{"id":38736,"date":"2024-11-02T00:56:41","date_gmt":"2024-11-02T00:56:41","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=38736"},"modified":"2024-11-26T06:43:29","modified_gmt":"2024-11-26T06:43:29","slug":"opencv-%ea%b0%95%ec%a2%8c-%ec%8a%a4%ed%85%8c%eb%a0%88%ec%98%a4-%eb%b9%84%ec%a0%84%ea%b3%bc-%ea%b9%8a%ec%9d%b4-%ec%a0%95%eb%b3%b4-%ec%b6%94%ec%b6%9c","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/38736\/","title":{"rendered":"OpenCV \uac15\uc88c, \uc2a4\ud14c\ub808\uc624 \ube44\uc804\uacfc \uae4a\uc774 \uc815\ubcf4 \ucd94\ucd9c"},"content":{"rendered":"

<\/p>\n

\uc2a4\ud14c\ub808\uc624 \ube44\uc804\uc740 \uc778\uac04\uc758 \ub208\uc774 \ubb3c\uccb4\uc758 \uae4a\uc774\ub97c \uc778\uc2dd\ud558\ub294 \ubc29\uc2dd\uc744 \ubaa8\ubc29\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. \uc774\ub294 \ub450 \uac1c\uc758 \uce74\uba54\ub77c\ub97c \uc0ac\uc6a9\ud558\uc5ec 3D \ud658\uacbd\uc5d0\uc11c \uae4a\uc774 \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\ub294 \uacfc\uc815\uc73c\ub85c, \ub85c\ubd07 \ube44\uc804, \uc790\uc728\uc8fc\ud589\ucc28, 3D \uc7ac\uad6c\uc131 \ubc0f \uc99d\uac15 \ud604\uc2e4\uc5d0 \uad11\ubc94\uc704\ud558\uac8c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

1. \uc2a4\ud14c\ub808\uc624 \ube44\uc804\uc758 \uae30\ucd08<\/h2>\n

\uc2a4\ud14c\ub808\uc624 \ube44\uc804\uc740 \ub450 \uac1c\uc758 \uce74\uba54\ub77c\uac00 \ub3d9\uc2dc\uc5d0 \ub3d9\uc77c\ud55c \uc7a5\uba74\uc744 \ucea1\ucc98\ud558\uc5ec \uae4a\uc774 \uc815\ubcf4\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uac01 \uce74\uba54\ub77c\uc758 \uc2dc\uc810 \ucc28\uc774 \ub54c\ubb38\uc5d0 \uac01 \uce74\uba54\ub77c\uc5d0\uc11c \ucc0d\ud78c \ub3d9\uc77c\ud55c \uc810\uc758 \uc704\uce58\uac00 \uc11c\ub85c \ub2e4\ub974\uac8c \ubcf4\uc774\uac8c \ub429\ub2c8\ub2e4. \uc774 \ub450 \uc774\ubbf8\uc9c0 \uac04\uc758 \ucc28\uc774\ub97c \ubd84\uc11d\ud558\uc5ec \uac01 \uc810\uc758 \uae4a\uc774\ub97c \ucd94\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1. \uc0bc\uac01\uce21\ub7c9 (Triangulation)<\/h3>\n

\uc0bc\uac01\uce21\ub7c9\uc740 \ub450 \uce74\uba54\ub77c\uc758 \uc704\uce58\uc640 \uac01 \uce74\uba54\ub77c\uc5d0\uc11c \ucea1\ucc98\ub41c \uc810\uc758 \uc704\uce58 \uc815\ubcf4\ub97c \uae30\ubc18\uc73c\ub85c \uae4a\uc774\ub97c \uacc4\uc0b0\ud558\ub294 \uae30\ubcf8\uc801\uc778 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub450 \uce74\uba54\ub77c\uc758 \uc704\uce58\uc640 \ub0b4\uc801 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n

2. OpenCV \uc124\uce58<\/h2>\n

OpenCV\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 OpenCV \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec OpenCV\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install opencv-python opencv-python-headless<\/code><\/pre>\n
3. \uc2a4\ud14c\ub808\uc624 \ube44\uc804 \uc2dc\uc2a4\ud15c \uad6c\ucd95<\/h2>\n
\uc2a4\ud14c\ub808\uc624 \ube44\uc804 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
    \n
  1. \uce74\uba54\ub77c \uce98\ub9ac\ube0c\ub808\uc774\uc158 (Camera Calibration)<\/li>\n
  2. \uc2a4\ud14c\ub808\uc624 \uc815\ud569 (Stereo Matching)<\/li>\n
  3. \uae4a\uc774 \ub9f5 \uc0dd\uc131 (Depth Map Generation)<\/li>\n<\/ol>\n
    3.1. \uce74\uba54\ub77c \uce98\ub9ac\ube0c\ub808\uc774\uc158<\/h3>\n
    \uce74\uba54\ub77c\uc758 \ub0b4\ubd80 \ub9e4\uac1c\ubcc0\uc218\uc640 \uc65c\uace1 \uacc4\uc218\ub97c \ucd94\uc815\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uce74\uba54\ub77c\uc758 \uc65c\uace1\uc744 \ubcf4\uc815\ud558\uace0, \uc815\ud655\ud55c 3D \uc88c\ud45c\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. OpenCV\uc5d0\uc11c\ub294 \uccb4\uc2a4\ud310 \ud328\ud134\uc744 \uc0ac\uc6a9\ud558\uc5ec \uce74\uba54\ub77c\ub97c \uce98\ub9ac\ube0c\ub808\uc774\uc158\ud558\ub294 \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
    \ucf54\ub4dc \uc608\uc81c: \uce74\uba54\ub77c \uce98\ub9ac\ube0c\ub808\uc774\uc158<\/h4>\n
    \nimport cv2\nimport numpy as np\nimport glob\n\n# \uccb4\uc2a4\ud310 \ud328\ud134\uc758 \ud06c\uae30\uc640 \ub0b4\ubd80 \ucf54\ub108 \uc218\nchessboard_size = (7, 6)  # \ub0b4\ubd80 \ucf54\ub108 \uc218\nsquare_size = 1.0  # \uccb4\uc2a4\ubcf4\ub4dc \uc815\uc0ac\uac01\ud615\uc758 \uc2e4\uc81c \ud06c\uae30\n\n# 3D \ud3ec\uc778\ud2b8\uc640 2D \ud3ec\uc778\ud2b8\ub97c \uc800\uc7a5\ud560 \ub9ac\uc2a4\ud2b8\nobjpoints = []  # 3D \ud3ec\uc778\ud2b8\nimgpoints = []  # 2D \ud3ec\uc778\ud2b8\n\n# 3D \ud3ec\uc778\ud2b8 \uc0dd\uc131\nobjp = np.zeros((chessboard_size[0] * chessboard_size[1], 3), np.float32)\nobjp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) * square_size\n\n# \uc774\ubbf8\uc9c0 \ud30c\uc77c \uacbd\ub85c\nimages = glob.glob('path_to_your_images\/*.jpg')\n\nfor fname in images:\n    img = cv2.imread(fname)\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n    # \uccb4\uc2a4\ud310 \ucc3e\uae30\n    ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None)\n\n    if ret:\n        imgpoints.append(corners)\n        objpoints.append(objp)\n\n# \uce74\uba54\ub77c \uce98\ub9ac\ube0c\ub808\uc774\uc158\nret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)\n\nprint(\"Camera Matrix:\\n\", mtx)\nprint(\"Distortion Coefficients:\\n\", dist)\n    <\/code><\/pre>\n
    3.2. \uc2a4\ud14c\ub808\uc624 \uc815\ud569<\/h3>\n
    \ub450 \uac1c\uc758 \uce74\uba54\ub77c\uc5d0\uc11c \ucd2c\uc601\ud55c \uc774\ubbf8\uc9c0 \uac04\uc758 \ub300\uc751 \uad00\uacc4\ub97c \ucc3e\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. OpenCV\uc5d0\uc11c\ub294 `StereoBM` \ubc0f `StereoSGBM` \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2a4\ud14c\ub808\uc624 \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    \ucf54\ub4dc \uc608\uc81c: \uc2a4\ud14c\ub808\uc624 \uc815\ud569<\/h4>\n
    \n# \ub450 \uac1c\uc758 \uc774\ubbf8\uc9c0 \ub85c\ub4dc\nimgL = cv2.imread('left_image.jpg', cv2.IMREAD_GRAYSCALE)\nimgR = cv2.imread('right_image.jpg', cv2.IMREAD_GRAYSCALE)\n\n# StereoSGBM \uac1d\uccb4 \uc0dd\uc131\nstereo = cv2.StereoSGBM_create(minDisparity=0,\n                                numDisparities=16,\n                                blockSize=5,\n                                P1=8 * 3 * blockSize**2,\n                                P2=32 * 3 * blockSize**2,\n                                disp12MaxDiff=1,\n                                uniquenessRatio=15,\n                                speckleWindowSize=0,\n                                speckleRange=2,\n                                mode=cv2.STEREO_SGBM_MODE_SGBM)\n    \n# Disparity \uacc4\uc0b0\ndisparity = stereo.compute(imgL, imgR).astype(np.float32) \/ 16.0\n\n# Disparity \ub9f5\uc744 \uc2dc\uac01\ud654\nplt.imshow(disparity, 'gray')\nplt.show()\n    <\/code><\/pre>\n
    3.3. \uae4a\uc774 \ub9f5 \uc0dd\uc131<\/h3>\n
    Disparity \ub9f5\uc744 \uae30\ubc18\uc73c\ub85c \uae4a\uc774 \uc815\ubcf4\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uae4a\uc774\ub294 disparity \uac12\uacfc \uce74\uba54\ub77c\uc758 \ub0b4\ubd80 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc774\uc6a9\ud558\uc5ec \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    \ucf54\ub4dc \uc608\uc81c: \uae4a\uc774 \uc815\ubcf4 \ucd94\ucd9c<\/h4>\n
    \n# \uae4a\uc774 \ub9f5 \uacc4\uc0b0 (\ub2e8\uc21c\ud55c \ube44\ub840 \uacc4\uc0b0)\nfocal_length = mtx[0, 0]  # \uce74\uba54\ub77c\uc758 \ucd08\uc810\uac70\ub9ac\nbaseline = 0.54  # \uce74\uba54\ub77c \uac04 \uac70\ub9ac (\ubbf8\ud130)\n\n# \uae4a\uc774 \ub9f5 \uacc4\uc0b0\ndepth_map = (focal_length * baseline) \/ disparity\ndepth_map[disparity == 0] = 0  # Disparity\uac00 0\uc778 \ubd80\ubd84\uc740 \uae4a\uc774 \uc815\ubcf4\uac00 \uc5c6\uc74c\n\n# \uae4a\uc774 \ub9f5 \uc2dc\uac01\ud654\nplt.imshow(depth_map, cmap='plasma')\nplt.colorbar()\nplt.title('Depth Map')\nplt.show()\n    <\/code><\/pre>\n
    4. \uacb0\ub860<\/h2>\n
    \uc2a4\ud14c\ub808\uc624 \ube44\uc804\uc740 3D \ud658\uacbd\uc5d0\uc11c \uae4a\uc774 \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\ub294 \uac15\ub825\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. OpenCV\ub97c \uc0ac\uc6a9\ud558\uba74 \uc774 \uacfc\uc815\uc744 \ub9e4\uc6b0 \ud6a8\uc728\uc801\uc73c\ub85c \uc218\ud589\ud560 \uc218 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c meaningful\ud55c \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uae30\ubcf8\uc801\uc778 \uc2a4\ud14c\ub808\uc624 \ube44\uc804 \uc2dc\uc2a4\ud15c \uad6c\ucd95 \ubc29\ubc95\uacfc OpenCV\ub97c \uc774\uc6a9\ud55c \ucf54\ub4dc \uc608\uc81c\ub97c \uc18c\uac1c\ud558\uc600\uc2b5\ub2c8\ub2e4.<\/p>\n
    \uc138\ubd80\uc801\uc778 \ub9e4\uac1c\ubcc0\uc218\uc640 \uc870\uc815\uc744 \ud1b5\ud574 \uac01 \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc758 \uc694\uad6c\uc5d0 \ub9de\uac8c \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ub354 \ubc1c\uc804\ub41c \uae30\ubc95\uc778 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc2a4\ud14c\ub808\uc624 \ube44\uc804\uacfc \uacb0\ud569\ud558\uc5ec \ub354\uc6b1 \uc815\uad50\ud55c \uc2dc\uc2a4\ud15c\uc744 \ub9cc\ub4e4\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    \ucc38\uace0 \uc790\ub8cc<\/h2>\n