Difference between revisions of "GstInference/Example pipelines with hierarchical metadata/PC"
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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=$OUTPUT_LAYER net.src_model ! inferencefilter filter-class=8 ! inferencedebug ! fakesink | GST_DEBUG=2,*inferencedebug*:6 gst-launch-1.0 v4l2src device=/dev/video0 ! videoconvert ! tee name=t t. ! videoscale ! queue ! net.sink_model t. ! queue ! net.sink_bypass tinyyolov2 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER net.src_model ! inferencefilter filter-class=8 ! inferencedebug ! fakesink | ||
| − | <syntaxhighlight> | + | </syntaxhighlight> |
* Output | * Output | ||
Revision as of 13:53, 21 February 2020
Tensorflow
Inceptionv1
Image file
- Get the graph used on this example from this link
- You will need a image file from one of ImageNet classes
- Pipeline
IMAGE_FILE=cat.jpg
MODEL_LOCATION='graph_inceptionv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
LABELS='imagenet_labels.txt'
GST_DEBUG=inceptionv1:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv1 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:00.626529976 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:150:gst_inceptionv1_preprocess:<net> Preprocess
0:00:00.643145025 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:162:gst_inceptionv1_postprocess_old:<net> Postprocess
0:00:00.643180120 6700 0x55a306b258a0 LOG inceptionv1 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 3804 : (4.191162)
0:00:00.643186095 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:187:gst_inceptionv1_postprocess_new:<net> Postprocess Meta
0:00:00.643211153 6700 0x55a306b258a0 LOG inceptionv1 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 7,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 14
Class : 3804
Label : (null)
Probability : 4.191162
Classes : 4004
},
],
predictions : [
]
}
Video file
- Get the graph used on this example from this link
- You will need a video file from one of ImageNet classes
- Pipeline
VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
LABELS='imagenet_labels.txt'
GST_DEBUG=inceptionv1:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! decodebin ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv1 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:00.881389256 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:150:gst_inceptionv1_preprocess:<net> Preprocess
0:00:00.898481750 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:162:gst_inceptionv1_postprocess_old:<net> Postprocess
0:00:00.898515118 6700 0x55a306b258a0 LOG inceptionv1 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 1016 : (4.182041)
0:00:00.898521200 6700 0x55a306b258a0 LOG inceptionv1 gstinceptionv1.c:187:gst_inceptionv1_postprocess_new:<net> Postprocess Meta
0:00:00.898546079 6700 0x55a306b258a0 LOG inceptionv1 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 22,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 44
Class : 1016
Label : (null)
Probability : 4.182041
Classes : 4004
},
],
predictions : [
]
}
Camera stream
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
LABELS='imagenet_labels.txt'
GST_DEBUG=inceptionv1:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv1 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:03.858432794 6899 0x558a68bf0e80 LOG inceptionv1 gstinceptionv1.c:150:gst_inceptionv1_preprocess:<net> Preprocess
0:00:03.875012119 6899 0x558a68bf0e80 LOG inceptionv1 gstinceptionv1.c:162:gst_inceptionv1_postprocess_old:<net> Postprocess
0:00:03.875053519 6899 0x558a68bf0e80 LOG inceptionv1 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 3022 : (9897291000005358165649701398904832.000000)
0:00:03.875061545 6899 0x558a68bf0e80 LOG inceptionv1 gstinceptionv1.c:187:gst_inceptionv1_postprocess_new:<net> Postprocess Meta
0:00:03.875089371 6899 0x558a68bf0e80 LOG inceptionv1 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 93,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 186
Class : 3022
Label : (null)
Probability : 9897291000005358165649701398904832.000000
Classes : 4004
},
],
predictions : [
]
}
Visualization with inference overlay
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv1_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV1/Logits/Predictions/Reshape_1'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! videoconvert ! tee name=t \
t. ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv1 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false
Inceptionv2
Image file
- Get the graph used on this example from this link
- You will need a image file from one of ImageNet classes
- Pipeline
IMAGE_FILE=cat.jpg
MODEL_LOCATION='graph_inceptionv2_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='Softmax'
GST_DEBUG=inceptionv2:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! videorate ! queue ! net.sink_model \
inceptionv2 name=net model-location=$MODEL_LOCATION backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:01.167111306 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:217:gst_inceptionv2_preprocess:<net> Preprocess
0:00:01.190633209 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:229:gst_inceptionv2_postprocess_old:<net> Postprocess
0:00:01.190667056 12853 0x55bc0eeb9770 LOG inceptionv2 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 2058 : (33799702613643740784668592694586507264.000000)
0:00:01.190673102 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:254:gst_inceptionv2_postprocess_new:<net> Postprocess Meta
0:00:01.190699590 12853 0x55bc0eeb9770 LOG inceptionv2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 23,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 46
Class : 2058
Label : (null)
Probability : 33799702613643740784668592694586507264.000000
Classes : 4004
},
],
predictions : [
]
}
Video file
- Get the graph used on this example from this link
- You will need a video file from one of ImageNet classes
- Pipeline
VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv2_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='Softmax'
LABELS='imagenet_labels.txt'
GST_DEBUG=inceptionv2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! decodebin ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv2 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:01.167111306 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:217:gst_inceptionv2_preprocess:<net> Preprocess
0:00:01.190633209 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:229:gst_inceptionv2_postprocess_old:<net> Postprocess
0:00:01.190667056 12853 0x55bc0eeb9770 LOG inceptionv2 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 2058 : (33799702613643740784668592694586507264.000000)
0:00:01.190673102 12853 0x55bc0eeb9770 LOG inceptionv2 gstinceptionv2.c:254:gst_inceptionv2_postprocess_new:<net> Postprocess Meta
0:00:01.190699590 12853 0x55bc0eeb9770 LOG inceptionv2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 23,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 46
Class : 2058
Label : (null)
Probability : 33799702613643740784668592694586507264.000000
Classes : 4004
},
],
predictions : [
]
}
Camera stream
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv2_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='Softmax'
LABELS='imagenet_labels.txt'
GST_DEBUG=inceptionv2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv2 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:01.647715258 12963 0x55be7ee48a80 LOG inceptionv2 gstinceptionv2.c:217:gst_inceptionv2_preprocess:<net> Preprocess
0:00:01.673402231 12963 0x55be7ee48a80 LOG inceptionv2 gstinceptionv2.c:229:gst_inceptionv2_postprocess_old:<net> Postprocess
0:00:01.673436695 12963 0x55be7ee48a80 LOG inceptionv2 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 3364 : (3.972995)
0:00:01.673445162 12963 0x55be7ee48a80 LOG inceptionv2 gstinceptionv2.c:254:gst_inceptionv2_postprocess_new:<net> Postprocess Meta
0:00:01.673476625 12963 0x55be7ee48a80 LOG inceptionv2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 26,
enabled : True,
bbox : {
x : 0
y : 0
width : 224
height : 224
},
classes : [
{
Id : 52
Class : 3364
Label : (null)
Probability : 3.972995
Classes : 4004
},
],
predictions : [
]
}
Visualization with inference overlay
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv2_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='Softmax'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! videoconvert ! tee name=t \
t. ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv2 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false
Inceptionv3
Image file
- Get the graph used on this example from this link
- You will need a image file from one of ImageNet classes
- Pipeline
IMAGE_FILE=cat.jpg
MODEL_LOCATION='graph_inceptionv3_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV3/Predictions/Reshape_1
GST_DEBUG=inceptionv3:6 gst-launch-1.0 \
multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv3 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:01.696274261 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:149:gst_inceptionv3_preprocess:<net> Preprocess
0:00:01.751348188 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:161:gst_inceptionv3_postprocess_old:<net> Postprocess
0:00:01.751379427 13153 0x55c06386e8a0 LOG inceptionv3 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 1353 : (9.000000)
0:00:01.751385353 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:186:gst_inceptionv3_postprocess_new:<net> Postprocess Meta
0:00:01.751511065 13153 0x55c06386e8a0 LOG inceptionv3 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 16,
enabled : True,
bbox : {
x : 0
y : 0
width : 299
height : 299
},
classes : [
{
Id : 32
Class : 1353
Label : (null)
Probability : 9.000000
Classes : 4004
},
],
predictions : [
]
}
Video file
- Get the graph used on this example from this link
- You will need a video file from one of ImageNet classes
- Pipeline
VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_inceptionv3_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV3/Predictions/Reshape_1
GST_DEBUG=inceptionv3:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! decodebin ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv3 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:01.643494169 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:149:gst_inceptionv3_preprocess:<net> Preprocess
0:00:01.696036720 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:161:gst_inceptionv3_postprocess_old:<net> Postprocess
0:00:01.696072019 13153 0x55c06386e8a0 LOG inceptionv3 gstinferencedebug.c:74:gst_inference_print_highest_probability:<net> Highest probability is label 2543 : (5.398693)
0:00:01.696079025 13153 0x55c06386e8a0 LOG inceptionv3 gstinceptionv3.c:186:gst_inceptionv3_postprocess_new:<net> Postprocess Meta
0:00:01.696208280 13153 0x55c06386e8a0 LOG inceptionv3 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 15,
enabled : True,
bbox : {
x : 0
y : 0
width : 299
height : 299
},
classes : [
{
Id : 30
Class : 2543
Label : (null)
Probability : 5.398693
Classes : 4004
},
],
predictions : [
]
}
Camera stream
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv3_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV3/Predictions/Reshape_1'
GST_DEBUG=inceptionv3:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! videoconvert ! videoscale ! queue ! net.sink_model \
inceptionv3 name=net model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend=tensorflow backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:14.614862363 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:199:gst_inceptionv3_preprocess:<net> Preprocess
0:00:15.737842669 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:231:gst_inceptionv3_postprocess:<net> Postprocess
0:00:15.737912053 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:252:gst_inceptionv3_postprocess:<net> Highest probability is label 838 : (0,105199)
0:00:15.738007534 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:199:gst_inceptionv3_preprocess:<net> Preprocess
0:00:16.855603761 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:231:gst_inceptionv3_postprocess:<net> Postprocess
0:00:16.855673578 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:252:gst_inceptionv3_postprocess:<net> Highest probability is label 838 : (0,093981)
0:00:16.855768558 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:199:gst_inceptionv3_preprocess:<net> Preprocess
0:00:17.980784789 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:231:gst_inceptionv3_postprocess:<net> Postprocess
0:00:17.980849612 27227 0x19cd4a0 LOG inceptionv3 gstinceptionv3.c:252:gst_inceptionv3_postprocess:<net> Highest probability is label 838 : (0,077824)
Visualization with inference overlay
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_inceptionv3_tensorflow.pb'
INPUT_LAYER='input'
OUTPUT_LAYER='InceptionV3/Predictions/Reshape_1'
LABELS='imagenet_labels.txt'
gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw, width=1280, height=720" ! videoconvert ! tee name=t \
t. ! videoscale ! queue ! net.sink_model \
t. ! queue ! net.sink_bypass \
inceptionv3 name=net model-location=$MODEL_LOCATION backend=tensorflow labels=$(cat $LABELS) backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER \
net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false
TinyYolov2
Image file
- Get the graph used on this example from this link
- You will need an image file from one of TinyYOLO classes
- Pipeline
CAMERA='/dev/video0' MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb' INPUT_LAYER='input/Placeholder' OUTPUT_LAYER='add_8' LABELS=labels.txt
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \ multifilesrc location=$IMAGE_FILE start-index=0 stop-index=0 loop=true ! jpegparse ! jpegdec ! videoconvert ! videoscale ! videorate ! tee name=t t. ! queue ! videoconvert ! videoscale ! net.sink_model t. ! queue ! videoconvert ! video/x-raw,format=RGB ! net.sink_bypass tinyyolov2 new-meta=true name=net backend=tensorflow model-location=$MODEL_LOCATION labels=$(cat labels.txt) backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false </syntaxhighlight>
- Output
0:00:03.050336570 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:286:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:03.097045162 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:325:gst_tinyyolov2_postprocess_old:<net> Postprocess
0:00:03.097080665 8194 0x55b131f7aad0 LOG tinyyolov2 gstinferencedebug.c:93:gst_inference_print_boxes:<net> Box: [class:7, x:87.942292, y:102.912900, width:244.945642, height:285.130143, prob:16.271288]
0:00:03.097087457 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:359:gst_tinyyolov2_postprocess_new:<net> Postprocess Meta
0:00:03.097095173 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:366:gst_tinyyolov2_postprocess_new:<net> Number of predictions: 1
0:00:03.097117947 8194 0x55b131f7aad0 LOG tinyyolov2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 346,
enabled : True,
bbox : {
x : 0
y : 0
width : 416
height : 416
},
classes : [
],
predictions : [
{
id : 347,
enabled : True,
bbox : {
x : 87
y : 102
width : 244
height : 285
},
classes : [
{
Id : 258
Class : 7
Label : cat
Probability : 16.271288
Classes : 20
},
],
predictions : [
]
},
]
}
Video file
- Get the graph used on this example from this link
- You will need a video file from one of TinyYOLO classes
- Pipeline
VIDEO_FILE='cat.mp4'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS=labels.txt
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
filesrc location=$VIDEO_FILE ! decodebin ! videoconvert ! videoscale ! queue ! tee name=t t. ! queue ! videoconvert ! videoscale ! net.sink_model t. ! queue ! videoconvert ! video/x-raw,format=RGB ! net.sink_bypass tinyyolov2 new-meta=true name=net backend=tensorflow model-location=$MODEL_LOCATION labels=$(cat $LABELS) backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false
- Output
0:00:02.992422192 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:286:gst_tinyyolov2_preprocess:<net> Preprocess
0:00:03.048734915 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:325:gst_tinyyolov2_postprocess_old:<net> Postprocess
0:00:03.048770315 8194 0x55b131f7aad0 LOG tinyyolov2 gstinferencedebug.c:93:gst_inference_print_boxes:<net> Box: [class:7, x:87.942292, y:102.912900, width:244.945642, height:285.130143, prob:16.271288]
0:00:03.048776786 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:359:gst_tinyyolov2_postprocess_new:<net> Postprocess Meta
0:00:03.048784401 8194 0x55b131f7aad0 LOG tinyyolov2 gsttinyyolov2.c:366:gst_tinyyolov2_postprocess_new:<net> Number of predictions: 1
0:00:03.048805819 8194 0x55b131f7aad0 LOG tinyyolov2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 338,
enabled : True,
bbox : {
x : 0
y : 0
width : 416
height : 416
},
classes : [
],
predictions : [
{
id : 339,
enabled : True,
bbox : {
x : 87
y : 102
width : 244
height : 285
},
classes : [
{
Id : 252
Class : 7
Label : cat
Probability : 16.271288
Classes : 20
},
],
predictions : [
]
},
]
}
Camera stream
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS=labels.txt
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw" ! tee name=t t. ! queue ! videoconvert ! videoscale ! net.sink_model t. ! queue ! videoconvert ! video/x-raw,format=RGB ! net.sink_bypass tinyyolov2 new-meta=true name=net backend=tensorflow model-location=$MODEL_LOCATION labels=$(cat labels.txt) backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER
- Output
0:00:02.493931842 8814 0x557dfec450f0 LOG tinyyolov2 gsttinyyolov2.c:286:gst_tinyyolov2_preprocess:<net> Preprocess
^Chandling interrupt.
Interrupt: Stopping pipeline ...
Execution ended after 0:00:01.951234668
Setting pipeline to PAUSED ...
Setting pipeline to READY ...
0:00:02.541405794 8814 0x557dfec450f0 LOG tinyyolov2 gsttinyyolov2.c:325:gst_tinyyolov2_postprocess_old:<net> Postprocess
0:00:02.541440570 8814 0x557dfec450f0 LOG tinyyolov2 gstinferencedebug.c:93:gst_inference_print_boxes:<net> Box: [class:14, x:82.788036, y:126.779761, width:250.107193, height:300.441625, prob:12.457702]
0:00:02.541447102 8814 0x557dfec450f0 LOG tinyyolov2 gsttinyyolov2.c:359:gst_tinyyolov2_postprocess_new:<net> Postprocess Meta
0:00:02.541454350 8814 0x557dfec450f0 LOG tinyyolov2 gsttinyyolov2.c:366:gst_tinyyolov2_postprocess_new:<net> Number of predictions: 1
0:00:02.541476722 8814 0x557dfec450f0 LOG tinyyolov2 gstinferencedebug.c:111:gst_inference_print_predictions:
{
id : 177,
enabled : True,
bbox : {
x : 0
y : 0
width : 416
height : 416
},
classes : [
],
predictions : [
{
id : 178,
enabled : True,
bbox : {
x : 82
y : 126
width : 250
height : 300
},
classes : [
{
Id : 101
Class : 14
Label : person
Probability : 12.457702
Classes : 20
},
],
predictions : [
]
},
]
}
Visualization with inference overlay
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
CAMERA='/dev/video0'
MODEL_LOCATION='graph_tinyyolov2_tensorflow.pb'
INPUT_LAYER='input/Placeholder'
OUTPUT_LAYER='add_8'
LABELS='labels.txt'
GST_DEBUG=tinyyolov2:6 gst-launch-1.0 \
v4l2src device=$CAMERA ! "video/x-raw" ! tee name=t t. ! queue ! videoconvert ! videoscale ! net.sink_model t. ! queue ! videoconvert ! video/x-raw,format=RGB ! net.sink_bypass tinyyolov2 new-meta=true name=net backend=tensorflow model-location=$MODEL_LOCATION labels=$(cat labels.txt) backend::input-layer=$INPUT_LAYER backend::output-layer=$OUTPUT_LAYER net.src_bypass ! inferenceoverlay ! videoconvert ! queue ! xvimagesink async=false sync=false qos=false
Using inference filter
- Get the graph used on this example from this link
- You will need a v4l2 compatible camera
- Pipeline
GST_DEBUG=2,*inferencedebug*:6 gst-launch-1.0 v4l2src device=/dev/video0 ! videoconvert ! tee name=t t. ! videoscale ! queue ! net.sink_model t. ! queue ! net.sink_bypass tinyyolov2 name=net model-location=$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 : [
]
},
]
}
