- For Realtime Video (or webcam): use Roboflow
inference; docs for realtime video streaming are available here. - For Async/Recorded Video: Roboflow now provides a new video inference API, please visit the Hosted Video Inference docs to learn more.
Example script to do inference on a video file with Roboflow Infer
An example video (gif) generated with this utility script from a Roboflow model with the following command:
ROBOFLOW_KEY=xxxxxxxx ./infer.sh rf-aquarium-merged/3 IMG_3203.mov fish.gif --fps_in 3 --fps_out 12 --scale 4
Colab Tutorial Here:
The shell script you need is infer.sh in this repo.
To use it
- copy that file to your local machine,
- add execute permissions
chmod +x infer.sh, - install the requirements (see below)
- and then use it:
# Simple usage with ROBOFLOW_KEY env var set inline
ROBOFLOW_KEY=xxxxx ./infer.sh [YOUR_DATASET]/[YOUR_VERSION] video_in.mp4 video_out.mov
To obtain your ROBOFLOW_KEY, go to
your account settings page and copy
your API Key (not your Publishable Key). Treat this API Key like a password;
it gives access to your Roboflow account and is meant to remain a secret.
The model ID ([YOUR_DATASET]/[YOUR_VERSION] in the command above) is obtained from the Roboflow
UI after training. Click "Example Web App" in the "Use Your Trained Model" section
and copy it from the pre-filled text field at the top of the page.
The script's main requirements are ffmpeg,
base64, and curl. On most modern Unix-like operating systems, you will only
need to install ffmpeg because the other requirements are preinstalled.
Already includes base64 and curl.
Use homebrew to install ffmpeg: brew install ffmpeg
Already includes base64 and curl.
Use apt to install ffmpeg: sudo apt install ffmpeg
Download ffmpeg from their website and
install git for Windows which conveniently
includes base64 and curl.
A ROBOFLOW_KEY environment variable with
your Roboflow API Key
is required.
The infer.sh script accepts the following parameters:
| Parameter | Example | Description |
|---|---|---|
--host |
--host "http://localhost:9001" |
The Roboflow Infer host; set for On-Device Inference (default: https://infer.roboflow.com). |
--confidence |
--confidence 50 |
The minimum threshold for the model to output box predictions (default: 50). |
--overlap |
--overlap 50 |
The maximum amount two predicted boxes of the same class can intersect before being combined (default: 50). |
--stroke |
--stroke 5 |
The thickness of the predicted bounding boxes (default: 5). |
--labels |
--labels |
Enable printing the class names (implicit default: off) |
--classes |
--classes car |
A comma separated (no whitespace) list of classes to predict (default: show all). |
--fps_in |
--fps_in 6 |
The sample rate from the input video in frames per second (default: 6). |
--fps_out |
--fps_out 24 |
The render speed; setting higher than fps_in gives a timelapse effect (default: 24). |
--scale |
--scale 2 |
The amount to shrink the video; eg 2 to make video_out width and height 2x smaller than video_in (default: 1). |
--tmp |
--tmp . |
The tmp directory; must be writable (default: /tmp). |
--retries |
--retries 3 |
The number of times to retry a failed inference (default: 3). |
--parallel |
--parallel 8 |
The number of concurrent frames to send to the model (default: 8). |
| model | xx-mymodel--1 |
The Roboflow model to use for inference (required). |
| video_in | video_in.mp4 |
The input video file (required). |
| video_out | video_out.mp4 |
The output video file (required). Can be of any format supported by ffmpeg's encoder (eg .mov, .mp4, .gif). |
The infer script is built with argbash
(a code generator that adds smart parameter parsing for bash scripts)
from source.m4.
To build, clone this repo, install argbash,
then run the following in the repo's top-level directory:
argbash source.m4 -o infer.sh
You can then use your newly generated infer.sh as described above.
This example repo is released under an MIT License. You will need to train a model with Roboflow Train which is goverened by the Roboflow Terms of Service.
**Note: Update from November 2023 - Roboflow now provides a new video inference API, please visit this link to learn more. This repository is archived. **