OpenCV图像匹配与拼接：使用ORB和FLANN进行特征点匹配并解决图像大小不一致问题

使用OpenCV进行图像匹配与拼接

本文将使用OpenCV库实现图像匹配和拼接，并解决图像大小不一致导致拼接错误的问题。

1. 读取图像和特征点提取

import cv2

# 读取图像
img1 = cv2.imread('1.png')
img2 = cv2.imread('2.png')
img3 = cv2.imread('3.png')
img4 = cv2.imread('4.png')

# 创建ORB对象
orb = cv2.ORB_create()

# 检测特征点并提取描述符
keypoints1, descriptors1 = orb.detectAndCompute(img1, None)
keypoints2, descriptors2 = orb.detectAndCompute(img2, None)
keypoints3, descriptors3 = orb.detectAndCompute(img3, None)
keypoints4, descriptors4 = orb.detectAndCompute(img4, None)

2. 使用FLANN匹配器进行特征点匹配

# 创建FLANN匹配器对象
FLANN_INDEX_LSH = 6
index_params = dict(algorithm=FLANN_INDEX_LSH, table_number=6, key_size=12, multi_probe_level=1)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)

# 对图像1和图像2的特征点进行匹配
matches12 = flann.knnMatch(descriptors1, descriptors2, k=2)
good_matches12 = []
for match in matches12:
    if len(match)<2:
        continue
    m,n = match
    if m.distance < 0.7 * n.distance:
        good_matches12.append(m)

# 对图像2和图像3的特征点进行匹配
matches23 = flann.knnMatch(descriptors2, descriptors3, k=2)
good_matches23 = []
for m, n in matches23:
    if m.distance < 0.7 * n.distance:
        good_matches23.append(m)

# 对图像3和图像4的特征点进行匹配
matches34 = flann.knnMatch(descriptors3, descriptors4, k=2)
good_matches34 = []
for m, n in matches34:
    if m.distance < 0.7 * n.distance:
        good_matches34.append(m)

# 对图像1和图像4进行匹配
matches14 = flann.knnMatch(descriptors1, descriptors4, k=2)
good_matches14 = []
for m, n in matches14:
    if m.distance < 0.7 * n.distance:
        good_matches14.append(m)

3. 解决图像大小不一致问题

如果四张图像的大小不一致，直接使用cv2.hconcat()进行拼接会报错。因此需要使用cv2.resize()函数将图像大小统一。

# 获取四张图像的大小
img1_size = img1.shape[:2]
img2_size = img2.shape[:2]
img3_size = img3.shape[:2]
img4_size = img4.shape[:2]

# 计算四张图像中最大的宽度和高度
max_width = max(img1_size[1], img2_size[1], img3_size[1], img4_size[1])
max_height = max(img1_size[0], img2_size[0], img3_size[0], img4_size[0])

# 使用cv2.resize()函数将四张图像大小统一
img1_resized = cv2.resize(img1, (max_width, max_height))
img2_resized = cv2.resize(img2, (max_width, max_height))
img3_resized = cv2.resize(img3, (max_width, max_height))
img4_resized = cv2.resize(img4, (max_width, max_height))

4. 绘制匹配结果

# 在绘制匹配结果时，使用resize后的图像进行绘制
img_matches12 = cv2.drawMatches(img1_resized, keypoints1, img2_resized, keypoints2, good_matches12, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
img_matches23 = cv2.drawMatches(img2_resized, keypoints2, img3_resized, keypoints3, good_matches23, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
img_matches34 = cv2.drawMatches(img3_resized, keypoints3, img4_resized, keypoints4, good_matches34, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
img_matches14 = cv2.drawMatches(img1_resized, keypoints1, img4_resized, keypoints4, good_matches14, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

5. 图像拼接

# 将四张resize后的图像拼接起来
img_concat = cv2.hconcat([img_matches12, img_matches23, img_matches34, img_matches14])

# 显示拼接后的图像
cv2.imshow('Matches', img_concat)
cv2.waitKey(0)

总结

本文介绍了使用OpenCV进行图像匹配和拼接的方法，包括特征点检测、描述符提取、特征点匹配和解决图像大小不一致问题。通过本文的学习，可以更好地理解图像匹配和拼接的原理，并应用于实际项目中。