Using OpenCV for Native Image Processing in Phimpme Android

OpenCV is very widely used open-source image processing library. After the integration of OpenCV Android SDK in the Phimpme Android application, the image processing functions can be written in Java part or native part. Taking runtime of the functions into consideration we used native functions for image processing in the Phimpme application.

We didn’t have the whole application written in native code, we just called the native functions on the Java OpenCV Mat object. Mat is short for the matrix in OpenCV. The image on which we perform image processing operations in the Phimpme Android application is stored as Mat object in OpenCV.

Creating a Java OpenCV Mat object

Mat object of OpenCV is same whether we use it in Java or C++. We have common OpenCV object in Phimpme for accessing from both Java part and native part of the application. We have a Java bitmap object on which we have to perform image processing operations using OpenCV. For doing that we need to create a Java Mat object and pass its address to native. Mat object of OpenCV can be created using the bitmap2Mat() function present in the OpenCV library. The implementation is shown below.

Mat inputMat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(bitmap, inputMat);

“bitmap” is the Java bitmap object which has the image to be processed. The third argument in the Mat function indicates that the Mat should be of type 8UC3 i.e. three color channels with 8-bit depth. With the second line above, the bitmap gets saved as the OpenCV Mat object.

Passing Mat Object to Native

We have the OpenCV Mat object in the memory. If we pass the whole object again to native, the same object gets copied from one memory location to another. In Phimpme application, instead of doing all that we can just get the memory location of the current OpenCV Mat object and pass it to native. As we have the address of the Mat, we can access it directly from native functions. Implementation of this is shown below.

Native Function Definition:

private static native void nativeApplyFilter(long inpAddr);

Native Function call:


Getting Native Mat Object to Java

We can follow the similar steps for getting the Mat from the native part after processing. In the Java part of Phimpme, we created an OpenCV Mat object before we pass the inputMat OpenCV Mat object to native for processing. So we have inputMat and outputMat in the memory before we send them to native. We get the memory locations of both the Mat objects and pass those addresses to native part. After the processing is done, the data gets written to the same memory location and can be accessed in Java. The above functions can be modified and rewritten for this purpose as shown below

Native Function Definition:

private static native void nativeApplyFilter(long inpAddr, long outAddr );

Native Function call:


if (outputMat !=null){
   Bitmap outbit = Bitmap.createBitmap(bitmap.getWidth(),bitmap.getHeight(),bitmap.getConfig());
   return outbit;

Native operations on Mat using OpenCV

The JNI function in the native part of Phimpme application receives the memory locations of both the OpenCV Mat objects. As we have the addresses, we can create Mat object pointing that memory location and can be passed to processing functions for performing native operations just like all OpenCV functions. This implementation is shown below.

#include <jni.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "enhance.h"
using namespace std;
using namespace cv;

Java_org_fossasia_phimpme_editor_editimage_filter_PhotoProcessing_nativeApplyFilter(JNIEnv *env, jclass type, jlong inpAddr,jlong outAddr) {
       Mat &src = *(Mat*)inpAddr;
       Mat &dst = *(Mat*)outAddr;
       applyFilter(src, dst);

applyFilter() function can have any image processing operation. The implementation of edge detection function using OpenCV in the Phimpme Android is shown below. We were able to do this in very few lines which otherwise would have needed an extremely large code.  

Mat grey,detected_edges;
cvtColor( src, grey, CV_BGR2GRAY );
blur( grey, detected_edges, Size(3,3) );
dst.create( grey.size(), grey.type() );
Canny( detected_edges, detected_edges, 70, 200, 3 );
dst = Scalar::all(0);
detected_edges.copyTo( dst, detected_edges);


The general structure of an OpenCV function which is necessary for implementing custom image processing operations can be understood by referring this below-mentioned brightness adjustment function.  

int x,y,bright;
dst = Mat::zeros( src.size(), src.type() );
for (y = 0; y < src.rows; y++) {
   for (x = 0; x < src.cols; x++) {<Vec3b>(y, x)[0] =
                  saturate_cast<uchar>((<Vec3b>(y, x)[0]) + bright);<Vec3b>(y, x)[1] =
               saturate_cast<uchar>((<Vec3b>(y, x)[1]) + bright);<Vec3b>(y, x)[2] =
               saturate_cast<uchar>((<Vec3b>(y, x)[2]) + bright);



Integrating OpenCV for Native Image Processing in Phimpme Android

OpenCV is an open source computer vision library written in C and C++. It has many pre-built image processing functions for all kinds of purposes. The filters and image enhancing functions which we implemented in Phimpme are not fully optimized. So we decided to integrate the Android SDK of OpenCV in the Phimpme Android application.

For integrating OpenCV in the application, the files inside the OpenCV Android SDK have to be properly placed inside the project. The build and make files of the project have to be configured correspondingly.

Here, in this post, I will mention how we fully integrated OpenCV Android SDK and configured the Phimpme project.

Setting Up

First, we downloaded the OpenCV-Android-SDK zip from the official release page here.

Now we extracted the file to navigated to SDK folder to see that there are three folders inside it. etc, Java and native.

All the build files are present inside the native directory and are necessarily copied to our project. The java directory inside the SDK folder is an external gradle module which has to imported and added as a dependency to our project.

So we copied all the files from jni directory of SDK folder inside OpenCV-Android-SDK to jni directory of the Phimpme project inside main. Similarly copied the 3rdparty directory into the main folder of the Phimpme project. Libs folder should also be copied into the main folder of the Phimpme but it has to be renamed to jniLibs. The project structure can be viewed in the below image.

Adding Module to Project

Now we headed to Android Studio and right clicked on the app in the project view, to add a new module. In that window select import Gradle project and browse to the java folder inside the SDK folder of OpenCV-Android-SDK as shown in figures.

Now we opened the build.gradle file of app level (not project level) and added the following line as a dependency.

  compile project(':openCVLibrary320')

Now open the build.gradle inside the openCVLibrary320 external module and change the platform tools version and  SDK versions.

Configuring Native Build Files

We removed all files related to cmake be we use ndk-build to compile native code. After removing the cmake files, added following lines in the file

APP_OPTIM := release
APP_ABI := all
APP_STL := gnustl_static
APP_CPPFLAGS := -frtti -fexceptions
APP_PLATFORM := android-25

Now we further added the following lines to the top of file

include $(LOCAL_PATH)/

The third line here is a flag indicating that the library has to be static and there is no need for external OpenCVLoader application.

Finally, find this line


in file and replace it with


And finally, add the following lines to java part. So that the OpenCV is initialized and linked to application

static {
   if (!OpenCVLoader.initDebug()) {
       Log.e( TAG + " - Error", "Unable to load OpenCV");
   } else {

With this, the integration of OpenCV and the configuration of the project is completed. Remove the build directories i.e. .build and .externalNativeBuild directories inside app folder for a fresh build so that you don’t face any wrong build error.