int Mat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) const

{

    return (depth() == _depth || _depth <= 0) &&

        (isContinuous() || !_requireContinuous) &&

        ((dims == 2 && (((rows == 1 || cols == 1) && channels() == _elemChannels) || (cols == _elemChannels))) ||

        (dims == 3 && channels() == 1 && size.p[2] == _elemChannels && (size.p[0] == 1 || size.p[1] == 1) &&

         (isContinuous() || step.p[1] == step.p[2]*size.p[2])))

    ? (int)(total()*channels()/_elemChannels) : -1;

}

#include <iostream>

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/core/core.hpp>
#include "copenni.cpp"

#include <iostream>

#define DEPTH_SCALE_FACTOR 255./4096.
#define ROI_HAND_WIDTH 140
#define ROI_HAND_HEIGHT 140
#define MEDIAN_BLUR_K 5
#define XRES  640
#define YRES  480
#define DEPTH_SEGMENT_THRESH 5
#define MAX_HANDS_COLOR 10
#define MAX_HANDS_NUMBER  10
#define HAND_LIKELY_AREA 2000
#define DELTA_POINT_DISTENCE 25     //手部中心點(diǎn)1和中心點(diǎn)2距離的閾值
#define SEGMENT_POINT1_DISTANCE 27  //凸點(diǎn)與手部中心點(diǎn)1遠(yuǎn)近距離的閾值
#define SEGMENT_POINT2_DISTANCE 30  //凸點(diǎn)與手部中心點(diǎn)2遠(yuǎn)近距離的閾值

using namespace cv;
using namespace xn;
using namespace std;


int main (int argc, char **argv)
{
    unsigned int convex_number_above_point1 = 0;
    unsigned int concave_number_above_point1 = 0;
    unsigned int convex_number_above_point2 = 0;
    unsigned int concave_number_above_point2 = 0;
    unsigned int convex_assist_above_point1 = 0;
    unsigned int convex_assist_above_point2 = 0;
    unsigned int point_y1 = 0;
    unsigned int point_y2 = 0;
    int number_result = -1;
    bool recognition_flag = false;  //開始手部數(shù)字識(shí)別的標(biāo)志

    vector<Scalar> color_array;//采用默認(rèn)的10種顏色
    {
        color_array.push_back(Scalar(255, 0, 0));
        color_array.push_back(Scalar(0, 255, 0));
        color_array.push_back(Scalar(0, 0, 255));
        color_array.push_back(Scalar(255, 0, 255));
        color_array.push_back(Scalar(255, 255, 0));
        color_array.push_back(Scalar(0, 255, 255));
        color_array.push_back(Scalar(128, 255, 0));
        color_array.push_back(Scalar(0, 128, 255));
        color_array.push_back(Scalar(255, 0, 128));
        color_array.push_back(Scalar(255, 128, 255));
    }
    vector<unsigned int> hand_depth(MAX_HANDS_NUMBER, 0);
    vector<Rect> hands_roi(MAX_HANDS_NUMBER, Rect(XRES/2, YRES/2, ROI_HAND_WIDTH, ROI_HAND_HEIGHT));

    namedWindow("color image", CV_WINDOW_AUTOSIZE);
    namedWindow("depth image", CV_WINDOW_AUTOSIZE);
    namedWindow("hand_segment", CV_WINDOW_AUTOSIZE);    //顯示分割出來的手的區(qū)域
    namedWindow("handrecognition", CV_WINDOW_AUTOSIZE); //顯示0~5數(shù)字識(shí)別的圖像

    COpenNI openni;
    if(!openni.Initial())
        return 1;

    if(!openni.Start())
        return 1;
    while(1) {
        if(!openni.UpdateData()) {
            return 1;
        }
        /*獲取并顯示色彩圖像*/
        Mat color_image_src(openni.image_metadata_.YRes(), openni.image_metadata_.XRes(),
                            CV_8UC3, (char *)openni.image_metadata_.Data());
        Mat color_image;
        cvtColor(color_image_src, color_image, CV_RGB2BGR);
        Mat hand_segment_mask(color_image.size(), CV_8UC1, Scalar::all(0));

        for(auto itUser = openni.hand_points_.cbegin(); itUser != openni.hand_points_.cend(); ++itUser) {

            point_y1 = itUser->second.Y;
            point_y2 = itUser->second.Y + DELTA_POINT_DISTENCE;
            circle(color_image, Point(itUser->second.X, itUser->second.Y),
                   5, color_array.at(itUser->first % color_array.size()), 3, 8);

            /*設(shè)置不同手部的深度*/
            hand_depth.at(itUser->first % MAX_HANDS_COLOR) = (unsigned int)(itUser->second.Z* DEPTH_SCALE_FACTOR);//itUser->first會(huì)導(dǎo)致程序出現(xiàn)bug

            /*設(shè)置不同手部的不同感興趣區(qū)域*/
            hands_roi.at(itUser->first % MAX_HANDS_NUMBER) = Rect(itUser->second.X - ROI_HAND_WIDTH/2, itUser->second.Y - ROI_HAND_HEIGHT/2,
                                               ROI_HAND_WIDTH, ROI_HAND_HEIGHT);
            hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x =  itUser->second.X - ROI_HAND_WIDTH/2;
            hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y =  itUser->second.Y - ROI_HAND_HEIGHT/2;
            hands_roi.at(itUser->first % MAX_HANDS_NUMBER).width = ROI_HAND_WIDTH;
            hands_roi.at(itUser->first % MAX_HANDS_NUMBER).height = ROI_HAND_HEIGHT;
            if(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x <= 0)
                hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x  = 0;
            if(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x > XRES)
                hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x =  XRES;
            if(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y <= 0)
                hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y = 0;
            if(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y > YRES)
                hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y =  YRES;
        }
        imshow("color image", color_image);

        /*獲取并顯示深度圖像*/
        Mat depth_image_src(openni.depth_metadata_.YRes(), openni.depth_metadata_.XRes(),
                            CV_16UC1, (char *)openni.depth_metadata_.Data());//因?yàn)閗inect獲取到的深度圖像實(shí)際上是無符號(hào)的16位數(shù)據(jù)
        Mat depth_image;
        depth_image_src.convertTo(depth_image, CV_8U, DEPTH_SCALE_FACTOR);
        imshow("depth image", depth_image);

        //取出手的mask部分
        //不管原圖像時(shí)多少通道的，mask矩陣聲明為單通道就ok
        for(auto itUser = openni.hand_points_.cbegin(); itUser != openni.hand_points_.cend(); ++itUser) {
            for(int i = hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x; i < std::min(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).x+hands_roi.at(itUser->first % MAX_HANDS_NUMBER).width, XRES); i++)
                for(int j = hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y; j < std::min(hands_roi.at(itUser->first % MAX_HANDS_NUMBER).y+hands_roi.at(itUser->first % MAX_HANDS_NUMBER).height, YRES); j++) {
                    hand_segment_mask.at<unsigned char>(j, i) = ((hand_depth.at(itUser->first % MAX_HANDS_NUMBER)-DEPTH_SEGMENT_THRESH) < depth_image.at<unsigned char>(j, i))
                                                                & ((hand_depth.at(itUser->first % MAX_HANDS_NUMBER)+DEPTH_SEGMENT_THRESH) > depth_image.at<unsigned char>(j,i));
                }
         }
        medianBlur(hand_segment_mask, hand_segment_mask, MEDIAN_BLUR_K);
        Mat hand_segment(color_image.size(), CV_8UC3);
        color_image.copyTo(hand_segment, hand_segment_mask);

        /*對(duì)mask圖像進(jìn)行輪廓提取,并在手勢(shì)識(shí)別圖像中畫出來*/
        std::vector< std::vector<Point> > contours;
        findContours(hand_segment_mask, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);//找出mask圖像的輪廓
        Mat hand_recognition_image = Mat::zeros(color_image.rows, color_image.cols, CV_8UC3);

        for(int i = 0; i < contours.size(); i++) {  //只有在檢測到輪廓時(shí)才會(huì)去求它的多邊形，凸包集，凹陷集
            recognition_flag = true;
            /*找出輪廓圖像多邊形擬合曲線*/
            Mat contour_mat = Mat(contours[i]);
            if(contourArea(contour_mat) > HAND_LIKELY_AREA) {   //比較有可能像手的區(qū)域
                std::vector<Point> approx_poly_curve;
                approxPolyDP(contour_mat, approx_poly_curve, 10, true);//找出輪廓的多邊形擬合曲線
                std::vector< std::vector<Point> > approx_poly_curve_debug;
                approx_poly_curve_debug.push_back(approx_poly_curve);

                 drawContours(hand_recognition_image, contours, i, Scalar(255, 0, 0), 1, 8); //畫出輪廓
    //            drawContours(hand_recognition_image, approx_poly_curve_debug, 0, Scalar(256, 128, 128), 1, 8); //畫出多邊形擬合曲線

                /*對(duì)求出的多邊形擬合曲線求出其凸包集*/
                vector<int> hull;
                convexHull(Mat(approx_poly_curve), hull, true);
                for(int i = 0; i < hull.size(); i++) {
                    circle(hand_recognition_image, approx_poly_curve[hull[i]], 2, Scalar(0, 255, 0), 2, 8);

                    /*統(tǒng)計(jì)在中心點(diǎn)1以上凸點(diǎn)的個(gè)數(shù)*/
                    if(approx_poly_curve[hull[i]].y <= point_y1) {
                        /*統(tǒng)計(jì)凸點(diǎn)與中心點(diǎn)1的y軸距離*/
                        long dis_point1 = abs(long(point_y1 - approx_poly_curve[hull[i]].y));
                        int dis1 = point_y1 - approx_poly_curve[hull[i]].y;
                        if(dis_point1 > SEGMENT_POINT1_DISTANCE && dis1 >= 0)  {
                            convex_assist_above_point1++;
                        }
                        convex_number_above_point1++;
                    }

                    /*統(tǒng)計(jì)在中心點(diǎn)2以上凸點(diǎn)的個(gè)數(shù)*/
                    if(approx_poly_curve[hull[i]].y <= point_y2)    {
                        /*統(tǒng)計(jì)凸點(diǎn)與中心點(diǎn)1的y軸距離*/
                        long dis_point2 = abs(long(point_y2 - approx_poly_curve[hull[i]].y));
                        int dis2 = point_y2 - approx_poly_curve[hull[i]].y;
                        if(dis_point2 > SEGMENT_POINT2_DISTANCE && dis2 >= 0)  {
                            convex_assist_above_point2++;
                        }
                        convex_number_above_point2++;
                    }
                }

    //            /*對(duì)求出的多邊形擬合曲線求出凹陷集*/
                std::vector<Vec4i> convexity_defects;
                if(Mat(approx_poly_curve).checkVector(2, CV_32S) > 3)
                    convexityDefects(approx_poly_curve, Mat(hull), convexity_defects);
                for(int i = 0; i < convexity_defects.size(); i++) {
                    circle(hand_recognition_image, approx_poly_curve[convexity_defects[i][2]] , 2, Scalar(0, 0, 255), 2, 8);

                    /*統(tǒng)計(jì)在中心點(diǎn)1以上凹陷點(diǎn)的個(gè)數(shù)*/
                    if(approx_poly_curve[convexity_defects[i][2]].y <= point_y1)
                        concave_number_above_point1++;

                    /*統(tǒng)計(jì)在中心點(diǎn)2以上凹陷點(diǎn)的個(gè)數(shù)*/
                    if(approx_poly_curve[convexity_defects[i][2]].y <= point_y2)
                        concave_number_above_point2++;
                }
            }
        }

        /**畫出手勢(shì)的中心點(diǎn)**/
        for(auto itUser = openni.hand_points_.cbegin(); itUser != openni.hand_points_.cend(); ++itUser) {
            circle(hand_recognition_image, Point(itUser->second.X, itUser->second.Y), 3, Scalar(0, 255, 255), 3, 8);
            circle(hand_recognition_image, Point(itUser->second.X, itUser->second.Y + 25), 3, Scalar(255, 0, 255), 3, 8);
        }

        /*手勢(shì)數(shù)字0~5的識(shí)別*/
        //"0"的識(shí)別
        if((convex_assist_above_point1 ==0 && convex_number_above_point2 >= 2 && convex_number_above_point2 <= 3 &&
                concave_number_above_point2 <= 1 && concave_number_above_point1 <= 1) || (concave_number_above_point1 ==0
                || concave_number_above_point2 == 0) && recognition_flag == true)
            number_result = 0;
        //"1"的識(shí)別
        if(convex_assist_above_point1 ==1 && convex_number_above_point1 >=1  && convex_number_above_point1 <=2 &&
                convex_number_above_point2 >=2 && convex_assist_above_point2 == 1)
            number_result = 1;
        //"2"的識(shí)別
        if(convex_number_above_point1 == 2 && concave_number_above_point1 == 1 && convex_assist_above_point2 == 2
                /*convex_assist_above_point1 <=1*/ && concave_number_above_point2 == 1)
            number_result = 2;
        //"3"的識(shí)別
        if(convex_number_above_point1 == 3 && concave_number_above_point1 <= 3 &&
                concave_number_above_point1 >=1 && convex_number_above_point2 >= 3 && convex_number_above_point2 <= 4 &&
                convex_assist_above_point2 == 3)
            number_result = 3;
        //"4"的識(shí)別
        if(convex_number_above_point1 == 4 && concave_number_above_point1 <=3 && concave_number_above_point1 >=2 &&
                convex_number_above_point2 == 4)
            number_result = 4;
        //"5"的識(shí)別
        if(convex_number_above_point1 >=4 && convex_number_above_point2 == 5 && concave_number_above_point2 >= 3 &&
                convex_number_above_point2 >= 4)
            number_result = 5;
        if(number_result !=0 && number_result != 1  && number_result != 2 && number_result != 3 && number_result != 4 && number_result != 5)
            number_result == -1;

        /*在手勢(shì)識(shí)別圖上顯示匹配的數(shù)字*/
        std::stringstream number_str;
        number_str << number_result;
        putText(hand_recognition_image, "Match: ", Point(0, 60), 4, 1, Scalar(0, 255, 0), 2, 0 );
        if(number_result == -1)
            putText(hand_recognition_image, " ", Point(120, 60), 4, 2, Scalar(255, 0 ,0), 2, 0);
        else
            putText(hand_recognition_image, number_str.str(), Point(150, 60), 4, 2, Scalar(255, 0 ,0), 2, 0);

        imshow("handrecognition", hand_recognition_image);
        imshow("hand_segment", hand_segment);

        /*一個(gè)循環(huán)中對(duì)有些變量進(jìn)行初始化操作*/
        convex_number_above_point1 = 0;
        convex_number_above_point2 = 0;
        concave_number_above_point1 = 0;
        concave_number_above_point2 = 0;
        convex_assist_above_point1 = 0;
        convex_assist_above_point2 = 0;
        number_result = -1;
        recognition_flag = false;
        number_str.clear();

        waitKey(20);
    }

}

# 作者：丹成學(xué)長 Q746876041， 需要完整代碼聯(lián)系學(xué)長獲取
import tensorflow as tf
IMAGE_SIZE = 100
NUM_CHANNELS = 1
CONV1_SIZE = 4
CONV1_KERNEL_NUM = 8
CONV2_SIZE = 2
CONV2_KERNEL_NUM = 16
FC_SIZE = 512
OUTPUT_NODE = 6

def get_weight(shape, regularizer):
	w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
	if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w)) 
	return w

def get_bias(shape): 
	b = tf.Variable(tf.zeros(shape))  
	return b

def conv2d(x,w):  
	return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_8x8(x):  
	return tf.nn.max_pool(x, ksize=[1, 8, 8, 1], strides=[1, 4, 4, 1], padding='SAME')

def max_pool_4x4(x):  
	return tf.nn.max_pool(x, ksize=[1, 4, 4, 1], strides=[1, 2, 2, 1], padding='SAME')

def forward(x, train, regularizer):
    
    conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer) 
    conv1_b = get_bias([CONV1_KERNEL_NUM]) 
    conv1 = conv2d(x, conv1_w) 
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b)) 
    pool1 = max_pool_8x8(relu1) 

    conv2_w = get_weight([CONV2_SIZE, CONV2_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM],regularizer) 
    conv2_b = get_bias([CONV2_KERNEL_NUM])
    conv2 = conv2d(pool1, conv2_w) 
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_b))
    pool2 = max_pool_4x4(relu2)

    pool_shape = pool2.get_shape().as_list() 
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3] 
    reshaped = tf.reshape(pool2, [pool_shape[0], nodes]) 

    fc1_w = get_weight([nodes, FC_SIZE], regularizer) 
    fc1_b = get_bias([FC_SIZE]) 
    fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b) 
    if train: fc1 = tf.nn.dropout(fc1, 0.5)

    fc2_w = get_weight([FC_SIZE, OUTPUT_NODE], regularizer)
    fc2_b = get_bias([OUTPUT_NODE])
    y = tf.matmul(fc1, fc2_w) + fc2_b
    return y 

# 作者：丹成學(xué)長 Q746876041， 需要完整代碼聯(lián)系學(xué)長獲取
import tensorflow as tf
import numpy as np
import gesture_forward
import gesture_backward
from image_processing import func5,func6
import cv2

def restore_model(testPicArr):
    with tf.Graph().as_default() as tg:
        
        x = tf.placeholder(tf.float32,[
            1,
            gesture_forward.IMAGE_SIZE,
            gesture_forward.IMAGE_SIZE,
            gesture_forward.NUM_CHANNELS])    
        #y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])
        y = gesture_forward.forward(x,False,None)
        
        preValue = tf.argmax(y, 1)

        variable_averages = tf.train.ExponentialMovingAverage(gesture_backward.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)
        
        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(gesture_backward.MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
                #global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] 
                preValue = sess.run(preValue, feed_dict={x:testPicArr})
                return preValue
            else:
                print("No checkpoint file found")
                return -1



def application01():
    testNum = input("input the number of test pictures:")
    testNum = int(testNum)
    for i in range(testNum):
        testPic = input("the path of test picture:")
        img = func5(testPic)
        cv2.imwrite(str(i)+'ttt.jpg',img)   
#        cv2.waitKey(0)
#        cv2.destroyAllWindows()
        img = img.reshape([1,100,100,1])
        img = img.astype(np.float32)
        img = np.multiply(img, 1.0/255.0)
#        print(img.shape)
#        print(type(img))        
        preValue = restore_model(img)
        print ("The prediction number is:", preValue)

def application02():
    
    #vc = cv2.VideoCapture('testVideo.mp4')
    vc = cv2.VideoCapture(0)
    # 設(shè)置每秒傳輸幀數(shù)
    fps = vc.get(cv2.CAP_PROP_FPS)
    # 獲取視頻的大小
    size = (int(vc.get(cv2.CAP_PROP_FRAME_WIDTH)),int(vc.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    # 生成一個(gè)空的視頻文件
    # 視頻編碼類型
    # cv2.VideoWriter_fourcc('X','V','I','D') MPEG-4 編碼類型
    # cv2.VideoWriter_fourcc('I','4','2','0') YUY編碼類型
    # cv2.VideoWriter_fourcc('P','I','M','I') MPEG-1 編碼類型
    # cv2.VideoWriter_fourcc('T','H','E','O') Ogg Vorbis類型，文件名為.ogv
    # cv2.VideoWriter_fourcc('F','L','V','1') Flask視頻，文件名為.flv
    #vw = cv2.VideoWriter('ges_pro.avi',cv2.VideoWriter_fourcc('X','V','I','D'), fps, size)
    # 讀取視頻第一幀的內(nèi)容
    success, frame = vc.read()
#    rows = frame.shape[0]    
#    cols = frame.shape[1]
#    t1 = int((cols-rows)/2)
#    t2 = int(cols-t1)
#    M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
#    frame = cv2.warpAffine(frame,M,(cols,rows))
#    frame = frame[0:rows, t1:t2]
#    cv2.imshow('sd',frame)
#    cv2.waitKey(0)
#    cv2.destroyAllWindows()
    while success:
        
        #90度旋轉(zhuǎn)        
#        img = cv2.warpAffine(frame,M,(cols,rows))
#        img = img[0:rows, t1:t2]
        img = func6(frame)
        img = img.reshape([1,100,100,1])
        img = img.astype(np.float32)
        img = np.multiply(img, 1.0/255.0)
        preValue = restore_model(img)
        # 寫入視頻
        cv2.putText(frame,"Gesture:"+str(preValue),(50,50),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),1)
        #vw.write(frame)
        cv2.imshow('gesture',frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        # 讀取視頻下一幀的內(nèi)容
        success, frame = vc.read()
    
    vc.release()
    cv2.destroyAllWindows()    
    print('viedo app over!')


def main():
    #application01()
    application02()
    
if __name__ == '__main__':
	main()