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fix bugs in the soft cascade detect method; add options for debug logging

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- WITH_DEBUG_OUT for logging cascade scales
- DEBUG_STORE_IMAGES for xml matrix serialization
- DEBUG_SHOW_RESULT to see detection result
pull/137/head
marina.kolpakova 12 years ago
parent ba27d89173
commit 765dea9ddf
3 changed files with 414 additions and 141 deletions
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  1. 2
      modules/objdetect/include/opencv2/objdetect/objdetect.hpp
  2. 551
      modules/objdetect/src/softcascade.cpp
  3. 2
      modules/objdetect/test/test_softcascade.cpp

2
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@ -512,7 +512,7 @@ public:
//! return vector of bounding boxes. Each box contains one detected object
virtual void detectMultiScale(const Mat& image, const std::vector<cv::Rect>& rois, std::vector<cv::Rect>& objects,
int step = 4, int rejectfactor = 1);
int rejectfactor = 1);
protected:
enum { BOOST = 0 };

551
modules/objdetect/src/softcascade.cpp
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@ -47,11 +47,23 @@
#include <vector>
#include <string>
#include <iostream>
#include <string>
#include <cstdio>
namespace {
char *itoa(long i, char* s, int /*dummy_radix*/)
{
sprintf(s, "%ld", i);
return s;
}
// used for noisy printfs
// #define WITH_DEBUG_OUT
struct Octave
{
int index;
float scale;
int stages;
cv::Size size;
@ -61,21 +73,17 @@ struct Octave
static const char *const SC_OCT_STAGES;
static const char *const SC_OCT_SHRINKAGE;
Octave() : scale(0), stages(0), size(cv::Size()), shrinkage(0) {}
Octave(cv::Size origObjSize, const cv::FileNode& fn)
: scale((float)fn[SC_OCT_SCALE]), stages((int)fn[SC_OCT_STAGES]),
Octave(const int i, cv::Size origObjSize, const cv::FileNode& fn)
: index(i), scale((float)fn[SC_OCT_SCALE]), stages((int)fn[SC_OCT_STAGES]),
size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)),
shrinkage((int)fn[SC_OCT_SHRINKAGE])
{}
int index() const {return (int)log(scale);}
};
const char *const Octave::SC_OCT_SCALE = "scale";
const char *const Octave::SC_OCT_STAGES = "stageNum";
const char *const Octave::SC_OCT_SHRINKAGE = "shrinkingFactor";
struct Stage
{
float threshold;
@ -94,10 +102,10 @@ struct Node
float threshold;
Node(){}
Node(cv::FileNodeIterator& fIt) : feature((int)(*(fIt +=2)++)), threshold((float)(*(fIt++))){}
Node(const int offset, cv::FileNodeIterator& fIt)
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))){}
};
struct Feature
{
int channel;
@ -112,40 +120,48 @@ struct Feature
cv::FileNode rn = fn[SC_F_RECT];
cv::FileNodeIterator r_it = rn.end();
rect = cv::Rect(*(--r_it), *(--r_it), *(--r_it), *(--r_it));
// std::cout << "feature: " << rect.x << " " << rect.y << " " << rect.width
//<< " " << rect.height << " " << channel << std::endl;
}
};
const char * const Feature::SC_F_CHANNEL = "channel";
const char * const Feature::SC_F_RECT = "rect";
struct Object
{
enum Class{PEDESTRIAN};
cv::Rect rect;
float confidence;
Class detType;
Object(const cv::Rect& r, const float c, Class dt = PEDESTRIAN) : rect(r), confidence(c), detType(dt) {}
};
struct Level
{
const Octave* octave;
float origScale;
float relScale;
float shrScale;
float shrScale; // used for marking detection
cv::Size workRect;
cv::Size objSize;
// TiDo not reounding
Level(const Octave& oct, const float scale, const int shrinkage, const int w, const int h)
: octave(&oct), origScale(scale), relScale(scale / oct.scale), shrScale (relScale / shrinkage),
: octave(&oct), origScale(scale), relScale(scale / oct.scale), shrScale (relScale / (float)shrinkage),
workRect(cv::Size(cvRound(w / (float)shrinkage),cvRound(h / (float)shrinkage))),
objSize(cv::Size(cvRound(oct.size.width * relScale), cvRound(oct.size.height * relScale)))
{}
void markDetection(const int x, const int dx, std::vector<cv::Rect>& detections) const
void markDetection(const int x, const int y, float confidence, std::vector<Object>& detections) const
{
int shrinkage = (*octave).shrinkage;
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
detections.push_back(Object(rect, confidence));
}
};
struct CascadeIntrinsics
{
static const float lambda = 1.099f, a = 0.89f;
@ -157,7 +173,7 @@ struct CascadeIntrinsics
if ((scaling - 1.f) < FLT_EPSILON)
return 1.f;
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool's and Dallal's papers
static const float A[2][2] =
{ //channel <= 6, otherwise
{ 0.89f, 1.f}, // down
@ -167,77 +183,164 @@ struct CascadeIntrinsics
static const float B[2][2] =
{ //channel <= 6, otherwise
{ 1.099f / log(2), 2.f}, // down
{ 2.f, 2.f} // up
{ 0.f, 2.f} // up
};
float a = A[(int)(scaling >= 1)][(int)(channel >= 6)];
float b = B[(int)(scaling >= 1)][(int)(channel >= 6)];
float a = A[(int)(scaling >= 1)][(int)(channel > 6)];
float b = B[(int)(scaling >= 1)][(int)(channel > 6)];
#if defined WITH_DEBUG_OUT
printf("scaling: %f %f %f %f\n", scaling, a, b, a * pow(scaling, b));
#endif
return a * pow(scaling, b);
}
};
// Feature rescale(float relScale)
// {
// Feature res(*this);
// res.rect = cv::Rect (cvRound(rect.x * relScale), cvRound(rect.y * relScale),
// cvRound(rect.width * relScale), cvRound(rect.height * relScale));
// res.threshold = threshold * CascadeIntrinsics::getFor(channel, relScale);
// return res;
// }
int qangle6(float dfdx, float dfdy)
{
static const float vectors[6][2] =
{
{std::cos(0), std::sin(0) },
{std::cos(M_PI / 6.f), std::sin(M_PI / 6.f) },
{std::cos(M_PI / 3.f), std::sin(M_PI / 3.f) },
{std::cos(M_PI / 2.f), std::sin(M_PI / 2.f) },
{std::cos(2.f * M_PI / 3.f), std::sin(2.f * M_PI / 3.f)},
{std::cos(5.f * M_PI / 6.f), std::sin(5.f * M_PI / 6.f)}
};
int index = 0;
float dot = fabs(dfdx * vectors[0][0] + dfdy * vectors[0][1]);
for(int i = 1; i < 6; ++i)
{
const float curr = fabs(dfdx * vectors[i][0] + dfdy * vectors[i][1]);
if(curr > dot)
{
dot = curr;
index = i;
}
}
return index;
}
//ToDo
void calcHistBins(const cv::Mat& grey, cv::Mat& magIntegral, std::vector<cv::Mat>& histInts,
const int bins, int shrinkage)
{
static const float magnitudeScaling = 1.f / sqrt(2);
CV_Assert( grey.type() == CV_8U);
float scale = 1.f / shrinkage;
const int rows = grey.rows + 1;
const int cols = grey.cols + 1;
cv::Size intSumSize(cols, rows);
histInts.clear();
std::vector<cv::Mat> hist;
for (int bin = 0; bin < bins; ++bin)
cv::Mat df_dx(grey.rows, grey.cols, CV_32F),
df_dy(grey.rows, grey.cols, CV_32F), mag, angle;
// cv::Sobel(grey, df_dx, CV_32F, 1, 0);
// cv::Sobel(grey, df_dy, CV_32F, 0, 1);
for (int y = 1; y < grey.rows -1; ++y)
{
hist.push_back(cv::Mat(rows, cols, CV_32FC1));
}
float* dx = df_dx.ptr<float>(y);
float* dy = df_dy.ptr<float>(y);
const uchar* gr = grey.ptr<uchar>(y);
const uchar* gr_down = grey.ptr<uchar>(y - 1);
const uchar* gr_up = grey.ptr<uchar>(y + 1);
for (int x = 1; x < grey.cols - 1; ++x)
{
float dx_a = gr[x + 1];
float dx_b = gr[x - 1];
dx[x] = dx_a - dx_b;
cv::Mat df_dx, df_dy, mag, angle;
cv::Sobel(grey, df_dx, CV_32F, 1, 0);
cv::Sobel(grey, df_dy, CV_32F, 0, 1);
float dy_a = gr_up[x];
float dy_b = gr_down[x];
dy[x] = dy_a - dy_b;
}
}
cv::cartToPolar(df_dx, df_dy, mag, angle, true);
const float magnitudeScaling = 1.0 / sqrt(2);
mag *= magnitudeScaling;
angle /= 60;
for (int h = 0; h < mag.rows; ++h)
cv::Mat saturatedMag(grey.rows, grey.cols, CV_8UC1);
for (int y = 0; y < grey.rows; ++y)
{
float* rm = mag.ptr<float>(y);
uchar* mg = saturatedMag.ptr<uchar>(y);
for (int x = 0; x < grey.cols; ++x)
{
mg[x] = cv::saturate_cast<uchar>(rm[x]);
}
}
mag = saturatedMag;
histInts.clear();
std::vector<cv::Mat> hist;
for (int bin = 0; bin < bins; ++bin)
{
hist.push_back(cv::Mat(rows, cols, CV_8UC1));
}
for (int h = 0; h < saturatedMag.rows; ++h)
{
float* magnitude = mag.ptr<float>(h);
float* ang = angle.ptr<float>(h);
uchar* magnitude = saturatedMag.ptr<uchar>(h);
float* dfdx = df_dx.ptr<float>(h);
float* dfdy = df_dy.ptr<float>(h);
for (int w = 0; w < mag.cols; ++w)
for (int w = 0; w < saturatedMag.cols; ++w)
{
hist[(int)ang[w]].ptr<float>(h)[w] = magnitude[w];
hist[ qangle6(dfdx[w], dfdy[w]) ].ptr<uchar>(h)[w] = magnitude[w];
}
}
angle /= 60;
// for (int h = 0; h < saturatedMag.rows; ++h)
// {
// uchar* magnitude = saturatedMag.ptr<uchar>(h);
// float* ang = angle.ptr<float>(h);
// for (int w = 0; w < saturatedMag.cols; ++w)
// {
// hist[ (int)ang[w] ].ptr<uchar>(h)[w] = magnitude[w];
// }
// }
char buffer[33];
for (int bin = 0; bin < bins; ++bin)
{
cv::Mat shrunk, sum;
cv::imshow(std::string("hist[bin]") + itoa(bin, buffer, 10), hist[bin]);
cv::resize(hist[bin], shrunk, cv::Size(), scale, scale, cv::INTER_AREA);
cv::imshow(std::string("shrunk") + itoa(bin, buffer, 10), shrunk);
cv::integral(shrunk, sum);
cv::imshow(std::string("sum") + itoa(bin, buffer, 10), sum);
histInts.push_back(sum);
// std::cout << shrunk << std::endl << std::endl;
}
cv::Mat shrMag;
cv::imshow("mag", mag);
cv::resize(mag, shrMag, cv::Size(), scale, scale, cv::INTER_AREA);
cv::FileStorage fs("/home/kellan/actualChannels.xml", cv::FileStorage::WRITE);
cv::imshow("shrunk_channel", shrMag);
fs << "shrunk_channel6" << shrMag;
// cv::imshow("shrMag", shrMag);
cv::integral(shrMag, magIntegral, mag.depth());
// cv::imshow("magIntegral", magIntegral);
histInts.push_back(magIntegral);
}
@ -252,39 +355,92 @@ struct ChannelStorage
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
ChannelStorage() {}
ChannelStorage(const cv::Mat& colored, int shr) : shrinkage(shr)
ChannelStorage(cv::Mat& colored, int shr) : shrinkage(shr)
{
cv::Mat _luv, shrLuv;
cv::cvtColor(colored, _luv, CV_BGR2Luv);
cv::resize(_luv, shrLuv, cv::Size(), 1.f / shr, 1.f / shr, cv::INTER_AREA);
hog.clear();
cv::FileStorage fs("/home/kellan/testInts.xml", cv::FileStorage::READ);
char buff[33];
float scale = 1.f / shrinkage;
for(int i = 0; i < 10; ++i)
{
cv::Mat channel;
fs[std::string("channel") + itoa(i, buff, 10)] >> channel;
cv::Mat shrunk, sum;
// cv::resize(channel, shrunk, cv::Size(), scale, scale, cv::INTER_AREA);
// cv::imshow(std::string("channel") + itoa(i, buff, 10), shrunk);
// cv::waitKey(0);
// cv::integral(channel, sum);
// if (i == 1)
// std::cout << channel << std::endl;
hog.push_back(channel);
}
// exit(1);
}
// {
// // add gauss
// cv::Mat gauss;
// cv::GaussianBlur(colored, gauss, cv::Size(3,3), 0 ,0);
cv::integral(shrLuv, luv);
// colored = gauss;
// // cv::imshow("colored", colored);
std::vector<cv::Mat> splited;
split(luv, splited);
// cv::Mat _luv, shrLuv;
// cv::cvtColor(colored, _luv, CV_BGR2Luv);
cv::Mat grey;
cv::cvtColor(colored, grey, CV_RGB2GRAY);
// // cv::imshow("_luv", _luv);
calcHistBins(grey, magnitude, hog, HOG_BINS, shrinkage);
// cv::resize(_luv, shrLuv, cv::Size(), 1.f / shr, 1.f / shr, cv::INTER_AREA);
hog.insert(hog.end(), splited.begin(), splited.end());
}
// // cv::imshow("shrLuv", shrLuv);
// cv::integral(shrLuv, luv);
// // cv::imshow("luv", luv);
// std::vector<cv::Mat> splited;
// split(luv, splited);
// char buffer[33];
// for (int i = 0; i < (int)splited.size(); i++)
// {
// // cv::imshow(itoa(i,buffer,10), splited[i]);
// }
// cv::Mat grey;
// cv::cvtColor(colored, grey, CV_RGB2GRAY);
// // cv::imshow("grey", grey);
// calcHistBins(grey, magnitude, hog, HOG_BINS, shrinkage);
// hog.insert(hog.end(), splited.begin(), splited.end());
// }
float get(const int x, const int y, const int channel, const cv::Rect& area) const
{
CV_Assert(channel < HOG_LUV_BINS);
const cv::Mat m = hog[channel];
float a = m.ptr(y + area.y)[x + area.x];
float b = m.ptr(y + area.y)[x + area.width];
float c = m.ptr(y + area.height)[x + area.width];
float d = m.ptr(y + area.height)[x + area.x];
#if defined WITH_DEBUG_OUT
printf("feature box %d %d %d %d ", area.x, area.y, area.width, area.height);
printf("get for channel %d\n", channel);
printf("!! %d\n", m.depth());
#endif
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];
#if defined WITH_DEBUG_OUT
printf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
#endif
return (a - b + c - d);
}
};
}
struct cv::SoftCascade::Filds
@ -299,25 +455,91 @@ struct cv::SoftCascade::Filds
std::vector<Octave> octaves;
std::vector<Stage> stages;
std::vector<Node> nodes;
std::vector<float> leaves;
std::vector<Node> nodes;
std::vector<float> leaves;
std::vector<Feature> features;
std::vector<Level> levels;
typedef std::vector<Octave>::iterator octIt_t;
float rescale(const Feature& feature, const float relScale, cv::Rect& scaledRect, const float threshold) const
{
float scaling = CascadeIntrinsics::getFor(feature.channel, relScale);
scaledRect = feature.rect;
#if defined WITH_DEBUG_OUT
printf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
scaledRect.width, scaledRect.height);
std::cout << "rescale: " << feature.channel << " " << relScale << " " << scaling << std::endl;
#endif
float farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// 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);
#if defined WITH_DEBUG_OUT
printf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
scaledRect.width, scaledRect.height);
std::cout << " new rect: " << scaledRect.x << " " << scaledRect.y
<< " " << scaledRect.width << " " << scaledRect.height << " ";
#endif
float sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
float approx = 1.f;
if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea * relScale * relScale;
approx = expected_new_area / sarea;
#if defined WITH_DEBUG_OUT
std::cout << " rel areas " << expected_new_area << " " << sarea << std::endl;
#endif
}
// compensation areas rounding
float rootThreshold = threshold / approx;/
rootThreshold *= scaling;
#if defined WITH_DEBUG_OUT
std::cout << "approximation " << approx << " " << threshold << " -> " << rootThreshold
<< " " << scaling << std::endl;
#endif
return rootThreshold;
}
void detectAt(const Level& level, const int dx, const int dy, const ChannelStorage& storage,
std::vector<cv::Rect>& detections) const
std::vector<Object>& detections) const
{
#if defined WITH_DEBUG_OUT
std::cout << "detect at: " << dx << " " << dy << std::endl;
#endif
float detectionScore = 0.f;
const Octave& octave = *(level.octave);
int stBegin = octave.index() * octave.stages, stEnd = stBegin + octave.stages;
int stBegin = octave.index * octave.stages, stEnd = stBegin + octave.stages;
#if defined WITH_DEBUG_OUT
std::cout << " octave stages: " << stBegin << " to " << stEnd << " index " << octave.index << " "
<< octave.scale << " level " << level.origScale << std::endl;
#endif
int st = stBegin;
for(; st < stEnd; ++st)
{
#if defined WITH_DEBUG_OUT
printf("index: %d\n", st);
#endif
const Stage& stage = stages[st];
{
int nId = st * 3;
@ -325,71 +547,55 @@ struct cv::SoftCascade::Filds
// work with root node
const Node& node = nodes[nId];
const Feature& feature = features[node.feature];
cv::Rect scaledRect;
float threshold = rescale(feature, level.relScale, scaledRect, node.threshold);
// rescaling
float scaling = CascadeIntrinsics::getFor(feature.channel, level.relScale);
cv::Rect scaledRect = feature.rect;
float farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// rescale
scaledRect.x = cvRound(scaling * scaledRect.x);
scaledRect.y = cvRound(scaling * scaledRect.y);
scaledRect.width = cvRound(scaling * scaledRect.width);
scaledRect.height = cvRound(scaling * scaledRect.height);
float sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
float sum = storage.get(dx, dy, feature.channel, scaledRect);
float approx = 1.f;
if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea*level.relScale*level.relScale;
approx = expected_new_area / sarea;
}
#if defined WITH_DEBUG_OUT
printf("root feature %d %f\n",feature.channel, sum);
#endif
float rootThreshold = node.threshold / approx; // ToDo check
rootThreshold *= scaling;
int next = (sum >= threshold)? 2 : 1;
// use rescaled
float sum = storage.get(dx, dy, feature.channel, scaledRect);
int next = (sum >= rootThreshold)? 2 : 1;
#if defined WITH_DEBUG_OUT
printf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
#endif
// leaces
// leaves
const Node& leaf = nodes[nId + next];
const Feature& fLeaf = features[node.feature];
// rescaling
scaling = CascadeIntrinsics::getFor(fLeaf.channel, level.relScale);
scaledRect = fLeaf.rect;
farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// rescale
scaledRect.x = cvRound(scaling * scaledRect.x);
scaledRect.y = cvRound(scaling * scaledRect.y);
scaledRect.width = cvRound(scaling * scaledRect.width);
scaledRect.height = cvRound(scaling * scaledRect.height);
sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
approx = 1.f;
if ((farea - 0.f) > FLT_EPSILON && (farea - 0.f) > FLT_EPSILON)
{
const float expected_new_area = farea*level.relScale*level.relScale;
approx = expected_new_area / sarea;
}
const Feature& fLeaf = features[leaf.feature];
rootThreshold = leaf.threshold / approx; // ToDo check
rootThreshold *= scaling;
threshold = rescale(fLeaf, level.relScale, scaledRect, leaf.threshold);
sum = storage.get(dx, dy, fLeaf.channel, scaledRect);
sum = storage.get(dx, dy, feature.channel, scaledRect);
int lShift = (next - 1) * 2 + (sum >= rootThreshold) ? 1 : 0;
float impact = leaves[nId + lShift];
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift];
#if defined WITH_DEBUG_OUT
printf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
#endif
detectionScore += impact;
}
#if defined WITH_DEBUG_OUT
printf("extracted stage:\n");
printf("ct %f\n", stage.threshold);
printf("computed score %f\n\n", detectionScore);
// if (st - stBegin > 100) break;
#endif
if (detectionScore <= stage.threshold) break;
}
if (st == octave.stages - 1)
level.markDetection(dx, dy, detections);
printf("x %d y %d: %d\n", dx, dy, st - stBegin);
if (st == stEnd)
{
std::cout << " got " << st << std::endl;
level.markDetection(dx, dy, detectionScore, detections);
}
}
octIt_t fitOctave(const float& logFactor)
@ -438,22 +644,17 @@ struct cv::SoftCascade::Filds
if (fabs(scale - maxScale) < FLT_EPSILON) break;
scale = std::min(maxScale, expf(log(scale) + logFactor));
// std::cout << "level scale "
// << levels[sc].origScale
// << " octeve "
// << levels[sc].octave->scale
// << " "
// << levels[sc].relScale
// << " " << levels[sc].shrScale
// << " [" << levels[sc].objSize.width
// << " " << levels[sc].objSize.height << "] ["
// << levels[sc].workRect.width << " " << levels[sc].workRect.height << std::endl;
std::cout << "level " << sc << " scale "
<< levels[sc].origScale
<< " octeve "
<< levels[sc].octave->scale
<< " "
<< levels[sc].relScale
<< " " << levels[sc].shrScale
<< " [" << levels[sc].objSize.width
<< " " << levels[sc].objSize.height << "] ["
<< levels[sc].workRect.width << " " << levels[sc].workRect.height << "]" << std::endl;
}
return;
std::cout << std::endl << std::endl << std::endl;
}
bool fill(const FileNode &root, const float mins, const float maxs)
@ -500,10 +701,12 @@ struct cv::SoftCascade::Filds
// octaves.reserve(noctaves);
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
int octIndex = 0;
for (; it != it_end; ++it)
{
FileNode fns = *it;
Octave octave(cv::Size(SoftCascade::ORIG_OBJECT_WIDTH, SoftCascade::ORIG_OBJECT_HEIGHT), fns);
Octave octave(octIndex, cv::Size(SoftCascade::ORIG_OBJECT_WIDTH, SoftCascade::ORIG_OBJECT_HEIGHT), fns);
CV_Assert(octave.stages > 0);
octaves.push_back(octave);
@ -527,7 +730,7 @@ struct cv::SoftCascade::Filds
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node(inIt));
nodes.push_back(Node(feature_offset, inIt));
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
@ -539,9 +742,31 @@ struct cv::SoftCascade::Filds
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
features.push_back(Feature(*st));
feature_offset += octave.stages * 3;
++octIndex;
}
shrinkage = octaves[0].shrinkage;
//debug print
// std::cout << "collected " << stages.size() << " stages" << std::endl;
// for (int i = 0; i < (int)stages.size(); ++i)
// {
// std::cout << "stage " << i << ": " << stages[i].threshold << std::endl;
// }
// std::cout << "collected " << nodes.size() << " nodes" << std::endl;
// for (int i = 0; i < (int)nodes.size(); ++i)
// {
// std::cout << "node " << i << ": " << nodes[i].threshold << " " << nodes[i].feature << std::endl;
// }
// std::cout << "collected " << leaves.size() << " leaves" << std::endl;
// for (int i = 0; i < (int)leaves.size(); ++i)
// {
// std::cout << "leaf " << i << ": " << leaves[i] << std::endl;
// }
return true;
}
};
@ -574,9 +799,11 @@ bool cv::SoftCascade::load( const string& filename, const float minScale, const
return true;
}
void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& rois,
std::vector<cv::Rect>& objects,
const int step, const int rejectfactor)
#define DEBUG_STORE_IMAGES
#define DEBUG_SHOW_RESULT
void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& /*rois*/,
std::vector<cv::Rect>& objects, const int /*rejectfactor*/)
{
typedef std::vector<cv::Rect>::const_iterator RIter_t;
// only color images are supperted
@ -589,20 +816,66 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
const Filds& fld = *filds;
cv::Mat image1;
cv::cvtColor(image, image1, CV_RGB2RGBA);
#if defined DEBUG_STORE_IMAGES
cv::FileStorage fs("/home/kellan/opencvInputImage.xml", cv::FileStorage::WRITE);
cv::imwrite("/home/kellan/opencvInputImage.jpg", image1);
fs << "opencvInputImage" << image1;
cv::Mat doppia;
cv::FileStorage fsr("/home/kellan/befireGause.xml", cv::FileStorage::READ);
fsr["input_gpu_mat"] >> doppia;
cv::Mat diff;
cv::absdiff(image1, doppia, diff);
fs << "absdiff" << diff;
fs.release();
#if defined DEBUG_STORE_IMAGES
// create integrals
ChannelStorage storage(image, fld.shrinkage);
ChannelStorage storage(image1, fld.shrinkage);
// object candidates
std::vector<cv::Rect> detections;
std::vector<Object> detections;
typedef std::vector<Level>::const_iterator lIt;
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
int total = 0, l = 0;
for (lIt it = fld.levels.begin() + 26; it != fld.levels.end(); ++it)
{
const Level& level = *it;
#if defined WITH_DEBUG_OUT
std::cout << "================================ " << l++ << std::endl;
#endif
for (int dy = 0; dy < level.workRect.height; ++dy)
{
for (int dx = 0; dx < level.workRect.width; ++dx)
{
fld.detectAt(level, dx, dy, storage, detections);
total++;
// break;
}
// break;
}
break;
}
cv::Mat out = image.clone();
#if defined DEBUG_SHOW_RESULT
printf("TOTAL: %d from %d\n", (int)detections.size(),total) ;
for(int i = 0; i < (int)detections.size(); ++i)
{
cv::rectangle(out, detections[i].rect, cv::Scalar(255, 0, 0, 255), 2);
}
std::swap(detections, objects);
cv::imshow("out", out);
cv::waitKey(0);
#endif
// std::swap(detections, objects);
}

2
modules/objdetect/test/test_softcascade.cpp
Unescape View File

@ -55,7 +55,7 @@ TEST(SoftCascade, detect)
cv::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000006_0.png");
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(colored.empty());
std::vector<cv::Rect> objectBoxes;

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