GStreamer Edge Impulse Plugin

A GStreamer plugin that enables real-time machine learning inference and data ingestion using Edge Impulse models and APIs. The plugin provides four elements for audio and video inference, visualization, and ingestion.

Public API: Inference and Ingestion Output

The plugin exposes results and ingestion status through standardized mechanisms:

GStreamer Bus Messages
- All inference elements emit structured messages on the GStreamer bus with the name edge-impulse-inference-result.
- The ingestion element (edgeimpulsesink) emits bus messages for ingestion results and errors:
  - edge-impulse-ingestion-result: Sent when a sample is successfully ingested (fields: filename, media type, length, label, category).
  - edge-impulse-ingestion-error: Sent when ingestion fails (fields: filename, media type, error, label, category).
- These messages allow applications to monitor both inference and ingestion events in real time.
Video Frame Metadata (VideoRegionOfInterestMeta)
- For video inference, results are also attached as metadata to each video frame using VideoRegionOfInterestMeta.
- This enables downstream elements (e.g., overlays, SDKs) to consume and visualize results directly.
- Available for video elements only.
VideoRegionOfInterestMeta Content:

Each VideoRegionOfInterestMeta attached to a video frame contains:
- x (u32): X coordinate of the top-left corner of the region (in pixels)
- y (u32): Y coordinate of the top-left corner of the region (in pixels)
- width (u32): Width of the region (in pixels)
- height (u32): Height of the region (in pixels)
- label (String): Class label or description for the region
For object detection, each detected object is represented as a separate ROI. For classification, a single ROI may cover the whole frame with the top label. For visual anomaly detection, the ROI may include anomaly scores and grid data as additional metadata.

Supported Model Types and Output Formats

1. Object Detection

Bus Message Example:

{
  "timestamp": 1234567890,
  "type": "object-detection",
  "result": {
    "bounding_boxes": [
      {
        "label": "person",
        "value": 0.95,
        "x": 24,
        "y": 145,
        "width": 352,
        "height": 239
      }
    ]
  }
}

Video Metadata:
- Each detected object is attached as a VideoRegionOfInterestMeta with bounding box coordinates, label, and confidence.

2. Classification

Bus Message Example:

{
  "timestamp": 1234567890,
  "type": "classification",
  "result": {
    "classification": {
      "cat": 0.85,
      "dog": 0.15
    }
  }
}

Video Metadata:
- For video, the top classification result may be attached as a single ROI covering the frame, with label and confidence.

3. Visual Anomaly Detection

Bus Message Example:

{
  "timestamp": 1234567890,
  "type": "anomaly-detection",
  "result": {
    "anomaly": 0.35,
    "classification": {
      "normal": 0.85,
      "anomalous": 0.15
    },
    "visual_anomaly_max": 0.42,
    "visual_anomaly_mean": 0.21,
    "visual_anomaly_grid": [
      { "x": 0, "y": 0, "width": 32, "height": 32, "value": 0.12 },
      { "x": 32, "y": 0, "width": 32, "height": 32, "value": 0.18 }
      // ... more grid cells ...
    ]
  }
}

Video Metadata:
- The frame will have a VideoAnomalyMeta attached, containing:
  - anomaly: The overall anomaly score for the frame
  - visual_anomaly_max: The maximum anomaly score in the grid
  - visual_anomaly_mean: The mean anomaly score in the grid
  - visual_anomaly_grid: A list of grid cells, each with its own region (x, y, width, height) and anomaly value
- Optionally, each grid cell may also be represented as a VideoRegionOfInterestMeta with the anomaly score as the label or as additional metadata, enabling visualization overlays.

Note: Audio elements only emit bus messages; video elements emit both bus messages and metadata.

Dependencies

This plugin depends on:

GStreamer 1.20 or newer
edge-impulse-runner-rs - Rust bindings for Edge Impulse Linux SDK
edge-impulse-ffi-rs - FFI bindings for Edge Impulse C++ SDK (used by runner-rs)
A trained Edge Impulse model file (.eim) or environment variables for FFI mode
Rust nightly toolchain (via rustup) - required for edition 2024 support

Note: The plugin inherits all build flags and environment variables supported by the underlying FFI crate. See the edge-impulse-ffi-rs documentation for the complete list of supported platforms, accelerators, and build options.

Installation

1. Install Rust

First, install the Rust toolchain using rustup:

# On Unix-like OS (Linux, macOS)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Follow the prompts to complete the installation. After installation, restart your terminal to ensure the Rust tools are in your PATH.

Note: This plugin requires Rust nightly due to the edge-impulse-runner dependency using edition 2024. Switch to nightly after installation:

rustup override set nightly

2. Install GStreamer

Download and install GStreamer from the official binaries:

macOS

Download and install both packages:

Linux

Install from your distribution's package manager. For example:

Ubuntu/Debian:

sudo apt-get install \
    libgstreamer1.0-dev \
    libgstreamer-plugins-base1.0-dev \
    libgstreamer-plugins-bad1.0-dev \
    gstreamer1.0-plugins-base \
    gstreamer1.0-plugins-good \
    gstreamer1.0-plugins-bad \
    gstreamer1.0-plugins-ugly \
    gstreamer1.0-libav \
    gstreamer1.0-tools \
    gstreamer1.0-x \
    gstreamer1.0-alsa \
    gstreamer1.0-gl \
    gstreamer1.0-gtk3 \
    gstreamer1.0-qt5 \
    gstreamer1.0-pulseaudio

3. Build the Plugin

Clone and build the plugin:

git clone https://github.com/edgeimpulse/gst-plugins-edgeimpulse.git
cd gst-plugins-edgeimpulse
cargo build --release

Build Features

The plugin supports two inference modes:

FFI Mode (Default):

Direct FFI calls to the Edge Impulse C++ SDK
Models are compiled into the binary
Faster startup and inference times
Usage: No model path needed - the model is statically linked
Requirement: Must have environment variables set for model download during build. Either:
- EI_PROJECT_ID: Your Edge Impulse project ID
- EI_API_KEY: Your Edge Impulse API key Or:
- EI_MODEL pointing to the path to your local Edge Impulse model directory.

# Set environment variables to download your model from Edge Impulse
export EI_PROJECT_ID="your_project_id"
export EI_API_KEY="your_api_key"
# Or
export EI_MODEL="~/Downloads/your-model-directory"  # Optional: for local models

# Build with FFI feature (default)
cargo build --release

EIM Mode (Legacy):

Uses Edge Impulse model files (.eim) for inference
Requires EIM model files to be present on the filesystem
Compatible with all Edge Impulse deployment targets
Usage: Set the model-path or model-path-with-debug property to the .eim file path

cargo build --release --no-default-features --features eim

Note:

The default build uses FFI mode. Use --no-default-features --features eim for EIM mode.
FFI mode will fail to build if the environment variables are not set, as it needs to download and compile the model during the build process.
When switching between different models, it's recommended to clean the build cache:
```
cargo clean
cargo cache -a
```

Environment Variables

Required for FFI Mode:

EI_PROJECT_ID: Your Edge Impulse project ID (found in your project dashboard)
EI_API_KEY: Your Edge Impulse API key (found in your project dashboard)

Common Optional Variables:

EI_MODEL: Path to a local Edge Impulse model directory (e.g., ~/Downloads/visual-ad-v16)
EI_ENGINE: Inference engine to use (tflite, tflite-eon, etc.)
USE_FULL_TFLITE: Set to 1 to use full TensorFlow Lite instead of EON

Platform-Specific Variables:

TARGET: Standard Rust target triple (e.g., aarch64-unknown-linux-gnu, x86_64-apple-darwin)
TARGET_MAC_ARM64=1: Build for Apple Silicon (M1/M2/M3) - legacy
TARGET_MAC_X86_64=1: Build for Intel Mac - legacy
TARGET_LINUX_X86=1: Build for Linux x86_64 - legacy
TARGET_LINUX_AARCH64=1: Build for Linux ARM64 - legacy
TARGET_LINUX_ARMV7=1: Build for Linux ARMv7 - legacy

Example:

export EI_PROJECT_ID="12345"
export EI_API_KEY="ei_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
export EI_ENGINE="tflite"
export USE_FULL_TFLITE="1"

Advanced Build Flags: For a complete list of advanced build flags including hardware accelerators, backends, and cross-compilation options, see the edge-impulse-ffi-rs documentation. This includes support for:

Apache TVM backend (USE_TVM=1)
ONNX Runtime backend (USE_ONNX=1)
Qualcomm QNN delegate (USE_QUALCOMM_QNN=1)
ARM Ethos-U delegate (USE_ETHOS=1)
BrainChip Akida backend (USE_AKIDA=1)
MemryX backend (USE_MEMRYX=1)
TensorRT for Jetson platforms (TENSORRT_VERSION=8.5.2)
And many more...

Note: The GStreamer plugin inherits all build flags and environment variables supported by the underlying edge-impulse-ffi-rs crate.

Troubleshooting

FFI Build Errors: If you get an error like could not find native static library 'edge_impulse_ffi_rs' when building with FFI mode, it means the environment variables are not set. The FFI mode requires:

EI_PROJECT_ID environment variable set to your Edge Impulse project ID
EI_API_KEY environment variable set to your Edge Impulse API key

These variables are used during the build process to download and compile your model into the binary.

Solution: Set the environment variables before building:

export EI_PROJECT_ID="your_project_id"
export EI_API_KEY="your_api_key"
cargo build --release

Model Switching: When switching between different models, the build cache may contain artifacts from the previous model. To ensure a clean build:

# Clean build artifacts
cargo clean

# Clean cargo cache (optional, but recommended when switching models)
cargo cache -a

# Rebuild with new model
export EI_MODEL="~/Downloads/new-model-directory"
cargo build --release

Docker-based Cross Compilation

For cross-compilation to ARM64 Linux from macOS or other platforms, we provide a Docker-based setup:

Prerequisites:

Docker and Docker Compose installed

Quick Start:

# Set up environment variables
export EI_PROJECT_ID="your_project_id"
export EI_API_KEY="your_api_key"
export EI_MODEL="/path/to/your/model"  # Optional: for local models

```bash
# Build the Docker image
docker-compose build

# Build the plugin for ARM64
docker-compose run --rm aarch64-build

# Test a specific example
docker-compose run --rm aarch64-build bash -c "
    ./target/aarch64-unknown-linux-gnu/release/examples/audio_inference --audio examples/assets/test_audio.wav
"

The compiled plugin will be available at target/aarch64-unknown-linux-gnu/release/libgstedgeimpulse.so.

Elements

edgeimpulseaudioinfer

Audio inference element that processes audio streams through Edge Impulse models.

Element Details:

Long name: Edge Impulse Audio Inference
Class: Filter/Audio/AI
Description: Runs audio inference on Edge Impulse models (EIM)

Pad Templates:

Sink pad (Always available):

audio/x-raw
  format: S16LE
  rate: [ 8000, 48000 ]
  channels: 1
  layout: interleaved

Source pad (Always available):

audio/x-raw
  format: S16LE
  rate: [ 8000, 48000 ]
  channels: 1
  layout: interleaved

Properties:

model-path (string):
- Path to Edge Impulse model file
- Default: null
- Flags: readable, writable
threshold (string):
- Format: blockId.type=value
- Types:
  - min_score: For object detection blocks
  - min_anomaly_score: For anomaly detection blocks
- Examples:
  - 5.min_score=0.6: Set minimum confidence score for block 5
  - 4.min_anomaly_score=0.35: Set minimum anomaly score for block 4
- Default: ""
- Flags: readable, writable

Key features:

Accepts S16LE mono audio at 8-48kHz
Passes audio through unchanged
Performs inference when model is loaded
Emits inference results as messages (see Public API)

Example pipeline:

# FFI mode (default)
gst-launch-1.0 autoaudiosrc ! \
    capsfilter caps="audio/x-raw,format=F32LE" ! \
    audioconvert ! \
    audioresample ! \
    capsfilter caps="audio/x-raw,format=S16LE,channels=1,rate=16000,layout=interleaved" ! \
    edgeimpulseaudioinfer ! \
    audioconvert ! \
    audioresample ! \
    capsfilter caps="audio/x-raw,format=F32LE,channels=2,rate=44100" ! \
    autoaudiosink

# EIM mode (legacy)
gst-launch-1.0 autoaudiosrc ! \
    capsfilter caps="audio/x-raw,format=F32LE" ! \
    audioconvert ! \
    audioresample ! \
    capsfilter caps="audio/x-raw,format=S16LE,channels=1,rate=16000,layout=interleaved" ! \
    edgeimpulseaudioinfer model-path=<path-to-model> ! \
    audioconvert ! \
    audioresample ! \
    capsfilter caps="audio/x-raw,format=F32LE,channels=2,rate=44100" ! \
    autoaudiosink

edgeimpulsevideoinfer

Video inference element that processes video frames through Edge Impulse models.

Element Details:

Long name: Edge Impulse Video Inference
Class: Filter/Video/AI
Description: Runs video inference on Edge Impulse models (EIM)

Pad Templates:

Sink pad (Always available):

video/x-raw
  format: RGB
  width: [ 1, 2147483647 ]
  height: [ 1, 2147483647 ]

Source pad (Always available):

video/x-raw
  format: RGB
  width: [ 1, 2147483647 ]
  height: [ 1, 2147483647 ]

Properties:

model-path (string):
- Path to Edge Impulse model file
- Default: null
- Flags: readable, writable
threshold (string):
- Format: blockId.type=value
- Types:
  - min_score: For object detection blocks (confidence threshold)
  - min_anomaly_score: For anomaly detection blocks
- Examples:
  - 5.min_score=0.6: Set minimum confidence score for block 5
  - 4.min_anomaly_score=0.35: Set minimum anomaly score for block 4
- Default: ""
- Flags: readable, writable

Key features:

Accepts RGB video frames of any resolution
Passes frames through unchanged
Performs inference when model is loaded
Supports classification, object detection and anomaly detection models
Emits inference results as messages (see Public API)
Attaches VideoRegionOfInterestMeta to each video frame (see Public API)

Example pipelines:

Basic pipeline with built-in overlay:

# FFI mode (default)
gst-launch-1.0  avfvideosrc ! \
  queue max-size-buffers=2 leaky=downstream ! \
  videoconvert n-threads=4 ! \
  videoscale method=nearest-neighbour ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  queue max-size-buffers=2 leaky=downstream ! \
  edgeimpulsevideoinfer ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

# EIM mode (legacy)
gst-launch-1.0  avfvideosrc ! \
  queue max-size-buffers=2 leaky=downstream ! \
  videoconvert n-threads=4 ! \
  videoscale method=nearest-neighbour ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  queue max-size-buffers=2 leaky=downstream ! \
  edgeimpulsevideoinfer model-path=<path-to-model> ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

Pipeline with threshold settings:

# FFI mode (default) - Set object detection threshold
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer threshold="5.min_score=0.6" ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

# FFI mode (default) - Set multiple thresholds
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer \
    threshold="5.min_score=0.6" \
    threshold="4.min_anomaly_score=0.35" ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

# EIM mode (legacy) - Set object detection threshold
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer model-path=<path-to-model> threshold="5.min_score=0.6" ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

# EIM mode (legacy) - Set multiple thresholds
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer model-path=<path-to-model> \
    threshold="5.min_score=0.6" \
    threshold="4.min_anomaly_score=0.35" ! \
  edgeimpulseoverlay ! \
  autovideosink sync=false

edgeimpulseoverlay

Video overlay element that visualizes inference results by drawing bounding boxes and labels on video frames.

Element Details:

Long name: Edge Impulse Overlay
Class: Filter/Effect/Video
Description: Draws bounding boxes on video frames based on ROI metadata

Pad Templates:

Sink/Source pads (Always available):

video/x-raw
  format: { RGB, BGR, RGBA, BGRA, UYVY, YUY2, YVYU, NV12, NV21, I420, YV12 }
  width: [ 1, 2147483647 ]
  height: [ 1, 2147483647 ]

Key features:

Draws bounding boxes for object detection and visual anomaly detection results (from VideoRegionOfInterestMeta)
Displays class labels with confidence scores
Supports wide range of video formats

Properties:

stroke-width (integer):
- Width of the bounding box lines in pixels
- Range: 1 - 100
- Default: 2
text-color (unsigned integer):
- Color of the text in RGB format
- Range: 0 - 4294967295
- Default: white (0xFFFFFF)
text-font (string):
- Font family to use for text rendering
- Default: "Sans"
text-font-size (unsigned integer):
- Size of the text font in pixels
- Range: 0 - 4294967295
- Default: 14

Example pipeline:

# FFI mode (default)
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer ! \
  edgeimpulseoverlay stroke-width=3 text-font-size=20 text-color=0x00FF00 ! \
  autovideosink sync=false

# EIM mode (legacy)
gst-launch-1.0 avfvideosrc ! \
  videoconvert ! \
  videoscale ! \
  video/x-raw,format=RGB,width=384,height=384 ! \
  edgeimpulsevideoinfer model-path=<path-to-model> ! \
  edgeimpulseoverlay stroke-width=3 text-font-size=20 text-color=0x00FF00 ! \
  autovideosink sync=false

The overlay element automatically processes VideoRegionOfInterestMeta from upstream elements (like edgeimpulsevideoinfer) and visualizes them with configurable styles.

edgeimpulsesink

Sink element that uploads audio or video buffers to Edge Impulse using the ingestion API.

Element Details:

Long name: Edge Impulse Ingestion Sink
Class: Sink/AI
Description: Uploads audio or video buffers to Edge Impulse ingestion API (WAV for audio, PNG for video)

Pad Templates:

Sink pad (Always available):

audio/x-raw
  format: S16LE
  channels: 1
  rate: 16000
video/x-raw
  format: { RGB, RGBA }
  width: [ 1, 2147483647 ]
  height: [ 1, 2147483647 ]

Properties:

api-key (string, required):
- Edge Impulse API key
- Flags: readable, writable
hmac-key (string, optional):
- Optional HMAC key for signing requests
- Flags: readable, writable
label (string, optional):
- Optional label for the sample
- Flags: readable, writable
category (string, default: "training"):
- Category for the sample (training, testing, anomaly)
- Flags: readable, writable
upload-interval-ms (u32, default: 0):
- Minimum interval in milliseconds between uploads (0 = every buffer)
- Flags: readable, writable

Key features:

Supports both audio (WAV) and video (PNG) ingestion
Batches and uploads buffers at a configurable interval
Emits bus messages for ingestion results and errors (see Public API)
Can be used in pipelines for automated dataset collection

Example pipeline:

gst-launch-1.0 autoaudiosrc ! audioconvert ! audioresample ! audio/x-raw,format=S16LE,channels=1,rate=16000 ! edgeimpulsesink api-key=<your-api-key> upload-interval-ms=1000 category=training

See examples/audio_ingestion.rs for a full example with bus message handling.

Examples

The repository includes examples demonstrating audio and video inference, as well as data ingestion. These examples have been tested on MacOS.

Audio Inference

Run the audio inference example:

# Basic usage (FFI mode - default)
cargo run --example audio_inference

# With threshold settings
cargo run --example audio_inference \
    --threshold "5.min_score=0.6" \
    --threshold "4.min_anomaly_score=0.35"

# With audio file input
cargo run --example audio_inference \
    --audio input.wav \
    --threshold "5.min_score=0.6"

# EIM mode (legacy)
cargo run --example audio_inference -- --model path/to/your/model.eim

This will capture audio from the default microphone (or audio file if specified) and display inference results:

For classification:

Got element message with name: edge-impulse-inference-result
Message structure: edge-impulse-inference-result {
    timestamp: (guint64) 9498000000,
    type: "classification",
    result: {
        "classification": {
            "no": 0.015625,
            "noise": 0.968750,
            "yes": 0.019531
        }
    }
}
Detected: noise (96.9%)

Video Inference

Run the video inference example:

# Basic usage (FFI mode - default)
cargo run --example video_inference

# With threshold settings
cargo run --example video_inference \
    --threshold "5.min_score=0.6" \
    --threshold "4.min_anomaly_score=0.35"

# EIM mode (legacy)
cargo run --example video_inference -- --model path/to/your/model.eim

This will capture video from your camera and display inference results with visualization. Example outputs:

For object detection:

Got element message with name: edge-impulse-inference-result
Message structure: edge-impulse-inference-result {
    timestamp: (guint64) 1234567890,
    type: "object-detection",
    result: {
        "bounding_boxes": [
            {
                "label": "person",
                "value": 0.95,
                "x": 24,
                "y": 145,
                "width": 352,
                "height": 239
            }
        ]
    }
}
Detected: person (95.0%)

For classification:

Got element message with name: edge-impulse-inference-result
Message structure: edge-impulse-inference-result {
    timestamp: (guint64) 1234567890,
    type: "classification",
    result: {
        "classification": {
            "cat": 0.85,
            "dog": 0.15
        }
    }
}
Detected: cat (85.0%)

For visual anomaly detection:

Got element message with name: edge-impulse-inference-result
Message structure: edge-impulse-inference-result {
    timestamp: (guint64) 1234567890,
    type: "anomaly-detection",
    result: {
        "anomaly": 0.35,
        "classification": {
            "normal": 0.85,
            "anomalous": 0.15
        },
        "visual_anomaly_max": 0.42,
        "visual_anomaly_mean": 0.21,
        "visual_anomaly_grid": [
            { "x": 0, "y": 0, "width": 32, "height": 32, "score": 0.12 },
            { "x": 32, "y": 0, "width": 32, "height": 32, "score": 0.18 }
            // ... more grid cells ...
        ]
    }
}
Detected: normal (85.0%)
Anomaly score: 35.0%
Max grid score: 42.0%
Mean grid score: 21.0%
Grid cells:
  Cell at (0, 0) size 32x32: score 12.0%
  Cell at (32, 0) size 32x32: score 18.0%
  ...

The element will automatically detect the model type and emit appropriate messages. Thresholds can be set for both object detection (min_score) and anomaly detection (min_anomaly_score) blocks. See Public API for output details.

Audio Ingestion

Run the audio ingestion example:

cargo run --example audio_ingestion -- --api-key <your-api-key> [--upload-interval-ms <interval>]

This will capture audio from the default microphone and upload samples to Edge Impulse using the ingestion API. Ingestion results and errors are printed as bus messages:

✅ Sample ingested: file='...', media_type='audio/wav', length=..., label=..., category='training'
❌ Ingestion error: file='...', media_type='audio/wav', error='...', label=..., category='training'

See the Public API and edgeimpulsesink sections for details.

Image Slideshow Example

The repository includes an image_slideshow example that demonstrates how to run Edge Impulse video inference on a folder of images as a configurable slideshow.

Usage

# FFI mode (default)
cargo run --example image_slideshow -- --folder <path-to-image-folder> -W <width> -H <height> [--framerate <fps>] [--max-images <N>]

# EIM mode (legacy)
cargo run --example image_slideshow -- --model <path-to-model.eim> --folder <path-to-image-folder> -W <width> -H <height> [--framerate <fps>] [--max-images <N>]

--model (optional): Path to the Edge Impulse model file (.eim) - only needed for EIM mode
--folder (required): Path to the folder containing images (jpg, jpeg, png)
-W, --width (required): Input width for inference
-H, --height (required): Input height for inference
--framerate (optional): Slideshow speed in images per second (default: 1)
--max-images (optional): Maximum number of images to process (default: 100)

How it works

All images in the folder are copied and converted to JPEG in a temporary directory for robust GStreamer playback.

The pipeline mimics the following structure:

multifilesrc ! decodebin ! videoconvert ! queue ! videoscale ! videorate ! video/x-raw,format=GRAY8,width=...,height=...,framerate=... ! edgeimpulsevideoinfer ! videoconvert ! video/x-raw,format=RGB,width=...,height=... ! edgeimpulseoverlay ! autovideosink

The slideshow speed is controlled by the --framerate argument.
Each image is shown for the correct duration, and the pipeline loops through all images.
Inference results are visualized and also available as bus messages and metadata (see above).

Example

# FFI mode (default)
cargo run --example image_slideshow -- --folder ./images -W 160 -H 160 --framerate 2

# EIM mode (legacy)
cargo run --example image_slideshow -- --model model.eim --folder ./images -W 160 -H 160 --framerate 2

This will show a 2 FPS slideshow of all images in ./images, running inference and overlaying results.

Debugging

Enable debug output with:

GST_DEBUG=edgeimpulseaudioinfer:4 # for audio inference element
GST_DEBUG=edgeimpulsevideoinfer:4 # for video inference element
GST_DEBUG=edgeimpulseoverlay:4 # for overlay element
GST_DEBUG=edgeimpulsesink:4 # for ingestion element

Acknowledgments

This crate is designed to work with Edge Impulse's machine learning models. For more information about Edge Impulse and their ML deployment solutions, visit Edge Impulse.

License

This project is licensed under the BSD 3-Clause Clear License - see the LICENSE file for details.

Name		Name	Last commit message	Last commit date
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.cargo		.cargo
.github/workflows		.github/workflows
examples		examples
src		src
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
docker-compose.yml		docker-compose.yml
docker-entrypoint.sh		docker-entrypoint.sh
test-cross-compilation.sh		test-cross-compilation.sh

License

edgeimpulse/gst-plugins-edgeimpulse

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GStreamer Edge Impulse Plugin

Public API: Inference and Ingestion Output

Supported Model Types and Output Formats

1. Object Detection

2. Classification

3. Visual Anomaly Detection

Dependencies

Installation

1. Install Rust

2. Install GStreamer

macOS

Linux

3. Build the Plugin

Build Features

Environment Variables

Troubleshooting

Docker-based Cross Compilation

Elements

edgeimpulseaudioinfer

edgeimpulsevideoinfer

edgeimpulseoverlay

edgeimpulsesink

Examples

Audio Inference

Video Inference

Audio Ingestion

Image Slideshow Example

Usage

How it works

Example

Debugging

Acknowledgments

License

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