OpenCV中的特征匹配+單應性以查找對象

• • 1. 效果圖
  • 2. 源碼
  • 參考

這篇博客將混合calib3d模塊中的特征匹配和單應性，在復雜圖像中查找已知對象。

1. 效果圖

特征匹配 & 單應性查找對象效果圖如下：

2. 源碼

# 在圖像中找到SIFT特征并應用比率測試來找到最佳匹配。import cv2
import numpy as np
from matplotlib import pyplot as pltMIN_MATCH_COUNT = 10img1 = cv2.imread('images/box.png', 0)  # 查詢圖像
img2 = cv2.imread('images/box_in_scene.png', 0)  # 訓練圖像# 初始化SIFT檢測器
sift = cv2.xfeatures2d.SIFT_create()# 使用SIFT檢測器尋找關鍵點和描述符
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)flann = cv2.FlannBasedMatcher(index_params, search_params)matches = flann.knnMatch(des1, des2, k=2)# 根據Lowe比率測試存儲所有良好匹配項
good = []
for m, n in matches:if m.distance < 0.7 * n.distance:good.append(m)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 = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)matchesMask = mask.ravel().tolist()h, w = img1.shapepts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)dst = cv2.perspectiveTransform(pts, M)img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
else:print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))matchesMask = None# 匹配點繪制為綠色，只繪制內置點
draw_params = dict(matchColor=(0, 255, 0),  # 繪制匹配點為綠色singlePointColor=None,matchesMask=matchesMask,  # 只繪制內置點flags=2)img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)plt.imshow(img3, 'gray')
plt.xticks([])
plt.yticks([])
plt.title("feature_match & homography res")
plt.show()

參考

• https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html#py-feature-homography

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