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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", |
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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/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", |
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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/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": { |
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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 Tracking\n", |
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"\n", |
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"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." |
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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": "markdown", |
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"source": [ |
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"## CLI" |
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], |
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"metadata": { |
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"id": "-ZF9DM6e6gz0" |
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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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"!yolo track source=\"/content/people walking gray.mp4\" save=True" |
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], |
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"metadata": { |
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"id": "-XJqhOwo6iqT" |
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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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"## Python\n", |
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"\n", |
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"- Draw Object tracking trails" |
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], |
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"metadata": { |
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"id": "XRcw0vIE6oNb" |
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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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"import cv2\n", |
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"import numpy as np\n", |
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"from ultralytics import YOLO\n", |
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"\n", |
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"from ultralytics.utils.checks import check_imshow\n", |
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"from ultralytics.utils.plotting import Annotator, colors\n", |
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"\n", |
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"from collections import defaultdict\n", |
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"\n", |
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"track_history = defaultdict(lambda: [])\n", |
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"model = YOLO(\"yolov8n.pt\")\n", |
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"names = model.model.names\n", |
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"\n", |
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"video_path = \"/path/to/video/file.mp4\"\n", |
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"cap = cv2.VideoCapture(video_path)\n", |
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"assert cap.isOpened(), \"Error reading video file\"\n", |
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"\n", |
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"frame_width = int(cap.get(3))\n", |
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"frame_height = int(cap.get(4))\n", |
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"size = (frame_width, frame_height)\n", |
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"result = cv2.VideoWriter('object_tracking.avi',\n", |
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" cv2.VideoWriter_fourcc(*'MJPG'),\n", |
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" int(cap.get(5)), size)\n", |
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"\n", |
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"\n", |
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"while cap.isOpened():\n", |
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" success, frame = cap.read()\n", |
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" if success:\n", |
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" results = model.track(frame, persist=True, verbose=False)\n", |
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" boxes = results[0].boxes.xyxy.cpu()\n", |
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"\n", |
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" if results[0].boxes.id is not None:\n", |
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"\n", |
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" # Extract prediction results\n", |
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" clss = results[0].boxes.cls.cpu().tolist()\n", |
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" track_ids = results[0].boxes.id.int().cpu().tolist()\n", |
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" confs = results[0].boxes.conf.float().cpu().tolist()\n", |
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"\n", |
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" # Annotator Init\n", |
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" annotator = Annotator(frame, line_width=2)\n", |
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"\n", |
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" for box, cls, track_id in zip(boxes, clss, track_ids):\n", |
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" annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)])\n", |
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"\n", |
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" # Store tracking history\n", |
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" track = track_history[track_id]\n", |
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" track.append((int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)))\n", |
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" if len(track) > 30:\n", |
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" track.pop(0)\n", |
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"\n", |
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" # Plot tracks\n", |
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" points = np.array(track, dtype=np.int32).reshape((-1, 1, 2))\n", |
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" cv2.circle(frame, (track[-1]), 7, colors(int(cls), True), -1)\n", |
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" cv2.polylines(frame, [points], isClosed=False, color=colors(int(cls), True), thickness=2)\n", |
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"\n", |
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" result.write(frame)\n", |
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" if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n", |
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" break\n", |
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" else:\n", |
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" break\n", |
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"\n", |
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"result.release()\n", |
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"cap.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": 3, |
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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/modes/track/\">Ultralytics Object Tracking 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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} |
Glenn Jocher
11 months ago
committed by
GitHub