pip install matplotlib
1简单的阈值化
cv2.threshold第一个参数是源图像，它应该是灰度图像. 第二个参数是用于对像素值进行分类的阈值, 第三个参数是maxVal，它表示如果像素值大于（有时小于）阈值则要给出的值. OpenCV提供不同类型的阈值，它由函数的第四个参数决定. 不同的类型是：
| cv2.THRESH_BINARY | 如果 src(x,y)>threshold ,dst(x,y) = max_value; 否则,dst（x,y）=0 |
| cv.THRESH_BINARY_INV | 如果 src(x,y)>threshold,dst(x,y) = 0; 否则,dst(x,y) = max_value |
| cv.THRESH_TRUNC | 如果 src(x,y)>threshold，dst(x,y) = max_value; 否则dst(x,y) = src(x,y) |
| cv.THRESH_TOZERO | 如果src(x,y)>threshold，dst(x,y) = src(x,y) ; 否则 dst(x,y) = 0 |
| cv.THRESH_TOZERO_INV | 如果 src(x,y)>threshold，dst(x,y) = 0 ; 否则dst(x,y) = src(x,y) |
代码
import cv2 import numpy as np import matplotlib.pylab as plt img = cv2.imread('lena.jpg',0) 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()
测试效果
2自适应阈值化
图像在不同区域具有不同照明条件时，应进行自适应阈值处理.因此，我们为同一图像的不同区域获得不同的阈值，并且它为具有不同照明的图像提供了更好的结果.cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst])
adaptiveMethod:决定如何计算阈值
- cv2.ADAPTIVE_THRESH_MEAN_C:阈值是邻域的平均值
- cv2.ADAPTIVE_THRESH_GAUSSIAN_C:阈值是邻域值的加权和，其中权重是高斯窗口
blockSize:决定了邻域的大小
C:从计算的平均值或加权平均值中减去的常数
import cv2 import numpy as np import matplotlib.pylab as plt img = cv2.imread('lena.jpg',0) img = cv2.medianBlur(img,5) ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\ cv2.THRESH_BINARY,11,2) th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\ cv2.THRESH_BINARY,11,2) titles = ['Original Image', 'Global Thresholding (v = 127)', 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding'] images = [img, th1, th2, th3] 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()
3 大津阈值法
根据双峰图像的图像直方图自动计算阈值。 （对于非双峰图像，二值化不准确。）
使用cv.threshold（）但是传递了一个额外的标志v.THRESH_OTSU.对于阈值，只需传递零.然后算法找到最佳阈值并返回为第二个输出retVal。如果未使用Otsu阈值法，则retVal与之前使用的阈值相同.
在第一种情况下，将全局阈值应用为值127.在第二种情况下，直接应用了Otsu的阈值.在第三种情况下，使用5x5高斯内核过滤图像以消除噪声，然后应用Otsu阈值处理.
代码：
import cv2 import numpy as np import matplotlib.pylab as plt img = cv2.imread('lena.jpg',0) # global thresholding ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) # Otsu's thresholding ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # Otsu's thresholding after Gaussian filtering blur = cv2.GaussianBlur(img,(5,5),0) ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # plot all the images and their histograms images = [img, 0, th1, img, 0, th2, blur, 0, th3] titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)', 'Original Noisy Image','Histogram',"Otsu's Thresholding", 'Gaussian filtered Image','Histogram',"Otsu's Thresholding"] for i in range(3): plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray') plt.title(titles[i*3]), plt.xticks([]), plt.yticks([]) plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256) plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([]) plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray') plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([]) plt.show()
