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<a href="https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc">
<img src="https://app.roboflow.com/images/download-dataset-badge.svg"></img>
</a>
<a href="https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc/model/">
<img src="https://app.roboflow.com/images/try-model-badge.svg"></img>
</a>
## 👋 hello
This repository contains examples of image and video inference via [Roboflow Inference (HTTP)](https://github.com/roboflow/inference) and stream inference via [Roboflow Inference (UDP)](https://github.com/roboflow/inference).
The HTTP examples take in an image or video and run inference, whereas the UDP example listens for predictions from a UDP stream and processes them.
## 💻 install client environment
```bash
# clone repository and navigate to root directory
git clone https://github.com/roboflow/inference-client.git
cd inference-client
# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate
# headless install
pip install -r requirements.txt
```
## 🐋 docker
You can learn more about Roboflow Inference Docker Image build, pull and run in our [documentation](https://roboflow.github.io/inference/quickstart/docker/).
### HTTP
- Run on x86 CPU:
```bash
docker run --net=host roboflow/roboflow-inference-server-cpu:latest
```
- Run on Nvidia GPU:
```bash
docker run --network=host --gpus=all roboflow/roboflow-inference-server-gpu:latest
```
### UDP
- Run on Nvidia GPU:
```bash
docker run --gpus=all --net=host -e STREAM_ID=0 -e MODEL_ID=<> -e API_KEY=<> roboflow/roboflow-inference-server-udp-gpu:latest
```
<details close>
<summary>👉 more docker run options</summary>
### HTTP
- Run on arm64 CPU:
```bash
docker run -p 9001:9001 roboflow/roboflow-inference-server-arm-cpu:latest
```
- Run on Nvidia GPU with TensorRT Runtime:
```bash
docker run --network=host --gpus=all roboflow/roboflow-inference-server-trt:latest
```
- Run on Nvidia Jetson with JetPack `4.x`:
```bash
docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson:latest
```
- Run on Nvidia Jetson with JetPack `5.x`:
```bash
docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson-5.1.1:latest
```
### UDP
We only support one UDP container at the moment. Refer to the UDP command from earlier to set up UDP.
</details>
## 🔑 keys
Before running the inference script, ensure that the `API_KEY` is set as an environment variable. This key provides access to the inference API.
- For Unix/Linux:
```bash
export API_KEY=your_api_key_here
```
- For Windows:
```bash
set API_KEY=your_api_key_here
```
Replace `your_api_key_here` with your actual API key.
## 📷 image inference example (HTTP)
To run the image inference script:
```bash
python image.py \
--image_path data/a9f16c_8_9.png \
--class_list "ball" "goalkeeper" "player" "referee" \
--dataset_id "football-players-detection-3zvbc" \
--version_id 2 \
--confidence 0.5
```
## 🎬 video inference example (HTTP)
To run the video inference script:
```bash
python video.py \
--video_path "data/40cd38_5.mp4" \
--class_list "ball" "goalkeeper" "player" "referee" \
--dataset_id "football-players-detection-3zvbc" \
--version_id 2 \
--confidence 0.5
```
## 📺 stream inference example (UDP)
To run the UDP receiver, run:
```bash
python udp.py --port=12345
``` |