opencv人体识别技术汇总

识别处理图像中人脸，人体是图像识别的一个重要分支，在很多场合都需要对人进行查找和处理，在拍照，自动驾驶，机器人，医学，安防等上都有广泛 的用途。opencv 有众多的级联分类器，可以进行简单的人脸，眼，鼻子，嘴，上体，全身，腿的分类。这些分类器还可以通过训练或者组合进一步强化识别能力，从而把几个弱分类器变成一个强分类器使用。

分类器都是一个概率问题，精确度高了，会有遗露，精确度低了，会有错选，通过更多的训练可以使用识别库日渐完善。使用OPENCV自带的训练有很多不尽如人意，不过在特定情况下或者某些要求不高的场合，使用一些手段还是可以使用的的。在查找人眼的过程中，会找到很多非人眼的信息，和人脸结合，人眼查找就精确了很多。

import org.opencv.core.*;
import org.opencv.imgcodecs.*;
import org.opencv.objdetect.*;
import org.opencv.imgproc.*;

public class DetectBody {
	public static void main(String[] args) {
		try {
			System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

			Mat src = Imgcodecs.imread("E:/work/qqq/b5.jpg");
			Imgcodecs.imwrite("E:/work/qqq/hh81.jpg", getUpperBody(src));
			Imgcodecs.imwrite("E:/work/qqq/hh82.jpg", getLefteye(src));
			Imgcodecs.imwrite("E:/work/qqq/hh83.jpg", getRighteye(src));
			// Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", getLeftear(src));
			// Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", getRightear(src));
			Imgcodecs.imwrite("E:/work/qqq/hh84.jpg", getMouth(src));
			Imgcodecs.imwrite("E:/work/qqq/hh85.jpg", getNose(src));
			// Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", getSmile(src));
			// Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", getLowerBody(src));
			// Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", getFullBody(src));
			Imgcodecs.imwrite("E:/work/qqq/hh86.jpg", getFace(src));
			Imgcodecs.imwrite("E:/work/qqq/hh87.jpg", getProfileFace(getFace(src)));

			CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_frontalface_alt2.xml");
			MatOfRect objDetections2 = new MatOfRect();
			faceDetector.detectMultiScale(src, objDetections2);
			for (Rect rect : objDetections2.toArray()) {
				Imgproc.rectangle(src, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
						new Scalar(0, 0, 255), 2);
				Mat s = src.submat(rect);
				getLefteye(s).copyTo(s);
			}

			Imgcodecs.imwrite("E:/work/qqq/hh88.jpg", src);
		} catch (Exception e) {
			System.out.println("例外：" + e);
		}

	}

	public static Mat getUpperBody(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_upperbody.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getLefteye(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_lefteye.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getRighteye(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_righteye.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getLeftear(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_leftear.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getRightear(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_rightear.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getMouth(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_mouth.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getNose(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_mcs_nose.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getSmile(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_smile.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getLowerBody(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_lowerbody.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getFullBody(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_fullbody.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getFace(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_frontalface_alt2.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}

	public static Mat getProfileFace(Mat src) {
		Mat result = src.clone();
		if (src.cols() > 1000 || src.rows() > 1000) {
			Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));
		}

		CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_profileface.xml");
		MatOfRect objDetections = new MatOfRect();
		faceDetector.detectMultiScale(result, objDetections);
		for (Rect rect : objDetections.toArray()) {
			Imgproc.rectangle(result, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
					new Scalar(0, 0, 255), 2);
		}
		return result;
	}
}

上半身的查找，感觉还是比较准确的，特别是在自拍相机中。由于距离人数固定，可以通过大小和人脸结合，很容易过滤掉不准确的分类

对人眼的查找，单纯查找人眼，这是左眼，有很多误选，最后结合人脸，就准确了。

右眼

嘴的选择，也需要结合人脸查找

鼻子

人脸

结合人脸的眼睛选择就变得准确了