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export to python/java

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This commit is contained in:
marina.kolpakova
2012-11-13 02:49:35 +04:00
parent 665bf430d5
commit bd9ca48fab
4 changed files with 57 additions and 57 deletions
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11
modules/objdetect/doc/soft_cascade.rst
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@@ -39,6 +39,7 @@ Implementation of soft (stageless) cascaded detector. ::
cv::AlgorithmInfo* info() const;
virtual bool load(const FileNode& fn);
virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
virtual void detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const;
};
@@ -80,10 +81,16 @@ SCascade::detect
--------------------------
Apply cascade to an input frame and return the vector of Decection objcts.
.. ocv:function:: bool SCascade::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
:param image: a frame on which detector will be applied.
:param rois: a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
:param objects: an output array of Detections.
:param objects: an output array of Detections.
:param rects: an output array of bounding rectangles for detected objects.
:param confs: an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th configence.

13
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@@ -490,7 +490,7 @@ protected:
// Implementation of soft (stageless) cascaded detector.
class CV_EXPORTS SCascade : public Algorithm
class CV_EXPORTS_W SCascade : public Algorithm
{
public:
@@ -539,24 +539,27 @@ public:
// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
// Param scales is a number of scales from minScale to maxScale.
// Param rejfactor is used for NMS.
SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejfactor = 1);
CV_WRAP SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejfactor = 1);
virtual ~SCascade();
CV_WRAP virtual ~SCascade();
cv::AlgorithmInfo* info() const;
// Load cascade from FileNode.
// Param fn is a root node for cascade. Should be <cascade>.
virtual bool load(const FileNode& fn);
CV_WRAP virtual bool load(const FileNode& fn);
// Load cascade config.
virtual void read(const FileNode& fn);
CV_WRAP virtual void read(const FileNode& fn);
// Return the vector of Decection objcts.
// Param image is a frame on which detector will be applied.
// Param rois is a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
// Param objects is an output array of Detections
virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
// Param rects is an output array of bounding rectangles for detected objects.
// Param confs is an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th configence.
CV_WRAP virtual void detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const;
private:
void detectNoRoi(const Mat& image, std::vector<Detection>& objects) const;

23
modules/objdetect/src/softcascade.cpp
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@@ -500,4 +500,27 @@ void cv::SCascade::detect(cv::InputArray _image, cv::InputArray _rois, std::vect
}
}
}
}
void cv::SCascade::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const
{
std::vector<Detection> objects;
detect( _image, _rois, objects);
_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, objects.size(), CV_32F);
cv::Mat confs = _confs.getMat();
float* confPtr = rects.ptr<float>(0);
typedef std::vector<Detection>::const_iterator IDet;
int i = 0;
for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
{
rectPtr[i] = (*it).bb;
confPtr[i] = (*it).confidence;
}
}

67
modules/objdetect/test/test_softcascade.cpp
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@@ -66,34 +66,26 @@ TEST(SCascade, detect)
std::vector<Detection> objects;
cascade.detect(colored, cv::noArray(), objects);
// cv::Mat out = colored.clone();
// int level = 0, total = 0;
// int levelWidth = objects[0].bb.width;
// for(int i = 0 ; i < (int)objects.size(); ++i)
// {
// if (objects[i].bb.width != levelWidth)
// {
// std::cout << "Level: " << level << " total " << total << std::endl;
// cv::imshow("out", out);
// cv::waitKey(0);
// out = colored.clone();
// levelWidth = objects[i].bb.width;
// total = 0;
// level++;
// }
// cv::rectangle(out, objects[i].bb, cv::Scalar(255, 0, 0, 255), 1);
// std::cout << "detection: " << objects[i].bb.x
// << " " << objects[i].bb.y
// << " " << objects[i].bb.width
// << " " << objects[i].bb.height << std::endl;
// total++;
// }
// std::cout << "detected: " << (int)objects.size() << std::endl;
ASSERT_EQ((int)objects.size(), 3498);
}
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()));
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(confs.cols, 3498);
}
TEST(SCascade, detectRoi)
{
typedef cv::SCascade::Detection Detection;
@@ -110,31 +102,6 @@ TEST(SCascade, detectRoi)
rois.push_back(cv::Rect(0, 0, 640, 480));
cascade.detect(colored, rois, objects);
// cv::Mat out = colored.clone();
// int level = 0, total = 0;
// int levelWidth = objects[0].bb.width;
// for(int i = 0 ; i < (int)objects.size(); ++i)
// {
// if (objects[i].bb.width != levelWidth)
// {
// std::cout << "Level: " << level << " total " << total << std::endl;
// cv::imshow("out", out);
// cv::waitKey(0);
// out = colored.clone();
// levelWidth = objects[i].bb.width;
// total = 0;
// level++;
// }
// cv::rectangle(out, objects[i].bb, cv::Scalar(255, 0, 0, 255), 1);
// std::cout << "detection: " << objects[i].bb.x
// << " " << objects[i].bb.y
// << " " << objects[i].bb.width
// << " " << objects[i].bb.height << std::endl;
// total++;
// }
// std::cout << "detected: " << (int)objects.size() << std::endl;
ASSERT_EQ((int)objects.size(), 3498);
}