void testSkinRecogWithThreshold(const std::vector<double> &mean, const Matrix &cov, ImageType &image, std::string out){
RGB white(255,255,255);
RGB black(0,0,0);
int height, width, levels;
image.getImageInfo(height,width,levels);
RGB val;
std::vector<double> pc(2); // pure color
double thR, thG;
for(int row = 0; row < height; row++){
for(int col = 0; col < width; col++){
image.getPixelVal(row, col, val);
pc[0] = val.r/float(val.r+val.g+val.b);
pc[1] = val.g/float(val.r+val.g+val.b);
thR = exp(-(cov[0][0] * pow((pc[0] - mean[0]),2) + cov[0][1] * (pc[0]- mean[0])));
thG = exp(-(cov[1][0] * (pc[1] - mean[1]) + cov[1][1] * pow((pc[1] - mean[1]),2)));
if((thR >= .9 && thG >= 1.0 && thG < 1.2)
|| (thR <= .8 && thR >= .7 && thG > 1.1)){
image.setPixelVal(row, col, white);
}
else{
image.setPixelVal(row, col, black);
}
}
} // end outer for loop
writeImage(out.c_str(), image);
}
/*
Expand image function
Writen by: Jeremiah Berns
Dependincies, image.cpp, image.h
Discription: Will accept the shrunken image, the grow size of the image, and then
expand the image back to 256x256
*/
void expandImage(ImageType oldImage, ImageType& newImage, int growVal, string newImageName)
{
//Variable decliration
int rows, cols, Q, tempValue;
//Variable setting
oldImage.getImageInfo(rows, cols, Q);
for(int i=0;i<rows;i++)
{
for(int j=0;j<cols;j++)
{
oldImage.getPixelVal(i,j, tempValue);
for(int k=0;k<growVal;k++)
{
for(int l=0;l<growVal;l++)
{
newImage.setPixelVal(i*growVal+k,j*growVal+l,tempValue);
}
}
}
}
writeImage(newImageName, newImage);
}
void crop(ImageType& image)
{
if(imageLoaded(image))
{
// initialize to -1 for input validation
int ULr = -1, ULc = -1, LRr = -1, LRc = -1;
int N, M, Q;
int errorCode = 0;
// get values
image.getImageInfo(N,M,Q);
// get inputs
cout << "Enter the upper left corner's row: ";
cin >> ULr;
cout << endl << "Enter the upper left corner's column: ";
cin >> ULc;
cout << endl << "Enter the lower right corner's row: ";
cin >> LRr;
cout << endl << "Enter the lower right corner's column: ";
cin >> LRc;
// check for errors
if(ULr < 0 || ULc < 0 || LRr < 0 || LRc < 0)
errorCode = 1;
else if(ULr > N || LRr > N || ULc > M || LRc > M)
errorCode = 2;
else if(ULr >= LRr || ULc >= LRc)
errorCode = 3;
switch(errorCode)
{
case 1:
cout << "ERROR: All inputs must be non-negative.";
break;
case 2:
cout << "ERROR: All crop boundaries must be within image boundaries.";
break;
case 3:
cout << "ERROR: All crop boundaries must be in the correct order.";
break;
}
// crop image if no error was found
if(errorCode == 0)
{
image.getSubImage(ULr, ULc, LRr, LRc, image);
cout << endl << endl << "Image has been cropped successfully.";
}
pressEnterToContinue();
}
}
void testSkinRecognition(const BayesianClassifier &classifier, ImageType &image, ImageType &ref, std::string out, bool YCbCr){
int height, width, levels;
image.getImageInfo(height,width,levels);
ImageType outImg(height,width,levels);
RGB val1, val2;
int label;
std::vector<double> color(2);
int TP = 0, TN = 0, FN = 0, FP = 0;
RGB white(255,255,255);
RGB black(0,0,0);
for(int row = 0; row < height; row++){
for(int col = 0; col < width; col++){
image.getPixelVal(row, col, val1);
ref.getPixelVal(row, col, val2);
if(YCbCr == true){
color[0] = -0.169*val1.r - 0.332*val1.g+ 0.500*val1.b;
color[1] = 0.500*val1.r - 0.419*val1.g - 0.081*val1.b;
}
else{
color[0] = val1.r/float(val1.r+val1.g+val1.b);
color[1] = val1.g/float(val1.r+val1.g+val1.b);
}
label = classifier.predict(color);
if(label == 0){
outImg.setPixelVal(row, col, white);
if(val2 != black){ TP++; } else{ FP++; }
}
else{
outImg.setPixelVal(row, col, black);
if(val2 == black){ TN++; } else{ FN++; }
}
}
} // end outer for loop
std::cout << std::endl
<< "TP: " << TP << std::endl
<< "TN: " << TN << std::endl
<< "FP: " << FP << std::endl
<< "FN: " << FN << std::endl;
/*std::stringstream ss;
ss << FP << " " << FN;
Debugger debugger("Data_Prog2/errors3a.txt",true);
debugger.debug(ss.str());
*/
writeImage(out.c_str(), outImg);
}
void makeColorMatrices(ImageType& img, ImageType& ref, Matrix &sk_cols,
Matrix &nsk_cols, bool YCbCr)
{
int height1, width1, levels1;
int height2, width2, levels2;
img.getImageInfo(height1, width1, levels1);
ref.getImageInfo(height2, width2, levels2);
assert(height1 == height2);
assert(width1 == width2);
assert(levels1 == levels2);
RGB val1, val2;
std::vector<double> color(2);
RGB black(0,0,0);
for(int row = 0; row < height1; row++){
for(int col = 0; col < width1; col++){
img.getPixelVal(row, col, val1);
ref.getPixelVal(row, col, val2);
if(YCbCr == true){
color[0] = -0.169*val1.r - 0.332*val1.g+ 0.500*val1.b;
color[1] = 0.500*val1.r - 0.419*val1.g - 0.081*val1.b;
}
else{
color[0] = val1.r/float(val1.r+val1.g+val1.b);
color[1] = val1.g/float(val1.r+val1.g+val1.b);
}
if(val2 != black){
sk_cols.push_back(color);
}
else{
nsk_cols.push_back(color);
}
}
}
}
void writeImage(const char fname[], ImageType& image)
/* write PPM image */
{
int i, j;
int N, M, Q;
unsigned char *charImage;
ofstream ofp;
image.getImageInfo(N, M, Q);
// make space for PPM
charImage = (unsigned char *) new unsigned char [3*M*N];
// convert the RGB to unsigned char
RGB val;
for(i=0; i<N; i++) {
for(j=0; j<3*M; j+=3) {
image.getPixelVal(i, j/3, val);
charImage[i*3*M+j]=(unsigned char)val.r;
charImage[i*3*M+j+1]=(unsigned char)val.g;
charImage[i*3*M+j+2]=(unsigned char)val.b;
}
}
ofp.open(fname, ios::out | ios::binary);
if (!ofp) {
cout << "Can't open file: " << fname << endl;
exit(1);
}
ofp << "P6" << endl;
ofp << M << " " << N << endl;
ofp << Q << endl;
ofp.write( reinterpret_cast<char *>(charImage), (3*M*N)*sizeof(unsigned char));
if (ofp.fail()) {
cout << "Can't write image " << fname << endl;
exit(0);
}
ofp.close();
delete [] charImage;
}
/*
Histogram Equalization function
Written by: Jeremiah Berns
Dependincies:image.h, image.cpp
Discription: This function will perform the histogram equalization algorithem to the oldImage
and will output the newImage with the given newImageName.
*/
void histogramEq(ImageType oldImage, ImageType& newImage, string newImageName)
{
int rows, cols, Q, pixelValue, pixelCount;
oldImage.getImageInfo(rows,cols,Q);
pixelCount = rows*cols;
int adjustedHistogram[Q];
double histogramArray[Q], equalizedHistogram[Q];
double probabilityArray[Q], cumulativeProbability[Q], probTotal=0;
for (int i = 0; i<Q;i++)
{
histogramArray[i] = 0;
equalizedHistogram[i] = 0;
}
for(int i=0; i<rows;i++)
{
for(int j=0; j<cols;j++)
{
oldImage.getPixelVal(i,j,pixelValue);
histogramArray[pixelValue]+=1;
}
}
for(int i=0;i<Q;i++)
{
probTotal+= histogramArray[i]/pixelCount;
cumulativeProbability[i] = probTotal;
cumulativeProbability[i] = cumulativeProbability[i]*255;
adjustedHistogram[i] = cumulativeProbability[i];
cout<<adjustedHistogram[i]<<endl;
}
for(int i=0; i<rows;i++)
{
for(int j=0; j<cols;j++)
{
oldImage.getPixelVal(i,j,pixelValue);
newImage.setPixelVal(i,j,adjustedHistogram[pixelValue-1]);
}
}
writeImage(newImageName, newImage);
}
void displayInfo(ImageType& image)
{
if(imageLoaded(image))
{
int N, M, Q;
// get values
image.getImageInfo(N,M,Q);
cout << "Height : " << N << endl
<< "Width : " << M << endl
<< "Max Pixel Value : " << Q << endl
<< "Mean Gray Value : " << image.meanGray();
pressEnterToContinue();
}
}
void writeImage(string fname, ImageType& image){
int i, j;
int N, M, Q;
unsigned char *charImage;
ofstream ofp;
image.getImageInfo(N, M, Q);
charImage = (unsigned char *) new unsigned char [M*N];
// convert the integer values to unsigned char
int val;
for(i=0; i<N; i++){
for(j=0; j<M; j++){
image.getPixelVal(i, j, val);
charImage[i*M+j]=(unsigned char)val;
}
}
ofp.open(fname.c_str(), ios::out | ios::binary);
if (!ofp) {
cout << "Can't open file: " << fname << endl;
exit(1);
}
ofp << "P5" << endl;
ofp << M << " " << N << endl;
ofp << Q << endl;
ofp.write( reinterpret_cast<char *>(charImage), (M*N)*sizeof(unsigned char));
if (ofp.fail()) {
cout << "Can't write image " << fname << endl;
exit(0);
}
ofp.close();
}
/*
shrink Image funtion.
Writen By Jeremiah Berns.
Dependincies: image.h, image.cpp
Discription: Will take in the old image, and the new image, and the pixel value
based apon the shrink value passed to it. It will place that value
from the old image into the new image, then save the new image with
the passed in file name.
*/
void shrinkImage(ImageType oldImage, ImageType& newImage, int shrinkVal, string newImageFname)
{
//Variable decliration
int rows, col, Q, tempValue;
//Variable setting
oldImage.getImageInfo(rows, col, Q);
for(int i=0; i<rows;i++)
{
for(int j=0;j<col;j++)
{
if(i%shrinkVal == 0 && j%shrinkVal ==0)
{
oldImage.getPixelVal(i,j, tempValue);
newImage.setPixelVal(i/shrinkVal,j/shrinkVal,tempValue);
}
}
}
writeImage(newImageFname, newImage);
}