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Extract horizontal and vertical lines by using morphological operations {#tutorial_moprh_lines_detection} |
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============= |
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|
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Goal |
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---- |
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|
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In this tutorial you will learn how to: |
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|
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- Apply two very common morphology operators (i.e. Dilation and Erosion), with the creation of custom kernels, in order to extract straight lines on the horizontal and vertical axes. For this purpose, you will use the following OpenCV functions: |
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- @ref cv::erode |
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- @ref cv::dilate |
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- @ref cv::getStructuringElement |
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|
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in an example where your goal will be to extract the music notes from a music sheet. |
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|
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Theory |
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------ |
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|
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### Morphology Operations |
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Morphology is a set of image processing operations that process images based on predefined *structuring elements* known also as kernels. The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and shape of the kernel, you can construct a morphological operation that is sensitive to specific shapes regarding the input image. |
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|
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Two of the most basic morphological operations are dilation and erosion. Dilation adds pixels to the boundaries of the object in an image, while erosion does exactly the opposite. The amount of pixels added or removed, respectively depends on the size and shape of the structuring element used to process the image. In general the rules followed from these two operations have as follows: |
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|
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- __Dilation__: The value of the output pixel is the <b><em>maximum</em></b> value of all the pixels that fall within the structuring element's size and shape. For example in a binary image, if any of the pixels of the input image falling within the range of the kernel is set to the value 1, the corresponding pixel of the output image will be set to 1 as well. The latter applies to any type of image (e.g. grayscale, bgr, etc). |
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|
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 |
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|
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 |
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|
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- __Erosion__: The vise versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below: |
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|
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 |
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|
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 |
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|
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### Structuring Elements |
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|
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As it can be seen above and in general in any morphological operation the structuring element used to probe the input image, is the most important part. |
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|
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A structuring element is a matrix consisting of only 0's and 1's that can have any arbitrary shape and size. Typically are much smaller than the image being processed, while the pixels with values of 1 define the neighborhood. The center pixel of the structuring element, called the origin, identifies the pixel of interest -- the pixel being processed. |
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|
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For example, the following illustrates a diamond-shaped structuring element of 7x7 size. |
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|
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 |
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|
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A structuring element can have many common shapes, such as lines, diamonds, disks, periodic lines, and circles and sizes. You typically choose a structuring element the same size and shape as the objects you want to process/extract in the input image. For example, to find lines in an image, create a linear structuring element as you will see later. |
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|
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Code |
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---- |
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This tutorial code's is shown lines below. You can also download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp). |
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@include samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp |
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|
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Explanation / Result |
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-------------------- |
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|
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-# Load the source image and check if it is loaded without any problem, then show it: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp load_image |
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 |
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|
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-# Then transform image to grayscale if it not already: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp gray |
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 |
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|
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-# Afterwards transform grayscale image to binary. Notice the ~ symbol which indicates that we use the inverse (i.e. bitwise_not) version of it: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp bin |
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 |
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|
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-# Now we are ready to apply morphological operations in order to extract the horizontal and vertical lines and as a consequence to separate the the music notes from the music sheet, but first let's initialize the output images that we will use for that reason: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp init |
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|
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-# As we specified in the theory in order to extract the object that we desire, we need to create the corresponding structure element. Since here we want to extract the horizontal lines, a corresponding structure element for that purpose will have the following shape: |
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 |
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and in the source code this is represented by the following code snippet: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp horiz |
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 |
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|
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-# The same applies for the vertical lines, with the corresponding structure element: |
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 |
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and again this is represented as follows: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp vert |
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 |
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|
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-# As you can see we are almost there. However, at that point you will notice that the edges of the notes are a bit rough. For that reason we need to refine the edges in order to obtain a smoother result: |
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@snippet samples/cpp/tutorial_code/ImgProc/Morphology_3.cpp smooth |
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 |
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@ -0,0 +1,194 @@ |
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Extract horizontal and vertical lines by using morphological operations {#tutorial_morph_lines_detection} |
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============= |
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|
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@prev_tutorial{tutorial_hitOrMiss} |
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@next_tutorial{tutorial_pyramids} |
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|
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Goal |
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---- |
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|
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In this tutorial you will learn how to: |
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|
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- Apply two very common morphology operators (i.e. Dilation and Erosion), with the creation of custom kernels, in order to extract straight lines on the horizontal and vertical axes. For this purpose, you will use the following OpenCV functions: |
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- **erode()** |
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- **dilate()** |
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- **getStructuringElement()** |
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|
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in an example where your goal will be to extract the music notes from a music sheet. |
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|
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Theory |
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------ |
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|
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### Morphology Operations |
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Morphology is a set of image processing operations that process images based on predefined *structuring elements* known also as kernels. The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and shape of the kernel, you can construct a morphological operation that is sensitive to specific shapes regarding the input image. |
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|
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Two of the most basic morphological operations are dilation and erosion. Dilation adds pixels to the boundaries of the object in an image, while erosion does exactly the opposite. The amount of pixels added or removed, respectively depends on the size and shape of the structuring element used to process the image. In general the rules followed from these two operations have as follows: |
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|
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- __Dilation__: The value of the output pixel is the <b><em>maximum</em></b> value of all the pixels that fall within the structuring element's size and shape. For example in a binary image, if any of the pixels of the input image falling within the range of the kernel is set to the value 1, the corresponding pixel of the output image will be set to 1 as well. The latter applies to any type of image (e.g. grayscale, bgr, etc). |
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|
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 |
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|
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 |
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|
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- __Erosion__: The vise versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below: |
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|
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 |
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|
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 |
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|
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### Structuring Elements |
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|
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As it can be seen above and in general in any morphological operation the structuring element used to probe the input image, is the most important part. |
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|
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A structuring element is a matrix consisting of only 0's and 1's that can have any arbitrary shape and size. Typically are much smaller than the image being processed, while the pixels with values of 1 define the neighborhood. The center pixel of the structuring element, called the origin, identifies the pixel of interest -- the pixel being processed. |
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|
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For example, the following illustrates a diamond-shaped structuring element of 7x7 size. |
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|
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 |
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|
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A structuring element can have many common shapes, such as lines, diamonds, disks, periodic lines, and circles and sizes. You typically choose a structuring element the same size and shape as the objects you want to process/extract in the input image. For example, to find lines in an image, create a linear structuring element as you will see later. |
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|
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Code |
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---- |
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|
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This tutorial code's is shown lines below. |
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@add_toggle_cpp |
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You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp). |
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@include samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp |
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@end_toggle |
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|
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@add_toggle_java |
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You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java). |
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@include samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java |
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@end_toggle |
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|
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@add_toggle_python |
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You can also download it from [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py). |
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@include samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py |
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@end_toggle |
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Explanation / Result |
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-------------------- |
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Get image from [here](https://raw.githubusercontent.com/opencv/opencv/master/doc/tutorials/imgproc/morph_lines_detection/images/src.png) . |
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#### Load Image |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp load_image |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java load_image |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py load_image |
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@end_toggle |
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 |
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#### Grayscale |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp gray |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java gray |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py gray |
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@end_toggle |
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 |
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#### Grayscale to Binary image |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp bin |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java bin |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py bin |
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@end_toggle |
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 |
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|
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#### Output images |
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|
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Now we are ready to apply morphological operations in order to extract the horizontal and vertical lines and as a consequence to separate the the music notes from the music sheet, but first let's initialize the output images that we will use for that reason: |
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|
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp init |
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@end_toggle |
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|
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java init |
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@end_toggle |
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|
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py init |
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@end_toggle |
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|
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#### Structure elements |
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As we specified in the theory in order to extract the object that we desire, we need to create the corresponding structure element. Since we want to extract the horizontal lines, a corresponding structure element for that purpose will have the following shape: |
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 |
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and in the source code this is represented by the following code snippet: |
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|
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp horiz |
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@end_toggle |
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|
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java horiz |
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@end_toggle |
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|
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py horiz |
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@end_toggle |
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|
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 |
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|
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The same applies for the vertical lines, with the corresponding structure element: |
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 |
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and again this is represented as follows: |
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|
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp vert |
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@end_toggle |
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|
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java vert |
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@end_toggle |
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|
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py vert |
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@end_toggle |
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 |
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#### Refine edges / Result |
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As you can see we are almost there. However, at that point you will notice that the edges of the notes are a bit rough. For that reason we need to refine the edges in order to obtain a smoother result: |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp smooth |
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@end_toggle |
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|
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@add_toggle_java |
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@snippet samples/java/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.java smooth |
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@end_toggle |
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|
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@add_toggle_python |
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@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py smooth |
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@end_toggle |
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 |
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@ -0,0 +1,152 @@ |
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/** |
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* @file Morphology_3.java |
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* @brief Use morphology transformations for extracting horizontal and vertical lines sample code |
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*/ |
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import org.opencv.core.*; |
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import org.opencv.highgui.HighGui; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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class Morphology_3Run { |
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public void run(String[] args) { |
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//! [load_image]
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// Check number of arguments
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if (args.length == 0){ |
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System.out.println("Not enough parameters!"); |
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System.out.println("Program Arguments: [image_path]"); |
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System.exit(-1); |
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} |
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// Load the image
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Mat src = Imgcodecs.imread(args[0]); |
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// Check if image is loaded fine
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if( src.empty() ) { |
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System.out.println("Error opening image: " + args[0]); |
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System.exit(-1); |
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} |
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// Show source image
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HighGui.imshow("src", src); |
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//! [load_image]
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//! [gray]
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// Transform source image to gray if it is not already
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Mat gray = new Mat(); |
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if (src.channels() == 3) |
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{ |
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Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY); |
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} |
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else |
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{ |
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gray = src; |
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} |
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// Show gray image
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showWaitDestroy("gray" , gray); |
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//! [gray]
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//! [bin]
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// Apply adaptiveThreshold at the bitwise_not of gray
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Mat bw = new Mat(); |
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Core.bitwise_not(gray, gray); |
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Imgproc.adaptiveThreshold(gray, bw, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, -2); |
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// Show binary image
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showWaitDestroy("binary" , bw); |
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//! [bin]
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//! [init]
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// Create the images that will use to extract the horizontal and vertical lines
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Mat horizontal = bw.clone(); |
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Mat vertical = bw.clone(); |
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//! [init]
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//! [horiz]
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// Specify size on horizontal axis
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int horizontal_size = horizontal.cols() / 30; |
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// Create structure element for extracting horizontal lines through morphology operations
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Mat horizontalStructure = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(horizontal_size,1)); |
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// Apply morphology operations
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Imgproc.erode(horizontal, horizontal, horizontalStructure); |
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Imgproc.dilate(horizontal, horizontal, horizontalStructure); |
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// Show extracted horizontal lines
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showWaitDestroy("horizontal" , horizontal); |
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//! [horiz]
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//! [vert]
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// Specify size on vertical axis
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int vertical_size = vertical.rows() / 30; |
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// Create structure element for extracting vertical lines through morphology operations
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Mat verticalStructure = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size( 1,vertical_size)); |
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// Apply morphology operations
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Imgproc.erode(vertical, vertical, verticalStructure); |
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Imgproc.dilate(vertical, vertical, verticalStructure); |
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// Show extracted vertical lines
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showWaitDestroy("vertical", vertical); |
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//! [vert]
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//! [smooth]
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// Inverse vertical image
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Core.bitwise_not(vertical, vertical); |
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showWaitDestroy("vertical_bit" , vertical); |
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// Extract edges and smooth image according to the logic
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// 1. extract edges
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// 2. dilate(edges)
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// 3. src.copyTo(smooth)
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// 4. blur smooth img
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// 5. smooth.copyTo(src, edges)
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// Step 1
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Mat edges = new Mat(); |
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Imgproc.adaptiveThreshold(vertical, edges, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 3, -2); |
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showWaitDestroy("edges", edges); |
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// Step 2
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Mat kernel = Mat.ones(2, 2, CvType.CV_8UC1); |
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Imgproc.dilate(edges, edges, kernel); |
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showWaitDestroy("dilate", edges); |
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// Step 3
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Mat smooth = new Mat(); |
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vertical.copyTo(smooth); |
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// Step 4
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Imgproc.blur(smooth, smooth, new Size(2, 2)); |
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// Step 5
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smooth.copyTo(vertical, edges); |
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// Show final result
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showWaitDestroy("smooth - final", vertical); |
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//! [smooth]
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System.exit(0); |
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} |
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private void showWaitDestroy(String winname, Mat img) { |
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HighGui.imshow(winname, img); |
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HighGui.moveWindow(winname, 500, 0); |
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HighGui.waitKey(0); |
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HighGui.destroyWindow(winname); |
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} |
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} |
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public class Morphology_3 { |
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public static void main(String[] args) { |
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// Load the native library.
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new Morphology_3Run().run(args); |
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} |
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} |
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@ -0,0 +1,136 @@ |
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""" |
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@file morph_lines_detection.py |
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@brief Use morphology transformations for extracting horizontal and vertical lines sample code |
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""" |
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import numpy as np |
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import sys |
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import cv2 |
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def show_wait_destroy(winname, img): |
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cv2.imshow(winname, img) |
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cv2.moveWindow(winname, 500, 0) |
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cv2.waitKey(0) |
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cv2.destroyWindow(winname) |
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def main(argv): |
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# [load_image] |
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# Check number of arguments |
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if len(argv) < 1: |
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print ('Not enough parameters') |
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print ('Usage:\nmorph_lines_detection.py < path_to_image >') |
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return -1 |
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|
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# Load the image |
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src = cv2.imread(argv[0], cv2.IMREAD_COLOR) |
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|
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# Check if image is loaded fine |
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if src is None: |
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print ('Error opening image: ' + argv[0]) |
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return -1 |
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|
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# Show source image |
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cv2.imshow("src", src) |
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# [load_image] |
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# [gray] |
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# Transform source image to gray if it is not already |
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if len(src.shape) != 2: |
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gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) |
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else: |
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gray = src |
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# Show gray image |
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show_wait_destroy("gray", gray) |
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# [gray] |
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# [bin] |
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# Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol |
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gray = cv2.bitwise_not(gray) |
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bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \ |
||||
cv2.THRESH_BINARY, 15, -2) |
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# Show binary image |
||||
show_wait_destroy("binary", bw) |
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# [bin] |
||||
|
||||
# [init] |
||||
# Create the images that will use to extract the horizontal and vertical lines |
||||
horizontal = np.copy(bw) |
||||
vertical = np.copy(bw) |
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# [init] |
||||
|
||||
# [horiz] |
||||
# Specify size on horizontal axis |
||||
cols = horizontal.shape[1] |
||||
horizontal_size = cols / 30 |
||||
|
||||
# Create structure element for extracting horizontal lines through morphology operations |
||||
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1)) |
||||
|
||||
# Apply morphology operations |
||||
horizontal = cv2.erode(horizontal, horizontalStructure) |
||||
horizontal = cv2.dilate(horizontal, horizontalStructure) |
||||
|
||||
# Show extracted horizontal lines |
||||
show_wait_destroy("horizontal", horizontal) |
||||
# [horiz] |
||||
|
||||
# [vert] |
||||
# Specify size on vertical axis |
||||
rows = vertical.shape[0] |
||||
verticalsize = rows / 30 |
||||
|
||||
# Create structure element for extracting vertical lines through morphology operations |
||||
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize)) |
||||
|
||||
# Apply morphology operations |
||||
vertical = cv2.erode(vertical, verticalStructure) |
||||
vertical = cv2.dilate(vertical, verticalStructure) |
||||
|
||||
# Show extracted vertical lines |
||||
show_wait_destroy("vertical", vertical) |
||||
# [vert] |
||||
|
||||
# [smooth] |
||||
# Inverse vertical image |
||||
vertical = cv2.bitwise_not(vertical) |
||||
show_wait_destroy("vertical_bit", vertical) |
||||
|
||||
''' |
||||
Extract edges and smooth image according to the logic |
||||
1. extract edges |
||||
2. dilate(edges) |
||||
3. src.copyTo(smooth) |
||||
4. blur smooth img |
||||
5. smooth.copyTo(src, edges) |
||||
''' |
||||
|
||||
# Step 1 |
||||
edges = cv2.adaptiveThreshold(vertical, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \ |
||||
cv2.THRESH_BINARY, 3, -2) |
||||
show_wait_destroy("edges", edges) |
||||
|
||||
# Step 2 |
||||
kernel = np.ones((2, 2), np.uint8) |
||||
edges = cv2.dilate(edges, kernel) |
||||
show_wait_destroy("dilate", edges) |
||||
|
||||
# Step 3 |
||||
smooth = np.copy(vertical) |
||||
|
||||
# Step 4 |
||||
smooth = cv2.blur(smooth, (2, 2)) |
||||
|
||||
# Step 5 |
||||
(rows, cols) = np.where(edges != 0) |
||||
vertical[rows, cols] = smooth[rows, cols] |
||||
|
||||
# Show final result |
||||
show_wait_destroy("smooth - final", vertical) |
||||
# [smooth] |
||||
|
||||
return 0 |
||||
|
||||
if __name__ == "__main__": |
||||
main(sys.argv[1:]) |
||||
Loading…
Reference in new issue