1 Sobel算子
dst = cv2.Sobel(src, ddepth, dx, dy, ksize)
- ddepth:图像的深度
- dx和dy分别表示水平和竖直方向
- ksize是Sobel算子的大小
import cv2
import numpy as np
img = cv2.imread('data/pie.png', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (400, 400))
def cv_show(img, name):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
sobelX = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
cv_show(sobelX, 'sobelX')
取绝对值
import cv2
import numpy as np
img = cv2.imread('data/pie.png', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (400, 400))
def cv_show(img, name):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
sobelX = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
sobelY = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)
# 白到黑是正数,黑到白是负数,所有的负数会被截断成0,所以要取绝对值
sobelX = cv2.convertScaleAbs(sobelX)
sobelY = cv2.convertScaleAbs(sobelY)
cv_show(sobelX, 'sobelX')
cv_show(sobelY, 'sobelY')
import cv2
import numpy as np
def cv_show(im, name):
cv2.imshow(name, im)
cv2.waitKey(0)
cv2.destroyAllWindows()
img = cv2.imread('data/test2.jpg', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (400, 400))
sobelX = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
sobelY = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)
# 白到黑是正数,黑到白是负数,所有的负数会被截断成0,所以要取绝对值
sobelX = cv2.convertScaleAbs(sobelX)
sobelY = cv2.convertScaleAbs(sobelY)
cv_show(sobelX, 'sobelX')
cv_show(sobelY, 'sobelY')
# 分别计算x和y,再求和
sobelXY = cv2.addWeighted(sobelX, 0.5, sobelY, 0.5, 0)
cv_show(sobelXY, 'sobelXY')
# 直接计算x和y(不推荐)
sobel_XY = cv2.Sobel(img, cv2.CV_64F, 1, 1, ksize=3)
cv_show(sobel_XY, 'sobel_XY')
只计算X方向
只计算Y方向
X方向+Y方向
X方向和Y方向一起计算
2 Scharr算子
对变化更加敏感
3 Laplacian算子
二阶推导,对噪声敏感
import cv2
import numpy as np
def cv_show(im, name):
cv2.imshow(name, im)
cv2.waitKey(0)
cv2.destroyAllWindows()
img = cv2.imread('data/test2.jpg', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (400, 400))
sobelX = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
sobelY = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)
# 白到黑是正数,黑到白是负数,所有的负数会被截断成0,所以要取绝对值
sobelX = cv2.convertScaleAbs(sobelX)
sobelY = cv2.convertScaleAbs(sobelY)
# 分别计算x和y,再求和
sobelXY = cv2.addWeighted(sobelX, 0.5, sobelY, 0.5, 0)
# 直接计算x和y(不推荐)
scharrX = cv2.Scharr(img, cv2.CV_64F, 1, 0)
scharrY = cv2.Scharr(img, cv2.CV_64F, 0, 1)
scharrX = cv2.convertScaleAbs(scharrX)
scharrY = cv2.convertScaleAbs(scharrY)
scharrXY = cv2.addWeighted(scharrX, 0.5, scharrY, 0.5, 0)
laplacian = cv2.Laplacian(img, cv2.CV_64F)
laplacian = cv2.convertScaleAbs(laplacian)
res = np.hstack([img, sobelXY, scharrXY, laplacian])
cv_show(res, 'res')