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,通常来说为灰度图
- dst: 输出图
- thresh: 阈值 0-255 一般是127
- maxval: 当像素值超过了阈值(或者小于阈值,根据type来决定),所赋予的值 最大值255
- type:二值化操作的类型,包含以下5种类型: cv2.THRESH_BINARY; cv2.THRESH_BINARY_INV; cv2.THRESH_TRUNC; cv2.THRESH_TOZERO;cv2.THRESH_TOZERO_INV
- cv2.THRESH_BINARY 超过阈值部分取maxval(最大值),否则取0
- cv2.THRESH_BINARY_INV THRESH_BINARY的反转
- cv2.THRESH_TRUNC 大于阈值部分设为阈值,否则不变
- cv2.THRESH_TOZERO 大于阈值部分不改变,否则设为0
- cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转
import cv2 import numpy as np import matplotlib.pyplot as plt#Matplotlib是RGB img=cv2.imread('d:/image0.jpg') img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 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 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()
滤波处理
import cv2 import numpy as np import matplotlib.pyplot as plt#Matplotlib是RGB img=cv2.imread('d:/image0.jpg') #cv2.imshow("image",img) #均值滤波 bluer=cv2.blur(img,(3,3)) #方框滤波 #基本和均值一样,可以选择归一化 box=cv2.boxFilter(img,-1,(3,3),normalize=True) #方框滤波 #基本和均值一样,可以选择归一化,容易越界 box2=cv2.boxFilter(img,-1,(3,3),normalize=False)
res=np.hstack((bluer,box,box2))
cv2.imshow("da",res)
cv2.waitKey(0)
cv2.destroyAllWindows()
#高斯滤波 #高斯滤波得卷积核里地数值满足高斯分布,相当于中间地分布 import cv2 import numpy as np img=cv2.imread("d:/image0.jpg") aussian=cv2.GaussianBlur(img,(3,3),1)
#均值滤波
bluer=cv2.blur(img,(5,5))
#中值滤波 median=cv2.medianBlur(img,5) res=np.hstack((aussian,bluer,median)) cv2.imshow("aussian vs averge",res) cv2.waitKey(0) cv2.destroyAllWindows()