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OpenCV笔记之阈值(二值化)处理 一共有5个方法• cv2.THRESH_BINARY（超过阈值部分取最大值, 如超过了阈值127变为255）• cv2.THRESH_BINARY_INV（反转上面的，可以理解为上面的颜色反转）• cv2.THRESH_TRUNC （截断，大于阈值部分设置阈值，比如大于127的就设置为127）• cv2.THRESH_TOZERO (大于阈值部分保持不变，小于等于阈值的全为0)• cv2.THRESH_TOZERO_INV（反转上面的,小于阈值不变，大于等于阈值为0）ret,thresh1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) ret,thresh2 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV) ret,thresh3 = cv2.threshold(gray,127,255,cv2.THRESH_TRUNC) ret,thresh4 = cv2.threshold(gray,127,255,cv2.THRESH_TOZERO) ret,thresh5 = cv2.threshold(gray,127,255,cv2.THRESH_TOZERO_INV)from matplotlib import pyplot as plt #thresh1在上面 titles = ['BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV'] images = [thresh1, thresh2, thresh3, thresh4, thresh5] for i in range(5): plt.subplot(2, 3, i+1) plt.imshow(images[i]) plt.title(titles[i]) plt.show() 滤波：#均值滤波 对一个3*3的像素颜色进行平均化 img=cv2.blur(img,(3,3)) #方框滤波 和均值滤波基本一样，-1是固定写法(颜色通道),normalize是处理像素颜色加起来不会越界 #不做处理的话 越界大于255就会用255来处理 img2=cv2.boxFilter(img,-1,(3,3),normalize=True) #高斯滤波的卷积核里的数值是满足高斯分布，相当于更重视中间的 img3=cv2.GaussianBlur(img,(3,3),1) # cv_show(img3) #中值滤波 取3*3范围内的中间数 img4=cv2.medianBlur(img,3)
