如何使用python在图片的频域数据做一些处理纠正歪斜的图片

可以使用Python中的OpenCV库来对图片进行频域处理和纠偏。以下是一些步骤：

1. 导入必要的库

import cv2
import numpy as np

2. 读取图像

img = cv2.imread('image.jpg', 0)

3. 计算傅里叶变换

f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)

4. 计算幅度谱和相位谱

magnitude_spectrum = 20*np.log(np.abs(fshift))
phase_spectrum = np.angle(fshift)

5. 对幅度谱进行滤波

可以使用高通滤波器和低通滤波器来进行滤波。在这里，我们将使用一个带通滤波器来滤波幅度谱。

rows, cols = img.shape
crow, ccol = rows//2, cols//2
fshift[crow-30:crow+30, ccol-30:ccol+30] = 0

6. 计算逆傅里叶变换

ishift = np.fft.ifftshift(fshift)
iimg = np.fft.ifft2(ishift)
iimg = np.abs(iimg)

7. 将图像转换为灰度图像并进行二值化

iimg = cv2.cvtColor(np.uint8(iimg), cv2.COLOR_GRAY2BGR)
gray = cv2.cvtColor(iimg, cv2.COLOR_BGR2GRAY)
(thresh, im_bw) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

8. 使用霍夫变换进行直线检测

edges = cv2.Canny(im_bw, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

9. 计算旋转角度并旋转图像

angle = np.rad2deg(lines[0][0][1]) - 90
rotated = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
rotated = cv2.rotate(rotated, cv2.ROTATE_90_CLOCKWISE)
M = cv2.getRotationMatrix2D((cols/2, rows/2), angle, 1)
rotated = cv2.warpAffine(rotated, M, (cols, rows))

最后，将旋转后的图像保存到文件中。

完整代码示例：

import cv2
import numpy as np

img = cv2.imread('image.jpg', 0)

f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)

magnitude_spectrum = 20*np.log(np.abs(fshift))
phase_spectrum = np.angle(fshift)

rows, cols = img.shape
crow, ccol = rows//2, cols//2
fshift[crow-30:crow+30, ccol-30:ccol+30] = 0

ishift = np.fft.ifftshift(fshift)
iimg = np.fft.ifft2(ishift)
iimg = np.abs(iimg)

iimg = cv2.cvtColor(np.uint8(iimg), cv2.COLOR_GRAY2BGR)
gray = cv2.cvtColor(iimg, cv2.COLOR_BGR2GRAY)
(thresh, im_bw) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

edges = cv2.Canny(im_bw, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

angle = np.rad2deg(lines[0][0][1]) - 90
rotated = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
rotated = cv2.rotate(rotated, cv2.ROTATE_90_CLOCKWISE)
M = cv2.getRotationMatrix2D((cols/2, rows/2), angle, 1)
rotated = cv2.warpAffine(rotated, M, (cols, rows))

cv2.imwrite('corrected.jpg', rotated)