Jul 20, 2011 Sorry to say, but i can't help you much on that issue because i could only test this app/plug with the built-in iSight. What kind of iSight have you got (USB built-in or IEEE-1394)? The only thing i could do is maybe let you manualy provide the domain offset of the camera like describe bellow. Jan 28, 2020 Download openCV and unzip it somewhere on your computer. (I hace tried it for 3.4.5) Create build folder inside it. Open CMake application Click Browse Source and navigate to your openCV folder. Click Browse Build and navigate to your build Folder. Click the configure button. You will be asked how you would like to generate the files. Aug 22, 2013 My previous post on installing OpenCV for Mac users is one of the most popular on this site (which is simultaneously surprising and fantastic). However, I recently switched from using MacPorts to Homebrew – users that need to migrate existing installs can check out this guide. Asked: 2017-12-15 06:01:08 -0500 Seen: 752 times Last updated: Dec 16 '17. Step 1: Find the OpenCV app. For any apps you don’t want or need, you can remove them to save a bit of space on your PC. To get to the app, click on the Start Button. Next, find the app you wish to remove. Step 2: Removing OpenCV from Windows Store. Right click on the app and click Uninstall. One last box will appear – click Uninstall again.
In this sample, we will build the OpenCV library for Windows and add it to a UWP C++ app, which will run facial and body recognition on a photo.
Create a new UWP C++ project
The sample code is available to download, but as an exercise, we will create this app from scratch.
Even if you download the sample, code, you'll need to follow the steps in Compile the OpenCV Libraries and Add the Libraries to your Project below.
Note: If this is the first project you create, Visual Studio will likely prompt you to enable developer mode for Windows 10.
Compile the OpenCV Libraries
Add the Libraries to your Project
Set up the User Interface
Open MainPage.xaml and replace the existing code with the following code to create the window UI:
To view the entire UI, change the dropdown in the top left corner from '5' Phone' to '12' Tablet'.
Modify the actual C++ FilesModify the Header File
Open MainPage.xaml.h. Replace the contents with the following code:
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The header file stablishes the functions we're going to declar in the main .cpp file, as well as a private variable (_stored_image) which stores the content of the storedImage XAML Image element once we upload it.
Add the Includes and Namespaces to the .cpp file
Add the following header files to the top of your code, right after the #include 'MainPage.xaml.h' line:
These lines allow us to use OpenCV library functions, along with some necessary default classes as well. We also define the locations of the features classifiers we'll use later.
Add the UpdateImage functionInstall Opencv Python On Mac
Add the following function right after the MainPage Constructor
This function changes the image contained in the 'storedImage' XAML Image element to the contents of the 'image' argument.
Add the Upload Button (loadImageButton) handler
Add the following function right after the UpdateImage function
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This function opens an image and uploads a default image to the StoredImage UI element.
Add the Canny Edge Button (cannyEdgesButton) handler
Add the following function right after the last handler:
This function applies Canny Edge detection to the image and updates the image container with the results.
Add the face and body classifiers
Add the following lines and function right after the last handler:
This function uses cascade classification to classify and detect bodies and faces in a video stream (or image) using two Haar classifiers, face_cascade and body_cascade, stored in the xml files we provided for you. It's a method of classification involving machine learning, as explained on OpenCV's website.
Add the 'Detect Faces and Bodies' button (detectFeaturesButton) event handler
Add the following lines and function right after the last helper function:
This function loads the classifiers, re-reads the image (the classification doesn't work on a Canny image in case the user clicked that button first), finds the faces and bodies using the helper function from the last step, and draws rectangles around the results: red for the faces, black for the bodies. It then pushes the updated image to the container.
Add in the Resources
Download the picture, face classifier, and body classifier and add them to your Assets folder within your project.
Optional: Build and test your app locally
Opencv Test CodeDeploy the app to your Windows 10 IoT Core Device
Notes:
Troubleshoot:Mac Install OpencvOpencv Test App For Mac Free
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