I tried to implement fourier descriptor to use it in machine learning algorithm but i got this error
OpenCV Error: Assertion failed (type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2) in void cv::dft(cv::InputArray, cv::OutputArray, int, int), file /home/maksim/workspace/android-pack/opencv/modules/core/src/dxt.cpp, line 2506
This is my code:
Mat trainData = new Mat();
Mat train_labels = new Mat();
String Newligne=System.getProperty("line.separator");
for (int i = 0; i <48; i++) {
String path1 = Environment.getExternalStorageDirectory().toString()
+ "/Pictures/images/" + "a"+i + ".jpg";
Mat img = Imgcodecs.imread(path1);
Log.i(TAG, "error" + i + img.empty());
img.convertTo(img, CvType.CV_32FC1);
Mat secondImage = new Mat(img.rows(), img.cols(), CvType.CV_64FC1);
img.convertTo(secondImage, CvType.CV_64FC1);
int m = Core.getOptimalDFTSize(img.rows());
int n = Core.getOptimalDFTSize(img.cols()); // on the border
// add zero values Imgproc.copyMakeBorder(image1, padded, 0, m - image1.rows(), 0, n
Mat padded = new Mat(new Size(n, m), CvType.CV_64FC1); // expand input
// image to
// optimal size
Core.copyMakeBorder(secondImage, padded, 0, m - secondImage.rows(), 0,
n - secondImage.cols(), Core.BORDER_CONSTANT);
List planes = new ArrayList();
planes.add(padded);
planes.add(Mat.zeros(padded.rows(), padded.cols(), CvType.CV_64FC1));
Mat complexI = Mat.zeros(padded.rows(), padded.cols(), CvType.CV_64FC2);
Mat complexI2 = Mat
.zeros(padded.rows(), padded.cols(), CvType.CV_64FC2);
Core.merge(planes, complexI); // Add to the expanded another plane with
// zeros
Core.dft(complexI, complexI2); // this way the result may fit in the
// source matrix
// compute the magnitude and switch to logarithmic scale
// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
Core.split(complexI2, planes); // planes[0] = Re(DFT(I), planes[1] =
// Im(DFT(I))
Mat mag = new Mat(planes.get(0).size(), planes.get(0).type());
Core.magnitude(planes.get(0), planes.get(1), mag);// planes[0]
// =
// magnitude
Mat magI = mag;
Mat magI2 = new Mat(magI.size(), magI.type());
Mat magI3 = new Mat(magI.size(), magI.type());
Mat magI4 = new Mat(magI.size(), magI.type());
Mat magI5 = new Mat(magI.size(), magI.type());
Core.add(magI, Mat.ones(padded.rows(), padded.cols(), CvType.CV_64FC1),
magI2); // switch to logarithmic scale
Core.log(magI2, magI3);
Mat crop = new Mat(magI3, new Rect(0, 0, magI3.cols() & -2,
magI3.rows() & -2));
magI4 = crop.clone();
// rearrange the quadrants of Fourier image so that the origin is at the
// image center
int cx = magI4.cols() / 2;
int cy = magI4.rows() / 2;
Rect q0Rect = new Rect(0, 0, cx, cy);
Rect q1Rect = new Rect(cx, 0, cx, cy);
Rect q2Rect = new Rect(0, cy, cx, cy);
Rect q3Rect = new Rect(cx, cy, cx, cy);
Mat q0 = new Mat(magI4, q0Rect); // Top-Left - Create a ROI per quadrant
Mat q1 = new Mat(magI4, q1Rect); // Top-Right
Mat q2 = new Mat(magI4, q2Rect); // Bottom-Left
Mat q3 = new Mat(magI4, q3Rect); // Bottom-Right
Mat tmp = new Mat(); // swap quadrants (Top-Left with Bottom-Right)
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left)
q2.copyTo(q1);
tmp.copyTo(q2);
Core.normalize(magI4, magI5, 0, 255, Core.NORM_MINMAX);
Mat realResult = new Mat(magI5.size(), CvType.CV_8UC1);
magI5.convertTo(realResult, CvType.CV_8UC1);
Mat imgResized = realResult.reshape(1, 1);
trainData.push_back( imgResized);
train_labels
.push_back(new Mat(1, 1, CvType.CV_32SC1, new Scalar(i)));
}
↧