{"id":64763,"date":"2026-01-24T01:24:27","date_gmt":"2026-01-23T17:24:27","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/64763.html"},"modified":"2026-01-24T01:24:27","modified_gmt":"2026-01-23T17:24:27","slug":"%e9%a1%b9%e7%9b%ae%e6%a1%88%e4%be%8b%ef%bc%9a%e6%8c%87%e7%ba%b9%e5%8c%b9%e9%85%8d%ef%bc%8c%e5%9b%be%e5%83%8f%e6%8b%bc%e6%8e%a5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/64763.html","title":{"rendered":"\u9879\u76ee\u6848\u4f8b\uff1a\u6307\u7eb9\u5339\u914d\uff0c\u56fe\u50cf\u62fc\u63a5"},"content":{"rendered":"

\u76ee\u5f55<\/p>\n

\u4e00\u3001SIFT\u6838\u5fc3\u539f\u7406\u901f\u89c8<\/p>\n

\u4e8c\u3001\u5b9e\u6218\u9879\u76ee\u4e00：\u57fa\u4e8eSIFT\u7684\u6307\u7eb9\u5339\u914d<\/p>\n

2.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0<\/p>\n

2.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3<\/p>\n

2.2.1 \u7279\u5f81\u63d0\u53d6\u4e0e\u5339\u914d\u70b9\u8ba1\u6570（\u6838\u5fc3\u51fd\u6570）<\/p>\n

2.2.2 \u6307\u7eb9\u5e93\u6bd4\u5bf9\u4e0e\u8eab\u4efd\u8bc6\u522b<\/p>\n

2.2.3 \u4e3b\u51fd\u6570\u8c03\u7528<\/p>\n

2.3 \u5173\u952e\u6ce8\u610f\u4e8b\u9879<\/p>\n

\u4e09\u3001\u5b9e\u6218\u9879\u76ee\u4e8c：\u57fa\u4e8eSIFT\u7684\u56fe\u7247\u5168\u666f\u62fc\u63a5<\/p>\n

3.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0<\/p>\n

3.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3<\/p>\n

3.2.1 \u8f85\u52a9\u51fd\u6570\u4e0e\u7279\u5f81\u63d0\u53d6<\/p>\n

3.2.2 \u7279\u5f81\u5339\u914d\u4e0e\u4f18\u8d28\u5339\u914d\u5bf9\u7b5b\u9009<\/p>\n

3.2.3 \u900f\u89c6\u53d8\u6362（\u6838\u5fc3\u73af\u8282）<\/p>\n

3.2.4 \u56fe\u50cf\u53d8\u6362\u4e0e\u62fc\u63a5<\/p>\n

3.3 \u5173\u952e\u6ce8\u610f\u4e8b\u9879<\/p>\n

\u56db\u3001\u603b\u7ed3\u4e0e\u62d3\u5c55<\/p>\n

\n

\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df，\u7279\u5f81\u63d0\u53d6\u662f\u5b9e\u73b0\u56fe\u50cf\u5339\u914d\u3001\u62fc\u63a5\u3001\u8bc6\u522b\u7b49\u4efb\u52a1\u7684\u6838\u5fc3\u57fa\u7840\u3002SIFT（\u5c3a\u5ea6\u4e0d\u53d8\u7279\u5f81\u53d8\u6362）\u4f5c\u4e3a\u7ecf\u5178\u7684\u7279\u5f81\u63d0\u53d6\u7b97\u6cd5，\u51ed\u501f\u5176**\u5c3a\u5ea6\u4e0d\u53d8\u6027\u3001\u65cb\u8f6c\u4e0d\u53d8\u6027\u3001\u5149\u7167\u9c81\u68d2\u6027**，\u5728\u5404\u7c7b\u89c6\u89c9\u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u5f02\u3002\u672c\u6587\u5c06\u7ed3\u5408\u4e24\u4e2a\u5b9e\u6218\u9879\u76ee\u2014\u2014\u6307\u7eb9\u5339\u914d\u4e0e\u56fe\u7247\u5168\u666f\u62fc\u63a5，\u4ece\u539f\u7406\u5230\u4ee3\u7801\u9010\u73af\u8282\u62c6\u89e3，\u5e26\u4f60\u5403\u900fSIFT\u7684\u5b9e\u9645\u5e94\u7528\u3002<\/p>\n

\u4e00\u3001SIFT\u6838\u5fc3\u539f\u7406\u901f\u89c8<\/h3>\n

SIFT\u7b97\u6cd5\u7684\u6838\u5fc3\u4ef7\u503c\u7684\u662f\u63d0\u53d6\u56fe\u50cf\u4e2d\u201c\u7a33\u5b9a\u4e14\u552f\u4e00\u201d\u7684\u7279\u5f81\u70b9，\u4e0d\u53d7\u56fe\u50cf\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u5149\u7167\u53d8\u5316\u7684\u5f71\u54cd，\u5176\u6d41\u7a0b\u53ef\u7b80\u5316\u4e3a\u4e09\u6b65：<\/p>\n

  • \n

    \u5173\u952e\u70b9\u68c0\u6d4b：\u901a\u8fc7\u9ad8\u65af\u5dee\u5206\u91d1\u5b57\u5854，\u5b9a\u4f4d\u56fe\u50cf\u4e2d\u5177\u6709\u5c3a\u5ea6\u3001\u65b9\u5411\u4fe1\u606f\u7684\u5173\u952e\u70b9（\u5982\u8fb9\u7f18\u7aef\u70b9\u3001\u89d2\u70b9\u3001\u7eb9\u7406\u5206\u53c9\u70b9），\u8fd9\u4e9b\u70b9\u662f\u56fe\u50cf\u7684\u201c\u6307\u7eb9\u201d\u3002<\/p>\n<\/li>\n

  • \n

    \u7279\u5f81\u63cf\u8ff0\u5b50\u751f\u6210：\u5bf9\u6bcf\u4e2a\u5173\u952e\u70b9，\u56f4\u7ed5\u5176\u6784\u5efa128\u7ef4\u7279\u5f81\u5411\u91cf，\u63cf\u8ff0\u5173\u952e\u70b9\u5468\u56f4\u7684\u50cf\u7d20\u7eb9\u7406\u5206\u5e03，\u4e3a\u540e\u7eed\u5339\u914d\u63d0\u4f9b\u4f9d\u636e\u3002<\/p>\n<\/li>\n

  • \n

    \u7279\u5f81\u5339\u914d：\u901a\u8fc7\u8ba1\u7b97\u4e24\u5f20\u56fe\u50cf\u7279\u5f81\u63cf\u8ff0\u5b50\u7684\u8ddd\u79bb，\u627e\u5230\u76f8\u4f3c\u7279\u5f81\u70b9，\u518d\u901a\u8fc7\u7b5b\u9009\u89c4\u5219\u5254\u9664\u9519\u8bef\u5339\u914d，\u5f97\u5230\u53ef\u9760\u5339\u914d\u5bf9\u3002<\/p>\n<\/li>\n

    \u4e8c\u3001\u5b9e\u6218\u9879\u76ee\u4e00：\u57fa\u4e8eSIFT\u7684\u6307\u7eb9\u5339\u914d<\/h3>\n

    2.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0<\/h4>\n

    \u672c\u9879\u76ee\u901a\u8fc7SIFT\u63d0\u53d6\u5f85\u8bc6\u522b\u6307\u7eb9\u4e0e\u6307\u7eb9\u5e93\u6a21\u677f\u7684\u7279\u5f81，\u5339\u914d\u4f18\u8d28\u7279\u5f81\u70b9\u6570\u91cf，\u627e\u5230\u6700\u76f8\u4f3c\u7684\u6a21\u677f\u6307\u7eb9，\u5b9e\u73b0\u8eab\u4efd\u8bc6\u522b；\u82e5\u5339\u914d\u70b9\u6570\u91cf\u4f4e\u4e8e\u9608\u503c，\u5219\u5224\u5b9a\u4e3a\u672a\u77e5\u8eab\u4efd\u3002<\/p>\n

    2.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3<\/h4>\n

    2.2.1 \u7279\u5f81\u63d0\u53d6\u4e0e\u5339\u914d\u70b9\u8ba1\u6570（\u6838\u5fc3\u51fd\u6570）<\/h5>\n

    \u8be5\u51fd\u6570\u8d1f\u8d23\u8bfb\u53d6\u4e24\u5f20\u6307\u7eb9\u56fe\u50cf，\u63d0\u53d6SIFT\u7279\u5f81，\u901a\u8fc7FLANN\u5339\u914d\u5668\u5feb\u901f\u5339\u914d\u5e76\u7b5b\u9009\u4f18\u8d28\u7279\u5f81\u70b9，\u8fd4\u56de\u6709\u6548\u5339\u914d\u70b9\u6570\u91cf\u3002<\/p>\n

    import os
    \nimport cv2
    \nimport numpy as np<\/p>\n

    def getNum(src, model):
    \n # \u8bfb\u53d6\u6307\u7eb9\u56fe\u50cf（\u5f85\u8bc6\u522b\u6307\u7eb9src，\u6a21\u677f\u6307\u7eb9model）
    \n img1 = cv2.imread(src)
    \n img2 = cv2.imread(model)<\/p>\n

    # \u521d\u59cb\u5316SIFT\u68c0\u6d4b\u5668，\u63d0\u53d6\u7279\u5f81\u70b9（kp）\u4e0e\u63cf\u8ff0\u5b50（des）
    \n sift = cv2.SIFT_create()
    \n kp1, des1 = sift.detectAndCompute(img1, None) # None\u8868\u793a\u65e0\u63a9\u819c，\u5904\u7406\u6574\u5f20\u56fe
    \n kp2, des2 = sift.detectAndCompute(img2, None)<\/p>\n

    # FLANN\u5339\u914d\u5668（\u5feb\u901f\u8fd1\u90bb\u641c\u7d22），\u9002\u5408\u6279\u91cf\u6a21\u677f\u5339\u914d
    \n flann = cv2.FlannBasedMatcher()
    \n # K\u8fd1\u90bb\u5339\u914d（k=2，\u4e3a\u6bcf\u4e2a\u7279\u5f81\u70b9\u627e\u6700\u76f8\u4f3c\u3001\u6b21\u76f8\u4f3c\u4e24\u4e2a\u5339\u914d）
    \n matches = flann.knnMatch(des1, des2, k=2)<\/p>\n

    # \u6bd4\u7387\u6d4b\u8bd5\u7b5b\u9009\u4f18\u8d28\u5339\u914d（SIFT\u4f5c\u8005\u63d0\u51fa，\u5254\u9664\u6a21\u7cca\u5339\u914d）
    \n good_matches = []
    \n for m, n in matches:
    \n # \u6700\u76f8\u4f3c\u5339\u914d\u8ddd\u79bb < 0.8*\u6b21\u76f8\u4f3c\u5339\u914d\u8ddd\u79bb，\u89c6\u4e3a\u6709\u6548\u5339\u914d
    \n if m.distance < 0.8 * n.distance:
    \n good_matches.append(m)
    \n return len(good_matches) # \u8fd4\u56de\u4f18\u8d28\u5339\u914d\u70b9\u6570\u91cf<\/p>\n

    2.2.2 \u6307\u7eb9\u5e93\u6bd4\u5bf9\u4e0e\u8eab\u4efd\u8bc6\u522b<\/h5>\n

    \u904d\u5386\u6307\u7eb9\u5e93，\u4e0e\u5f85\u8bc6\u522b\u6307\u7eb9\u9010\u4e00\u6bd4\u5bf9，\u8bb0\u5f55\u6700\u5927\u5339\u914d\u70b9\u6570\u91cf\u5bf9\u5e94\u7684\u6a21\u677f，\u7ed3\u5408\u9608\u503c\u5224\u65ad\u8eab\u4efd\u3002<\/p>\n

    def getID(src, database):
    \n max_matches = 0 # \u521d\u59cb\u5316\u6700\u5927\u5339\u914d\u70b9\u6570\u91cf
    \n match_name = "" # \u5339\u914d\u6210\u529f\u7684\u6a21\u677f\u6587\u4ef6\u540d<\/p>\n

    # \u904d\u5386\u6307\u7eb9\u5e93\u6240\u6709\u6a21\u677f\u6587\u4ef6
    \n for file in os.listdir(database):
    \n model_path = os.path.join(database, file)
    \n current_matches = getNum(src, model_path)
    \n print(f"\u6a21\u677f\u6587\u4ef6：{file}，\u5339\u914d\u70b9\u6570\u91cf：{current_matches}")<\/p>\n

    # \u66f4\u65b0\u6700\u5927\u5339\u914d\u70b9\u6570\u91cf\u4e0e\u5bf9\u5e94\u6a21\u677f
    \n if current_matches > max_matches:
    \n max_matches = current_matches
    \n match_name = file<\/p>\n

    # \u4ece\u6a21\u677f\u6587\u4ef6\u540d\u63d0\u53d6ID（\u4f9d\u8d56\u547d\u540d\u89c4\u8303：ID_\u59d3\u540d.bmp）
    \n if not match_name:
    \n return 9999 # \u6307\u7eb9\u5e93\u4e3a\u7a7a\u65f6\u8fd4\u56de\u672a\u77e5
    \n finger_id = match_name[0]
    \n # \u5339\u914d\u70b9\u5c11\u4e8e200（\u7ecf\u9a8c\u9608\u503c），\u5224\u5b9a\u4e3a\u672a\u77e5\u8eab\u4efd
    \n if max_matches < 200:
    \n finger_id = 9999
    \n return finger_id<\/p>\n

    # ID\u4e0e\u59d3\u540d\u6620\u5c04
    \ndef getName(finger_id):
    \n name_map = {0: '\u5f20\u4e09', 1: '\u674e\u56db', 2: '\u738b\u4e94', 3: '\u8d75\u516d', 4: '\u6731\u8001\u4e03',
    \n 5: '\u94b1\u516b', 6: '\u66f9\u4e5d', 7: '\u738b\u4e8c\u9ebb\u5b50', 8: 'Andy', 9: 'Anna', 9999: '\u672a\u77e5\u8eab\u4efd'}
    \n return name_map.get(int(finger_id), "\u672a\u77e5\u8eab\u4efd") <\/p>\n

    2.2.3 \u4e3b\u51fd\u6570\u8c03\u7528<\/h5>\n

    if __name__ == "__main__":
    \n target_finger = "mubiao_zhiwen.bmp" # \u5f85\u8bc6\u522b\u6307\u7eb9\u8def\u5f84
    \n finger_database = "database" # \u6307\u7eb9\u5e93\u6587\u4ef6\u5939\u8def\u5f84
    \n id_result = getID(target_finger, finger_database)
    \n name_result = getName(id_result)
    \n print(f"\\\\n\u8bc6\u522b\u7ed3\u679c：{name_result}") <\/p>\n

    2.2.4\u6267\u884c\u7ed3\u679c\"\"<\/h5>\n

    2.3 \u5173\u952e\u6ce8\u610f\u4e8b\u9879<\/h4>\n
      \n
    • \n

      \u6307\u7eb9\u5e93\u6a21\u677f\u547d\u540d\u9700\u89c4\u8303（\u5982\u201c0_\u5f20\u4e09.bmp\u201d），\u786e\u4fdd\u80fd\u4ece\u6587\u4ef6\u540d\u63d0\u53d6ID；<\/p>\n<\/li>\n

    • \n

      \u5339\u914d\u9608\u503c200\u4e3a\u7ecf\u9a8c\u503c，\u9700\u6839\u636e\u6307\u7eb9\u56fe\u50cf\u5206\u8fa8\u7387\u8c03\u6574（\u4f4e\u5206\u8fa8\u7387\u6307\u7eb9\u53ef\u964d\u4f4e\u9608\u503c）；<\/p>\n<\/li>\n

    • \n

      FLANN\u5339\u914d\u5668\u6bd4\u66b4\u529b\u5339\u914d\u5668\u901f\u5ea6\u66f4\u5feb，\u9002\u5408\u6307\u7eb9\u5e93\u6570\u91cf\u8f83\u591a\u7684\u573a\u666f\u3002<\/p>\n<\/li>\n<\/ul>\n

      \u4e09\u3001\u5b9e\u6218\u9879\u76ee\u4e8c：\u57fa\u4e8eSIFT\u7684\u56fe\u7247\u5168\u666f\u62fc\u63a5<\/h3>\n

      3.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0<\/h4>\n

      \u5bf9\u4e24\u5f20\u4e0d\u540c\u89d2\u5ea6\u62cd\u6444\u3001\u5b58\u5728\u91cd\u53e0\u533a\u57df\u7684\u7167\u7247，\u901a\u8fc7SIFT\u5339\u914d\u7279\u5f81\u70b9，\u8ba1\u7b97\u900f\u89c6\u53d8\u6362\u77e9\u9635\u5bf9\u9f50\u89c6\u89d2，\u6700\u7ec8\u62fc\u63a5\u6210\u5168\u666f\u56fe，\u6838\u5fc3\u89e3\u51b3\u201c\u4e0d\u540c\u89c6\u89d2\u56fe\u50cf\u5bf9\u9f50\u201d\u95ee\u9898\u3002\u539f\u56fe\u5982\u4e0b：\"\"<\/p>\n

      3.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3<\/h4>\n

      3.2.1 \u8f85\u52a9\u51fd\u6570\u4e0e\u7279\u5f81\u63d0\u53d6<\/h5>\n

      def cv_show(name, img):
      \n # \u5c01\u88c5\u56fe\u7247\u663e\u793a\u51fd\u6570，\u6309\u4e0b\u4efb\u610f\u952e\u5173\u95ed\u7a97\u53e3
      \n cv2.imshow(name, img)
      \n cv2.waitKey(0)<\/p>\n

      def detectAndDescribe(image):
      \n # \u63d0\u53d6SIFT\u7279\u5f81，\u8fd4\u56de\u7279\u5f81\u70b9\u5bf9\u8c61\u3001\u6d6e\u70b9\u578b\u5750\u6807\u3001\u63cf\u8ff0\u5b50
      \n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # SIFT\u57fa\u4e8e\u7070\u5ea6\u56fe\u63d0\u53d6\u7279\u5f81
      \n sift = cv2.SIFT_create()
      \n kps, des = sift.detectAndCompute(gray, None)
      \n # \u7279\u5f81\u70b9\u5750\u6807\u8f6c\u4e3afloat32（\u540e\u7eed\u900f\u89c6\u53d8\u6362\u8ba1\u7b97\u9700\u6c42）
      \n kps_float = np.float32([kp.pt for kp in kps])
      \n return (kps, kps_float, des)<\/p>\n

      3.2.2 \u7279\u5f81\u5339\u914d\u4e0e\u4f18\u8d28\u5339\u914d\u5bf9\u7b5b\u9009<\/h5>\n

      \u91c7\u7528\u66b4\u529b\u5339\u914d\u5668（BFMatcher），\u5339\u914d\u7cbe\u5ea6\u66f4\u9ad8，\u9002\u5408\u5c11\u91cf\u56fe\u7247\u62fc\u63a5\u573a\u666f，\u540c\u65f6\u53ef\u89c6\u5316\u5339\u914d\u6548\u679c\u3002<\/p>\n

      # \u8bfb\u53d6\u4e24\u5f20\u5f85\u62fc\u63a5\u56fe\u7247
      \nimageA = cv2.imread('1.jpg') # \u57fa\u51c6\u56fe（\u4e0d\u505a\u53d8\u6362）
      \nimageB = cv2.imread('2.jpg') # \u5f85\u53d8\u6362\u56fe
      \ncv_show("\u539f\u59cb\u56feA", imageA)
      \ncv_show("\u539f\u59cb\u56feB", imageB)<\/p>\n

      # \u63d0\u53d6\u4e24\u5f20\u56fe\u7684SIFT\u7279\u5f81
      \n(kpsA, kpsA_float, desA) = detectAndDescribe(imageA)
      \n(kpsB, kpsB_float, desB) = detectAndDescribe(imageB)<\/p>\n

      # \u66b4\u529b\u5339\u914d\u5668\u5339\u914d\u7279\u5f81\u63cf\u8ff0\u5b50
      \nmatcher = cv2.BFMatcher()
      \nraw_matches = matcher.knnMatch(desB, desA, k=2) # desB\u4e3a\u67e5\u8be2\u96c6，desA\u4e3a\u8bad\u7ec3\u96c6<\/p>\n

      # \u7b5b\u9009\u4f18\u8d28\u5339\u914d\u5bf9（\u9608\u503c0.65，\u6bd4\u6307\u7eb9\u5339\u914d\u66f4\u4e25\u683c，\u907f\u514d\u5bf9\u9f50\u504f\u5dee）
      \ngood_matches = []
      \nmatches = [] # \u5b58\u50a8\u5339\u914d\u5bf9\u7d22\u5f15（\u7528\u4e8e\u540e\u7eed\u5750\u6807\u63d0\u53d6）
      \nfor m in raw_matches:
      \n if len(m) == 2 and m[0].distance < 0.65 * m[1].distance:
      \n good_matches.append(m)
      \n # \u5b58\u50a8（desB\u7d22\u5f15，desA\u7d22\u5f15）
      \n matches.append((m[0].queryIdx, m[0].trainIdx))<\/p>\n

      # \u53ef\u89c6\u5316\u7279\u5f81\u5339\u914d\u7ed3\u679c
      \nvis = cv2.drawMatchesKnn(
      \n imageB, kpsB, imageA, kpsA, good_matches,
      \n outImg=None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
      \n)
      \ncv_show("\u7279\u5f81\u5339\u914d\u6548\u679c", vis) <\/p>\n

      3.2.3 \u900f\u89c6\u53d8\u6362（\u6838\u5fc3\u73af\u8282）<\/h5>\n

      \u901a\u8fc7\u5355\u5e94\u6027\u77e9\u9635（Homography）\u5b9e\u73b0\u89c6\u89d2\u5bf9\u9f50，\u5355\u5e94\u6027\u77e9\u9635\u63cf\u8ff0\u4e86\u4e24\u5f20\u56fe\u7684\u900f\u89c6\u53d8\u6362\u5173\u7cfb，\u9700\u81f3\u5c114\u4e2a\u5339\u914d\u5bf9\u8ba1\u7b97，\u7ed3\u5408RANSAC\u7b97\u6cd5\u5254\u9664\u5916\u70b9（\u9519\u8bef\u5339\u914d）\u3002<\/p>\n

      import sys<\/p>\n

      if len(matches) > 4:
      \n # \u63d0\u53d6\u5339\u914d\u5bf9\u5bf9\u5e94\u7684\u7279\u5f81\u70b9\u5750\u6807
      \n ptsB = np.float32([kpsB_float[i] for (i, _) in matches]) # imageB\u7279\u5f81\u70b9
      \n ptsA = np.float32([kpsA_float[i] for (_, i) in matches]) # imageA\u7279\u5f81\u70b9<\/p>\n

      # \u8ba1\u7b97\u5355\u5e94\u6027\u77e9\u9635（\u900f\u89c6\u53d8\u6362\u77e9\u9635）
      \n # cv2.RANSAC：\u901a\u8fc7\u968f\u673a\u6837\u672c\u4e00\u81f4\u6027\u7b97\u6cd5\u5254\u9664\u5916\u70b9，\u63d0\u5347\u77e9\u9635\u9c81\u68d2\u6027
      \n # ransacReprojThreshold=10：\u91cd\u6295\u5f71\u8bef\u5dee\u9608\u503c，\u8d85\u8fc710\u7684\u4e3a\u5916\u70b9
      \n (H, mask) = cv2.findHomography(ptsB, ptsA, cv2.RANSAC, 10.0)
      \nelse:
      \n print("\u672a\u627e\u5230\u8db3\u591f\u5339\u914d\u70b9（\u9700\u22654\u4e2a），\u65e0\u6cd5\u62fc\u63a5")
      \n sys.exit()<\/p>\n

      3.2.4 \u56fe\u50cf\u53d8\u6362\u4e0e\u62fc\u63a5<\/h5>\n

      # \u5bf9imageB\u6267\u884c\u900f\u89c6\u53d8\u6362，\u5bf9\u9f50\u5230imageA\u89c6\u89d2
      \n# \u8f93\u51fa\u5c3a\u5bf8：\u5bbd\u5ea6=\u4e24\u5f20\u56fe\u5bbd\u5ea6\u4e4b\u548c（\u9884\u7559\u62fc\u63a5\u7a7a\u95f4），\u9ad8\u5ea6=imageB\u9ad8\u5ea6
      \nresult = cv2.warpPerspective(imageB, H, (imageB.shape[1] + imageA.shape[1], imageB.shape[0]))
      \ncv_show("\u53d8\u6362\u540eimageB", result)<\/p>\n

      # \u5c06\u57fa\u51c6\u56feimageA\u53e0\u52a0\u5230\u53d8\u6362\u540e\u7684\u56fe\u50cf\u5de6\u4fa7
      \nresult[0:imageA.shape[0], 0:imageA.shape[1]] = imageA
      \ncv_show("\u6700\u7ec8\u62fc\u63a5\u5168\u666f\u56fe", result)<\/p>\n

      # \u4fdd\u5b58\u7ed3\u679c
      \ncv2.imwrite('pingjie.jpg', result) <\/p>\n

      3.2.5\u6267\u884c\u7ed3\u679c\"\"<\/h5>\n

      3.3 \u5173\u952e\u6ce8\u610f\u4e8b\u9879<\/h4>\n
        \n
      • \n

        \u4e24\u5f20\u56fe\u7247\u9700\u5b58\u5728\u91cd\u53e0\u533a\u57df（\u81f3\u5c1130%），\u5426\u5219\u65e0\u6cd5\u63d0\u53d6\u8db3\u591f\u5339\u914d\u70b9；<\/p>\n<\/li>\n

      • \n

        \u5355\u5e94\u6027\u77e9\u9635\u662f\u62fc\u63a5\u6838\u5fc3，RANSAC\u7b97\u6cd5\u80fd\u6709\u6548\u5254\u9664\u9519\u8bef\u5339\u914d，\u907f\u514d\u62fc\u63a5\u53d8\u5f62；<\/p>\n<\/li>\n

      • \n

        \u91cd\u53e0\u533a\u57df\u53ef\u901a\u8fc7\u52a0\u6743\u5e73\u5747\u505a\u6e10\u53d8\u878d\u5408，\u6d88\u9664\u76f4\u63a5\u8986\u76d6\u5bfc\u81f4\u7684\u62fc\u63a5\u75d5\u8ff9\u3002<\/p>\n<\/li>\n<\/ul>\n

        \u56db\u3001\u603b\u7ed3\u4e0e\u62d3\u5c55<\/h3>\n

        \u672c\u6587\u901a\u8fc7\u4e24\u4e2a\u5b9e\u6218\u9879\u76ee，\u5c55\u793a\u4e86SIFT\u7279\u5f81\u63d0\u53d6\u5728\u89c6\u89c9\u4efb\u52a1\u4e2d\u7684\u6838\u5fc3\u4f5c\u7528：\u6307\u7eb9\u5339\u914d\u4f9d\u8d56SIFT\u7684\u7a33\u5b9a\u6027\u5b9e\u73b0\u8eab\u4efd\u8bc6\u522b，\u56fe\u7247\u62fc\u63a5\u901a\u8fc7SIFT\u5339\u914d+\u900f\u89c6\u53d8\u6362\u89e3\u51b3\u89c6\u89d2\u5bf9\u9f50\u95ee\u9898\u3002\u4e24\u8005\u7684\u6838\u5fc3\u903b\u8f91\u4e00\u81f4\u2014\u2014\u63d0\u53d6\u7a33\u5b9a\u7279\u5f81\u2192\u5339\u914d\u7b5b\u9009\u2192\u57fa\u4e8e\u5339\u914d\u7ed3\u679c\u5b8c\u6210\u4efb\u52a1\u3002<\/p>\n

        \u62d3\u5c55\u65b9\u5411：<\/p>\n

      • \n

        \u7b97\u6cd5\u66ff\u4ee3：SIFT\u4e3a\u4e13\u5229\u7b97\u6cd5，\u53ef\u6539\u7528ORB（\u5feb\u901f\u3001\u5f00\u6e90）\u5b9e\u73b0\u5b9e\u65f6\u6027\u9700\u6c42；<\/p>\n<\/li>\n

      • \n

        \u591a\u56fe\u62fc\u63a5：\u5faa\u73af\u5339\u914d\u591a\u5f20\u56fe\u7247，\u9010\u6b65\u6269\u5c55\u5168\u666f\u56fe\u8303\u56f4；<\/p>\n<\/li>\n

      • \n

        \u7cbe\u5ea6\u4f18\u5316：\u8c03\u6574\u5339\u914d\u9608\u503c\u3001\u91cd\u6295\u5f71\u8bef\u5dee\u9608\u503c，\u7ed3\u5408\u56fe\u50cf\u9884\u5904\u7406（\u964d\u566a\u3001\u589e\u5f3a）\u63d0\u5347\u6548\u679c\u3002<\/p>\n<\/li>\n

        \u638c\u63e1SIFT\u7684\u6838\u5fc3\u539f\u7406\u4e0e\u5b9e\u64cd，\u80fd\u4e3a\u540e\u7eed\u56fe\u50cf\u8bc6\u522b\u3001\u76ee\u6807\u8ddf\u8e2a\u7b49\u590d\u6742\u89c6\u89c9\u4efb\u52a1\u5960\u5b9a\u57fa\u7840\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        \u76ee\u5f55
        \n\u4e00\u3001SIFT\u6838\u5fc3\u539f\u7406\u901f\u89c8
        \n\u4e8c\u3001\u5b9e\u6218\u9879\u76ee\u4e00：\u57fa\u4e8eSIFT\u7684\u6307\u7eb9\u5339\u914d
        \n2.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0
        \n2.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3
        \n2.2.1 \u7279\u5f81\u63d0\u53d6\u4e0e\u5339\u914d\u70b9\u8ba1\u6570（\u6838\u5fc3\u51fd\u6570）
        \n2.2.2 \u6307\u7eb9\u5e93\u6bd4\u5bf9\u4e0e\u8eab\u4efd\u8bc6\u522b
        \n2.2.3 \u4e3b\u51fd\u6570\u8c03\u7528
        \n2.3 \u5173\u952e\u6ce8\u610f\u4e8b\u9879
        \n\u4e09\u3001\u5b9e\u6218\u9879\u76ee\u4e8c：\u57fa\u4e8eSIFT\u7684\u56fe\u7247\u5168\u666f\u62fc\u63a5
        \n3.1 \u9879\u76ee\u529f\u80fd\u6982\u8ff0
        \n3.2 \u6838\u5fc3\u4ee3\u7801\u62c6\u89e3
        \n3.2.1 \u8f85\u52a9\u51fd\u6570\u4e0e\u7279\u5f81\u63d0\u53d6
        \n3.2.2 \u7279\u5f81\u5339\u914d\u4e0e\u4f18\u8d28\u5339\u914d\u5bf9\u7b5b\u9009<\/p>\n","protected":false},"author":2,"featured_media":64760,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,86,523],"topic":[],"class_list":["post-64763","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-50","tag-86","tag-523"],"yoast_head":"\n\u9879\u76ee\u6848\u4f8b\uff1a\u6307\u7eb9\u5339\u914d\uff0c\u56fe\u50cf\u62fc\u63a5 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/64763.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u9879\u76ee\u6848\u4f8b\uff1a\u6307\u7eb9\u5339\u914d\uff0c\u56fe\u50cf\u62fc\u63a5 - 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