[iOS OpenCV的使用，灰度和二值化]
看网上方法很多，但版本都不够新，我看了网上一些知识，总结了下，来个最新版Xcode6.1的.
最近主要想做iOS端的车牌识别，所以开始了解OpenCV。有兴趣的可以跟我交流下哈。
一.Opencv的使用：
步骤：
1.从官网下载iOS版本的Opencv2.framework。
2.拖进工程，选择copy items if needed
3.进入building settings，设置Framework SearchPath:
设置成$(PROJECT_DIR)/Newtest，这个Newtest是你的项目名，主要是为了定位到你存放的Opencv2.framework所在位置。
4.使用Opencv的方式：第（1）种全局pch：（不推荐）新建pch文件，修改成：
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
并在building setting里的 Incease Sharing of Precompiled Headers项目处：
设置成$(PROJECT_DIR)/Newtest，同理，这个Newtest是你的项目名，主要是为了定位到你存放的PCH文件所在位置。
PCH文件以前建工程默认生成，是全局性质的import。Xcode6不再自动生成。苹果引导开发者在某个类要用时才用。
第（2）种：在需要的地方#import <opencv2/opencv.hpp>
这里的重点是：使用opencv的类名一定要改成.mm！！
比如你专门写了各一个处理图片的类，Imageprocess。可以在.h里加入。
二：灰度化和二值化的主要实现过程：
其实过程就是这样：
UIImage（iOS图像类）-> cv::Mat（OpenCV图像类） -> Opencv灰度或二值处理函数 -> UIImage
三：Opencv类Imageprocess代码参考：
Imageprocess.h
// // Imageprocess.h // Chepaishibie // // Created by shen on 15/1/28. // Copyright (c) 2015年 shen. All rights reserved. // #import <Foundation/Foundation.h> #import <opencv2/opencv.hpp> #import <UIKit/UIKit.h>@interface Imageprocess : UIViewController- (cv::Mat)cvMatFromUIImage:(UIImage *)image;- (UIImage *)UIImageFromCVMat:(cv::Mat)cvMat;- (IplImage *)CreateIplImageFromUIImage:(UIImage *)image;- (UIImage *)UIImageFromIplImage:(IplImage *)image;- (UIImage *)Grayimage:(UIImage *)srcimage;- (UIImage *)Erzhiimage:(UIImage *)srcimage;int Otsu(unsigned char* pGrayImg , int iWidth , int iHeight);@end
Imageprocess.mm 里面包含了很多函数：
主要是 UIImage->cv::Mat ，cv::Mat->UIImage，UIImage->IplImage，IplImage->UIImage， 灰度化，二值化等，还有个OSTU计算阈值的方法。
//
// Imageprocess.mm
// Chepaishibie
//
// Created by shen on 15/1/28.
// Copyright (c) 2015年 shen. All rights reserved.
//
#import "Imageprocess.h"@implementation Imageprocess#pragma mark - opencv method
// UIImage to cvMat
- (cv::Mat)cvMatFromUIImage:(UIImage *)image
{CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);CGFloat cols = image.size.width;CGFloat rows = image.size.height;cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to datacols, // Width of bitmaprows, // Height of bitmap8, // Bits per componentcvMat.step[0], // Bytes per rowcolorSpace, // ColorspacekCGImageAlphaNoneSkipLast |kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);CGContextRelease(contextRef);CGColorSpaceRelease(colorSpace);return cvMat;
}// CvMat to UIImage
-(UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
{NSData *data = [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];CGColorSpaceRef colorSpace;if (cvMat.elemSize() == 1) {colorSpace = CGColorSpaceCreateDeviceGray();} else {colorSpace = CGColorSpaceCreateDeviceRGB();}CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);// Creating CGImage from cv::MatCGImageRef imageRef = CGImageCreate(cvMat.cols, //widthcvMat.rows, //height8, //bits per component8 * cvMat.elemSize(), //bits per pixelcvMat.step[0], //bytesPerRowcolorSpace, //colorspacekCGImageAlphaNone|kCGBitmapByteOrderDefault,// bitmap infoprovider, //CGDataProviderRefNULL, //decodefalse, //should interpolatekCGRenderingIntentDefault //intent
);// Getting UIImage from CGImageUIImage *finalImage = [UIImage imageWithCGImage:imageRef];CGImageRelease(imageRef);CGDataProviderRelease(provider);CGColorSpaceRelease(colorSpace);return finalImage;
}//由于OpenCV主要针对的是计算机视觉方面的处理，因此在函数库中，最重要的结构体是IplImage结构。
// NOTE you SHOULD cvReleaseImage() for the return value when end of the code.
- (IplImage *)CreateIplImageFromUIImage:(UIImage *)image {// Getting CGImage from UIImageCGImageRef imageRef = image.CGImage;CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();// Creating temporal IplImage for drawingIplImage *iplimage = cvCreateImage(cvSize(image.size.width,image.size.height), IPL_DEPTH_8U, 4);// Creating CGContext for temporal IplImageCGContextRef contextRef = CGBitmapContextCreate(iplimage->imageData, iplimage->width, iplimage->height,iplimage->depth, iplimage->widthStep,colorSpace, kCGImageAlphaPremultipliedLast|kCGBitmapByteOrderDefault);// Drawing CGImage to CGContext
CGContextDrawImage(contextRef,CGRectMake(0, 0, image.size.width, image.size.height),imageRef);CGContextRelease(contextRef);CGColorSpaceRelease(colorSpace);// Creating result IplImageIplImage *ret = cvCreateImage(cvGetSize(iplimage), IPL_DEPTH_8U, 3);cvCvtColor(iplimage, ret, CV_RGBA2BGR);cvReleaseImage(&iplimage);return ret;
}// NOTE You should convert color mode as RGB before passing to this function
- (UIImage *)UIImageFromIplImage:(IplImage *)image {CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();// Allocating the buffer for CGImageNSData *data =[NSData dataWithBytes:image->imageData length:image->imageSize];CGDataProviderRef provider =CGDataProviderCreateWithCFData((__bridge CFDataRef)data);// Creating CGImage from chunk of IplImageCGImageRef imageRef = CGImageCreate(image->width, image->height,image->depth, image->depth * image->nChannels, image->widthStep,colorSpace, kCGImageAlphaNone|kCGBitmapByteOrderDefault,provider, NULL, false, kCGRenderingIntentDefault);// Getting UIImage from CGImageUIImage *ret = [UIImage imageWithCGImage:imageRef];CGImageRelease(imageRef);CGDataProviderRelease(provider);CGColorSpaceRelease(colorSpace);return ret;
}#pragma mark - custom method// OSTU算法求出阈值
int Otsu(unsigned char* pGrayImg , int iWidth , int iHeight)
{if((pGrayImg==0)||(iWidth<=0)||(iHeight<=0))return -1;int ihist[256];int thresholdValue=0; // „–÷µint n, n1, n2 ;double m1, m2, sum, csum, fmax, sb;int i,j,k;memset(ihist, 0, sizeof(ihist));n=iHeight*iWidth;sum = csum = 0.0;fmax = -1.0;n1 = 0;for(i=0; i < iHeight; i++){for(j=0; j < iWidth; j++){ihist[*pGrayImg]++;pGrayImg++;}}pGrayImg -= n;for (k=0; k <= 255; k++){sum += (double) k * (double) ihist[k];}for (k=0; k <=255; k++){n1 += ihist[k];if(n1==0)continue;n2 = n - n1;if(n2==0)break;csum += (double)k *ihist[k];m1 = csum/n1;m2 = (sum-csum)/n2;sb = (double) n1 *(double) n2 *(m1 - m2) * (m1 - m2);if (sb > fmax){fmax = sb;thresholdValue = k;}}return(thresholdValue);
}-(UIImage *)Grayimage:(UIImage *)srcimage{UIImage *resimage;//openCV二值化过程：/*//1.Src的UIImage -> Src的IplImageIplImage* srcImage1 = [self CreateIplImageFromUIImage:srcimage];//2.设置Src的IplImage的ImageROIint width = srcImage1->width;int height = srcImage1->height;printf("图片大小%d,%d\n",width,height);// 分割矩形区域int x = 400;int y = 1100;int w = 1200;int h = 600;//cvSetImageROI:基于给定的矩形设置图像的ROI（感兴趣区域，region of interesting）cvSetImageROI(srcImage1, cvRect(x, y, w , h));//3.创建新的dstImage1的IplImage，并复制Src的IplImageIplImage* dstImage1 = cvCreateImage(cvSize(w, h), srcImage1->depth, srcImage1->nChannels);//cvCopy:如果输入输出数组中的一个是IplImage类型的话，其ROI和COI将被使用。cvCopy(srcImage1, dstImage1,0);//cvResetImageROI:释放基于给定的矩形设置图像的ROI（感兴趣区域，region of interesting）cvResetImageROI(srcImage1);resimage = [self UIImageFromIplImage:dstImage1];*///4.dstImage1的IplImage转换成cvMat形式的matImagecv::Mat matImage = [self cvMatFromUIImage:srcimage];cv::Mat matGrey;//5.cvtColor函数对matImage进行灰度处理//取得IplImage形式的灰度图像cv::cvtColor(matImage, matGrey, CV_BGR2GRAY);// 转换成灰色//6.使用灰度后的IplImage形式图像，用OSTU算法算阈值：threshold//IplImage grey = matGrey;
resimage = [self UIImageFromCVMat:matGrey];/*unsigned char* dataImage = (unsigned char*)grey.imageData;int threshold = Otsu(dataImage, grey.width, grey.height);printf("阈值：%d\n",threshold);//7.利用阈值算得新的cvMat形式的图像cv::Mat matBinary;cv::threshold(matGrey, matBinary, threshold, 255, cv::THRESH_BINARY);//8.cvMat形式的图像转UIImageUIImage* image = [[UIImage alloc ]init];image = [self UIImageFromCVMat:matBinary];resimage = image;*/return resimage;
}-(UIImage *)Erzhiimage:(UIImage *)srcimage{UIImage *resimage;//openCV二值化过程：/*//1.Src的UIImage -> Src的IplImageIplImage* srcImage1 = [self CreateIplImageFromUIImage:srcimage];//2.设置Src的IplImage的ImageROIint width = srcImage1->width;int height = srcImage1->height;printf("图片大小%d,%d\n",width,height);//// 分割矩形区域int x = 400;int y = 1100;int w = 1200;int h = 600;//cvSetImageROI:基于给定的矩形设置图像的ROI（感兴趣区域，region of interesting）cvSetImageROI(srcImage1, cvRect(x, y, w , h));//3.创建新的dstImage1的IplImage，并复制Src的IplImageIplImage* dstImage1 = cvCreateImage(cvSize(w, h), srcImage1->depth, srcImage1->nChannels);//cvCopy:如果输入输出数组中的一个是IplImage类型的话，其ROI和COI将被使用。cvCopy(srcImage1, dstImage1,0);//cvResetImageROI:释放基于给定的矩形设置图像的ROI（感兴趣区域，region of interesting）cvResetImageROI(srcImage1);resimage = [self UIImageFromIplImage:dstImage1];*///4.dstImage1的IplImage转换成cvMat形式的matImagecv::Mat matImage = [self cvMatFromUIImage:srcimage];cv::Mat matGrey;//5.cvtColor函数对matImage进行灰度处理//取得IplImage形式的灰度图像cv::cvtColor(matImage, matGrey, CV_BGR2GRAY);// 转换成灰色//6.使用灰度后的IplImage形式图像，用OSTU算法算阈值：thresholdIplImage grey = matGrey;unsigned char* dataImage = (unsigned char*)grey.imageData;int threshold = Otsu(dataImage, grey.width, grey.height);printf("阈值：%d\n",threshold);//7.利用阈值算得新的cvMat形式的图像
cv::Mat matBinary;cv::threshold(matGrey, matBinary, threshold, 255, cv::THRESH_BINARY);//8.cvMat形式的图像转UIImageUIImage* image = [[UIImage alloc ]init];image = [self UIImageFromCVMat:matBinary];resimage = image;return resimage;
}@end 四：可能问题：
1.出现'list' file not found: 检查类名是否改成.mm了！还不行的话，在Build Phases 中加入库：libc++.dylib 试试。
2.arm64不支持的问题：在Building settings里Build Active Architecture Only改为No，然后下面Valid Architectures把arm64删了。
五：样例参考：有两个很好的例子，一个是二值，一个是图像匹配。
1.二值
2.图像匹配
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