GstInference Signals

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
Contact Us

Overview

Metadata from GstInference is available to be obtained through GSignals and therefore can be used in other programs or processes such as using Python or C++.

Available Signals

Signals are created and listed in gst-libs/gst/r2inference/gstvideoinference.c

Signal Name	To be used by	Details
new-inference	C/C++	Format can be casted to struct that defines metadata and dereference pointers.
new-inference-string	Python, Javascript, Other	String format needs to be used because pointer dereference from other memory space is not available.

Inference String Signal

The following code show a simple capture of the signal in GStreamer using Python. It installs the function handler to the signal called "new-inference-string" from GstInference element. The signal sends a string formatted as json which can be parsed in python using json.loads function.

For details about what elements can be accessed in the serialized json string, check this section.

import gi
gi.require_version("Gst", "1.0")
gi.require_version("GstVideo", "1.0")
from gi.repository import Gst, GObject, GstVideo
import json

GObject.threads_init()
Gst.init(None)

def newPrediction(element, meta):
    # Parse data from string to json object
    data = json.loads(meta)
    print(data)

# Settings
video_dev = "/dev/video0"
arch = "mobilenetv2ssd"
backend = "coral"
model = "/home/coral/models/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite"
input_layer = "" # Needed by other backends such as Tensorflow
output_layer = "" # Needed by other backends such as Tensorflow

# Pipeline
inf_pipe_str = "v4l2src device=%s ! videoscale ! videoconvert ! \
                video/x-raw,width=640,height=480,format=I420 ! \
                videoconvert ! inferencebin arch=%s backend=%s \
                model-location=%s input-layer=%s output-layer=%s \
                overlay=true name=net ! \
                videoconvert ! autovideosink name=videosink sync=false" % \
                (video_dev,arch,backend,model,input_layer,output_layer)

# Load pipeline from string
inference_pipe = Gst.parse_launch(inf_pipe_str)
# Start pipeline
inference_pipe.set_state(Gst.State.PLAYING)

if (not inference_pipe):
    print("Unable to create pipeline")
    exit(1)

# Search for arch element from inferencebin
net = inference_pipe.get_by_name("arch")

# Connect to inference string signal
net.connect("new-inference-string", newPrediction)

# Launch loop
loop = GObject.MainLoop() 
loop.run()