OpenCV\u306f\u3001\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u3068\u753b\u50cf\u51e6\u7406\u306e\u5206\u91ce\u3067\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u308b\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u672c\u8a18\u4e8b\u3067\u306f\u3001OpenCV\u306e\u57fa\u672c\u6982\u5ff5\u304b\u3089\u5b9f\u8df5\u7684\u306a\u5fdc\u7528\u307e\u3067\u3092\u4e01\u5be7\u306b\u89e3\u8aac\u3057\u3001\u521d\u5fc3\u8005\u3067\u3082\u7406\u89e3\u3057\u3084\u3059\u3044\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u4ea4\u3048\u306a\u304c\u3089\u3001\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u3067OpenCV\u306e\u4f7f\u3044\u65b9\u3092\u5b66\u3079\u308b\u5185\u5bb9\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u76ee\u6b21 <\/p>\n
OpenCV\uff08Open Source Computer Vision Library\uff09\u306f\u3001\u30a4\u30f3\u30c6\u30eb\u304c\u958b\u767a\u3057\u305f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u5411\u3051\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u753b\u50cf\u51e6\u7406\u3084\u6a5f\u68b0\u5b66\u7fd2\u306a\u3069\u306e\u591a\u69d8\u306a\u6a5f\u80fd\u3092\u63d0\u4f9b\u3057\u3001C++\u3001Python\u3001Java\u306a\u3069\u8907\u6570\u306e\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u306b\u5bfe\u5fdc\u3057\u3066\u3044\u307e\u3059\u3002\u30af\u30ed\u30b9\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3067\u52d5\u4f5c\u3057\u3001BSD\u30e9\u30a4\u30bb\u30f3\u30b9\u306e\u4e0b\u3067\u5546\u7528\u5229\u7528\u3082\u53ef\u80fd\u306a\u3001\u975e\u5e38\u306b\u6c4e\u7528\u6027\u306e\u9ad8\u3044\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002<\/p>\n\n\n\n
OpenCV\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u7279\u5fb4\u3068\u30e1\u30ea\u30c3\u30c8\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3053\u308c\u3089\u306e\u7279\u5fb4\u304b\u3089\u3001OpenCV\u306f\u7814\u7a76\u958b\u767a\u304b\u3089\u5546\u7528\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u307e\u3067\u3001\u69d8\u3005\u306a\u5834\u9762\u3067\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
OpenCV\u304c\u63d0\u4f9b\u3059\u308b\u4e3b\u306a\u6a5f\u80fd\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
<\/p>\n\n\n\n
\u3053\u308c\u3089\u306f\u4e00\u90e8\u306e\u4f8b\u3067\u3059\u304c\u3001OpenCV\u3092\u4f7f\u3044\u3053\u306a\u3059\u3053\u3068\u3067\u3001\u753b\u50cf\u3084\u52d5\u753b\u306b\u95a2\u3059\u308b\u69d8\u3005\u306a\u554f\u984c\u306b\u53d6\u308a\u7d44\u3080\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
OpenCV\u3092\u4f7f\u3046\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u958b\u767a\u74b0\u5883\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
Python\u306e\u5834\u5408\u3001pip\u3092\u4f7f\u3063\u3066OpenCV\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
pip install opencv-python<\/pre>\n\n\n\nAnaconda\u3092\u4f7f\u3063\u3066\u3044\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u3066\u4eee\u60f3\u74b0\u5883\u3092\u4f5c\u6210\u3057\u3001OpenCV\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
conda create -n opencv_env python=3.7\nconda activate opencv_env\npip install opencv-python<\/pre>\n\n\n\n\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5b8c\u4e86\u3057\u305f\u3089\u3001\u4ee5\u4e0b\u306e\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3001OpenCV\u304c\u6b63\u3057\u304f\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u305f\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
import cv2\n\n# OpenCV\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u60c5\u5831\u3092\u8868\u793a\nprint(cv2.__version__)\n\n# \u30b5\u30f3\u30d7\u30eb\u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\nimg = cv2.imread('sample.jpg')\n\n# \u753b\u50cf\u3092\u30a6\u30a3\u30f3\u30c9\u30a6\u306b\u8868\u793a\ncv2.imshow('image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u4ee5\u4e0a\u304c\u3001OpenCV\u306e\u6982\u8981\u3068\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u306e\u8aac\u660e\u3067\u3059\u3002\u6b21\u7ae0\u4ee5\u964d\u3067\u3001\u3088\u308a\u5177\u4f53\u7684\u306a\u753b\u50cf\u51e6\u7406\u306e\u65b9\u6cd5\u3084\u5b9f\u8df5\u7684\u306a\u5fdc\u7528\u4f8b\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
OpenCV\u306e\u57fa\u672c\uff5c\u753b\u50cf\u51e6\u7406\u306e\u57fa\u790e\u77e5\u8b58\u3068\u5b9f\u88c5\u65b9\u6cd5<\/h2>\n\n\n\n
OpenCV\u3092\u4f7f\u3063\u305f\u753b\u50cf\u51e6\u7406\u306e\u57fa\u672c\u3068\u3057\u3066\u3001\u753b\u50cf\u306e\u5165\u51fa\u529b\u3001\u8272\u7a7a\u9593\u5909\u63db\u3001\u4e8c\u5024\u5316\u3001\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3001\u30a8\u30c3\u30b8\u691c\u51fa\u3001\u30b3\u30fc\u30ca\u30fc\u691c\u51fa\u306a\u3069\u306e\u624b\u6cd5\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306f\u753b\u50cf\u51e6\u7406\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3059\u308b\u4e0a\u3067\u983b\u7e41\u306b\u4f7f\u308f\u308c\u308b\u51e6\u7406\u3067\u3059\u3002<\/p>\n\n\n\n
\u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\u30fb\u8868\u793a\u30fb\u4fdd\u5b58<\/h3>\n\n\n\n
OpenCV\u3067\u753b\u50cf\u3092\u6271\u3046\u306b\u306f\u3001\u307e\u305a\u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002cv2.imread()\u95a2\u6570\u3092\u4f7f\u3063\u3066\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u307f\u3001cv2.imshow()\u95a2\u6570\u3067\u30a6\u30a3\u30f3\u30c9\u30a6\u306b\u8868\u793a\u3057\u307e\u3059\u3002\u307e\u305f\u3001cv2.imwrite()\u95a2\u6570\u3092\u4f7f\u3063\u3066\u51e6\u7406\u3057\u305f\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
import cv2\n\n# \u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\nimg = cv2.imread('input.jpg')\n\n# \u753b\u50cf\u306e\u8868\u793a\ncv2.imshow('image', img)\ncv2.waitKey(0)\n\n# \u753b\u50cf\u306e\u4fdd\u5b58 \ncv2.imwrite('output.jpg', img)\n\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u8272\u7a7a\u9593\u306e\u5909\u63db\u3068\u753b\u50cf\u306e\u4e8c\u5024\u5316<\/h3>\n\n\n\n
OpenCV\u3067\u306f\u3001cv2.cvtColor()\u95a2\u6570\u3092\u4f7f\u3063\u3066\u8272\u7a7a\u9593\u3092\u5909\u63db\u3067\u304d\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001BGR\u304b\u3089\u30b0\u30ec\u30fc\u30b9\u30b1\u30fc\u30eb\u3078\u306e\u5909\u63db\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u66f8\u3051\u307e\u3059\u3002<\/p>\n\n\n\n
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)<\/pre>\n\n\n\n\u753b\u50cf\u306e\u4e8c\u5024\u5316\u306b\u306f\u3001cv2.threshold()\u95a2\u6570\u3084 cv2.adaptiveThreshold()\u95a2\u6570\u3092\u4f7f\u3044\u307e\u3059\u3002\u524d\u8005\u306f\u5168\u4f53\u3067\u4e00\u3064\u306e\u95be\u5024\u3092\u4f7f\u3046\u306e\u306b\u5bfe\u3057\u3001\u5f8c\u8005\u306f\u753b\u50cf\u306e\u5c0f\u9818\u57df\u3054\u3068\u306b\u9069\u5fdc\u7684\u306b\u95be\u5024\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
# \u5358\u7d14\u306a\u4e8c\u5024\u5316\n_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n\n# \u9069\u5fdc\u7684\u95be\u5024\u51e6\u7406\u306b\u3088\u308b\u4e8c\u5024\u5316\nadaptive_binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)<\/pre>\n\n\n\n\u753b\u50cf\u306e\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u51e6\u7406<\/h3>\n\n\n\n
\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u51e6\u7406\u306f\u3001\u753b\u50cf\u306e\u5e73\u6ed1\u5316\u3084\u30ce\u30a4\u30ba\u9664\u53bb\u306b\u7528\u3044\u3089\u308c\u307e\u3059\u3002OpenCV\u306b\u306f\u3001\u5e73\u5747\u5316\u3001\u30ac\u30a6\u30b7\u30a2\u30f3\u3001\u30e1\u30c7\u30a3\u30a2\u30f3\u3001\u30d0\u30a4\u30e9\u30c6\u30e9\u30eb\u306a\u3069\u69d8\u3005\u306a\u30d5\u30a3\u30eb\u30bf\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
# \u30ab\u30fc\u30cd\u30eb\u306e\u5b9a\u7fa9\nkernel = np.ones((5,5), np.float32) \/ 25\n\n# \u5e73\u5747\u5316\u30d5\u30a3\u30eb\u30bf\ndst = cv2.filter2D(img, -1, kernel)\n\n# \u30ac\u30a6\u30b7\u30a2\u30f3\u30d5\u30a3\u30eb\u30bf\ngaussian = cv2.GaussianBlur(img, (5,5), 0)\n\n# \u30e1\u30c7\u30a3\u30a2\u30f3\u30d5\u30a3\u30eb\u30bf\nmedian = cv2.medianBlur(img, 5)\n\n# \u30d0\u30a4\u30e9\u30c6\u30e9\u30eb\u30d5\u30a3\u30eb\u30bf\nbilateral = cv2.bilateralFilter(img, 9, 75, 75)<\/pre>\n\n\n\n\u30a8\u30c3\u30b8\u691c\u51fa\u3068\u30b3\u30fc\u30ca\u30fc\u691c\u51fa<\/h3>\n\n\n\n
\u30a8\u30c3\u30b8\u691c\u51fa\u306f\u3001\u753b\u50cf\u5185\u306e\u7dda\u3084\u8f2a\u90ed\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u51e6\u7406\u3067\u3059\u3002OpenCV\u3067\u306f\u3001Canny\u3001Sobel\u3001Laplacian\u306a\u3069\u306e\u30a8\u30c3\u30b8\u691c\u51fa\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
# Canny\u30a8\u30c3\u30b8\u691c\u51fa\nedges = cv2.Canny(gray, 100, 200)\n\n# Sobel\u30d5\u30a3\u30eb\u30bf\nsobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)\nsobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)<\/pre>\n\n\n\n\u4e00\u65b9\u3001\u30b3\u30fc\u30ca\u30fc\u691c\u51fa\u306f\u753b\u50cf\u5185\u306e\u7279\u5fb4\u70b9\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306e\u51e6\u7406\u3067\u3059\u3002Harris\u6cd5\u3084Shi-Tomasi\u6cd5\u306a\u3069\u304c\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
# Harris corner\u691c\u51fa\nharris = cv2.cornerHarris(gray, 2, 3, 0.04)\n\n# Shi-Tomasi corner\u691c\u51fa \ncorners = cv2.goodFeaturesToTrack(gray, 25, 0.01, 10)\ncorners = np.int0(corners)\n\nfor corner in corners:\n x, y = corner.ravel()\n cv2.circle(img, (x,y), 3, (0,0,255), -1)<\/pre>\n\n\n\n\u4ee5\u4e0a\u3001OpenCV\u3092\u4f7f\u3063\u305f\u753b\u50cf\u51e6\u7406\u306e\u57fa\u672c\u7684\u306a\u624b\u6cd5\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u3089\u306e\u51e6\u7406\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u69d8\u3005\u306a\u753b\u50cf\u51e6\u7406\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u6b21\u7ae0\u3067\u306f\u3001\u3053\u308c\u3089\u306e\u57fa\u790e\u77e5\u8b58\u3092\u8e0f\u307e\u3048\u3066\u3001\u7269\u4f53\u691c\u51fa\u3084\u9854\u8a8d\u8b58\u306a\u3069\u3001\u3088\u308a\u5b9f\u8df5\u7684\u306a\u5fdc\u7528\u4f8b\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
OpenCV\u306e\u5b9f\u8df5\uff5c\u5177\u4f53\u7684\u306a\u753b\u50cf\u8a8d\u8b58\u30a2\u30d7\u30ea\u306e\u4f5c\u308a\u65b9<\/h2>\n\n\n\n
\u3053\u3053\u3067\u306f\u3001OpenCV\u3092\u4f7f\u3063\u305f\u5b9f\u8df5\u7684\u306a\u753b\u50cf\u8a8d\u8b58\u30a2\u30d7\u30ea\u306e\u4f5c\u308a\u65b9\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u7269\u4f53\u691c\u51fa\u3001\u9854\u8a8d\u8b58\u3001\u753b\u50cf\u5206\u985e\u306a\u3069\u3001\u4ee3\u8868\u7684\u306a\u624b\u6cd5\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u4ea4\u3048\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\u3067\u7269\u4f53\u691c\u51fa<\/h3>\n\n\n\n
\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\u306f\u3001\u4e88\u3081\u7528\u610f\u3057\u305f\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u3068\u5165\u529b\u753b\u50cf\u306e\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3001\u7269\u4f53\u306e\u4f4d\u7f6e\u3092\u7279\u5b9a\u3059\u308b\u624b\u6cd5\u3067\u3059\u3002OpenCV\u3067\u306f\u3001cv2.matchTemplate()\u95a2\u6570\u3092\u4f7f\u3063\u3066\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
import cv2\nimport numpy as np\n\nimg = cv2.imread('input.jpg')\ntemplate = cv2.imread('template.jpg', 0)\nh, w = template.shape\n\nmethods = ['cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF_NORMED']\n\nfor method_name in methods:\n img2 = img.copy()\n method = eval(method_name)\n\n res = cv2.matchTemplate(img2, template, method)\n min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n\n if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:\n top_left = min_loc\n else:\n top_left = max_loc\n\n bottom_right = (top_left[0] + w, top_left[1] + h)\n cv2.rectangle(img2, top_left, bottom_right, 255, 2)\n\n cv2.imshow(method_name, img2)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u30013\u7a2e\u985e\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u624b\u6cd5\u3092\u6bd4\u8f03\u3057\u3066\u3044\u307e\u3059\u3002\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u306e\u5927\u304d\u3055\u3084\u89d2\u5ea6\u304c\u5909\u5316\u3059\u308b\u5834\u5408\u306f\u3001\u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u691c\u51fa\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u306e\u4f5c\u6210\u6642\u306b\u306f\u3001\u80cc\u666f\u306e\u5f71\u97ff\u3092\u53d7\u3051\u306a\u3044\u3088\u3046\u524d\u51e6\u7406\u3092\u65bd\u3059\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u30ab\u30b9\u30b1\u30fc\u30c9\u5206\u985e\u5668\u3067\u9854\u691c\u51fa<\/h3>\n\n\n\n
\u30ab\u30b9\u30b1\u30fc\u30c9\u5206\u985e\u5668\u306f\u3001Haar-like\u7279\u5fb4\u91cf\u3068AdaBoost\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u308b\u7269\u4f53\u691c\u51fa\u624b\u6cd5\u3067\u3059\u3002OpenCV\u306b\u306f\u3001\u9854\u3084\u76ee\u3001\u7b11\u9854\u306a\u3069\u3092\u691c\u51fa\u3059\u308b\u305f\u3081\u306e\u5b66\u7fd2\u6e08\u307f\u30ab\u30b9\u30b1\u30fc\u30c9\u5206\u985e\u5668\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
import cv2\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n\nimg = cv2.imread('input.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\nfaces = face_cascade.detectMultiScale(gray, 1.3, 5)\nfor (x,y,w,h) in faces:\n cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = img[y:y+h, x:x+w]\n eyes = eye_cascade.detectMultiScale(roi_gray)\n for (ex,ey,ew,eh) in eyes:\n cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\n\ncv2.imshow('img',img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001\u9854\u3068\u76ee\u306e\u691c\u51fa\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002cv2.CascadeClassifier()\u3067\u5206\u985e\u5668\u3092\u8aad\u307f\u8fbc\u307f\u3001cv2.detectMultiScale()\u3067\u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u691c\u51fa\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\u691c\u51fa\u3055\u308c\u305f\u9854\u3084\u76ee\u306e\u4f4d\u7f6e\u306b\u77e9\u5f62\u3092\u63cf\u753b\u3057\u3066\u53ef\u8996\u5316\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u6a5f\u68b0\u5b66\u7fd2\u3092\u4f7f\u3063\u305f\u753b\u50cf\u5206\u985e<\/h3>\n\n\n\n
\u753b\u50cf\u5206\u985e\u3067\u306f\u3001\u753b\u50cf\u304b\u3089\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u30ab\u30c6\u30b4\u30ea\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002OpenCV\u3067\u306f\u3001Bag of Visual Words (BoW)\u3068\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30f3(SVM)\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u753b\u50cf\u5206\u985e\u306e\u5b9f\u88c5\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n\n\n\n
import cv2\nimport numpy as np\n\n# \u7279\u5fb4\u91cf\u62bd\u51fa\u5668\u3068BoW\u306e\u6e96\u5099\ndetector = cv2.SIFT()\nmatcher = cv2.BFMatcher(cv2.NORM_L2)\nbow_kmeans_trainer = cv2.BOWKMeansTrainer(50)\nextract_bow = cv2.BOWImgDescriptorExtractor(detector, matcher)\n\n# \u753b\u50cf\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3068BoW\u306e\u5b66\u7fd2\nfor i in range(num_images):\n img = cv2.imread('image_{}.jpg'.format(i))\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n kp, desc = detector.detectAndCompute(gray, None)\n bow_kmeans_trainer.add(desc)\n\nvoc = bow_kmeans_trainer.cluster()\nextract_bow.setVocabulary(voc)\n\n# BoW\u7279\u5fb4\u91cf\u306e\u62bd\u51fa\nbow_features = []\nlabels = []\nfor i in range(num_images):\n img = cv2.imread('image_{}.jpg'.format(i))\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n bow_feat = extract_bow.compute(gray, detector.detect(gray))\n bow_features.append(bow_feat)\n labels.append(image_labels[i])\n\nbow_features = np.array(bow_features)\nlabels = np.array(labels)\n\n# SVM\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2\u3068\u8a55\u4fa1\nsvm = cv2.SVM()\nsvm.train(bow_features, labels)\n\n_, pred_labels = svm.predict(bow_features)\nprint(pred_labels)<\/pre>\n\n\n\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001SIFT\u3068BoW\u3092\u4f7f\u3063\u3066\u753b\u50cf\u304b\u3089\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\u3001SVM\u3067\u5206\u985e\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u30fb\u8a55\u4fa1\u3057\u3066\u3044\u307e\u3059\u3002BoW\u306e\u5b66\u7fd2\u306b\u306f\u3001cv2.BOWKMeansTrainer()\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u7279\u5fb4\u91cf\u62bd\u51fa\u5668\u3068BoW\u306e\u7d44\u307f\u5408\u308f\u305b\u306f\u3001\u8ab2\u984c\u306b\u5fdc\u3058\u3066\u9069\u5207\u306b\u9078\u629e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u307e\u305f\u3001SVM\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3082\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u3001OpenCV\u3092\u4f7f\u3063\u305f\u5b9f\u8df5\u7684\u306a\u753b\u50cf\u8a8d\u8b58\u30a2\u30d7\u30ea\u306e\u4f5c\u308a\u65b9\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u7269\u4f53\u691c\u51fa\u3001\u9854\u8a8d\u8b58\u3001\u753b\u50cf\u5206\u985e\u306a\u3069\u3001\u305d\u308c\u305e\u308c\u306e\u624b\u6cd5\u306e\u7279\u5fb4\u3068\u4f7f\u3044\u65b9\u3092\u7406\u89e3\u3057\u3001\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u53c2\u8003\u306b\u5b9f\u88c5\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u753b\u50cf\u8a8d\u8b58\u306f\u5965\u304c\u6df1\u3044\u5206\u91ce\u3067\u3059\u304c\u3001OpenCV\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u521d\u5fc3\u8005\u3067\u3082\u6bd4\u8f03\u7684\u5bb9\u6613\u306b\u53d6\u308a\u7d44\u3080\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u521d\u5fc3\u8005\u304c\u9665\u308a\u3084\u3059\u3044\u7f60\u3068\u30c7\u30d0\u30c3\u30b0\u30c6\u30af\u30cb\u30c3\u30af<\/h2>\n\n\n\n
OpenCV\u3092\u4f7f\u3063\u305f\u958b\u767a\u3067\u306f\u3001\u521d\u5fc3\u8005\u304c\u9665\u308a\u3084\u3059\u3044\u7f60\u3084\u30a8\u30e9\u30fc\u304c\u5b58\u5728\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u4ee3\u8868\u7684\u306a\u30a8\u30e9\u30fc\u3068\u305d\u306e\u539f\u56e0\u3001\u30c7\u30d0\u30c3\u30b0\u306b\u5f79\u7acb\u3064\u30c6\u30af\u30cb\u30c3\u30af\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6539\u5584\u306e\u30b3\u30c4\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
OpenCV\u3067\u3088\u304f\u3042\u308b\u30a8\u30e9\u30fc\u3068\u305d\u306e\u539f\u56e0<\/h3>\n\n\n\n
OpenCV\u3092\u4f7f\u3063\u3066\u3044\u3066\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30a8\u30e9\u30fc\u306b\u906d\u9047\u3057\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f<\/p>\n\n\n\n
\n
- FileNotFoundError: \u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u304c\u898b\u3064\u304b\u3089\u306a\u3044<\/li>\n\n\n\n
- AttributeError: \u5b58\u5728\u3057\u306a\u3044\u30e1\u30bd\u30c3\u30c9\u3084\u5c5e\u6027\u3092\u547c\u3073\u51fa\u3057\u3066\u3044\u308b<\/li>\n\n\n\n
- TypeError: \u95a2\u6570\u306e\u5f15\u6570\u306e\u578b\u304c\u6b63\u3057\u304f\u306a\u3044<\/li>\n\n\n\n
- cv2.error: OpenCV\u306e\u95a2\u6570\u304c\u30a8\u30e9\u30fc\u3092\u8fd4\u3057\u305f<\/li>\n<\/ol>\n\n\n\n
\u3053\u308c\u3089\u306e\u30a8\u30e9\u30fc\u306f\u3001\u753b\u50cf\u30d1\u30b9\u306e\u6307\u5b9a\u30df\u30b9\u3001\u95a2\u6570\u306e\u5f15\u6570\u30a8\u30e9\u30fc\u3001\u30e1\u30e2\u30ea\u4e0d\u8db3\u3001\u30d0\u30fc\u30b8\u30e7\u30f3\u4e92\u63db\u6027\u306e\u554f\u984c\u306a\u3069\u304c\u539f\u56e0\u3067\u767a\u751f\u3057\u307e\u3059\u3002\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u6ce8\u610f\u6df1\u304f\u8aad\u307f\u3001\u539f\u56e0\u3092\u7279\u5b9a\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u30c7\u30d0\u30c3\u30b0\u306b\u5f79\u7acb\u3064\u95a2\u6570\u3068\u4f7f\u3044\u65b9<\/h3>\n\n\n\n
OpenCV\u306e\u30b3\u30fc\u30c9\u3092\u30c7\u30d0\u30c3\u30b0\u3059\u308b\u969b\u306f\u3001\u4ee5\u4e0b\u306e\u95a2\u6570\u3084\u65b9\u6cd5\u304c\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- cv2.imshow()\u3068cv2.waitKey(): \u51e6\u7406\u7d50\u679c\u306e\u8868\u793a\u3068\u30c7\u30d0\u30c3\u30b0<\/li>\n\n\n\n
- cv2.imwrite(): \u4e2d\u9593\u7d50\u679c\u306e\u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u4fdd\u5b58<\/li>\n\n\n\n
- print()\u3068assert: \u5909\u6570\u306e\u5024\u3084\u6761\u4ef6\u306e\u30c1\u30a7\u30c3\u30af<\/li>\n\n\n\n
- try-except\u6587: \u30a8\u30e9\u30fc\u51e6\u7406\u3068\u30ed\u30b0\u306e\u51fa\u529b<\/li>\n\n\n\n
- pdb\uff08Python Debugger\uff09: \u30d6\u30ec\u30fc\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u8a2d\u5b9a\u3068\u30b9\u30c6\u30c3\u30d7\u5b9f\u884c<\/li>\n<\/ol>\n\n\n\n
\u4ee5\u4e0b\u306e\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u306f\u3001\u753b\u50cf\u8aad\u307f\u8fbc\u307f\u6642\u306e\u30a8\u30e9\u30fc\u51e6\u7406\u3068\u30c7\u30d0\u30c3\u30b0\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
import cv2\n\n# \u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\ntry:\n img = cv2.imread('input.jpg')\n if img is None:\n raise ValueError(\"Image file could not be read, check file path and format.\")\nexcept FileNotFoundError as e:\n print(f\"Image file not found: {e}\")\n exit(1)\nexcept ValueError as e:\n print(f\"Error: {e}\")\n exit(1)\n\n# \u4e2d\u9593\u7d50\u679c\u306e\u8868\u793a\u3068\u30c7\u30d0\u30c3\u30b0\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\ncv2.imshow('grayscale', gray)\ncv2.imwrite('debug_gray.jpg', gray)\n\nprint(f\"Grayscale image shape: {gray.shape}\")\nassert gray.ndim == 2, \"Grayscale image should have 2 dimensions.\"\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/pre>\n\n\n\ntry-except\u6587\u3092\u4f7f\u3063\u3066\u30a8\u30e9\u30fc\u3092\u30ad\u30e3\u30c3\u30c1\u3057\u3001\u9069\u5207\u306a\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u8868\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u307e\u305f\u3001cv2.imshow()\u3084 cv2.imwrite()\u3092\u4f7f\u3063\u3066\u4e2d\u9593\u7d50\u679c\u3092\u78ba\u8a8d\u3057\u3001print()\u3068assert\u3067\u30c7\u30fc\u30bf\u306e\u5f62\u72b6\u3084\u6b21\u5143\u6570\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6539\u5584\u306e\u30b3\u30c4<\/h3>\n\n\n\n
OpenCV\u306e\u30b3\u30fc\u30c9\u3092\u9ad8\u901f\u5316\u3059\u308b\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30b3\u30c4\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- \u9069\u5207\u306a\u753b\u50cf\u30b5\u30a4\u30ba\u3068\u30c7\u30fc\u30bf\u578b\u306e\u9078\u629e<\/li>\n\n\n\n
- \u51e6\u7406\u9818\u57df\u306e\u9650\u5b9a\uff08ROI\u8a2d\u5b9a\uff09<\/li>\n\n\n\n
- \u4e26\u5217\u51e6\u7406\u306e\u6d3b\u7528\uff08cv2.parallel_for_\u3001cv2.setNumThreads()\uff09<\/li>\n\n\n\n
- GPU\u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u5229\u7528\uff08OpenCV GPU module\uff09<\/li>\n\n\n\n
- \u52b9\u7387\u7684\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u9078\u629e\u3068\u5b9f\u88c5<\/li>\n\n\n\n
- \u30b3\u30fc\u30c9\u306e\u6700\u9069\u5316\uff08\u30eb\u30fc\u30d7\u306e\u52b9\u7387\u5316\u3001\u4e0d\u8981\u306a\u51e6\u7406\u306e\u524a\u6e1b\u306a\u3069\uff09<\/li>\n<\/ol>\n\n\n\n
\u4f8b\u3048\u3070\u3001\u753b\u50cf\u30b5\u30a4\u30ba\u3092\u5c0f\u3055\u304f\u3059\u308b\u3053\u3068\u3067\u51e6\u7406\u6642\u9593\u3092\u77ed\u7e2e\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u51e6\u7406\u9818\u57df\u3092\u9650\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u4e0d\u8981\u306a\u8a08\u7b97\u3092\u7701\u7565\u3067\u304d\u307e\u3059\u3002\u4e26\u5217\u51e6\u7406\u3084GPU\u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u5927\u5e45\u306a\u9ad8\u901f\u5316\u304c\u671f\u5f85\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001ROI\u8a2d\u5b9a\u306b\u3088\u308b\u51e6\u7406\u9818\u57df\u306e\u9650\u5b9a\u306e\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import cv2\n\nimg = cv2.imread('input.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# ROI\u306e\u8a2d\u5b9a\nx, y, w, h = 100, 100, 200, 200\nroi = gray[y:y+h, x:x+w]\n\n# ROI\u306b\u5bfe\u3059\u308b\u51e6\u7406\nthreshold = cv2.threshold(roi, 127, 255, cv2.THRESH_BINARY)[1]\n\n# \u7d50\u679c\u306e\u8868\u793a\ncv2.imshow('original', gray)\ncv2.imshow('roi', roi)\ncv2.imshow('threshold', threshold)\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u753b\u50cf\u306e\u4e00\u90e8\u5206\u3092\u5207\u308a\u51fa\u3057\u3066ROI\u3068\u3057\u3001\u305d\u306e\u9818\u57df\u306b\u5bfe\u3057\u3066\u306e\u307f\u4e8c\u5024\u5316\u51e6\u7406\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
OpenCV\u3092\u4f7f\u3046\u4e0a\u3067\u3001\u521d\u5fc3\u8005\u304c\u9665\u308a\u3084\u3059\u3044\u7f60\u3092\u7406\u89e3\u3057\u3001\u9069\u5207\u306a\u30c7\u30d0\u30c3\u30b0\u30c6\u30af\u30cb\u30c3\u30af\u3092\u8eab\u306b\u3064\u3051\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u30a8\u30e9\u30fc\u51e6\u7406\u3092\u4e01\u5be7\u306b\u884c\u3044\u3001\u4e2d\u9593\u7d50\u679c\u3092\u78ba\u8a8d\u3057\u306a\u304c\u3089\u30b3\u30fc\u30c9\u3092\u66f8\u304f\u3053\u3068\u3067\u3001\u30d0\u30b0\u306e\u767a\u898b\u3068\u4fee\u6b63\u304c\u5bb9\u6613\u306b\u306a\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6539\u5584\u306e\u30b3\u30c4\u3092\u62bc\u3055\u3048\u308b\u3053\u3068\u3067\u3001\u52b9\u7387\u7684\u306a\u30b3\u30fc\u30c9\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002OpenCV\u3092\u6d3b\u7528\u3057\u3066\u3001\u3088\u308a\u5b9f\u8df5\u7684\u306a\u753b\u50cf\u51e6\u7406\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3057\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u767a\u5c55\u7de8\uff5c\u3088\u308a\u5b9f\u8df5\u7684\u306aOpenCV\u306e\u4f7f\u3044\u65b9<\/h2>\n\n\n\n
\u3053\u3053\u3067\u306f\u3001OpenCV\u3092\u3088\u308a\u5b9f\u8df5\u7684\u306b\u6d3b\u7528\u3059\u308b\u305f\u3081\u306e\u30c6\u30af\u30cb\u30c3\u30af\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u51e6\u7406\u3001\u30ab\u30e1\u30e9\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u30683\u6b21\u5143\u5fa9\u5143\u3001\u6a5f\u68b0\u5b66\u7fd2\u3068\u306e\u7d44\u307f\u5408\u308f\u305b\u306a\u3069\u3001\u5fdc\u7528\u7684\u306a\u8a71\u984c\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002<\/p>\n\n\n\n
\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u51e6\u7406\u306e\u30c6\u30af\u30cb\u30c3\u30af<\/h3>\n\n\n\n
OpenCV\u3092\u4f7f\u3063\u3066\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u753b\u50cf\u51e6\u7406\u3092\u884c\u3046\u5834\u5408\u3001\u51e6\u7406\u901f\u5ea6\u304c\u91cd\u8981\u306b\u306a\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c6\u30af\u30cb\u30c3\u30af\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u9ad8\u901f\u306a\u51e6\u7406\u3092\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- cv2.VideoCapture: \u30ab\u30e1\u30e9\u3084\u30d3\u30c7\u30aa\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u306e\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5165\u529b<\/li>\n\n\n\n
- cv2.VideoWriter: \u51e6\u7406\u7d50\u679c\u306e\u30d3\u30c7\u30aa\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u66f8\u304d\u51fa\u3057<\/li>\n\n\n\n
- \u30b9\u30ec\u30c3\u30c9\u4e26\u5217\u5316\u306b\u3088\u308b\u9ad8\u901f\u5316<\/li>\n\n\n\n
- \u30d5\u30ec\u30fc\u30e0\u30b9\u30ad\u30c3\u30d7\u3068\u30ea\u30b5\u30a4\u30ba\u306b\u3088\u308b\u51e6\u7406\u901f\u5ea6\u306e\u5411\u4e0a<\/li>\n<\/ol>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001\u30ab\u30e1\u30e9\u304b\u3089\u306e\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5165\u529b\u306b\u5bfe\u3057\u3066\u30a8\u30c3\u30b8\u691c\u51fa\u3092\u884c\u3046\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n\n\n\n
import cv2\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n ret, frame = cap.read()\n if not ret:\n break\n\n # \u30d5\u30ec\u30fc\u30e0\u306b\u5bfe\u3059\u308b\u51e6\u7406\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n edges = cv2.Canny(gray, 100, 200)\n\n cv2.imshow('frame', frame)\n cv2.imshow('edges', edges)\n\n if cv2.waitKey(1) == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()<\/pre>\n\n\n\ncv2.VideoCapture(0)\u3067\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30ab\u30e1\u30e9\u3092\u958b\u304d\u3001while\u30eb\u30fc\u30d7\u5185\u3067\u7d99\u7d9a\u7684\u306b\u30d5\u30ec\u30fc\u30e0\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002\u5404\u30d5\u30ec\u30fc\u30e0\u306b\u5bfe\u3057\u3066\u30a8\u30c3\u30b8\u691c\u51fa\u3092\u884c\u3044\u3001\u7d50\u679c\u3092\u8868\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30ab\u30e1\u30e9\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u30683\u6b21\u5143\u5fa9\u5143<\/h3>\n\n\n\n
\u30ab\u30e1\u30e9\u3092\u7528\u3044\u305f3\u6b21\u5143\u8a08\u6e2c\u3067\u306f\u3001\u30ab\u30e1\u30e9\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a8\u5b9a\uff08\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\uff09\u304c\u91cd\u8981\u3067\u3059\u3002OpenCV\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u95a2\u6570\u3092\u4f7f\u3063\u3066\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u30683\u6b21\u5143\u5fa9\u5143\u3092\u884c\u3048\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- cv2.findChessboardCorners: \u30c1\u30a7\u30b9\u30dc\u30fc\u30c9\u753b\u50cf\u304b\u3089\u306e\u30b3\u30fc\u30ca\u30fc\u691c\u51fa<\/li>\n\n\n\n
- cv2.calibrateCamera: \u30ab\u30e1\u30e9\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a8\u5b9a<\/li>\n\n\n\n
- cv2.stereoCalibrate: \u30b9\u30c6\u30ec\u30aa\u30ab\u30e1\u30e9\u306e\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3<\/li>\n\n\n\n
- cv2.triangulatePoints: \u4e09\u89d2\u6e2c\u91cf\u306b\u3088\u308b3\u6b21\u5143\u70b9\u306e\u5fa9\u5143<\/li>\n<\/ol>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001\u30c1\u30a7\u30b9\u30dc\u30fc\u30c9\u753b\u50cf\u3092\u7528\u3044\u305f\u30ab\u30e1\u30e9\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n\n\n\n
import cv2\nimport numpy as np\n\n# \u30c1\u30a7\u30b9\u30dc\u30fc\u30c9\u753b\u50cf\u304b\u3089\u306e\u30b3\u30fc\u30ca\u30fc\u691c\u51fa\nobjp = np.zeros((6*7,3), np.float32)\nobjp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)\n\nobjpoints = []\nimgpoints = []\n\nimg = cv2.imread('chessboard.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\nret, corners = cv2.findChessboardCorners(gray, (7,6), None)\n\nif ret == True:\n objpoints.append(objp)\n imgpoints.append(corners)\n\n # \u30ab\u30e1\u30e9\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a8\u5b9a\n ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)\n\n # \u6b6a\u307f\u88dc\u6b63\n h, w = img.shape[:2]\n newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))\n dst = cv2.undistort(img, mtx, dist, None, newcameramtx)\n\n cv2.imshow('calibrated', dst)\n cv2.waitKey(0)\n\ncv2.destroyAllWindows()<\/pre>\n\n\n\n\u30c1\u30a7\u30b9\u30dc\u30fc\u30c9\u753b\u50cf\u304b\u3089\u30b3\u30fc\u30ca\u30fc\u70b9\u3092\u691c\u51fa\u3057\u3001cv2.calibrateCamera()\u3067\u30ab\u30e1\u30e9\u884c\u5217\u3068\u6b6a\u307f\u4fc2\u6570\u3092\u63a8\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\u63a8\u5b9a\u3057\u305f\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u7528\u3044\u3066\u3001cv2.undistort()\u3067\u753b\u50cf\u306e\u6b6a\u307f\u88dc\u6b63\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
OpenCV\u3068\u6a5f\u68b0\u5b66\u7fd2\u306e\u7d44\u307f\u5408\u308f\u305b<\/h3>\n\n\n\n
OpenCV\u3068\u6a5f\u68b0\u5b66\u7fd2\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u7269\u4f53\u691c\u51fa\u3001\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3001\u59ff\u52e2\u63a8\u5b9a\u306a\u3069\u3001\u3088\u308a\u9ad8\u5ea6\u306a\u753b\u50cf\u8a8d\u8b58\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u624b\u6cd5\u304c\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- \u7279\u5fb4\u91cf\u62bd\u51fa\u3068\u30e2\u30c7\u30eb\u5b66\u7fd2\u306e\u7d44\u307f\u5408\u308f\u305b<\/li>\n\n\n\n
- \u7269\u4f53\u691c\u51fa\u3001\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3001\u59ff\u52e2\u63a8\u5b9a\u306a\u3069\u3078\u306e\u5fdc\u7528<\/li>\n\n\n\n
- OpenCV\u3068\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\uff08TensorFlow\u3001PyTorch\u306a\u3069\uff09\u306e\u9023\u643a<\/li>\n<\/ol>\n\n\n\n
OpenCV\u3067\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3055\u305b\u308b\u3053\u3068\u3067\u3001\u753b\u50cf\u5206\u985e\u3084\u7269\u4f53\u691c\u51fa\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001TensorFlow\u3084PyTorch\u306a\u3069\u306e\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3068\u9023\u643a\u3059\u308b\u3053\u3068\u3067\u3001CNN\u3084R-CNN\u306a\u3069\u306e\u9ad8\u5ea6\u306a\u30e2\u30c7\u30eb\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u3001OpenCV\u3092\u3088\u308a\u5b9f\u8df5\u7684\u306b\u6d3b\u7528\u3059\u308b\u305f\u3081\u306e\u30c6\u30af\u30cb\u30c3\u30af\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u51e6\u7406\u306e\u9ad8\u901f\u5316\u3001\u30ab\u30e1\u30e9\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u30683\u6b21\u5143\u5fa9\u5143\u3001\u6a5f\u68b0\u5b66\u7fd2\u3068\u306e\u7d44\u307f\u5408\u308f\u305b\u306a\u3069\u3001OpenCV\u306e\u5fdc\u7528\u7bc4\u56f2\u306f\u5e83\u7bc4\u56f2\u306b\u53ca\u3073\u307e\u3059\u3002\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3092\u53c2\u8003\u306b\u3001\u5b9f\u969b\u306bOpenCV\u3092\u4f7f\u3063\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u958b\u767a\u306b\u30c1\u30e3\u30ec\u30f3\u30b8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u753b\u50cf\u51e6\u7406\u3084\u8a8d\u8b58\u306e\u5206\u91ce\u3067\u3001OpenCV\u306f\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3068\u306a\u308b\u3053\u3068\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081\uff5c\u4eca\u5f8c\u306e\u5b66\u7fd2\u30b9\u30c6\u30c3\u30d7\u3068\u30a2\u30c9\u30d0\u30a4\u30b9<\/h2>\n\n\n\n
\u672c\u8a18\u4e8b\u3067\u306f\u3001OpenCV\u306e\u57fa\u672c\u6982\u5ff5\u304b\u3089\u5b9f\u8df5\u7684\u306a\u5fdc\u7528\u307e\u3067\u3092\u5e83\u304f\u30ab\u30d0\u30fc\u3057\u3066\u304d\u307e\u3057\u305f\u3002\u3053\u3053\u3067\u306f\u3001\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u91cd\u8981\u30dd\u30a4\u30f3\u30c8\u3092\u632f\u308a\u8fd4\u308a\u3001\u4eca\u5f8c\u306e\u5b66\u7fd2\u30b9\u30c6\u30c3\u30d7\u3068\u30a2\u30d7\u30ea\u958b\u767a\u306e\u30a2\u30c9\u30d0\u30a4\u30b9\u3092\u63d0\u793a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u304a\u3055\u3089\u3044\u3068\u91cd\u8981\u30dd\u30a4\u30f3\u30c8<\/h3>\n\n\n\n
\n
- OpenCV\u306e\u57fa\u672c\u6a5f\u80fd: \u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\u3001\u8868\u793a\u3001\u4fdd\u5b58\u3001\u8272\u7a7a\u9593\u5909\u63db\u3001\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u306a\u3069<\/li>\n\n\n\n
- \u753b\u50cf\u51e6\u7406\u306e\u5b9f\u88c5\u65b9\u6cd5: \u30a8\u30c3\u30b8\u691c\u51fa\u3001\u4e8c\u5024\u5316\u3001\u30e2\u30eb\u30d5\u30a9\u30ed\u30b8\u30fc\u5909\u63db\u3001\u7279\u5fb4\u91cf\u62bd\u51fa\u306a\u3069<\/li>\n\n\n\n
- \u7269\u4f53\u691c\u51fa\u3068\u8a8d\u8b58: \u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\u3001\u30ab\u30b9\u30b1\u30fc\u30c9\u5206\u985e\u5668\u3001\u6a5f\u68b0\u5b66\u7fd2\u3068\u306e\u7d44\u307f\u5408\u308f\u305b<\/li>\n\n\n\n
- \u30c7\u30d0\u30c3\u30b0\u3068\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6539\u5584: \u30a8\u30e9\u30fc\u51e6\u7406\u3001\u4e2d\u9593\u7d50\u679c\u306e\u53ef\u8996\u5316\u3001\u30b3\u30fc\u30c9\u306e\u6700\u9069\u5316<\/li>\n\n\n\n
- \u767a\u5c55\u7684\u306a\u30c8\u30d4\u30c3\u30af: \u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u51e6\u7406\u3001\u30ab\u30e1\u30e9\u30ad\u30e3\u30ea\u30d6\u30ec\u30fc\u30b7\u30e7\u30f3\u30013\u6b21\u5143\u5fa9\u5143\u306a\u3069<\/li>\n<\/ul>\n\n\n\n
\u3053\u308c\u3089\u306e\u77e5\u8b58\u3092\u8eab\u306b\u3064\u3051\u308b\u3053\u3068\u3067\u3001OpenCV\u3092\u4f7f\u3063\u305f\u753b\u50cf\u51e6\u7406\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u958b\u767a\u57fa\u76e4\u304c\u6574\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3088\u308a\u6df1\u304f\u5b66\u3076\u305f\u3081\u306e\u53c2\u8003\u8cc7\u6599<\/h3>\n\n\n\n
OpenCV\u306b\u3064\u3044\u3066\u3055\u3089\u306b\u6df1\u304f\u5b66\u3076\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u53c2\u8003\u8cc7\u6599\u304c\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- \u516c\u5f0f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8: OpenCV\u516c\u5f0f\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306b\u306f\u3001\u8c4a\u5bcc\u306a\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3068API \u30ea\u30d5\u30a1\u30ec\u30f3\u30b9\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
- \u66f8\u7c4d: \u300c\u5b9f\u8df5OpenCV4 for Python: \u753b\u50cf\u6620\u50cf\u60c5\u5831\u51e6\u7406\u3068\u6a5f\u68b0\u5b66\u7fd2\u300d\u3084\u300cOpenCV\u306b\u3088\u308b\u753b\u50cf\u51e6\u7406\u5165\u9580 \u6539\u8a02\u7b2c3\u7248 (KS\u60c5\u5831\u79d1\u5b66\u5c02\u9580\u66f8)\u300d\u306a\u3069\u3001OpenCV\u3092\u6271\u3063\u305f\u66f8\u7c4d\u304c\u591a\u6570\u51fa\u7248\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
- \u30aa\u30f3\u30e9\u30a4\u30f3\u5b66\u7fd2: Coursera\u3001Udemy\u3001OpenCV.org\u306a\u3069\u306e\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3067\u306f\u3001OpenCV\u306b\u7279\u5316\u3057\u305f\u30b3\u30fc\u30b9\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n
- \u8ad6\u6587: CVPR\u3084ICCV\u306a\u3069\u306e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u5206\u91ce\u306e\u30c8\u30c3\u30d7\u30ab\u30f3\u30d5\u30a1\u30ec\u30f3\u30b9\u306e\u8ad6\u6587\u3092\u8aad\u3080\u3053\u3068\u3067\u3001\u6700\u65b0\u306e\u7814\u7a76\u52d5\u5411\u3092\u628a\u63e1\u3067\u304d\u307e\u3059\u3002<\/li>\n\n\n\n
- GitHub: \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3084\u5b9f\u8df5\u7684\u306a\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u304c\u591a\u6570\u516c\u958b\u3055\u308c\u3066\u304a\u308a\u3001\u53c2\u8003\u306b\u306a\u308a\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n
\u3053\u308c\u3089\u306e\u30ea\u30bd\u30fc\u30b9\u3092\u6d3b\u7528\u3057\u3001\u7d99\u7d9a\u7684\u306b\u5b66\u7fd2\u3092\u6df1\u3081\u3066\u3044\u304f\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u30a2\u30d7\u30ea\u958b\u767a\u306e\u30a2\u30a4\u30c7\u30a2\u3068\u767a\u60f3\u6cd5<\/h3>\n\n\n\n
OpenCV\u3092\u4f7f\u3063\u305f\u30a2\u30d7\u30ea\u958b\u767a\u306e\u30a2\u30a4\u30c7\u30a2\u3092\u751f\u307f\u51fa\u3059\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u304c\u6709\u52b9\u3067\u3059\u3002<\/p>\n\n\n\n
\n
- \u65e2\u5b58\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u5206\u6790\u3057\u3001\u6539\u5584\u70b9\u3092\u898b\u3064\u3051\u308b<\/li>\n\n\n\n
- \u65e5\u5e38\u751f\u6d3b\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b\u30a2\u30d7\u30ea\u3092\u8003\u3048\u308b<\/li>\n\n\n\n
- \u6700\u65b0\u306e\u7814\u7a76\u30c8\u30ec\u30f3\u30c9\u3092\u8abf\u67fb\u3057\u3001\u5fdc\u7528\u53ef\u80fd\u6027\u3092\u63a2\u308b<\/li>\n\n\n\n
- \u30a2\u30a4\u30c7\u30a2\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u6280\u8853\u3092\u6574\u7406\u3059\u308b<\/li>\n\n\n\n
- \u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u3092\u7d20\u65e9\u304f\u4f5c\u6210\u3057\u3001\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u5f97\u308b<\/li>\n<\/ol>\n\n\n\n
\u5177\u4f53\u7684\u306a\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u30a2\u30a4\u30c7\u30a2\u3068\u3057\u3066\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u3082\u306e\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\n
- \u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u30a2\u30d7\u30ea: \u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u7269\u4f53\u8a8d\u8b58\u3001\u6587\u5b57\u8a8d\u8b58\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u30b9\u30ad\u30e3\u30ca\u306a\u3069<\/li>\n\n\n\n
- \u30ed\u30dc\u30c3\u30c8\u30d3\u30b8\u30e7\u30f3: \u81ea\u5f8b\u8d70\u884c\u8eca\u4e21\u3001\u30c9\u30ed\u30fc\u30f3\u3001\u7523\u696d\u7528\u30ed\u30dc\u30c3\u30c8\u306e\u8996\u899a\u30b7\u30b9\u30c6\u30e0<\/li>\n\n\n\n
- \u533b\u7642\u753b\u50cf\u89e3\u6790: \u304c\u3093\u8a3a\u65ad\u3001\u773c\u5e95\u753b\u50cf\u89e3\u6790\u3001MRI\u306e\u753b\u50cf\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3<\/li>\n\n\n\n
- \u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u30b7\u30b9\u30c6\u30e0: \u4e0d\u5be9\u8005\u691c\u77e5\u3001\u884c\u52d5\u5206\u6790\u3001\u9854\u8a8d\u8a3c\u306b\u3088\u308b\u5165\u9000\u5ba4\u7ba1\u7406<\/li>\n\n\n\n
- \u30b9\u30dd\u30fc\u30c4\u5206\u6790: \u9078\u624b\u306e\u30c8\u30e9\u30c3\u30ad\u30f3\u30b0\u3001\u30dc\u30fc\u30eb\u306e\u8ecc\u8de1\u5206\u6790\u3001\u6226\u8853\u5206\u6790<\/li>\n<\/ul>\n\n\n\n
\u3053\u308c\u3089\u306f\u307b\u3093\u306e\u4e00\u4f8b\u3067\u3059\u304c\u3001OpenCV\u306e\u5fdc\u7528\u53ef\u80fd\u6027\u306f\u5e83\u7bc4\u56f2\u306b\u53ca\u3073\u307e\u3059\u3002\u81ea\u5206\u306e\u8208\u5473\u95a2\u5fc3\u306b\u5408\u3063\u305f\u30c6\u30fc\u30de\u3092\u898b\u3064\u3051\u3001\u30a2\u30d7\u30ea\u958b\u767a\u306b\u30c1\u30e3\u30ec\u30f3\u30b8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u3001OpenCV\u306e\u5b66\u7fd2\u30b9\u30c6\u30c3\u30d7\u3068\u30a2\u30d7\u30ea\u958b\u767a\u306e\u30a2\u30c9\u30d0\u30a4\u30b9\u306b\u3064\u3044\u3066\u8ff0\u3079\u307e\u3057\u305f\u3002\u672c\u8a18\u4e8b\u3067\u7d39\u4ecb\u3057\u305f\u5185\u5bb9\u3092\u51fa\u767a\u70b9\u3068\u3057\u3066\u3001\u3055\u3089\u306b\u77e5\u8b58\u3092\u6df1\u3081\u3001\u5275\u9020\u7684\u306a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3057\u3066\u3044\u305f\u3060\u3051\u308c\u3070\u5e78\u3044\u3067\u3059\u3002OpenCV\u306e\u4e16\u754c\u306f\u5965\u6df1\u304f\u3001\u5e38\u306b\u65b0\u3057\u3044\u767a\u898b\u3068\u53ef\u80fd\u6027\u306b\u6e80\u3061\u3066\u3044\u307e\u3059\u3002\u4eca\u5f8c\u306e\u3054\u6d3b\u8e8d\u3092\u5fc3\u3088\u308a\u304a\u7948\u308a\u3057\u3066\u304a\u308a\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
OpenCV\u306f\u3001\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u3068\u753b\u50cf\u51e6\u7406\u306e\u5206\u91ce\u3067\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u308b\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002\u672c\u8a18\u4e8b\u3067\u306f\u3001OpenCV\u306e\u57fa\u672c\u6982\u5ff5\u304b\u3089\u5b9f\u8df5\u7684\u306a\u5fdc\u7528\u307e\u3067\u3092\u4e01\u5be7\u306b\u89e3\u8aac\u3057\u3001\u521d\u5fc3\u8005\u3067\u3082\u7406\u89e3\u3057\u3084\u3059\u3044\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002 … <\/p>\n","protected":false},"author":1,"featured_media":593,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":{"0":"post-592","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-python"},"_links":{"self":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=592"}],"version-history":[{"count":1,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/592\/revisions"}],"predecessor-version":[{"id":594,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/592\/revisions\/594"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/media\/593"}],"wp:attachment":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}