GStreamer example pipelines for Smart Parking use case.

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The Getting started with AI on NXP i.MX8M Plus guide is currently under active development. Some sections may be incomplete or change without notice.

Questions? Contact RidgeRun or email to support@ridgerun.com.

Getting started with AI on NXP i.MX8M Plus
Introduction
Overview i.MX8M Plus hardware specifications Development tools Development kit DART-MX8M-PLUS Development kit Quick getting started Yocto minimal image Flashing SD card First steps on the board
Development
Gatesgarth and Hardknott Yocto versions Downloading Yocto images for NXP based BSP Building an imx-core-image Flashing SD card Integrating multimedia software stack Installing GStreamer Installing OpenCV Integrating Artificial Intelligence software stack Installing TensorFlow Lite for NXP based images Installing R2Inference Installing GstInference Developing software for the board Building the SDK Crosscompiling apps for GStreamer, TensorFlow Lite, and OpenCV Exploring TensorFlow Lite delegates for prototyping
Neural Processing Unit
Introduction Profiling the NPU Installing gputop Setting the board on debug mode Use Case experiments: Smart Parking Introduction to the use case License Plate Detection TinyYOLO version 3 Training a model compatible with GstInference License Plate Recognition Rosetta Getting the model prototyping Bash scripts for CPU usage and time estimation Serial experiments Parallel Experiments Results
GStreamer
Exploring Rosetta element for character recognition Example pipelines Smart Parking Video inferences Image inferences Video streaming
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License Plate Detection with TinyYOLO version 3 pipeline

Prepare the image, model and labels location:

IMAGE_FILE=<your image>.jpg

MODEL_LOCATION=<your path>/car_plate_tinyYolo_model2.tflite

LABELS_LOCATION=<your path>/labels.txt

Using the CPU

GST_DEBUG="tinyyolov3:LOG,*inf*:DEBUG" gst-launch-1.0 multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true  ! jpegparse ! jpegdec ! videoconvert ! videoscale ! videorate ! queue ! net.sink_model tinyyolov3 name=net number-of-classes=1 model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS_LOCATION)"

Using the NPU backend:

GST_DEBUG="tinyyolov3:LOG,*inf*:DEBUG" gst-launch-1.0 multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true  ! jpegparse ! jpegdec ! videoconvert ! videoscale ! videorate ! queue ! net.sink_model tinyyolov3 name=net number-of-classes=1 model-location=$MODEL_LOCATION backend=nnapi labels="$(cat $LABELS_LOCATION)"

0:00:21.128664525   624 0xaaab0d856aa0 LOG               tinyyolov3 gsttinyyolov3.c:292:gst_tinyyolov3_postprocess:<net> Postprocess Meta
0:00:21.128757025   624 0xaaab0d856aa0 LOG               tinyyolov3 gsttinyyolov3.c:300:gst_tinyyolov3_postprocess:<net> Number of predictions: 1
0:00:21.128851651   624 0xaaab0d856aa0 LOG               tinyyolov3 gstinferencedebug.c:38:gst_inference_print_predictions: 
{
  "id" : 40,
  "enabled" : "True",
  "bbox" : {
    "x" : 0,
    "y" : 0,
    "width" : 416,
    "height" : 416
  },
  "classes" : [
    
  ],
  "predictions" : [
    {
      "id" : 41,
      "enabled" : "True",
      "bbox" : {
        "x" : 93,
        "y" : 110,
        "width" : 242,
        "height" : 209
      },
      "classes" : [
        {
          "Id" : 40,
          "Class" : 0,
          "Label" : "license",
          "Probability" : "1.000000",
          "Classes" : 1
        } 
      ],
      "predictions" : [
        
      ]
    }
  ]
}
0:00:21.129048153   624 0xaaab0d878d30 INFO          videoinference gstvideoinference.c:454:gst_video_inference_stop:<net> Stopping video inference

License Plate Recognition with Rosetta model pipeline

The following license plate is going to be the example for this example:

License Plate example.

IMAGE=<path to your image>.jpg

MODEL_LOCATION=<path to the Rosetta model>/crnn_dr.tflite

GST_DEBUG="*ros*:LOG,*inf*:DEBUG" gst-launch-1.0 multifilesrc location=$IMAGE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! videorate ! queue ! net.sink_model rosetta name=net model-location=$MODEL_LOCATION backend=tflite

NOTE: For this model do not use the NNAPI backend, because the model is not supported in 8 or 16 bit quantization.

The result of this pipe should be something like the following:

0:00:30.457070712   593 0xaaaac0759aa0 LOG                  rosetta gstrosetta.c:252:gst_rosetta_postprocess:<net> Rosetta prediction is done
0:00:30.457089962   593 0xaaaac0759aa0 LOG                  rosetta gstrosetta.c:256:gst_rosetta_postprocess:<net> The phrase is manisa

Smash License Plate detection and License Plate Recognition together

Inferences over images

IMAGE=Cars33.jpg

# TinyYOLO v3 model location
TY_MODEL_LOCATION=car_plate_tinyYolo_model2.tflite

LABELS=labels.txt

# Rosetta model location
RT_MODEL_LOCATION=crnn_dr.tflite

gst-launch-1.0 tinyyolov3 name=net model-location=$TY_MODEL_LOCATION backend=tflite labels="$(cat $LABELS)" multifilesrc location=$IMAGE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! queue ! tee name=t t. ! queue ! videoconvert ! videoscale !  net.sink_model t. ! queue ! videoconvert ! net.sink_bypass  net.src_model ! inferencecrop aspect-ratio=3/1 ! videoconvert ! videoscale ! videorate ! queue ! net1.sink_model rosetta name=net1 model-location=$RT_MODEL_LOCATION backend=tflite

Inference over videos

VIDEO=license_plate_video.mp4

# TinyYOLO v3 model location
TY_MODEL_LOCATION=car_plate_tinyYolo_model2.tflite

LABELS=labels.txt

# Rosetta model location
RT_MODEL_LOCATION=crnn_dr.tflite

gst-launch-1.0 tinyyolov3 name=net model-location=$TY_MODEL_LOCATION backend=tflite labels="$(cat $LABELS)" filesrc location=$VIDEO ! decodebin ! videoconvert ! videoscale ! queue ! tee name=t t. ! queue ! videoconvert ! videoscale !  net.sink_model t. ! queue ! videoconvert ! net.sink_bypass  net.src_model ! queue ! inferencecrop aspect-ratio=3/1 ! videoconvert ! videoscale ! videorate ! queue ! net1.sink_model rosetta name=net1 model-location=$RT_MODEL_LOCATION backend=tflite