OpenCV 之ios 轮廓矩
目标
在这节教程中您将学到:
- 使用OpenCV函数 moments 计算图像所有的矩(最高到3阶)
- 使用OpenCV函数 contourArea 来计算轮廓面积
- 使用OpenCV函数 arcLength 来计算轮廓或曲线长度
代码
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#import <opencv2/imgcodecs/ios.h>
#import <opencv2/imgproc.hpp>
#import <opencv2/highgui.hpp>
#import <opencv2/core/operations.hpp>
#import <opencv2/core/core_c.h>
using namespace cv;
using namespace std;
#endif
#import "MomentsViewController.h"
@interface MomentsViewController ()
@end
@implementation MomentsViewController
/// 全局变量
Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);
- (void)viewDidLoad {
[super viewDidLoad];
UIImage * srcImage = [UIImage imageNamed:@"pic.png"];
src = [self cvMatFromUIImage:srcImage];
UIImageView *imageView;
imageView = [self createImageViewInRect:CGRectMake(0, 100, 150, 150)];
[self.view addSubview:imageView];
imageView.image = [self UIImageFromCVMat:src];
cvtColor( src, src_gray, CV_BGR2GRAY );
blur( src_gray, src_gray, cv::Size(3,3) );
imageView = [self createImageViewInRect:CGRectMake(0, 250, 150, 150)];
[self.view addSubview:imageView];
imageView.image = [self UIImageFromCVMat:src_gray];
[self createSliderFrame:CGRectMake(150, 400, 150, 50) maxValue:max_thresh currentValue:thresh minValue:0 block:^(float value) {
thresh= value;
[self thresh_callback];
}];
[self thresh_callback];
}
-(void)thresh_callback{
Mat canny_output;
vector<vector<cv::Point> > contours;
vector<Vec4i> hierarchy;
/// 对图像进行二值化
threshold( src_gray, canny_output, thresh, 255, THRESH_BINARY );
/// 找到轮廓
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
Mat drawing1 = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing1, contours, i, color, 2, 8, hierarchy, 0, cv::Point() );
}
UIImageView *imageView;
imageView = [self createImageViewInRect:CGRectMake(150, 100, 150, 150)];
[self.view addSubview:imageView];
imageView.image = [self UIImageFromCVMat:drawing1];
/// 多边形逼近轮廓 + 获取矩形和圆形边界框
vector<Moments> mu(contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false ); }
/// 计算中心矩:
vector<Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
/// 绘制轮廓
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0,cv::Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
imageView = [self createImageViewInRect:CGRectMake(150, 250, 150, 150)];
[self.view addSubview:imageView];
imageView.image = [self UIImageFromCVMat:drawing];
printf("\t Info: Area and Contour Length \n");
for( int i = 0; i< contours.size(); i++ )
{
printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, cv::Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
}
#pragma mark - private
//brg
- (cv::Mat)cvMatFromUIImage:(UIImage *)image
{
CGColorSpaceRef colorSpace =CGColorSpaceCreateDeviceRGB();
CGFloat cols = image.size.width;
CGFloat rows = image.size.height;
Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels (color channels + alpha)
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to data
cols, // Width of bitmap
rows, // Height of bitmap
8, // Bits per component
cvMat.step[0], // Bytes per row
colorSpace, // Colorspace
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
Mat dst;
Mat src;
cvtColor(cvMat, dst, COLOR_RGBA2BGRA);
cvtColor(dst, src, COLOR_BGRA2BGR);
return src;
}
-(UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
{
// mat 是brg 而 rgb
Mat src;
NSData *data=nil;
CGBitmapInfo info =kCGImageAlphaNone|kCGBitmapByteOrderDefault;
CGColorSpaceRef colorSpace;
if (cvMat.depth()!=CV_8U) {
Mat result;
cvMat.convertTo(result, CV_8U,255.0);
cvMat = result;
}
if (cvMat.elemSize() == 1) {
colorSpace = CGColorSpaceCreateDeviceGray();
data= [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
} else if(cvMat.elemSize() == 3){
cvtColor(cvMat, src, COLOR_BGR2RGB);
data= [NSData dataWithBytes:src.data length:src.elemSize()*src.total()];
colorSpace = CGColorSpaceCreateDeviceRGB();
}else if(cvMat.elemSize() == 4){
colorSpace = CGColorSpaceCreateDeviceRGB();
cvtColor(cvMat, src, COLOR_BGRA2RGBA);
data= [NSData dataWithBytes:src.data length:src.elemSize()*src.total()];
info =kCGImageAlphaNoneSkipLast | kCGBitmapByteOrderDefault;
}else{
NSLog(@"[error:] 错误的颜色通道");
return nil;
}
CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
// Creating CGImage from cv::Mat
CGImageRef imageRef = CGImageCreate(cvMat.cols, //width
cvMat.rows, //height
8, //bits per component
8 * cvMat.elemSize(), //bits per pixel
cvMat.step[0], //bytesPerRow
colorSpace, //colorspace
kCGImageAlphaNone|kCGBitmapByteOrderDefault,// bitmap info
provider, //CGDataProviderRef
NULL, //decode
false, //should interpolate
kCGRenderingIntentAbsoluteColorimetric //intent
);
// Getting UIImage from CGImage
UIImage *finalImage = [UIImage imageWithCGImage:imageRef];
CGImageRelease(imageRef);
CGDataProviderRelease(provider);
CGColorSpaceRelease(colorSpace);
return finalImage;
}
@end
结果
打印结果如下
Info: Area and Contour Length
* Contour[0] - Area (M_00) = 57420.00 - Area OpenCV: 57420.00 - Length: 1082.00
* Contour[1] - Area (M_00) = 4255.50 - Area OpenCV: 4255.50 - Length: 429.12
* Contour[2] - Area (M_00) = 29.00 - Area OpenCV: 29.00 - Length: 27.31
* Contour[3] - Area (M_00) = 57.50 - Area OpenCV: 57.50 - Length: 28.73
* Contour[4] - Area (M_00) = 55.50 - Area OpenCV: 55.50 - Length: 27.90
* Contour[5] - Area (M_00) = 4078.00 - Area OpenCV: 4078.00 - Length: 432.48
* Contour[6] - Area (M_00) = 0.50 - Area OpenCV: 0.50 - Length: 3.41
* Contour[7] - Area (M_00) = 1.00 - Area OpenCV: 1.00 - Length: 6.83
* Contour[8] - Area (M_00) = 2.00 - Area OpenCV: 2.00 - Length: 6.00
* Contour[9] - Area (M_00) = 49.50 - Area OpenCV: 49.50 - Length: 26.73
* Contour[10] - Area (M_00) = 49.00 - Area OpenCV: 49.00 - Length: 26.14
* Contour[11] - Area (M_00) = 3878.00 - Area OpenCV: 3878.00 - Length: 364.19
* Contour[12] - Area (M_00) = 0.00 - Area OpenCV: 0.00 - Length: 0.00
* Contour[13] - Area (M_00) = 58.50 - Area OpenCV: 58.50 - Length: 28.73
* Contour[14] - Area (M_00) = 4409.50 - Area OpenCV: 4409.50 - Length: 493.55
* Contour[15] - Area (M_00) = 51.00 - Area OpenCV: 51.00 - Length: 82.97
* Contour[16] - Area (M_00) = 53.00 - Area OpenCV: 53.00 - Length: 28.14
* Contour[17] - Area (M_00) = 45.50 - Area OpenCV: 45.50 - Length: 26.73
* Contour[18] - Area (M_00) = 8091.50 - Area OpenCV: 8091.50 - Length: 494.86
* Contour[19] - Area (M_00) = 8.50 - Area OpenCV: 8.50 - Length: 14.24
* Contour[20] - Area (M_00) = 0.00 - Area OpenCV: 0.00 - Length: 4.00
* Contour[21] - Area (M_00) = 0.00 - Area OpenCV: 0.00 - Length: 4.00
* Contour[22] - Area (M_00) = 63.50 - Area OpenCV: 63.50 - Length: 32.73
* Contour[23] - Area (M_00) = 114.50 - Area OpenCV: 114.50 - Length: 40.38
* Contour[24] - Area (M_00) = 116.00 - Area OpenCV: 116.00 - Length: 40.97
