How to install cv2 python

Installing OpenCV for Python on Windows using Anaconda or WinPython

OpenCV is a popular library for Image processing and Computer Vision. Using python with OpenCV combines the simplicity of python with the capabilities of the versatile OpenCV library.

Using the Windows platform to foray into data science and computer vision is a popular choice especially among beginners. Anaconda is the first choice distribution for scientific python particularly on Windows. If a more portable setup is required to say run on a pendrive then I recommend using the WinPython distribution. Both setups allow for installing OpenCV in a straightforward way although it could be misleading for an absolute beginner. The first part explains the step-by-step process of installing OpenCV on Anaconda, the second part explains installing OpenCV on WinPython.

Step 1: Installing Anaconda

Download the latest Anaconda graphical installer for Windows from https://www.anaconda.com/distribution/ and check for the Windows architecture on your computer. If its 64-bit then choose the 64 bit graphical installer or else choose the 32-bit installer. Choose Python 3.7 for working with Python 3. This is the preferred option as python 2.7 is reaching its End-Of-Life by 2020.

By default the website prompts us to download the macOS installer so click Windows before clicking Download.

Launch the graphical installer and we will be prompted to choose for which user to install.

Choosing “Just Me” is recommended as choosing All Users would require administrator privileges whenever we are modifying Anaconda(as will be illustrated later).

If we chose “Just Me” then the above dialog will be displayed with the default location being “C:\Users\ \Anaconda3” whereas choosing “All Users” would display the following dialog:

The key is to use a path without spaces between folder names as this can create conflicts for Anaconda later( as it will prompt us when we try to choose such a name).

Continue to install with the default option of “Register Anaconda as the system Python 3.7” if we don’t have other versions of python or other distributions installed.

It takes a few minutes for installation to complete. Once completed we may be prompted to optionally install VSCode as an IDE. We may skip this option if we wish to do so.

Step 2: Installing OpenCV

Right. Lets now come to the actual goal of installing OpenCV for python.

Launch the Anaconda prompt from the start menu:

We should see a similar prompt if we went according to instructions.

if you chose “All users” while installing then you have to launch the prompt by Right-clicking and choosing “ Run as Administrator” to execute with administrator privileges. This is critical.

If we wish to install OpenCV in a separate environment, we need to create an new environment( replace opencv with any name in the command below, note the symbol “ — ” is a double hyphen, best to copy-paste followed by editing “opencv” to a name you prefer):

To install the OpenCV we need to type the following command at the prompt:

We can alternatively choose to install through anaconda navigator graphical interface. type in “ opencv” in search packages search bar. choose to install all the listed packages.

The prompt will show that it is “solving environment”. It takes quite a bit of time if you are on slower Internet connection.

Try to use faster Internet, preferably a wired connection for uninterrupted and best results. if you are in a work or institution based Internet connection then it is likely that an HTTP timeout will occur and we need to enter the command once again and restart the procedure.

Once the environment is resolved by conda it will list the packages that will be installed, namely: opencv, libopencv, py-opencv.

Enter y to proceed with the installation.

If you chose “All Users” then “executing transaction” will fail with the message “PermissionError(13,’Access is denied’)”

We can verify if the installation was successful by launching the python interpreter. opencv is referred to as cv2 in python. Type at the prompt:

if the prompt is displayed then opencv then python has successfully imported the opencv library. But we should also verify the version of opencv so we need to type:

as of March 2019 the version displayed is 3.4.1 which is the officially supported version of opencv by anaconda environment. If we want to work with a different version then while installing we can specify the version as “opencv=3.4.1″ as shown below

Similar steps can be followed to install OpenCV on anaconda for MacOS.

Installing through WinPython

Step 1: Install WinPython

It is preferable to download from the github Releases page of WinPython: https://github.com/winpython/winpython/releases. The reason being the default link from the homepage redirects to sourceforge.net which restricts the download speed quite considerably(around 200KBps). like in Anaconda choose the correct version for the target architecture(64 bit vs 32 bit). The Zero version is the minimalistic version with pretty much bare python without most of the other extra packages( no Spyder, Jupyter etc.)

Step 2: Installing OpenCV

Installing OpenCV in WinPython is quite easy. launch the WinPython Command Prompt.exe to get the prompt. Enter the command.

verify the installation like in anaconda with

Thats it! We have successfully installed openCV for python on Windows using Anaconda as well as WinPython.

Hoping this helps you to explore different computer vision algorithms available in OpenCV!

To install Tensorflow especially for GPU I found an article by Harveen Singh which was helpful for me :

How to Install OpenCV for Python on Windows?

OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. When it integrated with various libraries, such as Numpuy, python is capable of processing the OpenCV array structure for analysis. To Identify image patterns and its various features we use vector space and perform mathematical operations on these features.

To install OpenCV, one must have Python and PIP, preinstalled on their system. To check if your system already contains Python, go through the following instructions:

Open the Command line(search for cmd in the Run dialog( + R).
Now run the following command:

If Python is already installed, it will generate a message with the Python version available.

If Python is not present, go through How to install Python on Windows? and follow the instructions provided.

PIP is a package management system used to install and manage software packages/libraries written in Python. These files are stored in a large “on-line repository” termed as Python Package Index (PyPI).
To check if PIP is already installed on your system, just go to the command line and execute the following command:

If PIP is not present, go through How to install PIP on Windows? and follow the instructions provided.

Downloading and Installing OpenCV:

OpenCV can be directly downloaded and installed with the use of pip (package manager). To install OpenCV, just go to the command-line and type the following command:

Beginning with the installation:

To check if OpenCV is correctly installed, just run the following commands to perform a version check:

How to install cv2 python

In this tutorial

Below steps are tested in a Windows 7-64 bit machine with Visual Studio 2010 and Visual Studio 2012. The screenshots shows VS2012.

Installing OpenCV from prebuilt binaries

Building OpenCV from source

Download and install necessary Python packages to their default locations

Click on Configure.

It will open a new window to select the compiler. Choose appropriate compiler (here, Visual Studio 11) and click Finish.

You will see all the fields are marked in red. Click on the WITH field to expand it. It decides what extra features you need. So mark appropriate fields. See the below image:

Now click on BUILD field to expand it. First few fields configure the build method. See the below image:

Remaining fields specify what modules are to be built. Since GPU modules are not yet supported by OpenCV-Python, you can completely avoid it to save time (But if you work with them, keep it there). See the image below:

Now click on ENABLE field to expand it. Make sure ENABLE_SOLUTION_FOLDERS is unchecked (Solution folders are not supported by Visual Studio Express edition). See the image below:

Also make sure that in the PYTHON field, everything is filled. (Ignore PYTHON_DEBUG_LIBRARY). See image below:

Again, right-click on INSTALL and build it. Now OpenCV-Python will be installed.

Additional Resources

Exercises

If you have a windows machine, compile the OpenCV from source. Do all kinds of hacks. If you meet any problem, visit OpenCV forum and explain your problem.

How to install cv2 python

OpenCV on Wheels

Pre-built CPU-only OpenCV packages for Python.

Check the manual build section if you wish to compile the bindings from source to enable additional modules such as CUDA.

Installation and Usage

If you have previous/other manually installed (= not installed via pip ) version of OpenCV installed (e.g. cv2 module in the root of Python’s site-packages), remove it before installation to avoid conflicts.

Select the correct package for your environment:

There are four different packages (see options 1, 2, 3 and 4 below) and you should SELECT ONLY ONE OF THEM. Do not install multiple different packages in the same environment. There is no plugin architecture: all the packages use the same namespace ( cv2 ). If you installed multiple different packages in the same environment, uninstall them all with pip uninstall and reinstall only one package.

a. Packages for standard desktop environments (Windows, macOS, almost any GNU/Linux distribution)

b. Packages for server (headless) environments (such as Docker, cloud environments etc.), no GUI library dependencies

These packages are smaller than the two other packages above because they do not contain any GUI functionality (not compiled with Qt / other GUI components). This means that the packages avoid a heavy dependency chain to X11 libraries and you will have for example smaller Docker images as a result. You should always use these packages if you do not use cv2.imshow et al. or you are using some other package (such as PyQt) than OpenCV to create your GUI.

Import the package:

All packages contain Haar cascade files. cv2.data.haarcascades can be used as a shortcut to the data folder. For example:

Before opening a new issue, read the FAQ below and have a look at the other issues which are already open.

Frequently Asked Questions

Q: Do I need to install also OpenCV separately?

A: No, the packages are special wheel binary packages and they already contain statically built OpenCV binaries.

A: If the import fails on Windows, make sure you have Visual C++ redistributable 2015 installed. If you are using older Windows version than Windows 10 and latest system updates are not installed, Universal C Runtime might be also required.

Windows N and KN editions do not include Media Feature Pack which is required by OpenCV. If you are using Windows N or KN edition, please install also Windows Media Feature Pack.

If you have Windows Server 2012+, media DLLs are probably missing too; please install the Feature called «Media Foundation» in the Server Manager. Beware, some posts advise to install «Windows Server Essentials Media Pack», but this one requires the «Windows Server Essentials Experience» role, and this role will deeply affect your Windows Server configuration (by enforcing active directory integration etc.); so just installing the «Media Foundation» should be a safer choice.

If the above does not help, check if you are using Anaconda. Old Anaconda versions have a bug which causes the error, see this issue for a manual fix.

If you still encounter the error after you have checked all the previous solutions, download Dependencies and open the cv2.pyd (located usually at C:\Users\username\AppData\Local\Programs\Python\PythonXX\Lib\site-packages\cv2 ) file with it to debug missing DLL issues.

Q: I have some other import errors?

A: Make sure you have removed old manual installations of OpenCV Python bindings (cv2.so or cv2.pyd in site-packages).

Q: Function foo() or method bar() returns wrong result, throws exception or crashes interpreter. What should I do?

A: The repository contains only OpenCV-Python package build scripts, but not OpenCV itself. Python bindings for OpenCV are developed in official OpenCV repository and it’s the best place to report issues. Also please check

Q: Why the packages do not include non-free algorithms?

A: Non-free algorithms such as SURF are not included in these packages because they are patented / non-free and therefore cannot be distributed as built binaries. Note that SIFT is included in the builds due to patent expiration since OpenCV versions 4.3.0 and 3.4.10. See this issue for more info: #126

Q: Why the package and import are different (opencv-python vs. cv2)?

Documentation for opencv-python

The aim of this repository is to provide means to package each new OpenCV release for the most used Python versions and platforms.

CI build process

In Linux and MacOS build: get OpenCV’s optional C dependencies that we compile against

Checkout repository and submodules

Find OpenCV version from the sources

Rearrange OpenCV’s build result, add our custom files and generate wheel

Linux and macOS wheels are transformed with auditwheel and delocate, correspondingly

Install the generated wheel

Test that Python can import the library and run some sanity checks

Use twine to upload the generated wheel to PyPI (only in release builds)

The build can be customized with environment variables. In addition to any variables that OpenCV’s build accepts, we recognize:

See the next section for more info about manual builds outside the CI environment.

If some dependency is not enabled in the pre-built wheels, you can also run the build locally to create a custom wheel.

Manual debug builds

In order to build opencv-python in an unoptimized debug build, you need to side-step the normal process a bit.

If you would like the build produce all compiler commands, then the following combination of flags and environment variables has been tested to work on Linux:

See this issue for more discussion: #424

Since OpenCV version 4.3.0, also source distributions are provided in PyPI. This means that if your system is not compatible with any of the wheels in PyPI, pip will attempt to build OpenCV from sources. If you need a OpenCV version which is not available in PyPI as a source distribution, please follow the manual build guidance above instead of this one.

You can also force pip to build the wheels from the source distribution. Some examples:

If you need contrib modules or headless version, just change the package name (step 4 in the previous section is not needed). However, any additional CMake flags can be provided via environment variables as described in step 3 of the manual build section. If none are provided, OpenCV’s CMake scripts will attempt to find and enable any suitable dependencies. Headless distributions have hard coded CMake flags which disable all possible GUI dependencies.

On slow systems such as Raspberry Pi the full build may take several hours. On a 8-core Ryzen 7 3700X the build takes about 6 minutes.

Opencv-python package (scripts in this repository) is available under MIT license.

OpenCV itself is available under Apache 2 license.

Third party package licenses are at LICENSE-3RD-PARTY.txt.

All wheels ship with FFmpeg licensed under the LGPLv2.1.

Non-headless Linux wheels ship with Qt 5 licensed under the LGPLv3.

The packages include also other binaries. Full list of licenses can be found from LICENSE-3RD-PARTY.txt.

find_version.py script searches for the version information from OpenCV sources and appends also a revision number specific to this repository to the version string. It saves the version information to version.py file under cv2 in addition to some other flags.

A release is made and uploaded to PyPI when a new tag is pushed to master branch. These tags differentiate packages (this repo might have modifications but OpenCV version stays same) and should be incremented sequentially. In practice, release version numbers look like this:

cv_major.cv_minor.cv_revision.package_revision e.g. 3.1.0.0

The master branch follows OpenCV master branch releases. 3.4 branch follows OpenCV 3.4 bugfix releases.

Every commit to the master branch of this repo will be built. Possible build artifacts use local version identifiers:

cv_major.cv_minor.cv_revision+git_hash_of_this_repo e.g. 3.1.0+14a8d39

These artifacts can’t be and will not be uploaded to PyPI.

Linux wheels are built using manylinux2014. These wheels should work out of the box for most of the distros (which use GNU C standard library) out there since they are built against an old version of glibc.

The default manylinux2014 images have been extended with some OpenCV dependencies. See Docker folder for more info.

Supported Python versions

Python 3.x compatible pre-built wheels are provided for the officially supported Python versions (not in EOL):

Starting from 4.2.0 and 3.4.9 builds the macOS Travis build environment was updated to XCode 9.4. The change effectively dropped support for older than 10.13 macOS versions.

Conda Install Cv2

There are many Python IDEs that are available on the internet. One such Python IDE is Anaconda, open-source software and one of the most popular IDEs used by programmers worldwide for Python and R Programming. It contains a lot of plugins and packages like NumPy, Pandas, OpenCV, etc.

OpenCV (Open Source Computer Vision Library) is a library of programming functions fundamentally focused on real-time computer vision. It is essentially used to perform operations on pictures or videos. OpenCV is a cross-platform library, which means that it is accessible on several programming languages like Python, C++, etc.

This tutorial will discuss different methods to install the cv2 module on your device for Anaconda Users.

Use the pip Command to Install the OpenCV Module

The pip command can be used here to install this package on the system through the command terminal. pip is a package management system that can be utilized to manage and install all the software libraries and packages available to use in Python.

The opencv module can be installed by running the command below.

The above method works for programmers working with Anaconda applications as well.

Use the conda Command to Install the OpenCV Module

This method works only for programmers working on Anaconda IDE. The opencv module can be installed by running the command below.

Please enable JavaScript

The opencv module can be installed with the help of conda-forge by running the command below.

Use the Anaconda Navigator to Install the OpenCV Module

Additionally, to confirm if Anaconda can successfully import the OpenCV module, we can use the following code.

If the package is imported without any errors and the version is displayed in the output correctly, then the installation of the opencv module is successful.