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Merge remote-tracking branch 'upstream/3.4' into merge-3.4
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@alalek
alalek committed Jan 12, 2020
2 parents 1e32781 + 38e6d66 commit fb61f88
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3 changes: 3 additions & 0 deletions CMakeLists.txt
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Expand Up @@ -68,6 +68,9 @@ if(POLICY CMP0075)
cmake_policy(SET CMP0075 NEW) # CMake 3.12+: Include file check macros honor `CMAKE_REQUIRED_LIBRARIES`
endif()

if(POLICY CMP0077)
cmake_policy(SET CMP0077 NEW) # CMake 3.13+: option() honors normal variables.
endif()

#
# Configure OpenCV CMake hooks
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231 changes: 125 additions & 106 deletions ...als/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown
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Expand Up @@ -17,7 +17,7 @@ You'll find answers for the following questions:

Source code
-----------

@add_toggle_cpp
You can [download this from here
](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp) or find it in the
`samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp` of the OpenCV source code
Expand All @@ -26,102 +26,122 @@ library.
Here's a sample code of how to achieve all the stuff enumerated at the goal list.

@include cpp/tutorial_code/core/file_input_output/file_input_output.cpp
@end_toggle

@add_toggle_python
You can [download this from here
](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/core/file_input_output/file_input_output.py) or find it in the
`samples/python/tutorial_code/core/file_input_output/file_input_output.py` of the OpenCV source code
library.

Here's a sample code of how to achieve all the stuff enumerated at the goal list.

@include python/tutorial_code/core/file_input_output/file_input_output.py
@end_toggle

Explanation
-----------

Here we talk only about XML and YAML file inputs. Your output (and its respective input) file may
have only one of these extensions and the structure coming from this. They are two kinds of data
structures you may serialize: *mappings* (like the STL map) and *element sequence* (like the STL
structures you may serialize: *mappings* (like the STL map and the Python dictionary) and *element sequence* (like the STL
vector). The difference between these is that in a map every element has a unique name through what
you may access it. For sequences you need to go through them to query a specific item.

-# **XML/YAML File Open and Close.** Before you write any content to such file you need to open it
and at the end to close it. The XML/YAML data structure in OpenCV is @ref cv::FileStorage . To
specify that this structure to which file binds on your hard drive you can use either its
constructor or the *open()* function of this:
@code{.cpp}
string filename = "I.xml";
FileStorage fs(filename, FileStorage::WRITE);
//...
fs.open(filename, FileStorage::READ);
@endcode
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp open
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py open
@end_toggle
Either one of this you use the second argument is a constant specifying the type of operations
you'll be able to on them: WRITE, READ or APPEND. The extension specified in the file name also
determinates the output format that will be used. The output may be even compressed if you
specify an extension such as *.xml.gz*.

The file automatically closes when the @ref cv::FileStorage objects is destroyed. However, you
may explicitly call for this by using the *release* function:
@code{.cpp}
fs.release(); // explicit close
@endcode
-# **Input and Output of text and numbers.** The data structure uses the same \<\< output operator
that the STL library. For outputting any type of data structure we need first to specify its
name. We do this by just simply printing out the name of this. For basic types you may follow
this with the print of the value :
@code{.cpp}
fs << "iterationNr" << 100;
@endcode
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp close
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py close
@end_toggle
-# **Input and Output of text and numbers.** In C++, the data structure uses the \<\< output
operator in the STL library. In Python, @ref cv::FileStorage.write() is used instead. For
outputting any type of data structure we need first to specify its name. We do this by just
simply pushing the name of this to the stream in C++. In Python, the first parameter for the
write function is the name. For basic types you may follow this with the print of the value :
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp writeNum
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py writeNum
@end_toggle
Reading in is a simple addressing (via the [] operator) and casting operation or a read via
the \>\> operator :
@code{.cpp}
int itNr;
fs["iterationNr"] >> itNr;
itNr = (int) fs["iterationNr"];
@endcode
the \>\> operator. In Python, we address with getNode() and use real() :
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp readNum
@end_toggle
@add_toggle_python
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp readNum
@end_toggle
-# **Input/Output of OpenCV Data structures.** Well these behave exactly just as the basic C++
types:
@code{.cpp}
Mat R = Mat_<uchar >::eye (3, 3),
T = Mat_<double>::zeros(3, 1);

fs << "R" << R; // Write cv::Mat
fs << "T" << T;

fs["R"] >> R; // Read cv::Mat
fs["T"] >> T;
@endcode
and Python types:
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp iomati
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp iomatw
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp iomat
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py iomati
@snippet python/tutorial_code/core/file_input_output/file_input_output.py iomatw
@snippet python/tutorial_code/core/file_input_output/file_input_output.py iomat
@end_toggle
-# **Input/Output of vectors (arrays) and associative maps.** As I mentioned beforehand, we can
output maps and sequences (array, vector) too. Again we first print the name of the variable and
then we have to specify if our output is either a sequence or map.

For sequence before the first element print the "[" character and after the last one the "]"
character:
@code{.cpp}
fs << "strings" << "["; // text - string sequence
fs << "image1.jpg" << "Awesomeness" << "baboon.jpg";
fs << "]"; // close sequence
@endcode
character. With Python, the "]" character could be written with the name of the sequence or
the last element of the sequence depending on the number of elements:
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp writeStr
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py writeStr
@end_toggle
For maps the drill is the same however now we use the "{" and "}" delimiter characters:
@code{.cpp}
fs << "Mapping"; // text - mapping
fs << "{" << "One" << 1;
fs << "Two" << 2 << "}";
@endcode
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp writeMap
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py writeMap
@end_toggle
To read from these we use the @ref cv::FileNode and the @ref cv::FileNodeIterator data
structures. The [] operator of the @ref cv::FileStorage class returns a @ref cv::FileNode data
structures. The [] operator of the @ref cv::FileStorage class (or the getNode() function in Python) returns a @ref cv::FileNode data
type. If the node is sequential we can use the @ref cv::FileNodeIterator to iterate through the
items:
@code{.cpp}
FileNode n = fs["strings"]; // Read string sequence - Get node
if (n.type() != FileNode::SEQ)
{
cerr << "strings is not a sequence! FAIL" << endl;
return 1;
}

FileNodeIterator it = n.begin(), it_end = n.end(); // Go through the node
for (; it != it_end; ++it)
cout << (string)*it << endl;
@endcode
For maps you can use the [] operator again to access the given item (or the \>\> operator too):
@code{.cpp}
n = fs["Mapping"]; // Read mappings from a sequence
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
@endcode
items. In Python, the at() function can be used to address elements of the sequence and the
size() function returns the length of the sequence:
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp readStr
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py readStr
@end_toggle
For maps you can use the [] operator (at() function in Python) again to access the given item (or the \>\> operator too):
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp readMap
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py readMap
@end_toggle
-# **Read and write your own data structures.** Suppose you have a data structure such as:
@add_toggle_cpp
@code{.cpp}
class MyData
{
Expand All @@ -133,53 +153,52 @@ you may access it. For sequences you need to go through them to query a specific
string id;
};
@endcode
It's possible to serialize this through the OpenCV I/O XML/YAML interface (just as in case of
the OpenCV data structures) by adding a read and a write function inside and outside of your
class. For the inside part:
@code{.cpp}
void write(FileStorage& fs) const //Write serialization for this class
{
fs << "{" << "A" << A << "X" << X << "id" << id << "}";
}

void read(const FileNode& node) //Read serialization for this class
{
A = (int)node["A"];
X = (double)node["X"];
id = (string)node["id"];
}
@endcode
Then you need to add the following functions definitions outside the class:
@code{.cpp}
void write(FileStorage& fs, const std::string&, const MyData& x)
{
x.write(fs);
}

void read(const FileNode& node, MyData& x, const MyData& default_value = MyData())
{
if(node.empty())
x = default_value;
else
x.read(node);
}
@end_toggle
@add_toggle_python
@code{.py}
class MyData:
def __init__(self):
self.A = self.X = 0
self.name = ''
@endcode
@end_toggle
In C++, it's possible to serialize this through the OpenCV I/O XML/YAML interface (just as
in case of the OpenCV data structures) by adding a read and a write function inside and outside of your
class. In Python, you can get close to this by implementing a read and write function inside
the class. For the inside part:
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp inside
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py inside
@end_toggle
@add_toggle_cpp
In C++, you need to add the following functions definitions outside the class:
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp outside
@end_toggle
Here you can observe that in the read section we defined what happens if the user tries to read
a non-existing node. In this case we just return the default initialization value, however a
more verbose solution would be to return for instance a minus one value for an object ID.

Once you added these four functions use the \>\> operator for write and the \<\< operator for
read:
@code{.cpp}
MyData m(1);
fs << "MyData" << m; // your own data structures
fs["MyData"] >> m; // Read your own structure_
@endcode
read (or the defined input/output functions for Python):
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp customIOi
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp customIOw
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp customIO
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py customIOi
@snippet python/tutorial_code/core/file_input_output/file_input_output.py customIOw
@snippet python/tutorial_code/core/file_input_output/file_input_output.py customIO
@end_toggle
Or to try out reading a non-existing read:
@code{.cpp}
fs["NonExisting"] >> m; // Do not add a fs << "NonExisting" << m command for this to work
cout << endl << "NonExisting = " << endl << m << endl;
@endcode
@add_toggle_cpp
@snippet cpp/tutorial_code/core/file_input_output/file_input_output.cpp nonexist
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/core/file_input_output/file_input_output.py nonexist
@end_toggle

Result
------
Expand Down
16 changes: 16 additions & 0 deletions doc/tutorials/video/meanshift/meanshift.markdown
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Expand Up @@ -57,6 +57,14 @@ low light, low light values are discarded using **cv.inRange()** function.
@include samples/python/tutorial_code/video/meanshift/meanshift.py
@end_toggle

@add_toggle_java
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/meanshift/MeanshiftDemo.java)

- **Code at glance:**
@include samples/java/tutorial_code/video/meanshift/MeanshiftDemo.java
@end_toggle

Three frames in a video I used is given below:

![image](images/meanshift_result.jpg)
Expand Down Expand Up @@ -98,6 +106,14 @@ parameters (used to be passed as search window in next iteration). See the code
@include samples/python/tutorial_code/video/meanshift/camshift.py
@end_toggle

@add_toggle_java
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/meanshift/CamshiftDemo.java)

- **Code at glance:**
@include samples/java/tutorial_code/video/meanshift/CamshiftDemo.java
@end_toggle

Three frames of the result is shown below:

![image](images/camshift_result.jpg)
Expand Down
18 changes: 18 additions & 0 deletions doc/tutorials/video/optical_flow/optical_flow.markdown
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Expand Up @@ -109,6 +109,15 @@ below:
@include samples/python/tutorial_code/video/optical_flow/optical_flow.py
@end_toggle


@add_toggle_java
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/optical_flow/OpticalFlowDemo.java)

- **Code at glance:**
@include samples/java/tutorial_code/video/optical_flow/OpticalFlowDemo.java
@end_toggle

(This code doesn't check how correct are the next keypoints. So even if any feature point disappears
in image, there is a chance that optical flow finds the next point which may look close to it. So
actually for a robust tracking, corner points should be detected in particular intervals. OpenCV
Expand Down Expand Up @@ -151,6 +160,15 @@ corresponds to Value plane. See the code below:
@end_toggle


@add_toggle_java
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/optical_flow/OpticalFlowDenseDemo.java)

- **Code at glance:**
@include samples/java/tutorial_code/video/optical_flow/OpticalFlowDenseDemo.java
@end_toggle


See the result below:

![image](images/opticalfb.jpg)
4 changes: 2 additions & 2 deletions doc/tutorials/video/table_of_content_video.markdown
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Expand Up @@ -17,12 +17,12 @@ tracking and foreground extractions.

- @subpage tutorial_meanshift

*Languages:* C++, Python
*Languages:* C++, Java, Python

Learn how to use the Meanshift and Camshift algorithms to track objects in videos.

- @subpage tutorial_optical_flow

*Languages:* C++, Python
*Languages:* C++, Java, Python

We will learn how to use optical flow methods to track sparse features or to create a dense representation.
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