Perspective correction in OpenCV using python

Question

I am trying to do a perspective correction of a tilted rectangle ( a credit card), which is tilted in all the 4 directions. I could find its four corners and the respective

Answer 1

I have a better solution which is much easy:

• The red rectangle on original image and the corners points of the rectangle are source points

• We use cv2.getPerspectiveTransform(src, dst) that takes source points and destination points as arguments and returns the transformation matrix which transforms any image to destination image as show in the diagram

• We use this transformation matrix in cv2.warpPerspective()
  - As you can see results are better. You get a very nice bird view of the image

  import cv2
  import matplotlib.pyplot as plt
  import numpy as np
  
  
  def unwarp(img, src, dst, testing):
      h, w = img.shape[:2]
      # use cv2.getPerspectiveTransform() to get M, the transform matrix, and Minv, the inverse
      M = cv2.getPerspectiveTransform(src, dst)
      # use cv2.warpPerspective() to warp your image to a top-down view
      warped = cv2.warpPerspective(img, M, (w, h), flags=cv2.INTER_LINEAR)
  
      if testing:
          f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
          f.subplots_adjust(hspace=.2, wspace=.05)
          ax1.imshow(img)
          x = [src[0][0], src[2][0], src[3][0], src[1][0], src[0][0]]
          y = [src[0][1], src[2][1], src[3][1], src[1][1], src[0][1]]
          ax1.plot(x, y, color='red', alpha=0.4, linewidth=3, solid_capstyle='round', zorder=2)
          ax1.set_ylim([h, 0])
          ax1.set_xlim([0, w])
          ax1.set_title('Original Image', fontsize=30)
          ax2.imshow(cv2.flip(warped, 1))
          ax2.set_title('Unwarped Image', fontsize=30)
          plt.show()
      else:
          return warped, M
  im = cv2.imread("so.JPG")
  w, h = im.shape[0], im.shape[1]
  # We will first manually select the source points 
  # we will select the destination point which will map the source points in
  # original image to destination points in unwarped image
  src = np.float32([(20,     1),
                    (540,  130),
                    (20,    520),
                    (570,  450)])
  
  dst = np.float32([(600, 0),
                    (0, 0),
                    (600, 531),
                    (0, 531)])
  
  unwarp(im, src, dst, True)
  
  cv2.imshow("so", im)
  cv2.waitKey(0)[![enter image description here][1]][1]
  cv2.destroyAllWindows()