It is recommended to group the items under some suitable name.ĩ- Now you need to select the project from Xcode navigator from the left menu and go to “ Build Phases” tab. Now select the following OpenCV libraries and press “ add” button:Ĩ- The selected OpenCV libraries will start showing in Xcode. The OpenCV libraries naming convention is as libopencv_.dylib. You need to make sure that you are including the proper library, not the shortcut file. The /usr/local/lib directory will open.ħ- Select the required OpenCV libraries and press “ Add” Button. Type/usr/local/lib and press “ Go” Button. Press ( Alt Cmd a) or select ( File-> Add Files to project) option from menu. Let the process finish successfully.Ħ- Now open Xcode and make a new project to add OpenCV libraries in your OSX application project. Once you enter the password and give it a go, terminal will install some dependencies from the Internet and will install some dynamic libraries in /usr/local/includeand /usr/local/lib directory on your MAC machine. It will prompt you for MAC login and password. Once the process finish, type the following command in terminal: If cmake tool is already installed on your MAC then jump to next step.Ĥ- In terminal, navigate into the OpenCV directory and type the following commands:ĥ- Now you need to wait till terminal does its processing. If it is not installed, type “ sudo port install cmake” in terminal for installing cmake tool in your MAC machine. This will be the raw code of OpenCV and you have to build it.ģ- Now you need to check if cmake is installed on your MAC machine or not by typing “ cmake” command in terminal. Following are the steps to be followed:ġ- Download latest OpenCV source code for Linux/MAC OS from this link.Ģ- Once OpenCV source code download completes, extract the OpenCV directory and save it at some location. This article covers, how OpenCV libraries are used in a Linux/MAC application. In an OSX project, we had to implement OpenCV libraries in Swift language for MAC application. It should take just a few seconds to complete execution.OpenCV is basically an OpenSource library having the best frameworks for video processing. The final step here is to execute - sudo make install. After it’s done you should get an output like so. You can adjust the j option with respect to the hardware available. With all the eight cores ( j8 stands for eight cores here) chugging along, this step took ~8 minutes for me. Next, we launch the make command - make -j8. The compilation took ~3 minutes for me and it should produce outputs like so. Now, before you run the above cmake command, activate the conda environment you created in an earlier step ( conda activate ) if you haven’t already. For these two arguments, you would want to first determine the paths and then supply them accordingly. Also, please pay attention to the following arguments - OPENCV_EXTRA_MODULES_PATH and PYTHON3_EXECUTABLE. D PYTHON3_EXECUTABLE =/Users/sayakpaul/miniforge3/envs/dev/bin/python3 \Īs per this issue comment, DCMAKE_SYSTEM_PROCESSOR, DCMAKE_OSX_ARCHITECTURES, DWITH_OPENJPEG, and DWITH_IPP are needed to be set during the compilation step. D OPENCV_EXTRA_MODULES_PATH =/Users/sayakpaul/Downloads/opencv_contrib-4.5.0/modules \
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