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Merge remote-tracking branch 'origin/2.4' into merge-2.4

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Conflicts:
	modules/imgproc/src/opencl/integral_sqrsum.cl
	modules/nonfree/doc/feature_detection.rst
	modules/nonfree/include/opencv2/nonfree/ocl.hpp
	modules/nonfree/src/surf_ocl.cpp
	modules/nonfree/test/test_features2d.cpp
	modules/ocl/doc/image_processing.rst
	modules/ocl/include/opencv2/ocl/ocl.hpp
	modules/ocl/perf/perf_imgproc.cpp
	modules/ocl/perf/perf_match_template.cpp
	modules/ocl/src/haar.cpp
	modules/ocl/src/imgproc.cpp
	modules/ocl/src/match_template.cpp
	modules/ocl/src/opencl/haarobjectdetect.cl
	modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
	modules/ocl/test/test_imgproc.cpp
This commit is contained in:
Roman Donchenko
2014-03-31 14:42:00 +04:00
parent d6b54ff9d1 51530d4d8e
commit f77c68e0fa
13 changed files with 117 additions and 101 deletions
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38
doc/tutorials/core/mat-mask-operations/mat-mask-operations.rst
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@@ -32,14 +32,14 @@ Here's a function that will do this:
.. code-block:: cpp
void Sharpen(const Mat& myImage,Mat& Result)
void Sharpen(const Mat& myImage, Mat& Result)
{
CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
Result.create(myImage.size(),myImage.type());
Result.create(myImage.size(), myImage.type());
const int nChannels = myImage.channels();
for(int j = 1 ; j < myImage.rows-1; ++j)
for(int j = 1; j < myImage.rows - 1; ++j)
{
const uchar* previous = myImage.ptr<uchar>(j - 1);
const uchar* current = myImage.ptr<uchar>(j );
@@ -47,17 +47,17 @@ Here's a function that will do this:
uchar* output = Result.ptr<uchar>(j);
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
for(int i = nChannels; i < nChannels * (myImage.cols - 1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
*output++ = saturate_cast<uchar>(5 * current[i]
-current[i - nChannels] - current[i + nChannels] - previous[i] - next[i]);
}
}
Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.row(Result.rows - 1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
Result.col(Result.cols - 1).setTo(Scalar(0));
}
At first we make sure that the input images data is in unsigned char format. For this we use the :utilitysystemfunctions:`CV_Assert <cv-assert>` function that throws an error when the expression inside it is false.
@@ -70,14 +70,14 @@ We create an output image with the same size and the same type as our input. As
.. code-block:: cpp
Result.create(myImage.size(),myImage.type());
Result.create(myImage.size(), myImage.type());
const int nChannels = myImage.channels();
We'll use the plain C [] operator to access pixels. Because we need to access multiple rows at the same time we'll acquire the pointers for each of them (a previous, a current and a next line). We need another pointer to where we're going to save the calculation. Then simply access the right items with the [] operator. For moving the output pointer ahead we simply increase this (with one byte) after each operation:
.. code-block:: cpp
for(int j = 1 ; j < myImage.rows-1; ++j)
for(int j = 1; j < myImage.rows - 1; ++j)
{
const uchar* previous = myImage.ptr<uchar>(j - 1);
const uchar* current = myImage.ptr<uchar>(j );
@@ -85,21 +85,21 @@ We'll use the plain C [] operator to access pixels. Because we need to access mu
uchar* output = Result.ptr<uchar>(j);
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
for(int i = nChannels; i < nChannels * (myImage.cols - 1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
*output++ = saturate_cast<uchar>(5 * current[i]
-current[i - nChannels] - current[i + nChannels] - previous[i] - next[i]);
}
}
On the borders of the image the upper notation results inexistent pixel locations (like minus one - minus one). In these points our formula is undefined. A simple solution is to not apply the mask in these points and, for example, set the pixels on the borders to zeros:
On the borders of the image the upper notation results inexistent pixel locations (like minus one - minus one). In these points our formula is undefined. A simple solution is to not apply the kernel in these points and, for example, set the pixels on the borders to zeros:
.. code-block:: cpp
Result.row(0).setTo(Scalar(0)); // The top row
Result.row(Result.rows-1).setTo(Scalar(0)); // The bottom row
Result.col(0).setTo(Scalar(0)); // The left column
Result.col(Result.cols-1).setTo(Scalar(0)); // The right column
Result.row(0).setTo(Scalar(0)); // The top row
Result.row(Result.rows - 1).setTo(Scalar(0)); // The bottom row
Result.col(0).setTo(Scalar(0)); // The left column
Result.col(Result.cols - 1).setTo(Scalar(0)); // The right column
The filter2D function
=====================
@@ -116,7 +116,7 @@ Then call the :filtering:`filter2D <filter2d>` function specifying the input, th
.. code-block:: cpp
filter2D(I, K, I.depth(), kern );
filter2D(I, K, I.depth(), kern);
The function even has a fifth optional argument to specify the center of the kernel, and a sixth one for determining what to do in the regions where the operation is undefined (borders). Using this function has the advantage that it's shorter, less verbose and because there are some optimization techniques implemented it is usually faster than the *hand-coded method*. For example in my test while the second one took only 13 milliseconds the first took around 31 milliseconds. Quite some difference.

4
doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.rst
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@@ -45,7 +45,7 @@ All the above objects, in the end, point to the same single data matrix. Their h
:linenos:
Mat D (A, Rect(10, 10, 100, 100) ); // using a rectangle
Mat E = A(Range:all(), Range(1,3)); // using row and column boundaries
Mat E = A(Range::all(), Range(1,3)); // using row and column boundaries
Now you may ask if the matrix itself may belong to multiple *Mat* objects who takes responsibility for cleaning it up when it's no longer needed. The short answer is: the last object that used it. This is handled by using a reference counting mechanism. Whenever somebody copies a header of a *Mat* object, a counter is increased for the matrix. Whenever a header is cleaned this counter is decreased. When the counter reaches zero the matrix too is freed. Sometimes you will want to copy the matrix itself too, so OpenCV provides the :basicstructures:`clone() <mat-clone>` and :basicstructures:`copyTo() <mat-copyto>` functions.
@@ -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 :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.
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: