CVND_Exercises

by udacity

Exercise notebooks for CVND.

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Computer Vision Nanodegree Program, Exercises

This repository contains code exercises and materials for Udacity's Computer Vision Nanodegree program. It consists of tutorial notebooks that demonstrate, or challenge you to complete, various computer vision applications and techniques. These notebooks depend on a number of software packages to run, and so, we suggest that you create a local environment with these dependencies by following the instructions below.

Configure and Manage Your Environment with Anaconda

Per the Anaconda docs:

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

Overview

Using Anaconda consists of the following:

  1. Install
    miniconda
    on your computer, by selecting the latest Python version for your operating system. If you already have
    conda
    or
    miniconda
    installed, you should be able to skip this step and move on to step 2.
  2. Create and activate * a new
    conda
    environment.

* Each time you wish to work on any exercises, activate your

conda
environment!

1. Installation

Download the latest version of

miniconda
that matches your system.

NOTE: There have been reports of issues creating an environment using miniconda

v4.3.13
. If it gives you issues try versions
4.3.11
or
4.2.12
from here.

| | Linux | Mac | Windows | |--------|-------|-----|---------| | 64-bit | 64-bit (bash installer) | 64-bit (bash installer) | 64-bit (exe installer) | 32-bit | 32-bit (bash installer) | | 32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

  • Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
  • Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
  • Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

2. Create and Activate the Environment

For Windows users, these following commands need to be executed from the Anaconda prompt as opposed to a Windows terminal window. For Mac, a normal terminal window will work.

Git and version control

These instructions also assume you have

git
installed for working with Github from a terminal window, but if you do not, you can download that first with the command:
conda install git

If you'd like to learn more about version control and using

git
from the command line, take a look at our free course: Version Control with Git.

Now, we're ready to create our local environment!

  1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.

    git clone https://github.com/udacity/CVND_Exercises.git
    cd CVND_Exercises
    
  2. Create (and activate) a new environment, named

    cv-nd
    with Python 3.6. If prompted to proceed with the install
    (Proceed [y]/n)
    type y.
- __Linux__ or __Mac__: 
```
conda create -n cv-nd python=3.6
source activate cv-nd
```
- __Windows__: 
```
conda create --name cv-nd python=3.6
activate cv-nd
```

At this point your command line should look something like: (cv-nd) <user>:CVND_Exercises <user>$. The (cv-nd) indicates that your environment has been activated, and you can proceed with further package installations.

  1. Install PyTorch and torchvision; this should install the latest version of PyTorch.
- __Linux__ or __Mac__: 
```
conda install pytorch torchvision -c pytorch 
```
- __Windows__: 
```
conda install pytorch-cpu -c pytorch
pip install torchvision
```
  1. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).

    pip install -r requirements.txt
    
  2. That's it!

Now all of the

cv-nd
libraries are available to you. Assuming you're environment is still activated, you can navigate to the Exercises repo and start looking at the notebooks:
cd
cd CVND_Exercises
jupyter notebook

To exit the environment when you have completed your work session, simply close the terminal window.

Notes on environment creation and deletion

Verify that the

cv-nd
environment was created in your environments:
conda info --envs

Cleanup downloaded libraries (remove tarballs, zip files, etc):

conda clean -tp

Uninstall the environment (if you want); you can remove it by name:

conda env remove -n cv-nd

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