边缘检测（各类边缘算子）以及角点检测 python.pdf

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边缘检测（各类边缘算子）以及角点检测 python 边缘检测（各类边缘算子）以及角点检测边缘检测： 1.Roberts算子 2.Prewitt算子 3.Sobel算子 4.Laplacian算子 5.Canny函数代码如下： import cv2 import numpy as np import matplotlib.pyplot as plt image = cv2.imread('lenna.jpg', cv2.IMREAD_GRAYSCALE) cv2.imshow('origin', image) h, w = image.shape # 获取图像的高度和宽度 # Roberts算子 kernelx = np.array([[-1, 0], [0, 1]], dtype=int) kernely = np.array([[0, -1], [1, 0]], dtype=int) x = cv2.filter2D(image, cv2.CV_16S, kernelx) y = cv2.filter2D(image, cv2.CV_16S, kernely) absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) cv2.imshow('Roberts', Roberts ) # Prewitt算子 kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]], dtype=int) kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]], dtype=int) x = cv2.filter2D(image, cv2.CV_16S, kernelx) y = cv2.filter2D(image, cv2.CV_16S, kernely) absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) Prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) cv2.imshow('Prewitt', Prewitt) # Sobel 滤波器进行边的检测 sobel_horizontal = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5) # 水平方向 sobel_vetical = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=5) # 垂直方向 cv2.imshow('sobel_H', sobel_horizontal) # 水平方向 cv2.imshow('sobel_V', sobel_vetical) # 垂直方向 # 拉普拉斯算子进行边的检测 64F代表每一个像素点元素占64位浮点数 laplacian = cv2.Laplacian(image, cv2.CV_64F, ksize=5) cv2.imshow('laplacian', laplacian) # # Canny边检测器 canny = cv2.Canny(image, 50, 240) cv2.imshow('Canny', canny) cv2.waitKey(0) cv2.destroyAllWindows() 结果图如下 Roberts

prewitt Laplacian canny

Sobel_v sobel_h 设计检测具有特定角度边缘的算子；检测具有45º角的边缘的3×3算子；检测具有45º角的边缘的5×5算子；这里我使用的是prewitt模板 3×3代码如下： import cv2 import numpy as np import matplotlib.pyplot as plt image = cv2.imread('lenna.jpg', cv2.IMREAD_GRAYSCALE) cv2.imshow('origin', image) # Roberts算子 kernelx = np.array([[1, 1, 0], [1, 0, -1], [0, -1, -1]], dtype=int) kernely = np.array([[1, 1, 0], [1, 0, -1], [0, -1, -1]], dtype=int) x = cv2.filter2D(image, cv2.CV_16S, kernelx) y = cv2.filter2D(image, cv2.CV_16S, kernely) absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) Prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) cv2.imshow('Prewitt', Prewitt) cv2.waitKey(0) cv2.destroyAllWindows() 5×5代码如下 import cv2 import numpy as np import matplotlib.pyplot as plt image = cv2.imread('lenna.jpg', cv2.IMREAD_GRAYSCALE) cv2.imshow('origin', image)

# Roberts算子 kernelx = np.array([[1, 1, 1, 1, 0], [1, 1, 1, 0, -1], [1,1,0, -1, -1],[1,0,-1,-1,-1],[0,-1,-1,-1,-1]], dtype=int) kernely = np.array([[1, 1, 1, 1, 0], [1, 1, 1, 0, -1], [1,1,0, -1, -1],[1,0,-1,-1,-1],[0,-1,-1,-1,-1]], dtype=int) x = cv2.filter2D(image, cv2.CV_16S, kernelx) y = cv2.filter2D(image, cv2.CV_16S, kernely) absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) Prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) cv2.imshow('Prewitt', Prewitt) cv2.waitKey(0) cv2.destroyAllWindows() 角点检测 Harris import cv2 as cv import numpy as np from matplotlib import pyplot as plt def cornerHarri(imgae): gray = cv.cvtColor(imgae, cv.COLOR_BGR2GRAY) gray = np.float32(gray) dst = cv.cornerHarris(gray, 2, 3, 0.04) dst = cv.dilate(dst, None) imgae[dst>0.01*dst.max()]=[0,0,255] src = cv.imread("whiteandblack.jpg") rows, cols, channel = src.shape #绕图像的中心旋转 #参数：旋转中心旋转度数 scale M = cv.getRotationMatrix2D((cols/2, rows/2), 30, 1) #参数：原始图像旋转参数元素图像宽高 rotated = cv.warpAffine(src, M, (cols, rows)) cv.imshow("contours", src) cornerHarri(src) cv.imshow("corn",src) cornerHarri(rotated) cv.imshow("corn1",rotated) cv.waitKey(0) cv.destroyAllWindows() 这里是对原图及它旋转变换后的图像进行角点检测图像如下原图：角点检测后：

旋转后的角点检测：使用cv2.goodFeaturesToTrack()检测角点道理一样下面给出代码（结果图就不放了）： import numpy as np import cv2 from matplotlib import pyplot as plt

def track(imgae): gray = cv2.cvtColor(rotated,cv2.COLOR_BGR2GRAY) corners = cv2.goodFeaturesToTrack(gray,25,0.01,10) corners = np.int0(corners) for i in corners: x,y = i.ravel() cv2.circle(rotated,(x,y),3,255,-1) img = cv2.imread('whiteandblack.jpg') rows, cols, channel = img.shape #绕图像的中心旋转 #参数：旋转中心旋转度数 scale M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1) #参数：原始图像旋转参数元素图像宽高 rotated = cv2.warpAffine(img, M, (cols, rows)) cv2.imshow("contours", img) track(img) cv2.imshow("corn",img) track(rotated) cv2.imshow("corn1",rotated) cv.waitKey(0) cv.destroyAllWindows() 作者：XITMan

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