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LatchClassifier.cpp
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LatchClassifier.cpp
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#include "LatchClassifier.h"
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <limits>
#include <iostream>
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/cuda_stream_accessor.hpp"
#include "opencv2/cudaimgproc.hpp"
#include "bitMatcher.h"
#include "latch.h"
/* Helper functions. */
#define cudaCalloc(A, B) \
do { \
cudaError_t __cudaCalloc_err = cudaMalloc(A, B); \
if (__cudaCalloc_err == cudaSuccess) cudaMemset(*A, 0, B); \
} while (0)
#define checkError(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) {
if (code != cudaSuccess) {
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
#define checkLaunchError() \
do { \
/* Check synchronous errors, i.e. pre-launch */ \
cudaError_t err = cudaGetLastError(); \
if (cudaSuccess != err) { \
fprintf (stderr, "Cuda error in file '%s' in line %i : %s.\n",\
__FILE__, __LINE__, cudaGetErrorString(err) ); \
exit(EXIT_FAILURE); \
} \
/* Check asynchronous errors, i.e. kernel failed (ULF) */ \
err = cudaThreadSynchronize(); \
if (cudaSuccess != err) { \
fprintf (stderr, "Cuda error in file '%s' in line %i : %s.\n",\
__FILE__, __LINE__, cudaGetErrorString( err) ); \
exit(EXIT_FAILURE); \
} \
} while (0)
/* Main class definition */
LatchClassifier::LatchClassifier() :
m_maxKP(512 * NUM_SM),
m_matchThreshold(12),
m_detectorThreshold(10),
m_detectorTargetKP(3000),
m_detectorTolerance(200),
m_shouldBeTimed(false),
m_defects(0.0),
m_stream(cv::cuda::Stream::Null()),
m_stream1(cv::cuda::Stream::Null()),
m_stream2(cv::cuda::Stream::Null()) {
size_t sizeK = m_maxKP * sizeof(float) * 4; // K for keypoints
size_t sizeD = m_maxKP * (2048 / 32) * sizeof(unsigned int); // D for descriptor
size_t sizeM = m_maxKP * sizeof(int); // M for Matches
cudaMallocHost((void**) &m_hK1, sizeK);
cudaMallocHost((void**) &m_hK2, sizeK);
cudaMallocHost((void**) &m_hD1, sizeD);
cudaMallocHost((void**) &m_hD2, sizeD);
cudaCalloc((void**) &m_dK, sizeK);
cudaCalloc((void**) &m_dD1, sizeD);
cudaCalloc((void**) &m_dD2, sizeD);
cudaCalloc((void**) &m_dM1, sizeM);
cudaCalloc((void**) &m_dM2, sizeM);
cudaCalloc((void**) &m_dMask, sizeM);
cudaEventCreate(&m_latchFinished);
// The patch triplet locations for LATCH fits in memory cache.
loadPatchTriplets(m_patchTriplets);
float h_mask[64];
for (size_t i = 0; i < 64; i++) { h_mask[i] = 1.0f; }
initMask(&m_dMask, h_mask);
m_orbClassifier = cv::cuda::ORB::create();
m_orbClassifier->setBlurForDescriptor(true);
m_orbClassifierCPU = cv::ORB::create(10000);
m_latch = xfeatures2d::LATCH::create();
}
void LatchClassifier::setImageSize(int width, int height) {
size_t sizeI = width * height * sizeof(unsigned char);
cudaCalloc((void**) &m_dI, sizeI);
initImage(&m_dI, width, height, &m_pitch);
std::cout << "Finished setting image size: " << width << " " << height << std::endl;
}
std::vector<cv::KeyPoint> LatchClassifier::identifyFeaturePointsCPU(cv::Mat& img) {
// Convert image to grayscale
cv::Mat img1g;
cv::cvtColor(img, img1g, CV_BGR2GRAY);
// Find features using ORB/FAST
std::vector<cv::KeyPoint> keypoints;
m_orbClassifierCPU->detect(img1g, keypoints);
Mat desc;
m_latch->compute(img1g, keypoints, desc);
return keypoints;
}
void LatchClassifier::writeSIFTFile(const std::string& filename, int width, int height, unsigned int* desc, std::vector<cv::KeyPoint>& keys) {
FILE* f = fopen(filename.c_str(), "wb");
std::cout << "Total amount of matches for " << filename << " is " << keys.size() << std::endl;
fprintf(f, "%d %d \n", width, height);
int count = 0;
// Normalize descriptor
for (int i = 0; i < keys.size() ; i++) {
float norm = 0.0;
for (int j = 0; j < 64; j++) {
int index = i * 64 + j;
union {
unsigned int ui;
float f;
} conversion_union = { .ui = desc[index] };
float toBeAdded = conversion_union.f * conversion_union.f;
if (toBeAdded < std::numeric_limits<float>::max())
norm += toBeAdded;
else {
norm = std::numeric_limits<float>::max();
break;
}
}
norm = 512.0/std::max(std::sqrt(norm),1.19209290E-07F);
count++;
fprintf(f, "%f %f %f %f \n", keys.at(i).pt.y, keys.at(i).pt.x, keys.at(i).size, (keys.at(i).angle*M_PI/180.0));
for ( int j = 0; j < 16; j++) {
unsigned int tempInt = desc[i * 64 + j];
float tempFloat = static_cast<float>(static_cast<long>(tempInt)) * norm;
unsigned char* x = reinterpret_cast<unsigned char*>(&tempFloat);
for (int k = 0; k < 4; k++) {
//int bitShiftVal = 32 - 8 * (k+1);
//unsigned char x = (tempFloat >> bitShiftVal) & 0xFF;
fprintf(f, "0 ");
// fprintf(f, "%u ", x[k]);
if ((j * 4 + k + 1) % 19 == 0) fprintf(f, "\n");
}
count++;
}
for ( int j = 64; j < 128; j++) {
fprintf(f, "0 ");
if ((j + 1) % 19 == 0) fprintf(f, "\n");
}
fprintf(f, "\n");
}
// std::cout << "Count: " << count << std::endl;
fclose(f);
}
std::vector<cv::KeyPoint> LatchClassifier::identifyFeaturePoints(cv::Mat& img) {
cv::cuda::GpuMat imgGpu;
imgGpu.upload(img, m_stream);
// Convert image to grayscale
cv::cuda::GpuMat img1g;
cv::cuda::cvtColor(imgGpu, img1g, CV_BGR2GRAY, 0, m_stream);
// Find features using ORB/FAST
std::vector<cv::KeyPoint> keypoints;
cuda::GpuMat d_keypoints;
m_orbClassifier->detectAsync(img1g, d_keypoints, cv::noArray(), m_stream);
cudaStream_t copiedStream = cv::cuda::StreamAccessor::getStream(m_stream);
cudaStreamSynchronize(copiedStream);
m_orbClassifier->convert(d_keypoints, keypoints);
int numKP0;
latchGPU(img1g, m_pitch, m_hK1, m_dD1, &numKP0, m_maxKP, m_dK, &keypoints, m_dMask, copiedStream, m_latchFinished);
size_t sizeD = m_maxKP * (2048 / 32) * sizeof(unsigned int); // D for descriptor
cudaMemcpyAsync(m_hD1, m_dD1, sizeD, cudaMemcpyDeviceToHost, copiedStream);
m_stream.waitForCompletion();
return keypoints;
}
void LatchClassifier::identifyFeaturePointsAsync(cv::Mat& img,
cv::cuda::Stream::StreamCallback callback,
void* userData) {
cv::cuda::Stream& stream = cv::cuda::Stream::Null();
cv::cuda::GpuMat imgGpu;
imgGpu.upload(img, stream);
// Convert image to grayscale
cv::cuda::GpuMat img1g;
cv::cuda::cvtColor(imgGpu, img1g, CV_BGR2GRAY, 0, stream);
// Find features using ORB/FAST
std::vector<cv::KeyPoint> keypoints;
cuda::GpuMat d_keypoints;
m_orbClassifier->detectAsync(img1g, d_keypoints, cv::noArray(), stream);
cudaStream_t copiedStream = cv::cuda::StreamAccessor::getStream(stream);
cudaStreamSynchronize(copiedStream);
m_orbClassifier->convert(d_keypoints, keypoints);
int numKP0;
latchGPU(img1g, m_pitch, m_hK1, m_dD1, &numKP0, m_maxKP, m_dK, &keypoints, m_dMask, copiedStream, m_latchFinished);
size_t sizeD = m_maxKP * (2048 / 32) * sizeof(unsigned int); // D for descriptor
cudaMemcpyAsync(m_hD1, m_dD1, sizeD, cudaMemcpyDeviceToHost, copiedStream);
stream.enqueueHostCallback(callback, userData);
}
std::tuple<std::vector<cv::KeyPoint>,
std::vector<cv::KeyPoint>,
std::vector<cv::DMatch>>
LatchClassifier::identifyFeaturePointsBetweenImages(cv::Mat& img1, cv::Mat& img2) {
std::vector<cv::KeyPoint> goodMatches1;
std::vector<cv::KeyPoint> goodMatches2;
std::vector<cv::DMatch> goodMatches3;
// Images MUST match each other in width and height.
if (img2.cols != img1.cols || img2.rows != img2.rows)
return std::make_tuple(goodMatches1, goodMatches2, goodMatches3);
cv::cuda::GpuMat imgGpu1;
cv::cuda::GpuMat imgGpu2;
imgGpu1.upload(img1, m_stream1);
imgGpu2.upload(img2, m_stream2);
// Convert image to grayscale
cv::cuda::GpuMat img1g;
cv::cuda::GpuMat img2g;
cv::cuda::cvtColor(imgGpu1, img1g, CV_BGR2GRAY, 0, m_stream1);
cv::cuda::cvtColor(imgGpu2, img2g, CV_BGR2GRAY, 0, m_stream2);
// Find features using ORB/FAST
std::vector<cv::KeyPoint> keypoints0;
std::vector<cv::KeyPoint> keypoints1;
cv::cuda::GpuMat d_keypoints0;
cv::cuda::GpuMat d_keypoints1;
m_orbClassifier->detectAsync(img1g, d_keypoints0, cv::noArray(), m_stream1);
m_orbClassifier->detectAsync(img2g, d_keypoints1, cv::noArray(), m_stream2);
m_stream1.waitForCompletion();
m_stream2.waitForCompletion();
m_orbClassifier->convert(d_keypoints0, keypoints0);
m_orbClassifier->convert(d_keypoints1, keypoints1);
cudaStream_t copiedStream1 = cv::cuda::StreamAccessor::getStream(m_stream1);
cudaStream_t copiedStream2 = cv::cuda::StreamAccessor::getStream(m_stream2);
int numKP0;
latchGPU(img1g, m_pitch, m_hK1, m_dD1, &numKP0, m_maxKP, m_dK, &keypoints0, m_dMask, copiedStream1, m_latchFinished);
int numKP1;
latchGPU(img2g, m_pitch, m_hK2, m_dD2, &numKP1, m_maxKP, m_dK, &keypoints1, m_dMask, copiedStream2, m_latchFinished);
size_t sizeD = m_maxKP * (2048 / 32) * sizeof(unsigned int); // D for descriptor
cudaMemcpyAsync(m_hD1, m_dD1, sizeD, cudaMemcpyDeviceToHost, copiedStream1);
cudaMemcpyAsync(m_hD2, m_dD2, sizeD, cudaMemcpyDeviceToHost, copiedStream2);
bitMatcher(m_dD1, m_dD2, numKP0, numKP1, m_maxKP, m_dM1, m_matchThreshold, copiedStream1, m_latchFinished);
bitMatcher(m_dD2, m_dD1, numKP1, numKP0, m_maxKP, m_dM2, m_matchThreshold, copiedStream2, m_latchFinished);
cudaStreamSynchronize(copiedStream1);
cudaStreamSynchronize(copiedStream2);
// Recombine to find intersecting features. Need to declare arrays as static due to size.
int h_M1[m_maxKP];
int h_M2[m_maxKP];
getMatches(m_maxKP, h_M1, m_dM1);
getMatches(m_maxKP, h_M2, m_dM2);
for (size_t i = 0; i < numKP0; i++) {
if (h_M1[i] >= 0 && h_M1[i] < numKP1 && h_M2[h_M1[i]] == i) {
goodMatches1.push_back(keypoints0[i]);
goodMatches2.push_back(keypoints1[h_M1[i]]);
goodMatches3.push_back(cv::DMatch(i, h_M1[i], 0));
}
}
return std::make_tuple(goodMatches1, goodMatches2, goodMatches3);
}
LatchClassifier::~LatchClassifier() {
cudaStreamDestroy(cv::cuda::StreamAccessor::getStream(m_stream));
cudaStreamDestroy(cv::cuda::StreamAccessor::getStream(m_stream1));
cudaStreamDestroy(cv::cuda::StreamAccessor::getStream(m_stream2));
cudaFreeArray(m_patchTriplets);
cudaFree(m_dK);
cudaFree(m_dD1);
cudaFree(m_dD2);
cudaFree(m_dM1);
cudaFree(m_dM2);
cudaFreeHost(m_hK1);
cudaFreeHost(m_hK2);
}