Python + opencv: Fourier transform

Machine vision 001 2020-11-19 04:50:30
python opencv fourier transform


Python+OpenCV: The Fourier transform (Fourier Transform)

####################################################################################################
# Image Fourier transform (Image Fourier Transform)
def lmc_cv_image_fourier_transform():
"""
The functionality : Image Fourier transform (Image Fourier Transform).
"""
# Fourier Transform in Numpy
image = lmc_cv.imread('D:/99-Research/Python/Image/messi.jpg', flags=lmc_cv.IMREAD_GRAYSCALE)
fft_image = np.fft.fft2(image)
fft_shift_image = np.fft.fftshift(fft_image)
magnitude_spectrum = 20 * np.log(np.abs(fft_shift_image))
pyplot.figure('Fourier Transform in Numpy')
pyplot.subplot(1, 2, 1)
pyplot.imshow(image, cmap='gray')
pyplot.title('Original Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(1, 2, 2)
pyplot.imshow(magnitude_spectrum, cmap='gray')
pyplot.title('Magnitude Spectrum')
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# high pass filtering and reconstruct the image in Numpy
rows, cols = image.shape
crow, ccol = rows // 2, cols // 2
fft_shift_image[crow - 30:crow + 31, ccol - 30:ccol + 31] = 0
fft_ishift = np.fft.ifftshift(fft_shift_image)
inverse_image_complex = np.fft.ifft2(fft_ishift)
inverse_image_real = np.real(inverse_image_complex)
pyplot.figure('HPF Fourier Transform in Numpy')
pyplot.subplot(1, 3, 1)
pyplot.imshow(image, cmap='gray')
pyplot.title('Original Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(1, 3, 2)
pyplot.imshow(inverse_image_real, cmap='gray')
pyplot.title('Image after HPF')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(1, 3, 3)
pyplot.imshow(inverse_image_real)
pyplot.title('High Pass Filtering Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Fourier Transform in OpenCV
dft_image = lmc_cv.dft(np.float32(image), flags=lmc_cv.DFT_COMPLEX_OUTPUT)
dft_shift_image = np.fft.fftshift(dft_image)
magnitude_spectrum = 20 * np.log(lmc_cv.magnitude(dft_shift_image[:, :, 0], dft_shift_image[:, :, 1]))
pyplot.figure('Fourier Transform in OpenCV')
pyplot.subplot(1, 2, 1)
pyplot.imshow(image, cmap='gray')
pyplot.title('Original Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(1, 2, 2)
pyplot.imshow(magnitude_spectrum, cmap='gray')
pyplot.title('Magnitude Spectrum')
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# low pass filtering and reconstruct the image in OpenCV
rows, cols = image.shape
crow, ccol = rows // 2, cols // 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
# apply mask and inverse DFT
fft_shift_image = dft_shift_image * mask
fft_ishift = np.fft.ifftshift(fft_shift_image)
inverse_image = lmc_cv.idft(fft_ishift)
inverse_image = lmc_cv.magnitude(inverse_image[:, :, 0], inverse_image[:, :, 1])
pyplot.figure('LPF Fourier Transform in OpenCV')
pyplot.subplot(1, 2, 1)
pyplot.imshow(image, cmap='gray')
pyplot.title('Original Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(1, 2, 2)
pyplot.imshow(inverse_image, cmap='gray')
pyplot.title('Low Pass Filtering Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Performance Optimization of DFT
print("{} {}".format(rows, cols))
nrows = lmc_cv.getOptimalDFTSize(rows)
ncols = lmc_cv.getOptimalDFTSize(cols)
print("{} {}".format(nrows, ncols))
# pad zeros method 1
new_image = np.zeros((nrows, ncols))
new_image[:rows, :cols] = image
# pad zeros method 2
right = ncols - cols
bottom = nrows - rows
bordertype = lmc_cv.BORDER_CONSTANT # just to avoid line breakup in PDF file
new_image = lmc_cv.copyMakeBorder(image, 0, bottom, 0, right, bordertype, value=0)
# calculate the DFT performance comparison of Numpy function
number = 1000
start_time = time.time()
for i in range(number):
np.fft.fft2(image)
print(f"{number} loops, best of {(time.time() - start_time) / number} ms per loop")
start_time = time.time()
for i in range(number):
np.fft.fft2(image, [nrows, ncols])
print(f"{number} loops, best of {(time.time() - start_time) / number} ms per loop")
# calculate the DFT performance comparison of OpenCV function
start_time = time.time()
for i in range(number):
lmc_cv.dft(np.float32(image), flags=lmc_cv.DFT_COMPLEX_OUTPUT)
print(f"{number} loops, best of {(time.time() - start_time) / number} ms per loop")
start_time = time.time()
for i in range(number):
lmc_cv.dft(np.float32(new_image), flags=lmc_cv.DFT_COMPLEX_OUTPUT)
print(f"{number} loops, best of {(time.time() - start_time) / number} ms per loop")
# High Pass Filter or Low Pass Filter
# simple averaging filter without scaling parameter
mean_filter = np.ones((3, 3))
# creating a gaussian filter
x = lmc_cv.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]
magnitude_spectrum = [20 * np.log(np.abs(z) + 1.00) for z in fft_shift]
pyplot.figure('High Pass Filter or Low Pass Filter')
for i in range(6):
pyplot.subplot(2, 3, i + 1)
pyplot.imshow(magnitude_spectrum[i], cmap='gray')
pyplot.title(filter_name[i])
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close('all')
return

 

 

Performance Optimization of DFT

581 739
600 750

calculate the DFT performance comparison of Numpy function:
1000 loops, best of 0.057367880821228026 ms per loop
1000 loops, best of 0.022089494943618775 ms per loop

calculate the DFT performance comparison of OpenCV function:
1000 loops, best of 0.010666451215744019 ms per loop
1000 loops, best of 0.005198104381561279 ms per loop

版权声明
本文为[Machine vision 001]所创,转载请带上原文链接,感谢

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database