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pmlpp/mlpp/convolutions/convolutions.cpp

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//
// Convolutions.cpp
//
// Created by Marc Melikyan on 4/6/21.
//
#include <iostream>
#include "Convolutions/Convolutions.hpp"
#include "LinAlg/LinAlg.hpp"
#include "Stat/Stat.hpp"
#include <cmath>
namespace MLPP{
Convolutions::Convolutions()
: prewittHorizontal({{1,1,1}, {0,0,0}, {-1,-1,-1}}), prewittVertical({{1,0,-1}, {1,0,-1}, {1,0,-1}}),
sobelHorizontal({{1,2,1}, {0,0,0}, {-1,-2,-1}}), sobelVertical({{-1,0,1}, {-2,0,2}, {-1,0,1}}),
scharrHorizontal({{3,10,3}, {0,0,0}, {-3,-10,-3}}), scharrVertical({{3,0,-3}, {10,0,-10}, {3,0,-3}}),
robertsHorizontal({{0,1}, {-1,0}}), robertsVertical({{1,0}, {0,-1}})
{
}
std::vector<std::vector<double>> Convolutions::convolve(std::vector<std::vector<double>> input, std::vector<std::vector<double>> filter, int S, int P){
LinAlg alg;
std::vector<std::vector<double>> featureMap;
int N = input.size();
int F = filter.size();
int mapSize = (N - F + 2*P) / S + 1; // This is computed as ⌊mapSize⌋ by def- thanks C++!
if(P != 0){
std::vector<std::vector<double>> paddedInput;
paddedInput.resize(N + 2*P);
for(int i = 0; i < paddedInput.size(); i++){
paddedInput[i].resize(N + 2*P);
}
for(int i = 0; i < paddedInput.size(); i++){
for(int j = 0; j < paddedInput[i].size(); j++){
if(i - P < 0 || j - P < 0 || i - P > input.size() - 1 || j - P > input[0].size() - 1){
paddedInput[i][j] = 0;
}
else{
paddedInput[i][j] = input[i - P][j - P];
}
}
}
input.resize(paddedInput.size());
for(int i = 0; i < paddedInput.size(); i++){
input[i].resize(paddedInput[i].size());
}
input = paddedInput;
}
featureMap.resize(mapSize);
for(int i = 0; i < mapSize; i++){
featureMap[i].resize(mapSize);
}
for(int i = 0; i < mapSize; i++){
for(int j = 0; j < mapSize; j++){
std::vector<double> convolvingInput;
for(int k = 0; k < F; k++){
for(int p = 0; p < F; p++){
if(i == 0 && j == 0){
convolvingInput.push_back(input[i + k][j + p]);
}
else if(i == 0){
convolvingInput.push_back(input[i + k][j + (S - 1) + p]);
}
else if(j == 0){
convolvingInput.push_back(input[i + (S - 1) + k][j + p]);
}
else{
convolvingInput.push_back(input[i + (S - 1) + k][j + (S - 1) + p]);
}
}
}
featureMap[i][j] = alg.dot(convolvingInput, alg.flatten(filter));
}
}
return featureMap;
}
std::vector<std::vector<std::vector<double>>> Convolutions::convolve(std::vector<std::vector<std::vector<double>>> input, std::vector<std::vector<std::vector<double>>> filter, int S, int P){
LinAlg alg;
std::vector<std::vector<std::vector<double>>> featureMap;
int N = input[0].size();
int F = filter[0].size();
int C = filter.size() / input.size();
int mapSize = (N - F + 2*P) / S + 1; // This is computed as ⌊mapSize⌋ by def.
if(P != 0){
for(int c = 0; c < input.size(); c++){
std::vector<std::vector<double>> paddedInput;
paddedInput.resize(N + 2*P);
for(int i = 0; i < paddedInput.size(); i++){
paddedInput[i].resize(N + 2*P);
}
for(int i = 0; i < paddedInput.size(); i++){
for(int j = 0; j < paddedInput[i].size(); j++){
if(i - P < 0 || j - P < 0 || i - P > input[c].size() - 1 || j - P > input[c][0].size() - 1){
paddedInput[i][j] = 0;
}
else{
paddedInput[i][j] = input[c][i - P][j - P];
}
}
}
input[c].resize(paddedInput.size());
for(int i = 0; i < paddedInput.size(); i++){
input[c][i].resize(paddedInput[i].size());
}
input[c] = paddedInput;
}
}
featureMap.resize(C);
for(int i = 0; i < featureMap.size(); i++){
featureMap[i].resize(mapSize);
for(int j = 0; j < featureMap[i].size(); j++){
featureMap[i][j].resize(mapSize);
}
}
for(int c = 0; c < C; c++){
for(int i = 0; i < mapSize; i++){
for(int j = 0; j < mapSize; j++){
std::vector<double> convolvingInput;
for(int t = 0; t < input.size(); t++){
for(int k = 0; k < F; k++){
for(int p = 0; p < F; p++){
if(i == 0 && j == 0){
convolvingInput.push_back(input[t][i + k][j + p]);
}
else if(i == 0){
convolvingInput.push_back(input[t][i + k][j + (S - 1) + p]);
}
else if(j == 0){
convolvingInput.push_back(input[t][i + (S - 1) + k][j + p]);
}
else{
convolvingInput.push_back(input[t][i + (S - 1) + k][j + (S - 1) + p]);
}
}
}
}
featureMap[c][i][j] = alg.dot(convolvingInput, alg.flatten(filter));
}
}
}
return featureMap;
}
std::vector<std::vector<double>> Convolutions::pool(std::vector<std::vector<double>> input, int F, int S, std::string type){
LinAlg alg;
std::vector<std::vector<double>> pooledMap;
int N = input.size();
int mapSize = floor((N - F) / S + 1);
pooledMap.resize(mapSize);
for(int i = 0; i < mapSize; i++){
pooledMap[i].resize(mapSize);
}
for(int i = 0; i < mapSize; i++){
for(int j = 0; j < mapSize; j++){
std::vector<double> poolingInput;
for(int k = 0; k < F; k++){
for(int p = 0; p < F; p++){
if(i == 0 && j == 0){
poolingInput.push_back(input[i + k][j + p]);
}
else if(i == 0){
poolingInput.push_back(input[i + k][j + (S - 1) + p]);
}
else if(j == 0){
poolingInput.push_back(input[i + (S - 1) + k][j + p]);
}
else{
poolingInput.push_back(input[i + (S - 1) + k][j + (S - 1) + p]);
}
}
}
if(type == "Average"){
Stat stat;
pooledMap[i][j] = stat.mean(poolingInput);
}
else if(type == "Min"){
pooledMap[i][j] = alg.min(poolingInput);
}
else{
pooledMap[i][j] = alg.max(poolingInput);
}
}
}
return pooledMap;
}
std::vector<std::vector<std::vector<double>>> Convolutions::pool(std::vector<std::vector<std::vector<double>>> input, int F, int S, std::string type){
std::vector<std::vector<std::vector<double>>> pooledMap;
for(int i = 0; i < input.size(); i++){
pooledMap.push_back(pool(input[i], F, S, type));
}
return pooledMap;
}
double Convolutions::globalPool(std::vector<std::vector<double>> input, std::string type){
LinAlg alg;
if(type == "Average"){
Stat stat;
return stat.mean(alg.flatten(input));
}
else if(type == "Min"){
return alg.min(alg.flatten(input));
}
else{
return alg.max(alg.flatten(input));
}
}
std::vector<double> Convolutions::globalPool(std::vector<std::vector<std::vector<double>>> input, std::string type){
std::vector<double> pooledMap;
for(int i = 0; i < input.size(); i++){
pooledMap.push_back(globalPool(input[i], type));
}
return pooledMap;
}
double Convolutions::gaussian2D(double x, double y, double std){
double std_sq = std * std;
return 1/(2 * M_PI * std_sq) * std::exp(-(x * x + y * y)/2 * std_sq);
}
std::vector<std::vector<double>> Convolutions::gaussianFilter2D(int size, double std){
std::vector<std::vector<double>> filter;
filter.resize(size);
for(int i = 0; i < filter.size(); i++){
filter[i].resize(size);
}
for(int i = 0; i < size; i++){
for(int j = 0; j < size; j++){
filter[i][j] = gaussian2D(i - (size-1)/2, (size-1)/2 - j, std);
}
}
return filter;
}
/*
Indeed a filter could have been used for this purpose, but I decided that it would've just
been easier to carry out the calculation explicitly, mainly because it is more informative,
and also because my convolution algorithm is only built for filters with equally sized
heights and widths.
*/
std::vector<std::vector<double>> Convolutions::dx(std::vector<std::vector<double>> input){
std::vector<std::vector<double>> deriv; // We assume a gray scale image.
deriv.resize(input.size());
for(int i = 0; i < deriv.size(); i++){
deriv[i].resize(input[i].size());
}
for(int i = 0; i < input.size(); i++){
for(int j = 0; j < input[i].size(); j++){
if(j != 0 && j != input.size() - 1){
deriv[i][j] = input[i][j + 1] - input[i][j - 1];
}
else if(j == 0){
deriv[i][j] = input[i][j + 1] - 0; // Implicit zero-padding
}
else{
deriv[i][j] = 0 - input[i][j - 1]; // Implicit zero-padding
}
}
}
return deriv;
}
std::vector<std::vector<double>> Convolutions::dy(std::vector<std::vector<double>> input){
std::vector<std::vector<double>> deriv;
deriv.resize(input.size());
for(int i = 0; i < deriv.size(); i++){
deriv[i].resize(input[i].size());
}
for(int i = 0; i < input.size(); i++){
for(int j = 0; j < input[i].size(); j++){
if(i != 0 && i != input.size() - 1){
deriv[i][j] = input[i - 1][j] - input[i + 1][j];
}
else if(i == 0){
deriv[i][j] = 0 - input[i + 1][j]; // Implicit zero-padding
}
else{
deriv[i][j] = input[i - 1][j] - 0; // Implicit zero-padding
}
}
}
return deriv;
}
std::vector<std::vector<double>> Convolutions::gradMagnitude(std::vector<std::vector<double>> input){
LinAlg alg;
std::vector<std::vector<double>> xDeriv_2 = alg.hadamard_product(dx(input), dx(input));
std::vector<std::vector<double>> yDeriv_2 = alg.hadamard_product(dy(input), dy(input));
return alg.sqrt(alg.addition(xDeriv_2, yDeriv_2));
}
std::vector<std::vector<double>> Convolutions::gradOrientation(std::vector<std::vector<double>> input){
std::vector<std::vector<double>> deriv;
deriv.resize(input.size());
for(int i = 0; i < deriv.size(); i++){
deriv[i].resize(input[i].size());
}
std::vector<std::vector<double>> xDeriv = dx(input);
std::vector<std::vector<double>> yDeriv = dy(input);
for(int i = 0; i < deriv.size(); i++){
for(int j = 0; j < deriv[i].size(); j++){
deriv[i][j] = std::atan2(yDeriv[i][j], xDeriv[i][j]);
}
}
return deriv;
}
std::vector<std::vector<std::vector<double>>> Convolutions::computeM(std::vector<std::vector<double>> input){
double const SIGMA = 1;
double const GAUSSIAN_SIZE = 3;
double const GAUSSIAN_PADDING = ( (input.size() - 1) + GAUSSIAN_SIZE - input.size() ) / 2; // Convs must be same.
std::cout << GAUSSIAN_PADDING << std::endl;
LinAlg alg;
std::vector<std::vector<double>> xDeriv = dx(input);
std::vector<std::vector<double>> yDeriv = dy(input);
std::vector<std::vector<double>> gaussianFilter = gaussianFilter2D(GAUSSIAN_SIZE, SIGMA); // Sigma of 1, size of 3.
std::vector<std::vector<double>> xxDeriv = convolve(alg.hadamard_product(xDeriv, xDeriv), gaussianFilter, 1, GAUSSIAN_PADDING);
std::vector<std::vector<double>> yyDeriv = convolve(alg.hadamard_product(yDeriv, yDeriv), gaussianFilter, 1, GAUSSIAN_PADDING);
std::vector<std::vector<double>> xyDeriv = convolve(alg.hadamard_product(xDeriv, yDeriv), gaussianFilter, 1, GAUSSIAN_PADDING);
std::vector<std::vector<std::vector<double>>> M = {xxDeriv, yyDeriv, xyDeriv};
return M;
}
std::vector<std::vector<std::string>> Convolutions::harrisCornerDetection(std::vector<std::vector<double>> input){
double const k = 0.05; // Empirically determined wherein k -> [0.04, 0.06], though conventionally 0.05 is typically used as well.
LinAlg alg;
std::vector<std::vector<std::vector<double>>> M = computeM(input);
std::vector<std::vector<double>> det = alg.subtraction(alg.hadamard_product(M[0], M[1]), alg.hadamard_product(M[2], M[2]));
std::vector<std::vector<double>> trace = alg.addition(M[0], M[1]);
// The reason this is not a scalar is because xxDeriv, xyDeriv, yxDeriv, and yyDeriv are not scalars.
std::vector<std::vector<double>> r = alg.subtraction(det, alg.scalarMultiply(k, alg.hadamard_product(trace, trace)));
std::vector<std::vector<std::string>> imageTypes;
imageTypes.resize(r.size());
alg.printMatrix(r);
for(int i = 0; i < r.size(); i++){
imageTypes[i].resize(r[i].size());
for(int j = 0; j < r[i].size(); j++){
if(r[i][j] > 0){
imageTypes[i][j] = "C";
}
else if (r[i][j] < 0){
imageTypes[i][j] = "E";
}
else{
imageTypes[i][j] = "N";
}
}
}
return imageTypes;
}
std::vector<std::vector<double>> Convolutions::getPrewittHorizontal(){
return prewittHorizontal;
}
std::vector<std::vector<double>> Convolutions::getPrewittVertical(){
return prewittVertical;
}
std::vector<std::vector<double>> Convolutions::getSobelHorizontal(){
return sobelHorizontal;
}
std::vector<std::vector<double>> Convolutions::getSobelVertical(){
return sobelVertical;
}
std::vector<std::vector<double>> Convolutions::getScharrHorizontal(){
return scharrHorizontal;
}
std::vector<std::vector<double>> Convolutions::getScharrVertical(){
return scharrVertical;
}
std::vector<std::vector<double>> Convolutions::getRobertsHorizontal(){
return robertsHorizontal;
}
std::vector<std::vector<double>> Convolutions::getRobertsVertical(){
return robertsVertical;
}
}
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