GstInference/Metadatas/GstEmbeddingMeta

GstInference
Introduction
Getting started
Getting the code Building the plugin Quick Starting Guide
Supported architectures
InceptionV1 InceptionV2 InceptionV3 InceptionV4 TinyYoloV2 TinyYoloV3 YoloV2 ResNetV1 MobileNetV2 MobileNetV2 + SSD AlexNet
Supported backends
TensorFlow Tensorflow-Lite Coral from Google Caffe TensorRT ONNXRT ONNXRT ACL ONNXRT OpenVINO
Metadata and Signals
GstClassificationMeta GstInferenceMeta Signals
Overlay Elements
Inference Overlay
Utils Elements
InferenceFilter InferenceCrop InferenceDebug InferenceBin
Legacy pipelines
PC NANO TX2 Xavier IMX8
Example pipelines
Example applications
Classification Detection Hand Pose Recognition
Benchmarks
Model Zoo
Project Status
Releases Roadmap
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This metadata consist on an 128 elements array, that is filed with the embedding produced by facenet. This element draws a green border and writes "Pass" on the frame if its embedding is close to a known value or a red border and "Fail" otherwise.

Fields

The facenet element and embedding overlay uses similar metadata as the classification plugins. GstEmbeddingMeta consist on the following fields:

• num_dimensions: The number of labels outputted by the model. This can vary from model to model. Facenet uses 128.
• embedding: The embedding produced by the network

Access metadata

If you want to access this metadata from your custom Gstreamer element instead the process is fairly easy:

1. Add the Facenet element to your pipeline
2. Include GstInference metadata header: #include "gst/r2inference/gstinferencemeta.h"
3. Get a GstClassificationMeta object from the buffer: class_meta = (GstClassificationMeta *) gst_buffer_get_meta (frame->buffer, GST_CLASSIFICATION_META_API_TYPE);

FaceNet also raises a signal containing GstClassificationMeta, for details on how to use this signal please check the example applications section.