Opencv学习笔记（二）图像基本操作

图像基本操作

一、边界填充

二、图像融合

三、图像阈值

四、图像平滑

五、形态学预算

1、腐蚀操作

2、膨胀操作

3、开闭运算操作

4、梯度运算

5、顶帽运算

6、黑帽运算

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一、边界填充

• cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=?)

(1)cv2.BORDER_REPLICATE，复制法，赋值最边缘像素

(2)cv2.BORDER_REFLECT，反射法，对感兴趣图像中的像素在两边进行复制，如hgfedcba|abcdefgh|hgfedcba

(3)cv2.BORDER_REFLECT101，反射法，以最边缘级像素为轴对称，hgfedcb|abcdefgh|gfedcba

(4)cv2.BORDER_WRAP，外包装法，abcdefgh|abcdefgh|abcdefgh

(5)cv2.BORDER_CONSTANT，常量值填充。

import cv2
import matplotlib.pyplot as plt

img = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)

top_size, bottom_size, left_size, right_size = (50, 50, 50, 50)

replicate = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REPLICATE)
relect = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT)
relect101 = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT101)
wrap = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_WRAP)
constant = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_CONSTANT, value=0)

plt.subplot(231), plt.imshow(img, cmap='gray'), plt.title('ORIGINAL')
plt.subplot(232), plt.imshow(replicate, cmap='gray'), plt.title('REPLICATE')
plt.subplot(233), plt.imshow(relect, cmap='gray'), plt.title('REFLECT')
plt.subplot(234), plt.imshow(relect101, cmap='gray'), plt.title('REFLECT101')
plt.subplot(235), plt.imshow(wrap, cmap='gray'), plt.title('WRAP')
plt.subplot(236), plt.imshow(constant, cmap='gray'), plt.title('CONSTANT')
plt.show()

二、图像融合

• cv2.addWeighted(img1, alpha, img2, 1-alpha, gamma)

import cv2

img1 = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('dog.jpg', cv2.IMREAD_GRAYSCALE)
print(img1.shape)
print(img2.shape)
img_dog = cv2.resize(img2, (256, 256))
res = cv2.addWeighted(img1, 0.4, img_dog, 0.6, 0)

cv2.imshow('res', res)

cv2.waitKey(0)
cv2.destroyAllWindows()

三、图像阈值

ret, dst = cv2.threshold(src, thresh, maxval, type)

• src：输入图，只能输入单通道图像，通常来说为灰度图
• dst：输出图
• threshold：阈值
• maxval：当像素值超过了阈值（或者小于阈值，根据type来决定）所赋予的值
• type：二值化操作的类型包含以下五种：
  • cv2.THRESH_BINARY 超过阈值的部分取maxval
  • cv2.THRESH_BINARY_INV，cv2.THRESH_BINARY的反转
  • cv2.THRESH_TRUNC，大于阈值部分设为maxval，否则不变
  • cv2.THRESH_TOZERO，大于阈值的部分不变，否则为0
  • cv2.THRESH_TOZERO_INV，cv2.THRESH_TOZERO的反转

import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)

ret, thresh1 = cv2.threshold(img1, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img1, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img1, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img1, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img1, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Origin', 'binary', 'binary_inv', 'trunc', 'tozero', 'tozero_inv']
images = [img1, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in range(6):
    plt.subplot(2, 3, i+1), plt.imshow(images[i], cmap='gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])

plt.show()

四、图像平滑

• 均值滤波，cv2.blur(img, (kernel_size, kernel_size))
• 方框滤波，cv2.boxFilter(img, -1, (kernel_size, kernel_size), normaliza=True)，是否求平均。基本与均值滤波相同
• 高斯滤波：cv2.GaussianBlur(img, (kernel_size, kernel_size), sigma)
• 中值滤波：cv2.medianBlur(img, 5)

import cv2
import numpy as np

img = cv2.imread('lena.jpg')
cv2.waitKey(0)
cv2.destroyAllWindows()

blur = cv2.blur(img, (3, 3))

cv2.imshow('blur', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

median = cv2.medianBlur(img, 5)
cv2.imshow('median', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

gaussian = cv2.GaussianBlur(img, (5, 5), 1)
cv2.imshow('gaussian', gaussian)
cv2.waitKey(0)
cv2.destroyAllWindows()

res = np.hstack((blur, median, gaussian))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

五、形态学运算

就上图而言，膨胀和腐蚀都是针对白色部分而不是黑色部分，膨胀就是白色区域扩大，腐蚀就是白色区域缩小

1、腐蚀操作，cv2.erode(src, kernel, interation=?)

import cv2
import numpy as np

img = cv2.imread('zhinian.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((5, 5), np.uint8)

erosion1  = cv2.erode(img, kernel, iterations=1)
erosion2  = cv2.erode(img, kernel, iterations=2)
erosion3  = cv2.erode(img, kernel, iterations=3)

res = np.hstack((erosion1, erosion2, erosion3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

2、膨胀操作，cv2.dilate(src, kernel, interation=?)

import cv2
import numpy as np

img = cv2.imread('zhinian.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((5, 5), np.uint8)

dilate1  = cv2.dilate(img, kernel, iterations=1)
dilate2  = cv2.dilate(img, kernel, iterations=2)
dilate3  = cv2.dilate(img, kernel, iterations=3)

res = np.hstack((dilate1, dilate2, dilate3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

三、开闭运算，cv2.morphologyEx(img, type, kernel, iteration=?)

开运算：先腐蚀后膨胀,type=cv2.MORPH_OPEN

闭运算：先膨胀后腐蚀,type=cv2.MORPH_CLOSED

import cv2
import numpy as np

img = cv2.imread('zhinian.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((5, 5), np.uint8)
open1 = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel, iterations=1)
open2 = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel, iterations=2)
open3 = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel, iterations=3)

res1 = np.hstack((open1, open2, open3))
cv2.imshow('res', res1)
cv2.waitKey(0)
cv2.destroyAllWindows()

closed1 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=1)
closed2 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=2)
closed3 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=3)

res2 = np.hstack((closed1, closed1, closed1))
cv2.imshow('res', res2)
cv2.waitKey(0)
cv2.destroyAllWindows()

4、梯度运算 = 膨胀 - 腐蚀，cv2.morphologyEx(img, 2v.MORPH_GRADIENT, kernel, iteration=?)

gradient1 = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel, iterations=1)
gradient2 = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel, iterations=2)
gradient3 = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel, iterations=3)

res3 = np.hstack((gradient1, gradient2, gradient3))
cv2.imshow('res', res3)
cv2.waitKey(0)
cv2.destroyAllWindows()

5、顶帽运算 = 原始输入 - 开运算，cv2.morphologyEx(img, 2v.MORPH_TOPHAT, kernel, iteration=?)

tophat1 = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel, iterations=1)
tophat2 = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel, iterations=2)
tophat3 = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel, iterations=3)

res4 = np.hstack((tophat1, tophat2, tophat3))
cv2.imshow('res', res4)
cv2.waitKey(0)
cv2.destroyAllWindows()

6、黑帽运算 = 闭运算 - 原始输入，cv2.morphologyEx(img, 2v.MORPH_BLACKHAT, kernel, iteration=?)

blackhat1 = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel, iterations=1)
blackhat2 = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel, iterations=2)
blackhat3 = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel, iterations=3)

res5 = np.hstack((blackhat1, blackhat2, blackhat3))
cv2.imshow('res', res5)
cv2.waitKey(0)
cv2.destroyAllWindows()