Description
I have successfully tested segmentation using usb cam and masks overlay and detection seems working fine on output frames but I am unable to get the masks and detection as return which I want to use it for post processing.
#!/usr/bin/python
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
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# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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I have checked the API reference link for segnet as well and it says all the functions such ashttps://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet
Almost all function’s having None as returned type. So, please any help to get the detection boxes and masks points as return type ?
I have seen there’s option in object detection but there is so far couldn’t find any option in segmentation.
I will be very thank you.
Environment
**TensorRT **:GPU Type :Nvidia Driver :CUDA 10 :CUDNN 7 :Jetson Nano :Python3 :TensorFlow 1.15 :PyTorch :
Relevant Files
#!/usr/bin/python
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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Steps To Reproduce
<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg">
<p align="right"><sup><a href="jetpack-setup-2.md">Back</a> | <a href="imagenet-console-2.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
<br/>
<sup>System Setup</sup></p>
# Building the Project from Source
Provided with the repo is a library of TensorRT-accelerated deep learning networks for image recognition, object detection with localization (i.e. bounding boxes), and semantic segmentation. This inferencing library (`libjetson-inference`) is intended to be built & run on the Jetson, and includes support for both C++ and Python.
Various pre-trained DNN models are automatically downloaded to get you up and running quickly. It's also setup to accept customized models that you may have trained yourself, including support for Caffe, TensorFlow UFF, and ONNX.
The latest source can be obtained from [GitHub](http://github.com/dusty-nv/jetson-inference) and compiled onboard Jetson Nano, Jetson TX1/TX2, and Jetson AGX Xavier once they have been [flashed with JetPack](jetpack-setup-2.md) or setup with the pre-populated [SD card image](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#write) for Jetson Nano.
### Quick Reference
Here's a condensed form of the commands to download, build, and install the project:
``` bash
$ sudo apt-get update
$ sudo apt-get install git cmake libpython3-dev python3-numpy
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