Cambricon CNStream

CNStream is a streaming framework with plug-ins. It is used to connect other modules, includes basic functionality, libraries, and essential elements. CNStream provides following plug-in modules:

source: Supports RTSP, video file, and images（H.264, H.265, and JPEG decoding.）
inference: MLU-based inference accelerator for detection and classification.
osd (On-screen display): Module for highlighting objects and text overlay.
encode: Encodes on CPU.
display: Display the video on screen
tracker: Multi object tracking

Cambricon Dependencies

You can find the cambricon dependencies, including headers and libraries, in the MLU directory.

Quick Start

This section introduces how to quickly build instructions on CNStream and how to develop your own applications based on CNStream. We strongly recommend you to excute pre_required_helper.sh to prepare the environment. If not, please follow below commands.

Required environments

Before building instructions, you need to install the following software:

OpenCV2.4.9+
GFlags2.1.2
GLog0.3.4
Cmake2.8.7+
Live555 // if WITH_RTSP=ON, please run `download_live
SDL22.0.4+ // if build_display=ON

Ubuntu or Debian

If you are using Ubuntu or Debian, run the following commands:

  OpenCV2.4.9+  >>>>>>>>>   sudo apt-get install libopencv-dev
  GFlags2.1.2   >>>>>>>>>   sudo apt-get install libgflags-dev
  GLog0.3.4     >>>>>>>>>   sudo apt-get install libgoogle-glog-dev
  Cmake2.8.7+   >>>>>>>>>   sudo apt-get install cmake
  SDL22.0.4+    >>>>>>>>>   sudo apt-get install libsdl2-dev

Centos

If you are using Centos, run the following commands:

  OpenCV2.4.9+  >>>>>>>>>   sudo yum install opencv-devel.x86_64
  GFlags2.1.2   >>>>>>>>>   sudo yum install gflags.x86_64
  GLog0.3.4     >>>>>>>>>   sudo yum install glog.x86_64
  Cmake2.8.7+   >>>>>>>>>   sudo yum install cmake3.x86_64
  SDL22.0.4+    >>>>>>>>>   sudo yum install SDL2_gfx-devel.x86_64

Build Instructions Using CMake

After finished prerequiste, you can build instructions with the following steps:

Run the following command to save a directory for saving the output.
```
mkdir build       # Create a directory to save the output.
```
A Makefile is generated in the build folder.

Run the following command to generate a script for building instructions.

cd build
cmake ${CNSTREAM_DIR}  # Generate native build scripts.

Cambricon CNStream provides a CMake script (CMakeLists.txt) to build instructions. You can download CMake for free from http://www.cmake.org/.

${CNSTREAM_DIR} specifies the directory where CNStream saves for.

cmake option	range	default	description
build_display	ON / OFF	ON	build display module
build_encode	ON / OFF	ON	build encode module
build_fps_stats	ON / OFF	ON	build fps_stats module
build_inference	ON / OFF	ON	build inference module
build_osd	ON / OFF	ON	build osd module
build_source	ON / OFF	ON	build source module
build_track	ON / OFF	ON	build track module
build_discard_frame	ON / OFF	ON	build discard_frame module
build_tests	ON / OFF	ON	build tests
build_samples	ON / OFF	ON	build samples
build_apps	ON / OFF	ON	build apps
build_test_coverage	ON / OFF	OFF	build test coverage
MLU	MLU270 / MLU220_SOC	MLU270	specify MLU platform
RELEASE	ON / OFF	ON	release / debug
WITH_FFMPEG	ON / OFF	ON	build with FFMPEG
WITH_OPENCV	ON / OFF	ON	build with OPENCV
WITH_CHINESE	ON / OFF	OFF	build with CHINESE
WITH_RTSP	ON / OFF	ON	build with RTSP

If you want to build CNStream samples: a. Run the following command:
```
cmake -Dbuild_samples=ON ${CNSTREAM_DIR}
```
b. Run the following command to add the MLU platform definition. If you are using MLU220 SOC:
```
-DMLU=MLU220_SOC  // build the software support MLU220 soc
```
Run the following command to build instructions:
```
make
```

Samples

Demo Overview

The samples/demo is a cnstream-based target detection demo, which includes the following Plug-in modules：

source: With MLU to decode video streams, such as local video files, rtmp, and rtsp.
inferencer: With MLU for Neural Network Inferencing.
osd: Draw Inferencing results on images.
tracker: Track multi objects.
encoder: Encode images with inferencing results(detection result).
displayer: Displays inferencing results on the screen.
fps statistics: Prints the statistics on the terminal.

In the script run.sh, we set detection_config.json as the config file. If we check the config file, it will be found out that, resnet34_ssd.cambricon is the offline model used for inference, which means, after decoding, the data will be feed to a ssd model. And the results will be shown on the screen.

In addition, see the comments in cnstream/samples/demo/run.sh for details.)

Run samples

To run the CNStream sample:

Follow the steps above to build instructions.

Run the demo using the list below:

cd ${CNSTREAM_DIR}/samples/demo

./run.sh

Best Practices

How to create an application based on CNStream?

you should find a sample from "samples/example/example.cpp",that help developer eassily understand how to develop an application based on cnstream pipeline.

How to replace SSD offline model in a demo?

Modify the value of the model_path in run.sh and replace it with your own SSD offline model path.

How to change the input video file?

Modify the files.list_video file, which is under the cnstream/samples/demo directory, to replace the video path. It is recommended to use an absolute path or use a relative path relative to the executor path.

How to adapt other networks than SSD?

Modify pre-processing(optional). 2. Modify post-processing**.

Prospect Information： Currently, the inferencer plugin in CNStream provides two network preprocessing methods:
Specifies that cpu_preproc preprocesses the input image on the CPU. Applicable to situations where >b cannot complete pre-processing, such as yolov3.
If cpu_preproc is NULL, the MLU is used for pre-processing. The offline model needs to have the ability to reduce the mean and multiply the scale in the pre-processing. You can achieve the purpose by configuring the first-level convolution of the mean_value and std parameters. The inferencer plugin performs color space conversion (yuv various formats to RGBA format) and image reduction before performing offline inferencing.

a. Configure the pre-processing based on foreground information.

If the CPU is used for pre-processing, the corresponding pre-processing function is implemented first. Then modify the cpu_preproc parameter specified when creating the inferencer plugin in the demo, so that it points to the implemented pre-processing function.

b. Configure the post processing.
1. Implement the post-processing:
```
#include <cnstream.hpp>
class MyPostproc : public Postproc, virtual public edk::ReflexObjectEx<Postproc> {
 public:
  void Execute(std::vector<std::pair<float*, uint64_t>> net_outputs, CNFrameInfoPtr data) override {
    /*
     net_outputs : the result of the inference
     net_outputs[i].first : The data pointer of the i-th (starting from 0) output of the offline model.
     net_outputs[i].second : The length of the output data of the i-th (starting from 0) of the offline model.
     */


   /*Do something and put the detection information into data*/

  }

  DECLARE_REFLEX_OBJECT_EX(SsdPostproc, Postproc)
};  // class MyPostproc
```

. Modify the postproc_name parameter in cnstream/samples/demo/detection_config.json to the post-processing class name (MyPostproc).

Documentation

CNStream User Docs

Check out the Examples page for tutorials on how to use CNStream. Concepts page for basic definitions

Community forum

Discuss - General community discussion around CNStream

404hasbeenfound / cnstream Goto Github PK

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