OpenCV(八)-形态学

一、形态学概述

二、图像全局二值化

(function) def threshold(
    src: MatLike,
    thresh: float,
    maxval: float,
    type: int,
    dst: MatLike | None = ...
) -> tuple[float, MatLike]

import cv2
import numpy as np

img = cv2.imread('./dog.jpeg')
img1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, dst = cv2.threshold(img1, 180, 255, cv2.THRESH_BINARY)

cv2.imshow('img', img)
cv2.imshow('gray', img1)
cv2.imshow('dst', dst)

cv2.waitKey(0)

三、阈值类型

四、自适应阈值二值化

(function) def adaptiveThreshold(
    src: MatLike,
    maxValue: float,
    adaptiveMethod: int,
    thresholdType: int,
    blockSize: int,
    C: float,
    dst: MatLike | None = ...
) -> MatLike

import cv2
import numpy as np

img = cv2.imread('./math.png')
img1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

dst = cv2.adaptiveThreshold(img1, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 3, 0)

cv2.imshow('img', img)
cv2.imshow('gray', img1)
cv2.imshow('dst', dst)

cv2.waitKey(0)

五、OpenCV腐蚀

(function) def erode(
    src: MatLike,
    kernel: MatLike,		# 卷积核
    dst: MatLike | None = ...,
    anchor: Point = ...,
    iterations: int = ...,	# 腐蚀次数
    borderType: int = ...,
    borderValue: Scalar = ...
) -> MatLike

import cv2
import numpy as np

img = cv2.imread('./j.png')

kernal = np.ones((3,3), np.uint8)
dst = cv2.erode(img, kernal, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

六、获取形态学卷积核

(function) def getStructuringElement(
    shape: int,
    ksize: Size,
    anchor: Point = ...
) -> MatLike

import cv2
import numpy as np

img = cv2.imread('./j.png')

# kernal = np.ones((3,3), np.uint8)
kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
dst = cv2.erode(img, kernal, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

七、OpenCV膨胀

(function) def dilate(
    src: MatLike,
    kernel: MatLike,
    dst: MatLike | None = ...,
    anchor: Point = ...,
    iterations: int = ...,
    borderType: int = ...,
    borderValue: Scalar = ...
) -> MatLike

import cv2
import numpy as np

img = cv2.imread('./j.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
dst = cv2.dilate(img, kernal, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

八、开运算

import cv2
import numpy as np

img = cv2.imread('./dotj.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernal)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

九、闭运算

import cv2
import numpy as np

img = cv2.imread('./dotinj.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernal)
# 闭运算
dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernal)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

十、形态学梯度

import cv2
import numpy as np

# img = cv2.imread('./dotinj.png')
img = cv2.imread('./j.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernal)
# 闭运算
# dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernal)

# 梯度
dst = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernal)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

十一、顶帽运算

只留下小的

import cv2
import numpy as np

img = cv2.imread('./tophat.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (19,19))

# 顶帽运算
dst = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernal)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)

十二、黑帽运算

import cv2
import numpy as np

img = cv2.imread('./dotinj.png')

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

# 黑帽运算
dst = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernal)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

cv2.waitKey(0)