GstInference/Example pipelines/NANO: Difference between revisions

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{{GstInference/Head|previous=Example pipelines/PC|next=Example pipelines/TX2|title=GstInference GStreamer pipelines for Jetson NANO}}
{{GstInference/Head|previous=Example pipelines/PC|next=Example pipelines/TX2|title=GstInference GStreamer pipelines for Jetson NANO}}
</noinclude>
</noinclude>
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<br>
<br>
<table>
<tr>
<td><div class="clear; float:right">__TOC__</div></td>
<td valign=top>
{{GStreamer debug}}
{{GStreamer debug}}
</td>
</table>


== Tensorflow ==
== Tensorflow ==
Line 26: Line 34:
INPUT_LAYER='input'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
Line 48: Line 58:
INPUT_LAYER='input'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
Line 68: Line 80:
INPUT_LAYER='input'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
Line 89: Line 103:
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
OUTPUT_LAYER='InceptionV4/Logits/Predictions'
LABELS='imagenet_labels.txt'
LABELS='imagenet_labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
gst-launch-1.0 \
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
Line 98: Line 114:


*Output  
*Output  
[[File:Inceptionv2 barber.png|center|thumb|inceptionv2_barberchair]]
[[File:Inceptionv2 barber.png|center|thumb|inceptionv2_barberchair|link=]]


=== InceptionV1 ===
=== InceptionV1 ===
Line 108: Line 124:
<br>
<br>
'''Server Pipeline''' which runs on the host PC
'''Server Pipeline''' which runs on the host PC
* You will need to install a RidgeRun proprietary [https://developer.ridgerun.com/wiki/index.php?title=GstRtspSink gst-rtsp-sink] plugin on the PC. Please [https://developer.ridgerun.com/wiki/index.php?title=GstInference/Contact_us contact Ridegrun]   
* You will need to install a RidgeRun proprietary [[GstRtspSink | gst-rtsp-sink]] plugin on the PC. Please [[GstInference/Contact_us | contact Ridegrun]]   


<syntaxhighlight lang=bash>
<syntaxhighlight lang=bash>
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'''Install dependencies on the NANO board'''
'''Install dependencies on the NANO board'''
<syntaxhighlight lang="bash" line='line' style="background-color:cornsilk">
<!----
<syntaxhighlight lang="bash" line='line' style="background-color:FFFF66">
--->
<syntaxhighlight lang="bash" line="line" style="background-color:#FFFF66; color:blue;">
sudo apt install \
sudo apt install \
libgstrtspserver-1.0-dev \
libgstrtspserver-1.0-dev \
Line 140: Line 159:
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
export CUDA_VISIBLE_DEVICES=-1
export CUDA_VISIBLE_DEVICES=-1
</syntaxhighlight>
<syntaxhighlight lang=bash>
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=$OUTPUT_LAYER
GST_DEBUG=inceptionv1:6 gst-launch-1.0 -e rtspsrc location="rtsp://<server_ip_address>:5000/stream1" ! queue ! rtph265depay ! queue ! h265parse ! queue ! omxh265dec ! queue ! nvvidconv ! queue ! net.sink_model inceptionv1 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
</syntaxhighlight>
</syntaxhighlight>
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INPUT_LAYER='input/Placeholder'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
OUTPUT_LAYER='add_8'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
Line 180: Line 203:
INPUT_LAYER='input/Placeholder'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
OUTPUT_LAYER='add_8'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
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INPUT_LAYER='input/Placeholder'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
OUTPUT_LAYER='add_8'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
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OUTPUT_LAYER='add_8'
OUTPUT_LAYER='add_8'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
gst-launch-1.0 \
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
Line 230: Line 259:


*Output
*Output
[[File:TinyYolo barber chair label.png|center|thumb|tinyYolo barber chair by tinyYolo]]
[[File:TinyYolo barber chair label.png|center|thumb|tinyYolo barber chair by tinyYolo|link=]]


===FaceNet===
===FaceNet===
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LABELS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/labels.txt'
LABELS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/labels.txt'
EMBEDDINGS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/embeddings.txt'
EMBEDDINGS='$PATH_TO_GST_INFERENCE_ROOT_DIR/tests/examples/embedding/embeddings/embeddings.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
gst-launch-1.0 \
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
Line 266: Line 297:
MODEL_LOCATION='graph_inceptionv4.tflite'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
Line 287: Line 320:
MODEL_LOCATION='graph_inceptionv4.tflite'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
Line 306: Line 341:
MODEL_LOCATION='graph_inceptionv4.tflite'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
GST_DEBUG=inceptionv4:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
Line 325: Line 362:
MODEL_LOCATION='graph_inceptionv4.tflite'
MODEL_LOCATION='graph_inceptionv4.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
gst-launch-1.0 \
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
Line 334: Line 373:


*Output  
*Output  
[[File:Inceptionv2 barber.png|center|thumb|inceptionv2_barberchair]]
[[File:Inceptionv2 barber.png|center|thumb|inceptionv2_barberchair|link=]]


=== InceptionV1 ===
=== InceptionV1 ===
Line 344: Line 383:
<br>
<br>
'''Server Pipeline''' which runs on the host PC
'''Server Pipeline''' which runs on the host PC
* You will need to install a RidgeRun proprietary [https://developer.ridgerun.com/wiki/index.php?title=GstRtspSink gst-rtsp-sink] plugin on the PC. Please [https://developer.ridgerun.com/wiki/index.php?title=GstInference/Contact_us contact Ridegrun]
* You will need to install a RidgeRun proprietary [[GstRtspSink | gst-rtsp-sink]] plugin on the PC. Please [[GstInference/Contact_us | contact Ridegrun]]


<syntaxhighlight lang=bash>
<syntaxhighlight lang=bash>
Line 360: Line 399:


'''Install dependencies on the NANO board'''
'''Install dependencies on the NANO board'''
<!-----
<syntaxhighlight lang="bash" line='line' style="background-color:cornsilk">
<syntaxhighlight lang="bash" line='line' style="background-color:cornsilk">
---->
<syntaxhighlight lang="bash" line="line" style="background-color:#FFFF66; color:blue;">
sudo apt install \
sudo apt install \
libgstrtspserver-1.0-dev \
libgstrtspserver-1.0-dev \
Line 375: Line 417:
LABELS='labels.txt'
LABELS='labels.txt'
export CUDA_VISIBLE_DEVICES=-1
export CUDA_VISIBLE_DEVICES=-1
</syntaxhighlight>
<syntaxhighlight lang=bash>
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 $LABELS)"
GST_DEBUG=inceptionv1:6 gst-launch-1.0 -e rtspsrc location="rtsp://<server_ip_address>:5000/stream1" ! queue ! rtph265depay ! queue ! h265parse ! queue ! omxh265dec ! queue ! nvvidconv ! queue ! net.sink_model inceptionv1 name=net model-location=$MODEL_LOCATION backend=tflite labels="$(cat $LABELS)"
</syntaxhighlight>
</syntaxhighlight>
Line 397: Line 441:
MODEL_LOCATION='graph_tinyyolov2.tflite'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
multifilesrc location=$IMAGE_FILE ! jpegparse ! nvjpegdec ! 'video/x-raw' ! nvvidconv ! 'video/x-raw(memory:NVMM),format=NV12' ! nvvidconv ! queue ! net.sink_model \
Line 413: Line 459:
MODEL_LOCATION='graph_tinyyolov2.tflite'
MODEL_LOCATION='graph_tinyyolov2.tflite'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
filesrc location=$VIDEO_FILE ! qtdemux name=demux ! h264parse ! omxh264dec ! nvvidconv ! queue ! net.sink_model \
Line 430: Line 478:
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
LABELS='labels.txt'
LABELS='labels.txt'
</syntaxhighlight>
<syntaxhighlight lang=bash>
gst-launch-1.0 \
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! tee name=t \
Line 439: Line 489:


*Output
*Output
[[File:TinyYolo barber chair label.png|center|thumb|tinyYolo barber chair by tinyYolo]]
[[File:TinyYolo barber chair label.png|center|thumb|tinyYolo barber chair by tinyYolo|link=]]




Latest revision as of 12:02, 21 May 2024




Previous: Example pipelines/PC Index Next: Example pipelines/TX2


GstInference
GstInference
Introduction
Getting started
Supported architectures

InceptionV1

InceptionV3

YoloV2

AlexNet
Supported backends

Caffe

Metadata and Signals
Overlay Elements
Utils Elements
Legacy pipelines
Example pipelines
Example applications
Benchmarks
Model Zoo
Project Status
Contact Us

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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