[英]Extraction Texture Feature c++
I'm trying to extract a feature vector for each pixel of an image by using the gray-values of the surrounding pixels: http://img59.imageshack.us/img59/7398/texturemap.png The pixels marked in black are the pixels used, as the other pixels are redundant to the results of the SVM used later. 我试图通过使用周围像素的灰度值为图像的每个像素提取特征向量: http : //img59.imageshack.us/img59/7398/texturemap.png黑色标记的像素是所使用的像素,因为其他像素对于以后使用的SVM结果是多余的。
At the moment this code is used: 目前使用此代码:
vector<Histogram*> texture_based(image_file* image) {
int cat;
Mat img = cvLoadImage(image->getName().c_str(), CV_LOAD_IMAGE_GRAYSCALE);
Mat img_b(img.rows + 12, img.cols + 12, img.depth());
copyMakeBorder(img, img_b, 6, 6, 6, 6, IPL_BORDER_CONSTANT, cvScalarAll(0));
vector<Histogram*> result;
for(int i = 6; i < img_b.rows - 6; ++i) {
for(int j = 6; j < img_b.cols - 6; ++j) {
Mat hist = Mat::zeros(1, 49, CV_32FC1);
cat = 0;
hist.at<float>(0, 0) = (float)img_b.at<char>(i - 6, j - 6);
hist.at<float>(0, 1) = (float)img_b.at<char>(i - 5, j - 5);
hist.at<float>(0, 2) = (float)img_b.at<char>(i - 4, j - 4);
hist.at<float>(0, 3) = (float)img_b.at<char>(i - 3, j - 3);
hist.at<float>(0, 4) = (float)img_b.at<char>(i - 2, j - 2);
hist.at<float>(0, 5) = (float)img_b.at<char>(i - 1, j - 1);
hist.at<float>(0, 6) = (float)img_b.at<char>(i, j);
hist.at<float>(0, 7) = (float)img_b.at<char>(i + 1, j + 1);
hist.at<float>(0, 8) = (float)img_b.at<char>(i + 2, j + 2);
hist.at<float>(0, 9) = (float)img_b.at<char>(i + 3, j + 3);
hist.at<float>(0, 10) = (float)img_b.at<char>(i + 4, j + 4);
hist.at<float>(0, 11) = (float)img_b.at<char>(i + 5, j + 5);
hist.at<float>(0, 12) = (float)img_b.at<char>(i + 6, j + 6);
hist.at<float>(0, 13) = (float)img_b.at<char>(i + 6, j - 6);
hist.at<float>(0, 14) = (float)img_b.at<char>(i + 5, j - 5);
hist.at<float>(0, 15) = (float)img_b.at<char>(i + 4, j - 4);
hist.at<float>(0, 16) = (float)img_b.at<char>(i + 3, j - 3);
hist.at<float>(0, 17) = (float)img_b.at<char>(i + 2, j - 2);
hist.at<float>(0, 18) = (float)img_b.at<char>(i + 1, j - 1);
hist.at<float>(0, 19) = (float)img_b.at<char>(i - 1, j + 1);
hist.at<float>(0, 20) = (float)img_b.at<char>(i - 2, j + 2);
hist.at<float>(0, 21) = (float)img_b.at<char>(i - 3, j + 3);
hist.at<float>(0, 22) = (float)img_b.at<char>(i - 4, j + 4);
hist.at<float>(0, 23) = (float)img_b.at<char>(i - 5, j + 5);
hist.at<float>(0, 24) = (float)img_b.at<char>(i - 6, j + 6);
hist.at<float>(0, 25) = (float)img_b.at<char>(i, j - 6);
hist.at<float>(0, 26) = (float)img_b.at<char>(i, j - 5);
hist.at<float>(0, 27) = (float)img_b.at<char>(i, j - 4);
hist.at<float>(0, 28) = (float)img_b.at<char>(i, j - 3);
hist.at<float>(0, 29) = (float)img_b.at<char>(i, j - 2);
hist.at<float>(0, 30) = (float)img_b.at<char>(i, j - 1);
hist.at<float>(0, 31) = (float)img_b.at<char>(i, j + 1);
hist.at<float>(0, 32) = (float)img_b.at<char>(i, j + 2);
hist.at<float>(0, 33) = (float)img_b.at<char>(i, j + 3);
hist.at<float>(0, 34) = (float)img_b.at<char>(i, j + 4);
hist.at<float>(0, 35) = (float)img_b.at<char>(i, j + 5);
hist.at<float>(0, 36) = (float)img_b.at<char>(i, j + 6);
hist.at<float>(0, 37) = (float)img_b.at<char>(i - 6, j);
hist.at<float>(0, 38) = (float)img_b.at<char>(i - 5, j);
hist.at<float>(0, 39) = (float)img_b.at<char>(i - 4, j);
hist.at<float>(0, 40) = (float)img_b.at<char>(i - 3, j);
hist.at<float>(0, 41) = (float)img_b.at<char>(i - 2, j);
hist.at<float>(0, 42) = (float)img_b.at<char>(i - 1, j);
hist.at<float>(0, 43) = (float)img_b.at<char>(i + 1, j);
hist.at<float>(0, 44) = (float)img_b.at<char>(i + 2, j);
hist.at<float>(0, 45) = (float)img_b.at<char>(i + 3, j);
hist.at<float>(0, 46) = (float)img_b.at<char>(i + 4, j);
hist.at<float>(0, 47) = (float)img_b.at<char>(i + 5, j);
hist.at<float>(0, 48) = (float)img_b.at<char>(i + 6, j);
if(image->inAnyRec(i, j))
cat = 1;
Mat_<float> new_hist = hist;
Histogram* t = new Histogram(&new_hist, cat);
result.push_back(t);
}
}
return result;
}
Where image_file* a pointer is to a class with information about the image. 其中image_file *指向包含有关图像信息的类的指针。 I was wondering if there is a faster way of doing this. 我想知道是否有更快的方法。
You could calculate the operation in 4 passes; 您可以分四遍计算操作; each would initialize a vector of 12 (or 13) elements, move one pixel east, south, northeast or southeast and replace just one pixel from the vector. 每个对象都会初始化一个包含12(或13)个元素的向量,向东,向南,东北或东南移动一个像素,并从向量中仅替换一个像素。 This would also require initializing all the histogram vectors (width-12)*(height-12), 49 at once. 这还需要立即初始化所有直方图矢量(width-12)*(height-12)49。
A supporting option is to rotate/tilt the original image into just four arrays -- you have to profile if it makes sense to perform the char->float conversion at that point. 一个支持的选项是将原始图像旋转/倾斜成四个阵列-如果必须在此时执行char-> float转换,则必须进行剖析。
a b c d --> a e i --> a f k > i f c
e f g h b f j b g l j g d
i j k l c g k
d h l
From these new arrays the memory read pattern / cache locality can make a difference. 从这些新阵列中,内存读取模式/缓存位置可能会有所不同。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.