OpenCV是什么
OpenCV是一个基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows、Android和Mac OS操作系统上。它轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法。
如何在iOS开发中使用OpenCV
1.在OpenCV的官网下载iOS端的framework 地址：http://opencv.org/releases.html
2.将下载好的framework拖进Xcode，因为OpenCV是C++编写，所以需要在代码里面进行一些修改。将引入OpenCV头文件的.m文件后缀改为.mm。
人脸匹配是怎样实现的？
将包含人脸的图片作为基准图，然后与匹配图片做直方图比对
实现代码
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
#import <opencv2/videoio/cap_ios.h>
#import <opencv2/imgproc/imgproc_c.h>
#import <opencv2/core/core_c.h>
#import <opencv2/features2d/features2d.hpp>
@interface ViewController ()
@property(nonatomic,strong) UIImageView* imageView;
@property(nonatomic,strong) UIImageView* imageView1;
@end
@implementation ViewController
- (void)viewDidLoad {
[super viewDidLoad];
//识别的图片
UIImage *mImage = [UIImage imageNamed:@"31.jpeg"];
IplImage *srcIpl = [self convertToIplImage:mImage];
IplImage *dscIpl = cvCreateImage(cvGetSize(srcIpl), srcIpl->depth, 1);
IplImage *dscIplNew = cvCreateImage(cvGetSize(srcIpl), IPL_DEPTH_8U, 3);
cvCvtColor(dscIpl, dscIplNew, CV_GRAY2BGR);
//基准图
UIImage *mImage1 = [UIImage imageNamed:@"32.jpeg"];
IplImage *srcIpl1 = [self convertToIplImage:mImage1];
IplImage *dscIpl1 = cvCreateImage(cvGetSize(srcIpl1), srcIpl1 ->depth, 1);
IplImage *dscIplNew1 = cvCreateImage(cvGetSize(srcIpl1), IPL_DEPTH_8U, 3);
cvCvtColor(dscIpl1, dscIplNew1, CV_GRAY2BGR);
//基准图2
UIImage *tempImage = [UIImage imageNamed:@"30.jpeg"];
IplImage *iplTempImage = [self convertToIplImage:tempImage];
BOOL tf=[self ComparePPKImage:srcIpl withAnotherImage:srcIpl1 withTempleImage:iplTempImage];
if (tf) {
printf("匹配成功\n");
}else
{
printf("匹配失败\n");
}
}
//图片匹配
-(BOOL)ComparePPKImage:(IplImage*)mIplImage withAnotherImage:(IplImage*)mIplImage1 withTempleImage:(IplImage*)mTempleImage
{
//第一次模板标记
CvPoint minLoc =[self CompareTempleImage:mTempleImage withImage:mIplImage];
if (minLoc.x==mIplImage->width || minLoc.y==mIplImage->height) {
printf("第一个图片的模板标记失败\n");
return false;
}
//第二次模板标记
CvPoint minLoc1 =[self CompareTempleImage:mTempleImage withImage:mIplImage1];
if (minLoc1.x==mIplImage1->width || minLoc1.y==mIplImage1->height) {
printf("第二个图片的模板标记失败\n");
return false;
}
//裁切图片
IplImage *cropImage,*cropImage1;
cropImage =[self cropIplImage:mIplImage withStartPoint:minLoc withWidth:mTempleImage->width withHeight:mTempleImage->height];
cropImage1=[self cropIplImage:mIplImage1 withStartPoint:minLoc1 withWidth:mTempleImage->width withHeight:mTempleImage->height];
self.imageView.image=[self convertToUIImage:cropImage];
self.imageView1.image=[self convertToUIImage:cropImage1];
double rst = [self CompareHist:cropImage withParam2:cropImage1];
if (rst<0.18) {
return true;
}
else
{
return false;
}
}
/// 基于模板图片的标记识别
-(CvPoint)CompareTempleImage:(IplImage*)templeIpl withImage:(IplImage*)mIplImage
{
IplImage *src = mIplImage;
IplImage *templat = templeIpl;
IplImage *result;
int srcW, srcH, templatW, templatH, resultH, resultW;
srcW = src->width;
srcH = src->height;
templatW = templat->width;
templatH = templat->height;
resultW = srcW - templatW + 1;
resultH = srcH - templatH + 1;
result = cvCreateImage(cvSize(resultW, resultH), 32, 1);
cvMatchTemplate(src, templat, result, CV_TM_SQDIFF);
double minValue, maxValue;
CvPoint minLoc, maxLoc;
cvMinMaxLoc(result, &minValue, &maxValue, &minLoc, &maxLoc);
if (minLoc.y+templatH>srcH || minLoc.x+templatW>srcW) {
printf("未找到标记图片\n");
minLoc.x=srcW;
minLoc.y=srcH;
}
return minLoc;
}
// Do any additional setup after loading the view, typically from a nib.
/// UIImage类型转换为IPlImage类型
-(IplImage*)convertToIplImage:(UIImage*)image
{
CGImageRef imageRef = image.CGImage;
CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
IplImage *iplImage = cvCreateImage(cvSize(image.size.width, image.size.height), IPL_DEPTH_8U, 4);
CGContextRef contextRef = CGBitmapContextCreate(iplImage->imageData, iplImage->width, iplImage->height, iplImage->depth, iplImage->widthStep, colorSpace, kCGImageAlphaPremultipliedLast|kCGBitmapByteOrderDefault);
CGContextDrawImage(contextRef, CGRectMake(0, 0, image.size.width, image.size.height), imageRef);
CGContextRelease(contextRef);
CGColorSpaceRelease(colorSpace);
IplImage *ret = cvCreateImage(cvGetSize(iplImage), IPL_DEPTH_8U, 3);
cvCvtColor(iplImage, ret, CV_RGB2BGR);
cvReleaseImage(&iplImage);
return ret;
}
/// IplImage类型转换为UIImage类型
-(UIImage*)convertToUIImage:(IplImage*)image
{
cvCvtColor(image, image, CV_BGR2RGB);
CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
NSData *data = [NSData dataWithBytes:image->imageData length:image->imageSize];
CGDataProviderRef provider = CGDataProviderCreateWithCFData((CFDataRef)data);
CGImageRef imageRef = CGImageCreate(image->width, image->height, image->depth, image->depth * image->nChannels, image->widthStep, colorSpace, kCGImageAlphaNone | kCGBitmapByteOrderDefault, provider, NULL, false, kCGRenderingIntentDefault);
UIImage *ret = [UIImage imageWithCGImage:imageRef];
CGImageRelease(imageRef);
CGDataProviderRelease(provider);
CGColorSpaceRelease(colorSpace);
return ret;
}
-(IplImage*)cropIplImage:(IplImage*)srcIpl withStartPoint:(CvPoint)mPoint withWidth:(int)width withHeight:(int)height
{
//裁剪后的图片
IplImage *cropImage;
cvSetImageROI(srcIpl, cvRect(mPoint.x, mPoint.y, width, height));
cropImage = cvCreateImage(cvGetSize(srcIpl), IPL_DEPTH_8U, 3);
cvCopy(srcIpl, cropImage);
cvResetImageROI(srcIpl);
return cropImage;
}
// 多通道彩色图片的直方图比对
-(double)CompareHist:(IplImage*)image1 withParam2:(IplImage*)image2
{
int hist_size = 256;
IplImage *gray_plane = cvCreateImage(cvGetSize(image1), 8, 1);
cvCvtColor(image1, gray_plane, CV_BGR2GRAY);
float range[] = {0,255}; //灰度级的范围
float* ranges[]={range};
CvHistogram *gray_hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY,ranges,1);
cvCalcHist(&gray_plane, gray_hist);
IplImage *gray_plane2 = cvCreateImage(cvGetSize(image2), 8, 1);
cvCvtColor(image2, gray_plane2, CV_BGR2GRAY);
CvHistogram *gray_hist2 = cvCreateHist(1, &hist_size, CV_HIST_ARRAY,ranges,1);
cvCalcHist(&gray_plane2, gray_hist2);
double rst =cvCompareHist(gray_hist, gray_hist2, CV_COMP_BHATTACHARYYA);
printf("对比结果=%f\n",rst);
return rst;
}
注意事项
运行时出现下图错误，你会发现报错的是
enum { NO, GAIN, GAIN_BLOCKS };
这句代码，不要着急，用这句代码替换即可
enum { NO_EXPOSURE_COMPENSATOR = 0, GAIN, GAIN_BLOCKS };
错误截图
缺陷
该种方法对图片相似度要求较高，当背景差异较大或者干扰因素较多时，无法匹配成功
demo地址
https://github.com/Fairy-happy/Picture-matchingWithOpenCV
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