opencv 脚本不断被杀死

Question

我有以下 Python / OpenCV 代码，它应该获取填写好的文档 (new.png)，将其与参考文档 (ref.png) 对齐并将结果放入 output.png。 不幸的是，有时当我运行它时，我会在 bash 中收到一条“Killed”消息。我猜Python 会在资源耗尽时执行此操作，所以也许我的代码编写效率低下并且可以改进？

这是我的代码：

import sys
import cv2
import numpy as np

if len(sys.argv) != 4:
  print('USAGE')
  print('  python3 diff.py ref.png new.png output.png')
  sys.exit()

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.AKAZE_create()
  #orb = cv2.ORB_create(500)
  keypoints1, descriptors1 = orb.detectAndCompute(im1, None)
  keypoints2, descriptors2 = orb.detectAndCompute(im2, 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)
  print(matches[numGoodMatches].distance)
  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 = im2.shape
  im1Reg = cv2.warpPerspective(im1, h, (width, height))

  return im1Reg, h

def removeOverlap(refBW, newBW):
  # invert each
  refBW = 255 - refBW
  newBW = 255 - newBW

  # get absdiff
  xor = cv2.absdiff(refBW, newBW)

  result = cv2.bitwise_and(xor, newBW)

  # invert
  result = 255 - result

  return result

def offset(img, xOffset, yOffset):
  # The number of pixels
  num_rows, num_cols = img.shape[:2]

  # Creating a translation matrix
  translation_matrix = np.float32([ [1,0,xOffset], [0,1,yOffset] ])

  # Image translation
  img_translation = cv2.warpAffine(img, translation_matrix, (num_cols,num_rows), borderValue = (255,255,255))

  return img_translation


# the ink will often bleed out on printouts ever so slightly
# to eliminate that we'll apply a "jitter" of sorts

refFilename = sys.argv[1]
imFilename = sys.argv[2]
outFilename = sys.argv[3]

imRef = cv2.imread(refFilename, cv2.IMREAD_GRAYSCALE)
im = cv2.imread(imFilename, cv2.IMREAD_GRAYSCALE)

imNew, h = alignImages(im, imRef)

refBW = cv2.threshold(imRef, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
newBW = cv2.threshold(imNew, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

for x in range (-2, 2):
  for y in range (-2, 2):
    newBW = removeOverlap(offset(refBW, x, y), newBW)

cv2.imwrite(outFilename, newBW)

有什么明显的方法可以提高我的程序速度吗？

我正在运行 3.5.3 和 OpenCV 4.4.0。 uname -r返回 4.14.301-224.520.amzn2.x86_64 (Linux)

有任何想法吗？

Answer 1

由于您没有提供任何测试图像，所以我无法测试性能，但是使用 FLANN 进行 NN 搜索和使用 ORB 进行匹配应该会更快。

 def align_images(im1, im2):
  # Convert images to grayscale
  im1_gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
  im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)

  orb = cv2.ORB_create(500)
  keypoints1, descriptors1 = orb.detectAndCompute(im1_gray, None)
  keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None)

  # Use FLANN since it's probably will be faster.
  index_params = dict(algorithm = 6, table_number = 6, key_size = 12, multi_probe_level = 1)
  search_params = dict(checks = 500)
  flann = cv2.FlannBasedMatcher(index_params, search_params)
  matches = flann.knnMatch(descriptors1, descriptors2, k = 2)

  good_matches = []
  for m, n in matches:
    if m.distance < GOOD_MATCH_PERCENT * n.distance:
      good_matches.append(m)
  good_matches.sort(key = lambda x: x.distance, reverse = False)
# rest of the function