diff --git a/README.md b/README.md index 578fdc2e..551b7e76 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ For alternative installation methods including [Conda](https://anaconda.org/cond
Usage -#### CLI +### CLI YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command: @@ -76,7 +76,7 @@ yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples. -#### Python +### Python YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above: @@ -98,6 +98,18 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa
+### Notebooks + +Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics) tutorial, making it easy to learn and implement advanced YOLOv8 features. + +| Docs | Notebook | YouTube | +| --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| YOLOv8 Train, Val, Predict and Export Modes | Open In Colab |
Ultralytics Youtube Video
 | +| Ultralytics HUB QuickStart | Open In Colab |
Ultralytics Youtube Video
 | +| YOLOv8 Multi-Object Tracking in Videos | Open In Colab |
Ultralytics Youtube Video
 | +| YOLOv8 Object Counting in Videos | Open In Colab |
Ultralytics Youtube Video
 | +| YOLOv8 Heatmaps in Videos | Open In Colab |
Ultralytics Youtube Video
 | + ##
Models
YOLOv8 [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 YOLOv8 [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. diff --git a/README.zh-CN.md b/README.zh-CN.md index a85dd79a..225a7789 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -44,6 +44,8 @@ +以下是提供的内容的中文翻译： + ##
文档
请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。 @@ -66,7 +68,7 @@ pip install ultralytics
Usage -#### CLI +### CLI YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令： @@ -76,7 +78,7 @@ yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' `yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli)以获取示例。 -#### Python +### Python YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)： @@ -98,6 +100,18 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
+### 笔记本 + +Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://youtube.com/ultralytics) 教程，使学习和实现高级 YOLOv8 功能变得简单。 + +| 文档 | 笔记本 | YouTube | +| ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| YOLOv8 训练、验证、预测和导出模式 | 在 Colab 中打开 |
Ultralytics Youtube 视频
 | +| Ultralytics HUB 快速开始 | 在 Colab 中打开 |
Ultralytics Youtube 视频
 | +| YOLOv8 视频中的多对象跟踪 | 在 Colab 中打开 |
Ultralytics Youtube 视频
 | +| YOLOv8 视频中的对象计数 | 在 Colab 中打开 |
Ultralytics Youtube 视频
 | +| YOLOv8 视频中的热图 | 在 Colab 中打开 |
Ultralytics Youtube 视频
 | + ##
模型
在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [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)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。 diff --git a/docker/Dockerfile b/docker/Dockerfile index e3a32c8d..d70db238 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -28,7 +28,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u # Install pip packages RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -e ".[export]" albumentations comet pycocotools pytest-cov +RUN pip install --no-cache -e ".[export]" albumentations comet pycocotools lancedb pytest-cov # Run exports to AutoInstall packages RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 diff --git a/docker/Dockerfile-cpu b/docker/Dockerfile-cpu index 42e5ec32..52a93a68 100644 --- a/docker/Dockerfile-cpu +++ b/docker/Dockerfile-cpu @@ -26,7 +26,7 @@ RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED # Install pip packages RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache -e ".[export]" lancedb --extra-index-url https://download.pytorch.org/whl/cpu # Run exports to AutoInstall packages RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 diff --git a/docker/Dockerfile-python b/docker/Dockerfile-python index b227fa6a..e0e33117 100644 --- a/docker/Dockerfile-python +++ b/docker/Dockerfile-python @@ -26,7 +26,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u # Install pip packages RUN python3 -m pip install --upgrade pip wheel -RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache -e ".[export]" lancedb --extra-index-url https://download.pytorch.org/whl/cpu # Run exports to AutoInstall packages RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md index c08b75ce..54dcc060 100644 --- a/docs/en/guides/instance-segmentation-and-tracking.md +++ b/docs/en/guides/instance-segmentation-and-tracking.md @@ -91,16 +91,18 @@ There are two types of instance segmentation tracking available in the Ultralyti print("Video frame is empty or video processing has been successfully completed.") break - results = model.track(im0, persist=True) - masks = results[0].masks.xy - track_ids = results[0].boxes.id.int().cpu().tolist() - annotator = Annotator(im0, line_width=2) - for mask, track_id in zip(masks, track_ids): - annotator.seg_bbox(mask=mask, - mask_color=colors(track_id, True), - track_label=str(track_id)) + results = model.track(im0, persist=True) + + if results[0].boxes.id is not None: + masks = results[0].masks.xy + track_ids = results[0].boxes.id.int().cpu().tolist() + + for mask, track_id in zip(masks, track_ids): + annotator.seg_bbox(mask=mask, + mask_color=colors(track_id, True), + track_label=str(track_id)) out.write(im0) cv2.imshow("instance-segmentation-object-tracking", im0) diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md index 530d2eb8..53f86ae5 100644 --- a/docs/en/guides/vision-eye.md +++ b/docs/en/guides/vision-eye.md @@ -81,15 +81,17 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi print("Video frame is empty or video processing has been successfully completed.") break - results = model.track(im0, persist=True) - boxes = results[0].boxes.xyxy.cpu() - track_ids = results[0].boxes.id.int().cpu().tolist() - annotator = Annotator(im0, line_width=2) - for box, track_id in zip(boxes, track_ids): - annotator.box_label(box, label=str(track_id), color=colors(int(track_id))) - annotator.visioneye(box, center_point) + results = model.track(im0, persist=True) + boxes = results[0].boxes.xyxy.cpu() + + if results[0].boxes.id is not None: + track_ids = results[0].boxes.id.int().cpu().tolist() + + for box, track_id in zip(boxes, track_ids): + annotator.box_label(box, label=str(track_id), color=colors(int(track_id))) + annotator.visioneye(box, center_point) out.write(im0) cv2.imshow("visioneye-pinpoint", im0) diff --git a/examples/heatmaps.ipynb b/examples/heatmaps.ipynb new file mode 100644 index 00000000..25a3b9bd --- /dev/null +++ b/examples/heatmaps.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Open\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 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 heatmaps and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of Ultralytics Heatmaps. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ], + "metadata": { + "id": "PN1cAxdvd61e" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware." + ], + "metadata": { + "id": "o68Sg1oOeZm2" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dSwz_uOReMI" + }, + "outputs": [], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Ultralytics Heatmaps\n", + "\n", + "Heatmap is color-coded matrix, generated by Ultralytics YOLOv8, simplifies intricate data by using vibrant colors. This visual representation employs warmer hues for higher intensities and cooler tones for lower values. Heatmaps are effective in illustrating complex data patterns, correlations, and anomalies, providing a user-friendly and engaging way to interpret data across various domains." + ], + "metadata": { + "id": "m7VkxQ2aeg7k" + } + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "from ultralytics.solutions import heatmap\n", + "import cv2\n", + "\n", + "model = YOLO(\"yolov8n.pt\")\n", + "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n", + "assert cap.isOpened(), \"Error reading video file\"\n", + "\n", + "# Video writer\n", + "video_writer = cv2.VideoWriter(\"heatmap_output.avi\",\n", + " cv2.VideoWriter_fourcc(*'mp4v'),\n", + " int(cap.get(5)),\n", + " (int(cap.get(3)), int(cap.get(4))))\n", + "\n", + "# Init heatmap\n", + "heatmap_obj = heatmap.Heatmap()\n", + "heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,\n", + " imw=cap.get(4), # should same as cap height\n", + " imh=cap.get(3), # should same as cap width\n", + " view_img=True,\n", + " shape=\"circle\")\n", + "\n", + "while cap.isOpened():\n", + " success, im0 = cap.read()\n", + " if not success:\n", + " print(\"Video frame is empty or video processing has been successfully completed.\")\n", + " break\n", + " tracks = model.track(im0, persist=True, show=False)\n", + "\n", + " im0 = heatmap_obj.generate_heatmap(im0, tracks)\n", + " video_writer.write(im0)\n", + "\n", + "cap.release()\n", + "video_writer.release()\n", + "cv2.destroyAllWindows()" + ], + "metadata": { + "id": "Cx-u59HQdu2o" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Community Support\n", + "\n", + "For more information, you can explore Ultralytics Heatmaps Docs\n", + "\n", + "Ultralytics ⚡ resources\n", + "- About Us – https://ultralytics.com/about\n", + "- Join Our Team – https://ultralytics.com/work\n", + "- Contact Us – https://ultralytics.com/contact\n", + "- Discord – https://discord.gg/2wNGbc6g9X\n", + "- Ultralytics License – https://ultralytics.com/license\n", + "\n", + "YOLOv8 🚀 resources\n", + "- GitHub – https://github.com/ultralytics/ultralytics\n", + "- Docs – https://docs.ultralytics.com/" + ], + "metadata": { + "id": "QrlKg-y3fEyD" + } + } + ] +} diff --git a/examples/object_counting.ipynb b/examples/object_counting.ipynb new file mode 100644 index 00000000..03727cfd --- /dev/null +++ b/examples/object_counting.ipynb @@ -0,0 +1,146 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Open\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 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 Object Counting and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of Ultralytics Object Counting. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ], + "metadata": { + "id": "PN1cAxdvd61e" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware." + ], + "metadata": { + "id": "o68Sg1oOeZm2" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dSwz_uOReMI" + }, + "outputs": [], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Ultralytics Object Counting\n", + "\n", + "Counting objects using Ultralytics YOLOv8 entails the precise detection and enumeration of specific objects within videos and camera streams. YOLOv8 demonstrates exceptional performance in real-time applications, delivering efficient and accurate object counting across diverse scenarios such as crowd analysis and surveillance. This is attributed to its advanced algorithms and deep learning capabilities." + ], + "metadata": { + "id": "m7VkxQ2aeg7k" + } + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "from ultralytics.solutions import object_counter\n", + "import cv2\n", + "\n", + "model = YOLO(\"yolov8n.pt\")\n", + "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n", + "assert cap.isOpened(), \"Error reading video file\"\n", + "\n", + "# Define line points\n", + "line_points = [(20, 400), (1080, 400)]\n", + "\n", + "# Video writer\n", + "video_writer = cv2.VideoWriter(\"object_counting_output.avi\",\n", + " cv2.VideoWriter_fourcc(*'mp4v'),\n", + " int(cap.get(5)),\n", + " (int(cap.get(3)), int(cap.get(4))))\n", + "\n", + "# Init Object Counter\n", + "counter = object_counter.ObjectCounter()\n", + "counter.set_args(view_img=True,\n", + " reg_pts=line_points,\n", + " classes_names=model.names,\n", + " draw_tracks=True)\n", + "\n", + "while cap.isOpened():\n", + " success, im0 = cap.read()\n", + " if not success:\n", + " print(\"Video frame is empty or video processing has been successfully completed.\")\n", + " break\n", + " tracks = model.track(im0, persist=True, show=False)\n", + "\n", + " im0 = counter.start_counting(im0, tracks)\n", + " video_writer.write(im0)\n", + "\n", + "cap.release()\n", + "video_writer.release()\n", + "cv2.destroyAllWindows()" + ], + "metadata": { + "id": "Cx-u59HQdu2o" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Community Support\n", + "\n", + "For more information, you can explore Ultralytics Object Counting Docs\n", + "\n", + "Ultralytics ⚡ resources\n", + "- About Us – https://ultralytics.com/about\n", + "- Join Our Team – https://ultralytics.com/work\n", + "- Contact Us – https://ultralytics.com/contact\n", + "- Discord – https://discord.gg/2wNGbc6g9X\n", + "- Ultralytics License – https://ultralytics.com/license\n", + "\n", + "YOLOv8 🚀 resources\n", + "- GitHub – https://github.com/ultralytics/ultralytics\n", + "- Docs – https://docs.ultralytics.com/" + ], + "metadata": { + "id": "QrlKg-y3fEyD" + } + } + ] +} diff --git a/examples/object_tracking.ipynb b/examples/object_tracking.ipynb new file mode 100644 index 00000000..10a633d8 --- /dev/null +++ b/examples/object_tracking.ipynb @@ -0,0 +1,204 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Open\n", + "\n", + "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 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 Object Tracking and understand its features and capabilities.\n", + "\n", + "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n", + "\n", + "We hope that the resources in this notebook will help you get the most out of Ultralytics Object Tracking. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ], + "metadata": { + "id": "PN1cAxdvd61e" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Setup\n", + "\n", + "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware." + ], + "metadata": { + "id": "o68Sg1oOeZm2" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dSwz_uOReMI" + }, + "outputs": [], + "source": [ + "!pip install ultralytics" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Ultralytics Object Tracking\n", + "\n", + "Within the domain of video analytics, object tracking stands out as a crucial undertaking. It goes beyond merely identifying the location and class of objects within the frame; it also involves assigning a unique ID to each detected object as the video unfolds. The applications of this technology are vast, spanning from surveillance and security to real-time sports analytics." + ], + "metadata": { + "id": "m7VkxQ2aeg7k" + } + }, + { + "cell_type": "markdown", + "source": [ + "## CLI" + ], + "metadata": { + "id": "-ZF9DM6e6gz0" + } + }, + { + "cell_type": "code", + "source": [ + "!yolo track source=\"/content/people walking gray.mp4\" save=True" + ], + "metadata": { + "id": "-XJqhOwo6iqT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Python\n", + "\n", + "- Draw Object tracking trails" + ], + "metadata": { + "id": "XRcw0vIE6oNb" + } + }, + { + "cell_type": "code", + "source": [ + "import cv2\n", + "import numpy as np\n", + "from ultralytics import YOLO\n", + "\n", + "from ultralytics.utils.checks import check_imshow\n", + "from ultralytics.utils.plotting import Annotator, colors\n", + "\n", + "from collections import defaultdict\n", + "\n", + "track_history = defaultdict(lambda: [])\n", + "model = YOLO(\"yolov8n.pt\")\n", + "names = model.model.names\n", + "\n", + "video_path = \"/path/to/video/file.mp4\"\n", + "cap = cv2.VideoCapture(video_path)\n", + "assert cap.isOpened(), \"Error reading video file\"\n", + "\n", + "frame_width = int(cap.get(3))\n", + "frame_height = int(cap.get(4))\n", + "size = (frame_width, frame_height)\n", + "result = cv2.VideoWriter('object_tracking.avi',\n", + " cv2.VideoWriter_fourcc(*'MJPG'),\n", + " int(cap.get(5)), size)\n", + "\n", + "\n", + "while cap.isOpened():\n", + " success, frame = cap.read()\n", + " if success:\n", + " results = model.track(frame, persist=True, verbose=False)\n", + " boxes = results[0].boxes.xyxy.cpu()\n", + "\n", + " if results[0].boxes.id is not None:\n", + "\n", + " # Extract prediction results\n", + " clss = results[0].boxes.cls.cpu().tolist()\n", + " track_ids = results[0].boxes.id.int().cpu().tolist()\n", + " confs = results[0].boxes.conf.float().cpu().tolist()\n", + "\n", + " # Annotator Init\n", + " annotator = Annotator(frame, line_width=2)\n", + "\n", + " for box, cls, track_id in zip(boxes, clss, track_ids):\n", + " annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)])\n", + "\n", + " # Store tracking history\n", + " track = track_history[track_id]\n", + " track.append((int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)))\n", + " if len(track) > 30:\n", + " track.pop(0)\n", + "\n", + " # Plot tracks\n", + " points = np.array(track, dtype=np.int32).reshape((-1, 1, 2))\n", + " cv2.circle(frame, (track[-1]), 7, colors(int(cls), True), -1)\n", + " cv2.polylines(frame, [points], isClosed=False, color=colors(int(cls), True), thickness=2)\n", + "\n", + " result.write(frame)\n", + " if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n", + " break\n", + " else:\n", + " break\n", + "\n", + "result.release()\n", + "cap.release()\n", + "cv2.destroyAllWindows()" + ], + "metadata": { + "id": "Cx-u59HQdu2o" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Community Support\n", + "\n", + "For more information, you can explore Ultralytics Object Tracking Docs\n", + "\n", + "Ultralytics ⚡ resources\n", + "- About Us – https://ultralytics.com/about\n", + "- Join Our Team – https://ultralytics.com/work\n", + "- Contact Us – https://ultralytics.com/contact\n", + "- Discord – https://discord.gg/2wNGbc6g9X\n", + "- Ultralytics License – https://ultralytics.com/license\n", + "\n", + "YOLOv8 🚀 resources\n", + "- GitHub – https://github.com/ultralytics/ultralytics\n", + "- Docs – https://docs.ultralytics.com/" + ], + "metadata": { + "id": "QrlKg-y3fEyD" + } + } + ] +} diff --git a/tests/test_explorer.py b/tests/test_explorer.py index eedaf816..ce44d75c 100644 --- a/tests/test_explorer.py +++ b/tests/test_explorer.py @@ -1,4 +1,5 @@ from ultralytics import Explorer +from ultralytics.utils import ASSETS def test_similarity(): @@ -6,14 +7,14 @@ def test_similarity(): exp.create_embeddings_table() similar = exp.get_similar(idx=1) assert len(similar) == 25 - similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg') + similar = exp.get_similar(img=ASSETS / 'zidane.jpg') assert len(similar) == 25 similar = exp.get_similar(idx=[1, 2], limit=10) assert len(similar) == 10 sim_idx = exp.similarity_index() assert len(sim_idx) > 0 sql = exp.sql_query("WHERE labels LIKE '%person%'") - len(sql) > 0 + assert len(sql) > 0 def test_det(): diff --git a/ultralytics/data/explorer/explorer.py b/ultralytics/data/explorer/explorer.py index ed99525e..49fd37da 100644 --- a/ultralytics/data/explorer/explorer.py +++ b/ultralytics/data/explorer/explorer.py @@ -40,7 +40,7 @@ class ExplorerDataset(YOLODataset): return self.ims[i], self.im_hw0[i], self.im_hw[i] def build_transforms(self, hyp=None): - transforms = Format( + return Format( bbox_format='xyxy', normalize=False, return_mask=self.use_segments, @@ -49,7 +49,6 @@ class ExplorerDataset(YOLODataset): mask_ratio=hyp.mask_ratio, mask_overlap=hyp.overlap_mask, ) - return transforms class Explorer: @@ -161,8 +160,7 @@ class Explorer: embeds = self.model.embed(imgs) # Get avg if multiple images are passed (len > 1) embeds = torch.mean(torch.stack(embeds), 0).cpu().numpy() if len(embeds) > 1 else embeds[0].cpu().numpy() - query = self.table.search(embeds).limit(limit).to_arrow() - return query + return self.table.search(embeds).limit(limit).to_arrow() def sql_query(self, query, return_type='pandas'): """ @@ -223,8 +221,7 @@ class Explorer: """ result = self.sql_query(query, return_type='arrow') img = plot_similar_images(result, plot_labels=labels) - img = Image.fromarray(img) - return img + return Image.fromarray(img) def get_similar(self, img=None, idx=None, limit=25, return_type='pandas'): """ @@ -276,8 +273,7 @@ class Explorer: """ similar = self.get_similar(img, idx, limit, return_type='arrow') img = plot_similar_images(similar, plot_labels=labels) - img = Image.fromarray(img) - return img + return Image.fromarray(img) def similarity_index(self, max_dist=0.2, top_k=None, force=False): """ @@ -331,7 +327,6 @@ class Explorer: sim_table.add(_yield_sim_idx()) self.sim_index = sim_table - return sim_table.to_pandas() def plot_similarity_index(self, max_dist=0.2, top_k=None, force=False): @@ -373,8 +368,7 @@ class Explorer: buffer.seek(0) # Use Pillow to open the image from the buffer - image = Image.open(buffer) - return image + return Image.open(buffer) def _check_imgs_or_idxs(self, img, idx): if img is None and idx is None: @@ -385,8 +379,7 @@ class Explorer: idx = idx if isinstance(idx, list) else [idx] img = self.table.to_lance().take(idx, columns=['im_file']).to_pydict()['im_file'] - img = img if isinstance(img, list) else [img] - return img + return img if isinstance(img, list) else [img] def visualize(self, result): """ diff --git a/ultralytics/data/explorer/utils.py b/ultralytics/data/explorer/utils.py index a9aa563f..359c5868 100644 --- a/ultralytics/data/explorer/utils.py +++ b/ultralytics/data/explorer/utils.py @@ -1,4 +1,3 @@ -from pathlib import Path from typing import List import cv2 @@ -94,10 +93,12 @@ def plot_similar_images(similar_set, plot_labels=True): batch_idx = np.concatenate(batch_idx, axis=0) cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0) - fname = 'temp_exp_grid.jpg' - plot_images(imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, fname=fname, - max_subplots=len(images)).join() - img = cv2.imread(fname, cv2.IMREAD_COLOR) - img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - Path(fname).unlink() - return img_rgb + return plot_images(imgs, + batch_idx, + cls, + bboxes=boxes, + masks=masks, + kpts=kpts, + max_subplots=len(images), + save=False, + threaded=False) diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py index 88655d2f..c275f78f 100644 --- a/ultralytics/utils/__init__.py +++ b/ultralytics/utils/__init__.py @@ -736,16 +736,19 @@ class TryExcept(contextlib.ContextDecorator): def threaded(func): """ - Multi-threads a target function and returns thread. + Multi-threads a target function by default and returns the thread or function result. - Use as @threaded decorator. + Use as @threaded decorator. The function runs in a separate thread unless 'threaded=False' is passed. """ def wrapper(*args, **kwargs): - """Multi-threads a given function and returns the thread.""" - thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) - thread.start() - return thread + """Multi-threads a given function based on 'threaded' kwarg and returns the thread or function result.""" + if kwargs.pop('threaded', True): # run in thread + thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) + thread.start() + return thread + else: + return func(*args, **kwargs) return wrapper diff --git a/ultralytics/utils/plotting.py b/ultralytics/utils/plotting.py index 7d46ecf1..e6981624 100644 --- a/ultralytics/utils/plotting.py +++ b/ultralytics/utils/plotting.py @@ -125,7 +125,7 @@ class Annotator: if rotated: p1 = [int(b) for b in box[0]] # NOTE: cv2-version polylines needs np.asarray type. - cv2.polylines(self.im, [np.asarray(box, dtype=np.int)], True, color, self.lw) + cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw) else: p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA) @@ -580,7 +580,8 @@ def plot_images(images, fname='images.jpg', names=None, on_plot=None, - max_subplots=16): + max_subplots=16, + save=True): """Plot image grid with labels.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() @@ -596,7 +597,6 @@ def plot_images(images, batch_idx = batch_idx.cpu().numpy() max_size = 1920 # max image size - max_subplots = max_subplots # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs ** 0.5) # number of subplots (square) @@ -605,12 +605,9 @@ def plot_images(images, # Build Image mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init - for i, im in enumerate(images): - if i == max_subplots: # if last batch has fewer images than we expect - break + for i in range(bs): x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im + mosaic[y:y + h, x:x + w, :] = images[i].transpose(1, 2, 0) # Resize (optional) scale = max_size / ns / max(h, w) @@ -622,7 +619,7 @@ def plot_images(images, # Annotate fs = int((h + w) * ns * 0.01) # font size annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) - for i in range(i + 1): + for i in range(bs): x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders if paths: @@ -699,9 +696,12 @@ def plot_images(images, with contextlib.suppress(Exception): im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 annotator.fromarray(im) - annotator.im.save(fname) # save - if on_plot: - on_plot(fname) + if save: + annotator.im.save(fname) # save + if on_plot: + on_plot(fname) + else: + return np.asarray(annotator.im) @plt_settings()