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Commit dc74ce20 authored by marina.kolpakova's avatar marina.kolpakova
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OpenCV friendly xml format for soft cascade

parent c04725b6
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Showing with 299 additions and 261 deletions
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View file @ dc74ce20
......@@ -493,32 +493,36 @@ protected:
class CV_EXPORTS SoftCascade
{
public:
//! empty cascade will be created.
//! An empty cascade will be created.
SoftCascade();
//! cascade will be loaded from file "filename"
//! Cascade will be created from file for scales from minScale to maxScale.
//! Param filename is a path to xml-serialized cascade.
//! Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
//! Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
SoftCascade( const string& filename, const float minScale = 0.4f, const float maxScale = 5.f);
//! cascade will be loaded from file "filename". The previous cascade will be destroyed.
//! Param filename is a path to xml-serialized cascade.
//! Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
//! Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
bool load( const string& filename, const float minScale = 0.4f, const float maxScale = 5.f);
virtual ~SoftCascade();
//! return vector of bounding boxes. Each box contains detected object
//! 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);
protected:
virtual void detectForOctave(int octave);
// virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
// int stripSize, int yStep, double factor, vector<Rect>& candidates,
// vector<int>& rejectLevels, vector<double>& levelWeights, bool outputRejectLevels=false);
enum { BOOST = 0 };
enum
{
FRAME_WIDTH = 640,
FRAME_HEIGHT = 480,
TOTAL_SCALES = 55,
CLASSIFIERS = 5,
ORIG_OBJECT_WIDTH = 64,
FRAME_WIDTH = 640,
FRAME_HEIGHT = 480,
TOTAL_SCALES = 55,
CLASSIFIERS = 5,
ORIG_OBJECT_WIDTH = 64,
ORIG_OBJECT_HEIGHT = 128
};
......
modules/objdetect/src/softcascade.cpp
View file @ dc74ce20
......@@ -45,134 +45,160 @@
#include <vector>
#include <string>
#include <stdio.h>
#include <iostream>
namespace {
static const char* SC_OCT_SCALE = "scale";
static const char* SC_OCT_STAGES = "stageNum";
struct Octave
{
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_SHRINKAGE;
Octave(){}
Octave(const cv::FileNode& fn) : scale((float)fn[SC_OCT_SCALE]), stages((int)fn[SC_OCT_STAGES])
{/*printf("octave: %f %d\n", scale, stages);*/}
Octave(cv::Size origObjSize, const cv::FileNode& fn)
: 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])
{}
};
static const char *SC_STAGE_THRESHOLD = "stageThreshold";
static const char *SC_STAGE_WEIGHT = "weight";
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;
float weight;
static const char *const SC_STAGE_THRESHOLD;
Stage(){}
Stage(const cv::FileNode& fn) : threshold((float)fn[SC_STAGE_THRESHOLD]), weight((float)fn[SC_STAGE_WEIGHT])
{/*printf(" stage: %f %f\n",threshold, weight);*/}
Stage(const cv::FileNode& fn) : threshold((float)fn[SC_STAGE_THRESHOLD])
{ std::cout << " stage: " << threshold << std::endl; }
};
// according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
struct CascadeIntrinsics
{
static const float lambda = 1.099f, a = 0.89f;
static const float intrinsics[10][4];
static float getFor(int channel, float scaling)
{
CV_Assert(channel < 10);
if ((scaling - 1.f) < FLT_EPSILON)
return 1.f;
const char *const Stage::SC_STAGE_THRESHOLD = "stageThreshold";
int ud = (int)(scaling < 1.f);
return intrinsics[channel][(ud << 1)] * pow(scaling, intrinsics[channel][(ud << 1) + 1]);
}
struct Node
{
int feature;
float threshold;
Node(){}
Node(cv::FileNodeIterator& fIt) : feature((int)(*(fIt +=2)++)), threshold((float)(*(fIt++)))
{ std::cout << " Node: " << feature << " " << threshold << std::endl; }
};
const float CascadeIntrinsics::intrinsics[10][4] =
{ //da, db, ua, ub
// hog-like orientation bins
{a, lambda / log(2), 1, 2},
{a, lambda / log(2), 1, 2},
{a, lambda / log(2), 1, 2},
{a, lambda / log(2), 1, 2},
{a, lambda / log(2), 1, 2},
{a, lambda / log(2), 1, 2},
// gradient magnitude
{a, lambda / log(2), 1, 2},
// luv color channels
{1, 2, 1, 2},
{1, 2, 1, 2},
{1, 2, 1, 2}
};
static const char *SC_F_THRESHOLD = "threshold";
static const char *SC_F_DIRECTION = "direction";
static const char *SC_F_CHANNEL = "channel";
static const char *SC_F_RECT = "rect";
struct Feature
{
float threshold;
int direction;
int channel;
cv::Rect rect;
static const char * const SC_F_CHANNEL;
static const char * const SC_F_RECT;
Feature() {}
Feature(const cv::FileNode& fn)
: threshold((float)fn[SC_F_THRESHOLD]), direction((int)fn[SC_F_DIRECTION]),
channel((int)fn[SC_F_CHANNEL])
Feature(const cv::FileNode& fn) : channel((int)fn[SC_F_CHANNEL])
{
cv::FileNode rn = fn[SC_F_RECT];
cv::FileNodeIterator r_it = rn.begin();
rect = cv::Rect(*(r_it++), *(r_it++), *(r_it++), *(r_it++));
// printf(" feature: %f %d %d [%d %d %d %d]\n",threshold, direction, channel, rect.x, rect.y, rect.width, rect.height);
}
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;
}
};
struct Level
{
int index;
float factor;
float logFactor;
int width;
int height;
float octave;
cv::Size objSize;
Level(int i,float f, float lf, int w, int h): index(i), factor(f), logFactor(lf), width(w), height(h), octave(0.f) {}
void assign(float o, int detW, int detH)
{
octave = o;
objSize = cv::Size(cv::saturate_cast<int>(detW * o), cv::saturate_cast<int>(detH * o));
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;
}
float relScale() {return (factor / octave); }
// 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;
// }
};
struct Integral
{
cv::Mat magnitude;
std::vector<cv::Mat> hist;
cv::Mat luv;
Integral(cv::Mat m, std::vector<cv::Mat> h, cv::Mat l) : magnitude(m), hist(h), luv(l) {}
};
const char * const Feature::SC_F_CHANNEL = "channel";
const char * const Feature::SC_F_RECT = "rect";
// // according to R. Benenson, M. Mathias, R. Timofte and L. Van Gool paper
// struct CascadeIntrinsics
// {
// static const float lambda = 1.099f, a = 0.89f;
// static const float intrinsics[10][4];
// static float getFor(int channel, float scaling)
// {
// CV_Assert(channel < 10);
// if ((scaling - 1.f) < FLT_EPSILON)
// return 1.f;
// int ud = (int)(scaling < 1.f);
// return intrinsics[channel][(ud << 1)] * pow(scaling, intrinsics[channel][(ud << 1) + 1]);
// }
// };
// const float CascadeIntrinsics::intrinsics[10][4] =
// { //da, db, ua, ub
// // hog-like orientation bins
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// {a, lambda / log(2), 1, 2},
// // gradient magnitude
// {a, lambda / log(2), 1, 2},
// // luv color channels
// {1, 2, 1, 2},
// {1, 2, 1, 2},
// {1, 2, 1, 2}
// };
// struct Level
// {
// int index;
// float factor;
// float logFactor;
// int width;
// int height;
// Octave octave;
// cv::Size objSize;
// cv::Size dWinSize;
// static const float shrinkage = 0.25;
// Level(int i,float f, float lf, int w, int h): index(i), factor(f), logFactor(lf), width(w), height(h), octave(Octave())
// {}
// void assign(const Octave& o, int detW, int detH)
// {
// octave = o;
// objSize = cv::Size(cv::saturate_cast<int>(detW * o.scale), cv::saturate_cast<int>(detH * o.scale));
// }
// float relScale() {return (factor / octave.scale); }
// float srScale() {return (factor / octave.scale * shrinkage); }
// };
// struct Integral
// {
// cv::Mat magnitude;
// std::vector<cv::Mat> hist;
// cv::Mat luv;
// Integral(cv::Mat m, std::vector<cv::Mat> h, cv::Mat l) : magnitude(m), hist(h), luv(l) {}
// };
}
struct cv::SoftCascade::Filds
......@@ -183,68 +209,72 @@ struct cv::SoftCascade::Filds
int origObjWidth;
int origObjHeight;
int noctaves;
std::vector<Octave> octaves;
std::vector<Stage> stages;
std::vector<Feature> features;
std::vector<Level> levels;
typedef std::vector<Stage>::iterator stIter_t;
// carrently roi must be save for out of ranges.
void detectInRoi(const cv::Rect& roi, const Integral& ints, std::vector<cv::Rect>& objects, const int step)
{
for (int dy = roi.y; dy < roi.height; dy+=step)
for (int dx = roi.x; dx < roi.width; dx += step)
{
applyCascade(ints, dx, dy);
}
}
void applyCascade(const Integral& ints, const int x, const int y)
{
for (stIter_t sIt = sIt.begin(); sIt != stages.end(); ++sIt)
{
Stage stage& = *sIt;
}
}
std::vector<Node> nodes;
std::vector<float> leaves;
// compute levels of full pyramid
void calcLevels(int frameW, int frameH, int scales)
{
CV_Assert(scales > 1);
levels.clear();
float logFactor = (log(maxScale) - log(minScale)) / (scales -1);
float scale = minScale;
for (int sc = 0; sc < scales; ++sc)
{
Level level(sc, scale, log(scale) + logFactor,
std::max(0.0f, frameW - (origObjWidth * scale)), std::max(0.0f, frameH - (origObjHeight * scale)));
if (!level.width || !level.height)
break;
else
levels.push_back(level);
if (fabs(scale - maxScale) < FLT_EPSILON) break;
scale = std::min(maxScale, expf(log(scale) + logFactor));
}
std::vector<Feature> features;
for (std::vector<Level>::iterator level = levels.begin(); level < levels.end(); ++level)
{
float minAbsLog = FLT_MAX;
for (std::vector<Octave>::iterator oct = octaves.begin(); oct < octaves.end(); ++oct)
{
const Octave& octave =*oct;
float logOctave = log(octave.scale);
float logAbsScale = fabs((*level).logFactor - logOctave);
if(logAbsScale < minAbsLog)
(*level).assign(octave.scale, ORIG_OBJECT_WIDTH, ORIG_OBJECT_HEIGHT);
}
}
}
// typedef std::vector<Stage>::iterator stIter_t;
// // carrently roi must be save for out of ranges.
// void detectInRoi(const cv::Rect& roi, const Integral& ints, std::vector<cv::Rect>& objects, const int step)
// {
// for (int dy = roi.y; dy < roi.height; dy+=step)
// for (int dx = roi.x; dx < roi.width; dx += step)
// {
// applyCascade(ints, dx, dy);
// }
// }
// void applyCascade(const Integral& ints, const int x, const int y)
// {
// for (stIter_t sIt = stages.begin(); sIt != stages.end(); ++sIt)
// {
// Stage& stage = *sIt;
// }
// }
// // compute levels of full pyramid
// void calcLevels(int frameW, int frameH, int scales)
// {
// CV_Assert(scales > 1);
// levels.clear();
// float logFactor = (log(maxScale) - log(minScale)) / (scales -1);
// float scale = minScale;
// for (int sc = 0; sc < scales; ++sc)
// {
// Level level(sc, scale, log(scale), std::max(0.0f, frameW - (origObjWidth * scale)), std::max(0.0f, frameH - (origObjHeight * scale)));
// if (!level.width || !level.height)
// break;
// else
// levels.push_back(level);
// if (fabs(scale - maxScale) < FLT_EPSILON) break;
// scale = std::min(maxScale, expf(log(scale) + logFactor));
// }
// for (std::vector<Level>::iterator level = levels.begin(); level < levels.end(); ++level)
// {
// float minAbsLog = FLT_MAX;
// for (std::vector<Octave>::iterator oct = octaves.begin(); oct < octaves.end(); ++oct)
// {
// const Octave& octave =*oct;
// float logOctave = log(octave.scale);
// float logAbsScale = fabs((*level).logFactor - logOctave);
// if(logAbsScale < minAbsLog)
// {
// printf("######### %f %f %f %f\n", octave.scale, logOctave, logAbsScale, (*level).logFactor);
// minAbsLog = logAbsScale;
// (*level).assign(octave, ORIG_OBJECT_WIDTH, ORIG_OBJECT_HEIGHT);
// }
// }
// }
// }
bool fill(const FileNode &root, const float mins, const float maxs)
{
......@@ -252,19 +282,22 @@ struct cv::SoftCascade::Filds
maxScale = maxs;
// cascade properties
const char *SC_STAGE_TYPE = "stageType";
const char *SC_BOOST = "BOOST";
const char *SC_FEATURE_TYPE = "featureType";
const char *SC_ICF = "ICF";
const char *SC_TREE_TYPE = "stageTreeType";
const char *SC_STAGE_TH2 = "TH2";
const char *SC_NUM_OCTAVES = "octavesNum";
const char *SC_ORIG_W = "origObjWidth";
const char *SC_ORIG_H = "origObjHeight";
static const char *const SC_STAGE_TYPE = "stageType";
static const char *const SC_BOOST = "BOOST";
static const char *const SC_FEATURE_TYPE = "featureType";
static const char *const SC_ICF = "ICF";
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
const char* SC_OCTAVES = "octaves";
const char *SC_STAGES = "stages";
const char *SC_FEATURES = "features";
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_STAGES = "stages";
static const char *const SC_FEATURES = "features";
static const char *const SC_WEEK = "weakClassifiers";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
// only boost supported
......@@ -275,14 +308,6 @@ struct cv::SoftCascade::Filds
string featureTypeStr = (string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF);
// only trees of height 2
string stageTreeTypeStr = (string)root[SC_TREE_TYPE];
CV_Assert(stageTreeTypeStr == SC_STAGE_TH2);
// not empty
noctaves = (int)root[SC_NUM_OCTAVES];
CV_Assert(noctaves > 0);
origObjWidth = (int)root[SC_ORIG_W];
CV_Assert(origObjWidth == SoftCascade::ORIG_OBJECT_WIDTH);
......@@ -293,15 +318,17 @@ struct cv::SoftCascade::Filds
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
octaves.reserve(noctaves);
// octaves.reserve(noctaves);
FileNodeIterator it = fn.begin(), it_end = fn.end();
for (; it != it_end; ++it)
{
FileNode fns = *it;
Octave octave = Octave(fns);
Octave octave(cv::Size(SoftCascade::ORIG_OBJECT_WIDTH, SoftCascade::ORIG_OBJECT_HEIGHT), fns);
CV_Assert(octave.stages > 0);
octaves.push_back(octave);
stages.reserve(stages.size() + octave.stages);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
fns = fns[SC_STAGES];
if (fn.empty()) return false;
......@@ -313,14 +340,25 @@ struct cv::SoftCascade::Filds
fns = *st;
stages.push_back(Stage(fns));
fns = fns[SC_FEATURES];
// for each feature for tree. features stored in order {root, left, right}
fns = fns[SC_WEEK];
FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
for (; ftr != ft_end; ++ftr)
{
features.push_back(Feature(*ftr));
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
nodes.push_back(Node(inIt));
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
leaves.push_back((float)(*inIt));
}
}
st = ffs.begin(), st_end = ffs.end();
for (; st != st_end; ++st )
features.push_back(Feature(*st));
}
return true;
}
......@@ -349,7 +387,7 @@ bool cv::SoftCascade::load( const string& filename, const float minScale, const
filds = new Filds;
Filds& flds = *filds;
if (!flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale)) return false;
flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
// // flds.calcLevels(FRAME_WIDTH, FRAME_HEIGHT, TOTAL_SCALES);
return true;
}
......@@ -358,87 +396,84 @@ namespace {
void calcHistBins(const cv::Mat& grey, cv::Mat magIntegral, std::vector<cv::Mat>& histInts, const int bins)
{
CV_Assert( grey.type() == CV_8U);
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)
{
hist.push_back(cv::Mat(rows, cols, CV_32FC1));
}
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);
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)
{
float* magnitude = mag.ptr<float>(h);
float* ang = angle.ptr<float>(h);
for (int w = 0; w < mag.cols; ++w)
{
hist[(int)ang[w]].ptr<float>(h)[w] = magnitude[w];
}
}
for (int bin = 0; bin < bins; ++bin)
{
cv::Mat sum;
cv::integral(hist[bin], sum);
histInts.push_back(sum);
}
cv::integral(mag, magIntegral, mag.depth());
// CV_Assert( grey.type() == CV_8U);
// 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)
// {
// hist.push_back(cv::Mat(rows, cols, CV_32FC1));
// }
// 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);
// 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)
// {
// float* magnitude = mag.ptr<float>(h);
// float* ang = angle.ptr<float>(h);
// for (int w = 0; w < mag.cols; ++w)
// {
// hist[(int)ang[w]].ptr<float>(h)[w] = magnitude[w];
// }
// }
// for (int bin = 0; bin < bins; ++bin)
// {
// cv::Mat sum;
// cv::integral(hist[bin], sum);
// histInts.push_back(sum);
// }
// cv::integral(mag, magIntegral, mag.depth());
}
}
void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& rois, std::vector<cv::Rect>& objects,
const int step, const int rejectfactor)
const int step, const int rejectfactor)// add step scaling
{
typedef std::vector<cv::Rect>::const_iterator RIter_t;
// only color images are supperted
CV_Assert(image.type() == CV_8UC3);
// typedef std::vector<cv::Rect>::const_iterator RIter_t;
// // only color images are supperted
// CV_Assert(image.type() == CV_8UC3);
// only this window size allowed
CV_Assert(image.cols == 640 && image.rows == 480);
// // only this window size allowed
// CV_Assert(image.cols == 640 && image.rows == 480);
objects.clear();
// objects.clear();
// create integrals
cv::Mat luv;
cv::cvtColor(image, luv, CV_BGR2Luv);
// // create integrals
// cv::Mat luv;
// cv::cvtColor(image, luv, CV_BGR2Luv);
cv::Mat luvIntegral;
cv::integral(luv, luvIntegral);
// cv::Mat luvIntegral;
// cv::integral(luv, luvIntegral);
cv::Mat grey;
cv::cvtColor(image, grey, CV_RGB2GRAY);
// cv::Mat grey;
// cv::cvtColor(image, grey, CV_RGB2GRAY);
std::vector<cv::Mat> hist;
cv::Mat magnitude;
const int bins = 6;
calcHistBins(grey, magnitude, hist, bins);
// std::vector<cv::Mat> hist;
// cv::Mat magnitude;
// const int bins = 6;
// calcHistBins(grey, magnitude, hist, bins);
Integral integrals(magnitude, hist, luv);
// Integral integrals(magnitude, hist, luv);
for (RIter_t it = rois.begin(); it != rois.end(); ++it)
{
const cv::Rect& roi = *it;
(*filds).detectInRoi(roi, integrals, objects, step);
}
}
// for (RIter_t it = rois.begin(); it != rois.end(); ++it)
// {
// const cv::Rect& roi = *it;
// (*filds).detectInRoi(roi, integrals, objects, step);
// }
void cv::SoftCascade::detectForOctave(const int octave)
{}
\ No newline at end of file
}
\ No newline at end of file
modules/objdetect/test/test_softcascade.cpp
View file @ dc74ce20
......@@ -43,16 +43,15 @@
TEST(SoftCascade, readCascade)
{
std::string xml = "/home/kellan/icf-template.xml";
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/icf-template.xml";
cv::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
}
TEST(SoftCascade, Detect)
TEST(SoftCascade, detect)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/softcascade.xml";
std::cout << "PATH: "<< xml << std::endl;
cv::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
......
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