基于opencv.js实现二维码定位
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通过分析OpenCV.JS（官方下载地址 https://docs.opencv.org/_VERSION_/opencv.js)的白名单，我们可以了解目前官方PreBuild版本并没有实现QR识别。但是我们仍然可以通过轮廓分析的相关方法，去实现“基于opencv.js实现二维码定位”，这就是本篇BLOG的主要内容。
通过分析OpenCV.JS（官方下载地址 https://docs.opencv.org/_VERSION_/opencv.js)的白名单，我们可以了解目前官方PreBuild版本并没有实现QR识别。
# Classes and methods whitelist
core = {\'\': [\'absdiff\', \'add\', \'addWeighted\', \'bitwise_and\', \'bitwise_not\', \'bitwise_or\', \'bitwise_xor\', \'cartToPolar\',\\
\'compare\', \'convertScaleAbs\', \'copyMakeBorder\', \'countNonZero\', \'determinant\', \'dft\', \'divide\', \'eigen\', \\
\'exp\', \'flip\', \'getOptimalDFTSize\',\'gemm\', \'hconcat\', \'inRange\', \'invert\', \'kmeans\', \'log\', \'magnitude\', \\
\'max\', \'mean\', \'meanStdDev\', \'merge\', \'min\', \'minMaxLoc\', \'mixChannels\', \'multiply\', \'norm\', \'normalize\', \\
\'perspectiveTransform\', \'polarToCart\', \'pow\', \'randn\', \'randu\', \'reduce\', \'repeat\', \'rotate\', \'setIdentity\', \'setRNGSeed\', \\
\'solve\', \'solvePoly\', \'split\', \'sqrt\', \'subtract\', \'trace\', \'transform\', \'transpose\', \'vconcat\'],
\'Algorithm\': []}
imgproc = {\'\': [\'Canny\', \'GaussianBlur\', \'Laplacian\', \'HoughLines\', \'HoughLinesP\', \'HoughCircles\', \'Scharr\',\'Sobel\', \\
\'adaptiveThreshold\',\'approxPolyDP\',\'arcLength\',\'bilateralFilter\',\'blur\',\'boundingRect\',\'boxFilter\',\\
\'calcBackProject\',\'calcHist\',\'circle\',\'compareHist\',\'connectedComponents\',\'connectedComponentsWithStats\', \\
\'contourArea\', \'convexHull\', \'convexityDefects\', \'cornerHarris\',\'cornerMinEigenVal\',\'createCLAHE\', \\
\'createLineSegmentDetector\',\'cvtColor\',\'demosaicing\',\'dilate\', \'distanceTransform\',\'distanceTransformWithLabels\', \\
\'drawContours\',\'ellipse\',\'ellipse2Poly\',\'equalizeHist\',\'erode\', \'filter2D\', \'findContours\',\'fitEllipse\', \\
\'fitLine\', \'floodFill\',\'getAffineTransform\', \'getPerspectiveTransform\', \'getRotationMatrix2D\', \'getStructuringElement\', \\
\'goodFeaturesToTrack\',\'grabCut\',\'initUndistortRectifyMap\', \'integral\',\'integral2\', \'isContourConvex\', \'line\', \\
\'matchShapes\', \'matchTemplate\',\'medianBlur\', \'minAreaRect\', \'minEnclosingCircle\', \'moments\', \'morphologyEx\', \\
\'pointPolygonTest\', \'putText\',\'pyrDown\',\'pyrUp\',\'rectangle\',\'remap\', \'resize\',\'sepFilter2D\',\'threshold\', \\
\'undistort\',\'warpAffine\',\'warpPerspective\',\'warpPolar\',\'watershed\', \\
\'fillPoly\', \'fillConvexPoly\'],
\'CLAHE\': [\'apply\', \'collectGarbage\', \'getClipLimit\', \'getTilesGridSize\', \'setClipLimit\', \'setTilesGridSize\']}
objdetect = {\'\': [\'groupRectangles\'],
\'HOGDescriptor\': [\'load\', \'HOGDescriptor\', \'getDefaultPeopleDetector\', \'getDaimlerPeopleDetector\', \'setSVMDetector\', \'detectMultiScale\'],
\'CascadeClassifier\': [\'load\', \'detectMultiScale2\', \'CascadeClassifier\', \'detectMultiScale3\', \'empty\', \'detectMultiScale\']}
video = {\'\': [\'CamShift\', \'calcOpticalFlowFarneback\', \'calcOpticalFlowPyrLK\', \'createBackgroundSubtractorMOG2\', \\
\'findTransformECC\', \'meanShift\'],
\'BackgroundSubtractorMOG2\': [\'BackgroundSubtractorMOG2\', \'apply\'],
\'BackgroundSubtractor\': [\'apply\', \'getBackgroundImage\']}
dnn = {\'dnn_Net\': [\'setInput\', \'forward\'],
\'\': [\'readNetFromCaffe\', \'readNetFromTensorflow\', \'readNetFromTorch\', \'readNetFromDarknet\',
\'readNetFromONNX\', \'readNet\', \'blobFromImage\']}
features2d = {\'Feature2D\': [\'detect\', \'compute\', \'detectAndCompute\', \'descriptorSize\', \'descriptorType\', \'defaultNorm\', \'empty\', \'getDefaultName\'],
\'BRISK\': [\'create\', \'getDefaultName\'],
\'ORB\': [\'create\', \'setMaxFeatures\', \'setScaleFactor\', \'setNLevels\', \'setEdgeThreshold\', \'setFirstLevel\', \'setWTA_K\', \'setScoreType\', \'setPatchSize\', \'getFastThreshold\', \'getDefaultName\'],
\'MSER\': [\'create\', \'detectRegions\', \'setDelta\', \'getDelta\', \'setMinArea\', \'getMinArea\', \'setMaxArea\', \'getMaxArea\', \'setPass2Only\', \'getPass2Only\', \'getDefaultName\'],
\'FastFeatureDetector\': [\'create\', \'setThreshold\', \'getThreshold\', \'setNonmaxSuppression\', \'getNonmaxSuppression\', \'setType\', \'getType\', \'getDefaultName\'],
\'AgastFeatureDetector\': [\'create\', \'setThreshold\', \'getThreshold\', \'setNonmaxSuppression\', \'getNonmaxSuppression\', \'setType\', \'getType\', \'getDefaultName\'],
\'GFTTDetector\': [\'create\', \'setMaxFeatures\', \'getMaxFeatures\', \'setQualityLevel\', \'getQualityLevel\', \'setMinDistance\', \'getMinDistance\', \'setBlockSize\', \'getBlockSize\', \'setHarrisDetector\', \'getHarrisDetector\', \'setK\', \'getK\', \'getDefaultName\'],
# \'SimpleBlobDetector\': [\'create\'],
\'KAZE\': [\'create\', \'setExtended\', \'getExtended\', \'setUpright\', \'getUpright\', \'setThreshold\', \'getThreshold\', \'setNOctaves\', \'getNOctaves\', \'setNOctaveLayers\', \'getNOctaveLayers\', \'setDiffusivity\', \'getDiffusivity\', \'getDefaultName\'],
\'AKAZE\': [\'create\', \'setDescriptorType\', \'getDescriptorType\', \'setDescriptorSize\', \'getDescriptorSize\', \'setDescriptorChannels\', \'getDescriptorChannels\', \'setThreshold\', \'getThreshold\', \'setNOctaves\', \'getNOctaves\', \'setNOctaveLayers\', \'getNOctaveLayers\', \'setDiffusivity\', \'getDiffusivity\', \'getDefaultName\'],
\'DescriptorMatcher\': [\'add\', \'clear\', \'empty\', \'isMaskSupported\', \'train\', \'match\', \'knnMatch\', \'radiusMatch\', \'clone\', \'create\'],
\'BFMatcher\': [\'isMaskSupported\', \'create\'],
\'\': [\'drawKeypoints\', \'drawMatches\', \'drawMatchesKnn\']}
photo = {\'\': [\'createAlignMTB\', \'createCalibrateDebevec\', \'createCalibrateRobertson\', \\
\'createMergeDebevec\', \'createMergeMertens\', \'createMergeRobertson\', \\
\'createTonemapDrago\', \'createTonemapMantiuk\', \'createTonemapReinhard\', \'inpaint\'],
\'CalibrateCRF\': [\'process\'],
\'AlignMTB\' : [\'calculateShift\', \'shiftMat\', \'computeBitmaps\', \'getMaxBits\', \'setMaxBits\', \\
\'getExcludeRange\', \'setExcludeRange\', \'getCut\', \'setCut\'],
\'CalibrateDebevec\' : [\'getLambda\', \'setLambda\', \'getSamples\', \'setSamples\', \'getRandom\', \'setRandom\'],
\'CalibrateRobertson\' : [\'getMaxIter\', \'setMaxIter\', \'getThreshold\', \'setThreshold\', \'getRadiance\'],
\'MergeExposures\' : [\'process\'],
\'MergeDebevec\' : [\'process\'],
\'MergeMertens\' : [\'process\', \'getContrastWeight\', \'setContrastWeight\', \'getSaturationWeight\', \\
\'setSaturationWeight\', \'getExposureWeight\', \'setExposureWeight\'],
\'MergeRobertson\' : [\'process\'],
\'Tonemap\' : [\'process\' , \'getGamma\', \'setGamma\'],
\'TonemapDrago\' : [\'getSaturation\', \'setSaturation\', \'getBias\', \'setBias\', \\
\'getSigmaColor\', \'setSigmaColor\', \'getSigmaSpace\',\'setSigmaSpace\'],
\'TonemapMantiuk\' : [\'getScale\', \'setScale\', \'getSaturation\', \'setSaturation\'],
\'TonemapReinhard\' : [\'getIntensity\', \'setIntensity\', \'getLightAdaptation\', \'setLightAdaptation\', \\
\'getColorAdaptation\', \'setColorAdaptation\']
}
aruco = {\'\': [\'detectMarkers\', \'drawDetectedMarkers\', \'drawAxis\', \'estimatePoseSingleMarkers\', \'estimatePoseBoard\', \'estimatePoseCharucoBoard\', \'interpolateCornersCharuco\', \'drawDetectedCornersCharuco\'],
\'aruco_Dictionary\': [\'get\', \'drawMarker\'],
\'aruco_Board\': [\'create\'],
\'aruco_GridBoard\': [\'create\', \'draw\'],
\'aruco_CharucoBoard\': [\'create\', \'draw\'],
}
calib3d = {\'\': [\'findHomography\', \'calibrateCameraExtended\', \'drawFrameAxes\', \'estimateAffine2D\', \'getDefaultNewCameraMatrix\', \'initUndistortRectifyMap\', \'Rodrigues\']}
white_list = makeWhiteList([core, imgproc, objdetect, video, dnn, features2d, photo, aruco, calib3d]) 但是我们仍然可以通过轮廓分析的相关方法，去实现“基于opencv.js实现二维码定位”，这就是本篇BLOG的主要内容。
一、基本原理
主要内容请参考《OpenCV使用FindContours进行二维码定位》，这里重要的回顾一下。
使用过FindContours直接寻找联通区域的函数。典型的运用在二维码上面：
对于它的3个定位点
，这种重复包含的特性，在图上只有不容易重复的三处，这是具有排它性的。
，这种重复包含的特性，在图上只有不容易重复的三处，这是具有排它性的。 那么轮廓识别的结果是如何展示的了？比如在这幅图中（白色区域为有数据的区域，黑色为无数据),0,1,2是第一层，然后里面是3，3的里面是4和5。(2a表示是2的内部)，他们的关系应该是这样的：
所以我们只需要寻找某一个轮廓“有无爷爷轮廓”，就可以判断出来它是否“重复包含”
值得参考的C++代码应该是这样的，其中注释部分已经说明的比较清楚。
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
using namespace cv;
using namespace std;
//找到所提取轮廓的中心点
//在提取的中心小正方形的边界上每隔周长个像素提取一个点的坐标，求所提取四个点的平均坐标（即为小正方形的大致中心）
Point Center_cal(vector<vector<Point> > contours,int i)
{
int centerx=0,centery=0,n=contours[i].size();
centerx = (contours[i][n/4].x + contours[i][n*2/4].x + contours[i][3*n/4].x + contours[i][n-1].x)/4;
centery = (contours[i][n/4].y + contours[i][n*2/4].y + contours[i][3*n/4].y + contours[i][n-1].y)/4;
Point point1=Point(centerx,centery);
return point1;
}
int main( int argc, char** argv[] )
{
Mat src = imread( "e:/sandbox/qrcode.jpg", 1 );
resize(src,src,Size(800,600));//标准大小
Mat src_gray;
Mat src_all=src.clone();
Mat threshold_output;
vector<vector<Point> > contours,contours2;
vector<Vec4i> hierarchy;
//预处理
cvtColor( src, src_gray, CV_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) ); //模糊，去除毛刺
threshold( src_gray, threshold_output, 100, 255, THRESH_OTSU );
//寻找轮廓
//第一个参数是输入图像 2值化的
//第二个参数是内存存储器，FindContours找到的轮廓放到内存里面。
//第三个参数是层级，**[Next, Previous, First_Child, Parent]** 的vector
//第四个参数是类型，采用树结构
//第五个参数是节点拟合模式，这里是全部寻找
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CHAIN_APPROX_NONE, Point(0, 0) );
//轮廓筛选
int c=0,ic=0,area=0;
int parentIdx=-1;
for( int i = 0; i< contours.size(); i++ )
{
//hierarchy[i][2] != -1 表示不是最外面的轮廓
if (hierarchy[i][2] != -1 && ic==0)
{
parentIdx = i;
ic++;
}
else if (hierarchy[i][2] != -1)
{
ic++;
}
//最外面的清0
else if(hierarchy[i][2] == -1)
{
ic = 0;
parentIdx = -1;
}
//找到定位点信息
if ( ic >= 2)
{
contours2.push_back(contours[parentIdx]);
ic = 0;
parentIdx = -1;
}
}
//填充定位点
for(int i=0; i<contours2.size(); i++)
drawContours( src_all, contours2, i, CV_RGB(0,255,0) , -1 );
//连接定位点
Point point[3];
for(int i=0; i<contours2.size(); i++)
{
point[i] = Center_cal( contours2, i );
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