GstInference GStreamer pipelines for Jetson NANO

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Coral from Google




Problems running the pipelines shown on this page?
Please see our GStreamer Debugging guide for help.

Tensorflow

InceptionV4

  • Get the graph used on this example from this link.
  • You will need an image file from one of ImageNet classes.
  • Use the following pipelines as examples for different scenarios.

Image file

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graphs/InceptionV4_TensorFlow/graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:41.102961125  9500   0x55cd3e54a0 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,651213)
0:00:41.103261600  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:41.414504525  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:41.415032923  9500   0x55cd3e54a0 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,651213)
0:00:41.415468297  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:41.726504445  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess

Video file

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graphs/InceptionV4_TensorFlow/graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:43.428868204  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:43.436573728  9619   0x55b19b6b70 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,875079)
0:00:43.473135944  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:43.861247785  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:43.861550447  9619   0x55b19b6b70 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,872448)

Camera stream

CAMERA='/dev/video0'
MODEL_LOCATION='graphs/InceptionV4_TensorFlow/graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:47.149540519  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:47.149877140  9748   0x5592110b20 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 283 : (0,702133)
0:00:47.150562517  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:47.460348086  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:47.460709916  9748   0x5592110b20 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 283 : (0,705862)

Visualization with classification overlay

CAMERA='/dev/video0'
MODEL_LOCATION='graphs/InceptionV4_TensorFlow/graph_inceptionv4_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! classificationoverlay labels="$(cat $LABELS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false
  • Output
inceptionv2_barberchair

InceptionV1

RTSP Camera stream

  • Get the graph used on this example from this link
  • You will need a v4l2 compatible camera


Server Pipeline which runs on the host PC 

gst-launch-1.0 -e v4l2src device=/dev/video0 ! video/x-raw, format=YUY2, width=640, height=480, framerate=30/1 ! videoconvert ! video/x-raw, format=I420, width=640, height=480, framerate=30/1 ! queue ! x265enc option-string="keyint=30:min-keyint=30:repeat-headers=1" ! video/x-h265,  width=640, height=480, mapping=/stream1 ! queue ! rtspsink service=5000
  • Output
Setting pipeline to PAUSED ...
Pipeline is live and does not need PREROLL ...
Setting pipeline to PLAYING ...
New clock: GstSystemClock
Redistribute latency...

Install dependencies on the NANO board 

sudo apt install \
libgstrtspserver-1.0-dev \
libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
libgstreamer-plugins-good1.0-dev \
libgstreamer-plugins-bad1.0-dev


Client Pipeline which runs on the NANO board 

CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
export CUDA_VISIBLE_DEVICES=-1
GST_DEBUG=inceptionv1:6 gst-launch-1.0 -e rtspsrc location="rtsp://<server_ip_address>:5000/stream1" ! queue ! rtph265depay ! queue ! h265parse ! queue ! omxh265dec ! queue ! nvvidconv ! queue ! net.sink_model inceptionv1 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:08.679606626 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.295041)
0:00:08.679695321 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:142:gst_inceptionv1_preprocess:<net> Preprocess
0:00:08.892169471 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:153:gst_inceptionv1_postprocess:<net> Postprocess
0:00:08.892256499 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.256458)
0:00:08.892378058 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:142:gst_inceptionv1_preprocess:<net> Preprocess
0:00:09.101159620 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:153:gst_inceptionv1_postprocess:<net> Postprocess
0:00:09.101244877 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.243692)

TinyYoloV2

  • Get the graph used on this example from this link
  • You will need an image file from one of TinyYOLO classes

Image file

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graphs/TinyYoloV2_TensorFlow/graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:24.558985002  9909   0x557d3278a0 LOG               tinyyolov2 gsttinyyolov2.c:288:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:24.576012429  9909   0x557d3278a0 LOG               tinyyolov2 gstinferencedebug.c:92:gst_inference_print_boxes:<net> Box: [class:7, x:5,710080, y:115,575158, width:345,341579, height:304,490976, prob:14,346013]

Video file

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graphs/TinyYoloV2_TensorFlow/graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:07.245722660 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.360377432 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.360586455 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.105452, y:-9.139365, width:445.139551, height:487.967720, prob:14.592537]

Camera stream

  • Get the graph used on this example from this link
  • You will need a camera compatible with Nvidia Libargus API or V4l2.
  • LABELS and EMBEDDINGS files are in $PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings.
CAMERA='/dev/video0'
MODEL_LOCATION='graphs/TinyYoloV2_TensorFlow/graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER  backend::output-layer=$OUTPUT_LAYER
  • Output
0:00:39.754924355  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:39.876816786  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:39.876914225  5030      0x10ee590 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:4, x:147.260736, y:116.184709, width:134.389472, height:245.113627, prob:8.375733]

Visualization with detection overlay

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! detectionoverlay labels="$(cat $LABELS)" font-scale=1 thickness=2 ! videoconvert ! xvimagesink sync=false
  • Output
tinyYolo barber chair by tinyYolo

FaceNet

Visualization with detection overlay

  • Get the graph used on this example from this link
  • You will need a camera compatible with Nvidia Libargus API or V4l2.
  • LABELS and EMBEDDINGS files are in $PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings.
CAMERA='/dev/video1'
MODEL_LOCATION='graph_facenetv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='output'
LABELS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/labels.txt'
EMBEDDINGS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/embeddings.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
facenetv1 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! videoconvert ! embeddingoverlay labels="$(cat $LABELS)" embeddings="$(cat $EMBEDDINGS)" font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false

Tensorflow Lite

InceptionV4

  • Get the graph used on this example from this link.
  • You will need an image file from one of ImageNet classes.
  • Use the following pipelines as examples for different scenarios.

Image file

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:41.102961125  9500   0x55cd3e54a0 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,651213)
0:00:41.103261600  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:41.414504525  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:41.415032923  9500   0x55cd3e54a0 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,651213)
0:00:41.415468297  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:41.726504445  9500   0x55cd3e54a0 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess

Video file

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:43.428868204  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:43.436573728  9619   0x55b19b6b70 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,875079)
0:00:43.473135944  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:43.861247785  9619   0x55b19b6b70 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:43.861550447  9619   0x55b19b6b70 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 282 : (0,872448)

Camera stream

CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:47.149540519  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:47.149877140  9748   0x5592110b20 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 283 : (0,702133)
0:00:47.150562517  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:208:gst_inceptionv4_preprocess:<net> Preprocess
0:00:47.460348086  9748   0x5592110b20 LOG              inceptionv4 gstinceptionv4.c:219:gst_inceptionv4_postprocess:<net> Postprocess
0:00:47.460709916  9748   0x5592110b20 LOG              inceptionv4 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 283 : (0,705862)

Visualization with classification overlay

CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv4 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)" \
net.src_bypass ! videoconvert ! inferenceoverlay font-scale=4 thickness=4 ! videoconvert ! xvimagesink sync=false
  • Output
inceptionv2_barberchair

InceptionV1

RTSP Camera stream

  • Get the graph used on this example from this link
  • You will need a v4l2 compatible camera


Server Pipeline which runs on the host PC 

gst-launch-1.0 -e v4l2src device=/dev/video0 ! video/x-raw, format=YUY2, width=640, height=480, framerate=30/1 ! videoconvert ! video/x-raw, format=I420, width=640, height=480, framerate=30/1 ! queue ! x265enc option-string="keyint=30:min-keyint=30:repeat-headers=1" ! video/x-h265,  width=640, height=480, mapping=/stream1 ! queue ! rtspsink service=5000
  • Output
Setting pipeline to PAUSED ...
Pipeline is live and does not need PREROLL ...
Setting pipeline to PLAYING ...
New clock: GstSystemClock
Redistribute latency...

Install dependencies on the NANO board 

sudo apt install \
libgstrtspserver-1.0-dev \
libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
libgstreamer-plugins-good1.0-dev \
libgstreamer-plugins-bad1.0-dev


Client Pipeline which runs on the NANO board 

CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv1.tflite'
LABELS='labels.txt'
export CUDA_VISIBLE_DEVICES=-1
GST_DEBUG=inceptionv1:6 gst-launch-1.0 -e rtspsrc location="rtsp://<server_ip_address>:5000/stream1" ! queue ! rtph265depay ! queue ! h265parse ! queue ! omxh265dec ! queue ! nvvidconv ! queue ! net.sink_model inceptionv1 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:08.679606626 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.295041)
0:00:08.679695321 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:142:gst_inceptionv1_preprocess:<net> Preprocess
0:00:08.892169471 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:153:gst_inceptionv1_postprocess:<net> Postprocess
0:00:08.892256499 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.256458)
0:00:08.892378058 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:142:gst_inceptionv1_preprocess:<net> Preprocess
0:00:09.101159620 10086   0x5599c01cf0 LOG              inceptionv1 gstinceptionv1.c:153:gst_inceptionv1_postprocess:<net> Postprocess
0:00:09.101244877 10086   0x5599c01cf0 LOG              inceptionv1 gstinferencedebug.c:73:gst_inference_print_highest_probability:<net> Highest probability is label 665 : (0.243692)

TinyYoloV2

  • Get the graph used on this example from this link
  • You will need an image file from one of TinyYOLO classes

Image file

IMAGE_FILE='cat.jpg'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:24.558985002  9909   0x557d3278a0 LOG               tinyyolov2 gsttinyyolov2.c:288:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:24.576012429  9909   0x557d3278a0 LOG               tinyyolov2 gstinferencedebug.c:92:gst_inference_print_boxes:<net> Box: [class:7, x:5,710080, y:115,575158, width:345,341579, height:304,490976, prob:14,346013]

Video file

VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
  • Output
0:00:07.245722660 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:479:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:07.360377432 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:501:gst_tinyyolov2_postprocess:<net> Postprocess
0:00:07.360586455 30545       0x5ad000 LOG               tinyyolov2 gsttinyyolov2.c:384:print_top_predictions:<net> Box: [class:7, x:-46.105452, y:-9.139365, width:445.139551, height:487.967720, prob:14.592537]

Visualization with detection overlay

CAMERA='/dev/video1'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
LABELS='labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
t. ! videoconvert ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
tinyyolov2 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"  \
net.src_bypass ! videoconvert ! inferenceoverlay font-scale=1 thickness=2 ! videoconvert ! xvimagesink sync=false
  • Output
tinyYolo barber chair by tinyYolo


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