python计算机视觉——全景拼接

hello，大家好。今天要讲的是python全景拼接。
一、这里先介绍一下图片全景拼接的原理：
要实现两张图片的简单拼接，其实只需找出两张图片中相似的点 (至少四个，因为 homography 矩阵的计算需要至少四个点)，计算一张图片可以变换到另一张图片的变换矩阵 (homography 单应性矩阵)，用这个矩阵把那张图片变换后放到另一张图片相应的位置 ( 就是相当于把两张图片中定好的四个相似的点給重合在一起)。如此，就可以实现简单的全景拼接。当然，因为拼合之后图片会重叠在一起，所以需要重新计算图片重叠部分的像素值，否则结果会很难看。所以总结起来其实就两个步骤：

找两张图片中相似的点，计算变换矩阵
变换一张图片放到另一张图片合适的位置，并计算重叠区域新的像素值 (这里就是图片融合所需要采取的策略)

代码如下：

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

if __name__ == '__main__':
    top, bot, left, right = 100, 100, 0, 500
    img1 = cv.imread('image/5.jpg')
    img2 = cv.imread('image/6.jpg')
    srcImg = cv.copyMakeBorder(img1, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
    testImg = cv.copyMakeBorder(img2, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
    img1gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)
    img2gray = cv.cvtColor(testImg, cv.COLOR_BGR2GRAY)
    sift = cv.xfeatures2d_SIFT().create()
    # find the keypoints and descriptors with SIFT
    kp1, des1 = sift.detectAndCompute(img1gray, None)
    kp2, des2 = sift.detectAndCompute(img2gray, None)
    # FLANN parameters
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)
    flann = cv.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1, des2, k=2)

    # Need to draw only good matches, so create a mask
    matchesMask = [[0, 0] for i in range(len(matches))]

    good = []
    pts1 = []
    pts2 = []
    # ratio test as per Lowe's paper
    for i, (m, n) in enumerate(matches):
        if m.distance < 0.7 * n.distance:
            good.append(m)
            pts2.append(kp2[m.trainIdx].pt)
            pts1.append(kp1[m.queryIdx].pt)
            matchesMask[i] = [1, 0]

    draw_params = dict(matchColor=(0, 255, 0),
                       singlePointColor=(255, 0, 0),
                       matchesMask=matchesMask,
                       flags=0)
    img3 = cv.drawMatchesKnn(img1gray, kp1, img2gray, kp2, matches, None, **draw_params)
    plt.imshow(img3, ), plt.show()

    rows, cols = srcImg.shape[:2]
    MIN_MATCH_COUNT = 10
    if len(good) > MIN_MATCH_COUNT:
        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
        M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
        warpImg = cv.warpPerspective(testImg, np.array(M), (testImg.shape[1], testImg.shape[0]),
                                     flags=cv.WARP_INVERSE_MAP)

        for col in range(0, cols):
            if srcImg[:, col].any() and warpImg[:, col].any():
                left = col
                break
        for col in range(cols - 1, 0, -1):
            if srcImg[:, col].any() and warpImg[:, col].any():
                right = col
                break

        res = np.zeros([rows, cols, 3], np.uint8)
        for row in range(0, rows):
            for col in range(0, cols):
                if not srcImg[row, col].any():
                    res[row, col] = warpImg[row, col]
                elif not warpImg[row, col].any():
                    res[row, col] = srcImg[row, col]
                else:
                    srcImgLen = float(abs(col - left))
                    testImgLen = float(abs(col - right))
                    alpha = srcImgLen / (srcImgLen + testImgLen)
                    res[row, col] = np.clip(srcImg[row, col] * (1 - alpha) + warpImg[row, col] * alpha, 0, 255)

        # opencv is bgr, matplotlib is rgb
        res = cv.cvtColor(res, cv.COLOR_BGR2RGB)
        # show the result
        plt.figure()
        plt.imshow(res)
        plt.show()
    else:
        print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
        matchesMask = None

二、接下来针对不同的场景实现图片的全景拼接
1、室内场景
原图1：
python计算机视觉——全景拼接
原图2：

拼接后的图片：

2、室外景深落差较小的场景
原图1：

原图2：
拼接后的图片：

总结：从实验结果可以看出该方法对室内场景的拼接效果比室外场景更好。明天博主将会对室外景深落差较大的图片进行拼接，观察拼接结果。

python计算机视觉——全景拼接

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