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wrapped FlannBasedMatcher (and extended DescriptorMatcher wrapper)

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updated feature_homography.py sample to use new features
This commit is contained in:
Alexander Mordvintsev 2011-09-15 11:10:06 +00:00
parent d174c3db04
commit 028c44531f
4 changed files with 62 additions and 27 deletions
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20
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -2237,24 +2237,24 @@ public:
* Add descriptors to train descriptor collection.
* descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
*/
virtual void add( const vector<Mat>& descriptors );
CV_WRAP virtual void add( const vector<Mat>& descriptors );
/*
* Get train descriptors collection.
*/
const vector<Mat>& getTrainDescriptors() const;
CV_WRAP const vector<Mat>& getTrainDescriptors() const;
/*
* Clear train descriptors collection.
*/
virtual void clear();
CV_WRAP virtual void clear();
/*
* Return true if there are not train descriptors in collection.
*/
virtual bool empty() const;
CV_WRAP virtual bool empty() const;
/*
* Return true if the matcher supports mask in match methods.
*/
virtual bool isMaskSupported() const = 0;
CV_WRAP virtual bool isMaskSupported() const = 0;
/*
* Train matcher (e.g. train flann index).
@ -2267,7 +2267,7 @@ public:
* if it has not trained yet or if new descriptors have been added to the train
* collection).
*/
virtual void train();
CV_WRAP virtual void train();
/*
* Group of methods to match descriptors from image pair.
* Method train() is run in this methods.
@ -2291,9 +2291,9 @@ public:
* Group of methods to match descriptors from one image to image set.
* See description of similar methods for matching image pair above.
*/
void match( const Mat& queryDescriptors, vector<DMatch>& matches,
CV_WRAP void match( const Mat& queryDescriptors, CV_OUT vector<DMatch>& matches,
const vector<Mat>& masks=vector<Mat>() );
void knnMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
CV_WRAP void knnMatch( const Mat& queryDescriptors, CV_OUT vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
void radiusMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
@ -2562,10 +2562,10 @@ void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescriptors
/*
* Flann based matcher
*/
class CV_EXPORTS FlannBasedMatcher : public DescriptorMatcher
class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
{
public:
FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams() );
virtual void add( const vector<Mat>& descriptors );

13
modules/python/src2/cv2.cpp
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@ -74,6 +74,11 @@ typedef Ptr<FeatureDetector> Ptr_FeatureDetector;
typedef Ptr<DescriptorExtractor> Ptr_DescriptorExtractor;
typedef Ptr<DescriptorMatcher> Ptr_DescriptorMatcher;
typedef cvflann::flann_distance_t cvflann_flann_distance_t;
typedef cvflann::flann_algorithm_t cvflann_flann_algorithm_t;
typedef Ptr<flann::IndexParams> Ptr_flann_IndexParams;
typedef Ptr<flann::SearchParams> Ptr_flann_SearchParams;
static PyObject* failmsgp(const char *fmt, ...)
{
char str[1000];
@ -820,6 +825,14 @@ static bool pyopencv_to(PyObject *o, cv::flann::IndexParams& p, const char *name
return ok;
}
template <class T>
static bool pyopencv_to(PyObject *o, Ptr<T>& p, const char *name="<unknown>")
{
p = new T();
return pyopencv_to(o, *p, name);
}
static bool pyopencv_to(PyObject *o, cvflann::flann_distance_t& dist, const char *name="<unknown>")
{
int d = (int)dist;

2
modules/python/src2/hdr_parser.py
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@ -191,7 +191,7 @@ class CppHeaderParser(object):
if add_star:
arg_type += "*"
arg_type = self.batch_replace(arg_type, [("std::", ""), ("cv::", "")])
arg_type = self.batch_replace(arg_type, [("std::", ""), ("cv::", ""), ("::", "_")])
return arg_type, arg_name, modlist, argno

54
samples/python2/feature_homography.py
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@ -4,35 +4,53 @@ Feature homography
Example of using features2d framework for interactive video homography matching.
Usage
-----
feature_homography.py [<video source>]
Keys
----
SPACE - set reference frame
ESC - exit
'''
import numpy as np
import cv2
import video
from common import draw_str
from common import draw_str, clock
import sys
detector = cv2.FastFeatureDetector(16, True)
detector = cv2.GridAdaptedFeatureDetector(detector)
extractor = cv2.DescriptorExtractor_create('ORB')
FLANN_INDEX_KDTREE = 1
FLANN_INDEX_LSH = 6
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
green, red = (0, 255, 0), (0, 0, 255)
if __name__ == '__main__':
print __doc__
detector = cv2.FeatureDetector_create('ORB')
extractor = cv2.DescriptorExtractor_create('ORB')
matcher = cv2.DescriptorMatcher_create('BruteForce-Hamming') # 'BruteForce-Hamming' # FlannBased
try: src = sys.argv[1]
except: src = 0
cap = video.create_capture(src)
ref_desc = None
ref_kp = None
green, red = (0, 255, 0), (0, 0, 255)
cap = video.create_capture(0)
while True:
ret, img = cap.read()
vis = img.copy()
kp = detector.detect(img)
kp, desc = extractor.compute(img, kp)
for p in kp:
x, y = np.int32(p.pt)
@ -40,14 +58,17 @@ if __name__ == '__main__':
cv2.circle(vis, (x, y), r, (0, 255, 0))
draw_str(vis, (20, 20), 'feature_n: %d' % len(kp))
desc = extractor.compute(img, kp)
if ref_desc is not None:
raw_matches = matcher.knnMatch(desc, ref_desc, 2)
eps = 1e-5
matches = [(m1.trainIdx, m1.queryIdx) for m1, m2 in raw_matches if (m1.distance+eps) / (m2.distance+eps) < 0.7]
if ref_kp is not None:
raw_matches = matcher.knnMatch(desc, 2)
matches = []
for m in raw_matches:
if len(m) == 2:
m1, m2 = m
if m1.distance < m2.distance * 0.7:
matches.append((m1.trainIdx, m1.queryIdx))
match_n = len(matches)
inliner_n = 0
inlier_n = 0
if match_n > 10:
p0 = np.float32( [ref_kp[i].pt for i, j in matches] )
p1 = np.float32( [kp[j].pt for i, j in matches] )
@ -66,7 +87,8 @@ if __name__ == '__main__':
cv2.imshow('img', vis)
ch = cv2.waitKey(1)
if ch == ord(' '):
ref_desc = desc
matcher.clear()
matcher.add([desc])
ref_kp = kp
ref_img = img.copy()
if ch == 27: