SIFT特征匹配：图像的"指纹识别"技术

pip install opencv-python matplotlib

# 导入需要的库
import cv2
import numpy as np
import matplotlib.pyplot as plt

# 读取图像
img1 = cv2.imread('images/box.png')
img2 = cv2.imread('images/box_in_scene.png')

# 转换为灰度图像
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

# 创建SIFT对象
# 注意：在OpenCV 4.4.0及以上版本中，SIFT已移至cv2.SIFT_create()
sift = cv2.SIFT_create()

# 检测特征点并计算描述符
kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)

# 可视化特征点
img1_keypoints = cv2.drawKeypoints(gray1, kp1, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
img2_keypoints = cv2.drawKeypoints(gray2, kp2, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# 使用FLANN匹配器进行特征匹配
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)  # 检查次数越多，匹配越准确，但速度越慢
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# 应用Lowe's比率测试筛选良好的匹配点
good_matches = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)

# 绘制匹配结果
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None,
                              flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# 显示原图、特征点和匹配结果
plt.figure(figsize=(20, 8))
plt.subplot(141)
plt.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
plt.title('模板图像')
plt.axis('off')

plt.subplot(142)
plt.imshow(cv2.cvtColor(img1_keypoints, cv2.COLOR_BGR2RGB))
plt.title('模板图像特征点')
plt.axis('off')

plt.subplot(143)
plt.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
plt.title('场景图像')
plt.axis('off')

plt.subplot(144)
plt.imshow(cv2.cvtColor(img_matches, cv2.COLOR_BGR2RGB))
plt.title(f'SIFT特征匹配结果 (找到{len(good_matches)}个匹配点)')
plt.axis('off')

plt.tight_layout()
plt.show()

# 保存匹配结果图像
cv2.imwrite('images/sift_matches_result.png', img_matches)
print('匹配结果图像已保存！')