一，平滑均值滤波，奇数尺寸，参数和为1，缺点没有去除噪声，反而让图像模糊，代码，
"""
平滑滤波
"""
def average_filter():
img=cv2.imread('./data/opencv_logo.png')
kernel=np.ones(shape=(5,5),dtype=np.float32)/25
dst=cv2.filter2D(src=img,ddepth=-1,kernel=kernel)
plt.subplot(121)
plt.imshow(img)
plt.title('original')
plt.axis('off')
plt.subplot(122)
plt.imshow(dst)
plt.title('Average')
plt.axis('off')
plt.show()打印结果：
二，平滑高斯滤波,模拟人眼关注中心区域，有效去除高斯噪声
"""
高斯滤波
"""
def image_gauss():
img = cv2.imread('./data/img.png')
gauss_img = cv2.GaussianBlur(img, (7, 7),0)
plt.subplot(121)
plt.imshow(img)
plt.title('original')
plt.axis('off')
plt.subplot(122)
plt.imshow(gauss_img)
plt.title('gauss_img')
plt.axis('off')
plt.show()打印结果：
三，中值滤波，卷积域内的像素值从小到大排序,取中间值作为卷积输出,有效去除椒盐噪声
"""
中值滤波
"""
def image_median():
img = cv2.imread('./data/img1.png')
median_img = cv2.medianBlur(img,5)
plt.subplot(121)
plt.imshow(img)
plt.title('original')
plt.axis('off')
plt.subplot(122)
plt.imshow(median_img)
plt.title('medians_img')
plt.axis('off')
plt.show()打印结果：
四，高通滤波器（梯度滤波器）Sobel, and Laplacian,能够得到边缘信息。
https://docs.opencv.org/master/d5/d0f/tutorial_py_gradients.html
"""
三种高通滤波器
"""
def high_pass():
img = cv2.imread('./data/dave.png', 0)
laplacian = cv2.Laplacian(img, cv2.CV_64F)
sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
plt.subplot(2, 2, 1), plt.imshow(img, cmap='gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 2), plt.imshow(laplacian, cmap='gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 3), plt.imshow(sobelx, cmap='gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 4), plt.imshow(sobely, cmap='gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
plt.show()
五，傅里叶变换用来分析各种滤波器的频率特性，图片中的边缘点和噪声可看成是高频分量，因为变化明显，没有很大变化的就看成低频分量
https://docs.opencv.org/master/de/dbc/tutorial_py_fourier_transform.html
"""
傅利叶变换
"""
def FFT():
img = cv2.imread('./data/img3.png', 0)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20 * np.log(np.abs(fshift))
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(magnitude_spectrum, cmap='gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()
在中间部分更亮，表明低频分量多
用60×60窗口去掉低频分量
def FFT():
img = cv2.imread('./data/img3.png', 0)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
# magnitude_spectrum = 20 * np.log(np.abs(fshift))
# plt.subplot(121), plt.imshow(img, cmap='gray')
# plt.title('Input Image'), plt.xticks([]), plt.yticks([])
# plt.subplot(122), plt.imshow(magnitude_spectrum, cmap='gray')
# plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
# plt.show()
rows, cols = img.shape
crow, ccol = int(rows / 2), int(cols / 2)
fshift[crow - 30:crow + 30, ccol - 30:ccol + 30] = 0
f_ishift = np.fft.ifftshift(fshift)
img_back = np.fft.ifft2(f_ishift)
img_back = np.abs(img_back)
plt.subplot(131), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(132), plt.imshow(img_back, cmap='gray')
plt.title('Image after HPF'), plt.xticks([]), plt.yticks([])
plt.subplot(133), plt.imshow(img_back)
plt.title('Result in JET'), plt.xticks([]), plt.yticks([])
plt.show()
可见只保留了人的边缘信息，证明了中间亮的那些部分是低频分量。
六，Laplacian为啥是高通滤波器
def laplace_high_pass():
# simple averaging filter without scaling parameter
mean_filter = np.ones((3,3))
# creating a gaussian filter
x = cv2.getGaussianKernel(5,10)
gaussian = x*x.T
# different edge detecting filters
# scharr in x-direction
scharr = np.array([[-3, 0, 3],
[-10,0,10],
[-3, 0, 3]])
# sobel in x direction
sobel_x= np.array([[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1]])
# sobel in y direction
sobel_y= np.array([[-1,-2,-1],
[0, 0, 0],
[1, 2, 1]])
# laplacian
laplacian=np.array([[0, 1, 0],
[1,-4, 1],
[0, 1, 0]])
filters = [mean_filter, gaussian, laplacian, sobel_x, sobel_y, scharr]
filter_name = ['mean_filter', 'gaussian','laplacian', 'sobel_x', \
'sobel_y', 'scharr_x']
fft_filters = [np.fft.fft2(x) for x in filters]
fft_shift = [np.fft.fftshift(y) for y in fft_filters]
mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift]
for i in range(6):
plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray')
plt.title(filter_name[i]), plt.xticks([]), plt.yticks([])
plt.show()打印结果：
中间有白色的部分代表是低通滤波器，中间有黑色的部分代表是高通滤波器。