#include <stim/image/image.h>
#include <cmath>
#include <stim/visualization/colormap.h>

#define PI 3.1415926

void conv2(float* img, float* mask, float* cpu_copy, unsigned int w, unsigned int h, unsigned int M);
void array_abs(float* img, unsigned int N);
void array_multiply(float* lhs, float rhs, unsigned int N);

/// This function evaluates the gaussian derivative gradient given an one-channel image

/// @param img is the one-channel image
/// @param r is an array of radii for different scaled discs(filters)
/// @param sigma_n is the number of standard deviations used to define the sigma
/// @param theta is angle used for computing the gradient

stim::image<float> gaussian_derivative_filter_odd(stim::image<float> image, int r, unsigned int sigma_n, float theta){

	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
	int winsize = 2 * r + 1;           // set the winsdow size of disc(filter)
	float sigma  = float(r)/float(sigma_n); // calculate the sigma used in gaussian function

	stim::image<float> mask_x(winsize, winsize);    // allocate space for x-axis-oriented filter
	stim::image<float> mask_y(winsize, winsize);    // allocate space for y-axis-oriented filter
	stim::image<float> mask_theta(winsize, winsize);// allocate space for theta-oriented filter
	stim::image<float> derivative_theta(w, h);      // allocate space for theta-oriented gradient

	stim::image<float> mask_r; 


	float theta_r = (theta * PI)/180; //change angle unit from degree to rad

	//*************inseparable convolution****************
	for (int j = 0; j < winsize; j++){
		for (int i = 0; i< winsize; i++){

			int x = i - r;          //range of x
			int y = j - r;          //range of y

			// create the x-oriented gaussian derivative filter mask_x
			mask_x.data()[j*winsize + i] = (-1) * x * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2))) * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
			// create the y-oriented gaussian derivative filter mask_y
			mask_y.data()[j*winsize + i] = (-1) * y * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2))) * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
			// create the mask_theta
			mask_theta.data()[j*winsize + i] = cos(theta_r) * mask_x.data()[j*winsize + i] + sin(theta_r) * mask_y.data()[j*winsize + i] ;
		
		}
	}

	mask_r = mask_x.rotate(90, r, r);
	stim::cpu2image(mask_r.data(), "data_output/mask_r90_0915.bmp", winsize, winsize, stim::cmBrewer);  
	//stim::cpu2image(mask_theta.data(), "data_output/mask_0911_2.bmp", winsize, winsize, stim::cmBrewer); // (optional) show the mask result

	// do the 2D convolution with image and mask
	conv2(image.data(), mask_theta.data(), derivative_theta.data(), w, h, winsize);
	//*********************************************************/

	/*************separable convolution****************
	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_x
		array_x[i] = exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
		// create the 1D y-oriented gaussian derivative filter array_y
		array_y[i] = (-1) * y * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));

		
	}

	//stim::cpu2image(mask_theta.data(), "data_output/mask_0911_2.bmp", winsize, winsize, stim::cmBrewer); // (optional) show the mask result

	// do the 2D convolution with image and mask
	conv2(image.data(), mask_theta.data(), derivative_theta.data(), w, h, winsize);
	//*********************************************************/

	array_abs(derivative_theta.data(), N); // get the absolute value for each pixel (why slower than the "for loop" method sometimes?)

	float max = derivative_theta.maxv();						// get the maximum of gradient used for normalization
	array_multiply(derivative_theta.data(), 1/max, N);		// normalize the gradient


	//stim::cpu2image(derivative_theta.data(), "data_output/derivative_theta_0911.bmp", w, h, stim::cmBrewer); // (optional) show the gradient result

	return derivative_theta;

}