使用 OpenCV Python 查找图像之间的差异

Question

如何检测下面两张图片之间的差异？

我尝试对 2 个图像设置阈值并应用按位异或来查找差异，但仍然无法获得我正在寻找的结果。

图1

图2

Answer 1

您遇到的问题是，在进行差异异或之前，您的图像未对齐。这是在 Python/OpenCV 中使用 ORB 特征匹配来处理该问题的一种方法。

输入1：

输入2：

import cv2
import numpy as np
 
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15
  
def alignImages(im1, im2):

  # im2 is reference and im1 is to be warped to match im2
  # note: numbering is swapped in function
 
  # 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("circuit_matches.png", 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 = "circuit1.jpg"
  print("Reading reference image : ", refFilename)
  imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)
  hh, ww = imReference.shape[:2]
  
  # Read image to be aligned
  imFilename = "circuit2.jpg"
  print("Reading image to align : ", imFilename);  
  im = cv2.imread(imFilename, cv2.IMREAD_COLOR)
   
  # Aligned image will be stored in imReg. 
  # The estimated homography will be stored in h. 
  imReg, h = alignImages(im, imReference)
   
  # Print estimated homography
  print("Estimated homography : \n",  h)
  
  # Convert images to HSV and get saturation channel
  refSat = cv2.cvtColor(imReference, cv2.COLOR_BGR2HSV)[:,:,1]
  imSat = cv2.cvtColor(imReg, cv2.COLOR_BGR2HSV)[:,:,1]

  # Otsu threshold
  refThresh = cv2.threshold(refSat, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
  imThresh = cv2.threshold(imSat, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

  # apply morphology open and close
  kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
  refThresh = cv2.morphologyEx(refThresh, cv2.MORPH_OPEN, kernel, iterations=1)
  refThresh = cv2.morphologyEx(refThresh, cv2.MORPH_CLOSE, kernel, iterations=1).astype(np.float64)
  imThresh = cv2.morphologyEx(imThresh, cv2.MORPH_OPEN, kernel, iterations=1).astype(np.float64)
  imThresh = cv2.morphologyEx(imThresh, cv2.MORPH_CLOSE, kernel, iterations=1)
  
  # get absolute difference between the two thresholded images
  diff = np.abs(cv2.add(imThresh,-refThresh))
  
  # apply morphology open to remove small regions caused by slight misalignment of the two images
  kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (12,12))
  diff_cleaned = cv2.morphologyEx(diff, cv2.MORPH_OPEN, kernel, iterations=1).astype(np.uint8)

  # Filter using contour area and draw bounding boxes that do not touch the sides of the image
  cnts = cv2.findContours(diff_cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  cnts = cnts[0] if len(cnts) == 2 else cnts[1]
  result = imReference.copy()
  for c in cnts:
      x,y,w,h = cv2.boundingRect(c)
      if x>0 and y>0 and x+w<ww-1 and y+h<hh-1:
        cv2.rectangle(result, (x, y), (x+w, y+h), (0, 0, 255), 2)

  # save images
  cv2.imwrite('circuit2_aligned.jpg', imReg)
  cv2.imwrite('circuit_diff.png', diff_cleaned)
  cv2.imwrite('circuit_result.png', result)

 # show images
  cv2.imshow('reference', imReference)
  cv2.imshow('image', im)
  cv2.imshow('image_aligned', imReg)
  cv2.imshow('refThresh', refThresh)
  cv2.imshow('imThresh', imThresh)
  cv2.imshow('diff', diff)
  cv2.imshow('diff_cleaned', diff_cleaned)
  cv2.imshow('result', result)
  cv2.waitKey()

ORB 比赛地点：

图像 2 与图像 1 对齐：

阈值差异：

显示差异区域的结果：