Title: Install guide: Raspberry Pi 3 + Raspbian Jessie + OpenCV 3 - PyImageSearch

URL Source: https://pyimagesearch.com/2016/04/18/install-guide-raspberry-pi-3-raspbian-jessie-opencv-3/

Published Time: 2016-04-18T14:00:05+00:00

Markdown Content:
![Image 1: rpi3_board](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20446'%3E%3C/svg%3E)

Can you believe it’s been over _**four years**_ since the [original Raspberry Pi model B](http://www.amazon.com/gp/product/B009SQQF9C/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B009SQQF9C&linkCode=as2&tag=trndingcom-20&linkId=YZLP5GWTKJ5E7BGB) was released? Back then the Pi Model B shipped with only 256MB of RAM and a 700MHz single core processor.

Just over _**one year ago**_ the [Raspberry Pi 2](http://www.amazon.com/gp/product/B008XVAVAW/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B008XVAVAW&linkCode=as2&tag=trndingcom-20&linkId=KGMCPNATKSKQHAZX) was unleashed on the world. And man, for something called a _“Pi”_, this beast made an impact on the computer world like an asteroid. This board sported 1GB of RAM and a 900MHz _**quad-core**_ processor — quite the upgrade from the original single core, 700MHz system!

In my opinion, _**the Raspberry Pi 2 is what made computer vision possible on the Pi platform**_ (at least from a Python + OpenCV perspective). The original model B simply didn’t have the processing capacity (or the RAM) to be powerful enough to process images video streams for anything more than trivial operations — _the Pi 2 changed all that._

In fact, the Raspberry Pi 2 had _such a meaningful impact_ on the computer vision space, that I even took the time to make a _all_ code examples in _[Practical Python and OpenCV](https://pyimagesearch.com/practical-python-opencv/)_ compatible with the Pi.

And now we have the [Raspberry Pi 3](http://www.amazon.com/gp/product/B01C6FFNY4/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B01C6FFNY4&linkCode=as2&tag=trndingcom-20&linkId=M7C6OJOPQD3EOLIU):

*   1.2Ghz 64-bit quad-core processor.
*   1GB RAM.
*   Integrated 802.11n wireless and bluetooth.

Personally, I was hoping for a bit more RAM (perhaps in the range of 1.5-2GB). But upgrading to a _64-bit processor_ with _33% increased performance_ is _well worth it_.

Just as I have done in previous blog posts, I’ll be demonstrating **how to install OpenCV 3 with Python bindings on Raspbian Jessie**_._

If you are looking for previous installation instructions for different platforms, please consult this list:

*   [How to install OpenCV 3.0 on _**Raspbian Jessie**._](https://pyimagesearch.com/2015/10/26/how-to-install-opencv-3-on-raspbian-jessie/)
*   [Installing OpenCV on your _Raspberry Pi Zero_ running _**Raspbian Jessie**._](https://pyimagesearch.com/2015/12/14/installing-opencv-on-your-raspberry-pi-zero/)
*   [Installing OpenCV 3.0 for both Python 2.7 and Python 3+ on _**Raspbian Wheezy**_**.**](https://pyimagesearch.com/2015/07/27/installing-opencv-3-0-for-both-python-2-7-and-python-3-on-your-raspberry-pi-2/)
*   [Install OpenCV 2.4 for Python 2.7 on _**Raspbian Wheezy**_.](https://pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/)

Otherwise, let’s proceed with getting OpenCV 3 installed on your brand new Raspberry Pi 3!

Assumptions
-----------

In this tutorial, I am going to assume that you already own a **[Raspberry Pi 3](http://www.amazon.com/gp/product/B01C6FFNY4/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B01C6FFNY4&linkCode=as2&tag=trndingcom-20&linkId=KQ4HNH4UOHXJG44M)** with **[Raspbian Jessie installed](https://www.raspberrypi.org/downloads/raspbian/)**.

You should also have _either_:

*   _Physical access_ to your Raspberry Pi 3 so that you can open up a terminal and execute commands.
*   _Remote access_ via SSH.

I’ll be doing the majority of this tutorial via SSH, but as long as you have access to a terminal, you can easily follow along.

Installing OpenCV 3 on a Raspberry Pi 3 running Raspbian Jessie
---------------------------------------------------------------

If you’ve ever installed OpenCV on a Raspberry Pi (or any other platform before), you know that the process can be quite time consuming with many dependencies and pre-requisites that have to be installed. **The goal of this tutorial is to thus guide you step-by-step through the compile and installation process.**

In order to make the installation process go more smoothly, I’ve included timings for each step so you know when to take a break, grab a cup of coffee, and checkup on email while the Pi compiles OpenCV. That said, the _Pi 3 is substantially faster than the Pi 2_, so the time it takes to compile OpenCV has decreased **_dramatically_.**

Anyway, let’s go ahead and get started installing OpenCV 3 on your brand new Raspberry Pi 3 running Raspbian Jessie.

### Step #1: Expand filesystem

Are you using a _brand new_ install of Raspbian Jessie?

If so, the first thing you should do is expand your filesystem to include _all available space_ on your micro-SD card:

$ sudo raspi-config

![Image 2: Figure 1: Expanding the filesystem on your Raspberry Pi 3.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 1:** Expanding the filesystem on your Raspberry Pi 3.

Once prompted, you should select the first option, _**“1. Expand File System”**_, _**hit Enter**_ on your keyboard, arrow down to the _**“<Finish>”**_ button, and then reboot your Pi:

$ sudo reboot

After rebooting, your file system should have been expanded to include all available space on your micro-SD card. You can verify that the disk has been expanded by executing `df -h` and examining the output:

$ df -h
Filesystem      Size  Used Avail Use% Mounted on
/dev/root       7.2G  3.3G  3.6G  48% /
devtmpfs        459M     0  459M   0% /dev
tmpfs           463M     0  463M   0% /dev/shm
tmpfs           463M  6.4M  457M   2% /run
tmpfs           5.0M  4.0K  5.0M   1% /run/lock
tmpfs           463M     0  463M   0% /sys/fs/cgroup
/dev/mmcblk0p1   60M   20M   41M  34% /boot
tmpfs            93M     0   93M   0% /run/user/1000

As you can see, my Raspbian filesystem has been expanded to include all 8GB of the micro-SD card.

However, even with my filesystem expanded, I have already used 48% of my 8GB card!

OpenCV, along with all its dependencies, will need a few gigabytes during the compile, so you should delete the Wolfram engine to free up some space on your Pi:

$ sudo apt-get purge wolfram-engine

**After removing the Wolfram Engine, _you can reclaim almost 700mb!_**

### Step #2: Install dependencies

[This isn’t the first time I’ve discussed how to install OpenCV on the Raspberry Pi](https://pyimagesearch.com/opencv-tutorials-resources-guides/), so I’ll keep these instructions on the briefer side, allowing you to work through the installation process: I’ve also included the _**amount of time it takes to execute each command**_ so you can plan your OpenCV + Raspberry Pi 3 install accordingly (OpenCV itself takes **1h 12m** to compile).

The first step is to update and upgrade any existing packages:

$ sudo apt-get update
$ sudo apt-get upgrade

**Timing: 1m 26s**

We then need to install some developer tools, including [CMake](https://cmake.org/), which helps us configure the OpenCV build process:

$ sudo apt-get install build-essential cmake pkg-config

**Timing: 40s**

Next, we need to install some image I/O packages that allow us to load various image file formats from disk. Examples of such file formats include JPEG, PNG, TIFF, etc.:

$ sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev

**Timing: 32s**

Just as we need image I/O packages, we also need video I/O packages. These libraries allow us to read various video file formats from disk as well as work directly with video streams:

$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
$ sudo apt-get install libxvidcore-dev libx264-dev

**Timing: 34s**

The OpenCV library comes with a sub-module named `highgui` which is used to display images to our screen and build basic GUIs. In order to compile the `highgui` module, we need to install the GTK development library:

$ sudo apt-get install libgtk2.0-dev

**Timing: 3m 6s**

Many operations inside of OpenCV (namely matrix operations) can be optimized further by installing a few extra dependencies:

$ sudo apt-get install libatlas-base-dev gfortran

**Timing: 46s**

These optimization libraries are _especially important_ for resource constrained devices such as the Raspberry Pi.

Lastly, let’s install both the Python 2.7 and Python 3 header files so we can compile OpenCV with Python bindings:

$ sudo apt-get install python2.7-dev python3-dev

**Timing: 45s**

If you skip this step, you may notice an error related to the `Python.h` header file not being found when running `make` to compile OpenCV.

### Step #3: Download the OpenCV source code

Now that we have our dependencies installed, let’s grab the `3.1.0` archive of OpenCV from the official [OpenCV repository](https://github.com/Itseez/opencv). (_Note:_ As future versions of openCV are released, you can replace `3.1.0` with the latest version number):

$ cd ~
$ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.1.0.zip
$ unzip opencv.zip

**Timing: 1m 26s**

We’ll want the _full install_ of OpenCV 3 ([to have access to features such as SIFT and SURF](https://pyimagesearch.com/2015/07/16/where-did-sift-and-surf-go-in-opencv-3/), for instance), so we also need to grab the [opencv\_contrib](https://github.com/itseez/opencv_contrib) repository as well:

$ wget -O opencv\_contrib.zip https://github.com/Itseez/opencv\_contrib/archive/3.1.0.zip
$ unzip opencv\_contrib.zip

**Timing: 43s**

You might need to expand the command above using the “<=>” button during your copy and paste. The `.zip` in the `3.1.0.zip` may appear to be cutoff in some browsers. The full URL of the OpenCV 3.1.0 archive is:

[https://github.com/Itseez/opencv\_contrib/archive/3.1.0.zip](https://github.com/Itseez/opencv_contrib/archive/3.1.0.zip)

_**Note:** Make sure your `opencv` and `opencv_contrib` versions are the same (in this case, `3.1.0` ). If the versions numbers do not match up, then you’ll likely run into either compile-time or runtime._

### Step #4: Python 2.7 or Python 3?

Before we can start compiling OpenCV on our Raspberry Pi 3, we first need to install `pip` , a Python package manager:

$ wget https://bootstrap.pypa.io/get-pip.py
$ sudo python get-pip.py

**Timing: 20s**

If you’re a longtime PyImageSearch reader, then you’ll know that I’m a _huge fan_ of both [virtualenv](https://virtualenv.pypa.io/en/latest/) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.org/en/latest/). Installing these packages is not a requirement and you can _absolutely_ get OpenCV installed without them, but that said, _**I highly recommend you install them**_ as other PyImageSearch tutorials in the future will also leverage Python virtual environments. I’ll also be assuming that you have both `virtualenv` and `virtualenvwrapper` installed throughout the remainder of this guide.

**So, given that, what’s the point of using `virtualenv` and `virtualenvwrapper` ?**

First, it’s important to understand that a virtual environment is a _special tool_ used to keep the dependencies required by different projects in separate places by creating _isolated, independent_ Python environments for each of them.

In short, it solves the _“Project X depends on version 1.x, but Project Y needs 4.x”_ dilemma. It also keeps your global `site-packages` neat, tidy, and free from clutter.

If you would like a full explanation on why Python virtual environments are good practice, absolutely [give this excellent blog post on RealPython a read](https://realpython.com/blog/python/python-virtual-environments-a-primer/).

It’s _**standard practice**_ in the Python community to be using virtual environments of some sort, so I _highly recommend_ that you do the same:

$ sudo pip install virtualenv virtualenvwrapper
$ sudo rm -rf ~/.cache/pip

**Timing: 9s**

Now that both `virtualenv` and `virtualenvwrapper` have been installed, we need to update our `~/.profile` file to include the following lines at the _bottom_ of the file:

\# virtualenv and virtualenvwrapper
export WORKON\_HOME=$HOME/.virtualenvs
source /usr/local/bin/virtualenvwrapper.sh

In previous tutorials, I’ve recommended using your favorite terminal-based text editor such as `vim` , `emacs` , or `nano` to update the `~/.profile` file. If you’re comfortable with these editors, go ahead and update the file to reflect the changes mentioned above.

Otherwise, you should simply use `cat` and output redirection to handle updating `~/.profile` :

$ echo -e "\\n# virtualenv and virtualenvwrapper" >> ~/.profile
$ echo "export WORKON\_HOME=$HOME/.virtualenvs" >> ~/.profile
$ echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.profile

Now that we have our `~/.profile` updated, we need to reload it to make sure the changes take affect. You can force a reload of your `~/.profile` file by:

1.  Logging out and then logging back in.
2.  Closing a terminal instance and opening up a new one
3.  Or my personal favorite, _**just use the `source` command:**_

$ source ~/.profile

_**Note:** I recommend running the `source ~/.profile` file_ **each time** _you open up a new terminal to ensure your system variables have been setup correctly._

#### Creating your Python virtual environment

Next, let’s create the Python virtual environment that we’ll use for computer vision development:

$ mkvirtualenv cv -p python2

This command will create a new Python virtual environment named `cv` using _**Python 2.7**_.

If you instead want to use _**Python 3**_, you’ll want to use this command instead:

$ mkvirtualenv cv -p python3

Again, _**I can’t stress this point enough:**_ the `cv` Python virtual environment is _**entirely independent and sequestered**_ from the default Python version included in the download of Raspbian Jessie. Any Python packages in the _global_ `site-packages` directory _will not_ be available to the `cv` virtual environment. Similarly, any Python packages installed in `site-packages` of `cv` _will not_ be available to the global install of Python. Keep this in mind when you’re working in your Python virtual environment and it will help avoid a lot of confusion and headaches.

#### How to check if you’re in the “cv” virtual environment

If you ever reboot your Raspberry Pi; log out and log back in; or open up a new terminal, you’ll need to use the `workon` command to re-access the `cv` virtual environment. In previous blog posts, I’ve seen readers use the `mkvirtualenv` command — _**this is entirely unneeded!**_ The `mkvirtualenv` command is meant to be executed only once: to actually _create_ the virtual environment.

After that, you can use `workon` and you’ll be dropped down into your virtual environment:

$ source ~/.profile
$ workon cv

To validate and ensure you are in the `cv` virtual environment, examine your command line — _if you see the text `(cv)` preceding your prompt, then you **are** in the `cv` virtual environment:_

![Image 3: Figure 2: Make sure you see the "(cv)" text on your prompt, indicating that you are in the cv virtual environment.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 2:** Make sure you see the _“(cv)”_ text on your prompt, indicating that you _**are**_ in the _cv_ virtual environment.

Otherwise, if you _**do not**_ see the `(cv)` text, then you _**are not**_ in the `cv` virtual environment:

![Image 4: Figure 3: If you do not see the "(cv)" text on your prompt, then you are not in the cv virtual environment and need to run "source" and "workon" to resolve this issue.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 3:** If you do not see the _“(cv)”_ text on your prompt, then you _**are not**_ in the _cv_ virtual environment and need to run _“source”_ and _“workon”_ to resolve this issue.

To fix this, simply execute the `source` and `workon` commands mentioned above.

### Installing NumPy on your Raspberry Pi

Assuming you’ve made it this far, you should now be in the `cv` virtual environment (which you should stay in for the rest of this tutorial). Our only Python dependency is [NumPy](http://www.numpy.org/), a Python package used for numerical processing:

$ pip install numpy

**Timing: 9m 39s**

Be sure to grab a cup of coffee or go for a nice walk, the NumPy installation can take a bit of time.

_**Note:** Another question I’ve often seen is **“Help, my NumPy installation has hung and it’s not installing!”** Actually, it is installing, it just takes time to pull down the sources and compile. Be patient. The Raspberry Pi isn’t as fast as your laptop/desktop._

### Step #5: Compile and Install OpenCV

We are now ready to compile and install OpenCV! Double-check that you are in the `cv` virtual environment by examining your prompt (you should see the `(cv)` text preceding it), and if not, simply execute `workon` :

$ workon cv

Once you have ensured you are in the `cv` virtual environment, we can setup our build using CMake:

$ cd ~/opencv-3.1.0/
$ mkdir build
$ cd build
$ cmake -D CMAKE\_BUILD\_TYPE=RELEASE \\
    -D CMAKE\_INSTALL\_PREFIX=/usr/local \\
    -D INSTALL\_PYTHON\_EXAMPLES=ON \\
    -D OPENCV\_EXTRA\_MODULES\_PATH=~/opencv\_contrib-3.1.0/modules \\
    -D BUILD\_EXAMPLES=ON ..

**Timing: 1m 57s**

Now, before we move on to the actual compilation step, _**make sure you examine the output of CMake!**_

Start by scrolling down the section titled `Python 2` and `Python 3` .

_**If you are compiling OpenCV 3 for Python 2.7**_, then make sure your `Python 2` section includes valid paths to the `Interpreter` , `Libraries` , `numpy` and `packages path` , similar to my screenshot below:

![Image 5: Figure 4: Ensuring that Python 2.7 will be used when compiling OpenCV 3 for Raspbian Jessie on the Raspberry Pi 3.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 4:** Ensuring that Python 2.7 will be used when compiling OpenCV 3 for Raspbian Jessie on the Raspberry Pi 3.

Notice how the `Interpreter` points to our `python2.7` binary located in the `cv` virtual environment. The `numpy` variable also points to the NumPy installation in the `cv` environment.

Similarly, _**if you’re compiling OpenCV for Python 3**_, make sure the `Python 3` section looks like the figure below:

![Image 6: Figure 5: Checking that Python 3 will be used when compiling OpenCV 3 for Raspbian Jessie on the Raspberry Pi 3.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 5:** Checking that Python 3 will be used when compiling OpenCV 3 for Raspbian Jessie on the Raspberry Pi 3.

Again, the `Interpreter` points to our `python3.4` binary located in the `cv` virtual environment while `numpy` points to our NumPy install.

In either case, if you **_do not_** see the `cv` virtual environment in these variables paths, _**it’s almost certainly because you are NOT in the `cv` virtual environment prior to running CMake!**_

If this is the case, access the `cv` virtual environment using `workon cv` and re-run the `cmake` command outlined above.

Finally, we are now ready to compile OpenCV:

$ make -j4

**Timing: 1h 12m**

_**Note:** Compiling OpenCV in 72 minutes on the Raspberry Pi 3 is a **24%** improvement over the previous 95 minutes for the Raspberry Pi 2. That extra 300MHz makes a big difference!_

The `-j4` command controls the number of cores to leverage when compiling OpenCV 3. The Raspberry Pi 3 has _four cores_, thus we supply a value of `4` to allow OpenCV to compile faster.

However, due to race conditions, there are times when `make` errors out when using multiple cores. If this happens to you, I suggest starting the compilation over again and using only _one_ core:

$ make clean
$ make

Once OpenCV 3 has finished compiling, your output should look similar to mine below:

![Image 7: Figure 5: Our OpenCV 3 compile on Raspbian Jessie has completed successfully.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 5:** Our OpenCV 3 compile on Raspbian Jessie has completed successfully.

From there, all you need to do is install OpenCV 3 on your Raspberry Pi 3:

$ sudo make install
$ sudo ldconfig

**Timing: 52s**

### Step #6: Finish installing OpenCV on your Pi

We’re almost done — just a few more steps to go and you’ll be ready to use your Raspberry Pi 3 with OpenCV 3.

#### For Python 2.7:

Provided your **Step #5** finished without error, OpenCV should now be installed in `/usr/local/lib/python2.7/site-pacakges` . You can verify this using the `ls` command:

$ ls -l /usr/local/lib/python2.7/site-packages/
total 1852
-rw-r--r-- 1 root staff 1895772 Mar 20 20:00 cv2.so

_**Note:** In some cases, OpenCV can be installed in `/usr/local/lib/python2.7/dist-packages` (note the `dist-packages` rather than `site-packages` . If you do not find the `cv2.so` bindings in `site-packages` , we be sure to check `dist-packages` ._

Our final step is to [sym-link](https://en.wikipedia.org/wiki/Symbolic_link) the OpenCV bindings into our `cv` virtual environment for Python 2.7:

$ cd ~/.virtualenvs/cv/lib/python2.7/site-packages/
$ ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so

#### For Python 3:

After running `make install` , your OpenCV + Python bindings should be installed in `/usr/local/lib/python3.4/site-packages` . Again, you can verify this with the `ls` command:

$ ls -l /usr/local/lib/python3.4/site-packages/
total 1852
-rw-r--r-- 1 root staff 1895932 Mar 20 21:51 cv2.cpython-34m.so

I honestly don’t know why, perhaps it’s a bug in the CMake script, but when compiling OpenCV 3 bindings for Python 3+, the output `.so` file is named `cv2.cpython-34m.so` (or some variant of) rather than simply `cv2.so` (like in the Python 2.7 bindings).

Again, I’m not sure exactly _why_ this happens, but it’s an easy fix. All we need to do is rename the file:

$ cd /usr/local/lib/python3.4/site-packages/
$ sudo mv cv2.cpython-34m.so cv2.so

After renaming to `cv2.so` , we can sym-link our OpenCV bindings into the `cv` virtual environment for Python 3.4:

$ cd ~/.virtualenvs/cv/lib/python3.4/site-packages/
$ ln -s /usr/local/lib/python3.4/site-packages/cv2.so cv2.so

### Step #7: Testing your OpenCV 3 install

**Congratulations, you now have OpenCV 3 installed on your Raspberry Pi 3 running Raspbian Jessie!**

But before we pop the champagne and get drunk on our victory, let’s first verify that your OpenCV installation is working properly.

Open up a new terminal, execute the `source` and `workon` commands, and then finally attempt to import the Python + OpenCV bindings:

$ source ~/.profile 
$ workon cv
$ python
>>> import cv2
>>> cv2.\_\_version\_\_
'3.1.0'
>>>

As you can see from the screenshot of my own terminal, **OpenCV 3 has been successfully installed on my Raspberry Pi 3 + Python 2.7 environment:**

![Image 8: Figure 5: Confirming OpenCV 3 has been successfully installed on my Raspberry Pi 3 running Raspbian Jessie.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20402'%3E%3C/svg%3E)

**Figure 5:** Confirming OpenCV 3 has been successfully installed on my Raspberry Pi 3 running Raspbian Jessie.

Once OpenCV has been installed, you can remove both the `opencv-3.1.0` and `opencv_contrib-3.1.0` directories to free up a bunch of space on your disk:

$ rm -rf opencv-3.1.0 opencv\_contrib-3.1.0

However, be cautious with this command! Make sure OpenCV has been properly installed on your system before blowing away these directories. A mistake here could cost you _**hours**_ in compile time.

Troubleshooting and FAQ
-----------------------

_**Q.**_ When I try to execute `mkvirtualenv`  and `workon` , I get a “command not found error”.

_**A.**_ There are three reasons why this could be happening, all of them related to **Step #4**:

1.  Make certain that you have installed `virtualenv`  and `virtualenvwrapper`  via `pip` . You can check this by running `pip freeze`  and then examining the output, ensuring you see occurrences of both `virtualenv`  and `virtualenvwrapper` .
2.  You might not have updated your `~/.profile`  correctly. Use a text editor such as `nano`  to view your `~/.profile`  file and ensure that the proper `export`  and `source`  commands are present (again, check **Step #4** for the contents that should be appended to `~/.profile` .
3.  You did not `source`  your `~/.profile`  after editing it, rebooting, opening a new terminal, etc. Any time you open a new terminal and want to use a virtual environment, make sure you execute `source ~/.profile`  to load the contents — this will give you access to the `mkvirtualenv`  and `workon`  commands.

_**Q.**_ After I open a new terminal, logout, or reboot my Pi, I cannot execute `mkvirtualenv`  or `workon` .

_**A.**_ See **reason #3** from the previous question.

_**Q.**_ When I (1) open up a Python shell that imports OpenCV or (2) execute a Python script that calls OpenCV, I get an error: `ImportError: No module named cv2` .

_**A.**_ Unfortunately, this error is extremely hard to diagnose, mainly because there are multiple issues that could be causing the problem. To start, make sure you are in the `cv`  virtual environment by using `workon cv` . If the `workon`  command fails, then see the first question in this FAQ. If you’re _still_ getting an error, investigate the contents of the `site-packages`  directory for your `cv`  virtual environment. You can find the `site-packages`  directory in `~/.virtualenvs/cv/lib/python2.7/site-packages/`  or `~/.virtualenvs/cv/lib/python3.4/site-packages/`  (depending on which Python version you used for the install). Make sure that your sym-link to the `cv2.so`  file is valid and points to an existing file.

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Summary
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In this blog post, we learned how to install _**OpenCV 3**_ with either Python 2.7 or Python 3 bindings on your _**Raspberry Pi 3**_ running _**Raspbian Jessie**_.

If you are running a different version of Raspbian (such as _Raspbian Wheezy_) or want to install a different version of OpenCV (such as OpenCV 2.4), please consult the following tutorials:

*   [How to install OpenCV 3.0 on _**Raspbian Jessie**._](https://pyimagesearch.com/2015/10/26/how-to-install-opencv-3-on-raspbian-jessie/)
*   [Installing OpenCV on your _Raspberry Pi Zero_ running _**Raspbian Jessie**._](https://pyimagesearch.com/2015/12/14/installing-opencv-on-your-raspberry-pi-zero/)
*   [Installing OpenCV 3.0 for both Python 2.7 and Python 3+ on _**Raspbian Wheezy**_**.**](https://pyimagesearch.com/2015/07/27/installing-opencv-3-0-for-both-python-2-7-and-python-3-on-your-raspberry-pi-2/)
*   [Install OpenCV 2.4 for Python 2.7 on _**Raspbian Wheezy**_.](https://pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/)

_But before you go…_
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I tend to utilize the Raspberry Pi quite a bit on this blog, so if you’re interested in learning more about the Raspberry Pi + computer vision, _**enter your email address in the form below to be notified when these posts go live!**_

![Image 9](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20410%20567'%3E%3C/svg%3E)

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