I want to build a pipeline like this: video_decode_bin->nvstreammux->nvpreprocess(set rect roi on frame)->pgie(extract crnn feature with rect area).
nvpreprocess property set this:
[property]
enable=1
target-unique-ids=10
network-input-order=0
process-on-frame=1
unique-id=5
gpu-id=0
maintain-aspect-ratio=1
symmetric-padding=1
processing-width=160
processing-height=32
scaling-buf-pool-size=6
tensor-buf-pool-size=6
network-input-shape=1;1;32;160
network-color-format=2
tensor-data-type=5
tensor-name=input
3=NVBUF_MEM_CUDA_UNIFIED
scaling-pool-memory-type=0
2=NvBufSurfTransformCompute_VIC
scaling-pool-compute-hw=1
2=NvBufSurfTransformInter_Algo1
scaling-filter=0
custom-lib-path=/opt/nvidia/deepstream/deepstream/lib/gst-plugins/libcustom2d_preprocess.so
custom-tensor-preparation-function=CustomTensorPreparation
[user-configs]
pixel-normalization-factor=0.020319
offsets=149.94
[group-0]
src-ids=0
custom-input-transformation-function=CustomAsyncTransformation
process-on-roi=1
draw-roi=1
roi-params-src-0=385;345;66;29;992;357;67;30
input-object-min-width=160
input-object-min-height=32
input-object-max-width=160
input-object-max-height=32
roi_color=0;1;1;1
and pgie property set like this:
[primary-gie]
enable=1
gpu-id=0
input-tensor-meta=1
model-engine-file=./crnn.onnx_b1_dla0_fp16.engine
batch-size=1
gie-unique-id=10
nvbuf-memory-type=0
config-file=config_infer_crnn.txt
[property]
gpu-id=0
#enable-dla=1
#use-dla-core=1
model-color-format=2
net-scale-factor=1.0
onnx-file=crnn.onnx
model-engine-file=crnn.onnx_b1_dla0_fp16.engine
labelfile-path=crnn_num.txt
output-tensor-meta=1
force-implicit-batch-dim=0
batch-size=1
network-mode=2
process-mode=1
network-type=1
output-blob-names=output
classifier-async-mode=0
classifier-type=crnn
how I can get the pgie meta output?