标签:17 img res cv2 contours 图像 轮廓 255
# 图像轮廓 ## 2.1 图像二值化 import cv2 #opencv的缩写为cv2 import matplotlib.pyplot as plt # matplotlib库用于绘图展示 import numpy as np # numpy数值计算工具包 def cv_show(img,name): cv2.imshow(name,img) cv2.waitKey() cv2.destroyAllWindows() img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/08_Car.png') cv_show(img,'img') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于 17 的取 255,小于 127 的取 0 cv_show(thresh,'thresh') ## 2.2 轮廓检测 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # 做完二值后,再用图像轮廓检测函数再去做 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # cv_show(contours,'binary') # 返回的二值化后的图像 print(np.array(contours).shape) # 轮廓点的信息 print(hierarchy) # hierarchy 是把轮廓结果保存在层级结构当中,暂时用不上 ## 2.3 绘制所有轮廓 # 传入参数:图像、轮廓、轮廓索引(自适应,画所有轮廓),颜色模式,线条厚度 # 注意需要copy,要不原图会变。。。 cv_show(img,'img') draw_img = img.copy() # 若不用拷贝后的,而是用原图画轮廓,则画轮廓图绘把原始的输入图像重写,覆盖掉 res = cv2.drawContours(draw_img,contours,-1,(0,0,255),2) cv_show(res,'res') # 2.4 绘制某个轮廓 draw_img = img.copy() res = cv2.drawContours(draw_img,contours,7,(0,0,255),2) # 画 7 号轮廓 cv_show(res,'res') # 2.5 综合展示 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/10_contours.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) draw_img = img.copy() # 若不用拷贝后的,而是用原图画轮廓,则画轮廓图绘把原始的输入图像重写,覆盖掉 res = cv2.drawContours(draw_img,contours,-1,(0,0,255),2) #-1表示画出所有图形的里面轮廓,可以换不同的数字进行测试 cv_show(res,'res') # 3. 轮廓特征提取 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/10_contours.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt = contours[0] # 通过轮廓索引,拿到该索引对应的轮廓特征 print(cv2.contourArea(cnt)) # 该轮廓的面积 print(cv2.arcLength(cnt,True)) # 该轮廓的周长,True表示闭合的 # 4. 轮廓近似 # 4.2 正常轮廓展示 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/11_contours2.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) draw_img = img.copy() # 若不用拷贝后的,而是用原图画轮廓,则画轮廓图绘把原始的输入图像重写,覆盖掉 res = cv2.drawContours(draw_img,contours,-1,(0,0,255),2) # -1表示里外轮廓都显示 cv_show(res,'res') # 4.3 轮廓近似展示 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/11_contours2.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt = contours[0] draw_img = img.copy() res = cv2.drawContours(draw_img,[cnt],-1,(0,0,255),2) cv_show(res,'res') epsilon = 0.1 * cv2.arcLength(cnt,True) # 周长的百分比,这里用 0.1 的周长作阈值 approx = cv2.approxPolyDP(cnt,epsilon,True) # 第二个参数为阈值 draw_img = img.copy() res = cv2.drawContours(draw_img,[approx],-1,(0,0,255),2) cv_show(res,'res') # 5. 外接矩形 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/10_contours.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt = contours[6]#里面的数字不同得到不同的信息,画出不一样的轮廓 x,y,w,h = cv2.boundingRect(cnt) # 可以得到矩形四个坐标点的相关信息 img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255),2) cv_show(img,'img') area = cv2.contourArea(cnt) rect_area = w * h extent = float(area) / rect_area print('轮廓面具与边界矩形比:',extent) # 6. 外接圆 img = cv2.imread('D:/pycharm/pycharm-cope/opencv/resource/photo/10_contours.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) # 大于17的取255,小于127的取0 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt = contours[0] draw_img = img.copy() (x,y),redius = cv2.minEnclosingCircle(cnt) center = (int(x),int(y)) redius = int(redius) img = cv2.circle(draw_img,center,redius,(0,255,0),2) cv_show(img,'img')
标签:17,img,res,cv2,contours,图像,轮廓,255 来源: https://www.cnblogs.com/tuyin/p/16546347.html
本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享; 2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关; 3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关; 4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除; 5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。