eInfochips iMX8XML Manual de usuario
ML Demos User Manual
iMX8XML Reference Design
Version
Status
Date
0.2
Draft
14-Oct-2019
Confidentiality Notice
Copyright (c) 2019 eInfochips. - All rights reserved
This document is authored by eInfochips and is eInfochips intellectual property, including the copyrights in all
countries in the world. This document is provided under a license to use only with all other rights, including ownership
rights, being retained by eInfochips. This file may not be distributed, copied, or reproduced in any manner, electronic
or otherwise, without the express written consent of eInfochips
Contents
Document Details ..........................................................................................................................................4
Document History..............................................................................................................................4
Definition, Acronyms and Abbreviations...........................................................................................4
References .........................................................................................................................................4
Introduction ...................................................................................................................................................5
Purpose of the document ..................................................................................................................5
About the System ..............................................................................................................................5
Pre-requisite ......................................................................................................................................5
ML Demos Background..................................................................................................................................7
Copy Demos to SD Card.....................................................................................................................7
Run Setup.........................................................................................................................................11
Running ML Demos......................................................................................................................................12
1. Crowd Counting Demo.....................................................................................................................12
2. Object Detection Demo ...................................................................................................................16
3. Face Recognition Demo ...................................................................................................................21
4. Speech Recognition Demo...............................................................................................................28
5. Basler Camera Demo .......................................................................................................................39
6. Face Recognition using Tensorflow Lite demo ................................................................................42
7. Object Recognition using Arm NN Demo ........................................................................................45
Troubleshooting...........................................................................................................................................51
HDMI................................................................................................................................................51
Camera.............................................................................................................................................51
ML Demos references..................................................................................................................................53
Figures
Figure 1: iMX8XML RD..............................................................................................................................5
Figure 2: Hardware Setup..........................................................................................................................6
Figure 3: SD card partitions overview after flashing firmware .............................................................8
Figure 4: Create New EXT4 partition.......................................................................................................9
Figure 5: SD card partition after creating new one..............................................................................10
Figure 6: Run Crowd Count Demo.........................................................................................................12
Figure 7: Crowd Count Pre-Captured Mode.........................................................................................15
Figure 8: Crowd Count Live Mode..........................................................................................................15
Figure 9: Run Object Detection Demo...................................................................................................17
Figure 10: Setup for object detection.....................................................................................................19
Figure 11: Sample Input Image for Object Detection..........................................................................20
Figure 12: Sample Object detection output ..........................................................................................20
Figure 13: Face Recognition Demo Testing.........................................................................................22
Figure 14: Face Recognition Output......................................................................................................25
Figure 15: Run Speech Recognition Demo..........................................................................................28
Figure 16: Basler Camera logs...............................................................................................................39
Figure 17: Basler Pylon Viewer App......................................................................................................40
Figure 18: Pylon Viewer App display issue...........................................................................................41
Figure 19: Tensorflow based Face Recognition demo run screen ...................................................42
Figure 20: Tensorflow based Face Recognition demo output screen..............................................43
Figure 21: Arm NN Object Recognition run screen.............................................................................45
Figure 22: Arm NN Object Recognition output screen........................................................................47
Figure 23: Arm NN Object Recognition using MIPI camera run screen...........................................48
Figure 24: Arm NN Object Recognition using MIPI camera output screen .....................................50
Figure 25: No HDMI Connected Error ...................................................................................................51
Figure 26: No Camera connected error.................................................................................................52
Tables
Table 1: Documents History......................................................................................................................4
Table 2: Definition, Acronyms and Abbreviations..................................................................................4
Table 3: References...................................................................................................................................4
DOCUMENT DETAILS
Document History
Version
Author
Reviewer
Approver
Name
Date
(DD-MM-
YYYY)
Name
Date
(DD-MM-
YYYY)
Name
Date
(DD-MM-
YYYY)
0.1
Anil Patel
19-Apr-2019
Prajose
John
19-Apr-
2019
Bhavin
Patel
19-Apr-
2019
0.2
Anil Patel
15-Oct-2019
Prajose
John
15-Oct-
2019
Bhavin
Patel
15-Oct-
2019
Version
Description Of Changes
0.1
initial draft
0.2
Added EIQ support and demos
Table 1: Documents History
Definition, Acronyms and Abbreviations
Definition/Acronym/Abbreviation
Description
cd
Change directory
scp
Secure copy over the network
dfl
Default
Wi-Fi
Wireless fidelity
LTE
Long-Term Evolution
ML
Machine Learning
SVM
Support Vector Machine
CNN
Convolutional Neural Network
tf
Tensorflow
Table 2: Definition, Acronyms and Abbreviations
References
No.
Document
Version
Remarks
1
Refer the User guide V2.2
2.2
Prod Release.
Table 3: References
Introduction
Purpose of the document
•Purpose of this document is to use / understand / demonstrate Machine Learning
Demos to run on iMX8ML_RD AIML firmware.
About the System
•This system contains iMX8X reference design with multiple interfaces. This is used
for Machine learning experience.
Figure 1: iMX8XML RD
Pre-requisite
•x86 host system having Linux Ubuntu 16.04 LTS installed
•Basic understanding of Linux commands
•Flash the AIML firmware image to SD Card with all required python packages
(Refer User Guide)
•Webcam or Mezzanine D3 Camera
•USB HUB / mouse / Keyboard
•HDMI Display with HDMI connector
•Ethernet or WiFi with Internet Connectivity (for Audio google API Demo Only)
•Open board's terminal- console (minicom) on x86 Host PC (Refer User Guide)
Figure 2: Hardware Setup
ML DEMOS BACKGROUND
To demonstrate board’s capabilities for Machine Learning demos, we implemented few Audio and
video related ML demos. These demos mainly depend on OpenCV, Tensorflow, Caffe, ARM NN
and some python packages. All video ML demos required video source (webcam or D3
Mezzanine based OV5640 camera) to capture live stream and perform some action on it.
Moreover, Audio demos capture audio from DMIC or any other USB mic and perform speech
recognition on it.
All Demos are located on home folder of board under “ARROW_DEMOS” name.
Copy Demos to SD Card
(If we have constraint of size of board and want to copy demos to USB or another partition then only
follow this steps otherwise no need to do these steps.)
Our original firmware image took around 7GB space inside SD card. We can use rest of the
space of sdcard as storage device for ML demos. For that, we need to create FAT or EXT4
partition. We recommended ext4 partition as it is default Linux file system. To do so follow below
procedure.
•Flash SD card with required AIML firmware release. (Firmware release version must be
BETA release 0.3 or above). (Refer User Guide for this.)
•In Linux HOST OS, open “disk” utility and see sdcard partitions in it. You can see image
like below.
Figure 3: SD card partitions overview after flashing firmware
•As shown in above figure, in SD card partitions, we can see unused partition (10 GB) at
last. We can utilized it.
•Now Click on “+” sign to create new partition.
•Please select file system ext4 and name it as shown below.
Figure 4: Create New EXT4 partition
•It will take few times and create partition and we can able to mount that partition. (See
beow Figure for reference.)
Figure 5: SD card partition after creating new one
•Copy ARROW DEMOS on this partition. If you are unable to do that then kindly unmount
and mount again.
•After successful copy, boot our AIML board with this sdcard.
•After boot up we can see Demos at below location:
# ls -la /run/media/mmcblk1p3/ARROW_DEMOS/
total 40
drwxrwxrwx 7 1000 tracing 4096 Apr 9 14:22 .
drwx------ 5 1000 tracing 4096 Apr 9 14:22 ..
drwxrwxrwx 4 1000 tracing 4096 Apr 1 13:07 ai-crowd_count
drwxrwxrwx 6 1000 tracing 4096 Apr 9 14:40 face_recognition
drwxrwxrwx 4 1000 tracing 4096 Apr 9 12:24 real-time-object-detection
-rwxrwxrwx 1 1000 tracing 9322 Apr 9 14:18 run_ml_demos.sh
drwxrwxrwx 2 1000 tracing 4096 Apr 12 11:12 speech_recognition_tensorflow
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