55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
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import cv2
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import numpy as np
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# 读取左右视角图像（灰度模式）
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img1 = cv2.imread('images/left.jpg', 0)
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img2 = cv2.imread('images/right.jpg', 0)
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# 初始化SIFT特征检测器
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sift = cv2.SIFT_create() # 创建SIFT对象
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# 检测关键点并计算描述符
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kp1, des1 = sift.detectAndCompute(img1, None)
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kp2, des2 = sift.detectAndCompute(img2, None)
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# FLANN匹配器参数配置
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FLANN_INDEX_KDTREE = 1
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index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) # KD树算法参数
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search_params = dict(checks=50) # 搜索参数：检查次数
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# 创建FLANN匹配器实例
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flann = cv2.FlannBasedMatcher(index_params, search_params)
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matches = flann.knnMatch(des1, des2, k=2) # k近邻匹配
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pts1 = []
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pts2 = []
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good_matches = [] # 初始化优质匹配列表
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# 使用Lowe's比率测试筛选优质匹配
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for i, (m, n) in enumerate(matches):
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if m.distance < 0.7 * n.distance: # 修改此处：调整距离比阈值
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good_matches.append(m)
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pts2.append(kp2[m.trainIdx].pt) # 记录右图匹配点坐标
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pts1.append(kp1[m.queryIdx].pt) # 记录左图匹配点坐标
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if len(good_matches) > 8:
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pts1 = np.int32(pts1)
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pts2 = np.int32(pts2)
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# 计算基础矩阵（使用RANSAC算法）
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F, mask = cv2.findFundamentalMat(pts1, pts2, cv2.FM_RANSAC)
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print("基础矩阵:\n", F)
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# 通过基础矩阵恢复旋转和平移变换
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_, R, t, _ = cv2.recoverPose(F, pts1, pts2)
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print("旋转矩阵:\n", R)
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print("平移向量:\n", t)
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# 可视化优质匹配结果
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img_match = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None,
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flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
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cv2.imshow('特征匹配', img_match)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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else:
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print("匹配点不足，无法计算基础矩阵。")
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