init - 初始化项目
This commit is contained in:
Lee Nony
270
doc/tutorials/core/mat_operations.markdown
Normal file
270
doc/tutorials/core/mat_operations.markdown
Normal file
@@ -0,0 +1,270 @@
|
||||
Operations with images {#tutorial_mat_operations}
|
||||
======================
|
||||
|
||||
@tableofcontents
|
||||
|
||||
@prev_tutorial{tutorial_mat_mask_operations}
|
||||
@next_tutorial{tutorial_adding_images}
|
||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
| Compatibility | OpenCV >= 3.0 |
|
||||
|
||||
Input/Output
|
||||
------------
|
||||
|
||||
### Images
|
||||
|
||||
Load an image from a file:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Load an image from a file
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Load an image from a file
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Load an image from a file
|
||||
@end_toggle
|
||||
|
||||
If you read a jpg file, a 3 channel image is created by default. If you need a grayscale image, use:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Load an image from a file in grayscale
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Load an image from a file in grayscale
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Load an image from a file in grayscale
|
||||
@end_toggle
|
||||
|
||||
@note Format of the file is determined by its content (first few bytes). To save an image to a file:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Save image
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Save image
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Save image
|
||||
@end_toggle
|
||||
|
||||
@note Format of the file is determined by its extension.
|
||||
|
||||
@note Use cv::imdecode and cv::imencode to read and write an image from/to memory rather than a file.
|
||||
|
||||
Basic operations with images
|
||||
----------------------------
|
||||
|
||||
### Accessing pixel intensity values
|
||||
|
||||
In order to get pixel intensity value, you have to know the type of an image and the number of
|
||||
channels. Here is an example for a single channel grey scale image (type 8UC1) and pixel coordinates
|
||||
x and y:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 1
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Pixel access 1
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 1
|
||||
@end_toggle
|
||||
|
||||
C++ version only:
|
||||
intensity.val[0] contains a value from 0 to 255. Note the ordering of x and y. Since in OpenCV
|
||||
images are represented by the same structure as matrices, we use the same convention for both
|
||||
cases - the 0-based row index (or y-coordinate) goes first and the 0-based column index (or
|
||||
x-coordinate) follows it. Alternatively, you can use the following notation (**C++ only**):
|
||||
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 2
|
||||
|
||||
Now let us consider a 3 channel image with BGR color ordering (the default format returned by
|
||||
imread):
|
||||
|
||||
**C++ code**
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 3
|
||||
|
||||
**Python Python**
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 3
|
||||
|
||||
You can use the same method for floating-point images (for example, you can get such an image by
|
||||
running Sobel on a 3 channel image) (**C++ only**):
|
||||
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 4
|
||||
|
||||
The same method can be used to change pixel intensities:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 5
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Pixel access 5
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 5
|
||||
@end_toggle
|
||||
|
||||
There are functions in OpenCV, especially from calib3d module, such as cv::projectPoints, that take an
|
||||
array of 2D or 3D points in the form of Mat. Matrix should contain exactly one column, each row
|
||||
corresponds to a point, matrix type should be 32FC2 or 32FC3 correspondingly. Such a matrix can be
|
||||
easily constructed from `std::vector` (**C++ only**):
|
||||
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Mat from points vector
|
||||
|
||||
One can access a point in this matrix using the same method `Mat::at` (**C++ only**):
|
||||
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Point access
|
||||
|
||||
### Memory management and reference counting
|
||||
|
||||
Mat is a structure that keeps matrix/image characteristics (rows and columns number, data type etc)
|
||||
and a pointer to data. So nothing prevents us from having several instances of Mat corresponding to
|
||||
the same data. A Mat keeps a reference count that tells if data has to be deallocated when a
|
||||
particular instance of Mat is destroyed. Here is an example of creating two matrices without copying
|
||||
data (**C++ only**):
|
||||
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 1
|
||||
|
||||
As a result, we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. `pointsMat`
|
||||
uses data from points and will not deallocate the memory when destroyed. In this particular
|
||||
instance, however, developer has to make sure that lifetime of `points` is longer than of `pointsMat`
|
||||
If we need to copy the data, this is done using, for example, cv::Mat::copyTo or cv::Mat::clone:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 2
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Reference counting 2
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Reference counting 2
|
||||
@end_toggle
|
||||
|
||||
An empty output Mat can be supplied to each function.
|
||||
Each implementation calls Mat::create for a destination matrix.
|
||||
This method allocates data for a matrix if it is empty.
|
||||
If it is not empty and has the correct size and type, the method does nothing.
|
||||
If however, size or type are different from the input arguments, the data is deallocated (and lost) and a new data is allocated.
|
||||
For example:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 3
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Reference counting 3
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Reference counting 3
|
||||
@end_toggle
|
||||
|
||||
### Primitive operations
|
||||
|
||||
There is a number of convenient operators defined on a matrix. For example, here is how we can make
|
||||
a black image from an existing greyscale image `img`
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Set image to black
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Set image to black
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Set image to black
|
||||
@end_toggle
|
||||
|
||||
Selecting a region of interest:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Select ROI
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Select ROI
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Select ROI
|
||||
@end_toggle
|
||||
|
||||
Conversion from color to greyscale:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp BGR to Gray
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java BGR to Gray
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py BGR to Gray
|
||||
@end_toggle
|
||||
|
||||
Change image type from 8UC1 to 32FC1:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Convert to CV_32F
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Convert to CV_32F
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Convert to CV_32F
|
||||
@end_toggle
|
||||
|
||||
### Visualizing images
|
||||
|
||||
It is very useful to see intermediate results of your algorithm during development process. OpenCV
|
||||
provides a convenient way of visualizing images. A 8U image can be shown using:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp imshow 1
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java imshow 1
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py imshow 1
|
||||
@end_toggle
|
||||
|
||||
A call to waitKey() starts a message passing cycle that waits for a key stroke in the "image"
|
||||
window. A 32F image needs to be converted to 8U type. For example:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp imshow 2
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java imshow 2
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py imshow 2
|
||||
@end_toggle
|
||||
|
||||
@note Here cv::namedWindow is not necessary since it is immediately followed by cv::imshow.
|
||||
Nevertheless, it can be used to change the window properties or when using cv::createTrackbar
|
||||
Reference in New Issue
Block a user