OpenCV---阈值与平滑处理 图像阈值
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import cv2 # 读取的格式是BGR
import matplotlib.pyplot as plt
import numpy as np
def cv_show(name, img):
cv2.imshow('image', img) # 第一个为窗口名字
cv2.waitKey(0) # 等待时间，毫秒级，0表示任意键终止
cv2.destroyAllWindows()
'''
阈值与平滑处理
ret.dst=cv2.threshold(src,thresh,maxval,type)
src：输出图，只能输入单通道图像，通常来说为灰度图
dst：输出图
thresh：阈值
maxval：当像素超过了阈值（或者小于阈值，根据type来决定），所赋予的值
type：二值化操作的类型，包含以下5种类型：cv2.THRESH_BINARY超过阈值部分取maxval（最大值），否则取0;
cv2.THRESH_BINARY_INY 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；
'''
# flower = cv2.imread('flower.jpg')
# # cv_show('flower', flower)
# ret, thresh1 = cv2.threshold(flower, 127, 255, cv2.THRESH_BINARY)
# ret, thresh2 = cv2.threshold(flower, 127, 255, cv2.THRESH_BINARY_INV)
# ret, thresh3 = cv2.threshold(flower, 127, 255, cv2.THRESH_TRUNC)
# ret, thresh4 = cv2.threshold(flower, 127, 255, cv2.THRESH_TOZERO)
# ret, thresh5 = cv2.threshold(flower, 127, 255, cv2.THRESH_TOZERO_INV)
#
# titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
# images = [flower, 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()
# 图像平滑处理
moon = cv2.imread('timg.jpg')
# cv_show('moon', moon)
# # 均值滤波
# # 简单的平均卷积操作
# blur = cv2.blur(moon, (3, 3))
# cv_show('blur', blur)
# # 方框滤波
# # 基本和均值一样，可以选择归一化
# box = cv2.boxFilter(moon, -1, (3, 3), normalize=True) # normalize为True时和均值滤波一致，而为False时超过255的值设为255
# cv_show('box', box)
# # 高斯滤波
# # 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(moon, (5, 5), 1)
# cv_show('aussian', aussian)
# # 中值滤波
# # 相当于中值代替
# median = cv2.medianBlur(moon, 5) # 中值滤波
# cv_show('median', median)
# 展示所有
blur = cv2.blur(moon, (3, 3))
aussian = cv2.GaussianBlur(moon, (5, 5), 1)
median = cv2.medianBlur(moon, 5)
res = np.vstack((blur, aussian, median))
cv_show('median vs average', res)
只能输入灰度图
矩阵相乘 行对行操作
越界直接取到255
