![]() However, if you do a lot of scientific Python development, you might want to stick with Python 2.7 (for the time being at least). If you use Python 3 regularly and are comfortable with it, then go ahead and compile with Python 3 bindings. There are pros and cons of each, but the choice is honestly up to you. Are you going to compile OpenCV 3.0 with Python 2.7 bindings? Or are you going to compile OpenCV 3.0 Python 3 bindings? This section will show you how to verify your OpenCV 3.0 install and ensure it’s working correctly.īefore we get started, take a second and consider which version of Python you are going to use. After you have installed OpenCV 3.0 with Python support on your Raspberry Pi 2, you’ll want to confirm that is is indeed installed correctly and working as expected. Section 4: Verifying your OpenCV 3.0 install.Similarly, if you want to install OpenCV 3.0 with Python 3+ bindings on your Pi 2, then complete Section 1 and skip right to Section 3. Section 3: Compiling OpenCV 3.0 with Python 3+ support.After you complete this section, skip Section 3 and head right to Section 4. If you want to install OpenCV 3.0 with Python 2.7+ bindings on your Raspberry Pi, then this is the section that you’ll want to go to. Section 2: Compiling OpenCV 3.0 with Python 2.7+ support.Regardless of whether you are using Python 2.7 or Python 3+, we need to take some steps in order to prepare our Raspberry Pi for OpenCV 3.0 - these steps are mainly calls to apt-get, followed by installing the required packages and libraries. Section 1: Configuring your Raspberry Pi by installing the required packages and libraries.In order to keep this tutorial concise and organized, I have broken down the OpenCV 3.0 install process into four sections: These install instructions could also be used for the B+, but I highly recommend that you use the Pi 2 for running OpenCV applications - the added speed and memory makes the Pi 2 much more suitable for computer vision. The rest of this blog post will detail how to install OpenCV 3.0 for both Python 2.7 and Python 3+ on your Raspberry Pi 2. Install guide: Raspberry Pi 3 + Raspbian Jessie + OpenCV 3.How to install OpenCV 3.0 on Raspbian Jessie. ![]() Please use the following updated guides to help you install OpenCV + Python on your Raspberry Pi. Raspbian Jessie has now replaced Raspbian Wheezy and if this is the first time you are reading this tutorial then in all likelihood you are using Raspbian Jessie. UPDATE: The tutorial you are reading now covers how to install OpenCV 3 with Python 2.7 and Python 3 bindings on Raspbian Wheezy. Install OpenCV 3.0 for both Python 2.7+ and Python 3+ on your Raspberry Pi 2 ![]() ![]() So if you’re interested in building awesome computer vision based projects like this, then follow along with me and we’ll have OpenCV 3.0 with Python bindings installed on your Raspberry Pi 2 in no time. But now given the OpenCV 3.0 release, we can finally utilize Python 3+ in our projects.Īnd as you have seen elsewhere on the PyImageSearch blog, being able to utilize OpenCV on the Raspberry Pi has lead to be really great projects, such as an automated home surveillance and security system using Python + OpenCV + Dropbox + a Raspberry Pi 2: This is definitely an exciting tutorial - up until now only Python 2.7 was supported by OpenCV. By the end of this step-by-step guide you’ll have the brand new OpenCV 3.0 library installed on your Raspberry Pi 2, along with either Python 2.7+ or Python 3+ bindings. You see, we’re going to take a step forward and learn how to install the (just released) OpenCV 3.0 library for both Python 2.7 and Python 3+ on your Raspberry Pi. Given these benefits and applicability to a wide variety of domains, it’s perhaps comes as no surprise that my tutorial on installing OpenCV and Python on your Raspberry Pi 2 and B+ is still one of the most popular posts on the PyImageSearch blog.īut today, that’s going to change - because I think this blog post will over take its predecessor become the most popular article on the PyImageSearch blog. Given its lost-cost, we can now undertake large-scale, distributed computer vision research projects using a fleet of Raspberry Pis. The Raspberry Pi is also certainly prevalent in research and academia. It’s awesome for businesses and products as they can deploy computer vision algorithms on cost-affordable and reliable hardware. It’s great for hobbyists and garage-room hackers, as you get to learn on a cheap, but super fun piece of hardware. Honestly, I love the Raspberry Pi for teaching computer vision - it is perhaps one of the best teaching tools to expose programmers, developers, and students to the world of computer vision. Click here to download the source code to this post
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