public List <Point[]> detect(Mat im, float scaleFactor, int minNeighbours, OpenCVForUnity.Size minSize)
{
//convert image to greyscale
Mat gray = null;
if (im.channels() == 1)
{
gray = im;
}
else
{
gray = new Mat();
Imgproc.cvtColor(im, gray, Imgproc.COLOR_RGBA2GRAY);
}
using (Mat equalizeHistMat = new Mat())
using (MatOfRect faces = new MatOfRect()) {
Imgproc.equalizeHist(gray, equalizeHistMat);
detector.detectMultiScale(equalizeHistMat, faces, scaleFactor, minNeighbours, 0
| Objdetect.CASCADE_FIND_BIGGEST_OBJECT
| Objdetect.CASCADE_SCALE_IMAGE, minSize, new Size());
if (faces.rows() < 1)
{
return(new List <Point[]> ());
}
return(convertMatOfRectToPoints(faces));
}
}
public Point[] calc_peaks(Mat im,
Point[] points,
OpenCVForUnity.Size ssize)
{
int n = points.Length;
// Debug.Log ("n == int(patches.size()) " + patches.Count);
using (Mat pt = (new MatOfPoint2f(points)).reshape(1, 2 * n))
using (Mat S = calc_simil(pt))
using (Mat Si = inv_simil(S))
{
float[] pt_float = new float[pt.total()];
Utils.copyFromMat <float>(pt, pt_float);
int pt_cols = pt.cols();
float[] S_float = new float[S.total()];
Utils.copyFromMat <float>(S, S_float);
int S_cols = S.cols();
float[] A_float = new float[2 * 3];
Point[] pts = apply_simil(Si, points);
for (int i = 0; i < n; i++)
{
OpenCVForUnity.Size wsize = new OpenCVForUnity.Size(ssize.width + patches [i].patch_size().width, ssize.height + patches [i].patch_size().height);
using (Mat A = new Mat(2, 3, CvType.CV_32F))
{
Utils.copyFromMat <float>(A, A_float);
int A_cols = A.cols();
A_float [0] = S_float [0];
A_float [1] = S_float [1];
A_float [1 * A_cols] = S_float [1 * S_cols];
A_float [(1 * A_cols) + 1] = S_float [(1 * S_cols) + 1];
A_float [2] = (float)(pt_float [(2 * pt_cols) * i] -
(A_float [0] * (wsize.width - 1) / 2 + A_float [1] * (wsize.height - 1) / 2));
A_float [(1 * A_cols) + 2] = (float)(pt_float [((2 * pt_cols) * i) + 1] -
(A_float [1 * A_cols] * (wsize.width - 1) / 2 + A_float [(1 * A_cols) + 1] * (wsize.height - 1) / 2));
Utils.copyToMat(A_float, A);
using (Mat I = new Mat())
{
Imgproc.warpAffine(im, I, A, wsize, Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP);
using (Mat R = patches [i].calc_response(I, false))
{
Core.MinMaxLocResult minMaxLocResult = Core.minMaxLoc(R);
pts [i].x = pts [i].x + minMaxLocResult.maxLoc.x - 0.5 * ssize.width;
pts [i].y = pts [i].y + minMaxLocResult.maxLoc.y - 0.5 * ssize.height;
}
}
}
}
return(apply_simil(S, pts));
}
}
void Awake()
{
fa = 160; //opencv函数中用来区分红色与其他颜色的阈值。
mycap = new WebCamTexture(); //初始化摄像头
WebCamDevice[] devices = WebCamTexture.devices; //定义并获取摄像头数组
deviceName = devices[0].name; //获取当前摄像头名字
mycap = new WebCamTexture(deviceName, 640, 480); //用摄像头名字，宽度与高度来初始化摄像头
tu = new Mat(480, 640, CvType.CV_8UC3); //用高度，宽度，图像类别来初始化图片
a = new Mat(480, 640, CvType.CV_8UC3); //定义YUV空间的图片变量
b = new Mat(480, 640, CvType.CV_8UC1); //存放U通道的图像信息。
b1 = new Mat(480, 640, CvType.CV_8UC1); //存放变量b的备份
c = new List <Mat>(); //定义一个图像数组
size = new Size(50, 50); //将图片大小初始化为50*50的
color = new Scalar(255); //将颜色变量初始化为白色
hier = new Mat(); //findContours函数所需要的参数，无意义
rect1 = new OpenCVForUnity.Rect(1, 1, 1, 1); //定义一个手型轮廓边框（矩形框）变量
shishi = new Mat(50, 50, CvType.CV_8UC1); //定义一个新的图像变量，用来接收jie变量图像的缩放图像
}
IEnumerator GetPicture()
{
yield return(new WaitForEndOfFrame()); //攝影機讀取到的Frame繪製完畢後才進行拍照
//calculate mat of selected region and whole rawimage
OpenCVForUnity.Mat srcMat = new OpenCVForUnity.Mat(4, 1, CvType.CV_32FC2);
OpenCVForUnity.Mat dstMat = new OpenCVForUnity.Mat(4, 1, CvType.CV_32FC2);
dstMat.put(0, 0, rectTopLeft.x - rectBotLeft.x, rectTopLeft.y - rectBotLeft.y,
rectTopRight.x - rectBotLeft.x, rectTopRight.y - rectBotLeft.y,
rectBotLeft.x - rectBotLeft.x, rectBotLeft.y - rectBotLeft.y,
rectBotRight.x - rectBotLeft.x, rectBotRight.y - rectBotLeft.y);
//calculate transform matrix
transformMat = new OpenCVForUnity.Mat(3, 3, CvType.CV_32FC1);
transformMat = Imgproc.getPerspectiveTransform(srcMat, dstMat);
Texture2D sourceTex = ScreenCapture.CaptureScreenshotAsTexture();
Color[] pix = sourceTex.GetPixels((int)rectBotLeft.x, (int)rectBotLeft.y, width, height);
Texture2D destTex = new Texture2D(width, height);
destTex.SetPixels(pix);
destTex.Apply();
OpenCVForUnity.Size textureSize = new OpenCVForUnity.Size(width, height);
OpenCVForUnity.Mat rawImageSrcMat = new OpenCVForUnity.Mat(textureSize, CvType.CV_8UC4);
OpenCVForUnity.Mat rawImageSrcMatFlip = new OpenCVForUnity.Mat(textureSize, CvType.CV_8UC4);
Utils.texture2DToMat(destTex, rawImageSrcMat);
Core.flip(rawImageSrcMat, rawImageSrcMatFlip, 0);
OpenCVForUnity.Mat rawImageDstMat = new OpenCVForUnity.Mat(textureSize, CvType.CV_8UC4);
//Mat rawImageDstMatFlip = new Mat(textureSize, CvType.CV_8UC4);
Imgproc.warpPerspective(rawImageSrcMatFlip, rawImageDstMat, transformMat, textureSize);
texture = new Texture2D(width, height, TextureFormat.RGB24, false);
Utils.matToTexture2D(rawImageDstMat, texture);
rawImageRI.texture = texture;
}
public Point[] calc_peaks (Mat im,
Point[] points,
OpenCVForUnity.Size ssize)
{
int n = points.Length;
// Debug.Log ("n == int(patches.size()) " + patches.Count);
using (Mat pt = (new MatOfPoint2f (points)).reshape (1, 2 * n))
using (Mat S = calc_simil (pt))
using (Mat Si = inv_simil (S)) {
Point[] pts = apply_simil (Si, points);
for (int i = 0; i < n; i++) {
OpenCVForUnity.Size wsize = new OpenCVForUnity.Size (ssize.width + patches [i].patch_size ().width, ssize.height + patches [i].patch_size ().height);
using (Mat A = new Mat (2, 3, CvType.CV_32F)) {
A.put (0, 0, S.get (0, 0) [0]);
A.put (0, 1, S.get (0, 1) [0]);
A.put (1, 0, S.get (1, 0) [0]);
A.put (1, 1, S.get (1, 1) [0]);
A.put (0, 2, pt.get (2 * i, 0) [0] -
(A.get (0, 0) [0] * (wsize.width - 1) / 2 + A.get (0, 1) [0] * (wsize.height - 1) / 2));
A.put (1, 2, pt.get (2 * i + 1, 0) [0] -
(A.get (1, 0) [0] * (wsize.width - 1) / 2 + A.get (1, 1) [0] * (wsize.height - 1) / 2));
using (Mat I = new Mat ()) {
Imgproc.warpAffine (im, I, A, wsize, Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP);
using (Mat R = patches [i].calc_response (I, false)) {
Core.MinMaxLocResult minMaxLocResult = Core.minMaxLoc (R);
pts [i].x = pts [i].x + minMaxLocResult.maxLoc.x - 0.5 * ssize.width;
pts [i].y = pts [i].y + minMaxLocResult.maxLoc.y - 0.5 * ssize.height;
}
}
}
}
return apply_simil (S, pts);
}
}
public Point[] calc_peaks(Mat im,
Point[] points,
OpenCVForUnity.Size ssize)
{
int n = points.Length;
// Debug.Log ("n == int(patches.size()) " + patches.Count);
using (Mat pt = (new MatOfPoint2f(points)).reshape(1, 2 * n))
using (Mat S = calc_simil(pt))
using (Mat Si = inv_simil(S)) {
Point[] pts = apply_simil(Si, points);
for (int i = 0; i < n; i++)
{
OpenCVForUnity.Size wsize = new OpenCVForUnity.Size(ssize.width + patches [i].patch_size().width, ssize.height + patches [i].patch_size().height);
using (Mat A = new Mat(2, 3, CvType.CV_32F)) {
A.put(0, 0, S.get(0, 0) [0]);
A.put(0, 1, S.get(0, 1) [0]);
A.put(1, 0, S.get(1, 0) [0]);
A.put(1, 1, S.get(1, 1) [0]);
A.put(0, 2, pt.get(2 * i, 0) [0] -
(A.get(0, 0) [0] * (wsize.width - 1) / 2 + A.get(0, 1) [0] * (wsize.height - 1) / 2));
A.put(1, 2, pt.get(2 * i + 1, 0) [0] -
(A.get(1, 0) [0] * (wsize.width - 1) / 2 + A.get(1, 1) [0] * (wsize.height - 1) / 2));
using (Mat I = new Mat()) {
Imgproc.warpAffine(im, I, A, wsize, Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP);
using (Mat R = patches [i].calc_response(I, false)) {
Core.MinMaxLocResult minMaxLocResult = Core.minMaxLoc(R);
pts [i].x = pts [i].x + minMaxLocResult.maxLoc.x - 0.5 * ssize.width;
pts [i].y = pts [i].y + minMaxLocResult.maxLoc.y - 0.5 * ssize.height;
}
}
}
}
return(apply_simil(S, pts));
}
}
private Point[] fit(Mat image,
Point[] init,
OpenCVForUnity.Size ssize,
bool robust,
int itol,
double ftol)
{
int n = smodel.npts();
// assert((int(init.size())==n) && (pmodel.n_patches()==n));
// Debug.Log ("init.size())==n " + init.Length + " " + n);
// Debug.Log ("pmodel.n_patches()==n " + pmodel.n_patches () + " " + n);
smodel.calc_params(init, new Mat(), 3.0f);
Point[] pts = smodel.calc_shape();
//find facial features in image around current estimates
Point[] peaks = pmodel.calc_peaks(image, pts, ssize);
//optimise
if (!robust)
{
smodel.calc_params(peaks, new Mat(), 3.0f); //compute shape model parameters
pts = smodel.calc_shape(); //update shape
}
else
{
using (Mat weight = new Mat(n, 1, CvType.CV_32F))
using (Mat weight_sort = new Mat(n, 1, CvType.CV_32F))
{
float[] weight_float = new float[weight.total()];
Utils.copyFromMat <float>(weight, weight_float);
float[] weight_sort_float = new float[weight_sort.total()];
Point[] pts_old = pts;
for (int iter = 0; iter < itol; iter++)
{
//compute robust weight
for (int i = 0; i < n; i++)
{
using (MatOfPoint tmpMat = new MatOfPoint(new Point(pts [i].x - peaks [i].x, pts [i].y - peaks [i].y)))
{
weight_float [i] = (float)Core.norm(tmpMat);
}
}
Utils.copyToMat(weight_float, weight);
Core.sort(weight, weight_sort, Core.SORT_EVERY_COLUMN | Core.SORT_ASCENDING);
Utils.copyFromMat <float>(weight_sort, weight_sort_float);
double var = 1.4826 * weight_sort_float [n / 2];
if (var < 0.1)
{
var = 0.1;
}
Core.pow(weight, 2, weight);
Core.multiply(weight, new Scalar(-0.5 / (var * var)), weight);
Core.exp(weight, weight);
//compute shape model parameters
smodel.calc_params(peaks, weight, 3.0f);
//update shape
pts = smodel.calc_shape();
//check for convergence
float v = 0;
for (int i = 0; i < n; i++)
{
using (MatOfPoint tmpMat = new MatOfPoint(new Point(pts [i].x - pts_old [i].x, pts [i].y - pts_old [i].y)))
{
v += (float)Core.norm(tmpMat);
}
}
if (v < ftol)
{
break;
}
else
{
pts_old = pts;
}
}
}
}
return(pts);
}
