Improving Opencv9 Eroding and Dilating 和对opencv窗体上有控制按钮的理解

https://docs.opencv.org/4.3.0/db/df6/tutorial_erosion_dilatation.html

Goal

Morphological Operations

Dilation 膨胀

Erosion

C/C++

Python

对两函数的理解

窗体上有控制按钮

Goal

In this tutorial you will learn how to:

Apply two very common morphological operators: Erosion and Dilation. For this purpose, you will use the following OpenCV functions:
- cv::erode
- cv::dilate

Morphological Operations

In short: A set of operations that process images based on shapes. Morphological operations apply a structuring element to an input image and generate an output image.
The most basic morphological operations are: Erosion and Dilation. They have a wide array of uses, i.e. :
- Removing noise
- Isolation of individual elements and joining disparate elements in an image.
- Finding of intensity bumps or holes in an image

Dilation 膨胀

This operations consists of convolving an image A with some kernel ( B), which can have any shape or size, usually a square or circle.
The kernel B has a defined anchor point, usually being the center of the kernel.
As the kernel B is scanned over the image, we compute the maximal pixel value overlapped by B and replace the image pixel in the anchor point position with that maximal value. As you can deduce, this maximizing operation causes bright regions within an image to "grow" (therefore the name dilation).
The dilatation operation is: dst(x,y)=max(x′,y′):element(x′,y′)≠0src(x+x′,y+y′)
Take the above image as an example. Applying dilation we can get:

Erosion

This operation is the sister of dilation. It computes a local minimum over the area of given kernel.
As the kernel B is scanned over the image, we compute the minimal pixel value overlapped by B and replace the image pixel under the anchor point with that minimal value.
The erosion operation is: dst(x,y)=min(x′,y′):element(x′,y′)≠0src(x+x′,y+y′)
Analagously to the example for dilation, we can apply the erosion operator to the original image (shown above). You can see in the result below that the bright areas of the image get thinner, whereas the dark zones gets bigger.

C/C++

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>using namespace cv;
using namespace std;Mat src, erosion_dst, dilation_dst;
int erosion_elem = 0;
int erosion_size = 0;
int dilation_elem = 0;
int dilation_size = 0;
int const max_elem = 2;
int const max_kernel_size = 21;
void Erosion(int, void*);
void Dilation(int, void*);
int main(int argc, char** argv)
{//CommandLineParser parser(argc, argv, "{@input | LinuxLogo.jpg | input image}");//src = imread(samples::findFile(parser.get<String>("@input")), IMREAD_COLOR);src = imread("NWPUSZ.jpg");resize(src, src, Size(src.cols / 4, src.rows / 4));if (src.empty()){cout << "Could not open or find the image!\n" << endl;cout << "Usage: " << argv[0] << " <Input image>" << endl;return -1;}namedWindow("Erosion Demo", WINDOW_AUTOSIZE);namedWindow("Dilation Demo", WINDOW_AUTOSIZE);moveWindow("Dilation Demo", src.cols, 0);createTrackbar("Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",&erosion_elem, max_elem,Erosion);createTrackbar("Kernel size:\n 2n +1", "Erosion Demo",&erosion_size, max_kernel_size,Erosion);createTrackbar("Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",&dilation_elem, max_elem,Dilation);createTrackbar("Kernel size:\n 2n +1", "Dilation Demo",&dilation_size, max_kernel_size,Dilation);Erosion(0, 0);Dilation(0, 0);waitKey(0);return 0;
}void Erosion(int, void*)
{int erosion_type = 0;if (erosion_elem == 0){ erosion_type = MORPH_RECT; }else if (erosion_elem == 1){ erosion_type = MORPH_CROSS; }else if (erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }Mat element = getStructuringElement(erosion_type,Size(2 * erosion_size + 1, 2 * erosion_size + 1),Point(erosion_size, erosion_size));erode(src, erosion_dst, element);imshow("Erosion Demo", erosion_dst);
}void Dilation(int, void*)
{int dilation_type = 0;if (dilation_elem == 0){ dilation_type = MORPH_RECT; }else if (dilation_elem == 1){ dilation_type = MORPH_CROSS; }else if (dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }Mat element = getStructuringElement(dilation_type,Size(2 * dilation_size + 1, 2 * dilation_size + 1),Point(dilation_size, dilation_size));dilate(src, dilation_dst, element);imshow("Dilation Demo", dilation_dst);
}

Python

from __future__ import print_function
import cv2 as cverosion_size = 0
max_elem = 2
max_kernel_size = 21
title_trackbar_element_type = 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse'
title_trackbar_kernel_size = 'Kernel size:\n 2n +1'
title_erosion_window = 'Erosion Demo'
title_dilatation_window = 'Dilation Demo'def erosion(val):erosion_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_erosion_window)erosion_type = 0val_type = cv.getTrackbarPos(title_trackbar_element_type, title_erosion_window)if val_type == 0:erosion_type = cv.MORPH_RECTelif val_type == 1:erosion_type = cv.MORPH_CROSSelif val_type == 2:erosion_type = cv.MORPH_ELLIPSEelement = cv.getStructuringElement(erosion_type, (2*erosion_size + 1, 2*erosion_size+1), (erosion_size, erosion_size))erosion_dst = cv.erode(src, element)cv.imshow(title_erosion_window, erosion_dst)def dilatation(val):dilatation_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_dilatation_window)dilatation_type = 0val_type = cv.getTrackbarPos(title_trackbar_element_type, title_dilatation_window)if val_type == 0:dilatation_type = cv.MORPH_RECTelif val_type == 1:dilatation_type = cv.MORPH_CROSSelif val_type == 2:dilatation_type = cv.MORPH_ELLIPSEelement = cv.getStructuringElement(dilatation_type, (2*dilatation_size + 1, 2*dilatation_size+1), (dilatation_size, dilatation_size))dilatation_dst = cv.dilate(src, element)cv.imshow(title_dilatation_window, dilatation_dst)src = cv.imread("brid.jpg")
src = cv.resize(src, (int(src.shape[1]/4), int(src.shape[0]/4)))
# width = int(img.shape[1] )
# height = int(img.shape[0])if src is None:print('Could not open or find the image: ')exit(0)cv.namedWindow(title_erosion_window)
cv.createTrackbar(title_trackbar_element_type, title_erosion_window , 0, max_elem, erosion)
cv.createTrackbar(title_trackbar_kernel_size, title_erosion_window , 0, max_kernel_size, erosion)cv.namedWindow(title_dilatation_window)
cv.createTrackbar(title_trackbar_element_type, title_dilatation_window , 0, max_elem, dilatation)
cv.createTrackbar(title_trackbar_kernel_size, title_dilatation_window , 0, max_kernel_size, dilatation)erosion(0)
dilatation(0)
cv.waitKey()

对两函数的理解

膨胀和腐蚀不是用核与图像做卷积运算，将核在图像上滑动，对比核与图像重合的区域找到最大值，并将其赋给锚点的位置则是膨胀

对比核与图像重合的区域找到最小值，则是腐蚀

膨胀和腐蚀核都是0，1两个数值构成，不同类型只是由1形成的图像，如下图则是MORPH_RECT

https://docs.opencv.org/4.3.0/dd/dd7/tutorial_morph_lines_detection.html

窗体上有控制按钮

C/C++上需要控制的变量直接通过

createTrackbar("Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo", &erosion_elem, max_elem, Erosion);

createTrackbar的erosion_elem变化得到，其最大值就是max_elem，erosion_elem这个值会随着窗体上鼠标控制按钮的移动而变化

需要定义全局变量erosion_elem，如果需要控制和添加多个变量则，回调函数Erosion来确定出现在哪个窗口上

ceateTrackbar后面要调用 Erosion（0， 0）函数

python

cv2.createTrackbar(elementType, windowName, 0, max_kernelType, myDilation)

typePos = cv2.getTrackbarPos(elementType, windowName)

通过这两句确定，elementType的作用就是将两者变量的变化和获取关联起来，然后通过getTrackbarPos得知其变化的数值，再使用