图像处理-基于特征的图像对齐

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)