【全网独家】OpenCV 特征检测与匹配：ORB 特征，BRIEF 描述符，FAST 角点检测，FLANN 匹配器

    输入图像
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    检测 FAST 角点
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    计算每个角点的 Harris 响应值
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    保留响应值最高的 N 个角点
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    计算每个角点的方向
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    生成 BRIEF 描述符（考虑方向）
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    输出 ORB 特征点和描述符
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    输入图像和关键点
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    随机选取像素对
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    对每对像素进行强度比较
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    生成二进制字符串
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    输出 BRIEF 描述符
    
    
      
      
      
      
      
      
      
      
      
    
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    输入图像
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    遍历每个像素点
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    检查像素点周围的圆形邻域
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    判断是否存在一定数量的连续像素亮度差异
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    输出检测到的角点
    
    
      
      
      
      
      
      
      
      
      
    
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    输入特征描述符集合
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    构建近似最近邻结构（如 KD 树）
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    查询新特征描述符
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    在已有结构中进行近似最近邻搜索
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    输出匹配结果
    
    
      
      
      
      
      
      
      
      
      
    
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    import cv2
    
    img1 = cv2.imread('image1.jpg', 0)  # 读取灰度图
    img2 = cv2.imread('image2.jpg', 0)
    
    orb = cv2.ORB_create()
    
    kp1, des1 = orb.detectAndCompute(img1, None)
    kp2, des2 = orb.detectAndCompute(img2, None)
    
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(des1, des2)
    
    matches = sorted(matches, key=lambda x: x.distance)
    
    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
    
    cv2.imshow('Matches', img3)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    import cv2
    
    img = cv2.imread('image.jpg', 0)
    
    fast = cv2.FastFeatureDetector_create()
    
    kp = fast.detect(img, None)
    
    img2 = cv2.drawKeypoints(img, kp, None, color=(255, 0, 0))
    
    cv2.imshow('FAST Keypoints', img2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    import cv2
    import numpy as np
    
    img1 = cv2.imread('image1.jpg', 0)
    img2 = cv2.imread('image2.jpg', 0)
    
    sift = cv2.SIFT_create()
    
    kp1, des1 = sift.detectAndCompute(img1, None)
    kp2, des2 = sift.detectAndCompute(img2, None)
    
    index_params = dict(algorithm=1, trees=5)  # 使用 KD 树
    search_params = dict(checks=50)  # 指定递归次数
    
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    
    matches = flann.knnMatch(des1, des2, k=2)
    
    good_matches = []
    for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)
    
    img3 = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
    
    cv2.imshow('FLANN Matches', img3)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from flask import Flask, request, Response
    import cv2
    import numpy as np
    
    app = Flask(__name__)
    
    @app.route('/orb_feature_matching', methods=['POST'])
    def orb_feature_matching():
    file1 = request.files['image1']
    file2 = request.files['image2']
    img1 = cv2.imdecode(np.frombuffer(file1.read(), np.uint8), cv2.IMREAD_GRAYSCALE)
    img2 = cv2.imdecode(np.frombuffer(file2.read(), np.uint8), cv2.IMREAD_GRAYSCALE)
    
    orb = cv2.ORB_create()
    kp1, des1 = orb.detectAndCompute(img1, None)
    kp2, des2 = orb.detectAndCompute(img2, None)
    
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(des1, des2)
    matches = sorted(matches, key=lambda x: x.distance)
    
    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
    
    _, img_encoded = cv2.imencode('.jpg', img3)
    return Response(img_encoded.tobytes(), mimetype='image/jpeg')
    
    @app.route('/')
    def index():
    return '''
    <h1>OpenCV Feature Detection with Flask</h1>
    <form method="post" action="/orb_feature_matching" enctype="multipart/form-data">
      Select first image to upload: <input type="file```html
      name="image1"><br>
      Select second image to upload: <input type="file" name="image2"><br>
      <input type="submit" value="Submit">
    </form>
    '''
    
    if __name__ == '__main__':
    app.run(debug=True)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读