我想在处理中使用这样的代码，因为我不熟悉openFrameworks。 https://www.youtube.com/watch?v=pCq7u2TvlxU&list=UUtYM3-7ldtX7kf_sSoHt1Pw&index=1&feature=plcp
任何人都有机会听说过这样的项目进行处理吗？
由于我不是程序员，我试图使用与MarkerDetection混合的CalibrationDemo示例（来自opencv用于处理库） - 想知道我是否可以从复选框平面和相机获得一些转换矩阵... < / p>
关于opencv的大多数示例和教程都是用C语言编写的，所以在没有实际示例的情况下，我很难理解某些定义。
下面是正在进行的代码。它不适用于我想要的东西。正如我所说，它是处理opencv库的两个例子的混合。我的第一个目标是提取复选框平面的变换矩阵。
import gab.opencv.*;
import org.opencv.imgproc.Imgproc;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.CvType;
import org.opencv.core.Point;
import org.opencv.core.Size;
import processing.video.*;
//import java.util.list;
OpenCV opencv;
Capture cam;
PImage src, dst, markerImg;
ArrayList<MatOfPoint> contours;
ArrayList<MatOfPoint2f> approximations;
ArrayList<MatOfPoint2f> markers;
boolean[][] markerCells;
void setup() {
size(1000, 365);
//1000 × 730
cam = new Capture(this, 800, 480);
cam.start();
//src = cam.get();//opencv.getInput();
opencv = new OpenCV(this, 800, 480);
}
void update() {
//src = opencv.getInput();
opencv.loadImage(src);
// hold on to this for later, since adaptiveThreshold is destructive
Mat gray = OpenCV.imitate(opencv.getGray());
opencv.getGray().copyTo(gray);
Mat thresholdMat = OpenCV.imitate(opencv.getGray());
opencv.blur(5);
Imgproc.adaptiveThreshold(opencv.getGray(), thresholdMat, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 451, -65);
contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(thresholdMat, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_NONE);
image(opencv.getOutput(), 0, 0);
approximations = createPolygonApproximations(contours);
markers = new ArrayList<MatOfPoint2f>();
markers = selectMarkers(approximations);
MatOfPoint2f canonicalMarker = new MatOfPoint2f();
Point[] canonicalPoints = new Point[4];
canonicalPoints[0] = new Point(0, 350);
canonicalPoints[1] = new Point(0, 0);
canonicalPoints[2] = new Point(350, 0);
canonicalPoints[3] = new Point(350, 350);
canonicalMarker.fromArray(canonicalPoints);
if (markers.size() <= 0) return;
println("num points: " + markers.get(0).height());
Mat transform = Imgproc.getPerspectiveTransform(markers.get(0), canonicalMarker);
Mat unWarpedMarker = new Mat(50, 50, CvType.CV_8UC1);
Imgproc.warpPerspective(gray, unWarpedMarker, transform, new Size(350, 350));
Imgproc.threshold(unWarpedMarker, unWarpedMarker, 125, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
float cellSize = 350/7.0;
markerCells = new boolean[7][7];
for (int row = 0; row < 7; row++) {
for (int col = 0; col < 7; col++) {
int cellX = int(col*cellSize);
int cellY = int(row*cellSize);
Mat cell = unWarpedMarker.submat(cellX, cellX +(int)cellSize, cellY, cellY+ (int)cellSize);
markerCells[row][col] = (Core.countNonZero(cell) > (cellSize*cellSize)/2);
}
}
for (int col = 0; col < 7; col++) {
for (int row = 0; row < 7; row++) {
if (markerCells[row][col]) {
print(1);
} else {
print(0);
}
}
println();
}
dst = createImage(350, 350, RGB);
opencv.toPImage(unWarpedMarker, dst);
}
ArrayList<MatOfPoint2f> selectMarkers(ArrayList<MatOfPoint2f> candidates) {
float minAllowedContourSide = 50;
minAllowedContourSide = minAllowedContourSide * minAllowedContourSide;
ArrayList<MatOfPoint2f> result = new ArrayList<MatOfPoint2f>();
for (MatOfPoint2f candidate : candidates) {
if (candidate.size().height != 4) {
continue;
}
if (!Imgproc.isContourConvex(new MatOfPoint(candidate.toArray()))) {
continue;
}
// eliminate markers where consecutive
// points are too close together
float minDist = src.width * src.width;
Point[] points = candidate.toArray();
for (int i = 0; i < points.length; i++) {
Point side = new Point(points[i].x - points[(i+1)%4].x, points[i].y - points[(i+1)%4].y);
float squaredLength = (float)side.dot(side);
// println("minDist: " + minDist + " squaredLength: " +squaredLength);
minDist = min(minDist, squaredLength);
}
// println(minDist);
if (minDist < minAllowedContourSide) {
continue;
}
result.add(candidate);
}
return result;
}
ArrayList<MatOfPoint2f> createPolygonApproximations(ArrayList<MatOfPoint> cntrs) {
ArrayList<MatOfPoint2f> result = new ArrayList<MatOfPoint2f>();
double epsilon = cntrs.get(0).size().height * 0.01;
println(epsilon);
for (MatOfPoint contour : cntrs) {
MatOfPoint2f approx = new MatOfPoint2f();
Imgproc.approxPolyDP(new MatOfPoint2f(contour.toArray()), approx, epsilon, true);
result.add(approx);
}
return result;
}
void drawContours(ArrayList<MatOfPoint> cntrs) {
for (MatOfPoint contour : cntrs) {
beginShape();
Point[] points = contour.toArray();
for (int i = 0; i < points.length; i++) {
vertex((float)points[i].x, (float)points[i].y);
}
endShape();
}
}
void drawContours2f(ArrayList<MatOfPoint2f> cntrs) {
for (MatOfPoint2f contour : cntrs) {
beginShape();
Point[] points = contour.toArray();
for (int i = 0; i < points.length; i++) {
vertex((float)points[i].x, (float)points[i].y);
}
endShape(CLOSE);
}
}
void draw() {
//VIDEO
if (!cam.available()) {
println("no video available");
return;
}
cam.read();
src = cam.get();
pushMatrix();
background(125);
scale(0.7);
//image(src, 0, 0);
update();
noFill();
smooth();
strokeWeight(5);
stroke(0, 255, 0);
if (markers.size() > 0) drawContours2f(markers);
popMatrix();
if (markers.size() <= 0) {
drawContours2f(markers);
return;
}
pushMatrix();
translate(200 + src.width/2, 0);
strokeWeight(1);
image(dst, 0, 0);
float cellSize = dst.width/7.0;
for (int col = 0; col < 7; col++) {
for (int row = 0; row < 7; row++) {
if (markerCells[row][col]) {
fill(255);
} else {
fill(0);
}
stroke(0, 255, 0);
rect(col*cellSize, row*cellSize, cellSize, cellSize);
}
}
popMatrix();
}
任何帮助或指示都会很棒！ 乙