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opencv-python | Automated CI toolchain to produce precompiled opencv | 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.
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  • 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 4 major release(s) in the last 12 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
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  • opencv-python code analysis shows 0 unresolved vulnerabilities.
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  • It has 457 lines of code, 9 functions and 8 files.
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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

Manual builds

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export CMAKE_ARGS='-DCMAKE_VERBOSE_MAKEFILE=ON'
export VERBOSE=1

python3 setup.py bdist_wheel --build-type=Debug

Masking many images from two different path opencv

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import cv2 as cv
import os

img_path = r"image_folder_path"
od_images = r"od_img_folder_path"
for img,od in zip(os.listdir(img_path), os.listdir(od_images)):

    image = cv.imread(img_path+"\\"+img, cv.IMREAD_COLOR)
    od = cv.imread(od_images+"\\"+od, cv.IMREAD_GRAYSCALE)

    other = cv.bitwise_not(od)
    res =  cv.bitwise_and(image, image, mask=other)

     cv.imwrite('Output/masking/' +img+ '_masking.jpg', res)

Colab: (0) UNIMPLEMENTED: DNN library is not found

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!pip install tensorflow==2.7.0

'tensorflow==2.7.0',
'tf-models-official==2.7.0',
'tensorflow_io==0.23.1',

ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

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apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib  # or pip install manimlib

pip3 install manimce  # or pip install manimce

apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib  # or pip install manimlib

pip3 install manimce  # or pip install manimce

How to make conda use its own gcc version?

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# system gcc
which gcc && gcc --version
# /usr/bin/gcc
# gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0

# creating a conda env with gcc
conda create -n gcc gcc
# activate the environment
conda activating gcc
which gcc && gcc --version
# /opt/conda/envs/gcc/bin/gcc
# gcc (GCC) 11.2.0

# packages in environment at /opt/conda/envs/gcc:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       1_gnu    conda-forge
binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
gcc                       11.2.0               h702ea55_2    conda-forge
gcc_impl_linux-64         11.2.0              h82a94d6_11    conda-forge
kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
libgcc-devel_linux-64     11.2.0              h0952999_11    conda-forge
libgcc-ng                 11.2.0              h1d223b6_11    conda-forge
libgomp                   11.2.0              h1d223b6_11    conda-forge
libsanitizer              11.2.0              he4da1e4_11    conda-forge
libstdcxx-ng              11.2.0              he4da1e4_11    conda-forge
sysroot_linux-64          2.12                he073ed8_15    conda-forge

# system gcc
which gcc && gcc --version
# /usr/bin/gcc
# gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0

# creating a conda env with gcc
conda create -n gcc gcc
# activate the environment
conda activating gcc
which gcc && gcc --version
# /opt/conda/envs/gcc/bin/gcc
# gcc (GCC) 11.2.0

# packages in environment at /opt/conda/envs/gcc:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       1_gnu    conda-forge
binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
gcc                       11.2.0               h702ea55_2    conda-forge
gcc_impl_linux-64         11.2.0              h82a94d6_11    conda-forge
kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
libgcc-devel_linux-64     11.2.0              h0952999_11    conda-forge
libgcc-ng                 11.2.0              h1d223b6_11    conda-forge
libgomp                   11.2.0              h1d223b6_11    conda-forge
libsanitizer              11.2.0              he4da1e4_11    conda-forge
libstdcxx-ng              11.2.0              he4da1e4_11    conda-forge
sysroot_linux-64          2.12                he073ed8_15    conda-forge

ln -s /home/envs/segmentation_base/bin/x86_64-conda_cos6-linux-gnu-cc gcc
ln -s /home/envs/segmentation_base/bin/x86_64-conda_cos6-linux-gnu-cpp g++

Could not find a version that satisfies the requirement psycopg2

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  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --update --no-cache postgresql-dev musl-dev

  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --no-cache postgresql-dev gcc python3-dev musl-dev

  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --update --no-cache postgresql-dev musl-dev

  before_script:
    - pip install --upgrade pip setuptools wheel
    - apk update
    - apk add -q --no-cache postgresql-dev gcc python3-dev musl-dev

Gitlab CI: Failed building wheel for opencv-python

copy iconCopydownload iconDownload
gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -DTHREAD_STACK_SIZE=0x100000 -fPIC -DUSE__THREAD -DHAVE_SYNC_SYNCHRONIZE -I/usr/include/ffi -I/usr/include/libffi -I/usr/local/include/python3.9 -c c/_cffi_backend.c -o build/temp.linux-x86_64-3.9/c/_cffi_backend.o
c/_cffi_backend.c:15:10: fatal error: ffi.h: No such file or directory
  15 | #include <ffi.h>
    |          ^~~~~~~
compilation terminated.
error: command '/usr/bin/gcc' failed with exit code 1

apk add libffi-dev 

gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -DTHREAD_STACK_SIZE=0x100000 -fPIC -DUSE__THREAD -DHAVE_SYNC_SYNCHRONIZE -I/usr/include/ffi -I/usr/include/libffi -I/usr/local/include/python3.9 -c c/_cffi_backend.c -o build/temp.linux-x86_64-3.9/c/_cffi_backend.o
c/_cffi_backend.c:15:10: fatal error: ffi.h: No such file or directory
  15 | #include <ffi.h>
    |          ^~~~~~~
compilation terminated.
error: command '/usr/bin/gcc' failed with exit code 1

apk add libffi-dev

Tensorflow Object Detection API taking forever to install in a Google Colab and failing

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pip install --upgrade pip

ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from tensorflow.keras import backend as K


class CancerNet:
    @staticmethod
    def build(width, height, depth, classes):
        model = Sequential()
        shape = (height, width, depth)
        channelDim = -1

        if K.image_data_format() == "channels_first":
            shape = (depth, height, width)
            channelDim = 1

        model.add(SeparableConv2D(32, (3, 3), padding="same", input_shape=shape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))

        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

model = CancerNet()

from tensorflow.keras.layers import BatchNormalization

Tensorflow Object Detection Api M1 Macbook Conflict Error

copy iconCopydownload iconDownload
cd models/research
# Compile protos.
protoc object_detection/protos/*.proto --python_out=.
# Install TensorFlow Object Detection API.
cp object_detection/packages/tf1/setup.py .
python -m pip install --use-feature=2020-resolver .

python -m pip install --force --no-dependencies . 

conda create —-name=tf-m1
conda activate tf-m1
conda install python=3.8.6 -y
sh Desktop/PATH TO GITHUB DIR OF TENSORFLOW MAC(i used 0.1a3)/install_venv.sh /Users/stefan/miniforge3/envs/tf-m1
python -m pip install --upgrade pip
pip install ipykernel jupyter
python -m ipykernel install --user --name=tensorflow-m1.0
Tensorflow Test : ok  (import tensorflow as tf; print(tf.__version__))

conda install -c conda-forge matplotlib -y
conda install -c conda-forge scikit-learn -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y

cd Desktop/PATH/
mkdir -p Tensorflow/models
git clone https://github.com/tensorflow/models Tensorflow/models
cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install --force --no-dependencies . 

pip install tf-slim
pip install pycocotools
pip install lxml
pip install lvis
pip install contextlib2
pip install --no-dependencies tf-models-official
pip install avro-python3
pip install pyyaml
Pip install gin-config

python -m pip install --force --no-dependencies . 

conda create —-name=tf-m1
conda activate tf-m1
conda install python=3.8.6 -y
sh Desktop/PATH TO GITHUB DIR OF TENSORFLOW MAC(i used 0.1a3)/install_venv.sh /Users/stefan/miniforge3/envs/tf-m1
python -m pip install --upgrade pip
pip install ipykernel jupyter
python -m ipykernel install --user --name=tensorflow-m1.0
Tensorflow Test : ok  (import tensorflow as tf; print(tf.__version__))

conda install -c conda-forge matplotlib -y
conda install -c conda-forge scikit-learn -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y

cd Desktop/PATH/
mkdir -p Tensorflow/models
git clone https://github.com/tensorflow/models Tensorflow/models
cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install --force --no-dependencies . 

pip install tf-slim
pip install pycocotools
pip install lxml
pip install lvis
pip install contextlib2
pip install --no-dependencies tf-models-official
pip install avro-python3
pip install pyyaml
Pip install gin-config

python -m pip install --force --no-dependencies . 

conda create —-name=tf-m1
conda activate tf-m1
conda install python=3.8.6 -y
sh Desktop/PATH TO GITHUB DIR OF TENSORFLOW MAC(i used 0.1a3)/install_venv.sh /Users/stefan/miniforge3/envs/tf-m1
python -m pip install --upgrade pip
pip install ipykernel jupyter
python -m ipykernel install --user --name=tensorflow-m1.0
Tensorflow Test : ok  (import tensorflow as tf; print(tf.__version__))

conda install -c conda-forge matplotlib -y
conda install -c conda-forge scikit-learn -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y

cd Desktop/PATH/
mkdir -p Tensorflow/models
git clone https://github.com/tensorflow/models Tensorflow/models
cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install --force --no-dependencies . 

pip install tf-slim
pip install pycocotools
pip install lxml
pip install lvis
pip install contextlib2
pip install --no-dependencies tf-models-official
pip install avro-python3
pip install pyyaml
Pip install gin-config

python -m pip install --force --no-dependencies . 

conda create —-name=tf-m1
conda activate tf-m1
conda install python=3.8.6 -y
sh Desktop/PATH TO GITHUB DIR OF TENSORFLOW MAC(i used 0.1a3)/install_venv.sh /Users/stefan/miniforge3/envs/tf-m1
python -m pip install --upgrade pip
pip install ipykernel jupyter
python -m ipykernel install --user --name=tensorflow-m1.0
Tensorflow Test : ok  (import tensorflow as tf; print(tf.__version__))

conda install -c conda-forge matplotlib -y
conda install -c conda-forge scikit-learn -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y

cd Desktop/PATH/
mkdir -p Tensorflow/models
git clone https://github.com/tensorflow/models Tensorflow/models
cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install --force --no-dependencies . 

pip install tf-slim
pip install pycocotools
pip install lxml
pip install lvis
pip install contextlib2
pip install --no-dependencies tf-models-official
pip install avro-python3
pip install pyyaml
Pip install gin-config

python -m pip install --force --no-dependencies . 

conda create —-name=tf-m1
conda activate tf-m1
conda install python=3.8.6 -y
sh Desktop/PATH TO GITHUB DIR OF TENSORFLOW MAC(i used 0.1a3)/install_venv.sh /Users/stefan/miniforge3/envs/tf-m1
python -m pip install --upgrade pip
pip install ipykernel jupyter
python -m ipykernel install --user --name=tensorflow-m1.0
Tensorflow Test : ok  (import tensorflow as tf; print(tf.__version__))

conda install -c conda-forge matplotlib -y
conda install -c conda-forge scikit-learn -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y

cd Desktop/PATH/
mkdir -p Tensorflow/models
git clone https://github.com/tensorflow/models Tensorflow/models
cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install --force --no-dependencies . 

pip install tf-slim
pip install pycocotools
pip install lxml
pip install lvis
pip install contextlib2
pip install --no-dependencies tf-models-official
pip install avro-python3
pip install pyyaml
Pip install gin-config

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate

conda install -c apple tensorflow-deps
pip install tensorflow-macos
pip install tensorflow-metal

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate

conda install -c apple tensorflow-deps
pip install tensorflow-macos
pip install tensorflow-metal

OpenCV VideoCapture returns strange frame offset for different versions

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index = 0
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
while True:
    offset = cap.get(cv2.CAP_PROP_POS_MSEC)

    ok, frame = cap.read()
    if not ok:
        break
    
    # CAP_PROP_POS_MSEC
    print("CAP_PROP_POS_MSEC: ", index, offset)
    
    # Devide fps by frame number
    offset = cap.get(cv2.CAP_PROP_POS_FRAMES) / fps * 1000
    print("cv2.CAP_PROP_POS_FRAMES", index, offset)
     
    index += 1

Community Discussions

Trending Discussions on opencv-python
  • Masking many images from two different path opencv
  • Colab: (0) UNIMPLEMENTED: DNN library is not found
  • AWS Elastic Beanstalk - Failing to install requirements.txt on deployment
  • ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects
  • Combining Object Detection with Text to Speech Code
  • How to make conda use its own gcc version?
  • Could not find a version that satisfies the requirement psycopg2
  • Gitlab CI: Failed building wheel for opencv-python
  • Tensorflow Object Detection API taking forever to install in a Google Colab and failing
  • ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
Trending Discussions on opencv-python

QUESTION

Masking many images from two different path opencv

Asked 2022-Mar-31 at 04:06

Hello stackoverflow people:) I'm trying to masking many image from two different path, but I don't have an idea to do that. This an example for just two images and what I've do so far

image = cv.imread('Dataset/IDRiD_02.jpg', cv.IMREAD_COLOR)
od = cv.imread('od/IDRiD_02_OD.jpg', cv.IMREAD_GRAYSCALE)
mask = od
other = cv.bitwise_not(mask)
masking =  cv.bitwise_and(image, image, mask=other)
cv.imwrite('Output/masking/' + 'masking.jpg', masking)

Input is IDRiD_02.jpg and IDRiD_02_OD.jpg then Output is masking.jpg

Then I want to do the same but with many images

import cv2 as cv
import numpy as np
import os
import glob
import os.path

od_images = [] 

for directory_path in glob.glob("od/"):
    for mask_path in glob.glob(os.path.join(directory_path, "*.jpg")):
        mask = cv.imread(mask_path, cv.IMREAD_GRAYSCALE)
        od_images.append(mask)       
od_images = np.array(od_images)

path = "Dataset/*.jpg"

for file in glob.glob(path):
        
    #read image
    image = cv.imread(file, cv.IMREAD_COLOR)
    
    # e.g. MyPhoto.jpg
    basename = os.path.basename(file)
    # e.g. MyPhoto
    name = os.path.splitext(basename)[0]
    
    mask = cv.bitwise_not(od_images)
    
    masking =  cv.bitwise_and(image, image, mask = mask)
    
    cv.imwrite('Output/masking/' + name + '_masking.jpg', masking)

but then after I run the code, I'm getting the following error message

masking =  cv.bitwise_and(image, image, mask = mask)

error: OpenCV(4.5.5) D:\a\opencv-python\opencv-python\opencv\modules\core\src\arithm.cpp:230: error: (-215:Assertion failed) (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1) in function 'cv::binary_op'

anyone can understand and help me? Thank you before:)

ANSWER

Answered 2022-Mar-31 at 04:06

Hope it will work for you !

import cv2 as cv
import os

img_path = r"image_folder_path"
od_images = r"od_img_folder_path"
for img,od in zip(os.listdir(img_path), os.listdir(od_images)):

    image = cv.imread(img_path+"\\"+img, cv.IMREAD_COLOR)
    od = cv.imread(od_images+"\\"+od, cv.IMREAD_GRAYSCALE)

    other = cv.bitwise_not(od)
    res =  cv.bitwise_and(image, image, mask=other)

     cv.imwrite('Output/masking/' +img+ '_masking.jpg', res)

Source https://stackoverflow.com/questions/71659008

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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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