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Posted on Edited on In ,

It’s been quite a while before I write down this similar blog as my previous Single Board Computer blogs. Banana Pi Pro is a SBC with AllWinner A20 ARM CPU, which is also just of card size (Cited from LeMaker Official website).

Orange Pi Plus 2

Now, we follow our previous blog Install Armbian Debian Server onto NanoPi NEO to install Armbian Ubuntu Desktop onto Banana Pi Pro.

PART A: Install Ubuntu Desktop Built By Armbian onto Banana Pi Pro

1. Download Armbian Ubuntu Desktop for Banana Pi Pro

We FIRST go visiting the website https://www.armbian.com/banana-pi-pro/ and click Armbian Bionic icon, a file named Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65.7z will be automatically downloaded.

Then, we extract this .7z file by

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$ 7z x Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65.7z -oArmbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65

7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21
p7zip Version 16.02 (locale=en_CA.UTF-8,Utf16=on,HugeFiles=on,64 bits,8 CPUs Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz (506E3),ASM,AES-NI)

Scanning the drive for archives:
1 file, 202610698 bytes (194 MiB)

Extracting archive: Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65.7z
--
Path = Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65.7z
Type = 7z
Physical Size = 202610698
Headers Size = 290
Method = LZMA2:25
Solid = +
Blocks = 1

Everything is Ok                                              

Files: 4
Size:       885017676
Compressed: 202610698

2. Install Armbian Ubuntu Desktop for Banana Pi Pro

After the extracted image file is prepared, it’s the time to install the Armbian Ubuntu Desktop onto our TF card. We FIRST format the TF card:

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$ sudo umount /dev/mmcblk0p1 
[sudo] password for jiapei: 
$ sudo mkfs.ext4 /dev/mmcblk0 
mke2fs 1.44.1 (24-Mar-2018)
Found a dos partition table in /dev/mmcblk0
Proceed anyway? (y,N) y
Discarding device blocks: done                            
Creating filesystem with 7791744 4k blocks and 1949696 inodes
Filesystem UUID: 4e7bb315-0db1-4b62-a9d5-40c0ba78c7f0
Superblock backups stored on blocks: 
        32768, 98304, 163840, 229376, 294912, 819200, 884736, 1605632, 2654208, 
        4096000

Allocating group tables: done                            
Writing inode tables: done                            
Creating journal (32768 blocks): done
Writing superblocks and filesystem accounting information: done

Afterwards, use dd to install the downloaded Armbian Ubuntu Desktop image.

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$ cd Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65/
$ sudo dd bs=4M if=Armbian_5.59_Bananapipro_Ubuntu_bionic_next_4.14.65.img of=/dev/mmcblk0 conv=fsync
211+0 records in
211+0 records out
884998144 bytes (885 MB, 844 MiB) copied, 123.525 s, 7.2 MB/s

PART B: Boot Into Armbian, Network Configuration, Locale Configuration and Armbian Upgrading

NOTE: Whenever you met some unsolvable issue after you boot into Armbian, please use the command armbianmonitor -U to log the issue.

1. Boot Into Armbian

We now unplug the TF card from the host and put it into the Banana Pi Pro board, Armbian Ubuntu Desktop boots successfully. The default username and password are respectively: root and 1234.

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Ubuntu 18.04.1 LTS bananapipro tty1

bananapipro login: root
password:
You are required to change your password immediately (root enforced)
Changing password for root.
(current) UNIX password:
Enter new UNIX password:
Retype new UNIX password:
...

And, for the FIRST boot, we’ll be asked to create a NEW user besides root:

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Creating a new user account. Press <Ctrl-C> to abort

Please provide a username (eg. your forename): bananapipro
Trying to add user bananapipro
Adding user 'bananapipro' ...
Adding new group 'bananapipro' (1000) ...
Adding new user 'bananapipro' (1000) with group 'bananapipro' ...
Creating home directory '/home/bananapipro' ...
Copying files from '/etc/ske1' ...
Enter new UNIX password:
Retype new UNIX password:
passwd: password updated successfully
Changing the user information for orangepiplus2
Eter the new value, or press ENTER for the default
       Full Name []:
       Room Number []:
       Work Phone []:
       Home Phone []:
       Other []:
Is the information correct? [Y/n] Y

Dear bananapipro, your account bananapipro has been created and is sudo enabled.
Please use this account for your daily work from now on.
...

Then, Banana Pi Pro now boots into text mode with account root.

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root@bananapipro:~$

We may now re-login with the newly created account bananapipro:

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bananapipro@bananapipro:~$

2. Network Configuration

1). How to connect to wireless?

Please refer to Armbian docs - How to connect to wireless?, 

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bananapipro@bananapipro:~$ nmtui-connect SSID

and then input the corresponding password. Now, you should be able to connect to the Internet with a DHCP IP address.

2). How to set fixed IP?

Please refer to Armbian docs - How to set fixed IP?, and modify the file /etc/network/interfaces as follows:

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bananapipro@bananapipro:~$ sudo vim /etc/network/interfaces
source /etc/network/interfaces.d/*
# Network is managed by Network manager
auto lo
iface lo inet loopback

# Wifi
auto wlan0
iface wlan0 inet static
    address 192.168.0.82
    netmask 255.255.255.0
    network 192.168.0.0
    broadcast 192.168.0.1
    gateway 192.168.0.1
    dns-nameservers 192.168.0.1 8.8.8.8
    wpa-ssid XXXXXXXX
    wpa-psk YYYYYYYY

Afterwards, a reboot (meanwhile, you may have to set up your own router for a fixed IP) will bring your Banana Pi Pro a fixed IP address. Here in my case: 192.168.0.82.

3. SSH Into Banana Pi Pro

We can now access Banana Pi Pro via ssh as follows:

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$ ssh bananapipro@192.168.0.82
The authenticity of host '192.168.0.82 (192.168.0.82)' can't be established.
ECDSA key fingerprint is SHA256:SKJ/eU/a7Rm6T43jzwvNpQfG1w2FqDPg+eieM+sGynA.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added '192.168.0.82' (ECDSA) to the list of known hosts.
bananapipro@192.168.0.82's password: 
 ____                                  ____  _   ____            
| __ )  __ _ _ __   __ _ _ __   __ _  |  _ \(_) |  _ \ _ __ ___  
|  _ \ / _` | '_ \ / _` | '_ \ / _` | | |_) | | | |_) | '__/ _ \ 
| |_) | (_| | | | | (_| | | | | (_| | |  __/| | |  __/| | | (_) |
|____/ \__,_|_| |_|\__,_|_| |_|\__,_| |_|   |_| |_|   |_|  \___/ 
                                                                 

Welcome to ARMBIAN 5.59 stable Ubuntu 18.04.1 LTS 4.14.65-sunxi   
System load:   0.43 0.32 0.22   Up time:       15 min
Memory usage:  6 % of 992MB     IP:            192.168.0.82
CPU temp:      46°C           
Usage of /:    3% of 29G    

Last login: Tue Sep  4 21:43:26 2018

And, let’s have a look at current IP of Banana Pi Pro.

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bananapipro@bananapipro:~$ ip -c address
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000
    link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:0
    inet 127.0.0.1/8 scope host lo
       valid_lft forever preferred_lft forever
    inet6 ::1/128 scope host
       valid_lft forever preferred_lft forever
2: eth0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc mq state DOWN group default qlen 1000
    link/ether 02:86:02:42:2f:4e brd ff:ff:ff:ff:ff:ff
3: wlan0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc fq_codel state UP group default qlen 1000
    link/ether 98:3b:16:e9:79:43 brd ff:ff:ff:ff:ff:ff
    inet 192.168.0.82/24 brd 192.168.0.255 scope global dynamic noprefixroute wlan0
       valid_lft 3069218372sec preferred_lft 3069218372sec
    inet6 fe80::40a5:2ad5:cfa4:a2d8/64 scope link noprefixroute
       valid_lft forever preferred_lft forever

4. Locale Configuration

Clearly, from the above login info, our timezone seems to be wrong. Command dpkg-reconfigure tzdata is able to reset our timezone.

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bananapipro@bananapipro:~$ sudo dpkg-reconfigure tzdata
[sudo] password for bananapipro: 
perl: warning: Setting locale failed.
perl: warning: Please check that your locale settings:
        LANGUAGE = "en_US.UTF-8",
        LC_ALL = (unset),
        LC_MEASUREMENT = "en_CA.UTF-8",
        LC_PAPER = "en_CA.UTF-8",
        LC_MONETARY = "en_CA.UTF-8",
        LC_NAME = "en_CA.UTF-8",
        LC_ADDRESS = "en_CA.UTF-8",
        LC_NUMERIC = "en_CA.UTF-8",
        LC_MESSAGES = "en_US.UTF-8",
        LC_TELEPHONE = "en_CA.UTF-8",
        LC_IDENTIFICATION = "en_CA.UTF-8",
        LC_TIME = "en_CA.UTF-8",
        LANG = "en_US.UTF-8"
    are supported and installed on your system.
perl: warning: Falling back to a fallback locale ("en_US.UTF-8").
locale: Cannot set LC_ALL to default locale: No such file or directory
/usr/bin/locale: Cannot set LC_ALL to default locale: No such file or directory

Current default time zone: 'America/Vancouver'
Local time is now:      Tue Sep  4 15:33:36 PDT 2018.
Universal Time is now:  Tue Sep  4 22:33:36 UTC 2018.

During the above process, you’ll set the following 2 pages correspondingly:

System Locale America

System Locale Vancouver

If we logout and login Banana Pi Pro again, we’ll see the timezone has been successfully reset.

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$ exit
logout
Connection to 192.168.0.82 closed.
$ ssh bananapipro@192.168.0.82
bananapipro@192.168.0.82's password: 
 ____                                  ____  _   ____            
| __ )  __ _ _ __   __ _ _ __   __ _  |  _ \(_) |  _ \ _ __ ___  
|  _ \ / _` | '_ \ / _` | '_ \ / _` | | |_) | | | |_) | '__/ _ \ 
| |_) | (_| | | | | (_| | | | | (_| | |  __/| | |  __/| | | (_) |
|____/ \__,_|_| |_|\__,_|_| |_|\__,_| |_|   |_| |_|   |_|  \___/ 
                                                                 

Welcome to ARMBIAN 5.59 stable Ubuntu 18.04.1 LTS 4.14.65-sunxi   
System load:   0.16 0.20 0.18   Up time:       53 min
Memory usage:  6 % of 992MB     IP:            192.168.0.82
CPU temp:      46°C           
Usage of /:    3% of 29G    

Last login: Tue Sep  4 14:58:03 2018 from 192.168.0.60

5. Armbian Upgrading

Always these 3 commands:

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bananapipro@bananapipro:~$ sudo apt update
bananapipro@bananapipro:~$ apt list --upgradable
bananapipro@bananapipro:~$ sudo apt upgrade

6. Kernel Doublechecking

Finally, we have the system and kernel doublechecked.

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bananapipro@bananapipro:~$ uname -r
4.14.65-sunxi
bananapipro@bananapipro:~$ uname -a
Linux bananapipro 4.14.65-sunxi #68 SMP Tue Aug 21 19:57:06 CEST 2018 armv7l armv7l armv7l GNU/Linux
bananapipro@bananapipro:~$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 18.04.1 LTS
Release:        18.04
Codename:       bionic

Appendix

We can of course follow our previous blog Install Armbian Ubuntu Desktop with the Newest Supported Mainline Linux Kernel onto Orange Pi Plus 2 to upgrade the NEWEST U-Boot and Linux Kernel for Banana Pi Pro, which is NOT going to be discussed here again.

Posted on Edited on In ,

It seems Tensorflow evolves pretty fast. Today we are testing object tracking based on Tensorflow.

1. Environment

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➜  ~ python
Python 3.6.6 (default, Sep 12 2018, 18:26:19) 
[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.12.0-rc0'
>>> import cv2
>>> cv2.__version__
'3.4.3'

2. Object Tracking

2.1 Concepts

There are several fundamental concepts to be re-emphasized (Here, we took one single concerned object as our example. There might be multiple concerned objects):

  • detection: You don’t know whethere there is a concerned object in the field of view or not, which you will know after the detection. And, if there is such a concerned object in the view, the object location is to be given.
  • tracking: You know where the concerned object was. Based on the prior knowledge, you are to determine where this object is going to be next?
  • location: Both detection and tracking are looked on as locating the concerned object.
  • recognition: Only after the concerned object has been located, more detailed information may be recognized afterwards.

2.2 Testing

Now, let’s test out Deep Object Tracking Implementation in Tensorflow.

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➜  tf-adnet-tracking git:(master) ✗ python runner.py by_dataset  --vid-path=./data/freeman1/
VIDEOIO ERROR: V4L: can't open camera by index 0
Test:  0.0
Ratio:  0.0
Frame Rate:  0.0
Width:  0.0
Height:  0.0
Brightness:  0.0
Contrast:  0.0
Saturation:  0.0
Hue:  0.0
Gain:  0.0
Exposure:  0.0
2018-11-04 19:20:54.997017: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:993] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-04 19:20:54.997514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: GeForce GTX 980M major: 5 minor: 2 memoryClockRate(GHz): 1.1265
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 3.14GiB
2018-11-04 19:20:54.997565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-11-04 19:20:55.222053: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-04 19:20:55.222092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
2018-11-04 19:20:55.222100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
2018-11-04 19:20:55.222278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2854 MB memory) -> physical GPU (device: 0, name: GeForce GTX 980M, pci bus id: 0000:01:00.0, compute capability: 5.2)
[2018-11-04 19:20:55,738] [networks] [INFO] all global variables initialized
[2018-11-04 19:20:55,865] [networks] [INFO] conv1/weights:0 : original weights assigned. [0]=[[[-0.08093996 -0.03
[2018-11-04 19:20:55,877] [networks] [INFO] conv1/biases:0 : original weights assigned. [0]=-1.5706328
[2018-11-04 19:20:55,922] [networks] [INFO] conv2/weights:0 : original weights assigned. [0]=[[[-0.00856966 -0.00
[2018-11-04 19:20:55,936] [networks] [INFO] conv2/biases:0 : original weights assigned. [0]=-0.06253582
[2018-11-04 19:20:56,016] [networks] [INFO] conv3/weights:0 : original weights assigned. [0]=[[[ 0.00339902  0.00
[2018-11-04 19:20:56,033] [networks] [INFO] conv3/biases:0 : original weights assigned. [0]=-0.071603894
[2018-11-04 19:20:56,236] [networks] [INFO] fc4/weights:0 : original weights assigned. [0]=[[[ 2.6850728e-03  2
[2018-11-04 19:20:56,254] [networks] [INFO] fc4/biases:0 : original weights assigned. [0]=0.10009623
[2018-11-04 19:20:56,286] [networks] [INFO] fc5/weights:0 : original weights assigned. [0]=[[[-0.01758034  0.01
[2018-11-04 19:20:56,310] [networks] [INFO] fc5/biases:0 : original weights assigned. [0]=0.13894661
[2018-11-04 19:20:56,332] [networks] [INFO] fc6_1/weights:0 : original weights assigned. [0]=[[[ 0.001359    0.01
[2018-11-04 19:20:56,358] [networks] [INFO] fc6_1/biases:0 : original weights assigned. [0]=0.01
[2018-11-04 19:20:56,379] [networks] [INFO] fc6_2/weights:0 : original weights assigned. [0]=[[[-1.23225665e-02  
[2018-11-04 19:20:56,400] [networks] [INFO] fc6_2/biases:0 : original weights assigned. [0]=0.0
[]
[2018-11-04 19:20:56,482] [ADNetRunner] [INFO] ---- start dataset l=326
[2018-11-04 19:20:58,451] [ADNetRunner] [INFO] ADNetRunner.initial_finetune t=1541388056.484
[2018-11-04 19:20:59,194] [ADNetRunner] [DEBUG] redetection success=True
[2018-11-04 19:20:59,487] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:01,750] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:03,691] [ADNetRunner] [DEBUG] redetection success=False
[2018-11-04 19:21:03,975] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:04,878] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:07,524] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:08,925] [ADNetRunner] [DEBUG] redetection success=True
[2018-11-04 19:21:09,249] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:10,825] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:11,768] [ADNetRunner] [DEBUG] redetection success=True
[2018-11-04 19:21:12,096] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:14,021] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:16,867] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:19,710] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:22,677] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:25,711] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:26,202] [ADNetRunner] [DEBUG] redetection success=True
[2018-11-04 19:21:26,567] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:26,837] [ADNetRunner] [DEBUG] redetection success=True
[2018-11-04 19:21:27,152] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:29,387] [ADNetRunner] [DEBUG] finetuned
[2018-11-04 19:21:31,371] [ADNetRunner] [INFO] ----
[2018-11-04 19:21:31,371] [ADNetRunner] [INFO] total: 34.8886
initial_finetune: 1.9675
tracking.do_action: 5.4896
tracking.save_samples.roi: 0.5116
tracking.save_samples.feat: 17.9738
tracking.online_finetune: 6.2583
[2018-11-04 19:21:31,371] [ADNetRunner] [INFO] 9.344 FPS

Face Tracking Based on Tensorflow

Posted on Edited on In ,

Tensorflow is always problematic, particularly, for guys like me…

1. Configuration

Linux Kernel 4.17.3 + Ubuntu 18.04 + GCC 7.3.0 + Python

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$ uname -r
4.17.3-041703-generic
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$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 18.04 LTS
Release:        18.04
Codename:       bionic
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$ gcc --version
gcc (Ubuntu 7.3.0-16ubuntu3) 7.3.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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$ python --version
Python 3.6.5

2. Install Bazel

From Bazel’s official website Installing Bazel on Ubuntu:

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$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
$ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
$ sudo apt update && sudo apt install bazel

Current bazel 0.15.0 will be installed.

3. Let’s Build Tensorflow from Source

3.1 Configuration

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$ ./configure
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by com.google.protobuf.UnsafeUtil (file:~/.cache/bazel/_bazel_jiapei/install/ce085f519b017357185750fe457b4648/_embedded_binaries/A-server.jar) to field java.nio.Buffer.address
WARNING: Please consider reporting this to the maintainers of com.google.protobuf.UnsafeUtil
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.15.0 installed.
Please specify the location of python. [Default is /usr/bin/python]: 


Found possible Python library paths:
  /usr/local/lib/python3.6/dist-packages
  /usr/lib/python3/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib/python3.6/dist-packages]

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y
jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: Y
Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: Y
Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: Y
Amazon AWS Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: Y
Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: N
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: N
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: N
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 9.2


Please specify the location where CUDA 9.2 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1.4


Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:


Do you wish to build TensorFlow with TensorRT support? [y/N]: N
No TensorRT support will be enabled for TensorFlow.

Please specify the NCCL version you want to use. [Leave empty to default to NCCL 1.3]: 


Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.2]


Do you want to use clang as CUDA compiler? [y/N]: N
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 


Do you wish to build TensorFlow with MPI support? [y/N]: N
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: N
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
        --config=mkl            # Build with MKL support.
        --config=monolithic     # Config for mostly static monolithic build.
Configuration finished

3.2 FIRST Bazel Build

Follow Installing TensorFlow from Sources on Tensorflow‘s official website, have all required packages prepared, and run:

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bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

you will meet several ERROR messages, which requires you to carry out the following modifications.

3.3 Modifications

  • File ~/.cache/bazel/_bazel_jiapei/051cd94cedc722db8c69e42ce51064b5/external/jpeg/BUILD:
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config_setting(
    name = "armeabi-v7a",
-    values = {"android_cpu": "armeabi-v7a"},
+    values = {"cpu": "armeabi-v7a"},
)
  • Two Symbolic links
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$ sudo ln -s /usr/local/cuda/include/crt/math_functions.hpp /usr/local/cuda/include/math_functions.hpp
$ sudo ln -s /usr/local/cuda/nvvm/libdevice/libdevice.10.bc /usr/local/cuda/nvvm/lib64/libdevice.10.bc

3.4 Bazel Build Again

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bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

It’ll take around 30 minutes to have Tensorflow successfully built.

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......
Target //tensorflow/tools/pip_package:build_pip_package up-to-date:
  bazel-bin/tensorflow/tools/pip_package/build_pip_package
INFO: Elapsed time: 4924.451s, Critical Path: 197.91s
INFO: 8864 processes: 8864 local.
INFO: Build completed successfully, 11002 total actions

4. Tensorflow Installation

4.1 Build the pip File

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$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
Wed Jul 11 00:09:09 PDT 2018 : === Preparing sources in dir: /tmp/tmp.5X0zsqfxYo
/media/jiapei/Data/Downloads/machinelearning/deeplearning/tensorflow/tensorflow /media/jiapei/Data/Downloads/machinelearning/deeplearning/tensorflow/tensorflow
/media/jiapei/Data/Downloads/machinelearning/deeplearning/tensorflow/tensorflow
Wed Jul 11 00:09:35 PDT 2018 : === Building wheel
warning: no files found matching '*.dll' under directory '*'
warning: no files found matching '*.lib' under directory '*'
warning: no files found matching '*.h' under directory 'tensorflow/include/tensorflow'
warning: no files found matching '*' under directory 'tensorflow/include/Eigen'
warning: no files found matching '*.h' under directory 'tensorflow/include/google'
warning: no files found matching '*' under directory 'tensorflow/include/third_party'
warning: no files found matching '*' under directory 'tensorflow/include/unsupported'
Wed Jul 11 00:10:58 PDT 2018 : === Output wheel file is in: /tmp/tensorflow_pkg

Let’s have a look at what’s been built:

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$ ls /tmp/tensorflow_pkg/
tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl

4.2 Pip Installation

And, let’s have tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl installed.

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$ pip3 install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
Processing /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (0.31.1)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (0.2.0)
Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (1.14.5)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (0.7.1)
Requirement already satisfied: six>=1.10.0 in ./.local/lib/python3.6/site-packages (from tensorflow==1.9.0rc0) (1.11.0)
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (1.13.0)
Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (0.2.2)
Requirement already satisfied: tensorboard<1.9.0,>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (1.8.0)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (1.1.0)
Requirement already satisfied: setuptools<=39.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (39.1.0)
Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.9.0rc0) (3.6.0)
Requirement already satisfied: bleach==1.5.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow==1.9.0rc0) (1.5.0)
Requirement already satisfied: html5lib==0.9999999 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow==1.9.0rc0) (0.9999999)
Requirement already satisfied: werkzeug>=0.11.10 in ./.local/lib/python3.6/site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow==1.9.0rc0) (0.14.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow==1.9.0rc0) (2.6.11)
Successfully installed tensorflow-1.9.0rc0

Let’s test if Tensorflow has been successfully installed.

4.3 Check Tensorflow

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$ python
Python 3.6.5 (default, Apr  1 2018, 05:46:30) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.9.0-rc0'

5. Keras Installation

After successfully check out Keras, we can easily have Keras installed by command python setup.py install.

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$ python setup.py install
running install
running bdist_egg
running egg_info
creating Keras.egg-info
writing Keras.egg-info/PKG-INFO
writing dependency_links to Keras.egg-info/dependency_links.txt
writing requirements to Keras.egg-info/requires.txt
writing top-level names to Keras.egg-info/top_level.txt
writing manifest file 'Keras.egg-info/SOURCES.txt'
reading manifest file 'Keras.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'Keras.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
creating build
creating build/lib
creating build/lib/docs
copying docs/autogen.py -> build/lib/docs
copying docs/__init__.py -> build/lib/docs
creating build/lib/keras
copying keras/activations.py -> build/lib/keras
copying keras/callbacks.py -> build/lib/keras
copying keras/constraints.py -> build/lib/keras
copying keras/initializers.py -> build/lib/keras
copying keras/losses.py -> build/lib/keras
copying keras/metrics.py -> build/lib/keras
copying keras/models.py -> build/lib/keras
copying keras/objectives.py -> build/lib/keras
copying keras/optimizers.py -> build/lib/keras
copying keras/regularizers.py -> build/lib/keras
copying keras/__init__.py -> build/lib/keras
creating build/lib/keras/applications
copying keras/applications/densenet.py -> build/lib/keras/applications
copying keras/applications/imagenet_utils.py -> build/lib/keras/applications
copying keras/applications/inception_resnet_v2.py -> build/lib/keras/applications
copying keras/applications/inception_v3.py -> build/lib/keras/applications
copying keras/applications/mobilenet.py -> build/lib/keras/applications
copying keras/applications/mobilenetv2.py -> build/lib/keras/applications
copying keras/applications/nasnet.py -> build/lib/keras/applications
copying keras/applications/resnet50.py -> build/lib/keras/applications
copying keras/applications/vgg16.py -> build/lib/keras/applications
copying keras/applications/vgg19.py -> build/lib/keras/applications
copying keras/applications/xception.py -> build/lib/keras/applications
copying keras/applications/__init__.py -> build/lib/keras/applications
creating build/lib/keras/backend
copying keras/backend/cntk_backend.py -> build/lib/keras/backend
copying keras/backend/common.py -> build/lib/keras/backend
copying keras/backend/tensorflow_backend.py -> build/lib/keras/backend
copying keras/backend/theano_backend.py -> build/lib/keras/backend
copying keras/backend/__init__.py -> build/lib/keras/backend
creating build/lib/keras/datasets
copying keras/datasets/boston_housing.py -> build/lib/keras/datasets
copying keras/datasets/cifar.py -> build/lib/keras/datasets
copying keras/datasets/cifar10.py -> build/lib/keras/datasets
copying keras/datasets/cifar100.py -> build/lib/keras/datasets
copying keras/datasets/fashion_mnist.py -> build/lib/keras/datasets
copying keras/datasets/imdb.py -> build/lib/keras/datasets
copying keras/datasets/mnist.py -> build/lib/keras/datasets
copying keras/datasets/reuters.py -> build/lib/keras/datasets
copying keras/datasets/__init__.py -> build/lib/keras/datasets
creating build/lib/keras/engine
copying keras/engine/base_layer.py -> build/lib/keras/engine
copying keras/engine/input_layer.py -> build/lib/keras/engine
copying keras/engine/network.py -> build/lib/keras/engine
copying keras/engine/saving.py -> build/lib/keras/engine
copying keras/engine/sequential.py -> build/lib/keras/engine
copying keras/engine/topology.py -> build/lib/keras/engine
copying keras/engine/training.py -> build/lib/keras/engine
copying keras/engine/training_arrays.py -> build/lib/keras/engine
copying keras/engine/training_generator.py -> build/lib/keras/engine
copying keras/engine/training_utils.py -> build/lib/keras/engine
copying keras/engine/__init__.py -> build/lib/keras/engine
creating build/lib/keras/layers
copying keras/layers/advanced_activations.py -> build/lib/keras/layers
copying keras/layers/convolutional.py -> build/lib/keras/layers
copying keras/layers/convolutional_recurrent.py -> build/lib/keras/layers
copying keras/layers/core.py -> build/lib/keras/layers
copying keras/layers/cudnn_recurrent.py -> build/lib/keras/layers
copying keras/layers/embeddings.py -> build/lib/keras/layers
copying keras/layers/local.py -> build/lib/keras/layers
copying keras/layers/merge.py -> build/lib/keras/layers
copying keras/layers/noise.py -> build/lib/keras/layers
copying keras/layers/normalization.py -> build/lib/keras/layers
copying keras/layers/pooling.py -> build/lib/keras/layers
copying keras/layers/recurrent.py -> build/lib/keras/layers
copying keras/layers/wrappers.py -> build/lib/keras/layers
copying keras/layers/__init__.py -> build/lib/keras/layers
creating build/lib/keras/legacy
copying keras/legacy/interfaces.py -> build/lib/keras/legacy
copying keras/legacy/layers.py -> build/lib/keras/legacy
copying keras/legacy/__init__.py -> build/lib/keras/legacy
creating build/lib/keras/preprocessing
copying keras/preprocessing/image.py -> build/lib/keras/preprocessing
copying keras/preprocessing/sequence.py -> build/lib/keras/preprocessing
copying keras/preprocessing/text.py -> build/lib/keras/preprocessing
copying keras/preprocessing/__init__.py -> build/lib/keras/preprocessing
creating build/lib/keras/utils
copying keras/utils/conv_utils.py -> build/lib/keras/utils
copying keras/utils/data_utils.py -> build/lib/keras/utils
copying keras/utils/generic_utils.py -> build/lib/keras/utils
copying keras/utils/io_utils.py -> build/lib/keras/utils
copying keras/utils/layer_utils.py -> build/lib/keras/utils
copying keras/utils/multi_gpu_utils.py -> build/lib/keras/utils
copying keras/utils/np_utils.py -> build/lib/keras/utils
copying keras/utils/test_utils.py -> build/lib/keras/utils
copying keras/utils/vis_utils.py -> build/lib/keras/utils
copying keras/utils/__init__.py -> build/lib/keras/utils
creating build/lib/keras/wrappers
copying keras/wrappers/scikit_learn.py -> build/lib/keras/wrappers
copying keras/wrappers/__init__.py -> build/lib/keras/wrappers
creating build/bdist.linux-x86_64
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/docs
copying build/lib/docs/autogen.py -> build/bdist.linux-x86_64/egg/docs
copying build/lib/docs/__init__.py -> build/bdist.linux-x86_64/egg/docs
creating build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/activations.py -> build/bdist.linux-x86_64/egg/keras
creating build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/densenet.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/imagenet_utils.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/inception_resnet_v2.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/inception_v3.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/mobilenet.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/mobilenetv2.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/nasnet.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/resnet50.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/vgg16.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/vgg19.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/xception.py -> build/bdist.linux-x86_64/egg/keras/applications
copying build/lib/keras/applications/__init__.py -> build/bdist.linux-x86_64/egg/keras/applications
creating build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/backend/cntk_backend.py -> build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/backend/common.py -> build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/backend/tensorflow_backend.py -> build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/backend/theano_backend.py -> build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/backend/__init__.py -> build/bdist.linux-x86_64/egg/keras/backend
copying build/lib/keras/callbacks.py -> build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/constraints.py -> build/bdist.linux-x86_64/egg/keras
creating build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/boston_housing.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/cifar.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/cifar10.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/cifar100.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/fashion_mnist.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/imdb.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/mnist.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/reuters.py -> build/bdist.linux-x86_64/egg/keras/datasets
copying build/lib/keras/datasets/__init__.py -> build/bdist.linux-x86_64/egg/keras/datasets
creating build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/base_layer.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/input_layer.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/network.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/saving.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/sequential.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/topology.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/training.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/training_arrays.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/training_generator.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/training_utils.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/engine/__init__.py -> build/bdist.linux-x86_64/egg/keras/engine
copying build/lib/keras/initializers.py -> build/bdist.linux-x86_64/egg/keras
creating build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/advanced_activations.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/convolutional.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/convolutional_recurrent.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/core.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/cudnn_recurrent.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/embeddings.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/local.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/merge.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/noise.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/normalization.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/pooling.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/recurrent.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/wrappers.py -> build/bdist.linux-x86_64/egg/keras/layers
copying build/lib/keras/layers/__init__.py -> build/bdist.linux-x86_64/egg/keras/layers
creating build/bdist.linux-x86_64/egg/keras/legacy
copying build/lib/keras/legacy/interfaces.py -> build/bdist.linux-x86_64/egg/keras/legacy
copying build/lib/keras/legacy/layers.py -> build/bdist.linux-x86_64/egg/keras/legacy
copying build/lib/keras/legacy/__init__.py -> build/bdist.linux-x86_64/egg/keras/legacy
copying build/lib/keras/losses.py -> build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/metrics.py -> build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/models.py -> build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/objectives.py -> build/bdist.linux-x86_64/egg/keras
copying build/lib/keras/optimizers.py -> build/bdist.linux-x86_64/egg/keras
creating build/bdist.linux-x86_64/egg/keras/preprocessing
copying build/lib/keras/preprocessing/image.py -> build/bdist.linux-x86_64/egg/keras/preprocessing
copying build/lib/keras/preprocessing/sequence.py -> build/bdist.linux-x86_64/egg/keras/preprocessing
copying build/lib/keras/preprocessing/text.py -> build/bdist.linux-x86_64/egg/keras/preprocessing
copying build/lib/keras/preprocessing/__init__.py -> build/bdist.linux-x86_64/egg/keras/preprocessing
copying build/lib/keras/regularizers.py -> build/bdist.linux-x86_64/egg/keras
creating build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/conv_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/data_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/generic_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/io_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/layer_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/multi_gpu_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/np_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/test_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/vis_utils.py -> build/bdist.linux-x86_64/egg/keras/utils
copying build/lib/keras/utils/__init__.py -> build/bdist.linux-x86_64/egg/keras/utils
creating build/bdist.linux-x86_64/egg/keras/wrappers
copying build/lib/keras/wrappers/scikit_learn.py -> build/bdist.linux-x86_64/egg/keras/wrappers
copying build/lib/keras/wrappers/__init__.py -> build/bdist.linux-x86_64/egg/keras/wrappers
copying build/lib/keras/__init__.py -> build/bdist.linux-x86_64/egg/keras
byte-compiling build/bdist.linux-x86_64/egg/docs/autogen.py to autogen.cpython-36.pyc
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byte-compiling build/bdist.linux-x86_64/egg/keras/__init__.py to __init__.cpython-36.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying Keras.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO
copying Keras.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying Keras.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying Keras.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying Keras.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
zip_safe flag not set; analyzing archive contents...
creating dist
creating 'dist/Keras-2.2.0-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing Keras-2.2.0-py3.6.egg
Copying Keras-2.2.0-py3.6.egg to /usr/local/lib/python3.6/dist-packages
Adding Keras 2.2.0 to easy-install.pth file

Installed /usr/local/lib/python3.6/dist-packages/Keras-2.2.0-py3.6.egg
Processing dependencies for Keras==2.2.0
Searching for Keras-Preprocessing==1.0.1
Best match: Keras-Preprocessing 1.0.1
Adding Keras-Preprocessing 1.0.1 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Searching for Keras-Applications==1.0.2
Best match: Keras-Applications 1.0.2
Adding Keras-Applications 1.0.2 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Searching for h5py==2.8.0
Best match: h5py 2.8.0
Adding h5py 2.8.0 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Searching for PyYAML==3.13
Best match: PyYAML 3.13
Adding PyYAML 3.13 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Searching for six==1.11.0
Best match: six 1.11.0
Adding six 1.11.0 to easy-install.pth file

Using /usr/lib/python3/dist-packages
Searching for scipy==1.1.0
Best match: scipy 1.1.0
Adding scipy 1.1.0 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Searching for numpy==1.14.5
Best match: numpy 1.14.5
Adding numpy 1.14.5 to easy-install.pth file

Using /usr/local/lib/python3.6/dist-packages
Finished processing dependencies for Keras==2.2.0

That’s all for today. I think Python is seriously cool, handy indeed. I myself will still recommend Pytorch, but it seems Tensorflow and Keras are also very popular in North America.

Posted on Edited on In ,

I’m quite busy today. So, I’d just post some videos to show the performance about jiu bu gao su ni :laughing:
Click on the pictures to open an uploaded Facebook video.

1. Key Point Localization

1.1 Nobody - Yeah, it’s ME, 10 YEARS ago. How time flies…

Nobody - Yeah, it's ME

1.2 FRANCK - What a Canonical Annotated Face Dataset

FRANCK

2. Orientation

01_al_pacino 02_alanis_morissette 03_anderson_cooper
01_al_pacino.mp4 02_alanis_morissette.mp4 03_anderson_cooper.mp4
04_angelina_jolie 05_bill_clinton 06_bill_gates
04_angelina_jolie.mp4 05_bill_clinton.mp4 06_bill_gates.mp4
07_gloria_estefan 08_jet_li 09_julia_roberts
07_gloria_estefan.mp4 08_jet_li.mp4 09_julia_roberts.mp4
10_noam_chomsky 11_sylvester_stallone 12_tony_blair
10_noam_chomsky.mp4 11_sylvester_stallone.mp4 12_tony_blair.mp4
13_victoria_beckham 14_vladimir_putin
13_victoria_beckham.mp4 14_vladimir_putin.mp4

Posted on Edited on In ,

Yesterday is Canada Day… ^_^ Happy Canada Day everybody…

Happy Canada Day

Just notice this news about ImageAI today. So, I just had it tested for fun. I seriously don’t want to talk about ImageAI too much, you can follow the author’s github and it shouldn’t be that hard to have everything done in minutes.

1. Preparation

1.1 Prerequisite Dependencies

As described by on ImageAI’s Github, multiple Python dependencies need to be installed:

  • Tensorflow
  • Numpy
  • SciPy
  • OpenCV
  • Pillow
  • Matplotlib
  • h5py
  • Keras

All packages can be easily installed by command:

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pip3 install PackageName

Afterwards, ImageAI can be installed by a single command:

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pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl

1.2 CNN Models

Two models are adopted as in the examples prediction and detection.

2. Examples

2.1 Prediction

Simple examples are given at https://github.com/OlafenwaMoses/ImageAI/tree/master/imageai/Prediction.

I modified FirstPrediction.py a bit as follows:

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from imageai.Prediction import ImagePrediction
import os
import numpy as np

prediction = ImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath("/media/jiapei/Data/Programs/Python/ImageAI/resnet50_weights_tf_dim_ordering_tf_kernels.h5")
prediction.loadModel()


predictions, probabilities = prediction.predictImage(("./01.jpg"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
    print(eachPrediction + " : " + np.format_float_positional(eachProbability, precision=3))

For the 1st image:

ImageAI Test Image 1

From bash, you will get:

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$ python FirstPrediction.py 
2018-07-02 18:12:09.275412: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
convertible : 52.45954394340515
sports_car : 37.61279881000519
pickup : 3.1751133501529694
car_wheel : 1.817503571510315
minivan : 1.748703233897686

2.2 Detection

Simple examples are given at https://github.com/OlafenwaMoses/ImageAI/tree/master/imageai/Detection.

Trivial modification is also implemented upon FirstObjectDetection.py.

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from imageai.Detection import ObjectDetection
import os
import numpy as np

detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath( os.path.join("/media/jiapei/Data/Programs/Python/ImageAI/resnet50_coco_best_v2.1.0.h5"))
detector.loadModel()

input_image="./03.jpg"
output_image="./imageai_output_03.jpg"
detections = detector.detectObjectsFromImage(input_image, output_image)

for eachObject in detections:
    print(eachObject["name"] + " : " + np.format_float_positional(eachObject["percentage_probability"], precision=3))

2.2.1 For the 2nd image:

ImageAI Test Image 2

From bash, you will get

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$ python FirstDetection.py 
Using TensorFlow backend.
2018-07-02 18:23:09.634037: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-02 18:23:11.744790: W tensorflow/core/framework/allocator.cc:101] Allocation of 68300800 exceeds 10% of system memory.
2018-07-02 18:23:11.958081: W tensorflow/core/framework/allocator.cc:101] Allocation of 68403200 exceeds 10% of system memory.
2018-07-02 18:23:12.174739: W tensorflow/core/framework/allocator.cc:101] Allocation of 68403200 exceeds 10% of system memory.
2018-07-02 18:23:12.433540: W tensorflow/core/framework/allocator.cc:101] Allocation of 68403200 exceeds 10% of system memory.
2018-07-02 18:23:12.694631: W tensorflow/core/framework/allocator.cc:101] Allocation of 68403200 exceeds 10% of system memory.
2018-07-02 18:23:16.267111: W tensorflow/core/framework/allocator.cc:101] Allocation of 64224000 exceeds 10% of system memory.
2018-07-02 18:23:16.370939: W tensorflow/core/framework/allocator.cc:101] Allocation of 64224000 exceeds 10% of system memory.
2018-07-02 18:23:16.403353: W tensorflow/core/framework/allocator.cc:101] Allocation of 67435200 exceeds 10% of system memory.
person : 55.596935749053955
person : 66.90954566001892
person : 67.96322464942932
person : 50.80411434173584
bicycle : 64.87574577331543
bicycle : 72.0929205417633
person : 80.02063035964966
person : 85.82872748374939
truck : 59.56767797470093
person : 66.69963002204895
person : 79.37889695167542
person : 64.81361389160156
bus : 65.35580158233643
bus : 97.16107249259949
bus : 68.20474863052368
truck : 67.65954494476318
truck : 77.73774266242981
bus : 69.96590495109558
truck : 69.54039335250854
car : 61.26518249511719
car : 59.965676069259644

And, under the program folder, you will get an output image:

ImageAI Output Image for Test Image 2

2.2.2 For the 3rd image:

ImageAI Test Image 3

From bash, you will get

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$ python FirstDetection.py 
Using TensorFlow backend.
2018-07-02 18:25:24.919351: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
person : 53.27633619308472
person : 52.71329879760742
person : 63.67729902267456
person : 55.00321388244629
person : 74.53054189682007
person : 51.54905915260315
motorcycle : 59.057921171188354
bus : 93.79504919052124
bus : 86.21828556060791
bus : 77.143394947052
person : 59.69809293746948
car : 71.79147601127625
car : 60.15858054161072
person : 62.758803367614746
person : 58.786213397979736
person : 76.49624943733215
car : 56.977421045303345
person : 67.86248683929443
person : 50.977784395217896
person : 52.3215651512146
motorcycle : 52.81376242637634
person : 76.79281234741211
motorcycle : 74.65972304344177
person : 55.96961975097656
person : 68.15704107284546
motorcycle : 56.21282458305359
bicycle : 71.78951501846313
motorcycle : 69.68616843223572
bicycle : 91.09067916870117
motorcycle : 83.16765427589417
motorcycle : 61.57424449920654

And, under the program folder, you will get an output image:

ImageAI Output Image for Test Image 3

Posted on Edited on In ,

It has been quite a while that my VOSM has NOT been updated. My bad for sure. But today, I have it updated, and VOSM-0.3.5 is released. Just refer to the following 3 pages on github:

We’ll still explain a bit on How to use VOSM in the following:

1. Building

1.1 Building Commands

Currently, there are 7 types of models to be built. Namely, there are 7 choices for the parameter “-t”:

  • SM
  • TM
  • AM
  • IA
  • FM
  • SMLTC
  • SMNDPROFILE
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3
4
5
6
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$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "SM" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "TM" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "AM" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "IA" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "FM" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "SMLTC" -l 4 -p 0.95
$ testsmbuilding -o "./output" -a "./annotations/training/" -i "./images/training/" -s "../VOSM/shapeinfo/IMM/ShapeInfo.txt" -d "IMM" -c 1 -t "SMNDPROFILE" -l 4 -p 0.95

1.2 Output Folders

After these 7 commands, 9 folders will be generated:

  • Point2DDistributionModel
  • ShapeModel
  • TextureModel
  • AppearanceModel
  • AAMICIA
  • AFM
  • AXM
  • ASMLTCs
  • ASMNDProfiles

1.3 Output Images

Under folder TextureModel, 3 key images are generated:

Reference.jpg edges.jpg ellipses.jpg
Reference.jpg edges.jpg ellipses

Under folder AAMICIA, another 3 key images are generated:

m_IplImageTempFace.jpg m_IplImageTempFaceX.jpg m_IplImageTempFaceY.jpg
m_IplImageTempFace.jpg - same as Reference.jpg m_IplImageTempFaceX.jpg m_IplImageTempFaceY.jpg

2. Fitting

2.1 Fitting Commands

Current VOSM supports 5 fitting methods.

  • ASM_PROFILEND
  • ASM_LTC
  • AAM_BASIC
  • AAM_CMUICIA
  • AAM_IAIA
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$ testsmfitting -o "./output/" -t "ASM_PROFILEND" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true
$ testsmfitting -o "./output/" -t "ASM_LTC" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true
$ testsmfitting -o "./output/" -t "AAM_BASIC" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true
$ testsmfitting -o "./output/" -t "AAM_CMUICIA" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true
$ testsmfitting -o "./output/" -t "AAM_IAIA" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true

2.2 Fitting Results

Let’s just take ASM_PROFILEND as an example.

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$ testsmfitting -o "./output/" -t "ASM_PROFILEND" -i "./images/testing/" -a "./annotations/testing/" -d "IMM" -s true -r true

All fitted images are generated under current folder, some are well fitted:

11-1m.jpg 33-4m.jpg 40-6m.jpg
11-1m.jpg 33-4m.jpg 40-6m

others are NOT well fitted:

12-3f.jpg 20-6m.jpg 23-4m.jpg
12-3f.jpg 20-6m.jpg 23-4m

2.3 Process of Fitting

The fitting process can also be recorded for each image if the parameter “-r” is enabled by -r true. Let’s take a look at what’s in folder 40-6m.

00.jpg 01.jpg 02.jpg
00.jpg 01.jpg 02.jpg
03.jpg 04.jpg 05.jpg
03.jpg 04.jpg 05.jpg
06.jpg 07.jpg 08.jpg
06.jpg 07.jpg 08.jpg
09.jpg 10.jpg 11.jpg
09.jpg 10.jpg 11.jpg
09.jpg 10.jpg 11.jpg
12.jpg 13.jpg 14.jpg
15.jpg 16.jpg
15.jpg 16.jpg

Clearly, the technology of pyramids is adopted during the fitting process.

Posted on Edited on In

To download videos from Youtube is sometimes required. Some Chrome plugins can be used to download Youtube videos, such as: Youtube Downloader. Other methods can also be found on various resources, such as: WikiHow.

In this blog, I’m going to cite (copy and paste) from WikiHow about How to download Youtube videos by using VLC.

STEP1: Copy Youtube URL

Find the Youtube video that you would like to download, and copy the Youtube URL.

STEP 2: Broadcast Youtube Video in VLC

Paste the URL under VLC Media Player->Media->Open Network Stream->Network Tab->Please enter a network URL:, and Play:

Video Broadcasting

STEP 3: Get the Real Location of Youtube Video

Then, copy the URL under Tools->Codec Information->Codec->Location:

Current Media Information

STEP 4: Save Video As

Finally, paste the URL in Chrome:

Save Video As

and Save video as….

Posted on Edited on In

1. IRC and Freenode

People have been using IRC for decades.

Freenode is an IRC network used to discuss peer-directed projects.

cited from Wikipedia.

It’s recommended to have some background about IRC and Freenode on Wikipedia:

2. Popular IRC Clients

Lots of IRC clients can be found on Google. Here, we just summarize some from IRC Wikipedia:

3. 7 Best IRC Clients for Linux

Please just refer to https://www.tecmint.com/best-irc-clients-for-linux/.

4. WeeChat in Linux

4.1 Installation

We can of course directly download the package from WeeChat Download, and have it installed from source. However, installing WeeChat from repository is recommended.

Step 1: Add the WeeChat repository

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$ sudo sh -c 'echo "deb https://weechat.org/ubuntu $(lsb_release -cs) main" >> /etc/apt/sources.list.d/WeeChat.list'

Step 2: Add WeeChat repository key

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$ sudo apt-key adv --keyserver keys.gnupg.net --recv-keys 11E9DE8848F2B65222AA75B8D1820DB22A11534E

Step 3: WeeChat Installation

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$ sudo apt update
$ sudo apt install WeeChat

4.2 How to use WeeChat

Refer to WeeChat Official Documentation.

Step 1: Run WeeChat

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$ weechat

will display:

WeeChat

And we FIRST type in /help

WeeChat

Step 2: Connect Freenode

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/connect freenode

WeeChat

Step 3: Join a Channel

Here, we select channel OpenCV as an example:

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/join #opencv

WeeChat

Step 4: Close a Channel

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/close

5. Pidgin (Optional)

We’re NOT going to talk about it in this blog.

Posted on Edited on In

To DIY a home security camera is comparatively simple by using a low-cost embedded board with Linux installed. There are **ONLY 3 steps ** in total.

STEP1: Install MOTION from Repository

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$ sudo apt install motion

STEP 2: start_motion_daemon=yes

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$ sudo vim /etc/default/motion

Change start_motion_daemon from no to yes.

STEP 3: stream_localhost off

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$ sudo vim /etc/motion/motion.conf

Change stream_localhost on to stream_localhost off.

STEP 4: Restart Motion Service

Run the following command:

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$ sudo /etc/init.d/motion restart

STEP 5: Run Motion

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$ sudo motion

STEP 6: Video Surveillance Remotely

Open up Chrome and type in the IP address:8081 to show the captured video at 1FPS. In my case, 192.168.0.86:8081, and the video effect is as:

Motion Video Surveillance