怎么用Python OpenCV寻找两条曲线直接的最短距离
短信预约 -IT技能 免费直播动态提醒
这篇文章主要介绍了怎么用Python OpenCV寻找两条曲线直接的最短距离的相关知识,内容详细易懂,操作简单快捷,具有一定借鉴价值,相信大家阅读完这篇怎么用Python OpenCV寻找两条曲线直接的最短距离文章都会有所收获,下面我们一起来看看吧。
import numpy as npimport mathimport cv2def cal_pt_distance(pt1, pt2): dist = math.sqrt(pow(pt1[0]-pt2[0],2) + pow(pt1[1]-pt2[1],2)) return distfont = cv2.FONT_HERSHEY_SIMPLEXimg = cv2.imread('01.png')#cv2.imshow('class="lazy" data-src',img)gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)gray = cv2.GaussianBlur(gray, (3,3), 0)ret,thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)image,contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)#thresh,contours,hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)flag = FalseminDist = 10000minPt0 = (0,0)minPt1 = (0,0)for i in range(0,len(contours[1])):#遍历所有轮廓 pt = tuple(contours[1][i][0]) #print(pt) min_dis = 10000 min_pt = (0,0) #distance = cv2.pointPolygonTest(contours[1], pt, False) for j in range(0,len(contours[0])): pt2 = tuple(contours[0][j][0]) distance = cal_pt_distance(pt, pt2) #print(distance) if distance < min_dis: min_dis = distance min_pt = pt2 min_point = pt if min_dis < minDist: minDist = min_dis minPt0 = min_point minPt1 = min_pt temp = img.copy() cv2.drawContours(img,contours,1,(255,255,0),1) cv2.line(temp,pt,min_pt,(0,255,0),2,cv2.LINE_AA) cv2.circle(temp, pt,5,(255,0,255),-1, cv2.LINE_AA) cv2.circle(temp, min_pt,5,(0,255,255),-1, cv2.LINE_AA) cv2.imshow("img",temp) if cv2.waitKey(1)&0xFF ==27: #按下Esc键退出 flag = True break if flag: breakcv2.line(img,minPt0,minPt1,(0,255,0),2,cv2.LINE_AA)cv2.circle(img, minPt0,3,(255,0,255),-1, cv2.LINE_AA)cv2.circle(img, minPt1,3,(0,255,255),-1, cv2.LINE_AA)cv2.putText(img,("min_dist=%0.2f"%minDist), (minPt1[0],minPt1[1]+15), font, 0.7, (0,255,0), 2)cv2.imshow('result', img)cv2.imwrite('result.png',img)cv2.waitKey(0)cv2.destroyAllWindows()
原图:
结果图:
关于“怎么用Python OpenCV寻找两条曲线直接的最短距离”这篇文章的内容就介绍到这里,感谢各位的阅读!相信大家对“怎么用Python OpenCV寻找两条曲线直接的最短距离”知识都有一定的了解,大家如果还想学习更多知识,欢迎关注编程网行业资讯频道。
免责声明:
① 本站未注明“稿件来源”的信息均来自网络整理。其文字、图片和音视频稿件的所属权归原作者所有。本站收集整理出于非商业性的教育和科研之目的,并不意味着本站赞同其观点或证实其内容的真实性。仅作为临时的测试数据,供内部测试之用。本站并未授权任何人以任何方式主动获取本站任何信息。
② 本站未注明“稿件来源”的临时测试数据将在测试完成后最终做删除处理。有问题或投稿请发送至: 邮箱/279061341@qq.com QQ/279061341