Opencv android samples download

Sample — image-manipulations — this example demonstrates how OpenCV can be used as an image processing and manipulation library.

It supports several filters, demonstrates color space conversions and working with histograms. Sample — puzzle — shows how a simple game can be implemented with just a few calls to OpenCV. It is available on Google Play. Sample — face-detection — is the simplest implementation of the face detection functionality on Android. It supports 2 modes of execution: available by default Java wrapper for the cascade classifier, and manually crafted JNI call to a native class which supports tracking.

Even Java version is able to show close to the real-time performance on a Google Nexus One device. Skip to primary navigation Skip to main content Android. Of course you have to keep efficiency in mind, but please avoid premature optimization. OpenCV was designed to be high-performance, so measure your actual performance before you start to worry.

Keep in mind, that majority of modern mobile devices is surprisingly powerful. And do not forget to look into tutorials , they will help you to quickly understand what you can easily accomplish with OpenCV. Computer Vision field has a long history, but some problems are still unsolved.

We propose this way for the professional developers, since native development is a bit harder, but gives you larger opportunities. Javadoc html-files are included into the distribution and available online. To view the entire UI, change the dropdown in the top left corner from '5" Phone' to '12" Tablet'. The header file stablishes the functions we're going to declar in the main. Add the following header files to the top of your code, right after the include "MainPage.

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.

This function changes the image contained in the "storedImage" XAML Image element to the contents of the "image" argument. This function applies Canny Edge detection to the image and updates the image container with the results.

It's a method of classification involving machine learning, as explained on OpenCV's website. 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. Download the picture , face classifier , and body classifier and add them to your Assets folder within your project. If you've built the x86 version of OpenCV, you can test the program on your local machine.

Make sure the app builds correctly by invoking the Build Build Solution menu command. Press the "Detect" button to see the detected faces and bodies in the image indicated by rectangles. Click on the dropdown next to the Local Machine label and click on Remote Machine.

Make sure the dropdown just to the left says your device's architecture, either x86 , x64 , or ARM.