7.1 简单阈值

cv2.threshold(src, thresh, maxval, type, dst=None)图像阈值

• src：源图像，应该为灰度图。
• thresh：阈值，像素值小于阈值，则将其设置为0，否则将其设置为最大值。
• maxval：分配给超过阈值的像素值的最大值。
• type：提供了不同类型的阈值
  • cv.THRESH_BINARY
  • cv.THRESH_BINARY_INV
  • cv.THRESH_TRUNC
  • cv.THRESH_TOZERO
  • cv.THRESH_TOZERO_INV
• dst：输出图像

该方法返回两个输出。第一个是使用的阈值，第二个输出是阈值后的图像。

import cv2
import numpy as np
from matplotlib import pyplot as plt

# 读取图像（灰度图）
img = cv2.imread('gradient.png', 0)

# 阈值，返回的图像
ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img, 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()

7.2 自适应阈值

cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C, dst=None)自适应阈值化

• src：源图像
• maxValue：超过阈值的部分取值是多少
• adaptiveMethod：在一个邻域内计算阈值所采用的算法
  • ADAPTIVE_THRESH_MEAN_C：领域的平均值
  • ADAPTIVE_THRESH_GAUSSIAN_C：领域的高斯均值
• thresholdType：这是阈值类型
  • THRESH_BINARY
  • THRESH_BINARY_INV
• blockSize：计算单位是像素的邻域块大小选择，这是局部邻域大小，3、5、7等
• C：偏移值调整量，用均值和高斯计算阈值后，再减或加这个值就是最终阈值。

返回输出是阈值后的图像

import cv2
from matplotlib import pyplot as plt

# 读取图像
img = cv2.imread('lena.jpg', 0)

# 简单阈值
# threshold(src, thresh, maxval, type, dst=None)
ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
# 自适应阈值
# adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C, dst=None)
thresh2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 5, 2)
thresh3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 2)

titles = ['Original Image', 'BINARY', 'ADAPTIVE_THRESH_MEAN_C', 'ADAPTIVE_THRESH_GAUSSIAN_C']
images = [img, thresh1, thresh2, thresh3]
for i in range(4):
    plt.subplot(2, 2, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()