C++ ImageType::getImageInfo方法代码示例

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);
}