opencv-python-headless 4.0.0.21

Wrapper package for OpenCV python bindings.

OpenCV on Wheels

Unofficial pre-built OpenCV packages for Python.

Installation and Usage

1. 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.

2. Select the correct package for your environment:

   There are four different packages and you should select only one of them. Do not install multiple different packages in the same enviroment. 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)

   • run pip install opencv-python if you need only main modules
   • run pip install opencv-contrib-python if you need both main and contrib modules (check extra modules listing from OpenCV documentation)

   b. Packages for server (headless) environments

   These packages do not contain any GUI functionality. They are smaller and suitable for more restricted environments.

   • run pip install opencv-python-headless if you need only main modules
   • run pip install opencv-contrib-python-headless if you need both main and contrib modules (check extra modules listing from OpenCV documentation)

3. Import the package:

   import cv2

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

   cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

4. Read OpenCV documentation

5. 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.

Q: Pip fails with Could not find a version that satisfies the requirement ...?

A: Most likely the issue is related to too old pip and can be fixed by running pip install --upgrade pip. Note that the wheel (especially manylinux) format does not currently support properly ARM architecture so there are no packages for ARM based platforms in PyPI. However, opencv-python packages for Raspberry Pi can be found from https://www.piwheels.org/.

Q: Import fails on Windows: ImportError: DLL load failed: The specified module could not be found.?

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.

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.

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

A: It's easier for users to understand opencv-python than cv2 and it makes it easier to find the package with search engines. cv2 (old interface in old OpenCV versions was named as cv) is the name that OpenCV developers chose when they created the binding generators. This is kept as the import name to be consistent with different kind of tutorials around the internet. Changing the import name or behaviour would be also confusing to experienced users who are accustomed to the import 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.

Build process

The project is structured like a normal Python package with a standard setup.py file. The build process for a single entry in the build matrices is as follows (see for example appveyor.yml file):

1. Checkout repository and submodules

   • OpenCV is included as submodule and the version is updated manually by maintainers when a new OpenCV release has been made
   • Contrib modules are also included as a submodule

2. Find OpenCV version from the sources

3. Install dependencies (numpy)

4. Build OpenCV

   • tests are disabled, otherwise build time increases too much
   • there are 4 build matrix entries for each build combination: with and without contrib modules, with and without GUI (headless)
   • Linux builds run in manylinux Docker containers (CentOS 5)

5. Copy each .pyd/.so file to cv2 folder of this project and generate wheel

   • Linux and macOS wheels are checked with auditwheel and delocate

6. Install the generated wheel

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

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

The cv2.pyd/.so file is normally copied to site-packages. To avoid polluting the root folder this package wraps the statically built binary into cv2 package and __init__.py file in the package handles the import logic correctly.

Since all packages use the same cv2 namespace explained above, uninstall the other package before switching for example from opencv-python to opencv-contrib-python.

Licensing

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

OpenCV itself is available under 3-clause BSD License.

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

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

Linux and MacOS wheels ship with Qt 4.8.7 licensed under the LGPLv2.1.

Versioning

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.

Releases

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

Development builds

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.

Manylinux wheels

Linux wheels are built using manylinux. 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 manylinux images have been extended with some OpenCV dependencies. See Docker folder for more info.

Supported Python versions

Python 2.7 is the only supported version in 2.x series. Python 3.x releases follow Numpy releases. For example Python 3.3 is no longer supported by Numpy so support for it has been dropped in opencv-python, too.

Currently, builds for following Python versions are provided:

• 2.7
• 3.4
• 3.5
• 3.6
• 3.7