{"id":38766,"date":"2024-11-02T00:56:55","date_gmt":"2024-11-02T00:56:55","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=38766"},"modified":"2024-11-26T06:43:24","modified_gmt":"2024-11-26T06:43:24","slug":"opencv-%ea%b0%95%ec%a2%8c-%eb%8b%a4%ec%96%91%ed%95%9c-%ec%9d%b4%eb%af%b8%ec%a7%80-%eb%b6%84%ed%95%a0-%ea%b8%b0%eb%b2%95%ea%b3%bc-%ea%b7%b8-%ec%9d%91%ec%9a%a9","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/38766\/","title":{"rendered":"OpenCV \uac15\uc88c, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uacfc \uadf8 \uc751\uc6a9"},"content":{"rendered":"

<\/p>\n

\n \uc774\ubbf8\uc9c0 \ubd84\ud560\uc740 \ucef4\ud4e8\ud130 \ube44\uc804\uc5d0\uc11c \uc911\uc694\ud55c \uacfc\uc815\uc73c\ub85c, \uc774\ubbf8\uc9c0\ub97c \uc5ec\ub7ec \ubd80\ubd84\uc73c\ub85c \ub098\ub204\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \uac01 \uac1d\uccb4\ub97c \uc778\uc2dd\ud558\uace0 \ubd84\uc11d\ud558\ub294 \ub370 \ud544\uc694\ud55c \uae30\ucd08 \uc791\uc5c5\uc774 \ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 OpenCV\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uace0, \uc774 \uae30\ubc95\ub4e4\uc774 \uc2e4\uc81c\ub85c \uc5b4\ub5bb\uac8c \uc751\uc6a9\ub418\ub294\uc9c0\ub97c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. \uc774\ubbf8\uc9c0 \ubd84\ud560\uc774\ub780?<\/h2>\n

\n \uc774\ubbf8\uc9c0 \ubd84\ud560\uc740 \uc774\ubbf8\uc9c0 \ub0b4\uc758 \ud2b9\uc815 \uac1d\uccb4\ub97c \ubd84\ub9ac\ud558\uace0 \uac01\uac01\uc758 \uc601\uc5ed\uc73c\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub294 \uc774\ubbf8\uc9c0 \uc774\ud574, \uac1d\uccb4 \uc778\uc2dd, \uc774\ubbf8\uc9c0 \ud3b8\uc9d1, \uc758\ub8cc \uc774\ubbf8\uc9c0 \ubd84\uc11d \ubc0f \uc790\uc728\uc8fc\ud589\ucc28\uc640 \uac19\uc740 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub429\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ubd84\ud560\uc758 \uc8fc\ub41c \ubaa9\uc801\uc740 \uc774\ubbf8\uc9c0\uc758 \ud615\ud0dc\uc640 \uad6c\uc870\ub97c \ub354 \uc27d\uac8c \ubd84\uc11d\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

2. OpenCV \uc18c\uac1c<\/h2>\n

\n OpenCV(Open Source Computer Vision Library)\ub294 \uc2e4\uc2dc\uac04 \uc774\ubbf8\uc9c0 \ucc98\ub9ac \ubc0f \ucef4\ud4e8\ud130 \ube44\uc804 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. OpenCV\ub294 C++, Python, Java \ub4f1 \uc5ec\ub7ec \uc5b8\uc5b4\ub97c \uc9c0\uc6d0\ud558\uc5ec \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uc5d0\uc11c \ud65c\uc6a9\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 Python\uc744 \uc8fc\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc608\uc81c\ub97c \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95<\/h2>\n

3.1. \uc784\uacc4\uac12(thresholding)<\/h3>\n

\n \uc784\uacc4\uac12 \ucc98\ub9ac\ub294 \uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\uc220 \uc911 \ud558\ub098\ub85c, \ud53d\uc140 \uac12\uc744 \uae30\uc900\uc73c\ub85c \uc774\ubbf8\uc9c0\ub97c \uc774\uc9c4\ud654\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uac1d\uccb4\uc640 \ubc30\uacbd\uc744 \uba85\ud655\ud788 \uad6c\ubd84\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

\ucf54\ub4dc \uc608\uc81c:<\/h4>\n
\n        \n        import cv2\n        import numpy as np\n\n        # \uc774\ubbf8\uc9c0 \uc77d\uae30\n        img = cv2.imread('image.jpg', 0)\n\n        # \uc784\uacc4\uac12 \ucc98\ub9ac\n        ret, thresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)\n\n        # \uacb0\uacfc \ucd9c\ub825\n        cv2.imshow('Original Image', img)\n        cv2.imshow('Threshold Image', thresh)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        <\/code>\n    <\/pre>\n
\n        \uc704\uc758 \ucf54\ub4dc\ub294 \uc774\ubbf8\uc9c0 \ud30c\uc77c\uc744 \uc77d\uace0, 127\uc744 \uc784\uacc4\uac12\uc73c\ub85c \uc124\uc815\ud558\uc5ec \uc774\uc9c4\ud654\ud55c \uacb0\uacfc\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4.\n    <\/p>\n
3.2. Otsu\uc758 \ubc29\ubc95<\/h3>\n
\n        Otsu\uc758 \ubc29\ubc95\uc740 \uc784\uacc4\uac12 \ucc98\ub9ac\ub97c \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud55c \uae30\ubc95\uc73c\ub85c, \uc774\ubbf8\uc9c0\uc758 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ubd84\uc11d\ud558\uc5ec \uc790\ub3d9\uc73c\ub85c \ucd5c\uc801\uc758 \uc784\uacc4\uac12\uc744 \ucc3e\uc544\ub0c5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub354\uc6b1 \ud6a8\uacfc\uc801\uc73c\ub85c \uac1d\uccb4\uc640 \ubc30\uacbd\uc744 \ubd84\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\ucf54\ub4dc \uc608\uc81c:<\/h4>\n
\n        \n        import cv2\n\n        # \uc774\ubbf8\uc9c0 \uc77d\uae30\n        img = cv2.imread('image.jpg', 0)\n\n        # Otsu \uc784\uacc4\uac12 \ucc98\ub9ac\n        ret, otsu_thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n        # \uacb0\uacfc \ucd9c\ub825\n        cv2.imshow('Original Image', img)\n        cv2.imshow('Otsu Threshold Image', otsu_thresh)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        <\/code>\n    <\/pre>\n
\n        Otsu\uc758 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\uc758 \uc784\uacc4\uac12\uc744 \uc790\ub3d9\uc73c\ub85c \uacb0\uc815\ud558\uace0 \uacb0\uacfc\ub97c \ucd9c\ub825\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n    <\/p>\n
3.3. K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1<\/h3>\n
\n        K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ube44\uc9c0\ub3c4 \ud559\uc2b5 \uae30\ubc95\uc73c\ub85c, \uc774\ubbf8\uc9c0\ub97c k\uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub85c \ub098\ub204\uc5b4 \uac01 \ud074\ub7ec\uc2a4\ud130\uc5d0 \uc18d\ud558\ub294 \ud53d\uc140\uc744 \uadf8\ub8f9\ud654\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uc0c9\uc0c1 \uae30\ubc18 \ubd84\ud560\uc5d0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.\n    <\/p>\n
\ucf54\ub4dc \uc608\uc81c:<\/h4>\n
\n        \n        import cv2\n        import numpy as np\n\n        # \uc774\ubbf8\uc9c0 \uc77d\uae30\n        img = cv2.imread('image.jpg')\n        Z = img.reshape((-1, 3))\n\n        # \ub370\uc774\ud130\ub97c float32\ud615\uc73c\ub85c \ubcc0\ud658\n        Z = np.float32(Z)\n\n        # K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1\n        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)\n        k = 3\n        retval, labels, centers = cv2.kmeans(Z, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)\n\n        # \ud074\ub7ec\uc2a4\ud130 \uc911\uc2ec\uc73c\ub85c \uc774\ubbf8\uc9c0 \uc7ac\uad6c\uc131\n        centers = np.uint8(centers)\n        segmented_image = centers[labels.flatten()]\n        segmented_image = segmented_image.reshape(img.shape)\n\n        # \uacb0\uacfc \ucd9c\ub825\n        cv2.imshow('Original Image', img)\n        cv2.imshow('K-Means Segmented Image', segmented_image)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        <\/code>\n    <\/pre>\n
\n        K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub97c 3\uac1c\uc758 \uc0c9\uc0c1 \ud074\ub7ec\uc2a4\ud130\ub85c \ub098\ub208 \uacb0\uacfc\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n    <\/p>\n
3.4. Watershed \uc54c\uace0\ub9ac\uc998<\/h3>\n
\n        Watershed \uc54c\uace0\ub9ac\uc998\uc740 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\uc758 \uacbd\uacc4\ub97c \ucc3e\uae30 \uc704\ud55c \uae30\ubc95\uc73c\ub85c, \uc774\ubbf8\uc9c0\uc758 \uae4a\uc774 \uc815\ubcf4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \ubd84\ub9ac\ud569\ub2c8\ub2e4. \uc774 \uae30\ubc95\uc740 \uc885\uc885 \ub178\uc774\uc988\uac00 \ub9ce\uc740 \uc774\ubbf8\uc9c0\uc5d0\uc11c\ub3c4 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.\n    <\/p>\n
\ucf54\ub4dc \uc608\uc81c:<\/h4>\n
\n        \n        import cv2\n        import numpy as np\n\n        # \uc774\ubbf8\uc9c0 \uc77d\uae30 \ubc0f \uc804\ucc98\ub9ac\n        img = cv2.imread('image.jpg')\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\n        # \uac70\ub9ac \ubcc0\ud658 \ubc0f \uc774\uc9c4\ud654\n        dist_transform = cv2.distanceTransform(thresh, cv2.DIST_L2, 5)\n        ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)\n\n        # \ubc30\uacbd \ubc0f \uacbd\uacc4 \uc815\uc758\n        unknown = cv2.subtract(thresh, sure_fg)\n        ret, markers = cv2.connectedComponents(np.uint8(sure_fg))\n\n        # Watershed \uc801\uc6a9\n        markers = markers + 1\n        markers[unknown == 255] = 0\n        cv2.watershed(img, markers)\n\n        img[markers == -1] = [255, 0, 0]\n\n        # \uacb0\uacfc \ucd9c\ub825\n        cv2.imshow('Watershed Result', img)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        <\/code>\n    <\/pre>\n
\n        Watershed \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\ub97c \ubd84\ub9ac\ud558\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n    <\/p>\n
3.5. \ubd84\ud560 \uae30\ubc18 Segmentation<\/h3>\n
\n        \uc870\uac74\ubd80 \ub79c\ub364 \ud544\ub4dc\uc640 \uac19\uc740 \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ud560\ud558\ub294 \uc811\uadfc\ubc95\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc774\ubbf8\uc9c0\uc758 \uc804\ubc18\uc801\uc778 \uad6c\uc870\ub97c \ud65c\uc6a9\ud558\uc5ec \ub354 \uc815\uad50\ud55c \ubd84\ud560\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\n    <\/p>\n
\ucf54\ub4dc \uc608\uc81c:<\/h4>\n
\n        \n        import cv2\n        import numpy as np\n\n        # \uc774\ubbf8\uc9c0 \uc77d\uae30\n        img = cv2.imread('image.jpg')\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n        # \ucd08\uae30 \uc138\uadf8\uba58\ud14c\uc774\uc158\n        ret, thresh1 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n\n        # Morphological \uc5f0\uc0b0\uc744 \ud1b5\ud574 \ub178\uc774\uc988 \uc81c\uac70 \ubc0f \uacbd\uacc4 \uac15\ud654\n        kernel = np.ones((5,5),np.uint8)\n        opening = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel, iterations=2)\n\n        # \uacb0\uacfc \ucd9c\ub825\n        cv2.imshow('Segmentation Result', opening)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        <\/code>\n    <\/pre>\n
\n        \ubd84\ud560 \uae30\ubc18 \uc138\uadf8\uba58\ud14c\uc774\uc158\uc744 \uc218\ud589\ud558\uc5ec \uc774\ubbf8\uc9c0\uc758 \uad6c\uc870\ub97c \uac15\ud654\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n    <\/p>\n
4. \uc774\ubbf8\uc9c0 \ubd84\ud560\uc758 \uc751\uc6a9<\/h2>\n
\n        \uc774\ubbf8\uc9c0 \ubd84\ud560\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc758\ub8cc \uc601\uc0c1\uc5d0\uc11c \uc885\uc591\uc758 \uc704\uce58\ub97c \uc2dd\ubcc4\ud558\uac70\ub098 \uc790\uc728\uc8fc\ud589\ucc28\uc5d0\uc11c \ub3c4\ub85c \uacbd\uacc4\ub97c \uc778\uc2dd\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uae30\ubc95\ub9c8\ub2e4 \uadf8\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \uc801\uc6a9 \uc0ac\ub840\uac00 \uc788\uc73c\ubbc0\ub85c, \uc0c1\ud669\uc5d0 \ub9de\ub294 \ubd84\ud560 \uae30\ubc95\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n    <\/p>\n
4.1. \uc758\ub8cc \uc601\uc0c1 \ubd84\uc11d<\/h3>\n
\n        \uc758\ub8cc \uc601\uc0c1 \ubd84\uc11d\uc5d0\uc11c\ub294 MRI, CT \uc2a4\uce94 \ub4f1\uc5d0\uc11c \uc885\uc591, \uc7a5\uae30 \ub4f1\uc744 \ubd84\ub9ac\ud558\uae30 \uc704\ud574 \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uc774 \ud65c\uc6a9\ub429\ub2c8\ub2e4. Otsu\uc758 \ubc29\ubc95\uc774\ub098 Watershed \uc54c\uace0\ub9ac\uc998\uc774 \ud2b9\ud788 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n    <\/p>\n
4.2. \uc790\uc728\uc8fc\ud589\ucc28<\/h3>\n
\n        \uc790\uc728\uc8fc\ud589\ucc28\uc5d0\uc11c \ub3c4\ub85c, \ubcf4\ud589\uc790, \ub2e4\ub978 \ucc28\ub7c9 \ub4f1\uc744 \uc778\uc2dd\ud558\uae30 \uc704\ud574 K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1 \ub610\ub294 \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \uc138\uadf8\uba58\ud14c\uc774\uc158 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
4.3. \uc601\uc0c1 \ud3b8\uc9d1 \ubc0f \ud544\ud130\ub9c1<\/h3>\n
\n        \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uc740 \uc0ac\uc9c4 \ud3b8\uc9d1, \ubc30\uacbd \uc81c\uac70, \ud544\ud130 \uc801\uc6a9 \ub4f1\uc5d0\uc11c \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ud2b9\ud788, K-Means \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \ud1b5\ud574 \uc0c9\uc0c1\uc744 \ubcc0\uacbd\ud558\uac70\ub098 \ubd80\ubd84\uc801\uc778 \ud3b8\uc9d1\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
5. \uacb0\ub860<\/h2>\n
\n        \uc774\ubbf8\uc9c0 \ubd84\ud560\uc740 \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c \ud575\uc2ec\uc801\uc778 \uc5ed\ud560\uc744 \ud558\uba70, OpenCV\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc190\uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ud234\uc774 \ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0 \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc73c\uba70, \uc774 \uae30\ubc95\ub4e4\uc744 \uc2e4\uc81c\ub85c \uc801\uc6a9\ud558\uc5ec \uc2e4\ubb34\uc5d0\uc11c\uc758 \ud65c\uc6a9 \uac00\ub2a5\uc131\uc744 \ub17c\uc758\ud588\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\n        OpenCV\uc640 \uc774\ubbf8\uc9c0 \ubd84\ud560 \uae30\ubc95\uc744 \uc801\uc808\ud788 \ud65c\uc6a9\ud558\uba74 \ub354\uc6b1 \ud6a8\uacfc\uc801\uc774\uace0 \ud6a8\uc728\uc801\uc778 \uc774\ubbf8\uc9c0 \ubd84\uc11d\uc774 \uac00\ub2a5\ud560 \uac83\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc2dc\ub3c4\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n
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