02 计算机视觉-opencv阈值与滤波处理

1 灰度图

# opencv读取的格式是BGR
# matplotlib读取的格式是RGB
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

img = cv2.imread("cat.jpg")
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape

(414, 500)

cv2.imshow("img_gray",img_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
plt.imshow(img_gray)

<matplotlib.image.AxesImage at 0x1cc481f5128>

2 HSV

• H -色调（主波长）
• S -饱和度（纯度/颜色的阴影）
• V - 强度

hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

cv2.imshow("hsv",hsv)
cv2.waitKey(0)
cv2.destroyAllWindows()
plt.imshow(hsv)

<matplotlib.image.AxesImage at 0x1cc34feada0>

3 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

• thresh： 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5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV

• cv2.THRESH_BINARY 超过阈值部分取maxval（最大值），否则取0

• cv2.THRESH_BINARY_INV THRESH_BINARY的反转

• cv2.THRESH_TRUNC 大于阈值部分设为阈值，否则不变

• cv2.THRESH_TOZERO 大于阈值部分不改变，否则设为0

• cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转

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

titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

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

4 图像平滑

img = cv2.imread("lenaNoise.png")

cv2.imshow("img",img)
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.imshow(img)

<matplotlib.image.AxesImage at 0x1cc44d79080>

均值滤波

• 简单的平均卷积操作

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

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

plt.imshow(blur)

<matplotlib.image.AxesImage at 0x1cc35cbc748>

方框滤波

• 基本和均值一样，可以选择归一化

box = cv2.boxFilter(img,-1,(3,3), normalize=True)  

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

plt.imshow(box)

<matplotlib.image.AxesImage at 0x1cc48671400>

box = cv2.boxFilter(img,-1,(3,3), normalize=False)  

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

plt.imshow(box)

<matplotlib.image.AxesImage at 0x1cc44e33a90>

高斯滤波

• 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的

aussian = cv2.GaussianBlur(img, (5, 5), 1)  

cv2.imshow("aussian", aussian)
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.imshow(aussian)

<matplotlib.image.AxesImage at 0x1cc35020f60>

中值滤波

median = cv2.medianBlur(img,5)

cv2.imshow("median", median)
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.imshow(median)

<matplotlib.image.AxesImage at 0x1cc42c25b00>

# 展示所有的
res = np.hstack((blur,aussian,median))
cv2.imshow("median vs average",res)
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.imshow(res)

<matplotlib.image.AxesImage at 0x1cc486ae470>