阈值与平滑处理

图像阈值

 1 import cv2
 2 from matplotlib import pyplot as plt
 3 
 4 img = cv2.imread('default.png')
 5 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 6 cv2.imshow('test', img_gray)
 7 
 8 '''
 9 ret, thresh = cv2.threshold(src, thresh, maxval, type)
10 src：输入图，只能输入单通道图像，一般为灰度图
11 thresh：输出图
12 thresh：阈值
13 maxval：当像素值超过了阈值，所赋予的值
14 type：二值化操作类型，即如何处理阈值
15 '''
16 ret1, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)    # 超过阈值部分取maxval，否则取0
17 ret2, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)    # THRESH_BINARY的反转
18 ret3, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)     # 大于阈值部分设为阈值，否则不变
19 ret4, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)    # 大于阈值部分不改变，否则设为0
20 ret5, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)    # THRESH_TOZERO的反转
21 titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
22 images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
23 for i in range(6):
24     plt.subplot(2, 3, i+1)
25     plt.imshow(images[i], 'gray')
26     plt.title(titles[i])
27     plt.xticks([]), plt.yticks([])
28 plt.show()
29 cv2.waitKey(0)
30 cv2.destroyAllWindows()
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常见滤波函数

 1 import cv2
 2 
 3 img = cv2.imread('default.png')
 4 # 均值滤波，卷积核大小3*3
 5 blur = cv2.blur(img, (3, 3))
 6 # 方框滤波，如果归一化则normalize=True
 7 box = cv2.boxFilter(img, -1, (3, 3), normalize=False)
 8 # 高斯滤波，高斯模糊的卷积核里的数值满足高斯分布，更重视中间
 9 gaussian = cv2.GaussianBlur(img, (5, 5), 1)
10 # 中值滤波，相当于用中值代替
11 median = cv2.medianBlur(img, 5)
12 
13 images = [img, blur, box, gaussian, median]
14 titles = ['Original', 'Blur', 'Box', 'Gaussian', 'Median']
15 for i in range(5):
16     cv2.imshow(titles[i], images[i])
17 
18 cv2.waitKey(0)
19 cv2.destroyAllWindows()

 

posted @ 2021-09-12 23:13  Ocean允许访问  阅读(50)  评论(0)    收藏  举报