//
// Interactive illustration of gaussian filtering
// Works on an image sequence or picture from camera
//
// John Robinson, 2004.

#include "picture.h"
int main(int argc, char *argv[]) {
	colour_picture in(argc, argv, 240, 320);
	// Above line sets up in as the sequence-processing picture. Calls
	// to next() on this picture will either grab another picture from
	// the camera (if no arguments), or load the next input picture
	// from the list of arguments.
	picture_of_int inmono(in);
	picture_of_int out(in.nrows(),in.ncols());
	in.show("Input");
	out.show("Output");
	picture_of_int impulse(48,48);
	picture_of_int kern(48,48);
	colour_picture kernal_graph(80,100);
	colour_picture kernal_display(100,300);
	impulse = 0;
	impulse[24][24] = 10000;
	kernal_display.show("Kernel");
	parampic stddevcontrol(0,0.2,10,2,0,0,0,"standard deviation");
	double stddev = 2.0;
	while(!in.closerequested() && !out.closerequested()) {
		in.next();	// Get the next picture
		inmono = in;
		stddevcontrol.X(stddev);  // See if the user has changed param
		out.gaussfilter(inmono,stddev);
		kern.gaussfilter(impulse,stddev);
		in.reshow();
		out.reshow();
		// Make a cross section graph of the kernel
		kernal_graph = 0;
		kernal_graph.green.draw_graph(kern[24],48,kern.max(),-1,-1);
		// Note use of kern.max() for the shade colour, to ensure
		// that the graph is visible no matter how high the kernal
		// And put the image version and the graph version of the
		// kernel into a displayed picture
		kernal_display.paste_centred_on(kern,50,50);
		kernal_display.paste_centred_on(kernal_graph,200,50);
		kernal_display.reshow();
		}
	return 0;
	}