Install XCode from App Store. If XCode available on App Store is not compatible with. First we need to install Xcode. To install Xcode, fire up the Apple App Store, find the.
From Emgu CV: OpenCV in .NET (C#, VB, C++ and more)
- Open How To Download Opencv On Mac Windows 10
- How To Install Opencv
- Open How To Download Opencv On Mac Os
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-
1Windows
- 1.1Nuget
-
1.2Using the Downloadable packages
-
1.2.3Creating a New Project in Visual Studio
- 1.2.3.1Core Functionality
- 1.2.4The type initializer for 'Emgu.CV.CvInvoke' threw an exception.
-
1.2.3Creating a New Project in Visual Studio
-
1.3Building from Git
- 1.3.2Configuring the project
-
2Linux
- 2.1Getting ready
- 2.3Configuring & Building the project
-
3Mac OS
- 3.1Adding Emgu CV to your Xamarin Mac App
- 4iOS
- 5Android
Windows
- The Open Source Computer Vision Library, or OpenCV, if you prefer, houses over 2500 algorithms, extensive documentation and sample code for real-time computer vision. Advertisement OpenCV for Mac focuses mainly towards real-time image processing, as such, if it finds Intel's Integrated Performance Primitives on the system, it will use these.
- Problems installing opencv on mac with python. Wrong PYTHONPATH after updating.bashprofile for Mac. Svn authentification failed. Area of a single pixel object in OpenCV. Build problems for androidbinarypackage - Eclipse Indigo, Ubuntu 12.04. OpenCV DescriptorMatcher matches. OpenCV for Android (2.4.2): OpenCV Loader imports not resolved.
Nuget
Rayman 2 the great escape pc download. Using nuget package manager is probably the easiest way to include Emgu CV library in your project.
Open Source Release
- From your project, right click on 'References' and select 'Manager Nuget Packages.' option. It will open up nuget package manager. In package source, make sure that 'nuget.org' is selected. (If you are using the commercial release, please check the instruction in the commercial download area for instructions to setup the commercial release nuget repository.)
- Under 'Browse', enter the search text 'emgu.cv.runtime' and you should be able to find the Emgu.CV.runtime.windows nuget package.
Please make sure the package is created by 'Emgu Corporation' for the official release.
- Click the 'Install' button. Nuget will download Emgu.CV.runtime.windows and configure the project for you.
Commercial Release
- If you are using the commercial release, please check the instruction in the commercial download area for instructions to setup the commercial release nuget repository.
- Commercial release download link can be found in the 'Download Instructions' at the bottom of your purchase complete page. It is also available on the product delivery email, under the 'CUSTOMER SERVICE' session, it appears as a link under 'Your Order Data'. Please click on the link to access the Commercial release download session.
- If you have the Professional / Ultimate license. The instruction can be found in 'windows/nuget.txt' file.
- If you have the Windows only license. The instructions can be found in 'nuget.txt' file
- Once the commercial nuget repository is set up, you can browse it, there should be two packages: Emgu.CV.runtime.windows.dldt is the commercial release runtime without CUDA; Emgu.CV.runtime.windows.cuda.dldt is the commercial release runtime with CUDA support.
- Click the 'Install' button. Nuget will download Emgu CV commercial release and configure the project for you.
Using the Downloadable packages
If you are using the downloadable packages (.zip or .exe), you can follow the instructions below.
Getting the Dependency
- For Version 3.0+, the required vcrt dlls are included in the 'x86' and 'x64' folder. You will be ready as long as you copy all the unmanaged dlls in the 'x86' and 'x64' folder to the folder of executable.
- For version 2.4.x the bundled OpenCV binary is build with Visual Studio 2010, you will needs to installed MSVCRT 9.0 SP1 x86 or MSVCRT 9.0 SP1 x64 to resolve the dependency issue.
- For Version 2.0 - 2.3, the bundled OpenCV binary is build with Visual Studio 2008, you will needs to installed MSVCRT 9.0 SP1 to resolve the dependency issue.
- For Version 1.5, the bundled OpenCV pre1.1 binary is build with Visual Studio 2005, you will needs to installed MSVCRT 8.0 SP1 to resolve the dependency issue.
Building the Examples
- Follow this link to the file server on Source Forge.
- Download and extract the windows installer.
- Install the software
- Go to the 'SolutionWindows.Desktop' folder.
- Open
Emgu.CV.Example.sln
and build the solution. At this point, you should be able to run the example programs.
Creating a New Project in Visual Studio
To use the framework in Visual Studio, you need to
- Download and extract the binary files package Emgu.CV.Windows.Binary-{version}.zip
For a Full guide to using the dependencies under Visual Studio and C# see the C# Tutorial.
Core Functionality
Managed Code
- Create a new Visual Studio project or use an existing one
- Add reference
- For 4.4 release. Add the
Emgu.CV.Platform.NetStandard.dll
to Reference. AddEmgu.CV.RuntimeWindowsEmgu.CV.Runtime.Windows
shared project to include the native dlls into your project. - For 4.2 release. Add the
Emgu.CV.World.NetStandard.dll
to Reference. AddEmgu.CV.RuntimeWindowsEmgu.CV.Runtime.Windows
shared project to include the native dlls into your project. - For 3.1 release. Add the
Emgu.CV.World.dll
to Reference of the project. - For 3.0 release. Add the two files
Emgu.Utils.dll
andEmgu.CV.dll
to References of the project.
- For 4.4 release. Add the
- Optionally put the following lines in the top of your code to include the Emgu.CV namespace.
using Emgu.CV;
using Emgu.CV.Structure;
Open CV unmanaged dll
- For Emgu CV 4.2 and above, adding
Emgu.CV.RuntimeWindowsEmgu.CV.Runtime.Windows
shared project to your project will take care of deploying all the native files. - For older releases, copy the 'x86' and 'x64' folder, along with the DLLs inside those folder to the folder of the executable. Emgu CV is able to load the unmanaged binary from the 'x86' folder when running in 32bit mode, and load the unmanaged binary from the 'x64' folder when running in 64bit-mode. If you are only targeting the 'x64' platform, you only need to copy the 'x64' folder.
CUDA (GPU) package
- For Emgu CV 4.2 and above, adding
Emgu.CV.RuntimeWindowsEmgu.CV.Runtime.Windows
shared project to your project will take care of deploying all the native files. - For Emgu CV 3.x+, only package containing -cuda in its name (e.g.
libemgucv-xxx-cuda-xxx
) has CUDA processing enabled.- Install the latest cuda graphic card driver from NVIDIA on your development workstation.
- Adding reference: For 3.1 release. You don't need to add extra references. The Cuda namespace is part of the Emgu.CV.World.dll; For 3.0 release. Add
Emgu.CV.Cuda.dll
to References - Optionally put the following lines in the top of your code to include the Emgu.CV.Cuda namespace.
using Emgu.CV.Cuda;
- For Emgu CV 2.x, CUDA (GPU) for image processing is only available for Emgu CV rev 2.2.1 and later. Only package containing -gpu in its name (e.g.
libemgucv-xxx-gpu-xxx
) has CUDA (GPU) processing enabled.- Install the latest cuda graphic card driver from NVIDIA on your development workstation.
- Add
Emgu.CV.GPU.dll
to References - Optionally put the following lines in the top of your code to include the Emgu.CV.GPU namespace.
using Emgu.CV.GPU;
GUI
To display image using Emgu's ImageBox
- Add
Emgu.CV.UI.dll
to References - Optionally put the following lines in the top of your code to include the Emgu.CV.UI namespace.
using Emgu.CV.UI;
Machine Learning
- Adding reference
- For 3.1 release. You don't need to add extra reference.
- For release up to and including 3.0 release, add
Emgu.CV.ML.dll
to References
- Optionally put the following lines in the top of your code to include the Emgu.CV.ML namespace.
using Emgu.CV.ML;
Start Developing
- Follow the Tutorial to learn how to use Emgu CV.
- Hello World (C# or VB .NET) is a good starting point.
The type initializer for 'Emgu.CV.CvInvoke' threw an exception.
If you see this exception, please check the following
Have you installed MSVCRT?
- For Version 3.0+, the required vcrt dlls are included in the 'x86' and 'x64' folder. You will be ready as long as you copy all the unmanaged dlls in the 'x86' and 'x64' folder to the folder of executable.
- For Version 2.4+, the bundled OpenCV binary is build with Visual Studio 2010, you will needs to installed MSVCRT 10.0 SP1 x86 or MSVCRT 10.0 SP1 x64 to resolve the dependency issue.
- For Version 2.0+, the bundled OpenCV binary is build with Visual Studio 2008, you will needs to installed MSVCRT 9.0 SP1 to resolve the dependency issue.
- For Version 1.5, the bundled OpenCV pre1.1 binary is build with Visual Studio 2005, you will needs to installed MSVCRT 8.0 SP1 to resolve the dependency issue.
Have you copied the OpenCV dlls to the execution directory?
- Make sure the unmanaged DLLs are in the execution directory.
- For Emgu CV version >=2.4.2, this means the 'x86' and 'x64' folder and all the dlls within the folders. The folder names and file structures should not be altered when deploying with the application.
- For EMGU CV version 2.4
cudart64_42_9.dll, cvextern.dll, npp64_42_9.dll, opencv_calib3dXXX.dll, opencv_contribXXX.dll, opencv_coreXXX.dll, opencv_features2dXXX.dll, opencv_flannXXX.dll, opencv_highguiXXX.dll, opencv_imgprocXXX.dll, opencv_legacyXXX.dll, opencv_mlXXX.dll, opencv_nonfreXXX.dll, opencv_objectdetectXXX.dll, opencv_videoXXX.dll,</code?> where <code>XXX
is the OpenCV version number. - For Emgu CV version 2.2, 2.3 this means the following dlls:
opencv_calib3dXXX.dll, opencv_contribXXX.dll, opencv_coreXXX.dll, opencv_features2dXXX.dll, opencv_highguiXXX.dll, opencv_imgprocXXX.dll, opencv_legacyXXX.dll, opencv_mlXXX.dll, opencv_objectdetectXXX.dll, opencv_videoXXX.dll
whereXXX
is the OpenCV version number. - For Emgu CV version <= 2.1, this means the following dlls:
cvXXX.dll, cvauxXXX.dll, cxcoreXXX.dll, highguiXXX.dll, opencv_ffmpegXXX.dll, mlXXX.dllcvextern.dll
whereXXX
is the OpenCV version number.
- The best way to set up your project is:
- Copy the unmanaged DLLs to your project folder
- Right click on the project, click Add->Existing Item and select all unmanaged DLLs. Add them to the project.
- For each of the included Dlls, left click on it, find the 'Copy to Output Directory' option and select 'Copy if newer'
Are you missing any dependency?
Where to download mac os x lion iso. Download Dependency Walker and use it to open the 'cvextern.dll' file. Check if any dependency is missing.
I have checked all of above but I still got the Exception
In this case, please try to build and run the examples. After building the examples, try to run the 'Hello World' Program.
If 'Hello World' runs without any problem, compare it with you project, find the difference in configuration and fix it.
If 'Hello World' get the same 'The type initializer for 'Emgu.CV.CvInvoke' threw an exception.' message, try to find out the InnerException and report it to the discussion forum
Building from Git
If you wants to build the development version of Emgu CV from source code, you can to get it from GIT following instructions on This page.
Prerequisite
- You will need to install CMAKE in-order to build the unmanaged C++ code (OpenCV and cvextern.dll).
- You will need Visual Studio 2017/ Visual Studio 2015 to build the Managed code (Emgu CV)
Configuring the project
32-Bit Windows
Run
Build_Binary_x86_nocuda.bat
script located in the platformswindows
folder of GIT.
64-Bit Windows
Run
Build_Binary_x86-64_doc.bat
script located in the platformswindows
folder of GIT.
Building the unmanaged code
Double check if the
emgucv.sln
file exists in the root folder, if not, run the above step again.Open emgucv.sln
solution located in the root folder with the matching version of Visual Studio, switch the configuration to 'Release' and build the cvextern project.
Building the managed code
- Browse to the 'SolutionWindows.Desktop' Folder
- Open
Emgu.CV.sln
and build the solution.
At this point, the Emgu CV dlls should be available under the
bin
folder in the top most directory.
Linux
Getting ready
CentOS 7
- dotnet SDK
- For 4.3.0 release, you will need dotnet SDK 3.1
- OpenCV
- We will build a custom version of OpenCV in the next step. It is recommended to remove any OpenCV package if it is installed on your machine. You should remove OpenCV by running as root
- CMake
Emgu CV has adapted to use cmake to compile all it source code (as well as OpenCV). Make sure you have cmake installed.
- Installing GIT so you can check out the project folder, you can install GIT by running as root
Ubuntu 20.04
- dotnet SDK
- For 4.3.0 release, you will need dotnet SDK 3.1. Please follow this instruction to install asp .net core on Ubuntu. Once that is done, run the following command to verify dotnet is installed: It should show a message similar to the following
- OpenCV
- We will build a custom version of OpenCV in the next step. It is recommended to remove any OpenCV package if it is installed on your machine.
- CMake
Emgu CV has adapted to use cmake to compile its source code (as well as OpenCV).
- Installing GIT so you can check out the project folder, you can install GIT by running
Raspbian (Raspberry Pi)
- dotnet SDK
- For 4.3.0 release, you will need dotnet SDK 3.1
- OpenCV
- We will build a custom version of OpenCV in the next step. It is recommended to remove any OpenCV package if it is installed on your machine.
- CMake
Emgu CV has adapted to use cmake to compile its source code (as well as OpenCV).
- Installing GIT so you can check out the project folder, you can install GIT by running
Getting the source code
- To build from source, you will need a Git client to check out the source code from SourceForge. For more information, see GIT. The following command can be used to check out the source:
- Go to emgucv directory
- Initialize opencv, tesseract-ocr and cvblob submodules
Configuring & Building the project
Cent OS 7
- Got to the configuration folder
- Installing the perquisites. This only needs to be run once. You can install them by running
- Use the following command to configure and build the project:
- If you want to re-configure the modules you need. Call You can enable / disable modules as you need. e.g. If you do not want Emgu CV to build with tesseract. Set
EMGU_CV_WITH_TESSERACT
toOFF
. Once all flags are set, pressc
to re-configure. Pressq
to quite cmake. Rebuild the project with
Raspbian (Raspberry Pi)
- Got to the configuration folder
- Installing the perquisites
- Use the following command to configure and build the project:
- If you want to re-configure the modules you need. Call You can enable / disable modules as you need. e.g. If you do not want Emgu CV to build with tesseract. Set
EMGU_CV_WITH_TESSERACT
toOFF
. Once all flags are set, pressc
to re-configure. Pressq
to quite cmake. Rebuild the project with
Ubuntu
- Got to the configuration folder.
- Installing the prerequisites. This only needs to be run once. You can install them by running
- Use the following command to configure and build the project:
- If you want to re-configure the modules you need. Call You can enable / disable modules as you need. e.g. If you do not want Emgu CV to build with tesseract. Set
EMGU_CV_WITH_TESSERACT
toOFF
. Once all flags are set, pressc
to re-configure. Pressq
to quite cmake. Rebuild the project with
Running the Examples
- We have the native binary compiled. Now let's compile and run our first dot net core program.
- make sure dotnet can load the dynamic library from the current location by typing
- Try to compile and run the program
System.DllNotFoundException
If you encounter this exception, there might be missing dependencies. In this case, go to
libsx64
folder (or libsarm
folder, or libsx86
folder, depends on your system architecture). Run this command
ldd libcvextern.so
and check if there is any dependency missing.
If not, go back to
Emgu.CV.Example/BuildInfo.NetCore.Console
folder and run this command
gdb dotnet
then, on the (gdb) commandline, type
run run
to debug with GDB.
You can also try
LD_DEBUG=libs dotnet run
to find missing dependencies.
Mac OS
Emgu CV for Mac OS is available under our commercial license. The instructions below applies to the Emgu CV for Mac OS, Professional or Ultimate commercial release.
Adding Emgu CV to your Xamarin Mac App
You can either add Emgu CV to your project by directly adding the binary files, or by adding two projects. Car mechanic simulator 2015 download free macbook pro.
Using binary files
The 'libs' folder of the Mac OS (or Pro) release package should contains the files
Please add Emgu.CV.Platform.NetStandard.dll as a reference to your Xamarin Mac App. Yahoo messenger download mac latest. Deploy 'libcvextern.dylib' file to the folder of executable. Then you are ready to use Emgu CV in your Mac OS App.
If the files 'Emgu.CV.Platform.NetStandard.dll' does not exist, you can compile the visual studio solution under 'SolutionMacEmgu.CV.Mac.Example.sln' to build the dlls.
Using project files
![Install Install](/uploads/1/2/6/7/126727826/887126582.jpg)
Instead of using the binary files as mention above. You can also add this two projects as references into your existing project:
For v4.4.0
- Emgu.CV.PlatformNetstandardEmgu.CV.Platform.Netstandard.csproj
- Emgu.CV.RuntimeMacEmgu.CV.Runtime.Mac.shproj
Demos
The demo solution is available under the 'SolutionMac' folder.
iOS
Emgu CV for iOS is only available under our commercial license. The instructions below applies to the Emgu CV for iOS, Professional or Ultimate commercial release.
Adding Emgu CV to your Xamarin iOS App
The iOS (or Pro) release package should contains the following two files:
Adding this two dll files as a reference to your Xamarin iOS App should allow you to use Emgu CV in your App. After adding the references. You should call in the main ios app, to make sure the native binary is included in the compilation. Otherwise you may see a long list of missing native reference errors during compilation.
Size of the binary
The 'Emgu.CV.Platform.IOS.dll' file size is large. For example, in the 4.4.0 iOS release, this file is 724MB. It contains the native binary for all supported CPU architectures, including those for simulators.
However, if you are building an IPA for app store submission, and is only targeting ARM64 devices, you can select just ARM64 architecture. When the IPA is build, the compiler will strip out all the binary that are not used. It will significantly reduce the final size of the IPA. Depends on the number of functions you used, if you are only targeting a single ARM64 architecture, the final IPA size should be some where around 40MB.
Demos
The demo solution is available under the 'SolutioniOS' folder.
Android
Emgu CV for Android is only available under our commercial license. The instructions below applies to the Emgu CV for Android, Professional or Ultimate commercial release. Mindbody app for mac.
Adding Emgu CV to your Xamarin Android App
The 'libs' folder of the Android (or Pro) release package should contains these two files
Adding the above files as references to your Xamarin Android App should allow you to use Emgu CV in your App.
Open How To Download Opencv On Mac Windows 10
If the files does not exist, you can compile the visual studio solution under 'SolutionAndroidEmgu.CV.Android.sln' to build the dlls.
Size of the binary
The 'Emgu.CV.Platform.Android.dll' file size is large. For example, in the 4.4.0 Android release, this file is 67MB. It contains the native binary for all supported CPU architectures.
However, if you are building an app for Google Play Store submission, and is only targeting armeabi-v7 devices, you can select just armeabi-v7 architecture. When the final APK is build, the compiler will strip out all the binary for the architectures that are not used. It will significantly reduce the final size of the APK. Depends on the number of functions you used, if you are only targeting a single armeabi-v7 architecture, the final APK size should be some where around 20-30MB.
How To Install Opencv
Demos
The demo solution is available under the 'SolutionAndroid' folder.
Retrieved from 'http://www.emgu.com/wiki/index.php?title=Download_And_Installation&oldid=2391'
What’s OpenCV?
Open How To Download Opencv On Mac Os
Ahhh, computer vision, such a cool field! Lately, I’ve been trying to become more knowledgeable about CV and image processing in python. OpenCV (CV = ‘computer vision’) is an excellent open source computer vision software library written in C++ that supports C++, C, Python, Java, and Matlab API’s. OpenCV will supply you with functions that will let you detect faces in images, track objects in a video, and perform any number of image processing tasks.
The only problem is: how the hell do I install OpenCV so that I can use it in conjunction with a Jupyter notebook? Let’s be honest, most likely you’re either you’re using a Jupyter notebook, Spyder, or the ipython terminal (if you’re a real sadist) to test your python code. And especially if you’re coding for image processing, you’re going to want to view your progress without having (a) a million separate images open and (b) having to wait for Spyder to inevitably crash. That’s the beauty of a Jupyter notebook - when you’re using it with Matplotlib, you can just display your images and videos in a living document!
For me, my ideal OpenCV situation would be for me to be able to simply type and evaluate the following
import
statements with zero errors or package conficts:
Problems with traditional installation methods
There are many ways to install OpenCV. The standard approach is to download it from the OpenCV website and then compile and install OpenCV using the software building utility “CMake” all within a virutal Python environment. I’ve gone down this route according to Adrian Rosebrock’s fabulous installation walkthrough, and if you just want to have access to OpenCV 3.0, I suggest you consider it. But, at the end of the day, there are even more steps required after Adrian’s 9 steps to get OpenCV compatible with a Jupyter notebook. Other installation walkthroughs I’ve found tend to be generally convoluted and assume that you have Homebrew, XCode, maybe MacPorts, or just experience in general with installing and building software packages. Wouldn’t it be great if we could just run something analogous to
pip install opencv
?
If you’re like me (maybe you’re not) I often think that
pip install
‘ing a Python package is the same thing as R’s install.packages
function - while we get similar functionality, R packages come with the luxury of basically never interfering with other R package dependencies! If one package needs a newer or older version of some other package you’ve already installed, install.packages
will most likely just take care of everything for you. Python packages, on the other hand, will often have dependencies on specific versions of other packages, so if you pip install
one package, other package may fail to import because their dependent packages have been updated. That’s why we use virtual environments; my favorite method for creating and running virtual environments is with Anaconda, a Python distribution that comes with Sklearn, Scipy, NumPy, Jupyter notebook, and most of the other essential tools a data scientist needs when using Python.
Overall, I installed OpenCV cleanly in just a few steps:
- Install Anaconda, make Anaconda’s Python your system’s default Python (skip if you already have this).
- Create a virtual environment.
- Make sure all Conda packages are up-to-date.
- Run
conda install -c https://conda.binstar.org/menpo opencv
- Test.
(1) Install Anaconda. (Skip if you already have Anaconda).
First off, I’m still a python 2 guy. Yeah, there’s python 3, but I grew up on Py 2.7 and it’ll take a lot to pry it from my cold, dead hands. So I have a python 2.7 Anaconda environment running on my computer. Your choice.
I went to the Anaconda downloads page and got the Python 2.7 Mac OS X 64-Bit command-line installer, so that we can install everything from Terminal.
After downloading that, navigate to your Downloads directory (if you’re new to the Terminal, just open the Terminal application and type
cd $HOME/Downloads
).
While still in Terminal, enter
Awesome, now you’ve downloaded and installed Anaconda.
(1.b) Make Anaconda your default python installation.
For data science, Anaconda rules. Ideally, when you’re in Terminal and you type
python
, you’d like for the Anaconda python installation to be the default python that starts running instead of what comes installed by default on a typical Macbook. Why? Well, using Anaconda we can just import NumPy, import any Scikit Learn funciton, import Matplotlib, etc.
To see what I’m talking about, type this in Terminal:
If you get
/usr/bin/python2.7
, you’re not using the Anaconda installation. To change this, you’ll need to change your bash_profile so that the default path to the python installation in the Anaconda directory. If you don’t have a .bash_profile file in your home directory, do this:
This just created that file. Next, open the .bash_profile page and add this line:
export PATH=”~/anaconda/bin:$PATH”
Finally, you have to make your system update python path the with your new settings, so in Terminal type
(2) Make an Anaconda virtual environment
Anaconda has great documentation if you ever get lost using their tools, but otherwise they’re pretty easy to use. To create a virtual python 2.7 environment called “py27,” run this:
To enter this virtual environment, we use Conda’s
source activate
function:
If the environment is running properly, you should see
(py27)
preceding the $
sign at the command prompt in Terminal. In this environment we have access to Anaconda’s python package installer, conda install
, so that we can install packages at will in this “bubble” without messing up dependencies (basically breaking python) in any other environment. Side note: if you want to exit this py27 environment, just enter source deactivate
in Terminal.
(3) Update packages
Just to be safe, I updated all of my python packages while inside of my py27 environment. It’s ridiculously easy with Anaconda:
(4) Install OpenCV
With Anconda we can install python packages within a specific Conda environment using
conda install
instead of pip
, the typical python package management system.
Next, I would normally suggest just typing
conda install opencv
at the command prompt, but this (unsurprisingly) lead me to a package conflict with NumPy! Yep, the version of OpenCV that Conda installed relied on a specific release of the NumPy package that was actually in conflict with the one that was just updated in step (3). OK, to be honest, maybe I brought that upon myself with updating the packages the way I did. But, there’s a work around that functions with this latest update of NumPy: install OpenCV directly from the Menpo project:
(5) Fire up a Jupyter notebook and test!
The Anaconda environment should now have everything we need to start analyzing images in a self-contained little Jupyter notebook. Test it out. First, launch a Jupyter notebook from the terminal:
Next, see if everything is installed correctly; hopefully you’ll be able to run this sans errors:
If successful, you’ll be able to readily access OpenCV functions with the package prefix
cv2
!