【课程作业】直方图增强及频域图像处理技术_针对一幅数字图像,分别进行直方图均衡,空间域增强,频域增强。(作业里要体现出

绘制直方图

实现代码

import cv2 
import numpy as np 
img=cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png") 
hist = cv2.calcHist([img], [0], None, [256], [0,255]) 
print(type(hist)) 
print(hist.shape) 
print(hist.size) 
print(hist) 
# 下面是绘制直方图图像的代码
import cv2
import numpy as np
import matplotlib.pyplot as plt
src = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png") 
histb = cv2.calcHist([src], [0], None, [256], [0,255])
histg = cv2.calcHist([src], [1], None, [256], [0,255])
histr = cv2.calcHist([src], [2], None, [256], [0,255])
cv2.imshow("src", src)
plt.plot(histb, color='b')
plt.plot(histg, color='g')
plt.plot(histr, color='r')
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

实现效果

直方图均衡化

实现代码

import cv2
import matplotlib.pyplot as plt
import numpy as np
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")
# img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#-----------直方图均衡化处理---------------
equ = cv2.equalizeHist(img[:,:,0])
#-----------显示均衡化前后的图像---------------
cv2.imshow("original", img[:,:,0])
cv2.imshow("result", equ)
#-----------显示均衡化前后的直方图---------------
plt.figure("原始图像直方图") 
plt.hist(img.ravel(),256)
plt.figure("均衡化结果直方图") 
plt.hist(equ.ravel(),256)
plt.show()
cv2.waitKey()
cv2.destroyAllWindows()

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

实现效果

Numpy 实现傅里叶变换

实现代码

import numpy as np
import cv2
from matplotlib import pyplot as plt
import cv2 as cv
from matplotlib import pyplot as plt
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")[:,:,0]
#快速傅里叶变换算法得到频率分布
f = np.fft.fft2(img)
#默认结果中心点位置是在左上角,
#调用 fftshift()函数转移到中间位置
fshift = np.fft.fftshift(f) 
#fft 结果是复数, 其绝对值结果是振幅
fimg = np.log(np.abs(fshift))
#展示结果
plt.subplot(121), plt.imshow(img, 'gray'), plt.title('Original Fourier')
plt.axis('off')
plt.subplot(122), plt.imshow(fimg, 'gray'), plt.title('Fourier Fourier')
plt.axis('off')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

实现效果

OpenCV 傅里叶变换

实现代码

import numpy as np
import cv2
from matplotlib import pyplot as plt
#读取图像
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")
img = img[:,:,0]
#傅里叶变换
dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
#将频谱低频从左上角移动至中心位置
dft_shift = np.fft.fftshift(dft)
#频谱图像双通道复数转换为 0-255 区间
result = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))
#显示图像
plt.subplot(121), plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(result, cmap = 'gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

实现效果

Numpy 实现傅里叶逆变换

实现代码

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")[:,:,0]
#傅里叶变换
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
res = np.log(np.abs(fshift))
#傅里叶逆变换
ishift = np.fft.ifftshift(fshift)
iimg = np.fft.ifft2(ishift)
iimg = np.abs(iimg)
#展示结果
plt.subplot(131), plt.imshow(img, 'gray'), plt.title('Original Image')
plt.axis('off')
plt.subplot(132), plt.imshow(res, 'gray'), plt.title('Fourier Image')
plt.axis('off')
plt.subplot(133), plt.imshow(iimg, 'gray'), plt.title('Inverse Fourier Image')
plt.axis('off')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

实现效果

OpenCV 实现傅里叶逆变换

实现代码

import numpy as np
import cv2
from matplotlib import pyplot as plt
#读取图像
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")[:,:,0]
#傅里叶变换
dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
dftshift = np.fft.fftshift(dft)
res1= 20*np.log(cv2.magnitude(dftshift[:,:,0], dftshift[:,:,1]))
#傅里叶逆变换
ishift = np.fft.ifftshift(dftshift)
iimg = cv2.idft(ishift)
res2 = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
#显示图像
plt.subplot(131), plt.imshow(img, 'gray'), plt.title('Original Image')
plt.axis('off')
plt.subplot(132), plt.imshow(res1, 'gray'), plt.title('Fourier Image')
plt.axis('off')
plt.subplot(133), plt.imshow(res2, 'gray'), plt.title('Inverse Fourier Image')
plt.axis('off')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

实现效果

理想低通滤波器

实现代码

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")[:,:,0]
#傅里叶变换
dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
fshift = np.fft.fftshift(dft)
#设置低通滤波器
rows, cols = img.shape
crow,ccol = int(rows/2), int(cols/2) #中心位置
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 1
#掩膜图像和频谱图像乘积
f = fshift * mask
print (f.shape, fshift.shape, mask.shape)
#傅里叶逆变换
ishift = np.fft.ifftshift(f)
iimg = cv2.idft(ishift)
res = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
#显示原始图像和低通滤波处理图像
plt.subplot(121), plt.imshow(img, 'gray'), plt.title('Original Image')
plt.axis('off')
plt.subplot(122), plt.imshow(res, 'gray'), plt.title('Result Image')
plt.axis('off')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

实现效果

观察发现，存在振铃现象

理想高通滤波

实现代码

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread("D:/Project/PythonProject/GraphicAnalysis/class4/lena.png")[:,:,0]
#傅里叶变换
dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
fshift = np.fft.fftshift(dft)
#设置高通滤波器
rows, cols = img.shape
crow,ccol = int(rows/2), int(cols/2) #中心位置
mask = np.ones((rows, cols, 2), np.uint8)
mask[crow-80:crow+80, ccol-80:ccol+80] = 0
#掩膜图像和频谱图像乘积
f = fshift * mask
print (f.shape, fshift.shape, mask.shape)
#傅里叶逆变换
ishift = np.fft.ifftshift(f)
iimg = cv2.idft(ishift)
res = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
#显示原始图像和低通滤波处理图像
plt.subplot(131), plt.imshow(img, 'gray'), plt.title('Original Image')
plt.axis('off')
plt.subplot(132), plt.imshow(res, 'gray'), plt.title('Result Image')
plt.axis('off')
plt.subplot(133), plt.imshow(mask[:,:,1], 'gray'), plt.title('mask')
plt.axis('off')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

实现效果