Merge pull request #3 from intel-iot-devkit/document_revise

Document revise

Author: maozhong1

README.md

@@ -1 +1,77 @@
-# concurrent-video-anlalytic-pipeline-optimzation-sample
+# Concurrent Video Analytic Pipeline Optimzation Sample 
+Support users to quickly setup and adjust the core concurrent video analysis workload through configuration file to obtain the best performance of video codec, post-processing and inference based on Intel® integrated GPU according to their product requirements.
+Users can use the sample application video_e2e_sample to complete runtime performance evaluation or as a reference for debugging core video workload issues.
+
+## Typical workloads
+Sample par files can be found in par_files directory. Verfied on i7-8559U. Performance differs on other platforms.
+* 16 1080p H264 decoding, scaling, face detection inference, rendering inference results, composition, saving composition results to local H264 file, and display
+* 4 1080p H264 decoding, scaling, human pose estimation inference, rendering inference results, composition and display
+* 4 1080p H264 decoding, scaling, vehicle and vehicle attributes detection inference, rendering inference results, composition and display
+* 16 1080p RTSP H264 stream decoding, scaling, face detection inference, rendering inference results, composition and display.
+* 16 1080p H264 decoding, scaling, face detection inference, rendering inference results, composition and display. Plus 16 1080p H264 decoding, composition and showing on second display. 
+
+# Dependencies
+The sample application depends on [Intel® Media SDK](https://github.com/Intel-Media-SDK/), [Intel® OpenVINO™](https://software.intel.com/en-us/openvino-toolkit) and [FFmpeg](https://www.ffmpeg.org/)
+
+# FAQ
+See doc/FAQ.md
+
+# Table of contents
+
+  * [License](#license)
+  * [How to contribute](#how-to-contribute)
+  * [Documentation](#documentation)
+  * [System requirements](#system-requirements)
+  * [How to build](#how-to-build)
+    * [Build steps](#build-steps)
+  * [Known limitations](#know-limitations)
+
+# License
+The sample application is licensed under MIT license. See [LICENSE](./LICENSE) for details.
+
+# How to contribute
+See [CONTRIBUTING](./doc/CONTRIBUTING.md) for details. Thank you!
+
+# Documentation
+See [user guide](./doc/concurrent_video_analytic_sample_application_user_guide_2020.1.0.pdf)
+
+# System requirements
+
+**Operating System:**
+* Ubuntu 18.04.02
+
+**Software:**
+* [MediaSDK 19.4.0](https://github.com/Intel-Media-SDK/MediaSDK/releases/tag/intel-mediasdk-19.4.0)
+* [OpenVINO™ 2019 R3](https://software.intel.com/en-us/openvino-toolkit)
+
+**Hardware:** 
+* Intel® platforms supported by the MediaSDK 19.4.0 and OpenVINO 2019 R3. 
+* For Media SDK, the major platform dependency comes from the back-end media driver. https://github.com/intel/media-driver
+* For OpenVINO™, see details from here: https://software.intel.com/en-us/openvino-toolkit/documentation/system-requirements
+
+# How to build
+
+Run build_and_install.sh to install dependent software packages and build sample application video_e2e_sample. 
+
+Please refer to ”Installation Guide“ in [user guide](./doc/concurrent_video_analytic_sample_application_user_guide_2020.1.0.pdf) for details.
+
+## Build steps
+
+Get sources with the following git command: 
+```sh
+git clone https://github.com/intel-iot-devkit/concurrent-video-analytic-pipeline-optimization-sample-l.git cva_sample 
+```
+
+```sh
+cd cva_sample 
+./build_and_install.sh
+```
+This script will install the dependent software packages by running command "apt install". So it will ask for sudo password. Then it will download libva, libva-util, media-driver and MediaSDK source code and install these libraries. It might take 10 to 20 minutes depending on the network bandwidth.
+
+After the script finishing, the sample application video_e2e_sample can be found under ./bin. Please refer to "Run sample application" in [user guide](./doc/concurrent_video_analytic_sample_application_user_guide_2020.1.0.pdf) for details.
+
+# Known limitations
+
+The sample application has been validated on Intel® platforms Skylake(i7-6770HQ), Coffee Lake(i7-8559U i7-8700) and Whiskey Lake(i7-8665UE).
+
+

build_and_install.sh

@@ -63,7 +63,7 @@ then
         echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/opt/intel/mediasdk/lib"' >> ~/.bashrc
     fi
 
-    echo "VAAS sample application building has completed!"
+    echo "Sample application building has completed!"
     echo "Please use ./bin/video_e2e_sample for testing"
 fi
 

doc/CONTRIBUTING.md

@@ -0,0 +1,58 @@
+We welcome community contributions to SVET sample application. Thank you for your time!
+
+Please note that review and merge might take some time at this point.
+
+SVET sample application is licensed under MIT license. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
+
+Steps:
+ - In the commit message, explain what bug is fixed or what new feature is added in details.
+ - Validate that your changes don't break a build. [build instruction](../README.md#how-to-build)
+ - Pass [testing](#testing)
+ - Wait while your patchset is reviewed and tested by our internal validation cycle
+
+# Testing
+
+## Requirements
+
+Hardware Requirements: Coffee Lake or Whiskey Lake
+Software Requirements: Ubuntu 18.04, MediaSDK 19.4.0 and OpenVINO 2019 R3 
+
+## How to test your changes
+
+### 1. Build the SVET sample applicaton
+
+```sh
+./build_and_install.sh
+```
+Apply the changes to MediaSDK/samples/video_e2e_sample/. Then build video_e2e_sample:
+```sh
+cp msdk_build.sh MediaSDK
+cd MediaSDK
+./msdk_build.sh
+```
+
+### 2. Run tests to make sure below tests can run without error 
+
+Simple display test:
+```sh
+./bin/video_e2e_sample -par par_file/par_file_name.par
+```
+Basic video decoding and inference tests:
+n16_1080p_1080p_dp_noinfer.par
+n16_1080p_1080p_dp.par
+n16_1080p_4k_dp.par
+n4_1080p_1080p_dp.par
+n4_vehical_detect_1080p.par
+n64_d1_1080p_dp.par
+
+RTSP play and saving tests:
+rtsp_dump_only.par
+n16_1080p_rtsp_simu.pa
+n4_1080p_rtsp_simu_dp.par
+n16_1080p_rtsp_simu_dump.par
+
+Multiple display tests:
+```sh
+./bin/video_e2e_sample -par par_file/n16_1080p_1080p_dp_noinfer.par -par par_file/n16_1080p_1080p_dp.par
+```
+ 

doc/FAQ.md

@@ -0,0 +1,35 @@
+# Frequently asked questions (SVET sample application)
+
+## Where can I find the descritpion of options used in par file?
+See chapter 2.4 in doc/svet_sample_application_user_guide_2020.1.0.pdf
+Running the SVET sample applicton with option "-?" can show the usage of options.
+
+## Why does the system need to be switched to text mode before running the sample application
+The sample application uses libDRM to render the video directly to display, so it needs to act as master of the DRM display, which isn't allowed when X server is running. 
+If the par file doesn't include display session, there is no need to switch to text mode.
+
+## Why it needs "su -p" to switch to root user before running the sample application
+To become DRM master, it needs root privileges. With option "-p", it will preserve environment variables, like LIBVA_DRIVERS_PATH, LIBVA_DRIVER_NAME and LD_LIBRARY_PATH. If without "-p", these environment variables will be reset and the sample application will run into problems.
+
+## The loading time of 16-channel face detection demo is too long
+Please enable cl_cache by running command "export cl_cache_dir=/tmp/cl_cache" and "mkdir -p /tmp/cl_cache". Then after the first running of 16-channel face detection demo, the compiled OpenCL kernles are cached and the model loading time of next runnings of 16-channel face detection demo will only take about 10 seconds.
+More details about cl_cache can be found at https://github.com/intel/compute-runtime/blob/master/opencl/doc/FAQ.md
+
+## Can sources number for "-vpp_comp_only" or "-vpp_comp" be different from number of decoding sessions?  
+No. The sources number for "-vpp_comp_only" or "-vpp_comp" must be equal to the numer of decoding sessions. Otherwise, the sample application will fail during pipeline initialization or running. 
+
+## How to limit the fps of whole pipeline to 30?
+Add "-fps 30" to every decoding session.
+
+## How to limit the frame number of input to 1000?
+Add "-n 1000" to every decoding dessions. However this option won't work if both "-vpp_comp_only" and "-vpp_comp" are set.
+
+## Where can I find tutorials for inference engine?
+Please refer to https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html
+
+## Where can I find information for the models?
+Please refer to https://github.com/opencv/open_model_zoo/tree/master/models/intel. The names of models used in sample application are
+face-detection-retail-0004, human-pose-estimation-0001, vehicle-attributes-recognition-barrier-0039, vehicle-license-plate-detection-barrier-0106.
+
+## Can I use other OpenVINO version rather than 2019 R3?
+Yes, but you have to modify some code due to interfaces changing. And also you need to download the IR files and copy them to ./model manually. Please refer to script/download_and_copy_models.sh for how to download the IR files.

doc/concurrent_video_analytic_sample_application_release_notes_2020.1.0.pdf

@@ -1,6 +1,6 @@
 #!/bin/bash
-
-#use should use sudo to run this binaries
+#This script is supposed to run under directory svet_e2e_sample_l which is generated by script pack_binary.sh
+#Please use "sudo" to run this script for installing libva, media-driver and MediaSDK library binaries. 
 
 echo "If libva/media-driver/MediaSDK have been installed before, their libraies will be overwrote!"
 

doc/svet_sample_application_user_guide_2020.1.0.pdf renamed to doc/concurrent_video_analytic_sample_application_user_guide_2020.1.0.pdf

@@ -1,4 +1,6 @@
 #!/bin/bash
+#This script is used to pack all libva, media-driver and MediaSDK binaries to directory svet_e2e_sample_l.
+#Copy whole directory svet_e2e_sample_l to another device and run install_binary.sh to install the binaries
 
 root_path=$PWD
 release_folder=$root_path/svet_e2e_sample_l