图像阈值与平滑处理

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，通常来说说是灰度图dst:输出图thresh: 阈值maxval : 当图像像素值超过了阈值（或者小于阈值，根据type决定），所赋予的值type:二值化操作类型，包含以下5种类型： cv2.THRESH_BINARY; cv2.THRESH_BINARY_INV; cv2.THRESH_TRUNC;cv2.THRESH_TOZ ; cv2.THRESH_TOZERO_INVcv2.THRESH_BINARY 超过阈值部分去maxval(最大值)，否则取0cv2.THRESH_BINARY_INV THRESH_BINARY的反转cv2.THRESH_TRUNC 大于阈值部分设置为阈值，否则不便cv2.THRESH_TOZERO 大于阈值部分不改变，否则设为0cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转

代码

img = cv2.imread('../imgs/cat1.jpg') img_gray = cv2.imread('../imgs/cat1.jpg',cv2.IMREAD_GRAYSCALE) 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','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()

效果为

图像平滑

img = cv2.imread('../imgs/opencv.png') cv2.imshow('img',img) # cv2.waitKey(0) # cv2.destroyAllWindows() # 均值滤液 # 简单的屏均处理 blur = cv2.blur(img,(3,3)) cv2.imshow('blur',blur) # cv2.waitKey(0) # cv2.destroyAllWindows() # 方框滤液 # 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() # 方框滤液 # 基本和均值一样，可以选择归一化,容易越界 box = cv2.boxFilter(img,-1,(3,3),normalize=False) cv2.imshow('box',box) # cv2.waitKey(0) # cv2.destroyAllWindows() # 高斯滤液 # 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() # 中值滤液 # 相当于用中值代替 median = cv2.medianBlur(img,5) cv2.imshow('median',median) cv2.waitKey(0) cv2.destroyAllWindows()

小记

2020.7.1 9:43