\uc791\uc131\uc790: \uc870\uad11\ud615<\/p>\n
\ub0a0\uc9dc: 2024\ub144 11\uc6d4 26\uc77c<\/p>\n<\/header>\n \n \ucef4\ud4e8\ud130 \ube44\uc804\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uac1d\uccb4 \ucd94\uc801\uc740 \ub9ce\uc740 \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \n KLT Tracker\ub294 \uc774\ubbf8\uc9c0 \ub0b4\uc758 \ud2b9\uc9d5 \uc810\ub4e4\uc744 \uc0ac\uc6a9\ud558\uc5ec, \ud504\ub808\uc784 \uac04 \uac1d\uccb4\uc758 \uc774\ub3d9\uc744 \ucd94\uc801\ud569\ub2c8\ub2e4. \n KLT Tracker\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ubcf8 \ub2e8\uacc4\ub85c \ub3d9\uc791\ud569\ub2c8\ub2e4:\n <\/p>\n \n OpenCV\ub294 pip\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec OpenCV\ub97c \uc124\uce58\ud558\uc138\uc694.\n <\/p>\n \n \ub610\ud55c, NumPy\uc640 matplotlib\ub3c4 \uc124\uce58\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub4e4\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n \n KLT Tracker\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \ucd94\uc801\ud558\ub294 \uc608\uc81c \ucf54\ub4dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \n \uc704\uc758 \ucf54\ub4dc\ub294 \uc6f9\ucea0\uc73c\ub85c\ubd80\ud130 \uc2e4\uc2dc\uac04 \ube44\ub514\uc624 \uc2a4\ud2b8\ub9bc\uc744 \ubc1b\uc544\ub4e4\uc774\uace0, KLT Tracker\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc9d5 \uc810\uc744 \ucd94\uc801\ud569\ub2c8\ub2e4. \n \uc704 \uc608\uc81c\uc5d0\uc11c\ub294 \ub9ce\uc740 \uae30\ubcf8 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc774 \uc124\uc815\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc870\uc815\ud558\uc5ec \uacb0\uacfc\ub97c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n KLT Tracker\ub294 \ube60\ub974\uac8c \uc2e4\ud589\ub418\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \uba87 \uac00\uc9c0 \ud55c\uacc4\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \n KLT Tracker\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uacbd\uc6b0\uc5d0 \uc798 \uc791\ub3d9\ud569\ub2c8\ub2e4:\n <\/p>\n \n KLT Tracker \uc678\uc5d0\ub3c4 OpenCV\ub97c \ud1b5\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \uac1d\uccb4 \ucd94\uc801 \uc54c\uace0\ub9ac\uc998\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4. \n \uac01 \uc54c\uace0\ub9ac\uc998\uc740 \ud2b9\uc815\ud55c \uc0c1\ud669\uc5d0\uc11c \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\ubbc0\ub85c, \uc5ec\ub7ec\ubd84\uc758 \ud544\uc694\uc5d0 \ub530\ub77c \uc801\uc808\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n \n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 OpenCV\ub97c \uc774\uc6a9\ud55c KLT Tracker\uc758 \uae30\ubcf8 \uc0ac\uc6a9 \ubc29\ubc95\uacfc \uc608\uc81c\ub97c \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \uc758\uacac\uc774\ub098 \uc9c8\ubb38\uc740 \ub313\uae00\ub85c \ub0a8\uaca8\uc8fc\uc138\uc694!<\/p>\n<\/section>\n<\/article>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"1. \uc11c\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 OpenCV\ub97c \uc774\uc6a9\ud558\uc5ec KLT(Kanade-Lucas-Tomasi) Tracker\ub85c \uac1d\uccb4\ub97c \ucd94\uc801\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud558\uc5ec \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.
\n KLT \ud2b8\ub798\ucee4\ub294 \ud2b9\uc9d5 \uc810 \ud328\ud134\uc744 \uae30\ubc18\uc73c\ub85c\ud558\uc5ec, \uc601\uc0c1\uc5d0\uc11c \uc6c0\uc9c1\uc774\ub294 \uac1d\uccb4\ub97c \ucd94\uc801\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.\n <\/p>\n2. KLT Tracker \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\n \uc774 \ubc29\ubc95\uc740 \uac00\uc7a5\uc790\ub9ac, \ucf54\ub108 \ub4f1\uacfc \uac19\uc740 \uace0\uc720\ud55c \ud328\ud134\uc744 \uac00\uc9c4 \ud2b9\uc9d5 \uc810\uc744 \ucc3e\uc544\ub0b4\uace0,
\n \ub2e4\uc74c \ud504\ub808\uc784\uc5d0 \uc774\ub4e4 \uc810\uc758 \uc704\uce58\ub97c \ucd94\uc815\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uac1d\uccb4\uc758 \uc704\uce58\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucd94\uc801\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n\n
3. KLT Tracker\uc758 \uc124\uce58<\/h2>\n
pip install opencv-python opencv-python-headless<\/code><\/pre>\npip install numpy matplotlib<\/code><\/pre>\n4. KLT Tracker \uc0ac\uc6a9\ud558\uae30<\/h2>\n
\n \uc774 \ucf54\ub4dc\ub294 \uc6f9\ucea0\uc5d0\uc11c \uac1d\uccb4\ub97c \ucd94\uc801\ud558\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.\n <\/p>\n\nimport cv2\nimport numpy as np\n\n# \ube44\ub514\uc624 \ucea1\ucc98 \uac1d\uccb4 \uc0dd\uc131\ncap = cv2.VideoCapture(0)\n\n# \uccab \ubc88\uc9f8 \ud504\ub808\uc784 \uc77d\uae30\nret, old_frame = cap.read()\nold_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)\n\n# ShiTomasi \ucf54\ub108 \uac80\ucd9c\uae30 \uc0dd\uc131\nfeature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)\np0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params)\n\n# KLT Tracker\ub97c \uc704\ud55c Lucas-Kanade \ub9e4\uac1c\ubcc0\uc218 \uc124\uc815\nlk_params = dict(winSize=(15, 15), maxLevel=2,\n criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\n\n# \ubcc0\ud654 \ub9c8\uc2a4\ud06c \uc0dd\uc131\nmask = np.zeros_like(old_frame)\n\nwhile cap.isOpened():\n ret, frame = cap.read()\n if not ret:\n break\n \n frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Lucas-Kanade \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc9d5 \uc810 \ucd94\uc801\n p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)\n\n # \ucd94\uc801\uc5d0 \uc131\uacf5\ud55c \ud3ec\uc778\ud2b8\ub9cc \uc120\ud0dd\n good_new = p1[st == 1]\n good_old = p0[st == 1]\n\n # \uacb0\uacfc \uadf8\ub9ac\uae30\n for i, (new, old) in enumerate(zip(good_new, good_old)):\n a, b = new.ravel()\n c, d = old.ravel()\n mask = cv2.line(mask, (a, b), (c, d), (0, 255, 0), 2)\n frame = cv2.circle(frame, (a, b), 5, (0, 0, 255), -1)\n\n img = cv2.add(frame, mask)\n cv2.imshow('Frame', img)\n\n # ESC \ud0a4\ub85c \uc885\ub8cc\n if cv2.waitKey(30) & 0xFF == 27:\n break\n\n # \uc774\uc804 \uac12 \uc5c5\ub370\uc774\ud2b8\n old_gray = frame_gray.copy()\n p0 = good_new.reshape(-1, 1, 2)\n\ncap.release()\ncv2.destroyAllWindows()\n <\/code><\/pre>\n
\n \ucf54\ub4dc\uc758 \uc8fc\uc694 \ubd80\ubd84\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n\n
5. \ub2e4\uc591\ud55c \ub9e4\uac1c\ubcc0\uc218\ub97c \uc870\uc815\ud574\ubcf4\uae30<\/h3>\n
\n `feature_params`\uc640 `lk_params`\uc758 \ubcc0\uc218\ub97c \uc9c1\uc811 \uc870\uc815\ud574\ubcf4\uc138\uc694.
\n `maxCorners`, `qualityLevel`, `minDistance`, `blockSize` \uac12\ub4e4\uc744 \uc870\uc815\ud558\uc5ec \ub2e4\uc591\ud55c \ud2b9\uc9d5 \uc810 \uac80\ucd9c\uc744 \uc2dc\ud5d8\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n6. KLT Tracker\uc758 \uc7a5\uc810\uacfc \ud55c\uacc4<\/h2>\n
\n \ud2b9\ud788, \uc601\uc0c1\uc774 \uae09\uaca9\ud558\uac8c \ubcc0\ud558\uac70\ub098 \ud070 \uc6c0\uc9c1\uc784\uc774 \uc788\uc744 \ub54c\ub294 \uc815\ud655\ub3c4\uac00 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ub610\ud55c, \uc870\uba85\uc774 \ubcc0\ud558\uac70\ub098 \ubc30\uacbd\uc774 \ubcf5\uc7a1\ud574\uc9c8 \uacbd\uc6b0\uc5d0\ub294 \uc2e4\ud328\ud560 \uac00\ub2a5\uc131\uc774 \ub192\uc544\uc9d1\ub2c8\ub2e4.\n <\/p>\n\n
7. KLT Tracker\uc640 \ub2e4\ub978 \uc54c\uace0\ub9ac\uc998 \ube44\uad50<\/h2>\n
\n \uc774 \uc911 \uba87 \uac00\uc9c0\ub97c \ube44\uad50\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n\n
8. \uacb0\ub860<\/h2>\n
\n KLT Tracker\ub294 \ube60\ub974\uace0 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uac1d\uccb4\ub97c \ucd94\uc801\ud560 \uc218 \uc788\uc9c0\ub9cc, \uc0c1\ud669\uc5d0 \ub530\ub77c\uc11c \ud55c\uacc4\ub97c \ub098\ud0c0\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc5ec\ub7ec\ubd84\uc774 \uc774 \uc815\ubcf4\ub97c \ubc14\ud0d5\uc73c\ub85c \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc801\uc6a9\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n