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first debug integration of newly trained cascade

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pull/311/head
marina.kolpakova 12 years ago
parent dc0788c864
commit 3e7965a5a5
2 changed files with 173 additions and 93 deletions
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  1. 102
      modules/objdetect/src/softcascade.cpp
  2. 164
      modules/objdetect/test/test_softcascade.cpp

102
modules/objdetect/src/softcascade.cpp
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@ -47,18 +47,19 @@ namespace {
struct Octave
{
Octave(const int i, const 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]) {}
: index(i), weaks((int)fn[SC_OCT_WEAKS]), scale(pow(2,(float)fn[SC_OCT_SCALE])),
size(cvRound(origObjSize.width * scale), cvRound(origObjSize.height * scale)) {}
int index;
int weaks;
int index;
float scale;
int stages;
cv::Size size;
int shrinkage;
static const char *const SC_OCT_SCALE;
static const char *const SC_OCT_STAGES;
static const char *const SC_OCT_WEAKS;
static const char *const SC_OCT_SHRINKAGE;
};
@ -66,11 +67,11 @@ struct Octave
struct Weak
{
Weak(){}
Weak(const cv::FileNode& fn) : threshold((float)fn[SC_STAGE_THRESHOLD]){}
Weak(const cv::FileNode& fn) : threshold((float)fn[SC_WEAK_THRESHOLD]) {}
float threshold;
static const char *const SC_STAGE_THRESHOLD;
static const char *const SC_WEAK_THRESHOLD;
};
@ -78,9 +79,9 @@ struct Node
{
Node(){}
Node(const int offset, cv::FileNodeIterator& fIt)
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))){}
: feature((int)(*(fIt +=2)++) + offset), threshold((float)(*(fIt++))) {}
int feature;
int feature;
float threshold;
};
@ -96,7 +97,9 @@ struct Feature
int y = *r_it++;
int w = *r_it++;
int h = *r_it++;
rect = cv::Rect(x, y, w, h);
// ToDo: fix me
rect = cv::Rect(x, y, w + x, h + y);
// 1 / area
rarea = 1.f / ((rect.width - rect.x) * (rect.height - rect.y));
@ -112,9 +115,9 @@ struct Feature
};
const char *const Octave::SC_OCT_SCALE = "scale";
const char *const Octave::SC_OCT_STAGES = "stageNum";
const char *const Octave::SC_OCT_WEAKS = "weaks";
const char *const Octave::SC_OCT_SHRINKAGE = "shrinkingFactor";
const char *const Weak::SC_STAGE_THRESHOLD = "stageThreshold";
const char *const Weak::SC_WEAK_THRESHOLD = "treeThreshold";
const char *const Feature::SC_F_CHANNEL = "channel";
const char *const Feature::SC_F_RECT = "rect";
@ -144,7 +147,8 @@ struct Level
void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const
{
int shrinkage = (*octave).shrinkage;
// fix me
int shrinkage = 4;//(*octave).shrinkage;
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
detections.push_back(Detection(rect, confidence));
@ -237,41 +241,47 @@ struct cv::SCascade::Fields
float detectionScore = 0.f;
const Octave& octave = *(level.octave);
int stBegin = octave.index * octave.stages, stEnd = stBegin + octave.stages;
int stBegin = octave.index * octave.weaks, stEnd = stBegin + ((octave.index)? 1024 : 416;
int st = stBegin;
int offset = (octave.index)? -2: 0;
for(; st < stEnd; ++st)
{
const Weak& stage = stages[st];
{
int nId = st * 3;
int nId = st * 3 + offset;
// work with root node
const Node& node = nodes[nId];
const Feature& feature = features[node.feature];
const Feature& feature = features[node.feature + offset];
cv::Rect scaledRect(feature.rect);
float threshold = level.rescale(scaledRect, node.threshold,(int)(feature.channel > 6)) * feature.rarea;
float sum = storage.get(feature.channel, scaledRect);
// std::cout << "root: node.threshold " << node.threshold << " " << threshold << " " << sum << " node.feature " << node.feature << " " << feature.rect << std::endl;
int next = (sum >= threshold)? 2 : 1;
// leaves
const Node& leaf = nodes[nId + next];
const Feature& fLeaf = features[leaf.feature];
const Feature& fLeaf = features[leaf.feature + offset];
scaledRect = fLeaf.rect;
threshold = level.rescale(scaledRect, leaf.threshold, (int)(fLeaf.channel > 6)) * fLeaf.rarea;
sum = storage.get(fLeaf.channel, scaledRect);
// std::cout << "leaf: node.threshold " << leaf.threshold << " " << threshold << " " << sum << " node.feature " << leaf.feature << " " << fLeaf.rect << std::endl;
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift];
float impact = leaves[(st * 4 + offset) + lShift];
// std::cout << "impact " << impact;
detectionScore += impact;
}
// std::cout << dx << " " << dy << " " << detectionScore << " " << stage.threshold << std::endl;
if (detectionScore <= stage.threshold) return;
}
@ -364,55 +374,61 @@ struct cv::SCascade::Fields
origObjWidth = (int)root[SC_ORIG_W];
origObjHeight = (int)root[SC_ORIG_H];
// for each octave (~ one cascade in classic OpenCV xml)
shrinkage = (int)root["shrinkage"];
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
// octaves.reserve(noctaves);
// // octaves.reserve(noctaves);
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
int octIndex = 0;
// for each octave
for (; it != it_end; ++it)
{
FileNode fns = *it;
Octave octave(octIndex, cv::Size(origObjWidth, origObjHeight), fns);
CV_Assert(octave.stages > 0);
CV_Assert(octave.weaks > 0);
octaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_STAGES];
fns = fns["trees"];
if (fn.empty()) return false;
// for each stage (~ decision tree with H = 2)
// for each tree (~ decision tree with H = 2)
FileNodeIterator st = fns.begin(), st_end = fns.end();
// int i = 0;
for (; st != st_end; ++st )
{
fns = *st;
stages.push_back(Weak(fns));
stages.push_back(Weak(*st));
fns = fns[SC_WEEK];
FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
for (; ftr != ft_end; ++ftr)
fns = (*st)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node(feature_offset, inIt));
fns = (*st)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
int l = 0;
for (; inIt != inIt_end; ++inIt)
{
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node(feature_offset, inIt));
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
leaves.push_back((float)(*inIt));
leaves.push_back((float)(*inIt));
// l++;
// std::cout << ((float)(*inIt)) << std::endl;
}
// if (l =! 4) std::cout << "!!!!!!! " << i << std::endl;
// i++;
// std::cout << i << " nodes " << nodes.size() << " " << nodes.size() / 3.0 << std::endl;
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
features.push_back(Feature(*st));
feature_offset += octave.stages * 3;
feature_offset += octave.weaks * 3;
++octIndex;
}
@ -497,16 +513,20 @@ void cv::SCascade::detectNoRoi(const cv::Mat& image, std::vector<Detection>& obj
ChannelStorage storage(image, fld.shrinkage);
typedef std::vector<Level>::const_iterator lIt;
int i = 13;
for (lIt it = fld.levels.begin(); it != fld.levels.end(); ++it)
{
const Level& level = *it;
if (i++ == 26) return;
for (int dy = 0; dy < level.workRect.height; ++dy)
{
for (int dx = 0; dx < level.workRect.width; ++dx)
{
storage.offset = dy * storage.step + dx;
fld.detectAt(dx, dy, level, storage, objects);
// std::cout << std::endl << std::endl << std::endl;
}
}
}
@ -568,11 +588,11 @@ void cv::SCascade::detect(InputArray _image, InputArray _rois, OutputArray _rec
std::vector<Detection> objects;
detect( _image, _rois, objects);
_rects.create(1, (int)objects.size(), CV_32SC4);
_rects.create(1, objects.size(), CV_32SC4);
cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat();
cv::Rect* rectPtr = rects.ptr<cv::Rect>(0);
_confs.create(1, (int)objects.size(), CV_32F);
_confs.create(1, objects.size(), CV_32F);
cv::Mat confs = _confs.getMat();
float* confPtr = rects.ptr<float>(0);

164
modules/objdetect/test/test_softcascade.cpp
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@ -40,86 +40,146 @@
//
//M*/
#include "test_precomp.hpp"
#include <string>
#include <fstream>
TEST(SCascade, readCascade)
static std::string itoa(int i)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/icf-template.xml";
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
static char s[65];
sprintf(s, "%03d", i);
return std::string(s);
}
TEST(SCascade, detect)
#include "test_precomp.hpp"
#include <opencv2/highgui/highgui.hpp>
TEST(SCascade, detect1)
{
typedef cv::SCascade::Detection Detection;
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
// cascade.set("rejfactor", 0.5);
// cascade.set("minScale", 0.5);
// cascade.set("scales", 2);
cv::FileStorage fs("/home/kellan/soft-cascade-17.12.2012/first-soft-cascade-composide-octave_1.xml", cv::FileStorage::READ);
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
for (int sample = 0; sample < 1000; ++sample)
{
// std::cout << itoa(sample) << std::endl;
std::cout << std::string("/home/kellan/bahnhof-l/image_00000" + itoa(sample) + "_0.png") << std::endl;
cv::Mat colored = cv::imread(std::string("/home/kellan/bahnhof-l/image_00000" + itoa(sample) + "_0.png"));
ASSERT_FALSE(colored.empty());
std::vector<Detection> objects;
std::vector<Detection> objects;
cascade.detect(colored, cv::noArray(), objects);
ASSERT_EQ(1459, (int)objects.size());
for (int i = 0; i < (int)objects.size(); ++i)
cv::rectangle(colored, objects[i].bb, cv::Scalar(51, 160, 255, 255), 1);
// cv::Mat res;
// cv::resize(colored, res, cv::Size(), 4,4);
cv::imshow("detections", colored);
cv::waitKey(20);
// cv::imwrite(std::string("/home/kellan/res/image_00000" + itoa(sample) + ".png"), colored);
}
// ASSERT_EQ(1459, (int)objects.size());
}
TEST(SCascade, detectSeparate)
TEST(SCascade, readCascade)
{
typedef cv::SCascade::Detection Detection;
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/test-simple-cascade.xml";
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
cv::FileStorage fs("/home/kellan/soft-cascade-17.12.2012/first-soft-cascade-composide-octave_1.xml", cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
}
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(colored.empty());
// TEST(SCascade, detect)
// {
// typedef cv::SCascade::Detection Detection;
// std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
// cv::SCascade cascade;
// // cascade.set("maxScale", 0.5);
// // cascade.set("minScale", 0.5);
// // cascade.set("scales", 2);
cv::Mat rects, confs;
// cv::FileStorage fs("/home/kellan/soft-cascade-17.12.2012/first-soft-cascade-composide-octave_1.xml", cv::FileStorage::READ);
// ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cascade.detect(colored, cv::noArray(), rects, confs);
ASSERT_EQ(1459, confs.cols);
}
// // 454
// cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");//"/home/kellan/datasets/INRIA/training_set/pos/octave_-1/sample_1.png");//cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
// ASSERT_FALSE(colored.empty());
TEST(SCascade, detectRoi)
{
typedef cv::SCascade::Detection Detection;
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
// std::vector<Detection> objects;
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(colored.empty());
// cascade.detect(colored, cv::noArray(), objects);
std::vector<Detection> objects;
std::vector<cv::Rect> rois;
rois.push_back(cv::Rect(0, 0, 640, 480));
// for (int i = 0; i < objects.size(); ++i)
// cv::rectangle(colored, objects[i].bb, cv::Scalar::all(255), 1);
cascade.detect(colored, rois, objects);
ASSERT_EQ(1459, (int)objects.size());
}
// cv::Mat res;
// cv::resize(colored, res, cv::Size(), 4,4);
// cv::imshow("detections", colored);
// cv::waitKey(0);
TEST(SCascade, detectNoRoi)
{
typedef cv::SCascade::Detection Detection;
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
// // ASSERT_EQ(1459, (int)objects.size());
// }
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(colored.empty());
// TEST(SCascade, detectSeparate)
// {
// typedef cv::SCascade::Detection Detection;
// std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
// cv::SCascade cascade;
// cv::FileStorage fs(xml, cv::FileStorage::READ);
// ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
std::vector<Detection> objects;
std::vector<cv::Rect> rois;
// cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
// ASSERT_FALSE(colored.empty());
// cv::Mat rects, confs;
// cascade.detect(colored, cv::noArray(), rects, confs);
// ASSERT_EQ(1459, confs.cols);
// }
// TEST(SCascade, detectRoi)
// {
// typedef cv::SCascade::Detection Detection;
// std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
// cv::SCascade cascade;
// cv::FileStorage fs(xml, cv::FileStorage::READ);
// ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
// cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
// ASSERT_FALSE(colored.empty());
// std::vector<Detection> objects;
// std::vector<cv::Rect> rois;
// rois.push_back(cv::Rect(0, 0, 640, 480));
// cascade.detect(colored, rois, objects);
// ASSERT_EQ(1459, (int)objects.size());
// }
// TEST(SCascade, detectNoRoi)
// {
// typedef cv::SCascade::Detection Detection;
// std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
// cv::SCascade cascade;
// cv::FileStorage fs(xml, cv::FileStorage::READ);
// ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
// cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
// ASSERT_FALSE(colored.empty());
// std::vector<Detection> objects;
// std::vector<cv::Rect> rois;
cascade.detect(colored, rois, objects);
// cascade.detect(colored, rois, objects);
ASSERT_EQ(0, (int)objects.size());
}
// ASSERT_EQ(0, (int)objects.size());
// }
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