图像处理-1

灰度图

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import cv2 #opencv读取的格式是BGR import numpy as np import matplotlib.pyplot as plt#Matplotlib是RGB %matplotlib inline img=cv2.imread('cat.jpg') img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img_gray.shape cv2.imshow("img_gray", img_gray) cv2.waitKey(0) cv2.destroyAllWindows()

HSV

• H - 色调（主波长）。
• S - 饱和度（纯度/颜色的阴影）。
• V值（强度）

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  hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV) cv2.imshow("hsv", hsv) cv2.waitKey(0) cv2.destroyAllWindows()

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
• 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
• 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

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  ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV) # INV 反转 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()

图像平滑

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img = cv2.imread('lenaNoise.png') cv2.imshow('img', img) cv2.waitKey(0) cv2.destroyAllWindows() # 均值滤波 # 简单的平均卷积操作 blur = cv2.blur(img, (3, 3)) cv2.imshow('blur', blur) cv2.waitKey(0) cv2.destroyAllWindows() # 方框滤波 # 基本和均值一样，可以选择归一化 ，-1表示颜色通道是一至的 box = cv2.boxFilter(img,-1,(3,3), normalize=True) cv2.imshow('box', box) cv2.waitKey(0) cv2.destroyAllWindows() # 方框滤波 # 基本和均值一样，可以选择归一化,容易越界 ，做归一化的结果跟均值滤波是一样的 box = cv2.boxFilter(img,-1,(3,3), normalize=False) cv2.imshow('box', box) cv2.waitKey(0) cv2.destroyAllWindows() # 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(img, (5, 5), 1) cv2.imshow('aussian', aussian) cv2.waitKey(0) cv2.destroyAllWindows() # 中值滤波 # 相当于用中值代替 median = cv2.medianBlur(img, 5) # 中值滤波 cv2.imshow('median', median) cv2.waitKey(0) cv2.destroyAllWindows() # 展示所有的 res = np.hstack((blur,aussian,median)) #print (res) cv2.imshow('median vs average', res) cv2.waitKey(0) cv2.destroyAllWindows()

形态学-腐蚀操作

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img = cv2.imread('dige.png') cv2.imshow('img', img) cv2.waitKey(0) cv2.destroyAllWindows() kernel = np.ones((3,3),np.uint8) erosion = cv2.erode(img,kernel,iterations = 1) # erode函数 cv2.imshow('erosion', erosion) cv2.waitKey(0) cv2.destroyAllWindows() pie = cv2.imread('pie.png') cv2.imshow('pie', pie) cv2.waitKey(0) cv2.destroyAllWindows() kernel = np.ones((30,30),np.uint8) erosion_1 = cv2.erode(pie,kernel,iterations = 1) erosion_2 = cv2.erode(pie,kernel,iterations = 2) erosion_3 = cv2.erode(pie,kernel,iterations = 3) res = np.hstack((erosion_1,erosion_2,erosion_3)) cv2.imshow('res', res) cv2.waitKey(0) cv2.destroyAllWindows()

形态学-膨胀操作

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img = cv2.imread('dige.png') cv2.imshow('img', img) cv2.waitKey(0) cv2.destroyAllWindows() kernel = np.ones((3,3),np.uint8) dige_erosion = cv2.erode(img,kernel,iterations = 1) cv2.imshow('erosion', erosion) cv2.waitKey(0) cv2.destroyAllWindows() kernel = np.ones((3,3),np.uint8) dige_dilate = cv2.dilate(dige_erosion,kernel,iterations = 1) cv2.imshow('dilate', dige_dilate) cv2.waitKey(0) cv2.destroyAllWindows() pie = cv2.imread('pie.png') kernel = np.ones((30,30),np.uint8) dilate_1 = cv2.dilate(pie,kernel,iterations = 1) dilate_2 = cv2.dilate(pie,kernel,iterations = 2) dilate_3 = cv2.dilate(pie,kernel,iterations = 3) res = np.hstack((dilate_1,dilate_2,dilate_3)) cv2.imshow('res', res) cv2.waitKey(0) cv2.destroyAllWindows()

开运算与闭运算

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# 开：先腐蚀，再膨胀 img = cv2.imread('dige.png') kernel = np.ones((5,5),np.uint8) opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) cv2.imshow('opening', opening) cv2.waitKey(0) cv2.destroyAllWindows() # 闭：先膨胀，再腐蚀 img = cv2.imread('dige.png') kernel = np.ones((5,5),np.uint8) closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) cv2.imshow('closing', closing) cv2.waitKey(0) cv2.destroyAllWindows()

梯度运算

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# 梯度=膨胀-腐蚀 pie = cv2.imread('pie.png') kernel = np.ones((7,7),np.uint8) dilate = cv2.dilate(pie,kernel,iterations = 5) erosion = cv2.erode(pie,kernel,iterations = 5) res = np.hstack((dilate,erosion)) cv2.imshow('res', res) cv2.waitKey(0) cv2.destroyAllWindows() gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel) cv2.imshow('gradient', gradient) cv2.waitKey(0) cv2.destroyAllWindows()

礼帽与黑帽

• 礼帽 = 原始输入-开运算结果
• 黑帽 = 闭运算-原始输入

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  #礼帽 img = cv2.imread('dige.png') tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel) cv2.imshow('tophat', tophat) cv2.waitKey(0) cv2.destroyAllWindows() #黑帽 img = cv2.imread('dige.png') blackhat = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT, kernel) cv2.imshow('blackhat ', blackhat ) cv2.waitKey(0) cv2.destroyAllWindows()

参考资料： 唐宇迪 OpenCV计算机视觉实战(Python版)