python - 特征对齐 ORB 过程失败

Question

我正在尝试对齐无人机拍摄的几张图像，结合热成像和 RGB 相机。在做 ORB 对齐和计算单应矩阵之后，我调用warpPerspective方法，结果总是一个模糊的图像。我尝试了数百张图片，结果几乎相同。

我尝试将 MAX_FEATURES 提高到 1000、10000、10000，结果始终相同。

为什么warpPerspective会产生这种结果的任何想法？也许单应矩阵是错误的？

先感谢您

热像图

WarpPerspective 结果

另一个 WarpPerspective 结果

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 = "form.jpg"

  print("Reading reference image : ", refFilename)

  imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)

  # Read image to be aligned

  imFilename = "scanned-form.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)

标签： pythonc++image-processingorb