[OpenGL] Draw 3D and Texture with BMP image using OpenGL Part I

It has been more than half of year not posting any article in my blogger and that makes me a little bit embarrassed. Well, for breaking this situation, I just quickly explain a simple concept about OpenGL coordinate.

Before taking an adventure to OpenGL, we have to know the coordinate in OpenGL first. Please check out the following graph. As we can see, the perspective of z position is pointed to us and it's so different from OpenCV.

If we take a look closer, the following OpenGL code can be explained in the picture below:


glBegin(GL_QUADS) # Start Drawing The Cube

# Front Face (note that the texture's corners have to match the quad's corners)
glTexCoord2f(1.0, 0.0); glVertex3f(-1.0, -1.0, 1.0) # Bottom Left Of The Texture and Quad
glTexCoord2f(1.0, 1.0); glVertex3f( 1.0, -1.0, 1.0) # Bottom Right Of The Texture and Quad
glTexCoord2f(0.0, 1.0); glVertex3f( 1.0, 1.0, 1.0) # Top Right Of The Texture and Quad
glTexCoord2f(0.0, 0.0); glVertex3f(-1.0, 1.0, 1.0) # Top Left Of The Texture and Quad

# Back Face
glTexCoord2f(1.0, 0.0); glVertex3f(-1.0, -1.0, -1.0) # Bottom Right Of The Texture and Quad
glTexCoord2f(1.0, 1.0); glVertex3f(-1.0, 1.0, -1.0) # Top Right Of The Texture and Quad
glTexCoord2f(0.0, 1.0); glVertex3f( 1.0, 1.0, -1.0) # Top Left Of The Texture and Quad
glTexCoord2f(0.0, 0.0); glVertex3f( 1.0, -1.0, -1.0) # Bottom Left Of The Texture and Quad

# Top Face
glTexCoord2f(0.0, 1.0); glVertex3f(-1.0, 1.0, -1.0) # Top Left Of The Texture and Quad
glTexCoord2f(0.0, 0.0); glVertex3f(-1.0, 1.0, 1.0) # Bottom Left Of The Texture and Quad
glTexCoord2f(1.0, 0.0); glVertex3f( 1.0, 1.0, 1.0) # Bottom Right Of The Texture and Quad
glTexCoord2f(1.0, 1.0); glVertex3f( 1.0, 1.0, -1.0) # Top Right Of The Texture and Quad

# Bottom Face
glTexCoord2f(1.0, 1.0); glVertex3f(-1.0, -1.0, -1.0) # Top Right Of The Texture and Quad
glTexCoord2f(0.0, 1.0); glVertex3f( 1.0, -1.0, -1.0) # Top Left Of The Texture and Quad
glTexCoord2f(0.0, 0.0); glVertex3f( 1.0, -1.0, 1.0) # Bottom Left Of The Texture and Quad
glTexCoord2f(1.0, 0.0); glVertex3f(-1.0, -1.0, 1.0) # Bottom Right Of The Texture and Quad

# Right face
glTexCoord2f(1.0, 0.0); glVertex3f( 1.0, -1.0, -1.0) # Bottom Right Of The Texture and Quad
glTexCoord2f(1.0, 1.0); glVertex3f( 1.0, 1.0, -1.0) # Top Right Of The Texture and Quad
glTexCoord2f(0.0, 1.0); glVertex3f( 1.0, 1.0, 1.0) # Top Left Of The Texture and Quad
glTexCoord2f(0.0, 0.0); glVertex3f( 1.0, -1.0, 1.0) # Bottom Left Of The Texture and Quad

# Left Face
glTexCoord2f(0.0, 0.0); glVertex3f(-1.0, -1.0, -1.0) # Bottom Left Of The Texture and Quad
glTexCoord2f(1.0, 0.0); glVertex3f(-1.0, -1.0, 1.0) # Bottom Right Of The Texture and Quad
glTexCoord2f(1.0, 1.0); glVertex3f(-1.0, 1.0, 1.0) # Top Right Of The Texture and Quad
glTexCoord2f(0.0, 1.0); glVertex3f(-1.0, 1.0, -1.0) # Top Left Of The Texture and Quad

glEnd(); # Done Drawing The Cube



So, right now, if we just look at the first section of the code as follows, it represents the blue quadrilateral for the front face.

And, the texture coordinate represents the direction of the image.

In sum, we can see the result just like this:

[Haar Classifier] Train your own OpenCV Haar classifier

I just keep a record for myself because there are a lot of documents teaching how to train your haar classifier and almost of them seem to don't work well. The following 2 items are clear and easy to understand.

The Data Image Source (cars) I use.
http://cogcomp.cs.illinois.edu/Data/Car/

1. Train your own OpenCV Haar classifier
https://github.com/mrnugget/opencv-haar-classifier-training

find ./positive_images -iname "*.pgm" > positives.txt
find ./negative_images -iname "*.pgm" > negatives.txt

perl bin/createsamples.pl positives.txt negatives.txt samples 550\
  "opencv_createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1\
  -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 48 -h 24"

python ./tools/mergevec.py -v samples/ -o samples.vec

opencv_traincascade -data classifier -vec samples.vec -bg negatives.txt\
  -numStages 10 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 1000\
  -numNeg 600 -w 48 -h 24 -mode ALL -precalcValBufSize 1024\
  -precalcIdxBufSize 1024

2. OpenCV Tutorial: Training your own detector | packtpub.com
https://www.youtube.com/watch?v=WEzm7L5zoZE
find pos/ -name '*.pgm' -exec echo \{\} 1 0 0 100 40 \; > cars.info
find neg/ -name '*.pgm' > bg.txt

opencv_createsamples -info cars.info -num 550 -w 48 -h 24 -vec cars.vec
opencv_createsamples -w 48 -h 24 -vec cars.vec

opencv_traincascade -data data -vec cars.vec -bg bg.txt \
 -numPos 500 -numNeg 500 -numStages 10 -w 48 -h 24 -featureType LBP

P.S: Which one is best? I don't know...

[Image] How to resize, convert & modify images from the Linux

Installation
$ sudo apt-get install imagemagick

Converting Between Formats
$ convert howtogeek.png howtogeek.jpg

You can also specify a compression level for JPEG images:
$ convert howtogeek.png -quality 95 howtogeek.jpg

Resizing Images
$ convert example.png -resize 200×100 example.png
  - to force the image to become a specific size – even if it messes up the aspect ratio
$ convert example.png -resize 200×100! example.png

$ convert example.png -resize 200 example.png
$ convert example.png -resize x100 example.png

Rotating an Image
convert howtogeek.jpg -rotate 90 howtogeek-rotated.jpg

Applying Effects
ImageMagick can apply a variety of effects to an image.
  - For example,   the following command applies the “charcoal” effect to an image:
$ convert howtogeek.jpg -charcoal 2 howtogeek-charcoal.jpg
  - the “Implode” effect with a strength of 1:
# convert howtogeek.jpg -implode 1 howtogeek-imploded.jpg

Batch Processing
for file in *.png; do convert $file -rotate 90 rotated-$file; done



Reference:
http://www.howtogeek.com/109369/how-to-quickly-resize-convert-modify-images-from-the-linux-terminal/

[TensorFlow] My case to install TensorFlow with GPU enabled

My Operation System is Ubuntu 14.04 LTS 5 and GPU card is GeForce GTX 750Ti

1. Go to nvidia.com and download the driver (NVIDIA-Linux-x86_64-367.44.sh)

2. For Nvidia to find linux header files (*):
$ sudo apt-get install build-essential linux-headers-$(uname -r)

3. To enable full screen text mode (nomodeset):
$ sudo gedit /etc/default/grub
>> Edit GRUB_CMDLINE_LINUX_DEFAULT="quiet splash nomodeset"
Save it and reboot
$ sudo update-grub
$ sudo reboot

4. Log into with Ctl +Alt + F1

5. Stop the X Server service
$ sudo service lightdm stop

6. Install nVidia driver
$ sudo ./NVIDIA-Linux-x86_64-367.44.sh

7. Install CUDA (GPUs on Linux)

Download and install Cuda Toolkit

https://developer.nvidia.com/cuda-downloads

sudo dpkg -i cuda-repo-ubuntu1404-8-0-local_8.0.44-1_amd64.deb

sudo apt-get update

sudo apt-get install cuda

8. Download and install cuDNN

https://developer.nvidia.com/cudnn

tar xvzf cudnn-8.0-linux-x64-v5.1.tgz

cd cuda
sudo cp include/cudnn.h /usr/local/cuda-8.0/include
sudo cp lib64/* /usr/local/cuda-8.0/lib64
sudo chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*

9. You also need to set the LD_LIBRARY_PATH and CUDA_HOME environment variables. Consider adding the commands below to your ~/.bash_profile. These assume your CUDA installation is in /usr/local/cuda:

$ vim ~/.bashrc

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64"
export CUDA_HOME=/usr/local/cuda-8.0
export PATH="$CUDA_HOME/bin:$PATH"
export PATH="$PATH:$HOME/bin"

10. To install TensorFlow for Ubuntu/Linux 64-bit, GPU enabled:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.1-cp27-none-linux_x86_64.whl

To find out which device is used, you can enable log device placement like this:
$ python
>>>> import tensorflow as tf
>>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

[OpenCV] To install OpenCV on Debian and create a test project using Netbeans

This document is the steps to install OpenCV on Debian and create a test project using Netbeans for my reference in case.

#Prepare the build environment GCC、Cmake、pkgconfig

$sudo apt-get -y install build-essential cmake pkg-config

#Install ImageI/O libraries

$sudo apt-get -y install libjpeg62-dev libtiff4-dev libjasper-dev

#Install Viode I/O libraries

$sudo apt-get -y install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
$sudo apt-get -y install libdc1394-22-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev

#Install GTK+2.x and QT libraries

$sudo apt-get -y install  libgtk2.0-dev libqt4-dev

[Hadoop] Setting up a Single Node Cluster

Basically these resource links are good enough to do a single node cluster of Hadoop MapReduce. But I still want to add some comments for my reference.
http://www.thebigdata.cn/Hadoop/15184.html
http://www.powerxing.com/install-hadoop/

Login the user "hadoop"

# sudo su - hadoop

Go to the location of Hadoop

# /usr/local/hadoop

Add the variables in ~/.bashrc

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64
export HADOOP_HOME=/usr/local/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export HADOOP_INSTALL=/usr/local/hadoop
export PATH=$PATH:$HADOOP_INSTALL/bin
export PATH=$PATH:$HADOOP_INSTALL/sbin
export HADOOP_MAPRED_HOME=$HADOOP_INSTALL
export HADOOP_COMMON_HOME=$HADOOP_INSTALL
export HADOOP_HDFS_HOME=$HADOOP_INSTALL
export YARN_HOME=$HADOOP_INSTALL

Modify $JAVA_HOME in etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

Start dfs and yarn

# sbin/start-dfs.sh

# sbin/start-yarn.sh

Finally, we can try the Hadoop MapReduce example as follows:

# bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-z.]+'

P.S:
In order to forcefully let the namenode leave safemode, following command should be executed:

# hdfs dfsadmin -safemode leave

[Tensorflow] Fizz-Buzz example enhancement

I am just based on this Fizz-Buzz example as below to add 2nd convolution layer and guess what? The result is quicker to be learn. But, this is just the first step to learn "Deep Learning"...
There is still a lot of things and knowledge that need to learn more.
http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/

Before



After


Reference
http://www.slideshare.net/WrangleConf/wrangle-2016-lightning-talk-fizzbuzz-in-tensorflow

[Neutron] The first glance of L3HA mode in OpenStack Neutron ( Liberty version )

I just quickly take the first glance of L3HA mode in OpenStack Neutron ( Liberty version ) and is based on my tenant environment as follows:

My tenant environment

# neutron router-list

# neutron net-list

# neutron subnet-list

The Topology view looks like this:

Here I have 2 instances in my tenant:


So, if I use the instance: daanny_vm1 to ping danny_vm2, due to the different subnets, this action will trigger L3 vrouter function.

# ping 192.168.66.4 ( danny_vm2 )

# ip netns exec qrouter-f1e03fef-cccf-43de-9d35-56d11d636765 tcpdump -eln -i qr-4433f31f-5d icmp


The interface qr-4433f31f-5d is my subnet 192.168.44.0/24's gateway port as follows:

# neutron --os-tenant-name danny port-list | grep 4433f31f-5d
| 4433f31f-5d93-4fe4-868a-04ddcc38be20 |                                                 | fa:16:3e:25:22:b3 | {"subnet_id": "d169f180-4304-42f0-b11f-e094287bcd00", "ip_address": "192.168.44.1"}  |

Keepalived related

L3HA mode is havily relied on the daemon: Keepalived and this daemon is existed in qrouter namespace.

# vi /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765/keepalived.conf

vrrp_instance VR_1 {
    state BACKUP
    interface ha-857640ad-a6
    virtual_router_id 1
    priority 50
    garp_master_delay 60
    nopreempt
    advert_int 2
    track_interface {
        ha-857640ad-a6
    }
    virtual_ipaddress {
        169.254.0.1/24 dev ha-857640ad-a6
    }
    virtual_ipaddress_excluded {
        10.12.20.32/16 dev qg-f02984c6-dc
        10.12.20.33/32 dev qg-f02984c6-dc
        192.168.44.1/24 dev qr-4433f31f-5d
        192.168.55.1/24 dev qr-16e20a36-fc
        192.168.66.1/24 dev qr-35235c4f-64
        fe80::f816:3eff:fe0d:2702/64 dev qr-16e20a36-fc scope link
        fe80::f816:3eff:fe25:22b3/64 dev qr-4433f31f-5d scope link
        fe80::f816:3eff:fe51:30a1/64 dev qg-f02984c6-dc scope link
        fe80::f816:3eff:fe8f:a85b/64 dev qr-35235c4f-64 scope link
    }
    virtual_routes {
        0.0.0.0/0 via 10.12.0.254 dev qg-f02984c6-dc
    }
}

There are other two files under /var/lib/neutron/ha_confs/<< qrouter uuid >>/

neutron-keepalived-state-change.log ==> log file

state ==> HA status

# find -L /proc/[1-9]*/task/*/ns/net -samefile /run/netns/qrouter-f1e03fef-cccf-43de-9d35-56d11d636765 | cut -d/ -f5

2276895

2276896

2277216

2277217

3284547

# ps aux | grep -e "2276895|2276896|2277216|2277217|3284547"

neutron  2276895  0.0  0.0 126160 41364 ?        S    Jul22   0:00 /usr/bin/python2.7 /usr/bin/neutron-keepalived-state-change --router_id=f1e03fef-cccf-43de-9d35-56d11d636765 --namespace=qrouter-f1e03fef-cccf-43de-9d35-56d11d636765 --conf_dir=/var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765 --monitor_interface=ha-857640ad-a6 --monitor_cidr=169.254.0.1/24 --pid_file=/var/lib/neutron/external/pids/f1e03fef-cccf-43de-9d35-56d11d636765.monitor.pid --state_path=/var/lib/neutron --user=119 --group=125

root     2276896  0.0  0.0   6696   756 ?        S    Jul22   0:00 ip -o monitor address

root     2277216  0.0  0.0  44752   856 ?        Ss   Jul22   0:13 keepalived -P -f /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765/keepalived.conf -p /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765.pid -r /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765.pid-vrrp

root     2277217  0.0  0.0  51148  1712 ?        S    Jul22   0:24 keepalived -P -f /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765/keepalived.conf -p /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765.pid -r /var/lib/neutron/ha_confs/f1e03fef-cccf-43de-9d35-56d11d636765.pid-vrrp

neutron  3284547  0.0  0.0 172176 36032 ?        S    Jul22   0:00 /usr/bin/python2.7 /usr/bin/neutron-ns-metadata-proxy --pid_file=/var/lib/neutron/external/pids/f1e03fef-cccf-43de-9d35-56d11d636765.pid --metadata_proxy_socket=/var/lib/neutron/metadata_proxy --router_id=f1e03fef-cccf-43de-9d35-56d11d636765 --state_path=/var/lib/neutron --metadata_port=8775 --metadata_proxy_user=119 --metadata_proxy_group=125 --verbose --log-file=neutron-ns-metadata-proxy-f1e03fef-cccf-43de-9d35-56d11d636765.log --log-dir=/var/log/neutron

# neutron l3-agent-list-hosting-router f1e03fef-cccf-43de-9d35-56d11d636765

Then, we learn that the master vrouter is in node-8. 

There are other ways to know which node is master:

1. use the command to see if the interface qr-xxxxx and qg-xxxxx have ip address or not. If yes, this node is master.

• ip netns exec qrouter-f1e03fef-cccf-43de-9d35-56d11d636765 ip a

2. Check the following file that contains "master" or not.

• vim /var/lib/neutron/ha_confs/<< qrouter uuid >>/state

For more details:

http://www.slideshare.net/orimanabu/l3-ha-vrrp20141201