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opencv-python | Automated CI toolchain to produce precompiled opencvpython | Computer Vision library

 by   opencv Shell Version: 63 License: Non-SPDX

 by   opencv Shell Version: 63 License: Non-SPDX

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kandi X-RAY | opencv-python Summary

opencv-python is a Shell library typically used in Artificial Intelligence, Computer Vision, OpenCV, Numpy applications. opencv-python has no bugs, it has no vulnerabilities and it has medium support. However opencv-python has a Non-SPDX License. You can download it from GitHub.
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.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • opencv-python has a medium active ecosystem.
  • It has 2567 star(s) with 509 fork(s). There are 80 watchers for this library.
  • There were 2 major release(s) in the last 6 months.
  • There are 37 open issues and 482 have been closed. On average issues are closed in 51 days. There are 3 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of opencv-python is 63
opencv-python Support
Best in #Computer Vision
Average in #Computer Vision
opencv-python Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • opencv-python has 0 bugs and 0 code smells.
opencv-python Quality
Best in #Computer Vision
Average in #Computer Vision
opencv-python Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • opencv-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • opencv-python code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
opencv-python Security
Best in #Computer Vision
Average in #Computer Vision
opencv-python Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • opencv-python has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
opencv-python License
Best in #Computer Vision
Average in #Computer Vision
opencv-python License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • opencv-python releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
  • It has 457 lines of code, 9 functions and 8 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
opencv-python Reuse
Best in #Computer Vision
Average in #Computer Vision
opencv-python Reuse
Best in #Computer Vision
Average in #Computer Vision
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opencv-python Key Features

Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.

opencv-python Examples and Code Snippets

Community Discussions

Vulnerabilities

No vulnerabilities reported

Install opencv-python

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. Make sure that your pip version is up-to-date (19.3 is the minimum supported version): pip install --upgrade pip. Check version with pip -V. For example Linux distributions ship usually with very old pip versions which cause a lot of unexpected problems especially with the manylinux format.
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.
Make sure that your pip version is up-to-date (19.3 is the minimum supported version): pip install --upgrade pip. Check version with pip -V. For example Linux distributions ship usually with very old pip versions which cause a lot of unexpected problems especially with the manylinux format.
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) Option 1 - Main modules package: pip install opencv-python Option 2 - Full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python (check contrib/extra modules listing from OpenCV documentation) 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. Option 3 - Headless main modules package: pip install opencv-python-headless Option 4 - Headless full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python-headless (check contrib/extra modules listing from OpenCV documentation)
Import the package: import cv2 All packages contain Haar cascade 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")
Read OpenCV documentation
Before opening a new issue, read the FAQ below and have a look at the other issues which are already open.
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):. Steps 1--4 are handled by pip wheel.
In Linux and MacOS build: get OpenCV's optional C dependencies that we compile against
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
Find OpenCV version from the sources
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) source distributions are separate entries in the build matrix
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)
CI_BUILD. Set to 1 to emulate the CI environment build behaviour. Used only in CI builds to force certain build flags on in setup.py. Do not use this unless you know what you are doing.
ENABLE_CONTRIB and ENABLE_HEADLESS. Set to 1 to build the contrib and/or headless version
ENABLE_JAVA, Set to 1 to enable the Java client build. This is disabled by default.
CMAKE_ARGS. Additional arguments for OpenCV's CMake invocation. You can use this to make a custom build.

Support

A: No, the packages are special wheel binary packages and they already contain statically built OpenCV binaries. Since opencv-python version 4.3.0.*, manylinux1 wheels were replaced by manylinux2014 wheels. If your pip is too old, it will try to use the new source distribution introduced in 4.3.0.38 to manually build OpenCV because it does not know how to install manylinux2014 wheels. However, source build will also fail because of too old pip because it does not understand build dependencies in pyproject.toml. To use the new manylinux2014 pre-built wheels (or to build from source), your pip version must be >= 19.3. Please upgrade pip with pip install --upgrade pip. 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. A: Make sure you have removed old manual installations of OpenCV Python bindings (cv2.so or cv2.pyd in site-packages). 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 {OpenCV wiki](https://github.com/opencv/opencv/wiki) and the fficial OpenCV forum before file new bugs. 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: https://github.com/skvark/opencv-python/issues/126. 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.

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