OPENCV運動追蹤研究和PYTHON及JAVA實現

opencv運動追蹤可以用來捕捉到運行物體或者活物，在被動攝像頭上應用，可以在運行時錄相，節省寶貴的存儲空間。

一個外國老哥藉助樹黴派的攝像頭使用PYTHON做一個簡單的運行捕捉攝像頭，用於捕捉工作時間偷喝他冰箱裏啤酒的同事。代碼有一些轉義字符的亂碼，使用3.0API後有些問題，由於findContours不同版本返回值不同，我小修改了一下，可以完美運行於PC機帶的攝像頭，由於沒有紅外和輔助設備測距，所以需要離攝像頭一段距離才能完美展示和處理。

運行檢測的核心算法有很多，有些複雜，有些簡單，有些準確，有些粗糙。同時也一行業和機器學習，機器視覺結合後，每一天都在發生新變化，不停的有牛B的數學家，物理學家，程序員加入。這個算法的核心是,更詳細的算法可以去參考http://python.jobbole.com/81593/，他們禁止轉載，原理如下:

我們視頻流中的背景在連續的視頻幀內，多數時候應該是靜止不變的，因此如果我們可以建立背景模型，我們的就可以監視到顯著的變化。如果發生了顯著的變化，我們就可以檢測到它——通常這些變化和我們視頻中的運動有關。

上修改後的PYTHON代碼，如果2.0的API，需要修改這一行代碼，2.0返回兩個值，把前面的下劃線和逗號去掉，3.0不需要修改

(_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                             cv2.CHAIN_APPROX_SIMPLE)

：

# 導入必要的軟件包
import argparse
import datetime
import imutils
import time
import cv2

# 創建參數解析器並解析參數
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())

# 如果video參數爲None，那麼我們從攝像頭讀取數據
if args.get("video", None) is None:
    camera = cv2.VideoCapture(0)
    time.sleep(0.25)

# 否則我們讀取一個視頻文件
else:
    camera = cv2.VideoCapture(args["video"])

# 初始化視頻流的第一幀
firstFrame = None
# 遍歷視頻的每一幀
while True:
    # 獲取當前幀並初始化occupied/unoccupied文本
    (grabbed, frame) = camera.read()
    text = "Unoccupied"

    # 如果不能抓取到一幀，說明我們到了視頻的結尾
    if not grabbed:
        break

    # 調整該幀的大小，轉換爲灰階圖像並且對其進行高斯模糊
    frame = imutils.resize(frame, width=500)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (21, 21), 0)

    # 如果第一幀是None，對其進行初始化
    if firstFrame is None:
        firstFrame = gray
        continue
    # 計算當前幀和第一幀的不同
    frameDelta = cv2.absdiff(firstFrame, gray)
    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

    # 擴展閥值圖像填充孔洞，然後找到閥值圖像上的輪廓
    thresh = cv2.dilate(thresh, None, iterations=2)
    (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                                 cv2.CHAIN_APPROX_SIMPLE)

    # 遍歷輪廓
    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) < args["min_area"]:
            continue

        # compute the bounding box for the contour, draw it on the frame,
        # and update the text
        # 計算輪廓的邊界框，在當前幀中畫出該框
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        text = "Occupied"
    # draw the text and timestamp on the frame
    # 在當前幀上寫文字以及時間戳
    cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
                (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

    # 顯示當前幀並記錄用戶是否按下按鍵
    cv2.imshow("Security Feed", frame)
    cv2.imshow("Thresh", thresh)
    cv2.imshow("Frame Delta", frameDelta)
    key = cv2.waitKey(1) & 0xFF

    # 如果q鍵被按下，跳出循環
    if key == ord("q"):
        break
# 清理攝像機資源並關閉打開的窗口
camera.release()
cv2.destroyAllWindows()

下面上類似算法的JAVA代碼，算法大體上相當於翻譯python ：

import java.awt.EventQueue;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.awt.image.WritableRaster;
import java.util.ArrayList;
import java.util.List;

import javax.swing.ImageIcon;
import javax.swing.JButton;
import javax.swing.JFrame;
import javax.swing.JLabel;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import org.opencv.videoio.VideoCapture;

public class CameraBasic {

	static {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	}

	private JFrame frame;
	static JLabel label;
	static int flag = 0;

	/**
	 * Launch the application.
	 */
	public static void main(String[] args) {
		EventQueue.invokeLater(new Runnable() {
			public void run() {
				try {
					CameraBasic window = new CameraBasic();
					window.frame.setVisible(true);

				} catch (Exception e) {
					e.printStackTrace();
				}
			}
		});

		VideoCapture camera = new VideoCapture();
		camera.open(0);
		if (!camera.isOpened()) {
			System.out.println("Camera Error");
		} else {
			Mat frame = new Mat();
			Mat firstFrame = null;
			
			while (flag == 0) {
				camera.read(frame);

				// 捕捉動態
				Imgproc.resize(frame, frame, new Size(500, 500));
				Mat gray = new Mat();
				Imgproc.cvtColor(frame, gray, Imgproc.COLOR_BGR2GRAY);
				Imgproc.GaussianBlur(gray, gray, new Size(21, 21), 0);

				if (firstFrame == null) {
					firstFrame = gray;
					continue;
				}

				Mat frameDelta = new Mat();
				Core.absdiff(firstFrame, gray, frameDelta);
				Mat thresh = new Mat();
				Imgproc.threshold(frameDelta, thresh, 25, 255, Imgproc.THRESH_BINARY);

				List<MatOfPoint> contours = new ArrayList<>();
				Mat hierarchy = new Mat();

				Imgproc.findContours(thresh, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
				Imgproc.dilate(thresh, thresh, new Mat(), new Point(-1, -1), 2);

				for (MatOfPoint mf : contours) {

					if (Imgproc.contourArea(mf) < 2000) {
						continue;
					}
					Imgproc.drawContours(frame, contours, contours.indexOf(mf), new Scalar(0, 255, 255));
					Imgproc.fillConvexPoly(frame, mf, new Scalar(0, 255, 255));
					Rect r = Imgproc.boundingRect(mf);
					Imgproc.rectangle(frame, r.tl(), r.br(), new Scalar(0, 255, 0), 2);
				}
				firstFrame = gray;
				
				label.setIcon(new ImageIcon(matToBufferedImage(frame)));
				try {
					Thread.sleep(40);
				} catch (InterruptedException e) {
					// TODO Auto-generated catch block
					e.printStackTrace();
				}
			}
		}
	}

	/**
	 * Create the application.
	 */
	public CameraBasic() {
		initialize();
	}

	/**
	 * Initialize the contents of the frame.
	 */
	private void initialize() {
		frame = new JFrame();
		frame.setBounds(100, 100, 800, 450);
		frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
		frame.getContentPane().setLayout(null);

		JButton btnNewButton = new JButton("\u62CD\u7167");
		btnNewButton.addMouseListener(new MouseAdapter() {
			@Override
			public void mouseClicked(MouseEvent arg0) {
				flag = 1;
			}
		});
		btnNewButton.setBounds(33, 13, 113, 27);
		frame.getContentPane().add(btnNewButton);

		label = new JLabel("");
		label.setBounds(0, 0, 800, 450);
		frame.getContentPane().add(label);
	}

	public static BufferedImage matToBufferedImage(Mat mat) {
		if (mat.height() > 0 && mat.width() > 0) {
			BufferedImage image = new BufferedImage(mat.width(), mat.height(), BufferedImage.TYPE_3BYTE_BGR);
			WritableRaster raster = image.getRaster();
			DataBufferByte dataBuffer = (DataBufferByte) raster.getDataBuffer();
			byte[] data = dataBuffer.getData();
			mat.get(0, 0, data);
			return image;
		}

		return null;
	}
}

其實在OPENCV裏有很多運行追蹤的算法，使用起來更準確，也更簡單，下面是一個使用JAVA寫的調用BackgroundSubtractorMOG2運行追蹤算法實現，十分簡單，把加一個特效，給捕捉到人染色。

package javaCv;

import java.awt.EventQueue;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.awt.image.WritableRaster;
import java.util.ArrayList;
import java.util.List;

import javax.swing.ImageIcon;
import javax.swing.JButton;
import javax.swing.JFrame;
import javax.swing.JLabel;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.video.BackgroundSubtractorMOG2;
import org.opencv.video.Video;
import org.opencv.videoio.VideoCapture;

public class CameraBasic2 {

	static {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	}

	private JFrame frame;
	static JLabel label;
	static int flag = 0;

	/**
	 * Launch the application.
	 */
	public static void main(String[] args) {
		EventQueue.invokeLater(new Runnable() {
			public void run() {
				try {
					CameraBasic2 window = new CameraBasic2();
					window.frame.setVisible(true);

				} catch (Exception e) {
					e.printStackTrace();
				}
			}
		});

		VideoCapture camera = new VideoCapture();
		camera.open(0);
		if (!camera.isOpened()) {
			System.out.println("Camera Error");
		} else {
			Mat frame = new Mat();
			BackgroundSubtractorMOG2 bs = Video.createBackgroundSubtractorMOG2();
			bs.setHistory(100);
			Mat tmp = new Mat();

			while (flag == 0) {
				camera.read(frame);
				// 捕捉
				bs.apply(frame, tmp, 0.1f);

				List<MatOfPoint> contours = new ArrayList<>();
				Mat hierarchy = new Mat();
				Imgproc.findContours(tmp, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
				Imgproc.dilate(tmp, tmp, new Mat(), new Point(-1, -1), 2);

				for (MatOfPoint mf : contours) {
					if (Imgproc.contourArea(mf) < 1000) {
						continue;
					}
					// Imgproc.drawContours(frame, contours,
					// contours.indexOf(mf), new Scalar(0, 255, 255));
					Imgproc.fillConvexPoly(frame, mf, new Scalar(0, 255, 255));
					Rect r = Imgproc.boundingRect(mf);
					Imgproc.rectangle(frame, r.tl(), r.br(), new Scalar(0, 255, 0), 2);
					//Imgcodecs.imwrite("E:\\work\\qqq\\camera2\\" + "img" + System.currentTimeMillis() + ".jpg", frame);
				}

				label.setIcon(new ImageIcon(matToBufferedImage(frame)));
				try {
					Thread.sleep(40);
				} catch (InterruptedException e) {
					// TODO Auto-generated catch block
					e.printStackTrace();
				}
			}
		}
	}

	/**
	 * Create the application.
	 */
	public CameraBasic2() {
		initialize();
	}

	/**
	 * Initialize the contents of the frame.
	 */
	private void initialize() {
		frame = new JFrame();
		frame.setBounds(100, 100, 800, 450);
		frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
		frame.getContentPane().setLayout(null);

		JButton btnNewButton = new JButton("\u62CD\u7167");
		btnNewButton.addMouseListener(new MouseAdapter() {
			@Override
			public void mouseClicked(MouseEvent arg0) {
				flag = 1;
			}
		});
		btnNewButton.setBounds(33, 13, 113, 27);
		frame.getContentPane().add(btnNewButton);

		label = new JLabel("");
		label.setBounds(0, 0, 800, 450);
		frame.getContentPane().add(label);
	}

	public static BufferedImage matToBufferedImage(Mat mat) {
		if (mat.height() > 0 && mat.width() > 0) {
			BufferedImage image = new BufferedImage(mat.width(), mat.height(), BufferedImage.TYPE_3BYTE_BGR);
			WritableRaster raster = image.getRaster();
			DataBufferByte dataBuffer = (DataBufferByte) raster.getDataBuffer();
			byte[] data = dataBuffer.getData();
			mat.get(0, 0, data);
			return image;
		}

		return null;
	}
}

運行追蹤十分有用，可以用在家裏的攝像頭上，既節省空間，也節省觀看的時間,其實這個開發是比較有意思的，在捕捉動態對象的前提下，可以主動報警，有人入侵，也可以加入人臉識別，把好哥們都加進去，這樣如果不在家時，誰到了家裏都能知道，遇到不喜歡的人可以選擇性避開，遇到想見的人可以去見。也可以在攝像頭裏加入動作識別，來開啓空調電視之類，從而實現攝像頭的充分利用。

效果圖如下,我對人添加了染色效果：

參考：

http://python.jobbole.com/81593/

http://blog.csdn.net/jjddss/article/details/72674704

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