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我試圖在Cuda平臺上實現Viola Johns人臉檢測算法（我知道openCV已經做到了這一點，我爲我的學校做了... :) ）。使用OpenCV級聯 - 單獨使用haartraning XML文件
我的第一個階段是在CPU上實現算法。
我正在使用openCV庫，我知道openCV知道如何做人臉檢測，爲了理解，我想回到基本，並以我自己的方式做到這一點。
我使用openCV函數創建了積分和表示，以及squere和積分表示。
我迭代了級聯。通過階段，分類和rects迭代。 標準化每個窗口，計算每個分類器的總和，並與閾值進行比較，很遺憾，這似乎是我錯過了一些東西。因爲我無法檢測到臉部。
這似乎是我需要更好地瞭解cascade xml文件。
這裏有一個例子：
<!-- tree 158 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>3 6 2 2 -1.</_>
<_>3 6 1 1 2.</_>
<_>4 7 1 1 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>2.3729570675641298e-003</threshold>
<left_val>0.4750812947750092</left_val>
<right_val>0.7060170769691467</right_val></_></_>
<_>
<!-- tree 159 -->
<!-- tree 159 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>16 6 3 2 -1.</_>
<_>16 7 3 1 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>-1.4541699783876538e-003</threshold>
<left_val>0.3811730146408081</left_val>
<right_val>0.5330739021301270</right_val></_></_></trees>
<stage_threshold>79.2490768432617190</stage_threshold>
<parent>16</parent>
<next>-1</next></_>
<_>
我想了解什麼是left_val和right_val的含義是什麼？ 父，下一個值的含義是什麼？ 如何計算每個分類器歸一化總和？ 有什麼我在這裏做錯了嗎？看到我的代碼附加
基本上這是我正在做的一段時間，我想爲這個問題提供獎勵，但我沒有足夠的評級。任何幫助將非常感激。
Thnaks提前， 小號
int RunHaarClassifierCascadeSum(CascadeClassifier * face_cascade, CvMat* image , CvMat* sum , CvMat* sqsum,
CvMat* tilted,CvSize *scaningWindowSize, int iteratorRow, int iteratorCol)
{
// Normalize the current scanning window - Detection window
// Variance(x) = E(x^2) - (E(x))^2 = detectionWindowSquereExpectancy - detectionWindowExpectancy^2
// Expectancy(x) = E(x) = sum_of_pixels/size_of_window
double detectionWindowTotalSize = scaningWindowSize->height * scaningWindowSize->width;
// calculate the detection Window Expectancy , e.g the E(x)
double sumDetectionWindowPoint1,sumDetectionWindowPoint2,sumDetectionWindowPoint3,sumDetectionWindowPoint4; // ______________________
sumDetectionWindowPoint1 = cvGetReal2D(sum,iteratorRow,iteratorCol); // |R1 R2|
sumDetectionWindowPoint2 = cvGetReal2D(sum,iteratorRow+scaningWindowSize->width,iteratorCol); // | | Sum = R4-R2-R3+R1
sumDetectionWindowPoint3 = cvGetReal2D(sum,iteratorRow,iteratorCol+scaningWindowSize->height); // |R3________________R4|
sumDetectionWindowPoint4 = cvGetReal2D(sum,iteratorRow+scaningWindowSize->width,iteratorCol+scaningWindowSize->height);
double detectionWindowSum = calculateSum(sumDetectionWindowPoint1,sumDetectionWindowPoint2,sumDetectionWindowPoint3,sumDetectionWindowPoint4);
const double detectionWindowExpectancy = detectionWindowSum/detectionWindowTotalSize; // E(x)
// calculate the Square detection Window Expectancy , e.g the E(x^2)
double squareSumDetectionWindowPoint1,squareSumDetectionWindowPoint2,squareSumDetectionWindowPoint3,squareSumDetectionWindowPoint4; // ______________________
squareSumDetectionWindowPoint1 = cvGetReal2D(sqsum,iteratorRow,iteratorCol); // |R1 R2|
squareSumDetectionWindowPoint2 = cvGetReal2D(sqsum,iteratorRow+scaningWindowSize->width,iteratorCol); // | | Sum = R4-R2-R3+R1
squareSumDetectionWindowPoint3 = cvGetReal2D(sqsum,iteratorRow,iteratorCol+scaningWindowSize->height); // |R3________________R4|
squareSumDetectionWindowPoint4 = cvGetReal2D(sqsum,iteratorRow+scaningWindowSize->width,iteratorCol+scaningWindowSize->height);
double detectionWindowSquareSum = calculateSum(squareSumDetectionWindowPoint1,squareSumDetectionWindowPoint2,squareSumDetectionWindowPoint3,squareSumDetectionWindowPoint4);
const double detectionWindowSquareExpectancy = detectionWindowSquareSum/detectionWindowTotalSize; // E(x^2)
const double detectionWindowVariance = detectionWindowSquareExpectancy - std::pow(detectionWindowExpectancy,2); // Variance(x) = E(x^2) - (E(x))^2
const double detectionWindowStandardDeviation = std::sqrt(detectionWindowVariance);
if (detectionWindowVariance<=0)
return -1 ; // Error
// Normalize the cascade window to the normal scale window
double normalizeScaleWidth = double(scaningWindowSize->width/face_cascade->oldCascade->orig_window_size.width);
double normalizeScaleHeight = double(scaningWindowSize->height/face_cascade->oldCascade->orig_window_size.height);
// Calculate the cascade for each one of the windows
for(int stageIterator=0; stageIterator< face_cascade->oldCascade->count; stageIterator++) // Stage iterator
{
CvHaarStageClassifier* pCvHaarStageClassifier = face_cascade->oldCascade->stage_classifier + stageIterator;
for (int CvHaarStageClassifierIterator=0;CvHaarStageClassifierIterator<pCvHaarStageClassifier->count;CvHaarStageClassifierIterator++) // Classifier iterator
{
CvHaarClassifier* classifier = pCvHaarStageClassifier->classifier + CvHaarStageClassifierIterator;
float classifierSum=0.;
for(int CvHaarClassifierIterator = 0; CvHaarClassifierIterator < classifier->count;CvHaarClassifierIterator++) // Feature iterator
{
CvHaarFeature * pCvHaarFeature = classifier->haar_feature;
// Remark
if (pCvHaarFeature->tilted==1)
break;
// Remark
for(int CvHaarFeatureIterator = 0; CvHaarFeatureIterator< CV_HAAR_FEATURE_MAX; CvHaarFeatureIterator++) // 3 Features iterator
{
CvRect * currentRect = &(pCvHaarFeature->rect[CvHaarFeatureIterator].r);
// Normalize the rect to the scaling window scale
CvRect normalizeRec;
normalizeRec.x = (int)(currentRect->x*normalizeScaleWidth);
normalizeRec.y = (int)(currentRect->y*normalizeScaleHeight);
normalizeRec.width = (int)(currentRect->width*normalizeScaleWidth);
normalizeRec.height = (int)(currentRect->height*normalizeScaleHeight);
double sumRectPoint1,sumRectPoint2,sumRectPoint3,sumRectPoint4; // ______________________
sumRectPoint1 = cvGetReal2D(sum,normalizeRec.x,normalizeRec.y); // |R1 R2|
sumRectPoint2 = cvGetReal2D(sum,normalizeRec.x+normalizeRec.width,normalizeRec.y); // | | Sum = R4-R2-R3+R1
sumRectPoint3 = cvGetReal2D(sum,normalizeRec.x,normalizeRec.y+normalizeRec.height); // |R3________________R4|
sumRectPoint4 = cvGetReal2D(sum,normalizeRec.x+normalizeRec.width,normalizeRec.y+normalizeRec.height);
double nonNormalizeRect = calculateSum(sumRectPoint1,sumRectPoint2,sumRectPoint3,sumRectPoint4); //
double sumMean = detectionWindowExpectancy*(normalizeRec.width*normalizeRec.height); // sigma(Pi) = normalizeRect = (sigma(Pi- rect) - sigma(mean))/detectionWindowStandardDeviation
double normalizeRect = (nonNormalizeRect - sumMean)/detectionWindowStandardDeviation; //
classifierSum += (normalizeRect*(pCvHaarFeature->rect[CvHaarFeatureIterator].weight));
}
}
// if (classifierSum > (*(classifier->threshold)))
// return 0; // That's not a face !
if (classifierSum > ((*(classifier->threshold))*detectionWindowStandardDeviation))
return -stageIterator; // That's not a face ! , failed on stage number
}
}
return 1; // That's a face
}