[Job opening] Outstanding postdoc position for Computer vision and machine learning

I recently received an exciting research grant in computer vision and machine learning. We have an outstanding postdoctoral research associate position. Check the link below for how to apply. We are looking forward to your application.

See HERE on LinkedIn or HERE at LANL website (PDF here if it is not retrievable).

 

 

[Paper published] Check out our new paper for image classification with great results using only small sets of training data

Do you have great idea(s) using machine learning but stopped by the fact that you do not have enough training (image) data? Check out our newly accepted KDD workshop paper for a novel solution.

New KDD 2019 MLG (the 15th International Workshop on Mining and Learning with Graphs) Workshop  paper for computer vision and image analysis led by Liping has been accepted:

Image classification using topological features automatically extracted from graph representation of images

A PDF of the paper can be found at the Workshop website or HERE

BibTeX Entry:

@inproceedings{mlg2019_7,
title={Image classification using topological features automatically extracted from graph representation of images},
author={Yang, Liping and Oyen, Diane and Wohlberg, Brendt},
booktitle={Proceedings of the 15th International Workshop on Mining and Learning with Graphs (MLG)},
year={2019}
}

 

 

 

 

 

[Paper published] Check out our new computer vision and image analysis paper for skeleton extraction

New CVPR 2019 Workshop paper for computer vision and image analysis led by Liping has been published:

A Novel Algorithm for Skeleton Extraction From Images Using Topological Graph Analysis. 

A PDF of the paper can be found HERE. (Check HERE if it is not retrievable on the http://openaccess.thecvf.com) [Acceptance rate < 10/32 = 31.25%]

4. Yang, L. and Worboys, M. Generation of navigation graphs for indoor space. International Journal of Geographical Information Science, 29(10): 1737-1756, 2015. [Click here (PDF) to download a draft of this paper]

[Paper published] Check out our new (deep) machine learning paper for flood detection

New machine/deep learning paper led by Liping: Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event

A full-text view-only version of the paper can be found via the link: https://rdcu.be/bpUvx.

 

Conda commands (create virtual environments for python with conda)

This post provides some commonly used conda command.

(I use Mac and Linux OS, so the commands here assume that you use Mac and Linux OS.  If you are using Windows, most of the conda commands should be the same, but some command might be slightly different. For example, the one I am aware of is that the command for activating and deactivating conda environment is a bit different, you do not need to add “source” in the command like Mac and Linux OS do).

(To use conda, you need to have either Miniconda or Anaconda installed on your machine. Check out here for installing Miniconda on Mac, and here for installing Miniconda on CentOS7/RedHat7. Check here if you would like to know the difference between Miniconda and Anaconda.)

The first part of this post introduces common conda commands, and the second part of this post provides some package installation ( e.g., OpenCV, scikit-image, jupyter notebook etc.) using conda.

Common conda commands

Check whether conda is installed / check conda version info

In your terminal, type in the following command

$ conda -V 

#if you see something like below it means conda is installed, and it provides its version info. 

conda 4.5.11

Check conda is up to date

In your terminal, type in

$ conda update conda

# Upadate any packages if necessary by typing y to proceed.

Create a virtual environment using conda for your project

with conda, we can create virtual environment for different versions of pythons.

To see a list of available python versions available in conda repository, type the following command with regular expression and then press enter.

$ conda search "^python$"  # you should see a list of python versions, including python2.X and python3.X

Now, let us create a virtual environment with conda

use the following command to create a virtual environment for a python version you specified, replace x.x with the Python version you would like to use.

$ conda create -n yourenvname python=x.x 


# for example, the following command will create a Python 3.6 conda virtual environment called "conda-venv3_py36".

$ conda create -n conda-venv3_py36 python=3.6 

# the following command will create a Python 2.7 conda virtual environment called "conda-venv_py27".

$ conda create -n conda-venv_py27 python=2.7

Press y to proceed. This will install the Python version (and all the associated anaconda packaged libraries if you installed conda via Anaconda) at “path_to_your_anaconda_location/anaconda/envs/yourenvname” or at “path_to_your_miniconda_location/miniconda/envs/yourenvname”

Activate your virtual environment

Once we created a virtual environment using conda, before we start to using it, we need to activate it first each time we need to use the virtual environment.

To activate or switch into your virtual environment, simply type the following where yourenvname is the name you gave to your environement when creating.

$ source activate yourenvname

Activating a conda environment modifies the PATH and shell variables to point to the specific isolated Python  you created. Note that the command prompt will change to indicate which conda environemnt you are currently in by prepending (yourenvname)

If you do not remember your virtualenv or do not want to type it, you can use the following command to see a list of all your environments,

$ conda info -e

Install (additional) Python packages to a virtual environment

To install packages only to your virtual environment (not system wide), enter the following command

$ conda install -n yourenvname package-name

# yourenvname is the name of your environment, and package-name is the name of the package you would like to install.

# Note that if not specify “-n yourenvname” will install the package to the root Python installation.

Or you can simply first activate and into the virtual environment you would like to install packages [see the command above in (4)], and then use the following command

$ conda install package-name

(For some specific packages installation, check section 2 below in this post.)

Deactivate your virtual environment.

Each time once we finish working in your virtual environment created using conda, we will need to deactivate the virtual environment to exit from it.

To end a session in the current virtual environment, enter the following command .

$ source deactivate

# Note that we do not need to specify the envname - whichever is currently active will be deactivated, and the PATH and shell variables will return to normal.

Delete a virtual environment

When we do not need a virtual environment created by conda any more, we can simply remove it by the following command.

conda remove -n yourenvname -all # yourenvname is the name of the environment you would like to delete. # (You may instead use $ conda env remove -n myenv.) 

To verify that the environment was removed, in your Terminal window or an conda Prompt, run the following

$ conda info -e

The environments list that displays should not show the removed environment.

Cloning a conda virtual environment

To make  an exact copy of an environment by creating a clone of it, using the following command,

$ conda create --name myclone --clone myenv


# NOTE: replace myclone with the name of the new environment. Replace myenv with the name of the existing environment that you want to copy. # see the following for an example of cloning py35 and naming the new copy as py35-2 $ conda create --clone py35 --name py35-2 

To verify that the copy was made:

conda info -e

In the environments list that displays, you should see both the source environment and the new copy.

Viewing a list of your environments

To see a list of all of your conda virtual environments, in your Terminal or in one of your conda virtual environment, run one of the following commands:

$ conda info -e

OR

$ conda info --envs 

OR

$ conda env list

You will see a list of all your conda environments, and the active environment is shown with *.

Viewing a list of the packages in a conda environment

To see a list of all packages installed in a specific environment,

  • If the environment is not activated, in your Terminal window or a conda prompt, run the following:
$ conda list -n myenv
  • If the environment is activated, in your Terminal window or a conda prompt, run the following:
$ conda list

To see whether a specific package is installed in a conda environment

  • If the environment is not activated, in your Terminal window or a conda prompt, run the following:
$ conda list -n myenv package-name

# for example, the following command will list the opencv versions installed in the conda environment you specified

$ conda list -n myenv opencv
  • If the environment is activated, in your Terminal window or a conda prompt, run the following:
$ conda list package-name

# for example, the following command will list the opencv versions installed in the current active conda environment you are in

$ conda list opencv

 

Package installation using conda

Before installing packages using conda, make sure to first create a conda virtual environment [see the command at (3) in section 1 above in this post] and then  activate and into the environment you would like to install the packages into [see the command for (4) in section 1 above in this post].

Install Numpy

  $ conda install numpy

Install Matplotlib

  $ conda install matplotlib

Install Keras 

$ conda install keras

This should also install tensorflow

Install h5py

  $ conda install h5py

Install Jupyter Notebook

$ conda install jupyter

Install IPython

  $ conda install ipython

Install OpenCV3 (https://opencv.org/)

  $ conda install -c conda-forge opencv 

The command above will install (by default) the latest version of opencv available in the conda repository. if you would like to specifiy which version of openCV to install, you can first use the following comamnd to check OpenCV versions available.

$ conda search "^openCV$"   # you should see a list of openCV versions.

Then you could use the following command to install the version of OpenCV you would like to install,

 $ conda install -c conda-forge opencv=x.x # for example, the following command will install openCV 3..4.1, instead of the current lastest version 3.4.2. Note that is opencv==3.4.1, not opencv=3.4, if opencv=3.4, it will install openCV3.4.2 (the latest version in 3.4 series).

$ conda install -c conda-forge opencv==3.4.1

Install Scikit-image

$ conda install -c conda-forge scikit-image

Install Django

use the following command to search what vesion of django is available in your conda environment.

$ conda search "^django$"

use the following command to install specific version of django you would like to install into your conda environment.

$ conda install -c conda-forge django==1.11.8

use the following to test whether the django is installed successfully in your conda environment.

$ python
Python 3.6.7 |Anaconda, Inc.| (default, Oct 23 2018, 19:16:44)
[GCC 7.3.0] on linux
Type “help”, “copyright”, “credits” or “license” for more information.
>>> import django
>>> print(django.__version__)
1.11.8
>>>

 

References

 

Install Miniconda on CentOS 7 / RedHat 7

This post introduces how to install Miniconda on CentOS 7 / RedHat 7. 

(Tested on CentOS 7 / RedHat 7, but it should work for Ubuntu OS as well. Note that you can install Miniconda / Anaconda onto your Linux OS even when you are not a sudo / root user.)

(For installing Miniconda on Mac, check out this post.)

(For commonly used conda commands check this post.)

(For the comparison among Anaconda, Miniconda, and Virtualenv with pip, check this post.)

Step 1: Open a Terminal window, type

$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh

Step2:  Run the following bash script to install it

$ sh Miniconda3-latest-Linux-x86_64.sh

Step3: To make the changes take effect, close the terminal and open a new Terminal window. 

Step 4: Test conda

In the newly open Terminal window, type the following

$ conda -V

# If you see something like the following, it means Miniconda is successfully installed on your Linux OS.

conda 4.5.11

Step 5: Uninstalling Miniconda

To uninstall Python Anconda/Miniconda, we just simply remove the installation folder and remove the environment variables set in .bashrc file. For my installation, it will be just like this.

$ rm -rf /usr/local/miniconda/
$ rm -rf /usr/local/anaconda/

Then, you can edit the ~/.bashrc file and remove the following entries added for Anaconda/Miniconda directory from your PATH environment variable

$ export PATH=" /usr/local/anaconda/bin:$PATH" 
$ export PATH=" /usr/local/miniconda3/bin:$PATH" 

 

 

For commonly used conda commands check this post.

For the comparison among Anaconda, Miniconda, and Virtualenv with pip, check this post.

References:

 

Install Miniconda on Mac

This post introduces how to install Miniconda on Mac.

(For installing Miniconda on Linux OS, check out this post.)

(For commonly used conda commands check this post.)

(For the comparison among Anaconda, Miniconda, and Virtualenv with pip, check this post.)

Step 1: download  Miniconda (bash installer) from

https://docs.conda.io/en/latest/miniconda.html 

see the highlighted in the pic below.

A file called Miniconda3-latest-MacOSX-x86_64.sh will be shown in your Downloads folder.

Step 2: Open a Terminal window.

(If you don’t know how to open a terminal window,  through lauchpad type in Terminal, you will see the application.)

In the terminal window, type in

$ cd Downloads

Step 3: run the bash “shell” script to install Miniconda

In the terminal window, type in the following.

$ bash Miniconda3-latest-MacOSX-x86_64.sh

Scroll through the license (press the Space bar or Enter to move through quickly), type ‘yes’ to approve the terms, and then accept all the installation defaults.

Step 4: Close the Terminal window, and open a new Terminal window.

in the newly opened Terminal window.

Type the following:

$ conda -V

If you see something like the following, it means you have successfully installed conda via miniconda on your Mac.

conda 4.5.11

Step 5: Uninstalling Miniconda

To uninstall Python Anconda/Miniconda, we just simply remove the installation folder and remove the environment variables set in the hidden file .bash_profile in your home directory. For my installation, it will be just like this.

$ rm -rf /users/my-user-name/miniconda/
$ rm -rf /users/my-user-name/anaconda/

Then, you can edit the .bash_profile file and remove the following entries added for Anaconda/Miniconda directory from your PATH environment variable.

# added by Miniconda3 installer
$ export PATH="/Users/my-user-name/miniconda3/bin:$PATH"
# added by Anaconda3 installer
$ export PATH="/Users/my-user-name/anaconda3/bin:$PATH"

If you do not know where the hidden .bash_profile is located and how to edit it, see below for detailed instructions.

(1) Open a new terminal and go to your home directory. You can do this by using the command below.

$ cd

(2) use the following command to list all files, including hidden files in your home directory.

$ ls -a 
# you should see there is a file called .bash_profile.

(3) Use the cat command to see the contents of the hidden file .bash_profile. Type the following command into your terminal.

$ cat .bash_profile

You will see something like the following (depends on what you installed, if you installed Miniconda3, you will only see the first two lines. If you installed Anaconda3, you will see the bottom two lines.

# added by Miniconda3 installer 
$ export PATH="/Users/my-user-name/miniconda3/bin:$PATH" 

# added by Anaconda3 installer 
$ export PATH="/Users/my-user-name/anaconda3/bin:$PATH"

(4) To remove installed Miniconda/Anaconda from your .bash_profile use the command below to edit the file using the nano editor.

$ nano .bash_profile

Remove the Miniconda /Anoconda path in your .bash_profile hidden file.

Then Type control + X to exit out of nano

Save changes by typing Y.

Close the terminal, and now Miniconda/Anaconda should be successfully uninstalled from your Mac.

 

(Tested on macOS Mojave. Note that you can install Miniconda onto your Mac even when you are not an admin user.)

For commonly used conda commands check this post.

For the comparison among Anaconda, Miniconda, and Virtualenv with pip, check this post.

 

 

 

 

 

 

Anaconda vs. Miniconda vs. Virtualenv

This post briefly introduces which to choose among Anaconda, Miniconda, and Virtualenv.

If you have used pip and virtualenv in the past, you can use conda to perform all of the same operations. Pip is a package manager, and virtualenv is an environment manager; and conda is both.

Specifically, conda is a packaging tool and installer that aims to do more than what pip does; it handles library dependencies outside of the Python packages as well as the Python packages themselves. Conda also creates a virtual environment, like virtualenv does.

Both Anaconda and Miniconda uses Conda as the package manager. The difference among Anaconda and Miniconda is that Miniconda only comes the package management system. So when you install it, there is just the management system and not coming with a bundle of pre-installed packages like Anaconda does. Once Conda is installed, you can then install whatever package you need from scratch along with any desired version of Python.

Choose Anaconda if you:

  • Are new to conda or Python
  • Prefer having Python and 720+ open source certified packages automatically installed at once
  • Have the time and disk space (a few minutes and 3 GB), and/or
  • Don’t want to install each of the packages you want to use individually.

Choose Miniconda if you:

  • Know what package(s) you need to install
  • Do not have time or disk space (about 3 GB) to install over 720+ packages (many of the packages are never used and could be easily installed when needed), and/or
  • Just want fast access to Python and the conda commands, and prefer to sorting out the other packages later.

Choose Virtualenv only when you have sudo access to the machine you are working on. It is much easier to setup conda rather than virtualenv for a regular (i.e., non sudo/root) user on a linux/Mac machine.

I use Miniconda myself (because it is much more light weight than Anaconda) when I need to setup python programming environment and when I do not have sudo privilege, and I use Virtualenv when I have sudo access on the machine.

(Thanks to  Dr. Brendt Wohlberg  for introducing Miniconda — Miniconda makes me switching from pip & virtualenv to conda.)

References

 

Install OpenCV3 into virtualenv on Mac

This post introduces how to install OpenCV3 into a virtualenv on Mac.

If you have not setup virtualenv on your mac, check my post here to do that before you proceed the tutorial in this post.

Let us get started.

Step 1: Activate your virtualenv in your terminal

for example:

$ source ~/ipy-jupyter-venv3/bin/activate  

(ipy-jupyter-venv3)$  # Your prompt should change

Step 2: Install OpenCV (modules) according to your needs

  1. If you need only main modules, in your activated virtualenv in your terminal, run the following
(ipy-jupyter-venv3)$ pip3 install opencv-python 

2. If you need both main and contrib modules (check extra modules listing from OpenCV documentation),  in your activated virtualenv in your terminal, run the following

(ipy-jupyter-venv3)$ pip3 install opencv-contrib-python 

Step 3: Test whether openCV is installed correctly

To test whether OpenCV installed correctly into your virtualenv, in your terminal type in those command below in bold.

(ipy-jupyter-venv3) liping$ python3.6

Python 3.6.5 (default, Mar 30 2018, 06:41:53)

[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)] on darwin

Type “help”, “copyright”, “credits” or “license” for more information.

>>> import cv2

>>> cv2.__version__

‘3.4.3’

>>

Step 4 (optional): To uninstall opencv inside virtualenv, following the command below according to how you installed it.

(ipy-jupyter-venv3) Liping:~$ pip3 uninstall opencv-python 

or

(ipy-jupyter-venv3) Liping:~$ pip3 uninstall opencv-contrib-python

 

Summary:

In this post, you learned how to install and uninstall OpenCV into your virtualenv.