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1 灰度图

# opencv读取的格式是BGR# matplotlib读取的格式是RGBimport cv2import numpy as npimport matplotlib.pyplot as plt%matplotlib inlineimg = cv2.imread("cat.jpg")img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)img_gray.shape

(414, 500)

cv2.imshow("img_gray",img_gray)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(img_gray)

2 HSV

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

hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

cv2.imshow("hsv",hsv)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(hsv)

3 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

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()

4 图像平滑

img = cv2.imread("lenaNoise.png")cv2.imshow("img",img)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(img)

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()plt.imshow(blur)

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)  cv2.imshow('box', box)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(box)

box = cv2.boxFilter(img,-1,(3,3), normalize=False)  cv2.imshow('box', box)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(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(img, (5, 5), 1)  cv2.imshow("aussian", aussian)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(aussian)

中值滤波

median = cv2.medianBlur(img,5)cv2.imshow("median", median)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(median)

# 展示所有的res = np.hstack((blur,aussian,median))cv2.imshow("median vs average",res)cv2.waitKey(0)cv2.destroyAllWindows()plt.imshow(res)

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