Explorer Cleanup (#7364)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
aca8eb1fd4
commit
ed73c0fedc
15 changed files with 585 additions and 63 deletions
144
examples/heatmaps.ipynb
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144
examples/heatmaps.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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||||
"metadata": {
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||||
"colab": {
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||||
"provenance": [],
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||||
"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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||||
"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
|
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"<div align=\"center\">\n",
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"\n",
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" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
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" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
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"\n",
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" [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",
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"\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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"\n",
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"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">heatmaps</a> and understand its features and capabilities.\n",
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"\n",
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"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",
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"\n",
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"We hope that the resources in this notebook will help you get the most out of <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">Ultralytics Heatmaps</a>. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"\n",
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"</div>"
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],
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"metadata": {
|
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"id": "PN1cAxdvd61e"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Setup\n",
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"\n",
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"Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware."
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],
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"metadata": {
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"id": "o68Sg1oOeZm2"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "9dSwz_uOReMI"
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},
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"outputs": [],
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"source": [
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"!pip install ultralytics"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Ultralytics Heatmaps\n",
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"\n",
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"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."
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],
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"metadata": {
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"id": "m7VkxQ2aeg7k"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from ultralytics import YOLO\n",
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"from ultralytics.solutions import heatmap\n",
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"import cv2\n",
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"\n",
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"model = YOLO(\"yolov8n.pt\")\n",
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"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
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"assert cap.isOpened(), \"Error reading video file\"\n",
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"\n",
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"# Video writer\n",
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"video_writer = cv2.VideoWriter(\"heatmap_output.avi\",\n",
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" cv2.VideoWriter_fourcc(*'mp4v'),\n",
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" int(cap.get(5)),\n",
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" (int(cap.get(3)), int(cap.get(4))))\n",
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"\n",
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"# Init heatmap\n",
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"heatmap_obj = heatmap.Heatmap()\n",
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"heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,\n",
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" imw=cap.get(4), # should same as cap height\n",
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" imh=cap.get(3), # should same as cap width\n",
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" view_img=True,\n",
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" shape=\"circle\")\n",
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"\n",
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"while cap.isOpened():\n",
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" success, im0 = cap.read()\n",
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" if not success:\n",
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" print(\"Video frame is empty or video processing has been successfully completed.\")\n",
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" break\n",
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" tracks = model.track(im0, persist=True, show=False)\n",
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"\n",
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" im0 = heatmap_obj.generate_heatmap(im0, tracks)\n",
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" video_writer.write(im0)\n",
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"\n",
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"cap.release()\n",
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"video_writer.release()\n",
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"cv2.destroyAllWindows()"
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],
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"metadata": {
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"id": "Cx-u59HQdu2o"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"#Community Support\n",
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"\n",
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"For more information, you can explore <a href=\"https://docs.ultralytics.com/guides/heatmaps/#heatmap-colormaps\">Ultralytics Heatmaps Docs</a>\n",
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"\n",
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"Ultralytics ⚡ resources\n",
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"- About Us – https://ultralytics.com/about\n",
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"- Join Our Team – https://ultralytics.com/work\n",
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"- Contact Us – https://ultralytics.com/contact\n",
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"- Discord – https://discord.gg/2wNGbc6g9X\n",
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"- Ultralytics License – https://ultralytics.com/license\n",
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"\n",
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"YOLOv8 🚀 resources\n",
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"- GitHub – https://github.com/ultralytics/ultralytics\n",
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"- Docs – https://docs.ultralytics.com/"
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],
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"metadata": {
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"id": "QrlKg-y3fEyD"
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}
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}
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]
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}
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146
examples/object_counting.ipynb
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146
examples/object_counting.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"<div align=\"center\">\n",
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"\n",
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" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
|
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" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
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"\n",
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" [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",
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"\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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"\n",
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"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Object Counting</a> and understand its features and capabilities.\n",
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"\n",
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"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",
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"\n",
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"We hope that the resources in this notebook will help you get the most out of <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Ultralytics Object Counting</a>. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"\n",
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"</div>"
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],
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"metadata": {
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"id": "PN1cAxdvd61e"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Setup\n",
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"\n",
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"Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware."
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],
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"metadata": {
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"id": "o68Sg1oOeZm2"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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||||
"id": "9dSwz_uOReMI"
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},
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"outputs": [],
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"source": [
|
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"!pip install ultralytics"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Ultralytics Object Counting\n",
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"\n",
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"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."
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],
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"metadata": {
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"id": "m7VkxQ2aeg7k"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from ultralytics import YOLO\n",
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"from ultralytics.solutions import object_counter\n",
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"import cv2\n",
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"\n",
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"model = YOLO(\"yolov8n.pt\")\n",
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"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
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"assert cap.isOpened(), \"Error reading video file\"\n",
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"\n",
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"# Define line points\n",
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"line_points = [(20, 400), (1080, 400)]\n",
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"\n",
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"# Video writer\n",
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"video_writer = cv2.VideoWriter(\"object_counting_output.avi\",\n",
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" cv2.VideoWriter_fourcc(*'mp4v'),\n",
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" int(cap.get(5)),\n",
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" (int(cap.get(3)), int(cap.get(4))))\n",
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"\n",
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"# Init Object Counter\n",
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"counter = object_counter.ObjectCounter()\n",
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"counter.set_args(view_img=True,\n",
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" reg_pts=line_points,\n",
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" classes_names=model.names,\n",
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" draw_tracks=True)\n",
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"\n",
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"while cap.isOpened():\n",
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" success, im0 = cap.read()\n",
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" if not success:\n",
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" print(\"Video frame is empty or video processing has been successfully completed.\")\n",
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" break\n",
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" tracks = model.track(im0, persist=True, show=False)\n",
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"\n",
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" im0 = counter.start_counting(im0, tracks)\n",
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" video_writer.write(im0)\n",
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"\n",
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"cap.release()\n",
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"video_writer.release()\n",
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"cv2.destroyAllWindows()"
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],
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"metadata": {
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"id": "Cx-u59HQdu2o"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"#Community Support\n",
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"\n",
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"For more information, you can explore <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Ultralytics Object Counting Docs</a>\n",
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"\n",
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"Ultralytics ⚡ resources\n",
|
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"- About Us – https://ultralytics.com/about\n",
|
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"- Join Our Team – https://ultralytics.com/work\n",
|
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"- Contact Us – https://ultralytics.com/contact\n",
|
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"- Discord – https://discord.gg/2wNGbc6g9X\n",
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"- Ultralytics License – https://ultralytics.com/license\n",
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"\n",
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"YOLOv8 🚀 resources\n",
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"- GitHub – https://github.com/ultralytics/ultralytics\n",
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"- Docs – https://docs.ultralytics.com/"
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],
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"metadata": {
|
||||
"id": "QrlKg-y3fEyD"
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}
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}
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]
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}
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204
examples/object_tracking.ipynb
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204
examples/object_tracking.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
|
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"kernelspec": {
|
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
|
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"source": [
|
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"<div align=\"center\">\n",
|
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"\n",
|
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" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
|
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" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
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"\n",
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" [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",
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"\n",
|
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
|
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"\n",
|
||||
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the <a href=\"https://docs.ultralytics.com/modes/track/\">Object Tracking</a> and understand its features and capabilities.\n",
|
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"\n",
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||||
"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 <a href=\"https://docs.ultralytics.com/modes/track/\">Ultralytics Object Tracking</a>. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
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"\n",
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"</div>"
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],
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"metadata": {
|
||||
"id": "PN1cAxdvd61e"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
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"source": [
|
||||
"# Setup\n",
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"\n",
|
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"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"
|
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]
|
||||
},
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{
|
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"cell_type": "markdown",
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"source": [
|
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"# Ultralytics Object Tracking\n",
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"\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 <a href=\"https://docs.ultralytics.com/modes/track/\">Ultralytics Object Tracking Docs</a>\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"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue