我写了一个这样的示例代码,(1)首先,我们使用python c++ api来训练弓词汇并将其保存到文件中。(2) 编写 c++ 代码以获取图像的弓矢量化表示代码为:
vector<double> features;//store the feature
bool readVocabulary(const string& filename, Mat& vocabulary) {
FileStorage fs(filename, FileStorage::READ);
if (fs.isOpened()) {
fs["vocabulary"] >> vocabulary;
return true;
}
return false;
}
//imgpath is the image filepath, vocpath is the voc path
void getImgBow(char* imgpath, char* vocpath) {
cv::initModule_nonfree();
Ptr<FeatureDetector> featureDetector = FeatureDetector::create("SURF");
Ptr<DescriptorExtractor> descExtractor =
DescriptorExtractor::create("SURF");
Ptr<DescriptorMatcher> descMatcher =
DescriptorMatcher::create("FlannBased");
Ptr<BOWImgDescriptorExtractor> bowExtractor;
if (featureDetector.empty() || descExtractor.empty() || descMatcher.empty()) {
cout << "featureDetector or descExtractor was not created" << endl;
}
bowExtractor = new BOWImgDescriptorExtractor(descExtractor, descMatcher);
Mat vocabulary;
readVocabulary(vocpath, vocabulary);
bowExtractor->setVocabulary(vocabulary);
Mat img = imread(imgpath);
if (img.rows < img.cols)
cv::resize(img, img, Size(320, 240));
else
cv::resize(img, img, Size(240, 320));
vector<KeyPoint> keypoints;
Mat descriptors;
featureDetector->detect(img, keypoints);
bowExtractor->compute(img, keypoints, descriptors);
for (int j = 0; j < descriptors.cols; j++) {
float value = descriptors.at<float> (0, j);
features.push_back(value);
}
}
(3)、我们将c++代码编码为python模块:
PyObject* wrap_contentfilter(PyObject* self, PyObject* args) {
//parse the python parameters.
if (!PyArg_ParseTuple(args, "sssOO", &imgpath, &vocpath, &modelpath,
&candidate_list, &can_pro_lsit))
//you may use PyInt_AsSsize_t and so on to do type change.
//invoke getImgBow function.
//construct PyObject and return to python, use PyList_SetItem function,PyObject*
}
static PyMethodDef predictMethods[] = { { "content_filter", wrap_contentfilter,
METH_VARARGS, "get image's bow, predict" }, { NULL, NULL } };
extern "C"
void initcontentfilter() {
PyObject* m;
m = Py_InitModule("contentfilter", predictMethods);
}
(4)我们编写一个python例子来调用c++函数。
import contentfilter
contentfilter.content_filter(parameters)
(5)编译c++函数:
g++ -fPIC content_filter.cpp -o contentfilter.so -shared -I/usr/local/include -I/usr/include/python2.7 -I/usr/lib/python2.7/config -L/usr/local/lib -lopencv_highgui -lopencv_nonfree -lopencv_legacy -lopencv_ml -lopencv_features2d -lopencv_imgproc -lopencv_core
(6)python例子.py