工业相机拍摄的图像中,由于摄像质量的限制,图像中的直线经过处理后,会表现出比较严重的锯齿.在这种情况下求取直线的倾角(其实就是直线的斜率),如果是直接选取直线的开始点和结束点来计算,或是用opencv自带的哈夫曼直线方法,都会引起较大的角度偏差,一般会达到好几度.误差这么大,显然达不到工控要求.后来尝试采取直线点集做最小二乘拟合,误差缩小到0.5以下.以下是算法的代码: //最小二乘拟合计算直线的倾角 int pointCount = pointVect.size(); if (pointCou
以下代码为在Python3环境下利用OpenCV 抓取摄像头的实时图像, 通过OpenCV的 calHist函数计算直方图, 并显示在3个不同窗口中. import cv2 import numpy as np from matplotlib import pyplot as plt import time cap = cv2.VideoCapture(0) for i in range(0, 19): print(cap.get(i)) while(1): ret, fram
cv::groupRectangles void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2)¶ Groups the object candidate rectangles Parameters: rectList – The input/output vector of rectangles. On output there will be retained and group
#include <iostream>#include <opencv2/opencv.hpp> using namespace cv;using namespace std; int main(int argc,char** argv) { Mat img1, img2,img3,img1_1; img1 = imread("D://images//4.jpg"); if (img1.empty()) { cout <<"could no
需要注意的是下面r,b,g的类型和顺序 须用IPL_DEPTH_8U类型创建图像且[0][1][2]分量分别是b,g,r. 另外多谢郑乾师兄帮我发现了IPL_DEPTH_8U问题 uchar r,b,g;//notice 'uchar' type must be used instead of double int h=workImg->height; int w=workImg->width; IplImage *D=cvCreateImage(cvGetSize(workImg),IPL_
参考:http://wenhuix.github.io/research/denoise.html一.基本情况 (一)基本方法 Fast Non-Local MeansDenoising (FNLMD),论文为 Mahmoudi, Mona, and Guillermo Sapiro. "Fast image and video denoising via nonlocal means of similar neighborh