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In this tutorial you will will learn how to pip install OpenCV on Ubuntu, macOS, and the Raspberry Pi. In previous OpenCV install tutorials I have recommended compiling from source; however, in the past year it has become possible to install OpenCV via pip, Python’s very own package manager. While installing from source will give you the greatest control over your OpenCV configuration, it’s also the hardest and the most time consuming. If you’re looking for the fastest possible way to install OpenCV on your system, you want to use pip to install OpenCV (but there are a few things that may trip you up along the way, so make sure you read the rest of this guide). To learn how to pip install OpenCV on your system, just keep reading. Looking for the source code to this post? Pip install opencv In the remainder of this tutorial I’ll briefly describe the OpenCV packages you can install via pip, Python’s package manager.
This is supported on Linux, Windows, and GDAL-only Mac binaries are floating around the net somewhere. Also included in FWTools is the GUI application OpenEV, which can also read the *.adf format and export it. OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android.
From there, I’ll demonstrate how to pip install OpenCV on Ubuntu, macOS, and the Raspberry Pi. Finally, I’ll review some common problems you may encounter when using pip to install OpenCV. The two pip OpenCV packages: opencv-python and opencv-contrib-python Before we get started I want to remind you that the methods I’m coming here today are unofficial pre-built OpenCV packages that can be installed via pip — they are not official OpenCV packages released. Just because they are not official packages doesn’t mean you should feel uncomfortable using them, but it’s important for you to understand that they are not endorsed and supported directly by the official team.
All that said — there are four OpenCV packages that are pip-installable on the PyPI repository:.: This repository contains just the main modules of the OpenCV library. If you’re a PyImageSearch reader you do not want to install this package.: The opencv-contrib-python repository contains both the main modules along with the contrib modules — this is the library I recommend you install as it includes all OpenCV functionality.: Same as opencv-python but no GUI functionality. Useful for headless systems.: Same as opencv-contrib-python but no GUI functionality.
Useful for headless systems. Again, in the vast majority of situations you will want to install opencv - contrib - python on your system.
You DO NOT want to install both opencv - python and opencv - contrib - python — pick ONE of them. How to pip install OpenCV on Ubuntu You have two options to install OpenCV on Ubuntu with pip:.
Install into your system site - packages. Install into a virtual environment’s site - packages (preferred) First, install pip If you don’t have pip, you’ll need to obtain it first. $ sudo pip install opencv - contrib - python In a matter of seconds, OpenCV is ready to go in your system’s site-packages! Option B: Install OpenCV on Ubuntu into a virtual environment with pip There are huge benefits to Python virtual environments. The main benefit is that you can develop multiple projects on your system with isolated packages (many with version dependencies) without having to muddy the waters of your system.
You’re also free to add and remove virtual environments as you go. Put simply: Python virtual environments are a best practice for Python development. Chances are, you should jump on the bandwagon. My tools of choice are virtualenv and virtualenvwrapper but you could choose an alternative such as venv or Anaconda (conda for short). Here’s how to install virtualenv and virtualenvwrapper, both of which will live in your system site - packages and manage each project’s virtual environment site-packages. $ sudo pip install opencv - contrib - python In a matter of seconds, OpenCV is ready to go in your system’s site-packages. Option B: Install OpenCV on macOS into a virtual environment with pip Just like managing packages is a breeze with pip.
Managing projects and their dependencies is a breeze with virtual environments. You should use Python virtual environments if you’re serious about computer vision development (or any development for that matter). I don’t care what system you use (be it virtualenv, venv, or conda /Anaconda), just learn to use one and stick with it. Here’s how to install virtualenv and virtualenvwrapper, both of which will live in your system site-packages and manage each project’s virtual environment site-packages. $ source /.bashprofile You’ll see a few lines of terminal output indicating that virtualenvwrapper is set up.
You now have access to new terminal commands:. mkvirtualenv: Make a new virtual environment. workon: Activate/switch to a virtual environment. Remember, you can have as many environments as you’d like. deactivate: Jumps out of a virtual environment and you’ll be working with your system.
More (Read full review) • Written by Jeff F from Phoenix • Jan 20, 2016 1946 of 2726 people found this useful. I'll give my five stars right here without apology. All reviews for mac.
rmvirtualenv: Deletes a virtual environment. Be sure to! Let’s create a Python 3 virtual environment for OpenCV called cv. $ pip install opencv - contrib - python How to pip install OpenCV on Raspberry Pi Earlier in this post I mentioned that one of the downsides of installing OpenCV is that you don’t have any control over the compile itself — the binaries are prebuilt for you, which while nice, also means you can’t include any additional optimizations. For the Raspberry Pi, we’re in luck.
(creator of the picamera Python module) and of the Raspberry Pi community-run, a Python package repository providing ARM wheels (i.e., pre-compiled binaries packages) for the Raspberry Pi. Using PiWheels you’ll be able to pip install OpenCV in a matter of seconds (the same is true for other Python libraries that can take a long time to compile, including NumPy, SciPy, scikit-learn, etc.). So how do you instruct the pip command to use PiWheels? The short answer is “Nothing!” If you’re using Raspbian Stretch you’ll be pleased to know that the pip command will check PiWheels for a pre-compiled binary before it checks PyPI, enabling your Pi to save a bunch of CPU cycles (and you a bunch of install time). Furthermore, when Ben and Dave put together the OpenCV binary for PiWheels they asked me which instructions they should use — — which is exactly the instructions they followed! If you end up using pip to install OpenCV on your Raspberry Pi, rest assured, you’re using the optimized version. Let’s get started learning how to pip install OpenCV on our Raspberry Pi.
Install prerequisites on your Raspberry Pi The Raspberry Pi requires that you install a few system packages before you get started. $ sudo python3 get - pip.py Now you have two options:. Install OpenCV to your global Python site - packages on your Raspberry Pi.
Install OpenCV into a virtual environment on your Raspberry Pi Option A: Install OpenCV to your Raspberry Pi system with pip I wouldn’t recommend this option if you want to be able to use different versions of OpenCV in isolated environments. But a lot of people deploy their Raspberry Pis for only one purpose/project and don’t need virtual environments. That being said, it is quite a mess to clean up if you change your mind later and want to use virtual environments, so I’d recommend skipping this option and following Option B. To pip install OpenCV on your Raspberry Pi system, be sure to use sudo like this:.
$ sudo pip install opencv - contrib - python In a matter of seconds, OpenCV is ready to go in your Raspberry Pi’s site-packages along with any other packages you may have installed. Option B: Install OpenCV into a virtual environment with pip on your Raspberry Pi Virtual environments are definitely the way to go if your Raspberry Pi has multiple purposes (or if you’re like me and test code compatibility among various software versions for blog posts all the time?). Here’s how to install virtualenv and virtualenvwrapper, the tools I use to get it done.
$ source /.profile Terminal output will be printed indicating that virtualenvwrapper is ready. Be sure to inspect it for errors. You now have access to new terminal commands:. Create an environment with mkvirtualenv. Activate an environment (or switch to a different one) with workon. Deactivate an environment with deactivate. Remove an environment with rmvirtualenv.
Be sure to! To create a Python 3 virtual environment which will house OpenCV and other packages you install, simply use mkvirtualenv and the command below.
INFO approx. FPS: 27.83 Here I’ve created the illusion of fast 27 FPS on the Raspberry Pi while the neural network in the background is only capable of processing 0.9 FPS. How is this possible?
Threading and queues. It’s a little bit advanced, but if you read the (for the Raspberry Pi), you’ll understand the process.
Plus, you’ll be able to impress your friends and family. What to look out for when using pip to install OpenCV To start, not all Python distributions will have a version of OpenCV that is pip-installable. Newer versions of Python and newer operating systems (and not to mention, older versions which have reached their end of life) may not have a version of OpenCV ready to go in the PyPI repository as the open source community has not had a chance to release such a version yet. In those situations you can either:. Wait until the binaries for your combination of Python and OS are uploaded. Or what my recommendation would be — compile from source (, ).
Secondly, some readers, including Anaconda users, have reported problems using GUI functions such as cv2. Imshow and cv2. In these scenarios OpenCV will error out saying that it was not compiled with GTK or QT support. Simply put:.
You’ll be able to use all other OpenCV functions but you won’t be able to use any of the GUI functions, in particular the ones in the highgui module. The solution here is to compile from source (, ). Finally, I know readers have reported issues when executing import cv2 in their terminals, Jupyter Notebooks, or Python shells — this isn’t an issue with the pip install of OpenCV. In most, but not all, situations, the error is not related to your actual install of OpenCV. Instead, it’s more likely a problem with your understanding of some combination of:. The commands that were executed and how to utilize them properly. Thinking that a command executed correctly but instead resulted in an error.
Failing to access your Python virtual environment (if you are using one). You’ll want to double-check your commands, repeat the steps, and examine your output closely before reporting an issue importing the cv2 bindings. Summary In today’s tutorial, you learned how to pip install OpenCV on your operating system. Specifically, we covered how to install OpenCV via pip on Ubuntu, macOS, and Raspberry Pi.
While installing OpenCV via pip may be the easiest method to get you started, keep in mind that you may run into other issues. If you find yourself running into errors or problems using your pip install of OpenCV, be sure to refer to the “What to look out for when using pip to install OpenCV” section of this blog post. I hope you enjoyed today’s tutorial!
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About your honeymoon and openCV! Hopefully openCV 4 will be built soon after its release. I expect I will try it soon on a PiZero-W with Movidius. I know you recommended a minimal openCV installed for NCS but I have been using the NCS with OpenCV 3.4.2 compiled on Ubuntu 16.04 by building the NCS sdk with the old repo version of openCV, and then creating a python virtual environment and compiling ocv 3.4.2. I then got the ncs api to work by simply copying the mvnc python module to my virtual environment. It seems to work as I’ve pulled together several of your tutorials into a single python script that will use the ncs if one is plugged in and fall back to using the cpu only dnn module if no ncs devices are found or its running on Windows (limited testing so far).
I can select using multiple onvif netcameras (my preferred solution now since round robin sampling of multiple cameras is trivial), the picamera module, or a USB webcam. For performance, pretty much need the ncs on a Pi, on an i7 cpu dnn module is significantly faster. On an i3 (windows) the Pi with ncs is about the same as the cpu only dnn. My speed conclusions could be in error if the pre-compiled windows openCV is using gpu acceleration with the opencv-contrib-python version. Sorry if I missed it, but I didn’t saw any comments as to what’s the difference between opencv-python and opencv-contrib-python. I’m looking forward to this pip based install, but I’m just having a feeling that something will not work as intended. Maybe when it will be officially accepted by opencv.org I will change my mind, but up till then I will just stick to the old fashioned way (unless I need to do it fast on some machine).
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I mean, I’ve done it so many times and on so many machines (mostly thanks to you and your tutorials of course) that I really feel very comfortable installing it from scratch. Anyway, all the best and cheers for the new tutorial! Hi Bojan, The difference is that opencv-contrib-python includes modules in the contrib repository as well. The contrib repository contains algorithms such as SIFT, SURF, and others. In the past, these implementations were included in the default installation; however, they were moved beginning with OpenCV 3+. Modules that are actively being developed and/or modules that are patented (not free for commercial/industry use) are included in the contrib module.
For example, SIFT and SURF fall into this category. I can assure you that except for the issues mentioned in blog post with Anaconda, it works just as fine as compiling from the source. Thank you for providing this. It’s a trivial point to many people here, but it took me a little while to diagnose. I received the error INFO loading model INFO starting video stream Traceback (most recent call last): File “realtimeobjectdetection.py”, line 49, in frame = imutils.resize(frame, width=400) File “/home/david/.local/lib/python3.6/site-packages/imutils/convenience.py”, line 69, in resize (h, w) = image.shape:2 AttributeError: ‘NoneType’ object has no attribute ‘shape’ It was, of course, because there is no webcam on my laptop.
Once I had amended vs = VideoStream(src=0).start # (line 3 in file realtimeobjectdetection.py) to point to an mp4 video-file everything worked fine. Hi I tried the tutorial but had a problem in the first go. I was not able to install pip.
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I installed using the sudo apt command $ sudo apt install python3-pip This installed pip for me. But I realized that the pip was installed for python3 and not for python 2. That means I had to use pip3 instead of pip to install opencv.
The following code (as mentioned in the tutorial) did not work for me $ sudo pip install opencv-contrib-python But when I use pip3 instead of pip then the installation runs fine. $ sudo pip3 install opencv-contrib-python Is this something that has only happened for me or has someone else also faced the same problem. Form what I read online, pip3 is for python3 and pip is for python2. Hello, I installed opencv in my RPi Zero (Raspbian Stretch) by means of pip, my current version is 3.4.3. And when trying to compile the “piobjectdetection.py” code located in the download area, with python and python3 after the program shows “INFO loading model ” “INFO starting process ” “INFO starting video stream ” show this: Illegal instruction I do not know if it should be a problem with the opencv installed from piwheels, or if it is something in the code that needs to be changed, and if not, could anyone help me install version 3.3+ in the RPi Zero from the source?