1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
|
- ################################################################################
- # Copyright (c) 2019-2020, 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
- # DEALINGS IN THE SOFTWARE.
- ################################################################################
- Prerequisites:
- - DeepStreamSDK 5.1
- - Python 3.6
- - Gst-python
- - NumPy package
- - OpenCV package
- To install required packages:
- $ sudo apt update
- $ sudo apt install python3-numpy python3-opencv -y
- To run:
- $ python3 deepstream_segmentation.py <config_file> <jpeg/mjpeg stream> <FOLDER NAME TO SAVE FRAMES>
- This document shall describe the sample deepstream-segmentation application.
- It is meant for simple demonstration of how to use the various DeepStream SDK
- elements in the pipeline and extract meaningful insights from a video stream such
- as segmentation masks and respective color mapping for segmentation visualizaiton.
- This sample creates instance of "nvinfer" element. Instance of
- the "nvinfer" uses TensorRT API to execute inferencing on a model. Using a
- correct configuration for a nvinfer element instance is therefore very
- important as considerable behaviors of the instance are parameterized
- through these configs.
- For reference, here are the config files used for this sample :
- 1. The 4-class segmentation model configured through dstest_segmentation_config_semantic.txt
- 2. The 2-class segmentation model configured through dstest_segmentation_config_industrial.txt
- In this sample, we first create one instance of "nvinfer", referred as the pgie.
- for semantic segmentation, it needs semantic model which can get 4 classes map,
- including backgroud, car, person, bicycle. Then "nvsegvidsual" plugin chooses 4 different
- colors for them and to display. Similarlty for industrial segmentation, it needs industrial
- model which can only get defective area map. Later nvinfer element attach some MetaData
- to the buffer. By attaching the probe function at the end of the pipeline, one can
- extract meaningful information from this inference. Please refer
- the "tiler_src_pad_buffer_probe" function in the sample code. For details on the
- Metadata format, refer to the file "gstnvdsmeta.h". In this probe we demonstrate
- extracting the masks and color mapping for segmentation visualization using opencv
- and numpy.
|