generic-library/opencv
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improve pointer usage

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This commit is contained in:
marina.kolpakova 2012-10-06 20:18:36 +04:00
parent 2914f24521
commit 754fd7311b
1 changed files with 23 additions and 36 deletions
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57
modules/objdetect/src/softcascade.cpp
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@ -226,11 +226,6 @@ struct Level
float rescale(cv::Rect& scaledRect, const float threshold, int idx) const float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
{ {
// rescale // rescale
// scaledRect.x = cvRound(relScale * scaledRect.x);
// scaledRect.y = cvRound(relScale * scaledRect.y);
// scaledRect.width = cvRound(relScale * scaledRect.width);
// scaledRect.height = cvRound(relScale * scaledRect.height);
scaledRect.x = (scaleshift * scaledRect.x + R_SHIFT) >> 16; scaledRect.x = (scaleshift * scaledRect.x + R_SHIFT) >> 16;
scaledRect.y = (scaleshift * scaledRect.y + R_SHIFT) >> 16; scaledRect.y = (scaleshift * scaledRect.y + R_SHIFT) >> 16;
scaledRect.width = (scaleshift * scaledRect.width + R_SHIFT) >> 16; scaledRect.width = (scaleshift * scaledRect.width + R_SHIFT) >> 16;
@ -284,6 +279,8 @@ struct ChannelStorage
{ {
std::vector<cv::Mat> hog; std::vector<cv::Mat> hog;
int shrinkage; int shrinkage;
int offset;
int step;
enum {HOG_BINS = 6, HOG_LUV_BINS = 10}; enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
@ -391,31 +388,23 @@ struct ChannelStorage
hog.push_back(mag); hog.push_back(mag);
hog.insert(hog.end(), luvs.begin(), luvs.end()); hog.insert(hog.end(), luvs.begin(), luvs.end());
CV_Assert(hog.size() == 10);
step = hog[0].cols;
// CV_Assert(hog.size() == 10);
#endif #endif
} }
float get(const int x, const int y, const int channel, const cv::Rect& area) const float get(const int channel, const cv::Rect& area) const
{ {
CV_Assert(channel < HOG_LUV_BINS); // CV_Assert(channel < HOG_LUV_BINS);
const cv::Mat& m = hog[channel]; const cv::Mat& m = hog[channel];
int *ptr = ((int*)(m.data)) + offset;
dprintf("feature box %d %d %d %d ", area.x, area.y, area.width, area.height); int a = ptr[area.y * step + area.x];
dprintf("get for channel %d\n", channel); int b = ptr[area.y * step + area.width];
dprintf("!! %d\n", m.depth()); int c = ptr[area.height * step + area.width];
int d = ptr[area.height * step + area.x];
dprintf("extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",
x + area.x, y + area.y, x + area.width,y + area.y, x + area.width,y + area.height,
x + area.x, y + area.height);
dprintf("at point %d %d with offset %d\n", x, y, 0);
int a = m.ptr<int>(y + area.y)[x + area.x];
int b = m.ptr<int>(y + area.y)[x + area.width];
int c = m.ptr<int>(y + area.height)[x + area.width];
int d = m.ptr<int>(y + area.height)[x + area.x];
dprintf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
return (a - b + c - d); return (a - b + c - d);
} }
@ -443,8 +432,7 @@ struct cv::SoftCascade::Filds
typedef std::vector<Octave>::iterator octIt_t; typedef std::vector<Octave>::iterator octIt_t;
void detectAt(const Level& level, const int dx, const int dy, const ChannelStorage& storage, void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, std::vector<Object>& detections) const
std::vector<Object>& detections) const
{ {
dprintf("detect at: %d %d\n", dx, dy); dprintf("detect at: %d %d\n", dx, dy);
@ -473,7 +461,7 @@ struct cv::SoftCascade::Filds
float threshold = level.rescale(scaledRect, node.threshold,(int)(feature.channel > 6)) * feature.rarea; float threshold = level.rescale(scaledRect, node.threshold,(int)(feature.channel > 6)) * feature.rarea;
float sum = storage.get(dx, dy, feature.channel, scaledRect); float sum = storage.get(feature.channel, scaledRect);
dprintf("root feature %d %f\n",feature.channel, sum); dprintf("root feature %d %f\n",feature.channel, sum);
@ -488,7 +476,7 @@ struct cv::SoftCascade::Filds
scaledRect = fLeaf.rect; scaledRect = fLeaf.rect;
threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea; threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
sum = storage.get(dx, dy, fLeaf.channel, scaledRect); sum = storage.get(fLeaf.channel, scaledRect);
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0); int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift]; float impact = leaves[(st * 4) + lShift];
@ -506,17 +494,14 @@ struct cv::SoftCascade::Filds
if (st - stBegin > 50 ) break; if (st - stBegin > 50 ) break;
#endif #endif
if (detectionScore <= stage.threshold) break; if (detectionScore <= stage.threshold) return;
} }
dprintf("x %d y %d: %d\n", dx, dy, st - stBegin); dprintf("x %d y %d: %d\n", dx, dy, st - stBegin);
if (st == stEnd)
{
dprintf(" got %d\n", st); dprintf(" got %d\n", st);
level.markDetection(dx, dy, detectionScore, detections); level.markDetection(dx, dy, detectionScore, detections);
} }
}
octIt_t fitOctave(const float& logFactor) octIt_t fitOctave(const float& logFactor)
{ {
@ -738,13 +723,15 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
{ {
for (int dx = 0; dx < level.workRect.width; ++dx) for (int dx = 0; dx < level.workRect.width; ++dx)
{ {
fld.detectAt(level, dx, dy, storage, detections); storage.offset = dy * storage.step + dx;
fld.detectAt(dx, dy, level, storage, detections);
total++; total++;
} }
} }
cv::Mat out = image.clone();
#if defined DEBUG_SHOW_RESULT #if defined DEBUG_SHOW_RESULT
cv::Mat out = image.clone();
printf("TOTAL: %d from %d\n", (int)detections.size(),total) ; printf("TOTAL: %d from %d\n", (int)detections.size(),total) ;