{"id":97,"date":"2024-10-27T13:18:03","date_gmt":"2024-10-27T05:18:03","guid":{"rendered":"https:\/\/aimc.skyate.com\/?p=97"},"modified":"2024-10-28T09:28:27","modified_gmt":"2024-10-28T01:28:27","slug":"%e5%a4%9a%e6%91%84%e5%83%8f%e5%a4%b4%e5%9b%be%e5%83%8f%e5%af%b9%e9%bd%90%e8%9e%8d%e5%90%88","status":"publish","type":"post","link":"https:\/\/aimc.skyate.com\/index.php\/2024\/10\/27\/%e5%a4%9a%e6%91%84%e5%83%8f%e5%a4%b4%e5%9b%be%e5%83%8f%e5%af%b9%e9%bd%90%e8%9e%8d%e5%90%88\/","title":{"rendered":"\u591a\u6444\u50cf\u5934\u56fe\u50cf\u5bf9\u9f50\u878d\u5408"},"content":{"rendered":"\n

\u5728\u591a\u76ee\u6444\u50cf\u5934\u4e2d\u5f80\u5f80\u5b58\u5728\u591a\u4e2a\u6210\u50cf\u5668\u4ef6\uff0c\u5982\u56fe1\u6240\u793a\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe1 \u591a\u76ee\u6df1\u5ea6\u6444\u50cf\u5934 <\/p>\n\n\n\n

\u8fd9\u4e9b\u6444\u50cf\u5934\u7531\u4e8e\u4f4d\u7f6e\u3001\u6210\u50cf\u53c2\u6570\u4e4b\u95f4\u5b58\u5728\u5dee\u5f02\uff0c\u5bfc\u81f4\u5176\u89c6\u89d2\u5e76\u4e0d\u76f8\u540c\uff0c\u5982\u56fe2\u6240\u793a\u3002\u901a\u8fc7\u89c6\u89d2\u7684\u5dee\u5f02\uff0c\u53ef\u4ee5\u50cf\u4eba\u773c\u4e00\u6837\u4f30\u7b97\u7a7a\u95f4\u70b9\u5230\u76f8\u673a\u7684\u8ddd\u79bb\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe2 \u7ea2\u5916IR\u548c\u5f69\u8272RGB\u76f8\u673a\u89c6\u89d2\u5dee\u5f02 <\/p>\n\n\n\n

\u5982\u679c\u76f4\u63a5\u5c06\u4e24\u4e2a\u6444\u50cf\u5934\u7684\u753b\u9762\u8fdb\u884c\u53e0\u52a0\uff0c\u4f1a\u51fa\u73b0\u753b\u9762\u4e2d\u5143\u7d20\u9519\u4f4d\u7684\u60c5\u51b5\u3002\u8fd9\u79cd\u9519\u4f4d\u5c06\u4f1a\u4e3a\u540e\u7eed\u56fe\u50cf\u7684\u8bc6\u522b\u5e26\u6765\u9ebb\u70e6\uff0c\u56e0\u6b64\u9700\u8981\u5c06\u4e24\u4e2a\u6216\u591a\u4e2a\u6444\u50cf\u5934\u7684\u56fe\u50cf\u8fdb\u884c\u5bf9\u9f50\u77eb\u6b63\uff0c\u800c\u540e\u518d\u53e0\u52a0\u5904\u7406\u3002<\/p>\n\n\n\n

1 \u57fa\u672c\u539f\u7406<\/h1>\n\n\n\n

\u591a\u76ee\u6444\u50cf\u673a\u5728\u751f\u4ea7\u65f6\uff0c\u955c\u5934\u7684\u5b89\u88c5\u5b58\u5728\u4e00\u5b9a\u7684\u8bef\u5dee\uff0c\u5bfc\u81f4\u4e24\u4e2a\u6444\u50cf\u5934\u7684\u6210\u50cf\u9762\u5e76\u4e0d\u91cd\u53e0\uff0c\u5b58\u5728\u4e00\u5b9a\u7684\u504f\u8f6c\uff0c\u5982\u56fe3\u6240\u793a\u3002 <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u00a0\u56fe3 \u53cc\u9762\u6444\u50cf\u673a\u6210\u50cf\u793a\u610f\u56fe <\/p>\n\n\n\n

\u5728\u56fe3\u4e2d\u53cc\u76ee\u6444\u50cf\u673a\u4e2d\u7684\u4e24\u4e2a\u6444\u50cf\u5934\u867d\u7136\u6210\u50cf\u9762\u5b58\u5728\u4e00\u5b9a\u7684\u504f\u5dee\uff0c\u4f46\u662f\u6240\u62cd\u6444\u753b\u9762\u4e2d\u7684\u5143\u7d20\u57fa\u672c\u76f8\u540c\u3002 \u8bbeA\u4f20\u611f\u5668\u7684\u6210\u50cf\u9762\u53ef\u7528\u5750\u6807\u7cfbXa\u3001Ya\u3001Za\u63cf\u8ff0\uff1bB\u4f20\u611f\u5668\u7684\u6210\u50cf\u9762\u53ef\u7528Xb\u3001Yb\u3001Zb\u63cf\u8ff0\u3002\u5219\u540c\u4e00\u4e2a\u7269\u4f53\u5728\u4e24\u4e2a\u6210\u50cf\u9762\u4e2d\u5f62\u6210\u7684\u50cf\u7d20\u53ef\u4ee5\u7528\u4e24\u4e2a\u4e0d\u540c\u7684\u5750\u6807\u7cfb\u63cf\u8ff0\u3002\u56e0\u6b64\u5fc5\u7136\u5b58\u5728\u4e00\u4e2a\u6709A\u6210\u50cf\u9762\u5750\u6807\u7cfb\u5230B\u6210\u50cf\u9762\u5750\u6807\u7cfb\u7684\u53d8\u6362\u3002\u56e0\u6b64\uff0c\u53ef\u4ee5\u901a\u8fc7\u5728\u753b\u9762\u4e2d\u5bfb\u627e\u591a\u4e2a\u76f8\u540c\u5750\u6807\u70b9\uff0c\u5e76\u8ba1\u7b97\u51fa\u76f8\u5e94\u7684\u53d8\u6362\u77e9\u9635\u3002\u76f8\u5173\u57fa\u672c\u539f\u7406\u5728\u6444\u50cf\u5934\u6807\u5b9a\u4e2d\u5df2\u9610\u8ff0\u3002<\/p>\n\n\n\n

2 \u57fa\u4e8eopencv\u7684\u53cc\u76ee\u6444\u50cf\u5934\u6807\u5b9a<\/h1>\n\n\n\n

\u5728\u53cc\u76ee\u6444\u50cf\u5934\u7684\u6807\u5b9a\u4e2d\u4f9d\u65e7\u4f7f\u7528\u5f20\u6b63\u53cb\u68cb\u76d8\u6807\u5b9a\u683c\uff0812\u00d79\uff09\uff0c\u5982\u56fe4\u6240\u793a\u3002\u00a0<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u00a0\u56fe4 \u5f20\u6b63\u53cb\u68cb\u76d8\u6807\u5b9a\u677f\uff0812\u00d79\uff09<\/p>\n\n\n\n

\u9996\u5148\u83b7\u53d6\u4e24\u4e2a\u6444\u50cf\u5934\u62cd\u6444\u540c\u4e00\u6807\u5b9a\u677f\u7684\u7167\u7247\uff0c\u5982\u56fe5\u6240\u793a\u3002\u4e3a\u4e86\u540e\u7eed\u5904\u7406\u7b80\u5355\uff0c\u6b64\u65f6\u5e94\u5c06\u4e24\u5e45\u753b\u9762\u7684\u5c3a\u5bf8\u7edf\u4e00\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe5 \u53cc\u76ee\u76f8\u673a\u753b\u9762<\/p>\n\n\n\n

rgb_img2 = cv2.resize(cv2.imread(f\".\/img\/61.bmp\"), (320, 240), interpolation=cv2.INTER_NEAREST)\nir_img2 = cv2.imread(f\".\/img\/62.bmp\")<\/code><\/pre>\n\n\n\n
\u800c\u540e\u5e94\u5206\u522b\u5728\u4e24\u5e45\u753b\u9762\u4e2d\u5bfb\u627e\u51fa\u5bf9\u5e94\u7684\u7279\u5f81\u70b9\u3002<\/p>\n\n\n\n
    def find_chessboard(self, img, pattern=(9, 8), wind_name=\"rgb\", show_en=False):\n        # read input image\n        # img = cv2.imread(filename)\n        # cv2.imshow(\"raw\", img)\n        # img = cv2.undistort(img, camera_matrix, distortion_coefficients)\n\n        # convert the input image to a grayscale\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n        # Find the chess board corners\n        ret, corners = cv2.findChessboardCorners(gray, pattern, None)\n\n        # if chessboard corners are detected\n        if ret == True:\n            # Draw and display the corners\n            if show_en:\n                img = cv2.drawChessboardCorners(img.copy(), pattern, corners, ret)\n\n                # Draw number\uff0c\u6253\u5370\u89d2\u70b9\u7f16\u53f7\uff0c\u4fbf\u4e8e\u786e\u5b9a\u5bf9\u5e94\u70b9\n            corners = np.ceil(corners[:, 0, :])\n            if show_en:\n                for i, pt in enumerate(corners):\n                    cv2.putText(img, str(i), (int(pt[0]), int(pt[1])), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 255, 0), 1)\n                cv2.imshow(wind_name, img)\n\n            return corners\n        return None<\/code><\/pre>\n\n\n\n
img1_corners = self.find_chessboard(img1, chessboard_param, \"img1\", show_en)\nimg2_corners = self.find_chessboard(img2, chessboard_param, \"img2\", show_en)<\/code><\/pre>\n\n\n\n
\u8fd0\u884c\u7ed3\u679c\u5982\u56fe6\u6240\u793a\u3002\u56fe7\u4e3a\u7d22\u5f15\u53f7\u4e3a1\u7684\u7279\u5f81\u70b9\u3002\u00a0<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u56fe6 \u68cb\u76d8\u683c\u7279\u5f81\u70b9\u5bfb\u627e<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u56fe7 \u5355\u4e2a\u7279\u5f81\u70b9\u5bf9\u6bd4<\/p>\n\n\n\n
\u83b7\u53d6\u5230\u6240\u6709\u7279\u5f81\u70b9\u540e\uff0c\u5219\u5e94\u901a\u8fc7\u5bf9\u5e94\u7684\u7279\u5f81\u70b9\u8ba1\u7b97\u53d8\u6362\u77e9\u9635\u3002\u53ef\u901a\u8fc7cv2\u7684findHomography\u51fd\u6570\u5b9e\u73b0\u3002<\/p>\n\n\n\n
H\uff0cS=cv2.findHomography\uff08srcPoints\uff0cdstPoints\uff0cThreshold\uff0cmask\uff0cmaxIters\uff0cconfidence\uff09<\/code><\/pre>\n\n\n\n
\u53c2\u6570<\/th>\u89e3\u91ca<\/th><\/tr><\/thead>
srcPoints<\/td>\u76ee\u6807\u4e2d\u7684\u7279\u5f81\u70b9<\/td><\/tr>
dstPoints<\/td>\u4e0e\u76ee\u6807\u56fe\u7247\u76f8\u5339\u914d\u7684\u7279\u5f81\u70b9<\/td><\/tr>
method<\/td>\u8ba1\u7b97\u5355\u5e94\u77e9\u9635\u6240\u4f7f\u7528\u7684\u65b9\u6cd5\uff0c\u4e0d\u540c\u7684\u65b9\u6cd5\u5bf9\u5e94\u4e0d\u540c\u7684\u53c2\u6570\uff0c\u5177\u4f53\u5982\u4e0b\uff1a 0 – \u5229\u7528\u6240\u6709\u70b9\u7684\u5e38\u89c4\u65b9\u6cd5\uff0cRANSAC – RANSAC-\u57fa\u4e8eRANSAC\u7684\u9c81\u68d2\u7b97\u6cd5\uff0cLMEDS – \u6700\u5c0f\u4e2d\u503c\u9c81\u68d2\u7b97\u6cd5\uff0cRHO – PROSAC-\u57fa\u4e8ePROSAC\u7684\u9c81\u68d2\u7b97\u6cd5<\/td><\/tr>
Threshold<\/td>\u82e5srcPoints\u548cdstPoints\u662f\u4ee5\u50cf\u7d20\u4e3a\u5355\u4f4d\u7684\uff0c\u5219\u8be5\u53c2\u6570\u901a\u5e38\u8bbe\u7f6e\u57281\u523010\u7684\u8303\u56f4\u5185<\/td><\/tr>
mask<\/td>\u53ef\u9009\u8f93\u51fa\u63a9\u7801\u77e9\u9635\uff0c\u901a\u5e38\u7531\u9c81\u68d2\u7b97\u6cd5\uff08RANSAC\u6216LMEDS\uff09\u8bbe\u7f6e\u3002 \u8bf7\u6ce8\u610f\uff0c\u8f93\u5165\u63a9\u7801\u77e9\u9635\u662f\u4e0d\u9700\u8981\u8bbe\u7f6e\u7684<\/td><\/tr>
maxIters<\/td>RANSAC\u7b97\u6cd5\u7684\u6700\u5927\u8fed\u4ee3\u6b21\u6570\uff0c\u9ed8\u8ba4\u503c\u4e3a2000<\/td><\/tr>
confidence<\/td>\u53ef\u4fe1\u5ea6\u503c\uff0c\u53d6\u503c\u8303\u56f4\u4e3a0\u52301.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u5176\u8fd4\u56de\u503c\u6709\u4e24\u4e2a\uff0c\u4e00\u4e2a\u662f\u53d8\u6362\u77e9\u9635H\uff0c\u53e6\u4e00\u4e2a\u662fstatus\u5411\u91cf\uff0c\u7528\u6765\u5220\u9664\u9519\u8bef\u7684\u5339\u914d\u3002<\/p>\n\n\n\n
src_pts = []\nfor img_idx in range(len(img1_corners)): \n    src_pts.append(img2_corners[img_idx])\nh, status = cv2.findHomography(np.array(src_pts)[:, None, :], img1_corners[:, None, :])<\/code><\/pre>\n\n\n\n
\u5728\u83b7\u53d6\u53d8\u6362\u77e9\u9635\u540e\u5219\u53ef\u7ee7\u7eed\u5b8c\u6210\u753b\u9762\u5bf9\u9f50\uff0c\u5373\u5c06B\u7684\u753b\u9762\u6620\u5c04\u5230A\u7684\u6210\u50cf\u9762\u4e0a\u53bb\u3002\u8fd9\u4e00\u6b65\u901a\u8fc7cv2.warpPerspective\u5b9e\u73b0\u900f\u89c6\u53d8\u6362\u3002<\/p>\n\n\n\n
cv2.warpPerspective\u51fd\u6570\u662fOpenCV\u5e93\u4e2d\u7528\u4e8e\u6267\u884c\u900f\u89c6\u53d8\u6362\u7684\u51fd\u6570\u4e4b\u4e00\u3002\u5b83\u53ef\u4ee5\u5c06\u56fe\u50cf\u4ece\u4e00\u4e2a\u900f\u89c6\u6295\u5f71\u8f6c\u6362\u4e3a\u53e6\u4e00\u4e2a\u900f\u89c6\u6295\u5f71\uff0c\u5b9e\u73b0\u56fe\u50cf\u7684\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u5e73\u79fb\u7b49\u64cd\u4f5c\u3002\u8be5\u64cd\u4f5c\u53ef\u8868\u793a\u4e3a\uff1a<\/p>\n\n\n\n
DST = M * SRC<\/strong><\/p>\n\n\n\n
\u4e0a\u5f0f\u4e2dSRC\u8868\u793a\u539f\u56fe\u50cf\uff0cDST\u8868\u793a\u53d8\u6362\u540e\u56fe\u50cf\uff0cM\u8868\u793a\u900f\u89c6\u53d8\u6362\u77e9\u9635\uff083×3\uff09\u3002 \u8be5\u51fd\u6570\u53c2\u6570\u5982\u4e0b<\/p>\n\n\n\n
\u53c2\u6570<\/th>\u8bf4\u660e<\/th><\/tr><\/thead>
src<\/td>\u6e90\u56fe\u50cf\uff0c\u5373\u5f85\u8fdb\u884c\u900f\u89c6\u53d8\u6362\u7684\u56fe\u50cf\u3002<\/td><\/tr>
M<\/td>\u53d8\u6362\u77e9\u9635\uff0c\u53733×3\u7684\u900f\u89c6\u53d8\u6362\u77e9\u9635\u3002<\/td><\/tr>
dsize<\/td>\u76ee\u6807\u56fe\u50cf\u7684\u5927\u5c0f\uff0c\u4ee5\u5143\u7ec4\u5f62\u5f0f\u8868\u793a\uff0c\u4f8b\u5982(width, height)\u3002<\/td><\/tr>
flags<\/td>\u63d2\u503c\u65b9\u6cd5\u7684\u6807\u5fd7\uff0c\u7528\u4e8e\u6307\u5b9a\u63d2\u503c\u65b9\u6cd5\uff0c\u9ed8\u8ba4\u4e3acv2.INTER_LINEAR\u3002<\/td><\/tr>
borderMode<\/td>\u8fb9\u754c\u6a21\u5f0f\uff0c\u7528\u4e8e\u6307\u5b9a\u8d85\u51fa\u8fb9\u754c\u7684\u50cf\u7d20\u5904\u7406\u65b9\u5f0f\uff0c\u9ed8\u8ba4\u4e3acv2.BORDER_CONSTANT\u3002<\/td><\/tr>
borderValue<\/td>\u5f53\u8fb9\u754c\u6a21\u5f0f\u4e3acv2.BORDER_CONSTANT\u65f6\uff0c\u7528\u4e8e\u6307\u5b9a\u8fb9\u754c\u50cf\u7d20\u7684\u503c\uff0c\u9ed8\u8ba4\u4e3a0\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u8fd4\u56de\u503c \u76ee\u6807\u56fe\u50cf\uff0c\u5373\u7ecf\u8fc7\u900f\u89c6\u53d8\u6362\u540e\u7684\u56fe\u50cf\u3002<\/p>\n\n\n\n
\u901a\u8fc7\u4e0b\u9762\u7684\u7a0b\u5e8f\u8fdb\u884c\u53e0\u52a0\u753b\u9762\u3002<\/p>\n\n\n\n
img2_warp = cv2.warpPerspective(img2, self.H, (img2_w, img2_h), borderValue=(255, 255, 255))\nimg1_height, img1_width  = img1.shape[:2]\nimg2_warp_sized = cv2.resize(img2_warp, (img1_width, img1_height), cv2.INTER_AREA)\nmerge = cv2.addWeighted(img1, alpha, img2_warp_sized, 1 - alpha, gamma=0)<\/code><\/pre>\n\n\n\n
\u8fd0\u884c\u7ed3\u679c\u5982\u56fe8\u6240\u793a\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
 \u56fe8 \u753b\u9762\u77eb\u6b63\u53ca\u53e0\u52a0\u7ed3\u679c<\/p>\n\n\n\n
\u7531\u4e8e\u4e24\u4e2a\u6444\u50cf\u5934\u4e4b\u95f4\u7684\u76f8\u5bf9\u4f4d\u7f6e\u5173\u7cfb\u5728\u51fa\u5382\u540e\u6b63\u5e38\u60c5\u51b5\u4e0b\u57fa\u672c\u56fa\u5b9a\u3002\u56e0\u6b64\uff0c\u53d8\u6362\u77e9\u9635\u53ef\u4e0d\u5fc5\u7ecf\u5e38\u66f4\u65b0\u3002\u5982\u679c\u6444\u50cf\u5934\u7ecf\u8fc7\u4e86\u4e25\u91cd\u7684\u78d5\u78b0\uff0c\u6709\u53ef\u80fd\u5bfc\u81f4\u76f8\u5bf9\u4f4d\u7f6e\u51fa\u73b0\u53d8\u52a8\uff0c\u6b64\u65f6\u5e94\u91cd\u65b0\u6807\u5b9a\u6821\u51c6\u3002\u5982\u56fe9\u4e3a\u5904\u7406\u5b9e\u65f6\u89c6\u9891\u6d41\u7684\u6548\u679c\u3002 <\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
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