OpenCV—python 基于傅里叶变换的图像矫正

1. 基于傅里叶变换的图像矫正

2. import cv2
   import numpy as np
   import math
   
   def fourier_demo():
       #1、灰度化读取文件，
       img = cv2.imread('english_rotation.jpg',0)
   
       #2、图像延扩
       h, w = img.shape[:2]
       new_h = cv2.getOptimalDFTSize(h)
       new_w = cv2.getOptimalDFTSize(w)
       right = new_w - w
       bottom = new_h - h
       nimg = cv2.copyMakeBorder(img, 0, bottom, 0, right, borderType=cv2.BORDER_CONSTANT, value=0)
       cv2.imshow('new image', nimg)
   
       #3、执行傅里叶变换，并过得频域图像
       f = np.fft.fft2(nimg)
       fshift = np.fft.fftshift(f)
       magnitude = np.log(np.abs(fshift))
   
   
       #二值化
       magnitude_uint = magnitude.astype(np.uint8)
       ret, thresh = cv2.threshold(magnitude_uint, 11, 255, cv2.THRESH_BINARY)
       print(ret)
   
       cv2.imshow('thresh', thresh)
       print(thresh.dtype)
       #霍夫直线变换
       lines = cv2.HoughLinesP(thresh, 2, np.pi/180, 30, minLineLength=40, maxLineGap=100)
       print(len(lines))
   
       #创建一个新图像，标注直线
       lineimg = np.ones(nimg.shape,dtype=np.uint8)
       lineimg = lineimg * 255
   
       piThresh = np.pi/180
       pi2 = np.pi/2
       print(piThresh)
   
       for line in lines:
           x1, y1, x2, y2 = line[0]
           cv2.line(lineimg, (x1, y1), (x2, y2), (0, 255, 0), 2)
           if x2 - x1 == 0:
               continue
           else:
               theta = (y2 - y1) / (x2 - x1)
           if abs(theta) < piThresh or abs(theta - pi2) < piThresh:
               continue
           else:
               print(theta)
   
       angle = math.atan(theta)
       print(angle)
       angle = angle * (180 / np.pi)
       print(angle)
       angle = (angle - 90)/(w/h)
       print(angle)
   
       center = (w//2, h//2)
       M = cv2.getRotationMatrix2D(center, angle, 1.0)
       rotated = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
       cv2.imshow('line image', lineimg)
       cv2.imshow('rotated', rotated)
   
   fourier_demo()
   cv2.waitKey(0)
   cv2.destroyAllWindows()
   
   

    

3. 结果图像