iplimage | 易学教程

OpenCV——肤色检测

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一、RGB color space 检测代码如下： void SkinRGB(IplImage* src,IplImage* dst) { //RGB颜色空间 //均匀照明：R>95,G>40,B>20,R-B>15,R-G>15,R>B%R //侧向照明:R>200,G>210,B>170,R-B<=15,R>B,G>B int height = src->height, width = src->width, channel = src->nChannels, step = src->widthStep; int b = 0, g = 1, r = 2; cvZero(dst); unsigned char* p_src = (unsigned char*)src->imageData; unsigned char* p_dst = (unsigned char*)dst->imageData; for(int j = 0; j < height; j++){ for(int i = 0; i < width; i++){ if((p_src[j*step+i*channel+r] > 95 && p_src[j*step+i*channel+g] > 40 && p_src[j*step+i*channel+b] > 20 && (p_src[j*step+i*channel

opencv学习笔记[10]：IplImage数据结构

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1.IplImage数据结构 typedef struct _IplImage { int nSize; /* sizeof(IplImage) */ int ID; /* version (=0)*/ int nChannels; /* Most of OpenCV functions support 1,2,3 or 4 channels */ int alphaChannel; /* Ignored by OpenCV */ int depth; /* Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ char colorModel[4]; /* Ignored by OpenCV */ char channelSeq[4]; /* ditto */ int dataOrder; /* 0 - interleaved color channels, 1 - separate color channels. cvCreateImage can only create interleaved images */ int origin; /* 0 - top-left

opencv常用数据结构之：IplImage

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typedef struct_IplImage { int nSize; //IplImage大小 int ID; //版本(=0) int nChannels; //大多数OPENCV函数支持1、2、3或4个通道 int alphaChannel; //被opencv忽略 int depth; //像素的位深度，主要有以下支持格式：IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16U,IPL_DEPTH_16S, IPL_DEPTH_32S,IPL_DEPTH_32F 和 IPL_DEPTH_64F */ char colorMode[4]; //被opencv忽略 char channelSeq[4]; //被opencv忽略 int dataOder; //0:交叉存取颜色通道，1：分开的颜色通道；只有cvCreateImage可以创建交叉存取图像*/ int origin; //图像原点位置：0表示顶-左结构，1表示底-左结构 int align; //图像排列方式(4 or 8),在opencv被忽略，使用widthStep代替 int width; //图像宽像素数 int height; //图像高像素数 struct_IplROI * roi; //图像感兴趣区域，当该值为空时，只对该区域进行处理 struct_IplImage

[opencv] 图像几何变换：旋转，缩放，斜切

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几何变换几何变换可以看成图像中物体（或像素）空间位置改变，或者说是像素的移动。几何运算需要空间变换和灰度级差值两个步骤的算法，像素通过变换映射到新的坐标位置，新的位置可能是在几个像素之间，即不一定为整数坐标。这时就需要灰度级差值将映射的新坐标匹配到输出像素之间。最简单的插值方法是最近邻插值，就是令输出像素的灰度值等于映射最近的位置像素，该方法可能会产生锯齿。这种方法也叫零阶插值，相应比较复杂的还有一阶和高阶插值。插值算法感觉只要了解就可以了，图像处理中比较需要理解的还是空间变换。空间变换空间变换对应矩阵的仿射变换。一个坐标通过函数变换的新的坐标位置：所以在程序中我们可以使用一个2*3的数组结构来存储变换矩阵：以最简单的平移变换为例，平移（b1,b2）坐标可以表示为：因此，平移变换的变换矩阵及逆矩阵记为：缩放变换：将图像横坐标放大（或缩小）sx倍，纵坐标放大（或缩小）sy倍，变换矩阵及逆矩阵为：选择变换：图像绕原点逆时针旋转a角，其变换矩阵及逆矩阵（顺时针选择）为： OpenCV中的图像变换函数基本的放射变换函数： void cvWarpAffine( const CvArr* src,//输入图像 CvArr* dst, //输出图像 const CvMat* map_matrix, //2*3的变换矩阵 int flags=CV_INTER_LINEAR

Copying a portion of an IplImage into another Iplimage (that is of same size is the source)

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问题 I have a set of mask images that I need to use everytime I recognise a previously-known scene on my camera. All the mask images are in IplImage format. There will be instances where, for example, the camera has panned to a slightly different but nearby location. this means that if I do a template matching somewhere in the middle of the current scene, I will be able to recognise the scene with some amount of shift of the template in this scene. All I need to do is use those shifts to adjust

ISampleGrabber::BufferCB to IplImage; display in OpenCV shows garbled image - C++

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问题 I'm using DirectShow to access a video stream, and then using the SampleGrabber filter and interface to get samples from each frame for further image processing. I'm using a callback, so it gets called after each new frame. I've basically just worked from the PlayCap sample application and added a sample filter to the graph. The problem I'm having is that I'm trying to display the grabbed samples on a different OpenCV window. However, when I try to cast the information in the buffer to an

Compressing IplImage to JPEG using libjpeg in OpenCV

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问题 So I have this problem. I have an IplImage that i want to compress to JPEG and do something with it. I use libjpeg8b.The code exit when it goes the function of jpeg_start_compress() with an error of "Bogus input colorspace" .Here are my code. #include "highgui.h" #include <stdio.h> #include "jpeglib.h" #include "cv.h" #include <iostream> #include <fstream> using namespace std; using namespace cv; #pragma comment(lib, "jpeglib.lib") bool ipl2jpeg(IplImage *frame, unsigned char **outbuffer,

Fastest method to convert IplImage IPL_DEPTH_32S to QImage Format_RGB32

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问题 What is the fastest method to convert IplImage IPL_DEPTH_32S to QImage Format_RGB32? I need to catch pictures from cam and show it on form with frequency 30 frames in second. I tried to use QImage constructor: QImage qImage((uchar *) image->imageData, image->width, image->height, QImage::Format_RGB32); but image was corrupted after this. So, how can I do this fast (I think putting pixel by pixel into QImage is not a good decision)? 回答1: Before I start, OpenCV uses the BGR format by default .

OpenCV IplImage data to float

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问题 Is there a way to convert IplImage pointer to float pointer? Basically converting the imagedata to float. Appreciate any help on this. 回答1: Use cvConvert(src,dst) where src is the source image and dst is the preallocated floating point image. E.g. dst = cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_32F,1); cvConvert(src,dst); 回答2: // Original image gets loaded as IPL_DEPTH_8U IplImage* colored = cvLoadImage("coins.jpg", CV_LOAD_IMAGE_UNCHANGED); if (!colored) { printf("cvLoadImage

虹软人脸识别3.0 - 图像数据结构介绍（C++）

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从虹软开放了2.0版本SDK以来，由于具有免费、离线使用的特点，我们公司在人脸识别门禁应用中使用了虹软SDK，识别效果还不错，因此比较关注虹软SDK的官方动态。近期上线了ArcFace 3.0 SDK版本，确实做了比较大的更新。上一篇主要介绍了关于Android平台算法的改进，本篇将介绍一下关于Windows平台算法的更新。特征比对支持比对模型选择，有生活照比对模型和人证比对模型识别率、防***效果显著提升特征值更新，升级后人脸库需重新注册人脸检测同时支持全角度及单一角度新增了一种图像数据传入方式在V3.0版本接入过程中，发现使用新的图像数据结构还是具有一定难度的，本文将从以下几点对该图像数据结构及使用方式进行介绍 SDK接口变动图像数据结构步长的作用 OpenCV图像数据结构转换为虹软图像数据结构一、SDK 接口变动在接入ArcFace 3.0 SDK时，发现新增了ASFDetectFacesEx、ASFFaceFeatureExtractEx、ASFProcessEx、ASFProcessEx_IR一组接口，该组接口使用 LPASF_ImageData 结构体指针的方式传入图像数据，以人脸检测接口为例，具体接口比对如下：原始接口： MRESULT ASFDetectFaces( MHandle hEngine, // [in] 引擎handle

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