100 lines
4.2 KiB
Plaintext
100 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experimenting with Supervision Assets\n",
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"\n",
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"---\n",
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"\n",
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"<p align=\"center\">\n",
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" <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/supervision-assets.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
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"</p>\n",
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"\n",
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"When experimenting with interesting and useful features of the Supervision package, its important to have some sort of image or video data to experiment with. Luckily for us, Supervision ships with [Assets](https://supervision.roboflow.com/latest/assets/)! Assets is a collection of videos that you can utilize to start experimenting with the various features Supervision has to offer. Let's take a look at how to use this resource.\n",
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"\n",
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"## Install with Pip\n",
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"\n",
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"If you're running this code outside a Jupyter notebook, it's generally a best practice to create a virtual environment so that your projects deppendencies are isolated. Run the following ing your command line of choice to create and source one.\n",
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"\n",
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"```shell\n",
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"\n",
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"python -m venv venv\n",
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"source venv/bin/activate\n",
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"\n",
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"``` "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now that we've got a virtual enviornment installed and sourced, let's install the pip package with the assets \"extra\" package. Make sure to remove the \"!\" if running this in your command line."
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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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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"!pip install -q \"supervision[assets]\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Download a Video\n",
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"\n",
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"From here we can download and utilize a video asset directly from a python script! Note below that we're utilizing the method `download_assets` to download the `VideoAssets.SUBWAY` video to our local directory. This method returns the file path, so we can then utilize this path for additional experimentation. From here, you will see a video asset to experiment with in your local directory. "
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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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"outputs": [],
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"source": [
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"from supervision.assets import download_assets, VideoAssets\n",
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"\n",
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"# Download a video asset from the supervision package.\n",
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"path_to_video = download_assets(VideoAssets.SUBWAY)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We're now equipt with a video asset from Supervision to run some experiements on! For more information on available video assets, visit the [Supervision API Reference](https://supervision.roboflow.com/latest/assets/#videoassets). Happy building!"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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