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Update documentation and samples
This commit is contained in:
Suleyman TURKMEN
parent
1c73e66edf
commit
c61bc3a0cb
@ -1,6 +1,9 @@
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Changing the contrast and brightness of an image! {#tutorial_basic_linear_transform}
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=================================================
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@prev_tutorial{tutorial_adding_images}
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@next_tutorial{tutorial_discrete_fourier_transform}
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Goal
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----
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@ -1,7 +1,7 @@
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Discrete Fourier Transform {#tutorial_discrete_fourier_transform}
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==========================
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@prev_tutorial{tutorial_random_generator_and_text}
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@prev_tutorial{tutorial_basic_linear_transform}
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@next_tutorial{tutorial_file_input_output_with_xml_yml}
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Goal
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@ -1,6 +1,9 @@
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File Input and Output using XML and YAML files {#tutorial_file_input_output_with_xml_yml}
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==============================================
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@prev_tutorial{tutorial_discrete_fourier_transform}
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@next_tutorial{tutorial_interoperability_with_OpenCV_1}
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Goal
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----
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@ -1,6 +1,9 @@
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How to scan images, lookup tables and time measurement with OpenCV {#tutorial_how_to_scan_images}
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==================================================================
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@prev_tutorial{tutorial_mat_the_basic_image_container}
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@next_tutorial{tutorial_mat_mask_operations}
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Goal
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----
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@ -1,6 +1,8 @@
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How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
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==================================================================
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@prev_tutorial{tutorial_how_to_use_ippa_conversion}
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Goal
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----
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@ -1,6 +1,9 @@
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Intel® IPP Asynchronous C/C++ library in OpenCV {#tutorial_how_to_use_ippa_conversion}
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===============================================
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@prev_tutorial{tutorial_interoperability_with_OpenCV_1}
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@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
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Goal
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----
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@ -1,6 +1,9 @@
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Interoperability with OpenCV 1 {#tutorial_interoperability_with_OpenCV_1}
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==============================
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@prev_tutorial{tutorial_file_input_output_with_xml_yml}
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@next_tutorial{tutorial_how_to_use_ippa_conversion}
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Goal
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----
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@ -1,6 +1,9 @@
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Operations with images {#tutorial_mat_operations}
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======================
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@prev_tutorial{tutorial_mat_mask_operations}
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@next_tutorial{tutorial_adding_images}
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Input/Output
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------------
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@ -1,6 +1,8 @@
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Mat - The Basic Image Container {#tutorial_mat_the_basic_image_container}
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===============================
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@next_tutorial{tutorial_how_to_scan_images}
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Goal
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----
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@ -62,24 +62,6 @@ understanding how to manipulate the images on a pixel level.
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We will learn how to change our image appearance!
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- @subpage tutorial_basic_geometric_drawing
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*Languages:* C++, Java, Python
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*Compatibility:* \> OpenCV 2.0
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*Author:* Ana Huamán
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We will learn how to draw simple geometry with OpenCV!
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- @subpage tutorial_random_generator_and_text
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*Compatibility:* \> OpenCV 2.0
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*Author:* Ana Huamán
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We will draw some *fancy-looking* stuff using OpenCV!
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- @subpage tutorial_discrete_fourier_transform
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*Languages:* C++, Java, Python
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@ -1,7 +1,6 @@
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Basic Drawing {#tutorial_basic_geometric_drawing}
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=============
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@prev_tutorial{tutorial_basic_linear_transform}
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@next_tutorial{tutorial_random_generator_and_text}
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Goals
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@ -82,20 +81,20 @@ Code
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@add_toggle_cpp
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- This code is in your OpenCV sample folder. Otherwise you can grab it from
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp)
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@include samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp)
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@include samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp
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@end_toggle
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@add_toggle_java
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- This code is in your OpenCV sample folder. Otherwise you can grab it from
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java)
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@include samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java)
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@include samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java
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@end_toggle
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@add_toggle_python
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- This code is in your OpenCV sample folder. Otherwise you can grab it from
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py)
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@include samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py
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[here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py)
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@include samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py
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@end_toggle
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Explanation
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@ -104,42 +103,42 @@ Explanation
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Since we plan to draw two examples (an atom and a rook), we have to create two images and two
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windows to display them.
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp create_images
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp create_images
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py create_images
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py create_images
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@end_toggle
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We created functions to draw different geometric shapes. For instance, to draw the atom we used
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**MyEllipse** and **MyFilledCircle**:
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_atom
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_atom
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
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@end_toggle
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And to draw the rook we employed **MyLine**, **rectangle** and a **MyPolygon**:
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_rook
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_rook
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
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@end_toggle
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@ -149,15 +148,15 @@ Let's check what is inside each of these functions:
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<H4>MyLine</H4>
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_line
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_line
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_line
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_line
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@end_toggle
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- As we can see, **MyLine** just call the function **line()** , which does the following:
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@ -170,15 +169,15 @@ Let's check what is inside each of these functions:
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<H4>MyEllipse</H4>
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_ellipse
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_ellipse
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
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@end_toggle
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- From the code above, we can observe that the function **ellipse()** draws an ellipse such
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@ -194,15 +193,15 @@ Let's check what is inside each of these functions:
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<H4>MyFilledCircle</H4>
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_filled_circle
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_filled_circle
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
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@end_toggle
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- Similar to the ellipse function, we can observe that *circle* receives as arguments:
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@ -215,15 +214,15 @@ Let's check what is inside each of these functions:
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<H4>MyPolygon</H4>
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_polygon
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_polygon
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
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@end_toggle
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- To draw a filled polygon we use the function **fillPoly()** . We note that:
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@ -235,15 +234,15 @@ Let's check what is inside each of these functions:
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<H4>rectangle</H4>
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@add_toggle_cpp
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@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp rectangle
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@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp rectangle
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@end_toggle
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@add_toggle_java
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@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
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@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
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@end_toggle
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@add_toggle_python
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@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
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@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
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@end_toggle
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- Finally we have the @ref cv::rectangle function (we did not create a special function for
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Before Width: | Height: | Size: 16 KiB After Width: | Height: | Size: 16 KiB |
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Eroding and Dilating {#tutorial_erosion_dilatation}
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====================
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@prev_tutorial{tutorial_gausian_median_blur_bilateral_filter}
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@next_tutorial{tutorial_opening_closing_hats}
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Goal
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----
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Smoothing Images {#tutorial_gausian_median_blur_bilateral_filter}
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================
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@prev_tutorial{tutorial_random_generator_and_text}
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@next_tutorial{tutorial_erosion_dilatation}
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Goal
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@ -1,6 +1,9 @@
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Back Projection {#tutorial_back_projection}
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===============
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@prev_tutorial{tutorial_histogram_comparison}
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@next_tutorial{tutorial_template_matching}
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Goal
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----
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Histogram Calculation {#tutorial_histogram_calculation}
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=====================
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@prev_tutorial{tutorial_histogram_equalization}
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@next_tutorial{tutorial_histogram_comparison}
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Goal
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----
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Histogram Comparison {#tutorial_histogram_comparison}
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====================
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@prev_tutorial{tutorial_histogram_calculation}
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@next_tutorial{tutorial_back_projection}
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Goal
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----
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Histogram Equalization {#tutorial_histogram_equalization}
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======================
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@prev_tutorial{tutorial_warp_affine}
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@next_tutorial{tutorial_histogram_calculation}
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Goal
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----
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Canny Edge Detector {#tutorial_canny_detector}
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===================
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@prev_tutorial{tutorial_laplace_operator}
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@next_tutorial{tutorial_hough_lines}
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Goal
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----
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@ -1,6 +1,9 @@
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Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform}
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=============
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@prev_tutorial{tutorial_point_polygon_test}
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@next_tutorial{tutorial_out_of_focus_deblur_filter}
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Goal
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----
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@ -1,6 +1,9 @@
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Remapping {#tutorial_remap}
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=========
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@prev_tutorial{tutorial_hough_circle}
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@next_tutorial{tutorial_warp_affine}
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Goal
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----
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@ -1,6 +1,9 @@
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Affine Transformations {#tutorial_warp_affine}
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======================
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@prev_tutorial{tutorial_remap}
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@next_tutorial{tutorial_histogram_equalization}
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Goal
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----
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@ -1,6 +1,9 @@
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More Morphology Transformations {#tutorial_opening_closing_hats}
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===============================
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@prev_tutorial{tutorial_erosion_dilatation}
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@next_tutorial{tutorial_hitOrMiss}
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Goal
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----
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@ -1,6 +1,8 @@
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Out-of-focus Deblur Filter {#tutorial_out_of_focus_deblur_filter}
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==========================
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@prev_tutorial{tutorial_distance_transform}
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Goal
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----
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Before Width: | Height: | Size: 23 KiB After Width: | Height: | Size: 23 KiB |
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Before Width: | Height: | Size: 27 KiB After Width: | Height: | Size: 27 KiB |
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Before Width: | Height: | Size: 30 KiB After Width: | Height: | Size: 30 KiB |
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Before Width: | Height: | Size: 16 KiB After Width: | Height: | Size: 16 KiB |
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Before Width: | Height: | Size: 16 KiB After Width: | Height: | Size: 16 KiB |
@ -1,6 +1,9 @@
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Random generator and text with OpenCV {#tutorial_random_generator_and_text}
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=====================================
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@prev_tutorial{tutorial_basic_geometric_drawing}
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@next_tutorial{tutorial_gausian_median_blur_bilateral_filter}
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Goals
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-----
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@ -1,6 +1,9 @@
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Creating Bounding boxes and circles for contours {#tutorial_bounding_rects_circles}
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================================================
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@prev_tutorial{tutorial_hull}
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@next_tutorial{tutorial_bounding_rotated_ellipses}
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Goal
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----
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|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Creating Bounding rotated boxes and ellipses for contours {#tutorial_bounding_rotated_ellipses}
|
||||
=========================================================
|
||||
|
||||
@prev_tutorial{tutorial_bounding_rects_circles}
|
||||
@next_tutorial{tutorial_moments}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Finding contours in your image {#tutorial_find_contours}
|
||||
==============================
|
||||
|
||||
@prev_tutorial{tutorial_template_matching}
|
||||
@next_tutorial{tutorial_hull}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Convex Hull {#tutorial_hull}
|
||||
===========
|
||||
|
||||
@prev_tutorial{tutorial_find_contours}
|
||||
@next_tutorial{tutorial_bounding_rects_circles}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Image Moments {#tutorial_moments}
|
||||
=============
|
||||
|
||||
@prev_tutorial{tutorial_bounding_rotated_ellipses}
|
||||
@next_tutorial{tutorial_point_polygon_test}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Point Polygon Test {#tutorial_point_polygon_test}
|
||||
==================
|
||||
|
||||
@prev_tutorial{tutorial_moments}
|
||||
@next_tutorial{tutorial_distance_transform}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -3,6 +3,24 @@ Image Processing (imgproc module) {#tutorial_table_of_content_imgproc}
|
||||
|
||||
In this section you will learn about the image processing (manipulation) functions inside OpenCV.
|
||||
|
||||
- @subpage tutorial_basic_geometric_drawing
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
We will learn how to draw simple geometry with OpenCV!
|
||||
|
||||
- @subpage tutorial_random_generator_and_text
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
We will draw some *fancy-looking* stuff using OpenCV!
|
||||
|
||||
- @subpage tutorial_gausian_median_blur_bilateral_filter
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Basic Thresholding Operations {#tutorial_threshold}
|
||||
=============================
|
||||
|
||||
@prev_tutorial{tutorial_pyramids}
|
||||
@next_tutorial{tutorial_threshold_inRange}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
Thresholding Operations using inRange {#tutorial_threshold_inRange}
|
||||
=====================================
|
||||
|
||||
@prev_tutorial{tutorial_threshold}
|
||||
@next_tutorial{tutorial_filter_2d}
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
|
||||
@ -24,17 +24,7 @@ Explanation
|
||||
|
||||
The most important code part is:
|
||||
|
||||
@code{.cpp}
|
||||
Mat pano;
|
||||
Ptr<Stitcher> stitcher = Stitcher::create(mode, try_use_gpu);
|
||||
Stitcher::Status status = stitcher->stitch(imgs, pano);
|
||||
|
||||
if (status != Stitcher::OK)
|
||||
{
|
||||
cout << "Can't stitch images, error code = " << int(status) << endl;
|
||||
return -1;
|
||||
}
|
||||
@endcode
|
||||
@snippet cpp/stitching.cpp stitching
|
||||
|
||||
A new instance of stitcher is created and the @ref cv::Stitcher::stitch will
|
||||
do all the hard work.
|
||||
|
||||
@ -307,11 +307,11 @@ optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
|
||||
*/
|
||||
CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
|
||||
|
||||
/** @example pose_from_homography.cpp
|
||||
An example program about pose estimation from coplanar points
|
||||
/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp
|
||||
An example program about pose estimation from coplanar points
|
||||
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details
|
||||
*/
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Finds a perspective transformation between two planes.
|
||||
|
||||
@ -526,11 +526,11 @@ CV_EXPORTS_W void projectPoints( InputArray objectPoints,
|
||||
OutputArray jacobian = noArray(),
|
||||
double aspectRatio = 0 );
|
||||
|
||||
/** @example homography_from_camera_displacement.cpp
|
||||
An example program about homography from the camera displacement
|
||||
/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp
|
||||
An example program about homography from the camera displacement
|
||||
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details
|
||||
*/
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Finds an object pose from 3D-2D point correspondences.
|
||||
|
||||
@ -1966,11 +1966,11 @@ CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, Out
|
||||
size_t maxIters = 2000, double confidence = 0.99,
|
||||
size_t refineIters = 10);
|
||||
|
||||
/** @example decompose_homography.cpp
|
||||
An example program with homography decomposition.
|
||||
/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp
|
||||
An example program with homography decomposition.
|
||||
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details.
|
||||
*/
|
||||
Check @ref tutorial_homography "the corresponding tutorial" for more details.
|
||||
*/
|
||||
|
||||
/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
|
||||
|
||||
|
||||
@ -273,9 +273,11 @@ of p and len.
|
||||
*/
|
||||
CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
|
||||
|
||||
/** @example copyMakeBorder_demo.cpp
|
||||
An example using copyMakeBorder function
|
||||
*/
|
||||
/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
|
||||
An example using copyMakeBorder function.
|
||||
Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Forms a border around an image.
|
||||
|
||||
The function copies the source image into the middle of the destination image. The areas to the
|
||||
@ -474,9 +476,10 @@ The function can also be emulated with a matrix expression, for example:
|
||||
*/
|
||||
CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
|
||||
|
||||
/** @example AddingImagesTrackbar.cpp
|
||||
/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
|
||||
Check @ref tutorial_trackbar "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
*/
|
||||
/** @brief Calculates the weighted sum of two arrays.
|
||||
|
||||
The function addWeighted calculates the weighted sum of two arrays as follows:
|
||||
@ -2527,14 +2530,18 @@ public:
|
||||
Mat mean; //!< mean value subtracted before the projection and added after the back projection
|
||||
};
|
||||
|
||||
/** @example pca.cpp
|
||||
An example using %PCA for dimensionality reduction while maintaining an amount of variance
|
||||
*/
|
||||
/** @example samples/cpp/pca.cpp
|
||||
An example using %PCA for dimensionality reduction while maintaining an amount of variance
|
||||
*/
|
||||
|
||||
/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
|
||||
Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/**
|
||||
@brief Linear Discriminant Analysis
|
||||
@todo document this class
|
||||
*/
|
||||
@brief Linear Discriminant Analysis
|
||||
@todo document this class
|
||||
*/
|
||||
class CV_EXPORTS LDA
|
||||
{
|
||||
public:
|
||||
@ -2850,7 +2857,7 @@ public:
|
||||
use explicit type cast operators, as in the a1 initialization above.
|
||||
@param a lower inclusive boundary of the returned random number.
|
||||
@param b upper non-inclusive boundary of the returned random number.
|
||||
*/
|
||||
*/
|
||||
int uniform(int a, int b);
|
||||
/** @overload */
|
||||
float uniform(float a, float b);
|
||||
@ -2912,7 +2919,7 @@ public:
|
||||
|
||||
Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
|
||||
@todo document
|
||||
*/
|
||||
*/
|
||||
class CV_EXPORTS RNG_MT19937
|
||||
{
|
||||
public:
|
||||
@ -2930,17 +2937,11 @@ public:
|
||||
unsigned operator ()(unsigned N);
|
||||
unsigned operator ()();
|
||||
|
||||
/** @brief returns uniformly distributed integer random number from [a,b) range
|
||||
|
||||
*/
|
||||
/** @brief returns uniformly distributed integer random number from [a,b) range*/
|
||||
int uniform(int a, int b);
|
||||
/** @brief returns uniformly distributed floating-point random number from [a,b) range
|
||||
|
||||
*/
|
||||
/** @brief returns uniformly distributed floating-point random number from [a,b) range*/
|
||||
float uniform(float a, float b);
|
||||
/** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range
|
||||
|
||||
*/
|
||||
/** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/
|
||||
double uniform(double a, double b);
|
||||
|
||||
private:
|
||||
@ -2954,8 +2955,8 @@ private:
|
||||
//! @addtogroup core_cluster
|
||||
//! @{
|
||||
|
||||
/** @example kmeans.cpp
|
||||
An example on K-means clustering
|
||||
/** @example samples/cpp/kmeans.cpp
|
||||
An example on K-means clustering
|
||||
*/
|
||||
|
||||
/** @brief Finds centers of clusters and groups input samples around the clusters.
|
||||
@ -3067,7 +3068,7 @@ etc.).
|
||||
|
||||
Here is example of SimpleBlobDetector use in your application via Algorithm interface:
|
||||
@snippet snippets/core_various.cpp Algorithm
|
||||
*/
|
||||
*/
|
||||
class CV_EXPORTS_W Algorithm
|
||||
{
|
||||
public:
|
||||
@ -3083,8 +3084,8 @@ public:
|
||||
virtual void write(FileStorage& fs) const { (void)fs; }
|
||||
|
||||
/** @brief simplified API for language bindings
|
||||
* @overload
|
||||
*/
|
||||
* @overload
|
||||
*/
|
||||
CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
|
||||
|
||||
/** @brief Reads algorithm parameters from a file storage
|
||||
@ -3092,20 +3093,20 @@ public:
|
||||
CV_WRAP virtual void read(const FileNode& fn) { (void)fn; }
|
||||
|
||||
/** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
|
||||
*/
|
||||
*/
|
||||
CV_WRAP virtual bool empty() const { return false; }
|
||||
|
||||
/** @brief Reads algorithm from the file node
|
||||
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
cv::FileStorage fsRead("example.xml", FileStorage::READ);
|
||||
Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
|
||||
@endcode
|
||||
In order to make this method work, the derived class must overwrite Algorithm::read(const
|
||||
FileNode& fn) and also have static create() method without parameters
|
||||
(or with all the optional parameters)
|
||||
*/
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
cv::FileStorage fsRead("example.xml", FileStorage::READ);
|
||||
Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
|
||||
@endcode
|
||||
In order to make this method work, the derived class must overwrite Algorithm::read(const
|
||||
FileNode& fn) and also have static create() method without parameters
|
||||
(or with all the optional parameters)
|
||||
*/
|
||||
template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
|
||||
{
|
||||
Ptr<_Tp> obj = _Tp::create();
|
||||
@ -3115,16 +3116,16 @@ public:
|
||||
|
||||
/** @brief Loads algorithm from the file
|
||||
|
||||
@param filename Name of the file to read.
|
||||
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
|
||||
@param filename Name of the file to read.
|
||||
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
|
||||
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
|
||||
@endcode
|
||||
In order to make this method work, the derived class must overwrite Algorithm::read(const
|
||||
FileNode& fn).
|
||||
*/
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
|
||||
@endcode
|
||||
In order to make this method work, the derived class must overwrite Algorithm::read(const
|
||||
FileNode& fn).
|
||||
*/
|
||||
template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
|
||||
{
|
||||
FileStorage fs(filename, FileStorage::READ);
|
||||
@ -3138,14 +3139,14 @@ public:
|
||||
|
||||
/** @brief Loads algorithm from a String
|
||||
|
||||
@param strModel The string variable containing the model you want to load.
|
||||
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
|
||||
@param strModel The string variable containing the model you want to load.
|
||||
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
|
||||
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
|
||||
@endcode
|
||||
*/
|
||||
This is static template method of Algorithm. It's usage is following (in the case of SVM):
|
||||
@code
|
||||
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
|
||||
@endcode
|
||||
*/
|
||||
template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
|
||||
{
|
||||
FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
|
||||
@ -3156,11 +3157,11 @@ public:
|
||||
}
|
||||
|
||||
/** Saves the algorithm to a file.
|
||||
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
|
||||
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
|
||||
CV_WRAP virtual void save(const String& filename) const;
|
||||
|
||||
/** Returns the algorithm string identifier.
|
||||
This string is used as top level xml/yml node tag when the object is saved to a file or string. */
|
||||
This string is used as top level xml/yml node tag when the object is saved to a file or string. */
|
||||
CV_WRAP virtual String getDefaultName() const;
|
||||
|
||||
protected:
|
||||
|
||||
@ -575,7 +575,7 @@ protected:
|
||||
MatStep& operator = (const MatStep&);
|
||||
};
|
||||
|
||||
/** @example cout_mat.cpp
|
||||
/** @example samples/cpp/cout_mat.cpp
|
||||
An example demonstrating the serial out capabilities of cv::Mat
|
||||
*/
|
||||
|
||||
|
||||
@ -287,12 +287,12 @@ element is a structure of 2 integers, followed by a single-precision floating-po
|
||||
equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u`
|
||||
means that the array consists of bytes, and `2d` means the array consists of pairs of doubles.
|
||||
|
||||
@see @ref filestorage.cpp
|
||||
@see @ref samples/cpp/filestorage.cpp
|
||||
*/
|
||||
|
||||
//! @{
|
||||
|
||||
/** @example filestorage.cpp
|
||||
/** @example samples/cpp/filestorage.cpp
|
||||
A complete example using the FileStorage interface
|
||||
*/
|
||||
|
||||
|
||||
@ -59,6 +59,20 @@
|
||||
A network training is in principle not supported.
|
||||
@}
|
||||
*/
|
||||
/** @example samples/dnn/classification.cpp
|
||||
Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
|
||||
*/
|
||||
/** @example samples/dnn/colorization.cpp
|
||||
*/
|
||||
/** @example samples/dnn/object_detection.cpp
|
||||
Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details
|
||||
*/
|
||||
/** @example samples/dnn/openpose.cpp
|
||||
*/
|
||||
/** @example samples/dnn/segmentation.cpp
|
||||
*/
|
||||
/** @example samples/dnn/text_detection.cpp
|
||||
*/
|
||||
#include <opencv2/dnn/dnn.hpp>
|
||||
|
||||
#endif /* OPENCV_DNN_HPP */
|
||||
|
||||
@ -452,12 +452,13 @@ The function getWindowImageRect returns the client screen coordinates, width and
|
||||
*/
|
||||
CV_EXPORTS_W Rect getWindowImageRect(const String& winname);
|
||||
|
||||
/** @example samples/cpp/create_mask.cpp
|
||||
This program demonstrates using mouse events and how to make and use a mask image (black and white) .
|
||||
*/
|
||||
/** @brief Sets mouse handler for the specified window
|
||||
|
||||
@param winname Name of the window.
|
||||
@param onMouse Mouse callback. See OpenCV samples, such as
|
||||
<https://github.com/opencv/opencv/tree/3.4/samples/cpp/ffilldemo.cpp>, on how to specify and
|
||||
use the callback.
|
||||
@param onMouse Callback function for mouse events. See OpenCV samples on how to specify and use the callback.
|
||||
@param userdata The optional parameter passed to the callback.
|
||||
*/
|
||||
CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0);
|
||||
|
||||
@ -1191,7 +1191,7 @@ protected:
|
||||
//! @addtogroup imgproc_feature
|
||||
//! @{
|
||||
|
||||
/** @example lsd_lines.cpp
|
||||
/** @example samples/cpp/lsd_lines.cpp
|
||||
An example using the LineSegmentDetector
|
||||
\image html building_lsd.png "Sample output image" width=434 height=300
|
||||
*/
|
||||
@ -1349,11 +1349,12 @@ operation is shifted.
|
||||
*/
|
||||
CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
|
||||
|
||||
/** @example Smoothing.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp
|
||||
Sample code for simple filters
|
||||
|
||||
Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Blurs an image using the median filter.
|
||||
|
||||
The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
|
||||
@ -1556,11 +1557,12 @@ CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
|
||||
Point anchor = Point(-1,-1),
|
||||
double delta = 0, int borderType = BORDER_DEFAULT );
|
||||
|
||||
/** @example Sobel_Demo.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
|
||||
Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
|
||||
|
||||
Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
|
||||
|
||||
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
|
||||
@ -1656,8 +1658,8 @@ CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
|
||||
int dx, int dy, double scale = 1, double delta = 0,
|
||||
int borderType = BORDER_DEFAULT );
|
||||
|
||||
/** @example laplace.cpp
|
||||
An example using Laplace transformations for edge detection
|
||||
/** @example samples/cpp/laplace.cpp
|
||||
An example using Laplace transformations for edge detection
|
||||
*/
|
||||
|
||||
/** @brief Calculates the Laplacian of an image.
|
||||
@ -1692,10 +1694,10 @@ CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
|
||||
//! @addtogroup imgproc_feature
|
||||
//! @{
|
||||
|
||||
/** @example edge.cpp
|
||||
This program demonstrates usage of the Canny edge detector
|
||||
/** @example samples/cpp/edge.cpp
|
||||
This program demonstrates usage of the Canny edge detector
|
||||
|
||||
Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
|
||||
Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
|
||||
@ -1932,7 +1934,7 @@ CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
|
||||
InputArray mask, int blockSize,
|
||||
int gradientSize, bool useHarrisDetector = false,
|
||||
double k = 0.04 );
|
||||
/** @example houghlines.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp
|
||||
An example using the Hough line detector
|
||||
 
|
||||
*/
|
||||
@ -2021,7 +2023,7 @@ CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int
|
||||
double min_rho, double max_rho, double rho_step,
|
||||
double min_theta, double max_theta, double theta_step );
|
||||
|
||||
/** @example houghcircles.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp
|
||||
An example using the Hough circle detector
|
||||
*/
|
||||
|
||||
@ -2069,7 +2071,7 @@ CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
|
||||
//! @addtogroup imgproc_filter
|
||||
//! @{
|
||||
|
||||
/** @example morphology2.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp
|
||||
Advanced morphology Transformations sample code
|
||||
|
||||
Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details
|
||||
@ -2102,11 +2104,12 @@ CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
|
||||
int borderType = BORDER_CONSTANT,
|
||||
const Scalar& borderValue = morphologyDefaultBorderValue() );
|
||||
|
||||
/** @example Morphology_1.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp
|
||||
Erosion and Dilation sample code
|
||||
|
||||
Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Dilates an image by using a specific structuring element.
|
||||
|
||||
The function dilates the source image using the specified structuring element that determines the
|
||||
@ -2236,9 +2239,10 @@ CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
|
||||
int borderMode = BORDER_CONSTANT,
|
||||
const Scalar& borderValue = Scalar());
|
||||
|
||||
/** @example warpPerspective_demo.cpp
|
||||
/** @example samples/cpp/warpPerspective_demo.cpp
|
||||
An example program shows using cv::findHomography and cv::warpPerspective for image warping
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Applies a perspective transformation to an image.
|
||||
|
||||
The function warpPerspective transforms the source image using the specified matrix:
|
||||
@ -2434,7 +2438,7 @@ source image. The center must be inside the image.
|
||||
CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
|
||||
Point2f center, OutputArray patch, int patchType = -1 );
|
||||
|
||||
/** @example polar_transforms.cpp
|
||||
/** @example samples/cpp/polar_transforms.cpp
|
||||
An example using the cv::linearPolar and cv::logPolar operations
|
||||
*/
|
||||
|
||||
@ -2869,9 +2873,10 @@ CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
|
||||
//! @addtogroup imgproc_filter
|
||||
//! @{
|
||||
|
||||
/** @example Pyramids.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp
|
||||
An example using pyrDown and pyrUp functions
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Blurs an image and downsamples it.
|
||||
|
||||
By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
|
||||
@ -3120,7 +3125,7 @@ CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputA
|
||||
//! @addtogroup imgproc_hist
|
||||
//! @{
|
||||
|
||||
/** @example demhist.cpp
|
||||
/** @example samples/cpp/demhist.cpp
|
||||
An example for creating histograms of an image
|
||||
*/
|
||||
|
||||
@ -3317,9 +3322,9 @@ CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature
|
||||
|
||||
//! @} imgproc_hist
|
||||
|
||||
/** @example watershed.cpp
|
||||
/** @example samples/cpp/watershed.cpp
|
||||
An example using the watershed algorithm
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Performs a marker-based image segmentation using the watershed algorithm.
|
||||
|
||||
@ -3397,10 +3402,10 @@ CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
|
||||
//! @addtogroup imgproc_misc
|
||||
//! @{
|
||||
|
||||
/** @example grabcut.cpp
|
||||
/** @example samples/cpp/grabcut.cpp
|
||||
An example using the GrabCut algorithm
|
||||
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Runs the GrabCut algorithm.
|
||||
|
||||
@ -3424,11 +3429,10 @@ CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
|
||||
InputOutputArray bgdModel, InputOutputArray fgdModel,
|
||||
int iterCount, int mode = GC_EVAL );
|
||||
|
||||
/** @example distrans.cpp
|
||||
An example on using the distance transform\
|
||||
/** @example samples/cpp/distrans.cpp
|
||||
An example on using the distance transform
|
||||
*/
|
||||
|
||||
|
||||
/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
|
||||
|
||||
The function cv::distanceTransform calculates the approximate or precise distance from every binary
|
||||
@ -3500,8 +3504,8 @@ the first variant of the function and distanceType == #DIST_L1.
|
||||
CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
|
||||
int distanceType, int maskSize, int dstType=CV_32F);
|
||||
|
||||
/** @example ffilldemo.cpp
|
||||
An example using the FloodFill technique
|
||||
/** @example samples/cpp/ffilldemo.cpp
|
||||
An example using the FloodFill technique
|
||||
*/
|
||||
|
||||
/** @overload
|
||||
@ -3701,9 +3705,10 @@ enum TemplateMatchModes {
|
||||
TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f]
|
||||
};
|
||||
|
||||
/** @example MatchTemplate_Demo.cpp
|
||||
/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
|
||||
An example using Template Matching algorithm
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Compares a template against overlapped image regions.
|
||||
|
||||
The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
|
||||
@ -3735,6 +3740,10 @@ CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
|
||||
//! @addtogroup imgproc_shape
|
||||
//! @{
|
||||
|
||||
/** @example samples/cpp/connected_components.cpp
|
||||
This program demonstrates connected components and use of the trackbar
|
||||
*/
|
||||
|
||||
/** @brief computes the connected components labeled image of boolean image
|
||||
|
||||
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
|
||||
@ -3842,6 +3851,16 @@ CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays cont
|
||||
CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
|
||||
int mode, int method, Point offset = Point());
|
||||
|
||||
/** @example samples/cpp/squares.cpp
|
||||
A program using pyramid scaling, Canny, contours and contour simplification to find
|
||||
squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image.
|
||||
*/
|
||||
|
||||
/** @example samples/tapi/squares.cpp
|
||||
A program using pyramid scaling, Canny, contours and contour simplification to find
|
||||
squares in the input image.
|
||||
*/
|
||||
|
||||
/** @brief Approximates a polygonal curve(s) with the specified precision.
|
||||
|
||||
The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
|
||||
@ -3940,8 +3959,8 @@ The function finds the minimal enclosing circle of a 2D point set using an itera
|
||||
CV_EXPORTS_W void minEnclosingCircle( InputArray points,
|
||||
CV_OUT Point2f& center, CV_OUT float& radius );
|
||||
|
||||
/** @example minarea.cpp
|
||||
*/
|
||||
/** @example samples/cpp/minarea.cpp
|
||||
*/
|
||||
|
||||
/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
|
||||
|
||||
@ -3976,7 +3995,7 @@ The function compares two shapes. All three implemented methods use the Hu invar
|
||||
CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
|
||||
int method, double parameter );
|
||||
|
||||
/** @example convexhull.cpp
|
||||
/** @example samples/cpp/convexhull.cpp
|
||||
An example using the convexHull functionality
|
||||
*/
|
||||
|
||||
@ -4036,8 +4055,8 @@ CV_EXPORTS_W bool isContourConvex( InputArray contour );
|
||||
CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
|
||||
OutputArray _p12, bool handleNested = true );
|
||||
|
||||
/** @example fitellipse.cpp
|
||||
An example using the fitEllipse technique
|
||||
/** @example samples/cpp/fitellipse.cpp
|
||||
An example using the fitEllipse technique
|
||||
*/
|
||||
|
||||
/** @brief Fits an ellipse around a set of 2D points.
|
||||
@ -4253,9 +4272,10 @@ enum ColormapTypes
|
||||
COLORMAP_PARULA = 12 //!< 
|
||||
};
|
||||
|
||||
/** @example falsecolor.cpp
|
||||
/** @example samples/cpp/falsecolor.cpp
|
||||
An example using applyColorMap function
|
||||
*/
|
||||
|
||||
/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
|
||||
|
||||
@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
|
||||
@ -4342,9 +4362,10 @@ CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
|
||||
const Scalar& color, int thickness = 1,
|
||||
int lineType = LINE_8, int shift = 0);
|
||||
|
||||
/** @example Drawing_2.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp
|
||||
An example using drawing functions
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Draws a circle.
|
||||
|
||||
The function cv::circle draws a simple or filled circle with a given center and radius.
|
||||
@ -4468,9 +4489,11 @@ CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
|
||||
const Scalar& color, int lineType = LINE_8, int shift = 0,
|
||||
Point offset = Point() );
|
||||
|
||||
/** @example Drawing_1.cpp
|
||||
/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp
|
||||
An example using drawing functions
|
||||
*/
|
||||
Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @brief Fills the area bounded by one or more polygons.
|
||||
|
||||
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
|
||||
@ -4510,14 +4533,14 @@ CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
|
||||
bool isClosed, const Scalar& color,
|
||||
int thickness = 1, int lineType = LINE_8, int shift = 0 );
|
||||
|
||||
/** @example contours2.cpp
|
||||
An example program illustrates the use of cv::findContours and cv::drawContours
|
||||
\image html WindowsQtContoursOutput.png "Screenshot of the program"
|
||||
/** @example samples/cpp/contours2.cpp
|
||||
An example program illustrates the use of cv::findContours and cv::drawContours
|
||||
\image html WindowsQtContoursOutput.png "Screenshot of the program"
|
||||
*/
|
||||
|
||||
/** @example segment_objects.cpp
|
||||
/** @example samples/cpp/segment_objects.cpp
|
||||
An example using drawContours to clean up a background segmentation result
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Draws contours outlines or filled contours.
|
||||
|
||||
|
||||
@ -215,7 +215,7 @@ public:
|
||||
virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
|
||||
};
|
||||
|
||||
/** @example facedetect.cpp
|
||||
/** @example samples/cpp/facedetect.cpp
|
||||
This program demonstrates usage of the Cascade classifier class
|
||||
\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
|
||||
*/
|
||||
@ -443,7 +443,7 @@ public:
|
||||
*/
|
||||
CV_WRAP double getWinSigma() const;
|
||||
|
||||
/**@example peopledetect.cpp
|
||||
/**@example samples/cpp/peopledetect.cpp
|
||||
*/
|
||||
/**@brief Sets coefficients for the linear SVM classifier.
|
||||
@param _svmdetector coefficients for the linear SVM classifier.
|
||||
@ -478,7 +478,7 @@ public:
|
||||
*/
|
||||
virtual void copyTo(HOGDescriptor& c) const;
|
||||
|
||||
/**@example train_HOG.cpp
|
||||
/**@example samples/cpp/train_HOG.cpp
|
||||
*/
|
||||
/** @brief Computes HOG descriptors of given image.
|
||||
@param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
|
||||
@ -575,7 +575,7 @@ public:
|
||||
*/
|
||||
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
|
||||
|
||||
/**@example hog.cpp
|
||||
/**@example samples/tapi/hog.cpp
|
||||
*/
|
||||
/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
|
||||
*/
|
||||
|
||||
@ -730,7 +730,7 @@ CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray co
|
||||
//! @addtogroup photo_clone
|
||||
//! @{
|
||||
|
||||
/** @example cloning_demo.cpp
|
||||
/** @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp
|
||||
An example using seamlessClone function
|
||||
*/
|
||||
/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
|
||||
@ -836,7 +836,7 @@ CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flag
|
||||
CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
|
||||
float sigma_r = 0.15f);
|
||||
|
||||
/** @example npr_demo.cpp
|
||||
/** @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp
|
||||
An example using non-photorealistic line drawing functions
|
||||
*/
|
||||
/** @brief Pencil-like non-photorealistic line drawing
|
||||
|
||||
@ -53,7 +53,7 @@ namespace cv
|
||||
//! @addtogroup shape
|
||||
//! @{
|
||||
|
||||
/** @example shape_example.cpp
|
||||
/** @example samples/cpp/shape_example.cpp
|
||||
An example using shape distance algorithm
|
||||
*/
|
||||
/** @brief Abstract base class for shape distance algorithms.
|
||||
|
||||
@ -109,6 +109,14 @@ namespace cv {
|
||||
//! @addtogroup stitching
|
||||
//! @{
|
||||
|
||||
/** @example samples/cpp/stitching.cpp
|
||||
A basic example on image stitching
|
||||
*/
|
||||
|
||||
/** @example samples/cpp/stitching_detailed.cpp
|
||||
A detailed example on image stitching
|
||||
*/
|
||||
|
||||
/** @brief High level image stitcher.
|
||||
|
||||
It's possible to use this class without being aware of the entire stitching pipeline. However, to
|
||||
|
||||
@ -78,9 +78,10 @@ See the OpenCV sample camshiftdemo.c that tracks colored objects.
|
||||
*/
|
||||
CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
|
||||
TermCriteria criteria );
|
||||
/** @example camshiftdemo.cpp
|
||||
/** @example samples/cpp/camshiftdemo.cpp
|
||||
An example using the mean-shift tracking algorithm
|
||||
*/
|
||||
|
||||
/** @brief Finds an object on a back projection image.
|
||||
|
||||
@param probImage Back projection of the object histogram. See calcBackProject for details.
|
||||
@ -123,9 +124,10 @@ CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays py
|
||||
int derivBorder = BORDER_CONSTANT,
|
||||
bool tryReuseInputImage = true );
|
||||
|
||||
/** @example lkdemo.cpp
|
||||
/** @example samples/cpp/lkdemo.cpp
|
||||
An example using the Lucas-Kanade optical flow algorithm
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
||||
pyramids.
|
||||
|
||||
@ -263,9 +265,9 @@ enum
|
||||
MOTION_HOMOGRAPHY = 3
|
||||
};
|
||||
|
||||
/** @example image_alignment.cpp
|
||||
/** @example samples/cpp/image_alignment.cpp
|
||||
An example using the image alignment ECC algorithm
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
|
||||
|
||||
@ -322,9 +324,10 @@ CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray input
|
||||
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
|
||||
InputArray inputMask = noArray());
|
||||
|
||||
/** @example kalman.cpp
|
||||
/** @example samples/cpp/kalman.cpp
|
||||
An example using the standard Kalman filter
|
||||
*/
|
||||
|
||||
/** @brief Kalman filter class.
|
||||
|
||||
The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>,
|
||||
|
||||
@ -815,13 +815,18 @@ protected:
|
||||
|
||||
class IVideoWriter;
|
||||
|
||||
/** @example videowriter_basic.cpp
|
||||
/** @example samples/cpp/tutorial_code/videoio/video-write/video-write.cpp
|
||||
Check @ref tutorial_video_write "the corresponding tutorial" for more details
|
||||
*/
|
||||
|
||||
/** @example samples/cpp/videowriter_basic.cpp
|
||||
An example using VideoCapture and VideoWriter class
|
||||
*/
|
||||
*/
|
||||
|
||||
/** @brief Video writer class.
|
||||
|
||||
The class provides C++ API for writing video files or image sequences.
|
||||
*/
|
||||
*/
|
||||
class CV_EXPORTS_W VideoWriter
|
||||
{
|
||||
public:
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
|
||||
#include <opencv2/core/utility.hpp>
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
@ -32,44 +33,29 @@ static void on_trackbar(int, void*)
|
||||
imshow( "Connected Components", dst );
|
||||
}
|
||||
|
||||
static void help()
|
||||
{
|
||||
cout << "\n This program demonstrates connected components and use of the trackbar\n"
|
||||
"Usage: \n"
|
||||
" ./connected_components <image(../data/stuff.jpg as default)>\n"
|
||||
"The image is converted to grayscale and displayed, another image has a trackbar\n"
|
||||
"that controls thresholding and thereby the extracted contours which are drawn in color\n";
|
||||
}
|
||||
|
||||
const char* keys =
|
||||
{
|
||||
"{help h||}{@image|../data/stuff.jpg|image for converting to a grayscale}"
|
||||
};
|
||||
|
||||
int main( int argc, const char** argv )
|
||||
{
|
||||
CommandLineParser parser(argc, argv, keys);
|
||||
if (parser.has("help"))
|
||||
{
|
||||
help();
|
||||
return 0;
|
||||
}
|
||||
string inputImage = parser.get<string>(0);
|
||||
img = imread(inputImage.c_str(), 0);
|
||||
CommandLineParser parser(argc, argv, "{@image|../data/stuff.jpg|image for converting to a grayscale}");
|
||||
parser.about("\nThis program demonstrates connected components and use of the trackbar\n");
|
||||
parser.printMessage();
|
||||
cout << "\nThe image is converted to grayscale and displayed, another image has a trackbar\n"
|
||||
"that controls thresholding and thereby the extracted contours which are drawn in color\n";
|
||||
|
||||
String inputImage = parser.get<string>(0);
|
||||
img = imread(inputImage, IMREAD_GRAYSCALE);
|
||||
|
||||
if(img.empty())
|
||||
{
|
||||
cout << "Could not read input image file: " << inputImage << endl;
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
namedWindow( "Image", 1 );
|
||||
imshow( "Image", img );
|
||||
|
||||
namedWindow( "Connected Components", 1 );
|
||||
namedWindow( "Connected Components", WINDOW_AUTOSIZE);
|
||||
createTrackbar( "Threshold", "Connected Components", &threshval, 255, on_trackbar );
|
||||
on_trackbar(threshval, 0);
|
||||
|
||||
waitKey(0);
|
||||
return 0;
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
|
||||
// The "Square Detector" program.
|
||||
// It loads several images sequentially and tries to find squares in
|
||||
// each image
|
||||
@ -8,22 +9,18 @@
|
||||
#include "opencv2/highgui.hpp"
|
||||
|
||||
#include <iostream>
|
||||
#include <math.h>
|
||||
#include <string.h>
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
static void help()
|
||||
static void help(const char* programName)
|
||||
{
|
||||
cout <<
|
||||
"\nA program using pyramid scaling, Canny, contours, contour simpification and\n"
|
||||
"memory storage (it's got it all folks) to find\n"
|
||||
"squares in a list of images pic1-6.png\n"
|
||||
"\nA program using pyramid scaling, Canny, contours and contour simplification\n"
|
||||
"to find squares in a list of images (pic1-6.png)\n"
|
||||
"Returns sequence of squares detected on the image.\n"
|
||||
"the sequence is stored in the specified memory storage\n"
|
||||
"Call:\n"
|
||||
"./squares [file_name (optional)]\n"
|
||||
"./" << programName << " [file_name (optional)]\n"
|
||||
"Using OpenCV version " << CV_VERSION << "\n" << endl;
|
||||
}
|
||||
|
||||
@ -44,7 +41,6 @@ static double angle( Point pt1, Point pt2, Point pt0 )
|
||||
}
|
||||
|
||||
// returns sequence of squares detected on the image.
|
||||
// the sequence is stored in the specified memory storage
|
||||
static void findSquares( const Mat& image, vector<vector<Point> >& squares )
|
||||
{
|
||||
squares.clear();
|
||||
@ -93,7 +89,7 @@ static void findSquares( const Mat& image, vector<vector<Point> >& squares )
|
||||
{
|
||||
// approximate contour with accuracy proportional
|
||||
// to the contour perimeter
|
||||
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
|
||||
approxPolyDP(contours[i], approx, arcLength(contours[i], true)*0.02, true);
|
||||
|
||||
// square contours should have 4 vertices after approximation
|
||||
// relatively large area (to filter out noisy contours)
|
||||
@ -102,8 +98,8 @@ static void findSquares( const Mat& image, vector<vector<Point> >& squares )
|
||||
// area may be positive or negative - in accordance with the
|
||||
// contour orientation
|
||||
if( approx.size() == 4 &&
|
||||
fabs(contourArea(Mat(approx))) > 1000 &&
|
||||
isContourConvex(Mat(approx)) )
|
||||
fabs(contourArea(approx)) > 1000 &&
|
||||
isContourConvex(approx) )
|
||||
{
|
||||
double maxCosine = 0;
|
||||
|
||||
@ -144,7 +140,7 @@ int main(int argc, char** argv)
|
||||
{
|
||||
static const char* names[] = { "../data/pic1.png", "../data/pic2.png", "../data/pic3.png",
|
||||
"../data/pic4.png", "../data/pic5.png", "../data/pic6.png", 0 };
|
||||
help();
|
||||
help(argv[0]);
|
||||
|
||||
if( argc > 1)
|
||||
{
|
||||
@ -152,12 +148,11 @@ int main(int argc, char** argv)
|
||||
names[1] = "0";
|
||||
}
|
||||
|
||||
namedWindow( wndname, 1 );
|
||||
vector<vector<Point> > squares;
|
||||
|
||||
for( int i = 0; names[i] != 0; i++ )
|
||||
{
|
||||
Mat image = imread(names[i], 1);
|
||||
Mat image = imread(names[i], IMREAD_COLOR);
|
||||
if( image.empty() )
|
||||
{
|
||||
cout << "Couldn't load " << names[i] << endl;
|
||||
@ -167,7 +162,7 @@ int main(int argc, char** argv)
|
||||
findSquares(image, squares);
|
||||
drawSquares(image, squares);
|
||||
|
||||
char c = (char)waitKey();
|
||||
int c = waitKey();
|
||||
if( c == 27 )
|
||||
break;
|
||||
}
|
||||
|
||||
@ -20,8 +20,9 @@ int parseCmdArgs(int argc, char** argv);
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
int retval = parseCmdArgs(argc, argv);
|
||||
if (retval) return -1;
|
||||
if (retval) return EXIT_FAILURE;
|
||||
|
||||
//![stitching]
|
||||
Mat pano;
|
||||
Ptr<Stitcher> stitcher = Stitcher::create(mode, try_use_gpu);
|
||||
Stitcher::Status status = stitcher->stitch(imgs, pano);
|
||||
@ -29,12 +30,13 @@ int main(int argc, char* argv[])
|
||||
if (status != Stitcher::OK)
|
||||
{
|
||||
cout << "Can't stitch images, error code = " << int(status) << endl;
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
//![stitching]
|
||||
|
||||
imwrite(result_name, pano);
|
||||
cout << "stitching completed successfully\n" << result_name << " saved!";
|
||||
return 0;
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
|
||||
@ -63,7 +65,7 @@ int parseCmdArgs(int argc, char** argv)
|
||||
if (argc == 1)
|
||||
{
|
||||
printUsage(argv);
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
for (int i = 1; i < argc; ++i)
|
||||
@ -71,7 +73,7 @@ int parseCmdArgs(int argc, char** argv)
|
||||
if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
|
||||
{
|
||||
printUsage(argv);
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
else if (string(argv[i]) == "--try_use_gpu")
|
||||
{
|
||||
@ -82,7 +84,7 @@ int parseCmdArgs(int argc, char** argv)
|
||||
else
|
||||
{
|
||||
cout << "Bad --try_use_gpu flag value\n";
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
i++;
|
||||
}
|
||||
@ -104,7 +106,7 @@ int parseCmdArgs(int argc, char** argv)
|
||||
else
|
||||
{
|
||||
cout << "Bad --mode flag value\n";
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
i++;
|
||||
}
|
||||
@ -114,7 +116,7 @@ int parseCmdArgs(int argc, char** argv)
|
||||
if (img.empty())
|
||||
{
|
||||
cout << "Can't read image '" << argv[i] << "'\n";
|
||||
return -1;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
if (divide_images)
|
||||
@ -130,5 +132,5 @@ int parseCmdArgs(int argc, char** argv)
|
||||
imgs.push_back(img);
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
@ -1,45 +1,3 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
|
||||
@ -33,7 +33,7 @@ void Morphology_Operations( int, void* );
|
||||
int main( int argc, char** argv )
|
||||
{
|
||||
//![load]
|
||||
CommandLineParser parser( argc, argv, "{@input | ../data/LinuxLogo.jpg | input image}" );
|
||||
CommandLineParser parser( argc, argv, "{@input | ../data/baboon.jpg | input image}" );
|
||||
src = imread( parser.get<String>( "@input" ), IMREAD_COLOR );
|
||||
if (src.empty())
|
||||
{
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
// The "Square Detector" program.
|
||||
// It loads several images sequentially and tries to find squares in
|
||||
// each image
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/core/ocl.hpp"
|
||||
@ -9,7 +6,6 @@
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include <iostream>
|
||||
#include <string.h>
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
@ -31,7 +27,6 @@ static double angle( Point pt1, Point pt2, Point pt0 )
|
||||
|
||||
|
||||
// returns sequence of squares detected on the image.
|
||||
// the sequence is stored in the specified memory storage
|
||||
static void findSquares( const UMat& image, vector<vector<Point> >& squares )
|
||||
{
|
||||
squares.clear();
|
||||
@ -66,7 +61,7 @@ static void findSquares( const UMat& image, vector<vector<Point> >& squares )
|
||||
{
|
||||
// apply threshold if l!=0:
|
||||
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
|
||||
cv::threshold(gray0, gray, (l+1)*255/N, 255, THRESH_BINARY);
|
||||
threshold(gray0, gray, (l+1)*255/N, 255, THRESH_BINARY);
|
||||
}
|
||||
|
||||
// find contours and store them all as a list
|
||||
@ -80,7 +75,7 @@ static void findSquares( const UMat& image, vector<vector<Point> >& squares )
|
||||
// approximate contour with accuracy proportional
|
||||
// to the contour perimeter
|
||||
|
||||
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
|
||||
approxPolyDP(contours[i], approx, arcLength(contours[i], true)*0.02, true);
|
||||
|
||||
// square contours should have 4 vertices after approximation
|
||||
// relatively large area (to filter out noisy contours)
|
||||
@ -89,8 +84,8 @@ static void findSquares( const UMat& image, vector<vector<Point> >& squares )
|
||||
// area may be positive or negative - in accordance with the
|
||||
// contour orientation
|
||||
if( approx.size() == 4 &&
|
||||
fabs(contourArea(Mat(approx))) > 1000 &&
|
||||
isContourConvex(Mat(approx)) )
|
||||
fabs(contourArea(approx)) > 1000 &&
|
||||
isContourConvex(approx) )
|
||||
{
|
||||
double maxCosine = 0;
|
||||
|
||||
@ -150,7 +145,7 @@ int main(int argc, char** argv)
|
||||
|
||||
if(cmd.has("help"))
|
||||
{
|
||||
cout << "Usage : squares [options]" << endl;
|
||||
cout << "Usage : " << argv[0] << " [options]" << endl;
|
||||
cout << "Available options:" << endl;
|
||||
cmd.printMessage();
|
||||
return EXIT_SUCCESS;
|
||||
@ -158,7 +153,7 @@ int main(int argc, char** argv)
|
||||
if (cmd.has("cpu_mode"))
|
||||
{
|
||||
ocl::setUseOpenCL(false);
|
||||
std::cout << "OpenCL was disabled" << std::endl;
|
||||
cout << "OpenCL was disabled" << endl;
|
||||
}
|
||||
|
||||
string inputName = cmd.get<string>("i");
|
||||
@ -185,11 +180,11 @@ int main(int argc, char** argv)
|
||||
|
||||
do
|
||||
{
|
||||
int64 t_start = cv::getTickCount();
|
||||
int64 t_start = getTickCount();
|
||||
findSquares(image, squares);
|
||||
t_cpp += cv::getTickCount() - t_start;
|
||||
|
||||
t_start = cv::getTickCount();
|
||||
t_start = getTickCount();
|
||||
|
||||
cout << "run loop: " << j << endl;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user