Python|使用 OpenCV 的阈值技术 | Set-2（自适应阈值）

先决条件：使用 OpenCV 进行简单阈值处理

在上一篇文章中，使用不同类型的阈值技术解释了简单阈值。另一种阈值技术是自适应阈值。在简单阈值处理中，使用了一个全局阈值值，该值始终保持不变。因此，在不同区域照明条件变化的情况下，恒定的阈值将无济于事。自适应阈值是针对较小区域计算阈值的方法。这导致不同区域相对于照明变化的不同阈值。我们为此使用cv2.adaptiveThreshold 。

Syntax: cv2.adaptiveThreshold(source, maxVal, adaptiveMethod, thresholdType, blocksize, constant)

Parameters:
-> source: Input Image array(Single-channel, 8-bit or floating-point)
-> maxVal: Maximum value that can be assigned to a pixel.
-> adaptiveMethod: Adaptive method decides how threshold value is calculated.

cv2.ADAPTIVE_THRESH_MEAN_C: Threshold Value = (Mean of the neighbourhood area values – constant value). In other words, it is the mean of the blockSize×blockSize neighborhood of a point minus constant.

cv2.ADAPTIVE_THRESH_GAUSSIAN_C: Threshold Value = (Gaussian-weighted sum of the neighbourhood values – constant value). In other words, it is a weighted sum of the blockSize×blockSize neighborhood of a point minus constant.

-> thresholdType: The type of thresholding to be applied.
-> blockSize: Size of a pixel neighborhood that is used to calculate a threshold value.
-> constant: A constant value that is subtracted from the mean or weighted sum of the neighbourhood pixels.

编程需要懂一点英语

以下是Python实现：

# Python program to illustrate 
# adaptive thresholding type on an image
       
# organizing imports 
import cv2 
import numpy as np 
   
# path to input image is specified and  
# image is loaded with imread command 
image1 = cv2.imread('input1.jpg') 
   
# cv2.cvtColor is applied over the
# image input with applied parameters
# to convert the image in grayscale 
img = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
   
# applying different thresholding 
# techniques on the input image
thresh1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                                          cv2.THRESH_BINARY, 199, 5)
  
thresh2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                          cv2.THRESH_BINARY, 199, 5)
  
# the window showing output images
# with the corresponding thresholding 
# techniques applied to the input image
cv2.imshow('Adaptive Mean', thresh1)
cv2.imshow('Adaptive Gaussian', thresh2)
  
     
# De-allocate any associated memory usage  
if cv2.waitKey(0) & 0xff == 27: 
    cv2.destroyAllWindows()

输入图像：

输出：