Python-OpenCV：图像的全景拼接融合以及图片对齐处理

Opencv对图片切割和对图片对齐处理

以下原文转自：（作者： Wimb）
Python-OpenCV基础：图像的全景拼接

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('2.png')
    img2 = cv.imread('1.png')
    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

Opencv图片对齐（匹配）

#!usr/bin/python3
# -*-coding:utf-8-*-

from __future__ import print_function
import cv2
import numpy as np

# MAX_MATCHES = 500
MAX_MATCHES = 700
# MAX_MATCHES = 10000
# MAX_MATCHES = 15000
GOOD_MATCH_PERCENT = 0.25


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_MATCHES)
    # orb =  cv2.xfeatures2SURF_create()
    # orb = cv2.xfeatures2d.SURF_create()
    # orb = cv2.AKAZE_create(MAX_MATCHES)
    # detector = cv2.AKAZE_create(MAX_MATCHES)
    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)
    print(matches)

    # 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("./image_align/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 = "../success_image/good_img.jpg"
    # refFilename = "../staticimg/base.png"
    refFilename = "../success_images/001_success.jpg"
    # refFilename = "../staticimg/100_success.jpg"
    print("Reading reference image : ", refFilename)
    imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)

    # Read image to be aligned
    imFilename = "../staticimg/oldimg_04.jpg"
    # imFilename = "../cut_labels/cut_image.jpg"
    #

    # def point2area(points, img, color):
    #     """
    #     :param points: 点集合
    #     :param img: 图片位置
    #     :param color: BGR三色
    #     :return:将图片上点包围的区域涂上颜色
    #     """
    #     img = cv2.imread(img)
    #     res = cv2.fillPoly(img, [np.array(points)], color)
    #     cv2.imshow('fillpoly', res)
    #     cv2.waitKey(0)
    #     cv2.destroyAllWindows()
    #
    #
    # if __name__ == '__main__':
    #     points = [(20, 20), (70, 70), (120, 200)]
    #     img = 'lena.png'
    #     color = [255, 255, 255]
    #     point2area(points, img, color)


    # imFilename = "../staticimg/111.jpg"
    # imFilename = "../staticimg/100_success.jpg"
    print("Reading image to align : ", imFilename);
    im = cv2.imread(imFilename, cv2.IMREAD_COLOR)
    pointsone = [(350, 0),  (512, 205), (512, 0)]
    pointstwo = [(0, 0),  (175, 0), (0, 234)]
    # points = [(20, 20), (70, 70), (120, 200)]
    color = [0, 0, 0]
    res = cv2.fillPoly(im, [np.array(pointsone)], color)
    res = cv2.fillPoly(im, [np.array(pointstwo)], color)
    pointsthree = [(345, 0), (512, 270), (512, 0)]
    pointsfour = [(0, 0), (170, 0), (0, 260)]
    res = cv2.fillPoly(imReference, [np.array(pointsthree)], color)
    res = cv2.fillPoly(imReference, [np.array(pointsfour)], 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 = "./image_align/aligned.jpg"
    print("Saving aligned image : ", outFilename);
    cv2.imwrite(outFilename, imReg)

    # Print estimated homography
    print("Estimated homography : \n", h)

matches.jpg