https://github.com/ethanlee928/pi-inference
Computer Vision Inference Pipeline for Raspberry Pi
https://github.com/ethanlee928/pi-inference
computer-vision deep-learning edge-computing raspberry-pi
Last synced: 6 months ago
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Computer Vision Inference Pipeline for Raspberry Pi
- Host: GitHub
- URL: https://github.com/ethanlee928/pi-inference
- Owner: ethanlee928
- License: mit
- Created: 2024-08-12T16:07:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-04T16:05:57.000Z (over 1 year ago)
- Last Synced: 2025-07-20T15:24:50.495Z (7 months ago)
- Topics: computer-vision, deep-learning, edge-computing, raspberry-pi
- Language: Python
- Homepage:
- Size: 331 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pi-inference
A Computer Vision Inference Pipeline for Raspberry Pi inspired by [Jetson Inference](https://github.com/dusty-nv/jetson-inference).
The pipeline utilized `Gstreamer` and [`picamera2`](https://github.com/raspberrypi/picamera2) for video pipeline, and [`ncnn`](https://github.com/Tencent/ncnn) for optimized inference.
## 🖥️ Install
The pipeline is based on Gstreamer v1.22.0.
```bash
sudo scripts/install-packages.sh
```
Install the `pi-inference` package in a `Python>=3.8` environment.
```bash
pip install pi-inference
```
## 🚀 Quick Start
Inference using USB camera with pretrained `YOLOv8s` model, and display on GUI window.
```python
import logging
import supervision as sv
from ncnn.model_zoo import get_model
from pi_inference import VideoOutput, VideoSource
from pi_inference import functions as f
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s: %(message)s")
logger = logging.getLogger(__name__)
video_source = VideoSource("v4l2:///dev/video0", {"codec": "mjpg"})
video_output = VideoOutput("display://0", {})
net = get_model(
"yolov8s",
target_size=640,
prob_threshold=0.25,
nms_threshold=0.45,
num_threads=4,
use_gpu=False,
)
box_annotator = sv.BoxAnnotator()
labels_annotator = sv.LabelAnnotator()
fps_monitor = sv.FPSMonitor()
while True:
try:
frame = video_source.capture(timeout=300)
if frame is not None:
fps_monitor.tick()
detections = f.from_ncnn(frame, net)
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence in zip(detections["class_name"], detections.confidence)
]
frame = box_annotator.annotate(scene=frame, detections=detections)
frame = labels_annotator.annotate(scene=frame, detections=detections, labels=labels)
frame = f.draw_clock(frame)
frame = f.draw_text(frame, f"FPS: {fps_monitor.fps:.1f}", anchor_y=80)
video_output.render(frame)
except KeyboardInterrupt:
break
video_source.on_terminate()
video_output.on_terminate()
```
Find out more in [`examples`](examples).
## ⛏️ Development
Install the package using pip
```bash
# For raspberrypi
python3 -m venv --system-site-packages .venv
# For others
python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -e ".[dev]"
```