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stim/cuda/bsds500/dG1_conv2.cpp 3.6 KB
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91d8912e   David Mayerich   moved bsds500 fil... 
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  #include <stim/image/image.h>
  //#include <cmath>
  #include <stim/visualization/colormap.h>
  #include <stim/image/image_contour_detection.h>
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  #define SIGMA_N 3
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  /// This function generates the first-order gaussian derivative filter gx gy, 
  /// convolves the image with gx gy, 
  /// and returns an image class which channel(0) is Ix and channel(1) is Iy 
  
  /// @param img is the one-channel image
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  /// @param sigma is the parameter for gaussian function
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  void conv2_sep(float* img, unsigned int x, unsigned int y, float* kernel0, unsigned int k0, float* kernel1, unsigned int k1);
  //void array_abs(float* img, unsigned int N);
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  stim::image<float> dG1_conv2(stim::image<float> image, int sigma){
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  	unsigned int w = image.width();    // get the width of picture
  	unsigned int h = image.height();   // get the height of picture
  	unsigned N = w * h;				   // get the number of pixels of picture
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  	int r = SIGMA_N * sigma;  
  	int winsize = 2 * SIGMA_N * sigma + 1;           // set the winsdow size of filter
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  	stim::image<float> I(w, h, 1, 2);      // allocate space for return image class
  	stim::image<float> Ix(w, h);      // allocate space for Ix
  	stim::image<float> Iy(w, h);      // allocate space for Iy
  	Ix = image;  // initialize Ix
  	Iy = image;  // initialize Iy
  
  	float* array_x1;   
  	array_x1 = new float[winsize];  //allocate space for the 1D x-oriented gaussian derivative filter array_x1 for gx
  	float* array_y1;   
  	array_y1 = new float[winsize];  //allocate space for the 1D y-oriented gaussian derivative filter array_y1 for gx
  	float* array_x2;   
  	array_x2 = new float[winsize];  //allocate space for the 1D x-oriented gaussian derivative filter array_x2 for gy
  	float* array_y2;   
  	array_y2 = new float[winsize];  //allocate space for the 1D y-oriented gaussian derivative filter array_y2 for gy
  
  
  	for (int i = 0; i < winsize; i++){	
  
  		int x = i - r;          //range of x
  		int y = i - r;          //range of y
  
  		// create the 1D x-oriented gaussian derivative filter array_x1 for gx
  		array_x1[i] = (-1) * x * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
  		// create the 1D y-oriented gaussian derivative filter array_y1  for gx
  		array_y1[i] = exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
  		// create the 1D x-oriented gaussian derivative filter array_x2 for gy
  		array_x2[i] = exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
  		// create the 1D y-oriented gaussian derivative filter array_y2  for gy
  		array_y2[i] = (-1) * y * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
  	}
  
  	//stim::cpu2image(array_x1, "data_output/array_x1_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
  	//stim::cpu2image(array_y1, "data_output/array_y1_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
  	//stim::cpu2image(array_x2, "data_output/array_x2_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
  	//stim::cpu2image(array_y2, "data_output/array_y2_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
  
  	// get Ix by convolving the image with gx
  	conv2_sep(Ix.data(), w, h, array_x1, winsize, array_y1, winsize);
  	
  	//stim::cpu2image(Ix.data(), "data_output/Ix_0915.bmp", w, h, stim::cmBrewer); 
  	// get Iy by convolving the image with gy
  	conv2_sep(Iy.data(), w, h, array_x2, winsize, array_y2, winsize);
  	
  	//stim::cpu2image(Iy.data(), "data_output/Iy_0915.bmp", w, h, stim::cmBrewer); 
  
  	delete [] array_x1;            //free the memory of array_x1
  	delete [] array_y1;            //free the memory of array_y1
  	delete [] array_x2;            //free the memory of array_x2
  	delete [] array_y2;            //free the memory of array_y2
  
  	I.set_channel(0, Ix.data());
  	I.set_channel(1, Iy.data());
  
  	return I;
  
  }