我编写了以下非常简单的python代码来查找图像中的圆圈:
import cv
import numpy as np
WAITKEY_DELAY_MS = 10
STOP_KEY = 'q'
cv.NamedWindow("image - press 'q' to quit", cv.CV_WINDOW_AUTOSIZE);
cv.NamedWindow("post-process", cv.CV_WINDOW_AUTOSIZE);
key_pressed = False
while key_pressed != STOP_KEY:
# grab image
orig = cv.LoadImage('circles3.jpg')
# create tmp images
grey_scale = cv.CreateImage(cv.GetSize(orig), 8, 1)
processed = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.Smooth(orig, orig, cv.CV_GAUSSIAN, 3, 3)
cv.CvtColor(orig, grey_scale, cv.CV_RGB2GRAY)
# do some processing on the grey scale image
cv.Erode(grey_scale, processed, None, 10)
cv.Dilate(processed, processed, None, 10)
cv.Canny(processed, processed, 5, 70, 3)
cv.Smooth(processed, …Run Code Online (Sandbox Code Playgroud) 我是新的opencv.我写一个删除背景.
我的输入图片
我将我的程序编码为以下步骤:
- 计算平均像素
//define roi of image
cv::Rect roi(0, 0, 20 , 20 );
//copies input image in roi
cv::Mat image_roi = imgGray( roi );
//imshow("roi", image_roi);
//computes mean over roi
cv::Scalar avgPixelIntensity = cv::mean( image_roi );
//prints out only .val[0] since image was grayscale
cout << "Pixel intensity over ROI = " << avgPixelIntensity.val[0] << endl;
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- 根据平均像素值创建新的Mat图像:
//create new mat image base on avgPixelIntensity
cv::Mat areaSampleArv(imgGray.rows, imgGray.cols,imgGray.type(),avgPixelIntensity.val[0]);
imshow("areaSampleArv", areaSampleArv);
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- 反转图片:
void image_invert(Mat& image){
int height, …Run Code Online (Sandbox Code Playgroud)