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1. INSTALL PYTHON ON MAC ANACONDA MAC OS X
2. INSTALL PYTHON ON MAC ANACONDA SOFTWARE
3. INSTALL PYTHON ON MAC ANACONDA DOWNLOAD

I went to the Anaconda downloads page and got the Python 2.7 Mac OS X 64-Bit command-line installer, so that we can install everything from Terminal.Īfter downloading that, navigate to your Downloads directory (if you’re new to the Terminal, just open the Terminal application and type cd $HOME/Downloads). So I have a python 2.7 Anaconda environment running on my computer. Yeah, there’s python 3, but I grew up on Py 2.7 and it’ll take a lot to pry it from my cold, dead hands. (Skip if you already have Anaconda).įirst off, I’m still a python 2 guy.

Python packages, on the other hand, will often have dependencies on specific versions of other packages, so if you pip install one package, other package may fail to import because their dependent packages have been updated. If you’re like me (maybe you’re not) I often think that pip install‘ing a Python package is the same thing as R’s install.packages function - while we get similar functionality, R packages come with the luxury of basically never interfering with other R package dependencies! If one package needs a newer or older version of some other package you’ve already installed, install.packages will most likely just take care of everything for you.

Wouldn’t it be great if we could just run something analogous to pip install opencv?

INSTALL PYTHON ON MAC ANACONDA SOFTWARE

Other installation walkthroughs I’ve found tend to be generally convoluted and assume that you have Homebrew, XCode, maybe MacPorts, or just experience in general with installing and building software packages. But, at the end of the day, there are even more steps required after Adrian’s 9 steps to get OpenCV compatible with a Jupyter notebook. I’ve gone down this route according to Adrian Rosebrock’s fabulous installation walkthrough, and if you just want to have access to OpenCV 3.0, I suggest you consider it.

INSTALL PYTHON ON MAC ANACONDA DOWNLOAD

The standard approach is to download it from the OpenCV website and then compile and install OpenCV using the software building utility “CMake” all within a virutal Python environment. Problems with traditional installation methods That’s the beauty of a Jupyter notebook - when you’re using it with Matplotlib, you can just display your images and videos in a living document!įor me, my ideal OpenCV situation would be for me to be able to simply type and evaluate the following import statements with zero errors or package conficts: import opencv img = cv2. And especially if you’re coding for image processing, you’re going to want to view your progress without having (a) a million separate images open and (b) having to wait for Spyder to inevitably crash. The only problem is: how the hell do I install OpenCV so that I can use it in conjunction with a Jupyter notebook? Let’s be honest, most likely you’re either you’re using a Jupyter notebook, Spyder, or the ipython terminal (if you’re a real sadist) to test your python code. OpenCV will supply you with functions that will let you detect faces in images, track objects in a video, and perform any number of image processing tasks. OpenCV (CV = ‘computer vision’) is an excellent open source computer vision software library written in C++ that supports C++, C, Python, Java, and Matlab API’s. Ahhh, computer vision, such a cool field! Lately, I’ve been trying to become more knowledgeable about CV and image processing in python.