GstInference inferencedebug

GstInference
Introduction
Getting started
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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
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Our GstInferenceMeta behaves in a hierarchical manner; in order to print the whole metadata at a certain point in the pipeline, inferencedebug was created. This way, you can check classes. labels and the enabled status by enabling the debug on your pipeline and locating the inference debug element at the desired location.

GstInference inferencedebug example

In this example, we located the inferencedebug element right after the inferencefilter to check which classes are being enabled.

GST_DEBUG=2,*inferencedebug*:6 gst-launch-1.0 v4l2src device=/dev/video0 ! videoconvert ! tee name=t t. ! videoscale ! queue ! net.sink_model t. ! queue ! net.sink_bypass tinyyolov2 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER  net.src_model ! inferencefilter filter-class=8 ! inferencedebug ! fakesink

• Output

0:00:03.255231109 11277 0x55f5ce5cfde0 DEBUG         inferencedebug gstinferencedebug.c:131:gst_inference_debug_transform_ip:<inferencedebug0> transform_ip
0:00:03.255268289 11277 0x55f5ce5cfde0 DEBUG         inferencedebug gstinferencedebug.c:120:gst_inference_debug_print_predictions:<inferencedebug0> Prediction Tree: 
 {
  id : 169,
  enabled : False,
  bbox : {
    x : 0
    y : 0
    width : 416
    height : 416
  },
  classes : [
    
  ],
  predictions : [
    {
      id : 170,
      enabled : False,
      bbox : {
        x : 101
        y : 96
        width : 274
        height : 346
      },
      classes : [
        {
          Id : 81
          Class : 14
          Label : person
          Probability : 12.842868
          Classes : 20
        }, 
      ],
      predictions : [
        
      ]
    }, 
  ]
}