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Improved Documentation and Setup Guide for Google Colab #37

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Improved Documentation and Setup Guide for Google Colab #37

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87480fb
Added furthur documentation regarding colab setup
HudsonGri Feb 14, 2024
934374d
Fixed spelling of velocity
HudsonGri Feb 14, 2024
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138 changes: 65 additions & 73 deletions README.md
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Expand Up @@ -46,79 +46,71 @@ Input | Output

<h3>How to run</h3>

<p>This project requires compatible <b>GPU</b> to install tensorflow, you can run it on your local machine in case you have one or use <a href='https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwissLL5-MvxAhXwlYsKHbkBDEUQFnoECAMQAw&url=https%3A%2F%2Fcolab.research.google.com%2Fnotebooks%2F&usg=AOvVaw0eDNVclINNdlOuD-YTYiiB'>Google Colaboratory</a> with <b>Runtime Type</b> changed to <b>GPU</b>.</p>

- [ ] Input videos have to be rallies of the game and shouldn't contain any <strong>commercials, breaks or spectators</strong>.

<ol>
<li>
Clone this repository
</li>

```git
git clone https://github.com/ArtLabss/tennis-tracking.git
```

<li>
Download yolov3 weights (237 MB) from <a href="https://pjreddie.com/media/files/yolov3.weights">here</a> and add it to your <a href="/Yolov3">Yolov3 folder</a>.
</li>

<li>
Install the requirements using pip
</li>

```python
pip install -r requirements.txt
```

<li>
Run the following command in the command line
</li>

```python
python3 predict_video.py --input_video_path=VideoInput/video_input3.mp4 --output_video_path=VideoOutput/video_output.mp4 --minimap=0 --bounce=0
```

<li>If you are using Google Colab upload all the files to Google Drive, including yolov3 weights from step <strong>2.</strong></li>

<li>
Create a Google Colaboratory Notebook in the same directory as <code>predict_video.py</code>, change Runtime Type to <strong>GPU</strong> and connect it to Google drive
</li>

```python
from google.colab import drive
drive.mount('/content/drive')
```

<li>
Change the working directory to the one where the Colab Notebook and <code>predict_video.py</code> are. In my case,
</li>

```python
import os
os.chdir('drive/MyDrive/Colab Notebooks/tennis-tracking')
```

<li>
Install only 2 requirements, because Colab already has the rest
</li>

```python
!pip install filterpy sktime
```

<li>
Inside the notebook run <code>predict_video.py</code>
</li>

```
!python3 predict_video.py --input_video_path=VideoInput/video_input3.mp4 --output_video_path=VideoOutput/video_output.mp4 --minimap=0 --bounce=0
```

<p>After the compilation is completed, a new video will be created in <a href="/VideoOutput" target="_blank">VideoOutput folder</a> if <code>--minimap</code> was set <code>0</code>, if <code>--minimap=1</code> three videos will be created: video of the game, video of minimap and a combined video of both</p>
<p><i>P.S. If you stumble upon an <b>error</b> or have any questions feel free to open a new <a href='https://github.com/ArtLabss/tennis-tracking/issues'>Issue</a> </i></p>

</ol>
Running this project requires a compatible GPU with tensorflow installed, this can be done on your local machine or on [Google Colab](https://colab.research.google.com/notebooks) by following the steps below.

<a target="_blank" href="https://colab.research.google.com/github/ArtLabss/tennis-tracking/blob/main/Tennis_Tracking_Colab_Setup.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

**1. Set Up Your Environment**
- Ensure your Google Colab session is set to use a GPU. You can do this by going to `Runtime` > `Change runtime type` and selecting one of the GPU options as the hardware accelerator.

**2. Clone the Repository**
- Clone the Tennis Tracking repository directly into your Colab environment.
```python
!git clone https://github.com/ArtLabss/tennis-tracking.git
```

**3. YOLOv3 Weights Download**
- Use the following command to download the YOLOv3 weights directly into the Yolov3 folder within your cloned repository.
```python
!wget https://pjreddie.com/media/files/yolov3.weights -P tennis-tracking/Yolov3/
```

**4. Install Dependencies**
- Install the required libraries. Google Colab already includes many dependencies, so you may only need to install a few missing ones.
```python
!pip install -r tennis-tracking/requirements.txt filterpy sktime
```
- For any specific dependencies not included in the requirements file but necessary for Colab, install them using `!pip install` commands.

**5. Prepare Your Input Video**
- Upload your video to Google Drive or directly to Colab using the sidebar. If using Google Drive, mount your drive:
```python
from google.colab import drive
drive.mount('/content/drive')
```
- Make sure your input video follows the guidelines (e.g., contains only game rallies, no commerical breaks).

**6. Running the Script**
- Change the directory to the cloned repository's root.
```python
import os
os.chdir('/content/tennis-tracking')
```
- Execute the script with the necessary arguments. Here, replace `path_to_your_video` with the actual path to your video in Colab or Google Drive.
```python
!python predict_video.py --input_video_path=path_to_your_video --output_video_path=VideoOutput/video_output.mp4 --minimap=0 --bounce=0
```

### Additional Tips
- **Automating Video Uploads:** Use the Colab file upload utility to upload videos directly to your Colab session.
```python
from google.colab import files
uploaded = files.upload()
# Follow the prompt to select and upload your video file.
```

- **Accessing Output Videos:** Output videos are saved in the specified `VideoOutput` directory within the Colab environment. You can download them to your local machine using:
```python
from google.colab import files
files.download('VideoOutput/video_output.mp4')
# Replace 'VideoOutput/video_output.mp4' with the correct path if needed.
```

- **Troubleshooting:** If you encounter errors or issues, check the console output for any messages indicating missing dependencies or errors in the script. You can also refer to the [project's issues page](https://github.com/ArtLabss/tennis-tracking/issues) for solutions or to report new issues.

By following these streamlined steps, you can set up and run the Tennis Tracking project on Google Colab.


<h3>What's new?</h3>
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189 changes: 189 additions & 0 deletions Tennis_Tracking_Colab_Setup.ipynb
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@@ -0,0 +1,189 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "6eb77059",
"metadata": {},
"source": [
"\n",
"# Tennis Tracking Project Setup and Execution Guide\n",
"\n",
"This notebook provides a detailed guide on setting up and running the Tennis Tracking project in a Google Colab environment using a GPU."
]
},
{
"cell_type": "markdown",
"id": "0803b4d8",
"metadata": {},
"source": [
"\n",
"## 1. Set Up Your Environment\n",
"\n",
"Ensure your Google Colab session is set to use a GPU by going to `Runtime` > `Change runtime type` and selecting one of the GPU options as the hardware accelerator."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d89b3440",
"metadata": {},
"outputs": [],
"source": [
"\n",
"!git clone https://github.com/ArtLabss/tennis-tracking.git"
]
},
{
"cell_type": "markdown",
"id": "b2000b44",
"metadata": {},
"source": [
"\n",
"## 3. YOLOv3 Weights Download\n",
"\n",
"Use the following command to download the YOLOv3 weights directly into the Yolov3 folder within your cloned repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6414787f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"!wget https://pjreddie.com/media/files/yolov3.weights -P tennis-tracking/Yolov3/"
]
},
{
"cell_type": "markdown",
"id": "7835ce54",
"metadata": {},
"source": [
"\n",
"## 4. Install Dependencies\n",
"\n",
"Install the required libraries. Google Colab already includes many dependencies, so you may only need to install a few missing ones."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbacff01",
"metadata": {},
"outputs": [],
"source": [
"\n",
"!pip install -r tennis-tracking/requirements.txt filterpy sktime"
]
},
{
"cell_type": "markdown",
"id": "78633942",
"metadata": {},
"source": [
"\n",
"## 5. Prepare Your Input Video\n",
"\n",
"Upload your video to Google Drive or directly to Colab using the sidebar. If using Google Drive, mount your drive."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0020d632",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"id": "1047ac14",
"metadata": {},
"source": [
"\n",
"## 6. Running the Script\n",
"\n",
"Change the directory to the cloned repository's root and execute the script with the necessary arguments."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fb1cf42",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import os\n",
"os.chdir('/content/tennis-tracking')\n",
"\n",
"!python predict_video.py --input_video_path=path_to_your_video --output_video_path=VideoOutput/video_output.mp4 --minimap=0 --bounce=0"
]
},
{
"cell_type": "markdown",
"id": "7ddf9282",
"metadata": {},
"source": [
"\n",
"### Additional Tips\n",
"\n",
"Automate video uploads using the Colab file upload utility."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10b7ccdb",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from google.colab import files\n",
"uploaded = files.upload()\n",
"# Follow the prompt to select and upload your video file."
]
},
{
"cell_type": "markdown",
"id": "57ea720b",
"metadata": {},
"source": [
"\n",
"Access output videos for download to your local machine."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc50bd20",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from google.colab import files\n",
"files.download('VideoOutput/video_output.mp4')\n",
"# Replace 'VideoOutput/video_output.mp4' with the correct path if needed."
]
},
{
"cell_type": "markdown",
"id": "b34784f0",
"metadata": {},
"source": [
"\n",
"### Troubleshooting\n",
"\n",
"If you encounter errors, check the console output for messages indicating missing dependencies or errors in the script. Refer to the [project's issues page](https://github.com/ArtLabss/tennis-tracking/issues) for solutions or to report new issues."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
2 changes: 1 addition & 1 deletion predict_video.py
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Expand Up @@ -273,7 +273,7 @@
# interpolation
coords = interpolation(coords)

# velocty
# velocity
Vx = []
Vy = []
V = []
Expand Down