getHelpCallToActions: [
{
name: "Ask on Ultralytics GitHub",
url: "https://github.com/ultralytics/ultralytics",
icon: {
- builtIn: "FaGithub"
- }
+ builtIn: "FaGithub",
+ },
},
{
name: "Ask on Ultralytics Discourse",
url: "https://community.ultralytics.com/",
icon: {
- builtIn: "FaDiscourse"
- }
+ builtIn: "FaDiscourse",
+ },
},
{
name: "Ask on Ultralytics Discord",
url: "https://discord.com/invite/ultralytics",
icon: {
- builtIn: "FaDiscord"
- }
- }
+ builtIn: "FaDiscord",
+ },
+ },
],
},
},
diff --git a/docs/overrides/javascript/giscus.js b/docs/overrides/javascript/giscus.js
new file mode 100644
index 0000000000..a64e297342
--- /dev/null
+++ b/docs/overrides/javascript/giscus.js
@@ -0,0 +1,80 @@
+// Giscus functionality
+function loadGiscus() {
+ const giscusContainer = document.getElementById("giscus-container");
+ if (!giscusContainer || giscusContainer.querySelector("script")) return;
+
+ const script = document.createElement("script");
+ script.src = "https://giscus.app/client.js";
+ script.setAttribute("data-repo", "ultralytics/ultralytics");
+ script.setAttribute("data-repo-id", "R_kgDOH-jzvQ");
+ script.setAttribute("data-category", "Docs");
+ script.setAttribute("data-category-id", "DIC_kwDOH-jzvc4CWLkL");
+ script.setAttribute("data-mapping", "pathname");
+ script.setAttribute("data-strict", "1");
+ script.setAttribute("data-reactions-enabled", "1");
+ script.setAttribute("data-emit-metadata", "0");
+ script.setAttribute("data-input-position", "top");
+ script.setAttribute("data-theme", "preferred_color_scheme");
+ script.setAttribute("data-lang", "en");
+ script.setAttribute("data-loading", "lazy");
+ script.setAttribute("crossorigin", "anonymous");
+ script.setAttribute("async", "");
+
+ giscusContainer.appendChild(script);
+
+ // Synchronize Giscus theme with palette
+ var palette = __md_get("__palette");
+ if (palette && typeof palette.color === "object") {
+ var theme = palette.color.scheme === "slate" ? "dark" : "light";
+ script.setAttribute("data-theme", theme);
+ }
+
+ // Register event handlers for theme changes
+ var ref = document.querySelector("[data-md-component=palette]");
+ if (ref) {
+ ref.addEventListener("change", function () {
+ var palette = __md_get("__palette");
+ if (palette && typeof palette.color === "object") {
+ var theme = palette.color.scheme === "slate" ? "dark" : "light";
+
+ // Instruct Giscus to change theme
+ var frame = document.querySelector(".giscus-frame");
+ if (frame) {
+ frame.contentWindow.postMessage(
+ { giscus: { setConfig: { theme } } },
+ "https://giscus.app",
+ );
+ }
+ }
+ });
+ }
+}
+
+// Use Intersection Observer to load Giscus when the container is visible
+function setupGiscusLoader() {
+ const giscusContainer = document.getElementById("giscus-container");
+
+ if (giscusContainer) {
+ const observer = new IntersectionObserver((entries) => {
+ entries.forEach((entry) => {
+ if (entry.isIntersecting) {
+ loadGiscus();
+ observer.unobserve(entry.target);
+ }
+ });
+ }, { threshold: 0.1 }); // Trigger when 10% of the element is visible
+
+ observer.observe(giscusContainer);
+ }
+}
+
+// Hook into MkDocs' navigation system
+if (typeof document$ !== "undefined") {
+ document$.subscribe(() => {
+ // This function is called on every page load/change
+ setupGiscusLoader();
+ });
+} else {
+ console.warn("MkDocs document$ not found. Falling back to DOMContentLoaded.");
+ document.addEventListener("DOMContentLoaded", setupGiscusLoader);
+}
diff --git a/docs/overrides/partials/comments.html b/docs/overrides/partials/comments.html
index a99f4f814b..fdfce5d651 100644
--- a/docs/overrides/partials/comments.html
+++ b/docs/overrides/partials/comments.html
@@ -1,51 +1,7 @@
{% if page.meta.comments %}
![\"Ultralytics](\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\"){kind=icon}
\n",
" \n",
" \n",
- " \n",
+ " \n",
" ![\"Discord\"](\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\")
\n",
"\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
@@ -96,10 +96,7 @@
"source": [
"import cv2\n",
"\n",
- "from ultralytics import YOLO, solutions\n",
- "\n",
- "# Load YOLO model\n",
- "model = YOLO(\"yolo11n.pt\")\n",
+ "from ultralytics import solutions\n",
"\n",
"# Open video file\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@@ -113,10 +110,9 @@
"\n",
"# Initialize heatmap object\n",
"heatmap_obj = solutions.Heatmap(\n",
- " colormap=cv2.COLORMAP_PARULA,\n",
- " view_img=True,\n",
- " shape=\"circle\",\n",
- " names=model.names,\n",
+ " colormap=cv2.COLORMAP_PARULA, # Color of the heatmap\n",
+ " show=True, # Display the image during processing\n",
+ " model=yolo11n.pt, # Ultralytics YOLO11 model file\n",
")\n",
"\n",
"while cap.isOpened():\n",
@@ -125,11 +121,8 @@
" print(\"Video frame is empty or video processing has been successfully completed.\")\n",
" break\n",
"\n",
- " # Perform tracking on the current frame\n",
- " tracks = model.track(im0, persist=True, show=False)\n",
- "\n",
" # Generate heatmap on the frame\n",
- " im0 = heatmap_obj.generate_heatmap(im0, tracks)\n",
+ " im0 = heatmap_obj.generate_heatmap(im0)\n",
"\n",
" # Write the frame to the output video\n",
" video_writer.write(im0)\n",
diff --git a/examples/object_counting.ipynb b/examples/object_counting.ipynb
index 8356d592d2..50168f262e 100644
--- a/examples/object_counting.ipynb
+++ b/examples/object_counting.ipynb
@@ -16,7 +16,7 @@
" \n",
" \n",
" \n",
- " \n",
+ " \n",
" \n",
"\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
@@ -104,10 +104,7 @@
"source": [
"import cv2\n",
"\n",
- "from ultralytics import YOLO, solutions\n",
- "\n",
- "# Load the pre-trained YOLO11 model\n",
- "model = YOLO(\"yolo11n.pt\")\n",
+ "from ultralytics import solutions\n",
"\n",
"# Open the video file\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@@ -119,19 +116,15 @@
"# Define points for a line or region of interest in the video frame\n",
"line_points = [(20, 400), (1080, 400)] # Line coordinates\n",
"\n",
- "# Specify classes to count, for example: person (0) and car (2)\n",
- "classes_to_count = [0, 2] # Class IDs for person and car\n",
- "\n",
"# Initialize the video writer to save the output video\n",
"video_writer = cv2.VideoWriter(\"object_counting_output.avi\", cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n",
"\n",
"# Initialize the Object Counter with visualization options and other parameters\n",
"counter = solutions.ObjectCounter(\n",
- " view_img=True, # Display the image during processing\n",
- " reg_pts=line_points, # Region of interest points\n",
- " names=model.names, # Class names from the YOLO model\n",
- " draw_tracks=True, # Draw tracking lines for objects\n",
- " line_thickness=2, # Thickness of the lines drawn\n",
+ " show=True, # Display the image during processing\n",
+ " region=line_points, # Region of interest points\n",
+ " model=yolo11n.pt, # Ultralytics YOLO11 model file\n",
+ " line_width=2, # Thickness of the lines and bounding boxes\n",
")\n",
"\n",
"# Process video frames in a loop\n",
@@ -141,11 +134,8 @@
" print(\"Video frame is empty or video processing has been successfully completed.\")\n",
" break\n",
"\n",
- " # Perform object tracking on the current frame, filtering by specified classes\n",
- " tracks = model.track(im0, persist=True, show=False, classes=classes_to_count)\n",
- "\n",
" # Use the Object Counter to count objects in the frame and get the annotated image\n",
- " im0 = counter.start_counting(im0, tracks)\n",
+ " im0 = counter.count(im0)\n",
"\n",
" # Write the annotated frame to the output video\n",
" video_writer.write(im0)\n",
diff --git a/examples/object_tracking.ipynb b/examples/object_tracking.ipynb
index 53ca6b253c..7691fce9cd 100644
--- a/examples/object_tracking.ipynb
+++ b/examples/object_tracking.ipynb
@@ -16,7 +16,7 @@
" \n",
" \n",
" \n",
- " \n",
+ " \n",
" \n",
"\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index c52b03c990..98c659b864 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -30,7 +30,7 @@
" \n",
" \n",
" \n",
- " \n",
+ " \n",
"\n",
" \n",
" ![\"Ultralytics](\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\")
\n",
diff --git a/mkdocs.yml b/mkdocs.yml
index 771084066f..a7157ec942 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -96,8 +96,10 @@ extra: # version:
extra_css:
- stylesheets/style.css
+
extra_javascript:
- javascript/extra.js
+ - javascript/giscus.js
markdown_extensions:
- admonition
@@ -272,7 +274,7 @@ nav:
- VisDrone: datasets/detect/visdrone.md
- VOC: datasets/detect/voc.md
- xView: datasets/detect/xview.md
- - Roboflow 100: datasets/detect/roboflow-100.md
+ - RF100: datasets/detect/roboflow-100.md
- Brain-tumor: datasets/detect/brain-tumor.md
- African-wildlife: datasets/detect/african-wildlife.md
- Signature: datasets/detect/signature.md
@@ -555,6 +557,7 @@ nav:
- utils: reference/nn/modules/utils.md
- tasks: reference/nn/tasks.md
- solutions:
+ - solutions: reference/solutions/solutions.md
- ai_gym: reference/solutions/ai_gym.md
- analytics: reference/solutions/analytics.md
- distance_calculation: reference/solutions/distance_calculation.md
diff --git a/pyproject.toml b/pyproject.toml
index 3fb80e62af..f6cb23204a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "ultralytics"
dynamic = ["version"]
-description = "Ultralytics YOLO for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
+description = "Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
readme = "README.md"
requires-python = ">=3.8"
license = { "text" = "AGPL-3.0" }
diff --git a/tests/test_solutions.py b/tests/test_solutions.py
index d3ba2d5fc2..e01da6d818 100644
--- a/tests/test_solutions.py
+++ b/tests/test_solutions.py
@@ -17,10 +17,15 @@ def test_major_solutions():
cap = cv2.VideoCapture("solutions_ci_demo.mp4")
assert cap.isOpened(), "Error reading video file"
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
- counter = solutions.ObjectCounter(region=region_points, model="yolo11n.pt", show=False)
- heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False)
- speed = solutions.SpeedEstimator(region=region_points, model="yolo11n.pt", show=False)
- queue = solutions.QueueManager(region=region_points, model="yolo11n.pt", show=False)
+ counter = solutions.ObjectCounter(region=region_points, model="yolo11n.pt", show=False) # Test object counter
+ heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False) # Test heatmaps
+ speed = solutions.SpeedEstimator(region=region_points, model="yolo11n.pt", show=False) # Test queue manager
+ queue = solutions.QueueManager(region=region_points, model="yolo11n.pt", show=False) # Test speed estimation
+ line_analytics = solutions.Analytics(analytics_type="line", model="yolo11n.pt", show=False) # line analytics
+ pie_analytics = solutions.Analytics(analytics_type="pie", model="yolo11n.pt", show=False) # line analytics
+ bar_analytics = solutions.Analytics(analytics_type="bar", model="yolo11n.pt", show=False) # line analytics
+ area_analytics = solutions.Analytics(analytics_type="area", model="yolo11n.pt", show=False) # line analytics
+ frame_count = 0 # Required for analytics
while cap.isOpened():
success, im0 = cap.read()
if not success:
@@ -30,24 +35,23 @@ def test_major_solutions():
_ = heatmap.generate_heatmap(original_im0.copy())
_ = speed.estimate_speed(original_im0.copy())
_ = queue.process_queue(original_im0.copy())
+ _ = line_analytics.process_data(original_im0.copy(), frame_count)
+ _ = pie_analytics.process_data(original_im0.copy(), frame_count)
+ _ = bar_analytics.process_data(original_im0.copy(), frame_count)
+ _ = area_analytics.process_data(original_im0.copy(), frame_count)
cap.release()
- cv2.destroyAllWindows()
-
-@pytest.mark.slow
-def test_aigym():
- """Test the workouts monitoring solution."""
+ # Test workouts monitoring
safe_download(url=WORKOUTS_SOLUTION_DEMO)
- cap = cv2.VideoCapture("solution_ci_pose_demo.mp4")
- assert cap.isOpened(), "Error reading video file"
- gym = solutions.AIGym(line_width=2, kpts=[5, 11, 13])
- while cap.isOpened():
- success, im0 = cap.read()
+ cap1 = cv2.VideoCapture("solution_ci_pose_demo.mp4")
+ assert cap1.isOpened(), "Error reading video file"
+ gym = solutions.AIGym(line_width=2, kpts=[5, 11, 13], show=False)
+ while cap1.isOpened():
+ success, im0 = cap1.read()
if not success:
break
_ = gym.monitor(im0)
- cap.release()
- cv2.destroyAllWindows()
+ cap1.release()
@pytest.mark.slow
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 06ee07e308..ac22fe8620 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-__version__ = "8.3.13"
+__version__ = "8.3.21"
import os
diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py
index b6197d885d..fa2fe40a8c 100644
--- a/ultralytics/cfg/__init__.py
+++ b/ultralytics/cfg/__init__.py
@@ -1,6 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-import contextlib
import shutil
import subprocess
import sys
@@ -439,34 +438,60 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
def merge_equals_args(args: List[str]) -> List[str]:
"""
- Merges arguments around isolated '=' in a list of strings, handling three cases:
- 1. ['arg', '=', 'val'] becomes ['arg=val'],
- 2. ['arg=', 'val'] becomes ['arg=val'],
- 3. ['arg', '=val'] becomes ['arg=val'].
+ Merges arguments around isolated '=' in a list of strings and joins fragments with brackets.
+
+ This function handles the following cases:
+ 1. ['arg', '=', 'val'] becomes ['arg=val']
+ 2. ['arg=', 'val'] becomes ['arg=val']
+ 3. ['arg', '=val'] becomes ['arg=val']
+ 4. Joins fragments with brackets, e.g., ['imgsz=[3,', '640,', '640]'] becomes ['imgsz=[3,640,640]']
Args:
- args (List[str]): A list of strings where each element represents an argument.
+ args (List[str]): A list of strings where each element represents an argument or fragment.
Returns:
- (List[str]): A list of strings where the arguments around isolated '=' are merged.
+ List[str]: A list of strings where the arguments around isolated '=' are merged and fragments with brackets are joined.
Examples:
- >>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3"]
- >>> merge_equals_args(args)
- ['arg1=value', 'arg2=value2', 'arg3=value3']
+ >>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3", "imgsz=[3,", "640,", "640]"]
+ >>> merge_and_join_args(args)
+ ['arg1=value', 'arg2=value2', 'arg3=value3', 'imgsz=[3,640,640]']
"""
new_args = []
- for i, arg in enumerate(args):
+ current = ""
+ depth = 0
+
+ i = 0
+ while i < len(args):
+ arg = args[i]
+
+ # Handle equals sign merging
if arg == "=" and 0 < i < len(args) - 1: # merge ['arg', '=', 'val']
new_args[-1] += f"={args[i + 1]}"
- del args[i + 1]
+ i += 2
+ continue
elif arg.endswith("=") and i < len(args) - 1 and "=" not in args[i + 1]: # merge ['arg=', 'val']
new_args.append(f"{arg}{args[i + 1]}")
- del args[i + 1]
+ i += 2
+ continue
elif arg.startswith("=") and i > 0: # merge ['arg', '=val']
new_args[-1] += arg
- else:
- new_args.append(arg)
+ i += 1
+ continue
+
+ # Handle bracket joining
+ depth += arg.count("[") - arg.count("]")
+ current += arg
+ if depth == 0:
+ new_args.append(current)
+ current = ""
+
+ i += 1
+
+ # Append any remaining current string
+ if current:
+ new_args.append(current)
+
return new_args
@@ -483,7 +508,7 @@ def handle_yolo_hub(args: List[str]) -> None:
Examples:
```bash
- yolo hub login YOUR_API_KEY
+ yolo login YOUR_API_KEY
```
Notes:
diff --git a/ultralytics/cfg/datasets/coco128-seg.yaml b/ultralytics/cfg/datasets/coco128-seg.yaml
index dcd961c6e5..c8349cee66 100644
--- a/ultralytics/cfg/datasets/coco128-seg.yaml
+++ b/ultralytics/cfg/datasets/coco128-seg.yaml
@@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
# Example usage: yolo train data=coco128.yaml
# parent
diff --git a/ultralytics/cfg/datasets/coco128.yaml b/ultralytics/cfg/datasets/coco128.yaml
index 1b515592f2..a085f5e9a6 100644
--- a/ultralytics/cfg/datasets/coco128.yaml
+++ b/ultralytics/cfg/datasets/coco128.yaml
@@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
# Example usage: yolo train data=coco128.yaml
# parent
diff --git a/ultralytics/cfg/solutions/default.yaml b/ultralytics/cfg/solutions/default.yaml
index e4e1b845a0..a353fd2a21 100644
--- a/ultralytics/cfg/solutions/default.yaml
+++ b/ultralytics/cfg/solutions/default.yaml
@@ -15,3 +15,4 @@ down_angle: 90 # Workouts down_angle for counts, 90 is default value. You can ch
kpts: [6, 8, 10] # Keypoints for workouts monitoring, i.e. If you want to consider keypoints for pushups that have mostly values of [6, 8, 10].
colormap: # Colormap for heatmap, Only OPENCV supported colormaps can be used. By default COLORMAP_PARULA will be used for visualization.
analytics_type: "line" # Analytics type i.e "line", "pie", "bar" or "area" charts. By default, "line" analytics will be used for processing.
+json_file: # parking system regions file path.
diff --git a/ultralytics/data/split_dota.py b/ultralytics/data/split_dota.py
index f9acffe9bb..b745b3662c 100644
--- a/ultralytics/data/split_dota.py
+++ b/ultralytics/data/split_dota.py
@@ -13,9 +13,6 @@ from tqdm import tqdm
from ultralytics.data.utils import exif_size, img2label_paths
from ultralytics.utils.checks import check_requirements
-check_requirements("shapely")
-from shapely.geometry import Polygon
-
def bbox_iof(polygon1, bbox2, eps=1e-6):
"""
@@ -33,6 +30,9 @@ def bbox_iof(polygon1, bbox2, eps=1e-6):
Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4].
Bounding box format: [x_min, y_min, x_max, y_max].
"""
+ check_requirements("shapely")
+ from shapely.geometry import Polygon
+
polygon1 = polygon1.reshape(-1, 4, 2)
lt_point = np.min(polygon1, axis=-2) # left-top
rb_point = np.max(polygon1, axis=-2) # right-bottom
diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index dab9c69e60..6d403a2afb 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -194,9 +194,13 @@ class Exporter:
is_tf_format = any((saved_model, pb, tflite, edgetpu, tfjs))
# Device
+ dla = None
if fmt == "engine" and self.args.device is None:
LOGGER.warning("WARNING ⚠️ TensorRT requires GPU export, automatically assigning device=0")
self.args.device = "0"
+ if fmt == "engine" and "dla" in str(self.args.device): # convert int/list to str first
+ dla = self.args.device.split(":")[-1]
+ assert dla in {"0", "1"}, f"Expected self.args.device='dla:0' or 'dla:1, but got {self.args.device}."
self.device = select_device("cpu" if self.args.device is None else self.args.device)
# Checks
@@ -309,7 +313,7 @@ class Exporter:
if jit or ncnn: # TorchScript
f[0], _ = self.export_torchscript()
if engine: # TensorRT required before ONNX
- f[1], _ = self.export_engine()
+ f[1], _ = self.export_engine(dla=dla)
if onnx: # ONNX
f[2], _ = self.export_onnx()
if xml: # OpenVINO
@@ -398,7 +402,7 @@ class Exporter:
"""YOLO ONNX export."""
requirements = ["onnx>=1.12.0"]
if self.args.simplify:
- requirements += ["onnxslim==0.1.34", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
+ requirements += ["onnxslim", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
check_requirements(requirements)
import onnx # noqa
@@ -682,7 +686,7 @@ class Exporter:
return f, ct_model
@try_export
- def export_engine(self, prefix=colorstr("TensorRT:")):
+ def export_engine(self, dla=None, prefix=colorstr("TensorRT:")):
"""YOLO TensorRT export https://developer.nvidia.com/tensorrt."""
assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'"
f_onnx, _ = self.export_onnx() # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016
@@ -691,10 +695,10 @@ class Exporter:
import tensorrt as trt # noqa
except ImportError:
if LINUX:
- check_requirements("tensorrt>7.0.0,<=10.1.0")
+ check_requirements("tensorrt>7.0.0,!=10.1.0")
import tensorrt as trt # noqa
check_version(trt.__version__, ">=7.0.0", hard=True)
- check_version(trt.__version__, "<=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
+ check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
# Setup and checks
LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
@@ -717,6 +721,20 @@ class Exporter:
network = builder.create_network(flag)
half = builder.platform_has_fast_fp16 and self.args.half
int8 = builder.platform_has_fast_int8 and self.args.int8
+
+ # Optionally switch to DLA if enabled
+ if dla is not None:
+ if not IS_JETSON:
+ raise ValueError("DLA is only available on NVIDIA Jetson devices")
+ LOGGER.info(f"{prefix} enabling DLA on core {dla}...")
+ if not self.args.half and not self.args.int8:
+ raise ValueError(
+ "DLA requires either 'half=True' (FP16) or 'int8=True' (INT8) to be enabled. Please enable one of them and try again."
+ )
+ config.default_device_type = trt.DeviceType.DLA
+ config.DLA_core = int(dla)
+ config.set_flag(trt.BuilderFlag.GPU_FALLBACK)
+
# Read ONNX file
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(f_onnx):
diff --git a/ultralytics/hub/__init__.py b/ultralytics/hub/__init__.py
index 9c9c9dfa16..5b1fc9faeb 100644
--- a/ultralytics/hub/__init__.py
+++ b/ultralytics/hub/__init__.py
@@ -63,13 +63,13 @@ def login(api_key: str = None, save=True) -> bool:
return True
else:
# Failed to authenticate with HUB
- LOGGER.info(f"{PREFIX}Get API key from {api_key_url} and then run 'yolo hub login API_KEY'")
+ LOGGER.info(f"{PREFIX}Get API key from {api_key_url} and then run 'yolo login API_KEY'")
return False
def logout():
"""
- Log out of Ultralytics HUB by removing the API key from the settings file. To log in again, use 'yolo hub login'.
+ Log out of Ultralytics HUB by removing the API key from the settings file. To log in again, use 'yolo login'.
Example:
```python
@@ -79,7 +79,7 @@ def logout():
```
"""
SETTINGS["api_key"] = ""
- LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo hub login'.")
+ LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo login'.")
def reset_model(model_id=""):
diff --git a/ultralytics/hub/auth.py b/ultralytics/hub/auth.py
index 3c7c6d3d25..a12215bb20 100644
--- a/ultralytics/hub/auth.py
+++ b/ultralytics/hub/auth.py
@@ -68,7 +68,7 @@ class Auth:
if verbose:
LOGGER.info(f"{PREFIX}New authentication successful ✅")
elif verbose:
- LOGGER.info(f"{PREFIX}Get API key from {API_KEY_URL} and then run 'yolo hub login API_KEY'")
+ LOGGER.info(f"{PREFIX}Get API key from {API_KEY_URL} and then run 'yolo login API_KEY'")
def request_api_key(self, max_attempts=3):
"""
diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 12977f0184..0dcbb12f96 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -126,7 +126,7 @@ class AutoBackend(nn.Module):
fp16 &= pt or jit or onnx or xml or engine or nn_module or triton # FP16
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
stride = 32 # default stride
- model, metadata = None, None
+ model, metadata, task = None, None, None
# Set device
cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA
@@ -226,10 +226,10 @@ class AutoBackend(nn.Module):
import tensorrt as trt # noqa https://developer.nvidia.com/nvidia-tensorrt-download
except ImportError:
if LINUX:
- check_requirements("tensorrt>7.0.0,<=10.1.0")
+ check_requirements("tensorrt>7.0.0,!=10.1.0")
import tensorrt as trt # noqa
check_version(trt.__version__, ">=7.0.0", hard=True)
- check_version(trt.__version__, "<=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
+ check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239")
if device.type == "cpu":
device = torch.device("cuda:0")
Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
@@ -336,11 +336,15 @@ class AutoBackend(nn.Module):
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate
if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
- LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...")
+ device = device[3:] if str(device).startswith("tpu") else ":0"
+ LOGGER.info(f"Loading {w} on device {device[1:]} for TensorFlow Lite Edge TPU inference...")
delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[
platform.system()
]
- interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
+ interpreter = Interpreter(
+ model_path=w,
+ experimental_delegates=[load_delegate(delegate, options={"device": device})],
+ )
else: # TFLite
LOGGER.info(f"Loading {w} for TensorFlow Lite inference...")
interpreter = Interpreter(model_path=w) # load TFLite model
@@ -501,7 +505,7 @@ class AutoBackend(nn.Module):
# TensorRT
elif self.engine:
- if self.dynamic or im.shape != self.bindings["images"].shape:
+ if self.dynamic and im.shape != self.bindings["images"].shape:
if self.is_trt10:
self.context.set_input_shape("images", im.shape)
self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
index 60911e779f..4bc1fa25e7 100644
--- a/ultralytics/nn/modules/head.py
+++ b/ultralytics/nn/modules/head.py
@@ -28,6 +28,7 @@ class Detect(nn.Module):
shape = None
anchors = torch.empty(0) # init
strides = torch.empty(0) # init
+ legacy = False # backward compatibility for v3/v5/v8/v9 models
def __init__(self, nc=80, ch=()):
"""Initializes the YOLO detection layer with specified number of classes and channels."""
@@ -41,13 +42,17 @@ class Detect(nn.Module):
self.cv2 = nn.ModuleList(
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
)
- self.cv3 = nn.ModuleList(
- nn.Sequential(
- nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
- nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
- nn.Conv2d(c3, self.nc, 1),
+ self.cv3 = (
+ nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+ if self.legacy
+ else nn.ModuleList(
+ nn.Sequential(
+ nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
+ nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
+ nn.Conv2d(c3, self.nc, 1),
+ )
+ for x in ch
)
- for x in ch
)
self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 12de1cfbf6..1e69a8f25c 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -936,6 +936,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
import ast
# Args
+ legacy = True # backward compatibility for v3/v5/v8/v9 models
max_channels = float("inf")
nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))
depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
@@ -1027,8 +1028,10 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
}:
args.insert(2, n) # number of repeats
n = 1
- if m is C3k2 and scale in "mlx": # for M/L/X sizes
- args[3] = True
+ if m is C3k2: # for M/L/X sizes
+ legacy = False
+ if scale in "mlx":
+ args[3] = True
elif m is AIFI:
args = [ch[f], *args]
elif m in {HGStem, HGBlock}:
@@ -1047,6 +1050,8 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
args.append([ch[x] for x in f])
if m is Segment:
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
+ if m in {Detect, Segment, Pose, OBB}:
+ m.legacy = legacy
elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
args.insert(1, [ch[x] for x in f])
elif m is CBLinear:
diff --git a/ultralytics/solutions/ai_gym.py b/ultralytics/solutions/ai_gym.py
index 26f22d7032..0d131bd9d6 100644
--- a/ultralytics/solutions/ai_gym.py
+++ b/ultralytics/solutions/ai_gym.py
@@ -1,16 +1,40 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-from ultralytics.solutions.solutions import BaseSolution # Import a parent class
+from ultralytics.solutions.solutions import BaseSolution
from ultralytics.utils.plotting import Annotator
class AIGym(BaseSolution):
- """A class to manage the gym steps of people in a real-time video stream based on their poses."""
+ """
+ A class to manage gym steps of people in a real-time video stream based on their poses.
+
+ This class extends BaseSolution to monitor workouts using YOLO pose estimation models. It tracks and counts
+ repetitions of exercises based on predefined angle thresholds for up and down positions.
+
+ Attributes:
+ count (List[int]): Repetition counts for each detected person.
+ angle (List[float]): Current angle of the tracked body part for each person.
+ stage (List[str]): Current exercise stage ('up', 'down', or '-') for each person.
+ initial_stage (str | None): Initial stage of the exercise.
+ up_angle (float): Angle threshold for considering the 'up' position of an exercise.
+ down_angle (float): Angle threshold for considering the 'down' position of an exercise.
+ kpts (List[int]): Indices of keypoints used for angle calculation.
+ lw (int): Line width for drawing annotations.
+ annotator (Annotator): Object for drawing annotations on the image.
+
+ Methods:
+ monitor: Processes a frame to detect poses, calculate angles, and count repetitions.
+
+ Examples:
+ >>> gym = AIGym(model="yolov8n-pose.pt")
+ >>> image = cv2.imread("gym_scene.jpg")
+ >>> processed_image = gym.monitor(image)
+ >>> cv2.imshow("Processed Image", processed_image)
+ >>> cv2.waitKey(0)
+ """
def __init__(self, **kwargs):
- """Initialization function for AiGYM class, a child class of BaseSolution class, can be used for workouts
- monitoring.
- """
+ """Initializes AIGym for workout monitoring using pose estimation and predefined angles."""
# Check if the model name ends with '-pose'
if "model" in kwargs and "-pose" not in kwargs["model"]:
kwargs["model"] = "yolo11n-pose.pt"
@@ -31,12 +55,22 @@ class AIGym(BaseSolution):
def monitor(self, im0):
"""
- Monitor the workouts using Ultralytics YOLOv8 Pose Model: https://docs.ultralytics.com/tasks/pose/.
+ Monitors workouts using Ultralytics YOLO Pose Model.
+
+ This function processes an input image to track and analyze human poses for workout monitoring. It uses
+ the YOLO Pose model to detect keypoints, estimate angles, and count repetitions based on predefined
+ angle thresholds.
Args:
- im0 (ndarray): The input image that will be used for processing
- Returns
- im0 (ndarray): The processed image for more usage
+ im0 (ndarray): Input image for processing.
+
+ Returns:
+ (ndarray): Processed image with annotations for workout monitoring.
+
+ Examples:
+ >>> gym = AIGym()
+ >>> image = cv2.imread("workout.jpg")
+ >>> processed_image = gym.monitor(image)
"""
# Extract tracks
tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])[0]
diff --git a/ultralytics/solutions/analytics.py b/ultralytics/solutions/analytics.py
index 38489827af..aed7beed94 100644
--- a/ultralytics/solutions/analytics.py
+++ b/ultralytics/solutions/analytics.py
@@ -12,10 +12,41 @@ from ultralytics.solutions.solutions import BaseSolution # Import a parent clas
class Analytics(BaseSolution):
- """A class to create and update various types of charts (line, bar, pie, area) for visual analytics."""
+ """
+ A class for creating and updating various types of charts for visual analytics.
+
+ This class extends BaseSolution to provide functionality for generating line, bar, pie, and area charts
+ based on object detection and tracking data.
+
+ Attributes:
+ type (str): The type of analytics chart to generate ('line', 'bar', 'pie', or 'area').
+ x_label (str): Label for the x-axis.
+ y_label (str): Label for the y-axis.
+ bg_color (str): Background color of the chart frame.
+ fg_color (str): Foreground color of the chart frame.
+ title (str): Title of the chart window.
+ max_points (int): Maximum number of data points to display on the chart.
+ fontsize (int): Font size for text display.
+ color_cycle (cycle): Cyclic iterator for chart colors.
+ total_counts (int): Total count of detected objects (used for line charts).
+ clswise_count (Dict[str, int]): Dictionary for class-wise object counts.
+ fig (Figure): Matplotlib figure object for the chart.
+ ax (Axes): Matplotlib axes object for the chart.
+ canvas (FigureCanvas): Canvas for rendering the chart.
+
+ Methods:
+ process_data: Processes image data and updates the chart.
+ update_graph: Updates the chart with new data points.
+
+ Examples:
+ >>> analytics = Analytics(analytics_type="line")
+ >>> frame = cv2.imread("image.jpg")
+ >>> processed_frame = analytics.process_data(frame, frame_number=1)
+ >>> cv2.imshow("Analytics", processed_frame)
+ """
def __init__(self, **kwargs):
- """Initialize the Analytics class with various chart types."""
+ """Initialize Analytics class with various chart types for visual data representation."""
super().__init__(**kwargs)
self.type = self.CFG["analytics_type"] # extract type of analytics
@@ -31,8 +62,8 @@ class Analytics(BaseSolution):
figsize = (19.2, 10.8) # Set output image size 1920 * 1080
self.color_cycle = cycle(["#DD00BA", "#042AFF", "#FF4447", "#7D24FF", "#BD00FF"])
- self.total_counts = 0 # count variable for storing total counts i.e for line
- self.clswise_count = {} # dictionary for classwise counts
+ self.total_counts = 0 # count variable for storing total counts i.e. for line
+ self.clswise_count = {} # dictionary for class-wise counts
# Ensure line and area chart
if self.type in {"line", "area"}:
@@ -48,15 +79,28 @@ class Analytics(BaseSolution):
self.canvas = FigureCanvas(self.fig) # Set common axis properties
self.ax.set_facecolor(self.bg_color)
self.color_mapping = {}
- self.ax.axis("equal") if type == "pie" else None # Ensure pie chart is circular
+
+ if self.type == "pie": # Ensure pie chart is circular
+ self.ax.axis("equal")
def process_data(self, im0, frame_number):
"""
- Process the image data, run object tracking.
+ Processes image data and runs object tracking to update analytics charts.
Args:
- im0 (ndarray): Input image for processing.
- frame_number (int): Video frame # for plotting the data.
+ im0 (np.ndarray): Input image for processing.
+ frame_number (int): Video frame number for plotting the data.
+
+ Returns:
+ (np.ndarray): Processed image with updated analytics chart.
+
+ Raises:
+ ModuleNotFoundError: If an unsupported chart type is specified.
+
+ Examples:
+ >>> analytics = Analytics(analytics_type="line")
+ >>> frame = np.zeros((480, 640, 3), dtype=np.uint8)
+ >>> processed_frame = analytics.process_data(frame, frame_number=1)
"""
self.extract_tracks(im0) # Extract tracks
@@ -79,13 +123,22 @@ class Analytics(BaseSolution):
def update_graph(self, frame_number, count_dict=None, plot="line"):
"""
- Update the graph (line or area) with new data for single or multiple classes.
+ Updates the graph with new data for single or multiple classes.
Args:
frame_number (int): The current frame number.
- count_dict (dict, optional): Dictionary with class names as keys and counts as values for multiple classes.
- If None, updates a single line graph.
- plot (str): Type of the plot i.e. line, bar or area.
+ count_dict (Dict[str, int] | None): Dictionary with class names as keys and counts as values for multiple
+ classes. If None, updates a single line graph.
+ plot (str): Type of the plot. Options are 'line', 'bar', 'pie', or 'area'.
+
+ Returns:
+ (np.ndarray): Updated image containing the graph.
+
+ Examples:
+ >>> analytics = Analytics()
+ >>> frame_number = 10
+ >>> count_dict = {"person": 5, "car": 3}
+ >>> updated_image = analytics.update_graph(frame_number, count_dict, plot="bar")
"""
if count_dict is None:
# Single line update
diff --git a/ultralytics/solutions/distance_calculation.py b/ultralytics/solutions/distance_calculation.py
index 773b6086da..608aa97d7e 100644
--- a/ultralytics/solutions/distance_calculation.py
+++ b/ultralytics/solutions/distance_calculation.py
@@ -4,15 +4,41 @@ import math
import cv2
-from ultralytics.solutions.solutions import BaseSolution # Import a parent class
+from ultralytics.solutions.solutions import BaseSolution
from ultralytics.utils.plotting import Annotator, colors
class DistanceCalculation(BaseSolution):
- """A class to calculate distance between two objects in a real-time video stream based on their tracks."""
+ """
+ A class to calculate distance between two objects in a real-time video stream based on their tracks.
+
+ This class extends BaseSolution to provide functionality for selecting objects and calculating the distance
+ between them in a video stream using YOLO object detection and tracking.
+
+ Attributes:
+ left_mouse_count (int): Counter for left mouse button clicks.
+ selected_boxes (Dict[int, List[float]]): Dictionary to store selected bounding boxes and their track IDs.
+ annotator (Annotator): An instance of the Annotator class for drawing on the image.
+ boxes (List[List[float]]): List of bounding boxes for detected objects.
+ track_ids (List[int]): List of track IDs for detected objects.
+ clss (List[int]): List of class indices for detected objects.
+ names (List[str]): List of class names that the model can detect.
+ centroids (List[List[int]]): List to store centroids of selected bounding boxes.
+
+ Methods:
+ mouse_event_for_distance: Handles mouse events for selecting objects in the video stream.
+ calculate: Processes video frames and calculates the distance between selected objects.
+
+ Examples:
+ >>> distance_calc = DistanceCalculation()
+ >>> frame = cv2.imread("frame.jpg")
+ >>> processed_frame = distance_calc.calculate(frame)
+ >>> cv2.imshow("Distance Calculation", processed_frame)
+ >>> cv2.waitKey(0)
+ """
def __init__(self, **kwargs):
- """Initializes the DistanceCalculation class with the given parameters."""
+ """Initializes the DistanceCalculation class for measuring object distances in video streams."""
super().__init__(**kwargs)
# Mouse event information
@@ -21,14 +47,18 @@ class DistanceCalculation(BaseSolution):
def mouse_event_for_distance(self, event, x, y, flags, param):
"""
- Handles mouse events to select regions in a real-time video stream.
+ Handles mouse events to select regions in a real-time video stream for distance calculation.
Args:
- event (int): Type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).
+ event (int): Type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN).
x (int): X-coordinate of the mouse pointer.
y (int): Y-coordinate of the mouse pointer.
- flags (int): Flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY, etc.).
- param (dict): Additional parameters passed to the function.
+ flags (int): Flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY).
+ param (Dict): Additional parameters passed to the function.
+
+ Examples:
+ >>> # Assuming 'dc' is an instance of DistanceCalculation
+ >>> cv2.setMouseCallback("window_name", dc.mouse_event_for_distance)
"""
if event == cv2.EVENT_LBUTTONDOWN:
self.left_mouse_count += 1
@@ -43,13 +73,23 @@ class DistanceCalculation(BaseSolution):
def calculate(self, im0):
"""
- Processes the video frame and calculates the distance between two bounding boxes.
+ Processes a video frame and calculates the distance between two selected bounding boxes.
+
+ This method extracts tracks from the input frame, annotates bounding boxes, and calculates the distance
+ between two user-selected objects if they have been chosen.
Args:
- im0 (ndarray): The image frame.
+ im0 (numpy.ndarray): The input image frame to process.
Returns:
- (ndarray): The processed image frame.
+ (numpy.ndarray): The processed image frame with annotations and distance calculations.
+
+ Examples:
+ >>> import numpy as np
+ >>> from ultralytics.solutions import DistanceCalculation
+ >>> dc = DistanceCalculation()
+ >>> frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
+ >>> processed_frame = dc.calculate(frame)
"""
self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
self.extract_tracks(im0) # Extract tracks
diff --git a/ultralytics/solutions/heatmap.py b/ultralytics/solutions/heatmap.py
index d7dcf71cff..39352a9bd7 100644
--- a/ultralytics/solutions/heatmap.py
+++ b/ultralytics/solutions/heatmap.py
@@ -3,15 +3,40 @@
import cv2
import numpy as np
-from ultralytics.solutions.object_counter import ObjectCounter # Import object counter class
+from ultralytics.solutions.object_counter import ObjectCounter
from ultralytics.utils.plotting import Annotator
class Heatmap(ObjectCounter):
- """A class to draw heatmaps in real-time video stream based on their tracks."""
+ """
+ A class to draw heatmaps in real-time video streams based on object tracks.
+
+ This class extends the ObjectCounter class to generate and visualize heatmaps of object movements in video
+ streams. It uses tracked object positions to create a cumulative heatmap effect over time.
+
+ Attributes:
+ initialized (bool): Flag indicating whether the heatmap has been initialized.
+ colormap (int): OpenCV colormap used for heatmap visualization.
+ heatmap (np.ndarray): Array storing the cumulative heatmap data.
+ annotator (Annotator): Object for drawing annotations on the image.
+
+ Methods:
+ heatmap_effect: Calculates and updates the heatmap effect for a given bounding box.
+ generate_heatmap: Generates and applies the heatmap effect to each frame.
+
+ Examples:
+ >>> from ultralytics.solutions import Heatmap
+ >>> heatmap = Heatmap(model="yolov8n.pt", colormap=cv2.COLORMAP_JET)
+ >>> results = heatmap("path/to/video.mp4")
+ >>> for result in results:
+ ... print(result.speed) # Print inference speed
+ ... cv2.imshow("Heatmap", result.plot())
+ ... if cv2.waitKey(1) & 0xFF == ord("q"):
+ ... break
+ """
def __init__(self, **kwargs):
- """Initializes function for heatmap class with default values."""
+ """Initializes the Heatmap class for real-time video stream heatmap generation based on object tracks."""
super().__init__(**kwargs)
self.initialized = False # bool variable for heatmap initialization
@@ -23,10 +48,15 @@ class Heatmap(ObjectCounter):
def heatmap_effect(self, box):
"""
- Efficient calculation of heatmap area and effect location for applying colormap.
+ Efficiently calculates heatmap area and effect location for applying colormap.
Args:
- box (list): Bounding Box coordinates data [x0, y0, x1, y1]
+ box (List[float]): Bounding box coordinates [x0, y0, x1, y1].
+
+ Examples:
+ >>> heatmap = Heatmap()
+ >>> box = [100, 100, 200, 200]
+ >>> heatmap.heatmap_effect(box)
"""
x0, y0, x1, y1 = map(int, box)
radius_squared = (min(x1 - x0, y1 - y0) // 2) ** 2
@@ -48,9 +78,15 @@ class Heatmap(ObjectCounter):
Generate heatmap for each frame using Ultralytics.
Args:
- im0 (ndarray): Input image array for processing
+ im0 (np.ndarray): Input image array for processing.
+
Returns:
- im0 (ndarray): Processed image for further usage
+ (np.ndarray): Processed image with heatmap overlay and object counts (if region is specified).
+
+ Examples:
+ >>> heatmap = Heatmap()
+ >>> im0 = cv2.imread("image.jpg")
+ >>> result = heatmap.generate_heatmap(im0)
"""
if not self.initialized:
self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99
@@ -70,16 +106,17 @@ class Heatmap(ObjectCounter):
self.store_classwise_counts(cls) # store classwise counts in dict
# Store tracking previous position and perform object counting
- prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
+ prev_position = None
+ if len(self.track_history[track_id]) > 1:
+ prev_position = self.track_history[track_id][-2]
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
- self.display_counts(im0) if self.region is not None else None # Display the counts on the frame
+ if self.region is not None:
+ self.display_counts(im0) # Display the counts on the frame
# Normalize, apply colormap to heatmap and combine with original image
- im0 = (
- im0
- if self.track_data.id is None
- else cv2.addWeighted(
+ if self.track_data.id is not None:
+ im0 = cv2.addWeighted(
im0,
0.5,
cv2.applyColorMap(
@@ -88,7 +125,6 @@ class Heatmap(ObjectCounter):
0.5,
0,
)
- )
self.display_output(im0) # display output with base class function
return im0 # return output image for more usage
diff --git a/ultralytics/solutions/object_counter.py b/ultralytics/solutions/object_counter.py
index d576746421..6374920734 100644
--- a/ultralytics/solutions/object_counter.py
+++ b/ultralytics/solutions/object_counter.py
@@ -1,18 +1,40 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-from shapely.geometry import LineString, Point
-
-from ultralytics.solutions.solutions import BaseSolution # Import a parent class
+from ultralytics.solutions.solutions import BaseSolution
from ultralytics.utils.plotting import Annotator, colors
class ObjectCounter(BaseSolution):
- """A class to manage the counting of objects in a real-time video stream based on their tracks."""
+ """
+ A class to manage the counting of objects in a real-time video stream based on their tracks.
+
+ This class extends the BaseSolution class and provides functionality for counting objects moving in and out of a
+ specified region in a video stream. It supports both polygonal and linear regions for counting.
+
+ Attributes:
+ in_count (int): Counter for objects moving inward.
+ out_count (int): Counter for objects moving outward.
+ counted_ids (List[int]): List of IDs of objects that have been counted.
+ classwise_counts (Dict[str, Dict[str, int]]): Dictionary for counts, categorized by object class.
+ region_initialized (bool): Flag indicating whether the counting region has been initialized.
+ show_in (bool): Flag to control display of inward count.
+ show_out (bool): Flag to control display of outward count.
+
+ Methods:
+ count_objects: Counts objects within a polygonal or linear region.
+ store_classwise_counts: Initializes class-wise counts if not already present.
+ display_counts: Displays object counts on the frame.
+ count: Processes input data (frames or object tracks) and updates counts.
+
+ Examples:
+ >>> counter = ObjectCounter()
+ >>> frame = cv2.imread("frame.jpg")
+ >>> processed_frame = counter.count(frame)
+ >>> print(f"Inward count: {counter.in_count}, Outward count: {counter.out_count}")
+ """
def __init__(self, **kwargs):
- """Initialization function for Count class, a child class of BaseSolution class, can be used for counting the
- objects.
- """
+ """Initializes the ObjectCounter class for real-time object counting in video streams."""
super().__init__(**kwargs)
self.in_count = 0 # Counter for objects moving inward
@@ -26,14 +48,23 @@ class ObjectCounter(BaseSolution):
def count_objects(self, track_line, box, track_id, prev_position, cls):
"""
- Helper function to count objects within a polygonal region.
+ Counts objects within a polygonal or linear region based on their tracks.
Args:
- track_line (dict): last 30 frame track record
- box (list): Bounding box data for specific track in current frame
- track_id (int): track ID of the object
- prev_position (tuple): last frame position coordinates of the track
- cls (int): Class index for classwise count updates
+ track_line (Dict): Last 30 frame track record for the object.
+ box (List[float]): Bounding box coordinates [x1, y1, x2, y2] for the specific track in the current frame.
+ track_id (int): Unique identifier for the tracked object.
+ prev_position (Tuple[float, float]): Last frame position coordinates (x, y) of the track.
+ cls (int): Class index for classwise count updates.
+
+ Examples:
+ >>> counter = ObjectCounter()
+ >>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]}
+ >>> box = [130, 230, 150, 250]
+ >>> track_id = 1
+ >>> prev_position = (120, 220)
+ >>> cls = 0
+ >>> counter.count_objects(track_line, box, track_id, prev_position, cls)
"""
if prev_position is None or track_id in self.counted_ids:
return
@@ -42,7 +73,7 @@ class ObjectCounter(BaseSolution):
dx = (box[0] - prev_position[0]) * (centroid.x - prev_position[0])
dy = (box[1] - prev_position[1]) * (centroid.y - prev_position[1])
- if len(self.region) >= 3 and self.r_s.contains(Point(track_line[-1])):
+ if len(self.region) >= 3 and self.r_s.contains(self.Point(track_line[-1])):
self.counted_ids.append(track_id)
# For polygon region
if dx > 0:
@@ -52,7 +83,7 @@ class ObjectCounter(BaseSolution):
self.out_count += 1
self.classwise_counts[self.names[cls]]["OUT"] += 1
- elif len(self.region) < 3 and LineString([prev_position, box[:2]]).intersects(self.l_s):
+ elif len(self.region) < 3 and self.LineString([prev_position, box[:2]]).intersects(self.r_s):
self.counted_ids.append(track_id)
# For linear region
if dx > 0 and dy > 0:
@@ -64,20 +95,34 @@ class ObjectCounter(BaseSolution):
def store_classwise_counts(self, cls):
"""
- Initialize class-wise counts if not already present.
+ Initialize class-wise counts for a specific object class if not already present.
Args:
- cls (int): Class index for classwise count updates
+ cls (int): Class index for classwise count updates.
+
+ This method ensures that the 'classwise_counts' dictionary contains an entry for the specified class,
+ initializing 'IN' and 'OUT' counts to zero if the class is not already present.
+
+ Examples:
+ >>> counter = ObjectCounter()
+ >>> counter.store_classwise_counts(0) # Initialize counts for class index 0
+ >>> print(counter.classwise_counts)
+ {'person': {'IN': 0, 'OUT': 0}}
"""
if self.names[cls] not in self.classwise_counts:
self.classwise_counts[self.names[cls]] = {"IN": 0, "OUT": 0}
def display_counts(self, im0):
"""
- Helper function to display object counts on the frame.
+ Displays object counts on the input image or frame.
Args:
- im0 (ndarray): The input image or frame
+ im0 (numpy.ndarray): The input image or frame to display counts on.
+
+ Examples:
+ >>> counter = ObjectCounter()
+ >>> frame = cv2.imread("image.jpg")
+ >>> counter.display_counts(frame)
"""
labels_dict = {
str.capitalize(key): f"{'IN ' + str(value['IN']) if self.show_in else ''} "
@@ -91,12 +136,21 @@ class ObjectCounter(BaseSolution):
def count(self, im0):
"""
- Processes input data (frames or object tracks) and updates counts.
+ Processes input data (frames or object tracks) and updates object counts.
+
+ This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates
+ object counts, and displays the results on the input image.
Args:
- im0 (ndarray): The input image that will be used for processing
- Returns
- im0 (ndarray): The processed image for more usage
+ im0 (numpy.ndarray): The input image or frame to be processed.
+
+ Returns:
+ (numpy.ndarray): The processed image with annotations and count information.
+
+ Examples:
+ >>> counter = ObjectCounter()
+ >>> frame = cv2.imread("path/to/image.jpg")
+ >>> processed_frame = counter.count(frame)
"""
if not self.region_initialized:
self.initialize_region()
@@ -122,7 +176,9 @@ class ObjectCounter(BaseSolution):
)
# store previous position of track for object counting
- prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
+ prev_position = None
+ if len(self.track_history[track_id]) > 1:
+ prev_position = self.track_history[track_id][-2]
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
self.display_counts(im0) # Display the counts on the frame
diff --git a/ultralytics/solutions/parking_management.py b/ultralytics/solutions/parking_management.py
index ef58ad6274..fa815938ab 100644
--- a/ultralytics/solutions/parking_management.py
+++ b/ultralytics/solutions/parking_management.py
@@ -5,237 +5,232 @@ import json
import cv2
import numpy as np
-from ultralytics.utils.checks import check_imshow, check_requirements
+from ultralytics.solutions.solutions import LOGGER, BaseSolution, check_requirements
from ultralytics.utils.plotting import Annotator
class ParkingPtsSelection:
- """Class for selecting and managing parking zone points on images using a Tkinter-based UI."""
+ """
+ A class for selecting and managing parking zone points on images using a Tkinter-based UI.
+
+ This class provides functionality to upload an image, select points to define parking zones, and save the
+ selected points to a JSON file. It uses Tkinter for the graphical user interface.
+
+ Attributes:
+ tk (module): The Tkinter module for GUI operations.
+ filedialog (module): Tkinter's filedialog module for file selection operations.
+ messagebox (module): Tkinter's messagebox module for displaying message boxes.
+ master (tk.Tk): The main Tkinter window.
+ canvas (tk.Canvas): The canvas widget for displaying the image and drawing bounding boxes.
+ image (PIL.Image.Image): The uploaded image.
+ canvas_image (ImageTk.PhotoImage): The image displayed on the canvas.
+ rg_data (List[List[Tuple[int, int]]]): List of bounding boxes, each defined by 4 points.
+ current_box (List[Tuple[int, int]]): Temporary storage for the points of the current bounding box.
+ imgw (int): Original width of the uploaded image.
+ imgh (int): Original height of the uploaded image.
+ canvas_max_width (int): Maximum width of the canvas.
+ canvas_max_height (int): Maximum height of the canvas.
+
+ Methods:
+ setup_ui: Sets up the Tkinter UI components.
+ initialize_properties: Initializes the necessary properties.
+ upload_image: Uploads an image, resizes it to fit the canvas, and displays it.
+ on_canvas_click: Handles mouse clicks to add points for bounding boxes.
+ draw_box: Draws a bounding box on the canvas.
+ remove_last_bounding_box: Removes the last bounding box and redraws the canvas.
+ redraw_canvas: Redraws the canvas with the image and all bounding boxes.
+ save_to_json: Saves the bounding boxes to a JSON file.
+
+ Examples:
+ >>> parking_selector = ParkingPtsSelection()
+ >>> # Use the GUI to upload an image, select parking zones, and save the data
+ """
def __init__(self):
- """Initializes the UI for selecting parking zone points in a tkinter window."""
+ """Initializes the ParkingPtsSelection class, setting up UI and properties for parking zone point selection."""
check_requirements("tkinter")
+ import tkinter as tk
+ from tkinter import filedialog, messagebox
- import tkinter as tk # scope for multi-environment compatibility
+ self.tk, self.filedialog, self.messagebox = tk, filedialog, messagebox
+ self.setup_ui()
+ self.initialize_properties()
+ self.master.mainloop()
- self.tk = tk
- self.master = tk.Tk()
+ def setup_ui(self):
+ """Sets up the Tkinter UI components for the parking zone points selection interface."""
+ self.master = self.tk.Tk()
self.master.title("Ultralytics Parking Zones Points Selector")
-
- # Disable window resizing
self.master.resizable(False, False)
- # Setup canvas for image display
+ # Canvas for image display
self.canvas = self.tk.Canvas(self.master, bg="white")
+ self.canvas.pack(side=self.tk.BOTTOM)
- # Setup buttons
+ # Button frame with buttons
button_frame = self.tk.Frame(self.master)
button_frame.pack(side=self.tk.TOP)
- self.tk.Button(button_frame, text="Upload Image", command=self.upload_image).grid(row=0, column=0)
- self.tk.Button(button_frame, text="Remove Last BBox", command=self.remove_last_bounding_box).grid(
- row=0, column=1
- )
- self.tk.Button(button_frame, text="Save", command=self.save_to_json).grid(row=0, column=2)
-
- # Initialize properties
- self.image_path = None
- self.image = None
- self.canvas_image = None
- self.rg_data = [] # region coordinates
- self.current_box = []
- self.imgw = 0 # image width
- self.imgh = 0 # image height
+ for text, cmd in [
+ ("Upload Image", self.upload_image),
+ ("Remove Last BBox", self.remove_last_bounding_box),
+ ("Save", self.save_to_json),
+ ]:
+ self.tk.Button(button_frame, text=text, command=cmd).pack(side=self.tk.LEFT)
- # Constants
- self.canvas_max_width = 1280
- self.canvas_max_height = 720
-
- self.master.mainloop()
+ def initialize_properties(self):
+ """Initialize properties for image, canvas, bounding boxes, and dimensions."""
+ self.image = self.canvas_image = None
+ self.rg_data, self.current_box = [], []
+ self.imgw = self.imgh = 0
+ self.canvas_max_width, self.canvas_max_height = 1280, 720
def upload_image(self):
- """Upload an image and resize it to fit canvas."""
- from tkinter import filedialog
-
+ """Uploads and displays an image on the canvas, resizing it to fit within specified dimensions."""
from PIL import Image, ImageTk # scope because ImageTk requires tkinter package
- self.image_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")])
- if not self.image_path:
+ self.image = Image.open(self.filedialog.askopenfilename(filetypes=[("Image Files", "*.png;*.jpg;*.jpeg")]))
+ if not self.image:
return
- self.image = Image.open(self.image_path)
self.imgw, self.imgh = self.image.size
-
- # Calculate the aspect ratio and resize image
aspect_ratio = self.imgw / self.imgh
- if aspect_ratio > 1:
- # Landscape orientation
- canvas_width = min(self.canvas_max_width, self.imgw)
- canvas_height = int(canvas_width / aspect_ratio)
- else:
- # Portrait orientation
- canvas_height = min(self.canvas_max_height, self.imgh)
- canvas_width = int(canvas_height * aspect_ratio)
-
- # Check if canvas is already initialized
- if self.canvas:
- self.canvas.destroy() # Destroy previous canvas
-
- self.canvas = self.tk.Canvas(self.master, bg="white", width=canvas_width, height=canvas_height)
- resized_image = self.image.resize((canvas_width, canvas_height), Image.LANCZOS)
- self.canvas_image = ImageTk.PhotoImage(resized_image)
- self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
+ canvas_width = (
+ min(self.canvas_max_width, self.imgw) if aspect_ratio > 1 else int(self.canvas_max_height * aspect_ratio)
+ )
+ canvas_height = (
+ min(self.canvas_max_height, self.imgh) if aspect_ratio <= 1 else int(canvas_width / aspect_ratio)
+ )
- self.canvas.pack(side=self.tk.BOTTOM)
+ self.canvas.config(width=canvas_width, height=canvas_height)
+ self.canvas_image = ImageTk.PhotoImage(self.image.resize((canvas_width, canvas_height), Image.LANCZOS))
+ self.canvas.create_image(0, 0, anchor=self.tk.NW, image=self.canvas_image)
self.canvas.bind("