Working on Example Camera Code

[omsahmad]

Hello, I am trying to simply get the camera to feed me data, from the example code, but I have some questions. When I run it using the image flashed in the cRIO, it works well (I guess, as in I get constant data). However when I try to use the GetImage from the axis camera it no longer feeds data. I used a utility to get the HSV from my camera, but that did not help, in fact it made it worse. Does the code look right?

Also what do these lines do? I understand that they write images to the cRIO after throughout the image processing, but in what cases would I want to use them. Apparently do this is bad for the cRIO as well?

BinaryImage *thresholdImage = image->ThresholdHSV(threshold);	// get just the green target pixels
//thresholdImage->Write("/threshold.bmp");
BinaryImage *convexHullImage = thresholdImage->ConvexHull(false);  // fill in partial and full rectangles
//convexHullImage->Write("/ConvexHull.bmp");
BinaryImage *filteredImage = convexHullImage->ParticleFilter(criteria, 1);	//Remove small particles
//filteredImage->Write("Filtered.bmp");

#include "WPILib.h"
#include "Vision/RGBImage.h"
#include "Vision/BinaryImage.h"
#include "Math.h"
 
/**
 * Sample program to use NIVision to find rectangles in the scene that are illuminated
 * by a LED ring light (similar to the model from FIRSTChoice). The camera sensitivity
 * is set very low so as to only show light sources and remove any distracting parts
 * of the image.
 * 
 * The CriteriaCollection is the set of criteria that is used to filter the set of
 * rectangles that are detected. In this example we're looking for rectangles with
 * a minimum width of 30 pixels and maximum of 400 pixels.
 * 
 * The algorithm first does a color threshold operation that only takes objects in the
 * scene that have a bright green color component. Then a convex hull operation fills 
 * all the rectangle outlines (even the partially occluded ones). Then a small object filter
 * removes small particles that might be caused by green reflection scattered from other 
 * parts of the scene. Finally all particles are scored on rectangularity, aspect ratio,
 * and hollowness to determine if they match the target.
 *
 * Look in the VisionImages directory inside the project that is created for the sample
 * images as well as the NI Vision Assistant file that contains the vision command
 * chain (open it with the Vision Assistant)
 */

//Camera constants used for distance calculation
#define X_IMAGE_RES 320		//X Image resolution in pixels, should be 160, 320 or 640
//#define VIEW_ANGLE 48		//Axis 206 camera
#define VIEW_ANGLE 43.5  //Axis M1011 camera
#define PI 3.141592653

//Score limits used for target identification
#define RECTANGULARITY_LIMIT 60
#define ASPECT_RATIO_LIMIT 75
#define X_EDGE_LIMIT 40
#define Y_EDGE_LIMIT 60

//Minimum area of particles to be considered
#define AREA_MINIMUM 500

//Edge profile constants used for hollowness score calculation
#define XMAXSIZE 24
#define XMINSIZE 24
#define YMAXSIZE 24
#define YMINSIZE 48
const double xMax[XMAXSIZE] = {1, 1, 1, 1, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, 1, 1, 1, 1};
const double xMin[XMINSIZE] = {.4, .6, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, .1, 0.6, 0};
const double yMax[YMAXSIZE] = {1, 1, 1, 1, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, 1, 1, 1, 1};
const double yMin[YMINSIZE] = {.4, .6, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05,
								.05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05, .05,
								.05, .05, .6, 0};

class RobotDemo : public SimpleRobot
{
	//Structure to represent the scores for the various tests used for target identification
	struct Scores {
		double rectangularity;
		double aspectRatioInner;
		double aspectRatioOuter;
		double xEdge;
		double yEdge;

	};
	
//	RobotDrive myRobot; // robot drive system
//	Joystick stick; // only joystick
	Scores *scores;
	DriverStationLCD *lcd;
	SmartDashboard *sds;
	Jaguar *FRmotor; 			// Front Right motor,	ID: 1
	Jaguar *FLmotor; 			// Front Right motor,	ID: 2
	Jaguar *RRmotor; 			// Front Right motor,	ID: 3
	Jaguar *RLmotor; 			// Front Right motor,	ID: 4
	RobotDrive *drive;

public:
	RobotDemo(void)
	{	
		lcd = DriverStationLCD::GetInstance();
/**		
		FRmotor = new Jaguar(1); //(10);
		FLmotor = new Jaguar(2); //(13);
		RRmotor = new Jaguar(3); //(32);
		RLmotor = new Jaguar(4); //(31);
		
		drive = new RobotDrive(FLmotor, RLmotor, FRmotor, RRmotor);
		drive->SetInvertedMotor(RobotDrive::kFrontRightMotor, true);
		drive->SetInvertedMotor(RobotDrive::kFrontLeftMotor, false);
		drive->SetInvertedMotor(RobotDrive::kRearRightMotor, true);
		drive->SetInvertedMotor(RobotDrive::kRearLeftMotor, false);
		drive->SetExpiration(15);

// Motor Safety Enabled
		FRmotor->SetExpiration(0.1);
		FRmotor->SetSafetyEnabled(true);
		
		FLmotor->SetExpiration(0.1);
		FLmotor->SetSafetyEnabled(true);
		
		RRmotor->SetExpiration(0.1);
		RRmotor->SetSafetyEnabled(true);
		
		RLmotor->SetExpiration(0.1);
		RLmotor->SetSafetyEnabled(true);
*/			
	}

	void Autonomous(void)
	{
		Threshold threshold(60, 100, 90, 255, 20, 255);	//HSV threshold criteria, ranges are in that order ie. Hue is 60-100
//		Threshold threshold(80, 140, 0, 228, 15, 150);	//HSV threshold criteria, ranges are in that order ie. Hue is 60-100		
		ParticleFilterCriteria2 criteria[] = {
				{IMAQ_MT_AREA, AREA_MINIMUM, 65535, false, false}
		};												//Particle filter criteria, used to filter out small particles		
		while (IsAutonomous() && IsEnabled()) {
			lcd->Clear();
			/**
             * Do the image capture with the camera and apply the algorithm described above. This
             * sample will either get images from the camera or from an image file stored in the top
             * level directory in the flash memory on the cRIO. The file name in this case is "testImage.jpg"
             */
			AxisCamera &camera = AxisCamera::GetInstance("10.15.95.11");	//To use the Axis camera uncomment this line
			ColorImage *image;
			camera.GetImage(image);	//To get the images from the camera comment the line above and uncomment this one
			camera.WriteResolution(AxisCamera::kResolution_320x240);
			camera.WriteWhiteBalance(AxisCamera::kWhiteBalance_FixedOutdoor1);
	//		image = new RGBImage("/HybridLine_SmallGreen3.jpg");		// get the sample image from the cRIO flash
			printf("Width: %dx", image->GetWidth());
			printf("Height: %d", image->GetHeight());
			BinaryImage *thresholdImage = image->ThresholdHSV(threshold);	// get just the green target pixels
			//thresholdImage->Write("/threshold.bmp");
			BinaryImage *convexHullImage = thresholdImage->ConvexHull(false);  // fill in partial and full rectangles
			//convexHullImage->Write("/ConvexHull.bmp");
			BinaryImage *filteredImage = convexHullImage->ParticleFilter(criteria, 1);	//Remove small particles
			//filteredImage->Write("Filtered.bmp");

			vector<ParticleAnalysisReport> *reports = filteredImage->GetOrderedParticleAnalysisReports();  //get a particle analysis report for each particle
			scores = new Scores[reports->size()];

			//Iterate through each particle, scoring it and determining whether it is a target or not
			for (unsigned i = 0; i < reports->size(); i++) {
				ParticleAnalysisReport *report = &(reports->at(i));
				scores[i].rectangularity = scoreRectangularity(report);
				scores[i].aspectRatioOuter = scoreAspectRatio(filteredImage, report, true);
				scores[i].aspectRatioInner = scoreAspectRatio(filteredImage, report, false);			
				scores[i].xEdge = scoreXEdge(thresholdImage, report);
				scores[i].yEdge = scoreYEdge(thresholdImage, report);
				if(scoreCompare(scores[i], false))
				{
					lcd->Printf(DriverStationLCD::kUser_Line1, 1, "%dHi:%f Y:%f\n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
					lcd->Printf(DriverStationLCD::kUser_Line2, 1, "Dist: %f \n", computeDistance(thresholdImage, report, false));
					sds->PutNumber("Hi particle:", i);
					sds->PutNumber("Hi center X:", report->center_mass_x_normalized);
					sds->PutNumber("Hi Dist:", computeDistance(thresholdImage, report, false));
					printf("particle: %d  is a High Goal  centerX: %f  centerY: %f \n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
					printf("Distance: %f \n", computeDistance(thresholdImage, report, false));
				} else if (scoreCompare(scores[i], true)) {
					lcd->Printf(DriverStationLCD::kUser_Line1, 1, "particle: %d  is a Middle Goal  centerX: %f  centerY: %f \n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
					lcd->Printf(DriverStationLCD::kUser_Line2, 1, "Distance: %f \n", computeDistance(thresholdImage, report, true));
					sds->PutNumber("Mid particle:", i);
					sds->PutNumber("Mid center X:", report->center_mass_x_normalized);
					sds->PutNumber("Mid Dist:", computeDistance(thresholdImage, report, false));
					printf("particle: %d  is a Middle Goal  centerX: %f  centerY: %f \n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
					printf("Distance: %f \n", computeDistance(thresholdImage, report, true));
				} else {
					lcd->Printf(DriverStationLCD::kUser_Line1, 1, "particle: %d  is not a goal  centerX: %f  centerY: %f \n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
					printf("particle: %d  is not a goal  centerX: %f  centerY: %f \n", i, report->center_mass_x_normalized, report->center_mass_y_normalized);
				}
				lcd->Printf(DriverStationLCD::kUser_Line3,1, "r%fAR%f \n", scores[i].rectangularity, scores[i].aspectRatioInner);
				lcd->Printf(DriverStationLCD::kUser_Line4,1, "AR%fx%fy%f  \n", scores[i].aspectRatioOuter, scores[i].xEdge, scores[i].yEdge);	
				sds->PutNumber("rect:", scores[i].rectangularity);
				printf("rect: %f  ARinner: %f \n", scores[i].rectangularity, scores[i].aspectRatioInner);
				printf("ARouter: %f  xEdge: %f  yEdge: %f  \n", scores[i].aspectRatioOuter, scores[i].xEdge, scores[i].yEdge);	
			}
			lcd->UpdateLCD();
			printf("\n");
			
			// be sure to delete images after using them
			delete filteredImage;
			delete convexHullImage;
			delete thresholdImage;
			delete image;
			
			//delete allocated reports and Scores objects also
			delete scores;
			delete reports;
		}
	}

	/**
	 * Runs the motors with arcade steering. 
	 */
	void OperatorControl(void)
	{
		while (IsOperatorControl())
		{
			Wait(0.005);				// wait for a motor update time
		}
	}
	
	/**
	 * Computes the estimated distance to a target using the height of the particle in the image. For more information and graphics
	 * showing the math behind this approach see the Vision Processing section of the ScreenStepsLive documentation.
	 * 
	 * @param image The image to use for measuring the particle estimated rectangle
	 * @param report The Particle Analysis Report for the particle
	 * @param outer True if the particle should be treated as an outer target, false to treat it as a center target
	 * @return The estimated distance to the target in Inches.
	 */
	double computeDistance (BinaryImage *image, ParticleAnalysisReport *report, bool outer) {
		double rectShort, height;
		int targetHeight;
		
		imaqMeasureParticle(image->GetImaqImage(), report->particleIndex, 0, IMAQ_MT_EQUIVALENT_RECT_SHORT_SIDE, &rectShort);
		//using the smaller of the estimated rectangle short side and the bounding rectangle height results in better performance
		//on skewed rectangles
		height = min(report->boundingRect.height, rectShort);
		targetHeight = outer ? 29 : 21;
		
		return X_IMAGE_RES * targetHeight / (height * 12 * 2 * tan(VIEW_ANGLE*PI/(180*2)));
	}
	
	/**
	 * Computes a score (0-100) comparing the aspect ratio to the ideal aspect ratio for the target. This method uses
	 * the equivalent rectangle sides to determine aspect ratio as it performs better as the target gets skewed by moving
	 * to the left or right. The equivalent rectangle is the rectangle with sides x and y where particle area= x*y
	 * and particle perimeter= 2x+2y
	 * 
	 * @param image The image containing the particle to score, needed to perform additional measurements
	 * @param report The Particle Analysis Report for the particle, used for the width, height, and particle number
	 * @param outer	Indicates whether the particle aspect ratio should be compared to the ratio for the inner target or the outer
	 * @return The aspect ratio score (0-100)
	 */
	double scoreAspectRatio(BinaryImage *image, ParticleAnalysisReport *report, bool outer){
		double rectLong, rectShort, idealAspectRatio, aspectRatio;
		idealAspectRatio = outer ? (62/29) : (62/20);	//Dimensions of goal opening + 4 inches on all 4 sides for reflective tape
		
		imaqMeasureParticle(image->GetImaqImage(), report->particleIndex, 0, IMAQ_MT_EQUIVALENT_RECT_LONG_SIDE, &rectLong);
		imaqMeasureParticle(image->GetImaqImage(), report->particleIndex, 0, IMAQ_MT_EQUIVALENT_RECT_SHORT_SIDE, &rectShort);
		
		//Divide width by height to measure aspect ratio
		if(report->boundingRect.width > report->boundingRect.height){
			//particle is wider than it is tall, divide long by short
			aspectRatio = 100*(1-fabs((1-((rectLong/rectShort)/idealAspectRatio))));
		} else {
			//particle is taller than it is wide, divide short by long
			aspectRatio = 100*(1-fabs((1-((rectShort/rectLong)/idealAspectRatio))));
		}
		return (max(0, min(aspectRatio, 100)));		//force to be in range 0-100
	}
	
	/**
	 * Compares scores to defined limits and returns true if the particle appears to be a target
	 * 
	 * @param scores The structure containing the scores to compare
	 * @param outer True if the particle should be treated as an outer target, false to treat it as a center target
	 * 
	 * @return True if the particle meets all limits, false otherwise
	 */
	bool scoreCompare(Scores scores, bool outer){
		bool isTarget = true;

		isTarget &= scores.rectangularity > RECTANGULARITY_LIMIT;
		if(outer){
			isTarget &= scores.aspectRatioOuter > ASPECT_RATIO_LIMIT;
		} else {
			isTarget &= scores.aspectRatioInner > ASPECT_RATIO_LIMIT;
		}
		isTarget &= scores.xEdge > X_EDGE_LIMIT;
		isTarget &= scores.yEdge > Y_EDGE_LIMIT;

		return isTarget;
	}
	
	/**
	 * Computes a score (0-100) estimating how rectangular the particle is by comparing the area of the particle
	 * to the area of the bounding box surrounding it. A perfect rectangle would cover the entire bounding box.
	 * 
	 * @param report The Particle Analysis Report for the particle to score
	 * @return The rectangularity score (0-100)
	 */
	double scoreRectangularity(ParticleAnalysisReport *report){
		if(report->boundingRect.width*report->boundingRect.height !=0){
			return 100*report->particleArea/(report->boundingRect.width*report->boundingRect.height);
		} else {
			return 0;
		}	
	}
	
	/**
	 * Computes a score based on the match between a template profile and the particle profile in the X direction. This method uses the
	 * the column averages and the profile defined at the top of the sample to look for the solid vertical edges with
	 * a hollow center.
	 * 
	 * @param image The image to use, should be the image before the convex hull is performed
	 * @param report The Particle Analysis Report for the particle
	 * 
	 * @return The X Edge Score (0-100)
	 */
	double scoreXEdge(BinaryImage *image, ParticleAnalysisReport *report){
		double total = 0;
		LinearAverages *averages = imaqLinearAverages2(image->GetImaqImage(), IMAQ_COLUMN_AVERAGES, report->boundingRect);
		for(int i=0; i < (averages->columnCount); i++){
			if(xMin[i*(XMINSIZE-1)/averages->columnCount] < averages->columnAverages[i] 
			   && averages->columnAverages[i] < xMax[i*(XMAXSIZE-1)/averages->columnCount]){
				total++;
			}
		}
		total = 100*total/(averages->columnCount);		//convert to score 0-100
		imaqDispose(averages);							//let IMAQ dispose of the averages struct
		return total;
	}
	
	/**
	 * Computes a score based on the match between a template profile and the particle profile in the Y direction. This method uses the
	 * the row averages and the profile defined at the top of the sample to look for the solid horizontal edges with
	 * a hollow center
	 * 
	 * @param image The image to use, should be the image before the convex hull is performed
	 * @param report The Particle Analysis Report for the particle
	 * 
	 * @return The Y Edge score (0-100)
	 */
	double scoreYEdge(BinaryImage *image, ParticleAnalysisReport *report){
		double total = 0;
		LinearAverages *averages = imaqLinearAverages2(image->GetImaqImage(), IMAQ_ROW_AVERAGES, report->boundingRect);
		for(int i=0; i < (averages->rowCount); i++){
			if(yMin[i*(YMINSIZE-1)/averages->rowCount] < averages->rowAverages[i] 
			   && averages->rowAverages[i] < yMax[i*(YMAXSIZE-1)/averages->rowCount]){
				total++;
			}
		}
		total = 100*total/(averages->rowCount);		//convert to score 0-100
		imaqDispose(averages);						//let IMAQ dispose of the averages struct
		return total;
	}		
};

START_ROBOT_CLASS(RobotDemo);

Thanks!