360\u5ea6\u753b\u50cfAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u5b8c\u5168\u30ac\u30a4\u30c9<\/title>\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <style>\n body { font-family: \"Noto Sans JP\", Meiryo, sans-serif; background: #f9fbfc; color: #22333b; line-height: 2; margin: 0; padding: 2rem; }\n h1, h2, h3 { color: #115588; }\n h1 { font-size: 2.3rem; border-bottom: 4px solid #9ec9f5; margin-bottom: 2rem; }\n h2 { border-bottom: 2px solid #b3dafc; margin-top: 2.5rem; }\n h3 { font-size: 1.2rem; margin-top: 1.8rem; }\n pre, code { background: #eef4fa; border-radius: 5px; padding: .7em 1em; font-size: 1em; overflow-x: auto; }\n ul { margin-left: 2em; }\n .point { background: #d7ebfc; border-left: 4px solid #39a6e3; padding: .6em 1em; margin: 2em 0; }\n .alert { color: #e14242; font-weight: bold; }\n .footer { font-size: .96em; color: #667; border-top: 1px solid #ccd; margin-top: 3rem; padding-top: 1rem; }\n .codeblock { margin: 1.2em 0; }\n .step { font-weight: bold; color: #105a7c; }\n <\/style>\n<\/head>\n<body>\n\n\n\n<p>\n360\u5ea6\u30ab\u30e1\u30e9\uff08equirectangular\u753b\u50cf\uff09\u3092\u4f7f\u3063\u305fAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u3001<b>\u666e\u901a\u306e2D\u753b\u50cf\u89e3\u6790\u3068\u306f\u672c\u8cea\u7684\u306b\u7570\u306a\u308a\u307e\u3059<\/b>\u3002<br>\n\u306a\u305c\u306a\u3089\u3001360\u5ea6\u753b\u50cf\u306f<b>\u6295\u5f71\u6b6a\u307f\u304c\u5f37\u304f<\/b>\u3001\u307e\u305f1\u7269\u4f53\u304c\u8907\u6570\u30d1\u30c3\u30c1\u306b\u307e\u305f\u304c\u308b\u305f\u3081\u3001\u5358\u7d14\u306a\u77e9\u5f62\u3084\u30e9\u30d9\u30eb\u3060\u3051\u3067\u306f\u300c\u4f4d\u7f6e\u304c\u30ba\u30ec\u308b\u300d\u300c\u91cd\u8907\u3059\u308b\u300d\u306a\u3069\u306e\u554f\u984c\u304c\u591a\u767a\u3057\u307e\u3059\u3002<br>\n\u3053\u3053\u3067\u306f\u3001\u73fe\u5834\u5b9f\u88c5\u30ec\u30d9\u30eb\u3067<br>\n\u30fb\u30d1\u30c3\u30c1\u5206\u5272<br>\n\u30fb\u63a8\u8ad6\u5ea7\u6a19\u306e360\u5ea6\u753b\u50cf\u9006\u5909\u63db<br>\n\u30fb\u91cd\u8907\u7269\u4f53\u306e\u30de\u30fc\u30b8<br>\n\u30fbHTML\u53ef\u8996\u5316<br>\n\u307e\u3067\u3001\u8981\u70b9\u3068\u30b3\u30fc\u30c9\u3092\u30d5\u30eb\u7db2\u7f85\u3067\u89e3\u8aac\u3057\u307e\u3059\u3002\n<\/p>\n\n<h2>1. \u306a\u305c360\u5ea6\u753b\u50cf\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u7279\u6b8a\u306a\u306e\u304b<\/h2>\n<ul>\n <li>equirectangular\u753b\u50cf\u306f\u4e0a\u4e0b\u7aef\u3084\u7aef\u90e8\u3067\u6b6a\u307f\u304c\u5927\u304d\u304f\u3001\u901a\u5e38\u306e\u77e9\u5f62\u30a2\u30ce\u30c6\u304c\u901a\u7528\u3057\u306a\u3044<\/li>\n <li>YOLO\u3084SAM\u306a\u3069\u901a\u5e38\u7269\u4f53\u691c\u51faAI\u3092\u305d\u306e\u307e\u307e\u4f7f\u3046\u3068\u3001\u8a8d\u8b58\u7cbe\u5ea6\u3082\u30a2\u30ce\u30c6\u7cbe\u5ea6\u3082\u5927\u5e45\u306b\u843d\u3061\u308b<\/li>\n <li>1\u7269\u4f53\u304c\u8907\u6570\u65b9\u5411\u304b\u3089\u64ae\u3089\u308c\u308b\uff1d\u8907\u6570\u30d1\u30c3\u30c1\u3067\u300c\u591a\u91cd\u691c\u51fa\u300d\u304c\u5fc5\u305a\u8d77\u3053\u308b<\/li>\n<\/ul>\n\n<div class=\"point\">\n<strong>\u7d50\u8ad6\uff1a<\/strong><br>\n<b>\u30d1\u30c3\u30c1\u5206\u5272\u2192\u500b\u5225\u63a8\u8ad6\u2192\u9006\u5909\u63db\u2192\u30de\u30fc\u30b8\uff08\u91cd\u8907\u6392\u9664\uff09<\/b>\u3068\u3044\u3046\u6bb5\u968e\u3092\u8e0f\u3080\u306e\u304c\u201c\u552f\u4e00\u307e\u3068\u3082\u306a\u73fe\u5b9f\u89e3\u201d\u3067\u3059\u3002\n<\/div>\n\n<h2>2. \u30d1\u30c3\u30c1\u5206\u5272\uff08equirectangular\u2192perspective\uff09<\/h2>\n<h3>2-1. \u30d1\u30c3\u30c1\u5206\u5272\u8a2d\u8a08<\/h3>\n<ul>\n <li>\u6c34\u5e73\u65b9\u5411\uff08yaw\uff09: 0, 45, 90, \u2026, 315\u5ea6<\/li>\n <li>\u5782\u76f4\u65b9\u5411\uff08pitch\uff09: -60, -30, 0, 30, 60\u5ea6<\/li>\n <li>FOV\uff08\u8996\u91ce\u89d2\uff09\u306f90\u5ea6\u304c\u5b9a\u756a<\/li>\n <li>\u96a3\u308a\u5408\u3046\u30d1\u30c3\u30c1\u3067\u5fc5\u305a\u91cd\u8907\uff08\u30aa\u30fc\u30d0\u30fc\u30e9\u30c3\u30d7\uff09\u3059\u308b\u8a2d\u8a08<\/li>\n<\/ul>\n<h3>2-2. Python\u5b9f\u88c5\u4f8b<\/h3>\n<div class=\"codeblock\">\n<pre><code>import cv2\nimport py360convert\nimport os\nimport itertools\n\ninput_file = \"scene_360.jpg\"\noutput_dir = \"patches\"\nos.makedirs(output_dir, exist_ok=True)\nfov_deg = 90\nout_hw = (640, 640)\n\nyaw_list = list(range(0, 360, 45)) # 8\u65b9\u5411\npitch_list = [-60, -30, 0, 30, 60] # 5\u6bb5\n\nimg_360 = cv2.imread(input_file)\n\nfor u_deg, v_deg in itertools.product(yaw_list, pitch_list):\n persp_img = py360convert.e2p(\n img_360,\n fov_deg=fov_deg,\n u_deg=u_deg,\n v_deg=v_deg,\n out_hw=out_hw\n )\n fname = f\"patch_yaw{u_deg}_pitch{v_deg}.jpg\"\n cv2.imwrite(os.path.join(output_dir, fname), persp_img)\n<\/code><\/pre>\n<\/div>\n\n<h2>3. \u30d1\u30c3\u30c1\u753b\u50cf\u3067\u7269\u4f53\u691c\u51fa \u2192 \u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3txt\u751f\u6210<\/h2>\n<p>\n\u5404\u30d1\u30c3\u30c1\u753b\u50cf\u306b\u5bfe\u3057\u3066\u3001\u7269\u4f53\u691c\u51fa\u3092\u884c\u3044\u3001<b>YOLO\u5f62\u5f0f(txt)<\/b>\u3067\u7d50\u679c\u3092\u4fdd\u5b58\u3057\u307e\u3059\u3002\u3053\u306e\u51e6\u7406\u306f\u5f0a\u793e\u88fd\u30c4\u30fc\u30eb\u3092\u5229\u7528\u3057\u307e\u3057\u305f\u3002<br>\n<b>\u4f8b\uff1a<\/b>patch_yaw90_pitch0.jpg \u2192 patch_yaw90_pitch0.txt\n<\/p>\n<pre><code>\n\u30af\u30e9\u30b9 x_center y_center width height\n\uff08\u4f8b\uff090 0.512 0.440 0.20 0.12\n<\/code><\/pre>\n\n<h2>4. \u691c\u51fa\u70b9\u306e\u300c360\u5ea6\u753b\u50cf\u5ea7\u6a19\u300d\u3078\u306e\u9006\u5909\u63db<\/h2>\n<h3>4-1. \u306a\u305c\u9006\u5909\u63db\u304c\u5fc5\u8981\u304b<\/h3>\n<p>\nYOLO\u7b49\u306f\u300c\u5206\u5272\u3057\u305f\u30d1\u30c3\u30c1\u5185\u306e\u30d4\u30af\u30bb\u30eb\u5ea7\u6a19\u300d\u3067\u691c\u51fa\u70b9\u3092\u8fd4\u3057\u307e\u3059\u304c\u3001<b>360\u5ea6\u753b\u50cf\u306b\u30de\u30fc\u30af\u3092\u63cf\u304f\u306b\u306f\u5143\u306eequirectangular\u5ea7\u6a19\u306b\u623b\u3059\u5fc5\u8981\u304c\u3042\u308b<\/b>\u305f\u3081\u3067\u3059\u3002\n<\/p>\n<h3>4-2. p2e\u95a2\u6570\uff08\u30d1\u30c3\u30c1\u2192360\u753b\u50cf\u5ea7\u6a19 \u9006\u5909\u63db\uff09<\/h3>\n<div class=\"codeblock\">\n<pre><code>import numpy as np\ndef p2e(x, y, fov_deg, u_deg, v_deg, w_p, h_p, w_e, h_e):\n nx = (x \/ w_p - 0.5) * 2\n ny = (y \/ h_p - 0.5) * 2\n fov = np.deg2rad(fov_deg)\n z = 1 \/ np.tan(fov \/ 2)\n vec = np.stack([nx, -ny, -z * np.ones_like(nx)], axis=-1)\n vec = vec \/ np.linalg.norm(vec, axis=-1, keepdims=True)\n yaw = np.deg2rad(u_deg)\n pitch = np.deg2rad(v_deg)\n Ryaw = np.array([\n [np.cos(yaw), 0, np.sin(yaw)],\n [0, 1, 0],\n [-np.sin(yaw), 0, np.cos(yaw)]\n ])\n Rpitch = np.array([\n [1, 0, 0],\n [0, np.cos(pitch), -np.sin(pitch)],\n [0, np.sin(pitch), np.cos(pitch)]\n ])\n R = Ryaw @ Rpitch\n vec_rot = vec @ R.T\n theta = np.arctan2(vec_rot[..., 0], -vec_rot[..., 2])\n phi = np.arcsin(vec_rot[..., 1])\n x_e = (theta \/ (2 * np.pi) + 0.5) * w_e\n y_e = (0.5 - phi \/ np.pi) * h_e\n return x_e, y_e\n<\/code><\/pre>\n<\/div>\n\n<h2>5. \u9006\u5909\u63db\u2192\u691c\u51fa\u70b9\u3092360\u5ea6\u753b\u50cf\u3078\u30de\u30fc\u30af<\/h2>\n<ol>\n <li>\u30d1\u30c3\u30c1\u540d\uff08yaw, pitch\uff09\u304b\u3089\u4e2d\u5fc3\u65b9\u4f4d\u3092\u53d6\u5f97<\/li>\n <li>txt\u304b\u3089\uff08x_center, y_center, class\uff09\u3092\u53d6\u5f97<\/li>\n <li>YOLO\u306e\u76f8\u5bfe\u5024\u3092\u30d4\u30af\u30bb\u30eb\u5024\u306b\u5909\u63db\u2192p2e\u3067equirectangular\u5ea7\u6a19\u3078<\/li>\n <li>360\u753b\u50cf\u4e0a\u306b\u30de\u30fc\u30af\u3092\u63cf\u753b\uff08cv2.circle\u306a\u3069\uff09<\/li>\n<\/ol>\n<div class=\"codeblock\">\n<pre><code>\nfor patch_path in glob.glob('patches\/patch_yaw*_pitch*.jpg'):\n # yaw, pitch\u53d6\u5f97\u7701\u7565\n txt_path = patch_path.replace('.jpg', '.txt')\n # \u8aad\u307f\u8fbc\u307f\u7701\u7565\n x_p = float(x_center) * patch_hw[0]\n y_p = float(y_center) * patch_hw[1]\n x_e, y_e = p2e(x_p, y_p, fov_deg, yaw, pitch, patch_hw[0], patch_hw[1], w_e, h_e)\n all_points.append((x_e, y_e, int(class_id)))\n<\/code><\/pre>\n<\/div>\n\n<h2>6. \u91cd\u8907\u7269\u4f53\u306e\u81ea\u52d5\u30de\u30fc\u30b8\uff08DBSCAN\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\uff09<\/h2>\n<div class=\"point\">\n\u5358\u7d14\u306a\u8ddd\u96e2\u3057\u304d\u3044\u5024\u3067\u306f\u300c\u30b4\u30df\u300d\u3084\u300c\u91cd\u8907\uff1d\u5206\u5272\u753b\u50cf\u3067\u540c\u3058\u5bfe\u8c61\u7269\u3092\u9055\u3063\u305f\u89d2\u5ea6\u304b\u3089\u8a08\u7b97\u3057\u3066\u3057\u307e\u3044\u3001\u591a\u91cd\u306b\u4e2d\u5fc3\u70b9\u3092\u751f\u6210\u3057\u3066\u3057\u307e\u3046\u300d\u304c\u591a\u304f\u6b8b\u308b\u305f\u3081\u3001<b>DBSCAN<\/b>\u306b\u3088\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u73fe\u5834\u3067\u306e\u6b63\u653b\u6cd5\u3067\u3059\u3002\n<\/div>\n<h3>DBSCAN\u306b\u3088\u308b\u91cd\u8907\u6392\u9664\u30ed\u30b8\u30c3\u30af<\/h3>\n<div class=\"codeblock\">\n<pre><code>\nfrom sklearn.cluster import DBSCAN\ndef merge_points_dbscan(points, eps=30, min_samples=2):\n if not points:\n return []\n arr = np.array([[x, y, class_id] for x, y, class_id in points])\n merged = []\n for class_id in np.unique(arr[:,2]):\n arr_c = arr[arr[:,2]==class_id]\n coords = arr_c[:,:2]\n if len(coords) == 0:\n continue\n clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(coords)\n labels = clustering.labels_\n for lbl in set(labels):\n if lbl == -1:\n continue\n idxs = np.where(labels == lbl)[0]\n cx, cy = np.mean(coords[idxs], axis=0)\n merged.append((cx, cy, int(class_id)))\n return merged\n<\/code><\/pre>\n<\/div>\n\n<h2>7. \u7d71\u5408\u30b9\u30af\u30ea\u30d7\u30c8\uff08\u30d5\u30eb\u30d0\u30fc\u30b8\u30e7\u30f3\uff09<\/h2>\n<div class=\"codeblock\">\n<pre><code>import cv2\nimport numpy as np\nimport glob\nimport re\nimport os\nfrom sklearn.cluster import DBSCAN\n\n# p2e\u95a2\u6570\u30fbmerge_points_dbscan\u95a2\u6570\u306f\u4e0a\u8a18\u53c2\u7167\n\nbase_img = 'scene_360.jpg'\npatch_dir = 'patches'\nfov_deg = 90\npatch_hw = (640, 640)\noutput_img = 'scene_360_annotated.jpg'\neps = 30\nmin_samples = 2\n\nimg_360 = cv2.imread(base_img)\nh_e, w_e = img_360.shape[:2]\npattern = re.compile(r'patch_yaw(-?\\d+)_pitch(-?\\d+).jpg')\nall_points = []\n\nfor patch_path in glob.glob(os.path.join(patch_dir, 'patch_yaw*_pitch*.jpg')):\n m = pattern.search(patch_path)\n if not m:\n continue\n yaw, pitch = int(m.group(1)), int(m.group(2))\n txt_path = patch_path.replace('.jpg', '.txt')\n if not os.path.exists(txt_path):\n continue\n with open(txt_path, 'r') as f:\n for line in f:\n vals = line.strip().split()\n if len(vals) < 5:\n continue\n class_id, x_center, y_center, w, h = vals\n x_p = float(x_center) * patch_hw[0]\n y_p = float(y_center) * patch_hw[1]\n x_e, y_e = p2e(\n x_p, y_p,\n fov_deg, yaw, pitch,\n patch_hw[0], patch_hw[1],\n w_e, h_e\n )\n all_points.append((x_e, y_e, int(class_id)))\n\nprint(f\"\u5168\u30d1\u30c3\u30c1\u5408\u8a08\u691c\u51fa\u70b9\u6570: {len(all_points)}\")\n\nmerged_points = merge_points_dbscan(all_points, eps=eps, min_samples=min_samples)\nprint(f\"\u30af\u30e9\u30b9\u30bf\u4ee3\u8868\u70b9\uff08\uff1d\u7269\u4f53\u6570\uff09: {len(merged_points)}\")\n\nfor x_e, y_e, class_id in merged_points:\n center = (int(round(x_e)), int(round(y_e)))\n color = (0, 0, 255)\n cv2.circle(img_360, center, 10, color, 2)\n\ncv2.imwrite(output_img, img_360)\nprint(f\"{output_img} \u306b\u4fdd\u5b58\u3057\u307e\u3057\u305f\u3002\")\n<\/code><\/pre>\n<\/div>\n<p>\n\u3053\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3001360\u5ea6\u753b\u50cf\u4e0a\u306b\u300c\u91cd\u8907\u6392\u9664\u6e08\u307f\u300d\u306e\u7269\u4f53\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u304c\u63cf\u753b\u3055\u308c\u307e\u3059\u3002<br>\n<code>scikit-learn<\/code>\uff08DBSCAN\u306e\u305f\u3081\uff09\u3082\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n<\/p>\n\n<h2>8. \u5fdc\u7528\uff1a\u8272\u5206\u3051\u3084\u77e9\u5f62\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u3001Web\u53ef\u8996\u5316<\/h2>\n<ul>\n <li>\u30af\u30e9\u30b9\u756a\u53f7\u3054\u3068\u306b\u8272\u5206\u3051\u3059\u308b\uff08<code>cv2.circle<\/code>\u306ecolor\u5f15\u6570\u3092\u8f9e\u66f8\u7b49\u3067\u5207\u308a\u66ff\u3048\uff09<\/li>\n <li>\u77e9\u5f62\u3082\u51fa\u3057\u305f\u3044\u5834\u5408\u306f\u56db\u9685\uff08xmin,ymin\/xmax,ymax\uff09\u5168\u3066\u9006\u5909\u63db\u2192<code>cv2.rectangle<\/code>\u7b49\u3067\u63cf\u753b<\/li>\n <li>\u7d50\u679cJSON\u5316\u3057\u3001<b>Pannellum<\/b>\u306a\u3069360\u5ea6\u30d3\u30e5\u30fc\u30a2\u306ehotspot\u60c5\u5831\u306b\u6d41\u3057\u8fbc\u3080<\/li>\n<\/ul>\n\n<h2>9. \u3088\u304f\u3042\u308b\u7591\u554f\u3068\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0<\/h2>\n<ul>\n <li><b>Q. \u70b9\u304c\u591a\u3059\u304e\u308b\/\u6d88\u3048\u306a\u3044\uff01<\/b><br>DBSCAN eps\u3084min_samples\u3092\u4e0a\u3052\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u308c\u3067\u3082\u99c4\u76ee\u306a\u5834\u5408\u306f\u691c\u51fa\u7cbe\u5ea6\u305d\u306e\u3082\u306e\u3084FOV\/\u5206\u5272\u9593\u9694\u3092\u898b\u76f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n <li><b>Q. \u306a\u305c\u3053\u306e\u624b\u9806\u304c\u5fc5\u8981\uff1f<\/b><br>360\u5ea6\u753b\u50cf\u306f\u6295\u5f71\u4e0a\u201c\u6b6a\u3080\u201d\u305f\u3081\u3001\u3069\u3093\u306aAI\u3082\u300c\u666e\u901a\u306b\u77e9\u5f62\u30a2\u30ce\u30c6\u3059\u308b\u3060\u3051\u300d\u3067\u306f\u6b63\u3057\u304f\u691c\u51fa\u30fb\u53ef\u8996\u5316\u3067\u304d\u306a\u3044\u305f\u3081\u3002<\/li>\n <li><b>Q. py360convert\u306bp2e\u304c\u306a\u3044\uff01<\/b><br>\u3053\u306e\u30da\u30fc\u30b8\u306e\u81ea\u4f5cp2e\u95a2\u6570\u3067\u5341\u5206\u5b9f\u7528\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<\/ul>\n\n<div class=\"point\">\n<b>\u7dcf\u307e\u3068\u3081\uff1a<\/b>360\u5ea6\u753b\u50cf\u306eAI\u30a2\u30ce\u30c6\u306f\u3001\u300c\u30d1\u30c3\u30c1\u5206\u5272\u2192\u63a8\u8ad6\u2192\u9006\u5909\u63db\u2192DBSCAN\u30de\u30fc\u30b8\u300d\u304c\u5b9f\u7528\u73fe\u5834\u306e\u9244\u677f\u30d1\u30bf\u30fc\u30f3\u306e\u3088\u3046\u306a\u6c17\u304c\u3057\u307e\u3059\u3002<br>\n\u6642\u9593\u8ef8\u3092\u8003\u3048\u308c\u3070\u3001\u52d5\u753b\u7b49\u306b\u3082\u5fdc\u7528\u3082\u53ef\u80fd\u306a\u306e\u3067\u3001\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u30fb\u76f8\u8ac7\u306f\u3044\u3064\u3067\u3082\u3069\u3046\u305e\u3002\n<\/div>\n\n<div class=\"footer\">\n2025 © \u682a\u5f0f\u4f1a\u793e\u30d3\u30fc\u30fb\u30ca\u30ec\u30c3\u30b8\u30fb\u30c7\u30b6\u30a4\u30f3\n\u672c\u8cc7\u6599\u306f\u73fe\u5834\u5c0e\u5165\u30fb\u30b7\u30b9\u30c6\u30e0\u958b\u767a\u8005\u306e\u305f\u3081\u306e\u5b9f\u8df5\u77e5\u8b58\u96c6\u3067\u3059\u3002\u3054\u8cea\u554f\u30fb\u76f8\u8ac7\u306f\u3044\u3064\u3067\u3082\u3002<br>\n\u3010\u5099\u8003\u3011\u3053\u306e\u30da\u30fc\u30b8\u306e\u3059\u3079\u3066\u306e\u30b3\u30fc\u30c9\u306f\u518d\u5229\u7528\u30fb\u5546\u7528OK\u3002\u8cea\u554f\u306f\u30b3\u30e1\u30f3\u30c8\u6b04\u306b\u3066\u3002\n<\/div>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>360\u5ea6\u753b\u50cfAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u5b8c\u5168\u30ac\u30a4\u30c9 360\u5ea6\u30ab\u30e1\u30e9\uff08equirectangular\u753b\u50cf\uff09\u3092\u4f7f\u3063\u305fAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u3001\u666e\u901a\u306e2D\u753b\u50cf\u89e3\u6790\u3068\u306f\u672c\u8cea\u7684\u306b\u7570\u306a\u308a\u307e\u3059\u3002 \u306a\u305c\u306a\u3089\u3001360\u5ea6\u753b\u50cf\u306f\u6295\u5f71\u6b6a\u307f\u304c\u5f37\u304f\u3001\u307e\u305f1\u7269\u4f53 […]<\/p>\n","protected":false},"author":1,"featured_media":1512,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","vkExUnit_EyeCatch_disable":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[5,6,64],"tags":[],"class_list":["post-1511","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-programing","category-64"],"aioseo_notices":[],"veu_head_title_object":{"title":"","add_site_title":""},"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/07\/scene_360_annotated2.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1511"}],"version-history":[{"count":3,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511\/revisions"}],"predecessor-version":[{"id":1516,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511\/revisions\/1516"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1512"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

$\"\"$ <\/div>\n\n\n\n \n 360\u5ea6\u753b\u50cfAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u5b8c\u5168\u30ac\u30a4\u30c9<\/title>\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <style>\n body { font-family: \"Noto Sans JP\", Meiryo, sans-serif; background: #f9fbfc; color: #22333b; line-height: 2; margin: 0; padding: 2rem; }\n h1, h2, h3 { color: #115588; }\n h1 { font-size: 2.3rem; border-bottom: 4px solid #9ec9f5; margin-bottom: 2rem; }\n h2 { border-bottom: 2px solid #b3dafc; margin-top: 2.5rem; }\n h3 { font-size: 1.2rem; margin-top: 1.8rem; }\n pre, code { background: #eef4fa; border-radius: 5px; padding: .7em 1em; font-size: 1em; overflow-x: auto; }\n ul { margin-left: 2em; }\n .point { background: #d7ebfc; border-left: 4px solid #39a6e3; padding: .6em 1em; margin: 2em 0; }\n .alert { color: #e14242; font-weight: bold; }\n .footer { font-size: .96em; color: #667; border-top: 1px solid #ccd; margin-top: 3rem; padding-top: 1rem; }\n .codeblock { margin: 1.2em 0; }\n .step { font-weight: bold; color: #105a7c; }\n <\/style>\n<\/head>\n<body>\n\n\n\n<p>\n360\u5ea6\u30ab\u30e1\u30e9\uff08equirectangular\u753b\u50cf\uff09\u3092\u4f7f\u3063\u305fAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u3001<b>\u666e\u901a\u306e2D\u753b\u50cf\u89e3\u6790\u3068\u306f\u672c\u8cea\u7684\u306b\u7570\u306a\u308a\u307e\u3059<\/b>\u3002<br>\n\u306a\u305c\u306a\u3089\u3001360\u5ea6\u753b\u50cf\u306f<b>\u6295\u5f71\u6b6a\u307f\u304c\u5f37\u304f<\/b>\u3001\u307e\u305f1\u7269\u4f53\u304c\u8907\u6570\u30d1\u30c3\u30c1\u306b\u307e\u305f\u304c\u308b\u305f\u3081\u3001\u5358\u7d14\u306a\u77e9\u5f62\u3084\u30e9\u30d9\u30eb\u3060\u3051\u3067\u306f\u300c\u4f4d\u7f6e\u304c\u30ba\u30ec\u308b\u300d\u300c\u91cd\u8907\u3059\u308b\u300d\u306a\u3069\u306e\u554f\u984c\u304c\u591a\u767a\u3057\u307e\u3059\u3002<br>\n\u3053\u3053\u3067\u306f\u3001\u73fe\u5834\u5b9f\u88c5\u30ec\u30d9\u30eb\u3067<br>\n\u30fb\u30d1\u30c3\u30c1\u5206\u5272<br>\n\u30fb\u63a8\u8ad6\u5ea7\u6a19\u306e360\u5ea6\u753b\u50cf\u9006\u5909\u63db<br>\n\u30fb\u91cd\u8907\u7269\u4f53\u306e\u30de\u30fc\u30b8<br>\n\u30fbHTML\u53ef\u8996\u5316<br>\n\u307e\u3067\u3001\u8981\u70b9\u3068\u30b3\u30fc\u30c9\u3092\u30d5\u30eb\u7db2\u7f85\u3067\u89e3\u8aac\u3057\u307e\u3059\u3002\n<\/p>\n\n<h2>1. \u306a\u305c360\u5ea6\u753b\u50cf\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u7279\u6b8a\u306a\u306e\u304b<\/h2>\n<ul>\n <li>equirectangular\u753b\u50cf\u306f\u4e0a\u4e0b\u7aef\u3084\u7aef\u90e8\u3067\u6b6a\u307f\u304c\u5927\u304d\u304f\u3001\u901a\u5e38\u306e\u77e9\u5f62\u30a2\u30ce\u30c6\u304c\u901a\u7528\u3057\u306a\u3044<\/li>\n <li>YOLO\u3084SAM\u306a\u3069\u901a\u5e38\u7269\u4f53\u691c\u51faAI\u3092\u305d\u306e\u307e\u307e\u4f7f\u3046\u3068\u3001\u8a8d\u8b58\u7cbe\u5ea6\u3082\u30a2\u30ce\u30c6\u7cbe\u5ea6\u3082\u5927\u5e45\u306b\u843d\u3061\u308b<\/li>\n <li>1\u7269\u4f53\u304c\u8907\u6570\u65b9\u5411\u304b\u3089\u64ae\u3089\u308c\u308b\uff1d\u8907\u6570\u30d1\u30c3\u30c1\u3067\u300c\u591a\u91cd\u691c\u51fa\u300d\u304c\u5fc5\u305a\u8d77\u3053\u308b<\/li>\n<\/ul>\n\n<div class=\"point\">\n<strong>\u7d50\u8ad6\uff1a<\/strong><br>\n<b>\u30d1\u30c3\u30c1\u5206\u5272\u2192\u500b\u5225\u63a8\u8ad6\u2192\u9006\u5909\u63db\u2192\u30de\u30fc\u30b8\uff08\u91cd\u8907\u6392\u9664\uff09<\/b>\u3068\u3044\u3046\u6bb5\u968e\u3092\u8e0f\u3080\u306e\u304c\u201c\u552f\u4e00\u307e\u3068\u3082\u306a\u73fe\u5b9f\u89e3\u201d\u3067\u3059\u3002\n<\/div>\n\n<h2>2. \u30d1\u30c3\u30c1\u5206\u5272\uff08equirectangular\u2192perspective\uff09<\/h2>\n<h3>2-1. \u30d1\u30c3\u30c1\u5206\u5272\u8a2d\u8a08<\/h3>\n<ul>\n <li>\u6c34\u5e73\u65b9\u5411\uff08yaw\uff09: 0, 45, 90, \u2026, 315\u5ea6<\/li>\n <li>\u5782\u76f4\u65b9\u5411\uff08pitch\uff09: -60, -30, 0, 30, 60\u5ea6<\/li>\n <li>FOV\uff08\u8996\u91ce\u89d2\uff09\u306f90\u5ea6\u304c\u5b9a\u756a<\/li>\n <li>\u96a3\u308a\u5408\u3046\u30d1\u30c3\u30c1\u3067\u5fc5\u305a\u91cd\u8907\uff08\u30aa\u30fc\u30d0\u30fc\u30e9\u30c3\u30d7\uff09\u3059\u308b\u8a2d\u8a08<\/li>\n<\/ul>\n<h3>2-2. Python\u5b9f\u88c5\u4f8b<\/h3>\n<div class=\"codeblock\">\n<pre><code>import cv2\nimport py360convert\nimport os\nimport itertools\n\ninput_file = \"scene_360.jpg\"\noutput_dir = \"patches\"\nos.makedirs(output_dir, exist_ok=True)\nfov_deg = 90\nout_hw = (640, 640)\n\nyaw_list = list(range(0, 360, 45)) # 8\u65b9\u5411\npitch_list = [-60, -30, 0, 30, 60] # 5\u6bb5\n\nimg_360 = cv2.imread(input_file)\n\nfor u_deg, v_deg in itertools.product(yaw_list, pitch_list):\n persp_img = py360convert.e2p(\n img_360,\n fov_deg=fov_deg,\n u_deg=u_deg,\n v_deg=v_deg,\n out_hw=out_hw\n )\n fname = f\"patch_yaw{u_deg}_pitch{v_deg}.jpg\"\n cv2.imwrite(os.path.join(output_dir, fname), persp_img)\n<\/code><\/pre>\n<\/div>\n\n<h2>3. \u30d1\u30c3\u30c1\u753b\u50cf\u3067\u7269\u4f53\u691c\u51fa \u2192 \u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3txt\u751f\u6210<\/h2>\n<p>\n\u5404\u30d1\u30c3\u30c1\u753b\u50cf\u306b\u5bfe\u3057\u3066\u3001\u7269\u4f53\u691c\u51fa\u3092\u884c\u3044\u3001<b>YOLO\u5f62\u5f0f(txt)<\/b>\u3067\u7d50\u679c\u3092\u4fdd\u5b58\u3057\u307e\u3059\u3002\u3053\u306e\u51e6\u7406\u306f\u5f0a\u793e\u88fd\u30c4\u30fc\u30eb\u3092\u5229\u7528\u3057\u307e\u3057\u305f\u3002<br>\n<b>\u4f8b\uff1a<\/b>patch_yaw90_pitch0.jpg \u2192 patch_yaw90_pitch0.txt\n<\/p>\n<pre><code>\n\u30af\u30e9\u30b9 x_center y_center width height\n\uff08\u4f8b\uff090 0.512 0.440 0.20 0.12\n<\/code><\/pre>\n\n<h2>4. \u691c\u51fa\u70b9\u306e\u300c360\u5ea6\u753b\u50cf\u5ea7\u6a19\u300d\u3078\u306e\u9006\u5909\u63db<\/h2>\n<h3>4-1. \u306a\u305c\u9006\u5909\u63db\u304c\u5fc5\u8981\u304b<\/h3>\n<p>\nYOLO\u7b49\u306f\u300c\u5206\u5272\u3057\u305f\u30d1\u30c3\u30c1\u5185\u306e\u30d4\u30af\u30bb\u30eb\u5ea7\u6a19\u300d\u3067\u691c\u51fa\u70b9\u3092\u8fd4\u3057\u307e\u3059\u304c\u3001<b>360\u5ea6\u753b\u50cf\u306b\u30de\u30fc\u30af\u3092\u63cf\u304f\u306b\u306f\u5143\u306eequirectangular\u5ea7\u6a19\u306b\u623b\u3059\u5fc5\u8981\u304c\u3042\u308b<\/b>\u305f\u3081\u3067\u3059\u3002\n<\/p>\n<h3>4-2. p2e\u95a2\u6570\uff08\u30d1\u30c3\u30c1\u2192360\u753b\u50cf\u5ea7\u6a19 \u9006\u5909\u63db\uff09<\/h3>\n<div class=\"codeblock\">\n<pre><code>import numpy as np\ndef p2e(x, y, fov_deg, u_deg, v_deg, w_p, h_p, w_e, h_e):\n nx = (x \/ w_p - 0.5) * 2\n ny = (y \/ h_p - 0.5) * 2\n fov = np.deg2rad(fov_deg)\n z = 1 \/ np.tan(fov \/ 2)\n vec = np.stack([nx, -ny, -z * np.ones_like(nx)], axis=-1)\n vec = vec \/ np.linalg.norm(vec, axis=-1, keepdims=True)\n yaw = np.deg2rad(u_deg)\n pitch = np.deg2rad(v_deg)\n Ryaw = np.array([\n [np.cos(yaw), 0, np.sin(yaw)],\n [0, 1, 0],\n [-np.sin(yaw), 0, np.cos(yaw)]\n ])\n Rpitch = np.array([\n [1, 0, 0],\n [0, np.cos(pitch), -np.sin(pitch)],\n [0, np.sin(pitch), np.cos(pitch)]\n ])\n R = Ryaw @ Rpitch\n vec_rot = vec @ R.T\n theta = np.arctan2(vec_rot[..., 0], -vec_rot[..., 2])\n phi = np.arcsin(vec_rot[..., 1])\n x_e = (theta \/ (2 * np.pi) + 0.5) * w_e\n y_e = (0.5 - phi \/ np.pi) * h_e\n return x_e, y_e\n<\/code><\/pre>\n<\/div>\n\n<h2>5. \u9006\u5909\u63db\u2192\u691c\u51fa\u70b9\u3092360\u5ea6\u753b\u50cf\u3078\u30de\u30fc\u30af<\/h2>\n<ol>\n <li>\u30d1\u30c3\u30c1\u540d\uff08yaw, pitch\uff09\u304b\u3089\u4e2d\u5fc3\u65b9\u4f4d\u3092\u53d6\u5f97<\/li>\n <li>txt\u304b\u3089\uff08x_center, y_center, class\uff09\u3092\u53d6\u5f97<\/li>\n <li>YOLO\u306e\u76f8\u5bfe\u5024\u3092\u30d4\u30af\u30bb\u30eb\u5024\u306b\u5909\u63db\u2192p2e\u3067equirectangular\u5ea7\u6a19\u3078<\/li>\n <li>360\u753b\u50cf\u4e0a\u306b\u30de\u30fc\u30af\u3092\u63cf\u753b\uff08cv2.circle\u306a\u3069\uff09<\/li>\n<\/ol>\n<div class=\"codeblock\">\n<pre><code>\nfor patch_path in glob.glob('patches\/patch_yaw*_pitch*.jpg'):\n # yaw, pitch\u53d6\u5f97\u7701\u7565\n txt_path = patch_path.replace('.jpg', '.txt')\n # \u8aad\u307f\u8fbc\u307f\u7701\u7565\n x_p = float(x_center) * patch_hw[0]\n y_p = float(y_center) * patch_hw[1]\n x_e, y_e = p2e(x_p, y_p, fov_deg, yaw, pitch, patch_hw[0], patch_hw[1], w_e, h_e)\n all_points.append((x_e, y_e, int(class_id)))\n<\/code><\/pre>\n<\/div>\n\n<h2>6. \u91cd\u8907\u7269\u4f53\u306e\u81ea\u52d5\u30de\u30fc\u30b8\uff08DBSCAN\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\uff09<\/h2>\n<div class=\"point\">\n\u5358\u7d14\u306a\u8ddd\u96e2\u3057\u304d\u3044\u5024\u3067\u306f\u300c\u30b4\u30df\u300d\u3084\u300c\u91cd\u8907\uff1d\u5206\u5272\u753b\u50cf\u3067\u540c\u3058\u5bfe\u8c61\u7269\u3092\u9055\u3063\u305f\u89d2\u5ea6\u304b\u3089\u8a08\u7b97\u3057\u3066\u3057\u307e\u3044\u3001\u591a\u91cd\u306b\u4e2d\u5fc3\u70b9\u3092\u751f\u6210\u3057\u3066\u3057\u307e\u3046\u300d\u304c\u591a\u304f\u6b8b\u308b\u305f\u3081\u3001<b>DBSCAN<\/b>\u306b\u3088\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u73fe\u5834\u3067\u306e\u6b63\u653b\u6cd5\u3067\u3059\u3002\n<\/div>\n<h3>DBSCAN\u306b\u3088\u308b\u91cd\u8907\u6392\u9664\u30ed\u30b8\u30c3\u30af<\/h3>\n<div class=\"codeblock\">\n<pre><code>\nfrom sklearn.cluster import DBSCAN\ndef merge_points_dbscan(points, eps=30, min_samples=2):\n if not points:\n return []\n arr = np.array([[x, y, class_id] for x, y, class_id in points])\n merged = []\n for class_id in np.unique(arr[:,2]):\n arr_c = arr[arr[:,2]==class_id]\n coords = arr_c[:,:2]\n if len(coords) == 0:\n continue\n clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(coords)\n labels = clustering.labels_\n for lbl in set(labels):\n if lbl == -1:\n continue\n idxs = np.where(labels == lbl)[0]\n cx, cy = np.mean(coords[idxs], axis=0)\n merged.append((cx, cy, int(class_id)))\n return merged\n<\/code><\/pre>\n<\/div>\n\n<h2>7. \u7d71\u5408\u30b9\u30af\u30ea\u30d7\u30c8\uff08\u30d5\u30eb\u30d0\u30fc\u30b8\u30e7\u30f3\uff09<\/h2>\n<div class=\"codeblock\">\n<pre><code>import cv2\nimport numpy as np\nimport glob\nimport re\nimport os\nfrom sklearn.cluster import DBSCAN\n\n# p2e\u95a2\u6570\u30fbmerge_points_dbscan\u95a2\u6570\u306f\u4e0a\u8a18\u53c2\u7167\n\nbase_img = 'scene_360.jpg'\npatch_dir = 'patches'\nfov_deg = 90\npatch_hw = (640, 640)\noutput_img = 'scene_360_annotated.jpg'\neps = 30\nmin_samples = 2\n\nimg_360 = cv2.imread(base_img)\nh_e, w_e = img_360.shape[:2]\npattern = re.compile(r'patch_yaw(-?\\d+)_pitch(-?\\d+).jpg')\nall_points = []\n\nfor patch_path in glob.glob(os.path.join(patch_dir, 'patch_yaw*_pitch*.jpg')):\n m = pattern.search(patch_path)\n if not m:\n continue\n yaw, pitch = int(m.group(1)), int(m.group(2))\n txt_path = patch_path.replace('.jpg', '.txt')\n if not os.path.exists(txt_path):\n continue\n with open(txt_path, 'r') as f:\n for line in f:\n vals = line.strip().split()\n if len(vals) < 5:\n continue\n class_id, x_center, y_center, w, h = vals\n x_p = float(x_center) * patch_hw[0]\n y_p = float(y_center) * patch_hw[1]\n x_e, y_e = p2e(\n x_p, y_p,\n fov_deg, yaw, pitch,\n patch_hw[0], patch_hw[1],\n w_e, h_e\n )\n all_points.append((x_e, y_e, int(class_id)))\n\nprint(f\"\u5168\u30d1\u30c3\u30c1\u5408\u8a08\u691c\u51fa\u70b9\u6570: {len(all_points)}\")\n\nmerged_points = merge_points_dbscan(all_points, eps=eps, min_samples=min_samples)\nprint(f\"\u30af\u30e9\u30b9\u30bf\u4ee3\u8868\u70b9\uff08\uff1d\u7269\u4f53\u6570\uff09: {len(merged_points)}\")\n\nfor x_e, y_e, class_id in merged_points:\n center = (int(round(x_e)), int(round(y_e)))\n color = (0, 0, 255)\n cv2.circle(img_360, center, 10, color, 2)\n\ncv2.imwrite(output_img, img_360)\nprint(f\"{output_img} \u306b\u4fdd\u5b58\u3057\u307e\u3057\u305f\u3002\")\n<\/code><\/pre>\n<\/div>\n<p>\n\u3053\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3001360\u5ea6\u753b\u50cf\u4e0a\u306b\u300c\u91cd\u8907\u6392\u9664\u6e08\u307f\u300d\u306e\u7269\u4f53\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u304c\u63cf\u753b\u3055\u308c\u307e\u3059\u3002<br>\n<code>scikit-learn<\/code>\uff08DBSCAN\u306e\u305f\u3081\uff09\u3082\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n<\/p>\n\n<h2>8. \u5fdc\u7528\uff1a\u8272\u5206\u3051\u3084\u77e9\u5f62\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u3001Web\u53ef\u8996\u5316<\/h2>\n<ul>\n <li>\u30af\u30e9\u30b9\u756a\u53f7\u3054\u3068\u306b\u8272\u5206\u3051\u3059\u308b\uff08<code>cv2.circle<\/code>\u306ecolor\u5f15\u6570\u3092\u8f9e\u66f8\u7b49\u3067\u5207\u308a\u66ff\u3048\uff09<\/li>\n <li>\u77e9\u5f62\u3082\u51fa\u3057\u305f\u3044\u5834\u5408\u306f\u56db\u9685\uff08xmin,ymin\/xmax,ymax\uff09\u5168\u3066\u9006\u5909\u63db\u2192<code>cv2.rectangle<\/code>\u7b49\u3067\u63cf\u753b<\/li>\n <li>\u7d50\u679cJSON\u5316\u3057\u3001<b>Pannellum<\/b>\u306a\u3069360\u5ea6\u30d3\u30e5\u30fc\u30a2\u306ehotspot\u60c5\u5831\u306b\u6d41\u3057\u8fbc\u3080<\/li>\n<\/ul>\n\n<h2>9. \u3088\u304f\u3042\u308b\u7591\u554f\u3068\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0<\/h2>\n<ul>\n <li><b>Q. \u70b9\u304c\u591a\u3059\u304e\u308b\/\u6d88\u3048\u306a\u3044\uff01<\/b><br>DBSCAN eps\u3084min_samples\u3092\u4e0a\u3052\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u308c\u3067\u3082\u99c4\u76ee\u306a\u5834\u5408\u306f\u691c\u51fa\u7cbe\u5ea6\u305d\u306e\u3082\u306e\u3084FOV\/\u5206\u5272\u9593\u9694\u3092\u898b\u76f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n <li><b>Q. \u306a\u305c\u3053\u306e\u624b\u9806\u304c\u5fc5\u8981\uff1f<\/b><br>360\u5ea6\u753b\u50cf\u306f\u6295\u5f71\u4e0a\u201c\u6b6a\u3080\u201d\u305f\u3081\u3001\u3069\u3093\u306aAI\u3082\u300c\u666e\u901a\u306b\u77e9\u5f62\u30a2\u30ce\u30c6\u3059\u308b\u3060\u3051\u300d\u3067\u306f\u6b63\u3057\u304f\u691c\u51fa\u30fb\u53ef\u8996\u5316\u3067\u304d\u306a\u3044\u305f\u3081\u3002<\/li>\n <li><b>Q. py360convert\u306bp2e\u304c\u306a\u3044\uff01<\/b><br>\u3053\u306e\u30da\u30fc\u30b8\u306e\u81ea\u4f5cp2e\u95a2\u6570\u3067\u5341\u5206\u5b9f\u7528\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<\/ul>\n\n<div class=\"point\">\n<b>\u7dcf\u307e\u3068\u3081\uff1a<\/b>360\u5ea6\u753b\u50cf\u306eAI\u30a2\u30ce\u30c6\u306f\u3001\u300c\u30d1\u30c3\u30c1\u5206\u5272\u2192\u63a8\u8ad6\u2192\u9006\u5909\u63db\u2192DBSCAN\u30de\u30fc\u30b8\u300d\u304c\u5b9f\u7528\u73fe\u5834\u306e\u9244\u677f\u30d1\u30bf\u30fc\u30f3\u306e\u3088\u3046\u306a\u6c17\u304c\u3057\u307e\u3059\u3002<br>\n\u6642\u9593\u8ef8\u3092\u8003\u3048\u308c\u3070\u3001\u52d5\u753b\u7b49\u306b\u3082\u5fdc\u7528\u3082\u53ef\u80fd\u306a\u306e\u3067\u3001\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u30fb\u76f8\u8ac7\u306f\u3044\u3064\u3067\u3082\u3069\u3046\u305e\u3002\n<\/div>\n\n<div class=\"footer\">\n2025 © \u682a\u5f0f\u4f1a\u793e\u30d3\u30fc\u30fb\u30ca\u30ec\u30c3\u30b8\u30fb\u30c7\u30b6\u30a4\u30f3\n\u672c\u8cc7\u6599\u306f\u73fe\u5834\u5c0e\u5165\u30fb\u30b7\u30b9\u30c6\u30e0\u958b\u767a\u8005\u306e\u305f\u3081\u306e\u5b9f\u8df5\u77e5\u8b58\u96c6\u3067\u3059\u3002\u3054\u8cea\u554f\u30fb\u76f8\u8ac7\u306f\u3044\u3064\u3067\u3082\u3002<br>\n\u3010\u5099\u8003\u3011\u3053\u306e\u30da\u30fc\u30b8\u306e\u3059\u3079\u3066\u306e\u30b3\u30fc\u30c9\u306f\u518d\u5229\u7528\u30fb\u5546\u7528OK\u3002\u8cea\u554f\u306f\u30b3\u30e1\u30f3\u30c8\u6b04\u306b\u3066\u3002\n<\/div>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>360\u5ea6\u753b\u50cfAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u5b8c\u5168\u30ac\u30a4\u30c9 360\u5ea6\u30ab\u30e1\u30e9\uff08equirectangular\u753b\u50cf\uff09\u3092\u4f7f\u3063\u305fAI\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f\u3001\u666e\u901a\u306e2D\u753b\u50cf\u89e3\u6790\u3068\u306f\u672c\u8cea\u7684\u306b\u7570\u306a\u308a\u307e\u3059\u3002 \u306a\u305c\u306a\u3089\u3001360\u5ea6\u753b\u50cf\u306f\u6295\u5f71\u6b6a\u307f\u304c\u5f37\u304f\u3001\u307e\u305f1\u7269\u4f53 […]<\/p>\n","protected":false},"author":1,"featured_media":1512,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","vkExUnit_EyeCatch_disable":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[5,6,64],"tags":[],"class_list":["post-1511","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-programing","category-64"],"aioseo_notices":[],"veu_head_title_object":{"title":"","add_site_title":""},"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/07\/scene_360_annotated2.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1511"}],"version-history":[{"count":3,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511\/revisions"}],"predecessor-version":[{"id":1516,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1511\/revisions\/1516"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1512"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}