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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?
#Opencv python download mac 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. 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 I don't understand why.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:
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I'm writing code to face recognition in python and i'm using open cv on mac (P圜harm).
OpenCV waitKey in python doesn't work on mac. Once I install Python, how do I get to a point where I can click on an application on my dock that opens that allows me to code in Python using OpenCV? Sorry for the basic questions- I'm new to Mac and this has been infinitely more complex than on PC. 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! 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.
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