Extract imgcodecs module from highgui

2026-07-30 15:53:03 +04:00 · 2014-07-04 18:48:15 +04:00
parent 964b260937
commit 4286f60387
204 changed files with 968 additions and 439 deletions
@@ -25,7 +25,7 @@ Here's a sample usage of :operationsonarrays:`dft() <dft>` :
   :language: cpp
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-   :lines: 1-3, 5, 19-20, 23-78
+   :lines: 1-4, 6, 20-21, 24-79

 Explanation
 ===========
@@ -45,7 +45,7 @@ The final argument is optional. If given the image will be loaded in gray scale
 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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-   :lines: 48-60
+   :lines: 49-61

 Here we first use the C++ *stringstream* class to convert the third command line argument from text to an integer format. Then we use a simple look and the upper formula to calculate the lookup table. No OpenCV specific stuff here.

@@ -99,7 +99,7 @@ When it comes to performance you cannot beat the classic C style operator[] (poi
 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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-   :lines: 125-152
+   :lines: 126-153

 Here we basically just acquire a pointer to the start of each row and go through it until it ends. In the special case that the matrix is stored in a continues manner we only need to request the pointer a single time and go all the way to the end. We need to look out for color images: we have three channels so we need to pass through three times more items in each row.

@@ -122,7 +122,7 @@ In case of the efficient way making sure that you pass through the right amount
 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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-   :lines: 154-182
+   :lines: 155-183

 In case of color images we have three uchar items per column. This may be considered a short vector of uchar items, that has been baptized in OpenCV with the *Vec3b* name. To access the n-th sub column we use simple operator[] access. It's important to remember that OpenCV iterators go through the columns and automatically skip to the next row. Therefore in case of color images if you use a simple *uchar* iterator you'll be able to access only the blue channel values.

@@ -134,7 +134,7 @@ The final method isn't recommended for scanning. It was made to acquire or modif
 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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+   :lines: 185-217

 The functions takes your input type and coordinates and calculates on the fly the address of the queried item. Then returns a reference to that. This may be a constant when you *get* the value and non-constant when you *set* the value. As a safety step in **debug mode only*** there is performed a check that your input coordinates are valid and does exist. If this isn't the case you'll get a nice output message of this on the standard error output stream. Compared to the efficient way in release mode the only difference in using this is that for every element of the image you'll get a new row pointer for what we use the C operator[] to acquire the column element.

@@ -148,14 +148,14 @@ This is a bonus method of achieving lookup table modification in an image. Becau
 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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-   :lines: 107-110
+   :lines: 108-111

 Finally call the function (I is our input image and J the output one):

 .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
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+   :lines: 116

 Performance Difference
 ======================
@@ -77,7 +77,7 @@ Now that you have the basics done :download:`here's <../../../../samples/cpp/tut
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+   :lines: 1-10, 23-26, 29-46

 Here you can observe that with the new structure we have no pointer problems, although it is possible to use the old functions and in the end just transform the result to a *Mat* object.

@@ -85,7 +85,7 @@ Here you can observe that with the new structure we have no pointer problems, al
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+   :lines: 48-53

 Because, we want to mess around with the images luma component we first convert from the default RGB to the YUV color space and then split the result up into separate planes. Here the program splits: in the first example it processes each plane using one of the three major image scanning algorithms in OpenCV (C [] operator, iterator, individual element access). In a second variant we add to the image some Gaussian noise and then mix together the channels according to some formula.

@@ -95,7 +95,7 @@ The scanning version looks like:
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+   :lines: 57-77

 Here you can observe that we may go through all the pixels of an image in three fashions: an iterator, a C pointer and an individual element access style. You can read a more in-depth description of these in the :ref:`howToScanImagesOpenCV` tutorial. Converting from the old function names is easy. Just remove the cv prefix and use the new *Mat* data structure. Here's an example of this by using the weighted addition function:

@@ -103,7 +103,7 @@ Here you can observe that we may go through all the pixels of an image in three
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 As you may observe the *planes* variable is of type *Mat*. However, converting from *Mat* to *IplImage* is easy and made automatically with a simple assignment operator.

@@ -111,7 +111,7 @@ As you may observe the *planes* variable is of type *Mat*. However, converting f
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 The new *imshow* highgui function accepts both the *Mat* and *IplImage* data structures. Compile and run the program and if the first image below is your input you may get either the first or second as output:

@@ -86,7 +86,7 @@ Each of the building components has their own valid domains. This leads to the d
 Creating a *Mat* object explicitly
 ==================================

-In the :ref:`Load_Save_Image` tutorial you have already learned how to write a matrix to an image file by using the :readwriteimagevideo:`imwrite() <imwrite>` function. However, for debugging purposes it's much more convenient to see the actual values. You can do this using the << operator of *Mat*. Be aware that this only works for two dimensional matrices.
+In the :ref:`Load_Save_Image` tutorial you have already learned how to write a matrix to an image file by using the :readwriteimage:`imwrite() <imwrite>` function. However, for debugging purposes it's much more convenient to see the actual values. You can do this using the << operator of *Mat*. Be aware that this only works for two dimensional matrices.

 Although *Mat* works really well as an image container, it is also a general matrix class. Therefore, it is possible to create and manipulate multidimensional matrices. You can create a Mat object in multiple ways:

@@ -200,7 +200,6 @@ Here you will learn the about the basic building blocks of the library. A must r
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-  =============== ======================================================
 +
  .. tabularcolumns:: m{100pt} m{300pt}
  .. cssclass:: toctableopencv
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  .. |Author_ElenaG| unicode:: Elena U+0020 Gvozdeva

-  =============== ======================================================
-
 .. raw:: latex

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