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submodule
opencv
Commits
ef8182e1
Commit
ef8182e1
authored
May 21, 2015
by
Vadim Pisarevsky
Browse files
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Browse Files
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Plain Diff
Merge pull request #4025 from vpisarev:features2d_fixes
parents
021473e9
af47b655
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Showing
6 changed files
with
224 additions
and
139 deletions
+224
-139
persistence.hpp
modules/core/include/opencv2/core/persistence.hpp
+2
-0
persistence.cpp
modules/core/src/persistence.cpp
+29
-0
mser.cpp
modules/features2d/src/mser.cpp
+0
-0
test_descriptors_regression.cpp
modules/features2d/test/test_descriptors_regression.cpp
+94
-0
test_matchers_algorithmic.cpp
modules/features2d/test/test_matchers_algorithmic.cpp
+10
-0
test_mser.cpp
modules/features2d/test/test_mser.cpp
+89
-139
No files found.
modules/core/include/opencv2/core/persistence.hpp
View file @
ef8182e1
...
@@ -660,6 +660,7 @@ CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value
...
@@ -660,6 +660,7 @@ CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
Mat
&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
Mat
&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
SparseMat
&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
SparseMat
&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
std
::
vector
<
KeyPoint
>&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
std
::
vector
<
KeyPoint
>&
value
);
CV_EXPORTS
void
write
(
FileStorage
&
fs
,
const
String
&
name
,
const
std
::
vector
<
DMatch
>&
value
);
CV_EXPORTS
void
writeScalar
(
FileStorage
&
fs
,
int
value
);
CV_EXPORTS
void
writeScalar
(
FileStorage
&
fs
,
int
value
);
CV_EXPORTS
void
writeScalar
(
FileStorage
&
fs
,
float
value
);
CV_EXPORTS
void
writeScalar
(
FileStorage
&
fs
,
float
value
);
...
@@ -678,6 +679,7 @@ CV_EXPORTS void read(const FileNode& node, String& value, const String& default_
...
@@ -678,6 +679,7 @@ CV_EXPORTS void read(const FileNode& node, String& value, const String& default_
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
Mat
&
mat
,
const
Mat
&
default_mat
=
Mat
()
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
Mat
&
mat
,
const
Mat
&
default_mat
=
Mat
()
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
SparseMat
&
mat
,
const
SparseMat
&
default_mat
=
SparseMat
()
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
SparseMat
&
mat
,
const
SparseMat
&
default_mat
=
SparseMat
()
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
std
::
vector
<
KeyPoint
>&
keypoints
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
std
::
vector
<
KeyPoint
>&
keypoints
);
CV_EXPORTS
void
read
(
const
FileNode
&
node
,
std
::
vector
<
DMatch
>&
matches
);
template
<
typename
_Tp
>
static
inline
void
read
(
const
FileNode
&
node
,
Point_
<
_Tp
>&
value
,
const
Point_
<
_Tp
>&
default_value
)
template
<
typename
_Tp
>
static
inline
void
read
(
const
FileNode
&
node
,
Point_
<
_Tp
>&
value
,
const
Point_
<
_Tp
>&
default_value
)
{
{
...
...
modules/core/src/persistence.cpp
View file @
ef8182e1
...
@@ -5594,6 +5594,35 @@ void read(const FileNode& node, std::vector<KeyPoint>& keypoints)
...
@@ -5594,6 +5594,35 @@ void read(const FileNode& node, std::vector<KeyPoint>& keypoints)
}
}
}
}
void
write
(
FileStorage
&
fs
,
const
String
&
objname
,
const
std
::
vector
<
DMatch
>&
matches
)
{
cv
::
internal
::
WriteStructContext
ws
(
fs
,
objname
,
CV_NODE_SEQ
+
CV_NODE_FLOW
);
int
i
,
n
=
(
int
)
matches
.
size
();
for
(
i
=
0
;
i
<
n
;
i
++
)
{
const
DMatch
&
m
=
matches
[
i
];
cv
::
write
(
fs
,
m
.
queryIdx
);
cv
::
write
(
fs
,
m
.
trainIdx
);
cv
::
write
(
fs
,
m
.
imgIdx
);
cv
::
write
(
fs
,
m
.
distance
);
}
}
void
read
(
const
FileNode
&
node
,
std
::
vector
<
DMatch
>&
matches
)
{
matches
.
resize
(
0
);
FileNodeIterator
it
=
node
.
begin
(),
it_end
=
node
.
end
();
for
(
;
it
!=
it_end
;
)
{
DMatch
m
;
it
>>
m
.
queryIdx
>>
m
.
trainIdx
>>
m
.
imgIdx
>>
m
.
distance
;
matches
.
push_back
(
m
);
}
}
int
FileNode
::
type
()
const
{
return
!
node
?
NONE
:
(
node
->
tag
&
TYPE_MASK
);
}
int
FileNode
::
type
()
const
{
return
!
node
?
NONE
:
(
node
->
tag
&
TYPE_MASK
);
}
bool
FileNode
::
isNamed
()
const
{
return
!
node
?
false
:
(
node
->
tag
&
NAMED
)
!=
0
;
}
bool
FileNode
::
isNamed
()
const
{
return
!
node
?
false
:
(
node
->
tag
&
NAMED
)
!=
0
;
}
...
...
modules/features2d/src/mser.cpp
View file @
ef8182e1
This diff is collapsed.
Click to expand it.
modules/features2d/test/test_descriptors_regression.cpp
View file @
ef8182e1
...
@@ -347,3 +347,97 @@ TEST( Features2d_DescriptorExtractor_AKAZE, regression )
...
@@ -347,3 +347,97 @@ TEST( Features2d_DescriptorExtractor_AKAZE, regression )
Hamming
(),
AKAZE
::
create
());
Hamming
(),
AKAZE
::
create
());
test
.
safe_run
();
test
.
safe_run
();
}
}
TEST
(
Features2d_DescriptorExtractor
,
batch
)
{
string
path
=
string
(
cvtest
::
TS
::
ptr
()
->
get_data_path
()
+
"detectors_descriptors_evaluation/images_datasets/graf"
);
vector
<
Mat
>
imgs
,
descriptors
;
vector
<
vector
<
KeyPoint
>
>
keypoints
;
int
i
,
n
=
6
;
Ptr
<
ORB
>
orb
=
ORB
::
create
();
for
(
i
=
0
;
i
<
n
;
i
++
)
{
string
imgname
=
format
(
"%s/img%d.png"
,
path
.
c_str
(),
i
+
1
);
Mat
img
=
imread
(
imgname
,
0
);
imgs
.
push_back
(
img
);
}
orb
->
detect
(
imgs
,
keypoints
);
orb
->
compute
(
imgs
,
keypoints
,
descriptors
);
ASSERT_EQ
((
int
)
keypoints
.
size
(),
n
);
ASSERT_EQ
((
int
)
descriptors
.
size
(),
n
);
for
(
i
=
0
;
i
<
n
;
i
++
)
{
EXPECT_GT
((
int
)
keypoints
[
i
].
size
(),
100
);
EXPECT_GT
(
descriptors
[
i
].
rows
,
100
);
}
}
TEST
(
Features2d_Feature2d
,
no_crash
)
{
const
String
&
pattern
=
string
(
cvtest
::
TS
::
ptr
()
->
get_data_path
()
+
"shared/*.png"
);
vector
<
String
>
fnames
;
glob
(
pattern
,
fnames
,
false
);
sort
(
fnames
.
begin
(),
fnames
.
end
());
Ptr
<
AKAZE
>
akaze
=
AKAZE
::
create
();
Ptr
<
ORB
>
orb
=
ORB
::
create
();
Ptr
<
KAZE
>
kaze
=
KAZE
::
create
();
Ptr
<
BRISK
>
brisk
=
BRISK
::
create
();
size_t
i
,
n
=
fnames
.
size
();
vector
<
KeyPoint
>
keypoints
;
Mat
descriptors
;
orb
->
setMaxFeatures
(
5000
);
for
(
i
=
0
;
i
<
n
;
i
++
)
{
printf
(
"%d. image: %s:
\n
"
,
(
int
)
i
,
fnames
[
i
].
c_str
());
if
(
strstr
(
fnames
[
i
].
c_str
(),
"MP.png"
)
!=
0
)
continue
;
bool
checkCount
=
strstr
(
fnames
[
i
].
c_str
(),
"templ.png"
)
==
0
;
Mat
img
=
imread
(
fnames
[
i
],
-
1
);
printf
(
"
\t
AKAZE ... "
);
fflush
(
stdout
);
akaze
->
detectAndCompute
(
img
,
noArray
(),
keypoints
,
descriptors
);
printf
(
"(%d keypoints) "
,
(
int
)
keypoints
.
size
());
fflush
(
stdout
);
if
(
checkCount
)
{
EXPECT_GT
((
int
)
keypoints
.
size
(),
0
);
}
ASSERT_EQ
(
descriptors
.
rows
,
(
int
)
keypoints
.
size
());
printf
(
"ok
\n
"
);
printf
(
"
\t
KAZE ... "
);
fflush
(
stdout
);
kaze
->
detectAndCompute
(
img
,
noArray
(),
keypoints
,
descriptors
);
printf
(
"(%d keypoints) "
,
(
int
)
keypoints
.
size
());
fflush
(
stdout
);
if
(
checkCount
)
{
EXPECT_GT
((
int
)
keypoints
.
size
(),
0
);
}
ASSERT_EQ
(
descriptors
.
rows
,
(
int
)
keypoints
.
size
());
printf
(
"ok
\n
"
);
printf
(
"
\t
ORB ... "
);
fflush
(
stdout
);
orb
->
detectAndCompute
(
img
,
noArray
(),
keypoints
,
descriptors
);
printf
(
"(%d keypoints) "
,
(
int
)
keypoints
.
size
());
fflush
(
stdout
);
if
(
checkCount
)
{
EXPECT_GT
((
int
)
keypoints
.
size
(),
0
);
}
ASSERT_EQ
(
descriptors
.
rows
,
(
int
)
keypoints
.
size
());
printf
(
"ok
\n
"
);
printf
(
"
\t
BRISK ... "
);
fflush
(
stdout
);
brisk
->
detectAndCompute
(
img
,
noArray
(),
keypoints
,
descriptors
);
printf
(
"(%d keypoints) "
,
(
int
)
keypoints
.
size
());
fflush
(
stdout
);
if
(
checkCount
)
{
EXPECT_GT
((
int
)
keypoints
.
size
(),
0
);
}
ASSERT_EQ
(
descriptors
.
rows
,
(
int
)
keypoints
.
size
());
printf
(
"ok
\n
"
);
}
}
modules/features2d/test/test_matchers_algorithmic.cpp
View file @
ef8182e1
...
@@ -543,3 +543,13 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
...
@@ -543,3 +543,13 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
DescriptorMatcher
::
create
(
"FlannBased"
),
0.04
f
);
DescriptorMatcher
::
create
(
"FlannBased"
),
0.04
f
);
test
.
safe_run
();
test
.
safe_run
();
}
}
TEST
(
Features2d_DMatch
,
read_write
)
{
FileStorage
fs
(
".xml"
,
FileStorage
::
WRITE
+
FileStorage
::
MEMORY
);
vector
<
DMatch
>
matches
;
matches
.
push_back
(
DMatch
(
1
,
2
,
3
,
4.5
f
));
fs
<<
"Match"
<<
matches
;
String
str
=
fs
.
releaseAndGetString
();
ASSERT_NE
(
strstr
(
str
.
c_str
(),
"4.5"
),
(
char
*
)
0
);
}
modules/features2d/test/test_mser.cpp
View file @
ef8182e1
...
@@ -41,171 +41,121 @@
...
@@ -41,171 +41,121 @@
//M*/
//M*/
#include "test_precomp.hpp"
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/highgui.hpp"
#if 0
#include <vector>
#include <vector>
#include <string>
#include <string>
using
namespace
std
;
using
namespace
std
;
using
namespace
cv
;
using
namespace
cv
;
class CV_MserTest : public cvtest::BaseTest
#undef RENDER_MSERS
{
#define RENDER_MSERS 0
public:
CV_MserTest();
protected:
void run(int);
int LoadBoxes(const char* path, vector<CvBox2D>& boxes);
int SaveBoxes(const char* path, const vector<CvBox2D>& boxes);
int CompareBoxes(const vector<CvBox2D>& boxes1,const vector<CvBox2D>& boxes2, float max_rel_diff = 0.01f);
};
CV_MserTest::CV_MserTest()
{
}
int CV_MserTest::LoadBoxes(const char* path, vector<CvBox2D>& boxes)
#if defined RENDER_MSERS && RENDER_MSERS
static
void
renderMSERs
(
const
Mat
&
gray
,
Mat
&
img
,
const
vector
<
vector
<
Point
>
>&
msers
)
{
{
boxes.clear();
cvtColor
(
gray
,
img
,
COLOR_GRAY2BGR
);
FILE* f = fopen(path,"r");
RNG
rng
((
uint64
)
1749583
);
for
(
int
i
=
0
;
i
<
(
int
)
msers
.
size
();
i
++
)
if (f==NULL)
{
{
return 0;
uchar
b
=
rng
.
uniform
(
0
,
256
);
uchar
g
=
rng
.
uniform
(
0
,
256
);
uchar
r
=
rng
.
uniform
(
0
,
256
);
Vec3b
color
(
b
,
g
,
r
);
const
Point
*
pt
=
&
msers
[
i
][
0
];
size_t
j
,
n
=
msers
[
i
].
size
();
for
(
j
=
0
;
j
<
n
;
j
++
)
img
.
at
<
Vec3b
>
(
pt
[
j
])
=
color
;
}
}
while (!feof(f))
{
CvBox2D box;
int values_read = fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height);
CV_Assert(values_read == 5);
boxes.push_back(box);
}
fclose(f);
return 1;
}
}
#endif
int CV_MserTest::SaveBoxes(const char* path, const vector<CvBox2D>& box
es)
TEST
(
Features2d_MSER
,
cas
es
)
{
{
FILE* f = fopen(path,"w");
uchar
buf
[]
=
if (f==NULL)
{
return 0;
}
for (int i=0;i<(int)boxes.size();i++)
{
{
fprintf(f,"%f,%f,%f,%f,%f\n",boxes[i].angle,boxes[i].center.x,boxes[i].center.y,boxes[i].size.width,boxes[i].size.height);
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
}
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
fclose(f);
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
return 1;
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
}
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
0
,
0
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
,
255
};
Mat
big_image
=
imread
(
cvtest
::
TS
::
ptr
()
->
get_data_path
()
+
"mser/puzzle.png"
,
0
);
Mat
small_image
(
14
,
26
,
CV_8U
,
buf
);
static
const
int
thresharr
[]
=
{
0
,
70
,
120
,
180
,
255
};
const
int
kDelta
=
5
;
Ptr
<
MSER
>
mserExtractor
=
MSER
::
create
(
kDelta
);
vector
<
vector
<
Point
>
>
msers
;
vector
<
Rect
>
boxes
;
int CV_MserTest::CompareBoxes(const vector<CvBox2D>& boxes1,const vector<CvBox2D>& boxes2, float max_rel_diff)
RNG
rng
((
uint64
)
123456
);
{
if (boxes1.size() != boxes2.size())
return 0;
for
(int i=0; i<(int)boxes1.size();i++
)
for
(
int
i
=
0
;
i
<
100
;
i
++
)
{
{
float rel_diff;
bool
use_big_image
=
rng
.
uniform
(
0
,
7
)
!=
0
;
if (!((boxes1[i].angle == 0.0f) && (abs(boxes2[i].angle) < max_rel_diff)))
bool
invert
=
rng
.
uniform
(
0
,
2
)
!=
0
;
{
bool
binarize
=
use_big_image
?
rng
.
uniform
(
0
,
5
)
!=
0
:
false
;
float angle_diff = (float)fmod(boxes1[i].angle - boxes2[i].angle, 180);
bool
blur
=
rng
.
uniform
(
0
,
2
)
!=
0
;
// for angular correctness, it makes no sense to use a "relative" error.
int
thresh
=
thresharr
[
rng
.
uniform
(
0
,
5
)];
// a 1-degree error around 5 degrees is equally bas as around 250 degrees.
// in correct cases, angle_diff can now be a bit above 0 or a bit below 180
if (angle_diff > 90.0f)
{
angle_diff -= 180.0f;
}
rel_diff = (float)fabs(angle_diff);
if (rel_diff > max_rel_diff)
return i;
}
if (!((boxes1[i].center.x == 0.0f) && (abs(boxes2[i].center.x) < max_rel_diff)))
{
rel_diff = abs(boxes1[i].center.x-boxes2[i].center.x)/abs(boxes1[i].center.x);
if (rel_diff > max_rel_diff)
return i;
}
if (!((boxes1[i].center.y == 0.0f) && (abs(boxes2[i].center.y) < max_rel_diff)))
{
rel_diff = abs(boxes1[i].center.y-boxes2[i].center.y)/abs(boxes1[i].center.y);
if (rel_diff > max_rel_diff)
return i;
}
if (!((boxes1[i].size.width == 0.0f) && (abs(boxes2[i].size.width) < max_rel_diff)))
{
rel_diff = abs(boxes1[i].size.width-boxes2[i].size.width)/abs(boxes1[i].size.width);
if (rel_diff > max_rel_diff)
return i;
}
if (!((boxes1[i].size.height == 0.0f) && (abs(boxes2[i].size.height) < max_rel_diff))
)
/*if( i == 0
)
{
{
rel_diff = abs(boxes1[i].size.height-boxes2[i].size.height)/abs(boxes1[i].size.height);
use_big_image = true;
if (rel_diff > max_rel_diff)
invert = binarize = blur = false;
return i;
}*/
}
}
return -1
;
const
Mat
&
src0
=
use_big_image
?
big_image
:
small_image
;
}
Mat
src
=
src0
.
clone
();
void CV_MserTest::run(int)
int
kMinArea
=
use_big_image
?
256
:
10
;
{
int
kMaxArea
=
(
int
)
src
.
total
()
/
4
;
string image_path = string(ts->get_data_path()) + "mser/puzzle.png";
Mat img = imread( image_path );
mserExtractor
->
setMinArea
(
kMinArea
);
if (img.empty())
mserExtractor
->
setMaxArea
(
kMaxArea
);
{
ts->printf( cvtest::TS::LOG, "Unable to open image mser/puzzle.png\n");
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
return;
}
Mat yuv;
if
(
invert
)
cvtColor(img, yuv, COLOR_BGR2YCrCb);
bitwise_not
(
src
,
src
);
vector<vector<Point> > msers;
if
(
binarize
)
MSER()(yuv, msers);
threshold
(
src
,
src
,
thresh
,
255
,
THRESH_BINARY
);
if
(
blur
)
GaussianBlur
(
src
,
src
,
Size
(
5
,
5
),
1.5
,
1.5
);
vector<CvBox2D> boxes;
int
minRegs
=
use_big_image
?
7
:
2
;
vector<CvBox2D> boxes_orig;
int
maxRegs
=
use_big_image
?
1000
:
15
;
for ( size_t i = 0; i < msers.size(); i++ )
if
(
binarize
&&
(
thresh
==
0
||
thresh
==
255
)
)
{
minRegs
=
maxRegs
=
0
;
RotatedRect box = fitEllipse(msers[i]);
box.angle=(float)CV_PI/2-box.angle;
boxes.push_back(box);
}
string boxes_path = string(ts->get_data_path()) + "mser/boxes.txt";
mserExtractor
->
detectRegions
(
src
,
msers
,
boxes
);
string calc_boxes_path = string(ts->get_data_path()) + "mser/boxes.calc.txt";
int
nmsers
=
(
int
)
msers
.
size
();
ASSERT_EQ
(
nmsers
,
(
int
)
boxes
.
size
());
if (!LoadBoxes(boxes_path.c_str(),boxes_orig))
if
(
maxRegs
<
nmsers
||
minRegs
>
nmsers
)
{
{
SaveBoxes(boxes_path.c_str(),boxes);
printf
(
"%d. minArea=%d, maxArea=%d, nmsers=%d, minRegs=%d, maxRegs=%d, "
ts->printf( cvtest::TS::LOG, "Unable to open data file mser/boxes.txt\n");
"image=%s, invert=%d, binarize=%d, thresh=%d, blur=%d
\n
"
,
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
i
,
kMinArea
,
kMaxArea
,
nmsers
,
minRegs
,
maxRegs
,
use_big_image
?
"big"
:
"small"
,
return;
(
int
)
invert
,
(
int
)
binarize
,
thresh
,
(
int
)
blur
);
}
#if defined RENDER_MSERS && RENDER_MSERS
Mat
image
;
imshow
(
"source"
,
src
);
renderMSERs
(
src
,
image
,
msers
);
imshow
(
"result"
,
image
);
waitKey
();
#endif
}
const float dissimularity = 0.01f;
ASSERT_LE
(
minRegs
,
nmsers
);
int n_box = CompareBoxes(boxes_orig,boxes,dissimularity);
ASSERT_GE
(
maxRegs
,
nmsers
);
if (n_box < 0)
{
ts->set_failed_test_info(cvtest::TS::OK);
}
else
{
SaveBoxes(calc_boxes_path.c_str(), boxes);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
ts->printf( cvtest::TS::LOG, "Incorrect correspondence in box %d\n",n_box);
}
}
}
}
TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
#endif
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