如何使用python在图片的频域做一些处理用于纠正歪斜的图片
时间: 2024-04-30 08:18:59 浏览: 16
可以使用Python的NumPy和OpenCV库进行图片的频域处理和纠正。具体步骤如下:
1. 加载图片并转为灰度图像
```python
import cv2
img = cv2.imread('image.jpg', 0)
```
2. 对图像进行傅里叶变换
```python
import numpy as np
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20 * np.log(np.abs(fshift))
```
3. 找到频域中的主要方向
```python
rows, cols = img.shape
crow, ccol = rows // 2, cols // 2
magnitude_spectrum[crow - 30: crow + 30, ccol - 30: ccol + 30] = 0
orientation_map = np.arctan2(np.imag(fshift), np.real(fshift))
orientation_map[orientation_map < 0] += np.pi
histogram = np.zeros(np.pi, dtype=np.float32)
for i in range(rows):
for j in range(cols):
histogram[int(orientation_map[i, j] * 180 / np.pi)] += magnitude_spectrum[i, j]
orientation = np.argmax(histogram) * np.pi / 180
```
4. 旋转图像
```python
M = cv2.getRotationMatrix2D((cols // 2, rows // 2), orientation * 180 / np.pi, 1)
img_corrected = cv2.warpAffine(img, M, (cols, rows))
```
完整代码如下:
```python
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))
rows, cols = img.shape
crow, ccol = rows // 2, cols // 2
magnitude_spectrum[crow - 30: crow + 30, ccol - 30: ccol + 30] = 0
orientation_map = np.arctan2(np.imag(fshift), np.real(fshift))
orientation_map[orientation_map < 0] += np.pi
histogram = np.zeros(np.pi, dtype=np.float32)
for i in range(rows):
for j in range(cols):
histogram[int(orientation_map[i, j] * 180 / np.pi)] += magnitude_spectrum[i, j]
orientation = np.argmax(histogram) * np.pi / 180
M = cv2.getRotationMatrix2D((cols // 2, rows // 2), orientation * 180 / np.pi, 1)
img_corrected = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow('Original Image', img)
cv2.imshow('Frequency Domain', magnitude_spectrum.astype(np.uint8))
cv2.imshow('Corrected Image', img_corrected)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
运行后可以看到三张图片:原始图片、频域图像和纠正后的图片。