OpenCV 3.0 + Install and Usage

cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local -DWITH_TBB=ON -DBUILD_NEW_PYTHON_SUPPORT=ON -DWITH_V4L=ON -DBUILD_EXAMPLES=ON -DWITH_CUDA=OFF -DWITH_QT=ON -DWITH_GTK=ON -DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules (-DBUILD_opencv_legacy=OFF) <opencv_source_directory>

cd opencv×.×
mkdir release
cd release
cmake ..
sudo make -j4
sudo make install
sudo /bin/bash -c 'echo "/usr/local/lib">/etc/ld.so.conf.d/opencv.conf'
sudo ldconfig

pkg-config opencv --cflags --libs #查看opencv安装情况 参考pkg-config --help
pkg-confg --modversion opencv

img = cv2.resize(img,(0,0), fx = 0.3, fy = 0.3)

img = cv2.resize(img,dsize=(0,0), fx = 0.3, fy = 0.3)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

image_matche = cv2.cvtColor(np.zeros((image1.shape[0], image1.shape[1] * 2), dtype=np.uint8), cv2.COLOR_GRAY2BGR)

    if cv2.__version__.split('.')[0] == '2':
        detector = cv2.FeatureDetector_create('SIFT')
        descriptor = cv2.DescriptorExtractor_create('SIFT')
        detector.setDouble("contrastThreshold", 0.1)
        detector.setDouble('edgeThreshold', 10)
        points = detector.detect(gray)
        points, desc = descriptor.compute(gray, points)
    else:
        sift = cv2.xfeatures2d.SIFT_create()
        points, desc = sift.detectAndCompute(gray, None)

def match_low(feature1,feature2):
    FLANN_INDEX_KDTREE = 1
    flann_params = dict(algorithm=FLANN_INDEX_KDTREE, tree=4)
    flann = cv2.flann_Index(feature1, flann_params)
    idx, dist = flann.knnSearch(feature2, 2, params={})
    good1 = dist[:, 0] < dist[:, 1] * 0.8 * 0.8
    matches_good1 = zip(idx[good1, 0], good1.nonzero()[0])
    # (a, b) = np.array(matches_good, dtype=int)
    flann = cv2.flann_Index(feature2, flann_params)
    idx, dist = flann.knnSearch(feature1, 2, params={})
    good2 = dist[:, 0] < dist[:, 1] * 0.8 ** 2
    matches_good2 = zip(idx[good2, 0], good2.nonzero()[0])
    matches1 = [(a, b) for a, b in np.array(matches_good1, dtype=int)]
    matches2 = [(b, a) for a, b in np.array(matches_good2, dtype=int)]
    matches = set(matches1).intersection(matches2)
    return np.array(list(matches), dtype=int)

    Summary:
    This method does a fast local approximate nearest neighbors (FLANN) calculation between two sets of feature vectors. The result are two numpy arrays the first one is a list of indexes of the matches and the second one is the match distance value. For the match indices or idx, the index values correspond to the values of td, and the value in the array is the index in td. I.
    I.e. j = idx[i] is where td[i] matches sd[j].
    The second numpy array (dist), at the index i is the match distance between td[i] and sd[j].
    Lower distances mean better matches.

    Parameters:
    sd - A numpy array of feature vectors of any size.         (t is template)
    td - A numpy array of feature vectors of any size, this vector is used for indexing
         and the result arrays will have a length matching this vector. (s is source)

    Returns:
    Two numpy arrays, the first one, idx, is the idx of the matches of the vector td with sd.
    The second one, dist, is the distance value for the closest match.

    try:
        import cv2
    except:
        logger.warning("Can't run FLANN Matches without OpenCV >= 2.3.0")
        return
    FLANN_INDEX_KDTREE = 1  # bug: flann enums are missing
    flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 4)
    flann = cv2.flann_Index(sd, flann_params)
    idx, dist = flann.knnSearch(td, 1, params = {}) # bug: need to provide empty dict
    del flann
    return idx,dist

set (OPNECV_DIR ".../share/OpenCV")  NOTE: The OPENCV_DIR should have file called OpenCV-config.cmake

#define CV_8U   0
#define CV_8S   1
#define CV_16U  2
#define CV_16S  3
#define CV_32S  4
#define CV_32F  5
#define CV_64F  6
#define CV_USRTYPE1 7

string type2str(int type) {
  string r;

  uchar depth = type & CV_MAT_DEPTH_MASK;
  uchar chans = 1 + (type >> CV_CN_SHIFT);

  switch ( depth ) {
    case CV_8U:  r = "8U"; break;
    case CV_8S:  r = "8S"; break;
    case CV_16U: r = "16U"; break;
    case CV_16S: r = "16S"; break;
    case CV_32S: r = "32S"; break;
    case CV_32F: r = "32F"; break;
    case CV_64F: r = "64F"; break;
    default:     r = "User"; break;
  }
  r += "C";
  r += (chans+'0');

  return r;
}

int main(int argc, char**　argv)
{
  mat = imread("C:\someimage.jpg");
  type = mat.type();
  string ty =  type2str( M.type() );
  printf("Matrix: %s %dx%d \n", ty.c_str(), M.cols, M.rows );
  //std::cout<< ty<< std::endl;
}