JNIEXPORT void JNICALL Java_org_opencv_video_BackgroundSubtractor_apply_11
(JNIEnv* env, jclass , jlong self, jlong image_nativeObj, jlong fgmask_nativeObj)
{
try {
LOGD("video::apply_11()");
BackgroundSubtractor* me = (BackgroundSubtractor*) self; //TODO: check for NULL
Mat& image = *((Mat*)image_nativeObj);
Mat& fgmask = *((Mat*)fgmask_nativeObj);
me->operator ()( image, fgmask );
return;
} catch(cv::Exception e) {
LOGD("video::apply_11() catched cv::Exception: %s", e.what());
jclass je = env->FindClass("org/opencv/core/CvException");
if(!je) je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, e.what());
return;
} catch (...) {
LOGD("video::apply_11() catched unknown exception (...)");
jclass je = env->FindClass("java/lang/Exception");
env->ThrowNew(je, "Unknown exception in JNI code {video::apply_11()}");
return;
}
}
bool Pipeline::apply_backgroundSubtractor(json_t *pStage, json_t *pStageModel, Model &model) {
validateImage(model.image);
int history = jo_int(pStage, "history", 0, model.argMap);
float varThreshold = jo_float(pStage, "varThreshold", 16, model.argMap);
bool bShadowDetection = jo_bool(pStage, "bShadowDetection", TRUE, model.argMap);
string background = jo_string(pStage, "background", "", model.argMap);
string method = jo_string(pStage, "method", "MOG2", model.argMap);
string stageName = jo_string(pStage, "name", method.c_str(), model.argMap);
int maxval = 255;
double learningRate = jo_double(pStage, "learningRate", -1, model.argMap);
const char *errMsg = NULL;
StageDataPtr pStageData = model.stageDataMap[stageName];
BackgroundSubtractor *pSubtractor;
bool is_absdiff = false;
if (!errMsg) {
if (method.compare("MOG2") == 0) {
if (pStageData) {
pSubtractor = ((SubtractorStageData *) pStageData)->pSubtractor;
} else {
pSubtractor = new BackgroundSubtractorMOG2(history, varThreshold, bShadowDetection);
model.stageDataMap[stageName] = new SubtractorStageData(stageName, pSubtractor);
}
} else if (method.compare("absdiff") == 0) {
is_absdiff = true;
} else {
errMsg = "Expected method: MOG2";
}
}
Mat bgImage;
if (!background.empty()) {
if (history != 0) {
errMsg = "Expected history=0 if background image is specified";
} else {
if (model.image.channels() == 1) {
bgImage = imread(background.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
} else {
bgImage = imread(background.c_str(), CV_LOAD_IMAGE_COLOR);
}
if (bgImage.data) {
LOGTRACE2("apply_backgroundSubtractor(%s) %s", background.c_str(), matInfo(bgImage).c_str());
if (model.image.rows!=bgImage.rows || model.image.cols!=bgImage.cols) {
errMsg = "Expected background image of same size as pipeline image";
}
} else {
errMsg = "Could not load background image";
}
}
}
if (!errMsg) {
if (history < 0) {
errMsg = "Expected history >= 0";
}
}
if (!errMsg) {
Mat fgMask;
if (is_absdiff) {
absdiff(model.image, bgImage, fgMask);
if (fgMask.channels() > 1) {
cvtColor(fgMask, fgMask, CV_BGR2GRAY);
}
threshold(fgMask, model.image, varThreshold, maxval, THRESH_BINARY);
} else {
if (bgImage.data) {
pSubtractor->operator()(bgImage, fgMask, learningRate);
}
pSubtractor->operator()(model.image, fgMask, learningRate);
model.image = fgMask;
}
}
return stageOK("apply_backgroundSubtractor(%s) %s", errMsg, pStage, pStageModel);
}
