机器视觉：基于特征的图像对齐（使用opencv和python）

from __future__ import print_function
import cv2
import numpy as np


MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15


def alignImages(im1, im2):

  # Convert images to grayscale
  im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
  im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)

  # Detect ORB features and compute descriptors.
  orb = cv2.ORB_create(MAX_FEATURES)
  keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
  keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)

  # Match features.
  matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
  matches = matcher.match(descriptors1, descriptors2, None)

  # Sort matches by score
  matches.sort(key=lambda x: x.distance, reverse=False)

  # Remove not so good matches
  numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
  matches = matches[:numGoodMatches]

  # Draw top matches
  imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
  cv2.imwrite("matches.jpg", imMatches)

  # Extract location of good matches
  points1 = np.zeros((len(matches), 2), dtype=np.float32)
  points2 = np.zeros((len(matches), 2), dtype=np.float32)

  for i, match in enumerate(matches):
    points1[i, :] = keypoints1[match.queryIdx].pt
    points2[i, :] = keypoints2[match.trainIdx].pt

  # Find homography
  h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)

  # Use homography
  height, width, channels = im2.shape
  im1Reg = cv2.warpPerspective(im1, h, (width, height))

  return im1Reg, h


if __name__ == '__main__':

  # Read reference image
  refFilename = "D:/mywindows/Python Sample/target.jpg"
  print("Reading reference image : ", refFilename)
  imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)

  # Read image to be aligned
  imFilename = "D:/mywindows/Python Sample/to_be_align.jpg"
  print("Reading image to align : ", imFilename);  
  im = cv2.imread(imFilename, cv2.IMREAD_COLOR)

  print("Aligning images ...")
  # Registered image will be resotred in imReg. 
  # The estimated homography will be stored in h. 
  imReg, h = alignImages(im, imReference)

  # Write aligned image to disk. 
  outFilename = "aligned.jpg"
  print("Saving aligned image : ", outFilename); 
  cv2.imwrite(outFilename, imReg)

  # Print estimated homography
  print("Estimated homography : \n",  h)

  

什么是图像对齐或者图像配准

图像对齐(或者图像配准)可以扭曲旋转（其实是仿射变换）一张图使它和另一个图可以很完美的对齐。 
下面是一个例子，中间的表在经过图像对齐技术处理之后，可以和左边的模板一样。对齐之后就可以根据模板的格式对用户填写的内容进行分析了。

图像对齐的应用场景

上面说的表单分析就是，先把拍摄照向模板对齐，易于下一步处理。

在一些医学应用中，可能需要把多次拍摄的照片拼接起来。

最有意思的应用应该是合成全景照片。

图像对齐：基础理论

图像对齐技术的核心是一种维数3X3的单应性矩阵（Homography ）。

 

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