图像处理-基于特征对齐的二景图像拼接

import numpy as np

import cv2 as cv

# 导入自己写的一个工具库

import opencv_utils

MAX_FEATURES = 500

GOOD_MATCH_PERCENT = 0.15

def stitchImage(img1, img2):

    # Detect ORB features and compute descriptors.

    orb = cv.ORB_create(MAX_FEATURES)

    kp1, des1 = orb.detectAndCompute(img1, None)

    kp2, des2 = orb.detectAndCompute(img2, None)

    # Match features.

    matcher = cv.DescriptorMatcher_create(cv.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)

    matches = matcher.match(des1, des2, 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 = cv.drawMatches(img1, kp1, img2, kp2, matches, None)

    cv.imwrite("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, :] = kp1[match.queryIdx].pt

        points2[i, :] = kp2[match.trainIdx].pt

    # 通过两个图像的特征点计算变换矩阵，获得变换矩阵和掩模

    (M, mask) = cv.findHomography(points1, points2, cv.RANSAC)

    # 对 img1 透视变换，M 是变换矩阵， 变换后的大小是 (img1.w + img2.w, img1.h)

    result = cv.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0]))

    # 将 img2 的值赋给结果图像

    result[0:img2.shape[0], 0:img2.shape[1]] = img2

    return result

if __name__ == "__main__":

    img1 = cv.imread(r"F:\opencvTest\stitch_right.png")

    img2 = cv.imread(r"F:\opencvTest\stitch_left.png")

    # 顺时针旋转 90 度

    img1_90 = opencv_utils.rotateClockWise90(img1)

    img2_90 = opencv_utils.rotateClockWise90(img2)

    # 进行拼接

    stitched = stitchImage(img1_90, img2_90)

    # 结果图逆时针旋转 90 度

    stitched = opencv_utils.rotateAntiClockWise90(stitched)

    cv.imwrite('stitched.png', stitched)

    # 原图与结果图一起展示，易于对比

    result = opencv_utils.merge3Image(img1, img2, stitched)

    cv.imshow('result', result)

    cv.imwrite('result.png', result)

    cv.waitKey(0)

    cv.destroyAllWindows()

# opencv_utils.py

import cv2 as cv

import numpy as np

# 合并两张图片为一张图片

def merge2Image(src1, src2):

    if not src1.shape == src2.shape:

        print("图片的尺寸不相等")

        return

    y = src1.shape[0]

    x = src1.shape[1]

    res = np.zeros((y, x * 2, 3), dtype=src1.dtype)

    res[:, :x, :] = src1

    res[:, x:, :] = src2

    return res

# 合并三张图象为一张图像，第一张第二张上下合并，新图像再与第三张左右合并

def merge3Image(src1, src2, src3):

    if (not src1.shape == src2.shape) or (not src1.shape[0] + src2.shape[0] == src3.shape[0]):

        print("图片的尺寸不合适")

        return

    height = src1.shape[0] + src2.shape[0]

    width = src1.shape[1] + src3.shape[1]

    res = np.zeros((height, width, 3), dtype=src1.dtype)

    res[:src1.shape[0], :src1.shape[1], :] = src1

    res[src1.shape[0]:, :src2.shape[1], :] = src2

    res[:, src1.shape[1]:, :] = src3

    return res

# 顺时针旋转 90 度

def rotateClockWise90(img):

    trans_img = cv.transpose(img)

    new_img = cv.flip(trans_img, 1)

    return new_img

# 逆时针旋转 90 度

def rotateAntiClockWise90(img):

    trans_img = cv.transpose(img)

    new_img = cv.flip(trans_img, 0)

    return new_img

if __name__ == "__main__":

    src1 = cv.imread(r"F:\opencvTest\stitch_left.png")

    src2 = cv.imread(r"F:\opencvTest\stitch_right.png")

    res = merge2Image(src1, src2)

    cv.imshow("res", res)

    cv.imwrite("src_merge.png", res)

    cv.waitKey()

    cv.destroyAllWindows()