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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-09-04 19:35:38 +03:00
committed by Alexander Alekhin
parent e0bfe44cac 351ee2e3a5
commit d74b98c3d9
117 changed files with 6913 additions and 3713 deletions
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samples/cpp/CMakeLists.txt
-3
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@@ -36,9 +36,6 @@ endif()
if(NOT BUILD_opencv_viz OR NOT VTK_USE_FILE)
ocv_list_filterout(cpp_samples "/viz/")
endif()
if(NOT HAVE_IPP_A)
ocv_list_filterout(cpp_samples "/ippasync/")
endif()
ocv_list_filterout(cpp_samples "real_time_pose_estimation/")
foreach(sample_filename ${cpp_samples})
if(sample_filename MATCHES "/viz/")
samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp
Executable
+184
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@@ -0,0 +1,184 @@
/**
* @brief You will learn how to recover an image with motion blur distortion using a Wiener filter
* @author Karpushin Vladislav, karpushin@ngs.ru, https://github.com/VladKarpushin
*/
#include <iostream>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
using namespace cv;
using namespace std;
void help();
void calcPSF(Mat& outputImg, Size filterSize, int len, double theta);
void fftshift(const Mat& inputImg, Mat& outputImg);
void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H);
void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr);
void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma = 5.0, double beta = 0.2);
const String keys =
"{help h usage ? | | print this message }"
"{image |input.png | input image name }"
"{LEN |125 | length of a motion }"
"{THETA |0 | angle of a motion in degrees }"
"{SNR |700 | signal to noise ratio }"
;
int main(int argc, char *argv[])
{
help();
CommandLineParser parser(argc, argv, keys);
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
int LEN = parser.get<int>("LEN");
double THETA = parser.get<double>("THETA");
int snr = parser.get<int>("SNR");
string strInFileName = parser.get<String>("image");
if (!parser.check())
{
parser.printErrors();
return 0;
}
Mat imgIn;
imgIn = imread(strInFileName, IMREAD_GRAYSCALE);
if (imgIn.empty()) //check whether the image is loaded or not
{
cout << "ERROR : Image cannot be loaded..!!" << endl;
return -1;
}
Mat imgOut;
//! [main]
// it needs to process even image only
Rect roi = Rect(0, 0, imgIn.cols & -2, imgIn.rows & -2);
//Hw calculation (start)
Mat Hw, h;
calcPSF(h, roi.size(), LEN, THETA);
calcWnrFilter(h, Hw, 1.0 / double(snr));
//Hw calculation (stop)
imgIn.convertTo(imgIn, CV_32F);
edgetaper(imgIn, imgIn);
// filtering (start)
filter2DFreq(imgIn(roi), imgOut, Hw);
// filtering (stop)
//! [main]
imgOut.convertTo(imgOut, CV_8U);
normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);
imwrite("result.jpg", imgOut);
return 0;
}
void help()
{
cout << "2018-08-14" << endl;
cout << "Motion_deblur_v2" << endl;
cout << "You will learn how to recover an image with motion blur distortion using a Wiener filter" << endl;
}
//! [calcPSF]
void calcPSF(Mat& outputImg, Size filterSize, int len, double theta)
{
Mat h(filterSize, CV_32F, Scalar(0));
Point point(filterSize.width / 2, filterSize.height / 2);
ellipse(h, point, Size(0, cvRound(float(len) / 2.0)), 90.0 - theta, 0, 360, Scalar(255), FILLED);
Scalar summa = sum(h);
outputImg = h / summa[0];
}
//! [calcPSF]
//! [fftshift]
void fftshift(const Mat& inputImg, Mat& outputImg)
{
outputImg = inputImg.clone();
int cx = outputImg.cols / 2;
int cy = outputImg.rows / 2;
Mat q0(outputImg, Rect(0, 0, cx, cy));
Mat q1(outputImg, Rect(cx, 0, cx, cy));
Mat q2(outputImg, Rect(0, cy, cx, cy));
Mat q3(outputImg, Rect(cx, cy, cx, cy));
Mat tmp;
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp);
q2.copyTo(q1);
tmp.copyTo(q2);
}
//! [fftshift]
//! [filter2DFreq]
void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H)
{
Mat planes[2] = { Mat_<float>(inputImg.clone()), Mat::zeros(inputImg.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);
dft(complexI, complexI, DFT_SCALE);
Mat planesH[2] = { Mat_<float>(H.clone()), Mat::zeros(H.size(), CV_32F) };
Mat complexH;
merge(planesH, 2, complexH);
Mat complexIH;
mulSpectrums(complexI, complexH, complexIH, 0);
idft(complexIH, complexIH);
split(complexIH, planes);
outputImg = planes[0];
}
//! [filter2DFreq]
//! [calcWnrFilter]
void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr)
{
Mat h_PSF_shifted;
fftshift(input_h_PSF, h_PSF_shifted);
Mat planes[2] = { Mat_<float>(h_PSF_shifted.clone()), Mat::zeros(h_PSF_shifted.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);
dft(complexI, complexI);
split(complexI, planes);
Mat denom;
pow(abs(planes[0]), 2, denom);
denom += nsr;
divide(planes[0], denom, output_G);
}
//! [calcWnrFilter]
//! [edgetaper]
void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma, double beta)
{
int Nx = inputImg.cols;
int Ny = inputImg.rows;
Mat w1(1, Nx, CV_32F, Scalar(0));
Mat w2(Ny, 1, CV_32F, Scalar(0));
float* p1 = w1.ptr<float>(0);
float* p2 = w2.ptr<float>(0);
float dx = float(2.0 * CV_PI / Nx);
float x = float(-CV_PI);
for (int i = 0; i < Nx; i++)
{
p1[i] = float(0.5 * (tanh((x + gamma / 2) / beta) - tanh((x - gamma / 2) / beta)));
x += dx;
}
float dy = float(2.0 * CV_PI / Ny);
float y = float(-CV_PI);
for (int i = 0; i < Ny; i++)
{
p2[i] = float(0.5 * (tanh((y + gamma / 2) / beta) - tanh((y - gamma / 2) / beta)));
y += dy;
}
Mat w = w2 * w1;
multiply(inputImg, w, outputImg);
}
//! [edgetaper]
samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp
-168
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@@ -1,168 +0,0 @@
#include <stdio.h>
#include "opencv2/core/utility.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "cvconfig.h"
using namespace std;
using namespace cv;
#ifdef HAVE_IPP_A
#include "opencv2/core/ippasync.hpp"
#define CHECK_STATUS(STATUS, NAME)\
if(STATUS!=HPP_STATUS_NO_ERROR){ printf("%s error %d\n", NAME, STATUS);\
if (virtMatrix) {hppStatus delSts = hppiDeleteVirtualMatrices(accel, virtMatrix); CHECK_DEL_STATUS(delSts,"hppiDeleteVirtualMatrices");}\
if (accel) {hppStatus delSts = hppDeleteInstance(accel); CHECK_DEL_STATUS(delSts, "hppDeleteInstance");}\
return -1;}
#define CHECK_DEL_STATUS(STATUS, NAME)\
if(STATUS!=HPP_STATUS_NO_ERROR){ printf("%s error %d\n", NAME, STATUS); return -1;}
#endif
static void help()
{
printf("\nThis program shows how to use the conversion for IPP Async.\n"
"This example uses the Sobel filter.\n"
"You can use cv::Sobel or hppiSobel.\n"
"Usage: \n"
"./ipp_async_sobel [--camera]=<use camera,if this key is present>, \n"
" [--file_name]=<path to movie or image file>\n"
" [--accel]=<accelerator type: auto (default), cpu, gpu>\n\n");
}
const char* keys =
{
"{c camera | | use camera or not}"
"{fn file_name|../data/baboon.jpg | image file }"
"{a accel |auto | accelerator type: auto (default), cpu, gpu}"
};
//this is a sample for hppiSobel functions
int main(int argc, const char** argv)
{
help();
VideoCapture cap;
CommandLineParser parser(argc, argv, keys);
Mat image, gray, result;
#ifdef HAVE_IPP_A
hppiMatrix* src,* dst;
hppAccel accel = 0;
hppAccelType accelType;
hppStatus sts;
hppiVirtualMatrix * virtMatrix;
bool useCamera = parser.has("camera");
string file = parser.get<string>("file_name");
string sAccel = parser.get<string>("accel");
parser.printMessage();
if( useCamera )
{
printf("used camera\n");
cap.open(0);
}
else
{
printf("used image %s\n", file.c_str());
cap.open(file.c_str());
}
if( !cap.isOpened() )
{
printf("can not open camera or video file\n");
return -1;
}
accelType = sAccel == "cpu" ? HPP_ACCEL_TYPE_CPU:
sAccel == "gpu" ? HPP_ACCEL_TYPE_GPU:
HPP_ACCEL_TYPE_ANY;
//Create accelerator instance
sts = hppCreateInstance(accelType, 0, &accel);
CHECK_STATUS(sts, "hppCreateInstance");
accelType = hppQueryAccelType(accel);
sAccel = accelType == HPP_ACCEL_TYPE_CPU ? "cpu":
accelType == HPP_ACCEL_TYPE_GPU ? "gpu":
accelType == HPP_ACCEL_TYPE_GPU_VIA_DX9 ? "gpu dx9": "?";
printf("accelType %s\n", sAccel.c_str());
virtMatrix = hppiCreateVirtualMatrices(accel, 1);
for(;;)
{
cap >> image;
if(image.empty())
break;
cvtColor( image, gray, COLOR_BGR2GRAY );
result.create( image.rows, image.cols, CV_8U);
double execTime = (double)getTickCount();
//convert Mat to hppiMatrix
src = hpp::getHpp(gray,accel);
dst = hpp::getHpp(result,accel);
sts = hppiSobel(accel,src, HPP_MASK_SIZE_3X3,HPP_NORM_L1,virtMatrix[0]);
CHECK_STATUS(sts,"hppiSobel");
sts = hppiConvert(accel, virtMatrix[0], 0, HPP_RND_MODE_NEAR, dst, HPP_DATA_TYPE_8U);
CHECK_STATUS(sts,"hppiConvert");
// Wait for tasks to complete
sts = hppWait(accel, HPP_TIME_OUT_INFINITE);
CHECK_STATUS(sts, "hppWait");
execTime = ((double)getTickCount() - execTime)*1000./getTickFrequency();
printf("Time : %0.3fms\n", execTime);
imshow("image", image);
imshow("rez", result);
waitKey(15);
sts = hppiFreeMatrix(src);
CHECK_DEL_STATUS(sts,"hppiFreeMatrix");
sts = hppiFreeMatrix(dst);
CHECK_DEL_STATUS(sts,"hppiFreeMatrix");
}
if (!useCamera)
waitKey(0);
if (virtMatrix)
{
sts = hppiDeleteVirtualMatrices(accel, virtMatrix);
CHECK_DEL_STATUS(sts,"hppiDeleteVirtualMatrices");
}
if (accel)
{
sts = hppDeleteInstance(accel);
CHECK_DEL_STATUS(sts, "hppDeleteInstance");
}
printf("SUCCESS\n");
#else
printf("IPP Async not supported\n");
#endif
return 0;
}