{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nerfstudio.cameras import camera_utils\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import json\n",
    "import copy\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_matrix = './site1_large/transform_matrix.json'\n",
    "with open(transform_matrix, 'r') as file:\n",
    "    t_matrix = json.load(file)\n",
    "\n",
    "\n",
    "# bim_camera = './site1_large/input_bim_camera.json'\n",
    "# with open(bim_camera, 'r') as file:\n",
    "#     bim_path = json.load(file)\n",
    "\n",
    "# bim_path_raw = copy.deepcopy(bim_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# bim_path_list = [[[-5.95399755e-02,  1.56655982e-01, -9.85856934e-01, -1.08250000e+03],\n",
    "#                 [ 9.98214828e-01,  1.39999050e-02, -5.80616878e-02, -4.55000000e+02],\n",
    "#                 [ 4.70619269e-03, -9.87554002e-01, -1.57209878e-01,  1.50000000e+02],\n",
    "#                 [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]],\n",
    "#                 [[-5.95399755e-02,  1.56655982e-01, -9.85856934e-01, -1.08250000e+03],\n",
    "#                 [ 9.98214828e-01,  1.39999050e-02, -5.80616878e-02, -4.55000000e+02],\n",
    "#                 [ 4.70619269e-03, -9.87554002e-01, -1.57209878e-01,  3.50000000e+02],\n",
    "#                 [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]]]\n",
    "\n",
    "\n",
    "bim_path_list = [[[ 1.07741548e-01,  3.23598376e-01,  9.40040345e-01, -1.08595013e+03],\n",
    "                [ 1.83026241e-01, -9.35839245e-01,  3.01174871e-01, -4.51663558e+02],\n",
    "                [ 9.77186346e-01,  1.39603003e-01, -1.60055763e-01,  1.20258615e+02],\n",
    "                [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]],\n",
    "                [[ 1.07741548e-01,  3.23598376e-01,  9.40040345e-01, -1.08595013e+03],\n",
    "                [ 1.83026241e-01, -9.35839245e-01,  3.01174871e-01, -4.51663558e+02],\n",
    "                [ 9.77186346e-01,  1.39603003e-01, -1.60055763e-01,  3.20258615e+02],\n",
    "                [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BIM to Camera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "camera_world_transforms = []\n",
    "\n",
    "# for frame in bim_path['frames']:\n",
    "#     bim_transform_4x4 = frame['transform_matrix']\n",
    "\n",
    "#     matrix_1 = np.array(bim_transform_4x4)\n",
    "#     matrix_2 = np.array(t_matrix['b2c_transform'])\n",
    "\n",
    "#     camera_world_transform_4x4 = np.dot(matrix_2, matrix_1)\n",
    "#     camera_world_transforms.append(list(list(row) for row in camera_world_transform_4x4))\n",
    "\n",
    "for matrix in bim_path_list:\n",
    "    bim_transform_4x4 = matrix\n",
    "\n",
    "    matrix_1 = np.array(bim_transform_4x4)\n",
    "    matrix_2 = np.array(t_matrix['b2c_transform'])\n",
    "\n",
    "    # Transform the extrinsic matrix\n",
    "    camera_world_transform_4x4 = np.dot(matrix_2, matrix_1)\n",
    "\n",
    "    R = camera_world_transform_4x4[:3, :3]\n",
    "    t = camera_world_transform_4x4[:3, 3]\n",
    "\n",
    "    # Construct the 4x4 transformation matrix\n",
    "    transform_matrix = np.eye(4)\n",
    "    transform_matrix[:3, :3] = R.T\n",
    "\n",
    "    transform_matrix[0, 0] *= -1\n",
    "    transform_matrix[1, 0] *= -1\n",
    "    transform_matrix[2, 1] *= -1\n",
    "    transform_matrix[2, 2] *= -1\n",
    "\n",
    "    transform_matrix[0, 3] = float(-t[0])\n",
    "    transform_matrix[1, 3] = float(-t[1])\n",
    "    transform_matrix[2, 3] = float(t[2])\n",
    "\n",
    "    camera_world_transforms.append(list(list(row) for row in transform_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Camera to Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_camera_world_transforms = []\n",
    "\n",
    "for transform in camera_world_transforms:\n",
    "\n",
    "    matrix_1 = np.array(transform)\n",
    "    matrix_2 = np.array(t_matrix['c2n_transform'])\n",
    "\n",
    "    normed_camera_world_transform_4x4 = np.dot(matrix_2, matrix_1)\n",
    "    normed_camera_world_transform_4x4[:3, 3] *= t_matrix['norm_scale']\n",
    "\n",
    "    normed_camera_world_transforms.append(list(list(row) for row in normed_camera_world_transform_4x4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[-0.018663635203528976,\n",
       "   0.01978361252148456,\n",
       "   0.120437957889028,\n",
       "   0.9215669200457903],\n",
       "  [-0.013450979278442571,\n",
       "   -0.1214291915929607,\n",
       "   0.01786201445354012,\n",
       "   0.2688063339987272],\n",
       "  [0.12130856765245047,\n",
       "   -0.010420604923397365,\n",
       "   0.020510275709091662,\n",
       "   -0.0383849907185625],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.018663635203528976,\n",
       "   0.01978361252148456,\n",
       "   0.120437957889028,\n",
       "   0.9197317157416658],\n",
       "  [-0.013450979278442571,\n",
       "   -0.1214291915929607,\n",
       "   0.01786201445354012,\n",
       "   0.27242520666418885],\n",
       "  [0.12130856765245047,\n",
       "   -0.010420604923397365,\n",
       "   0.020510275709091662,\n",
       "   0.16926187493976144],\n",
       "  [0.0, 0.0, 0.0, 1.0]]]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_camera_world_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Camera Pose for NeRF Render"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "nerf_template = './nerf_template.json'\n",
    "with open(nerf_template, 'r') as file:\n",
    "    tmp = json.load(file)\n",
    "\n",
    "# Write the updated dictionary back to the JSON file\n",
    "with open(nerf_template, 'w') as f:\n",
    "    json.dump(tmp, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "nerf_template = './nerf_template.json'\n",
    "with open(nerf_template, 'r') as file:\n",
    "    tmp = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "stretched_matrix = np.array(normed_camera_world_transforms).reshape(-1,16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01866364,  0.01978361,  0.12043796,  0.92156692, -0.01345098,\n",
       "        -0.12142919,  0.01786201,  0.26880633,  0.12130857, -0.0104206 ,\n",
       "         0.02051028, -0.03838499,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ],\n",
       "       [-0.01866364,  0.01978361,  0.12043796,  0.91973172, -0.01345098,\n",
       "        -0.12142919,  0.01786201,  0.27242521,  0.12130857, -0.0104206 ,\n",
       "         0.02051028,  0.16926187,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ]])"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stretched_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps = 1.0\n",
    "seconds = math.ceil(len(stretched_matrix) / fps)\n",
    "\n",
    "\n",
    "keyframes = []\n",
    "for matrix in stretched_matrix:\n",
    "    temp_dic = {}\n",
    "    temp_dic[\"matrix\"] = list(matrix)\n",
    "    temp_dic[\"fov\"] = 75.0\n",
    "    temp_dic[\"aspect\"] = 0.5625\n",
    "    temp_dic[\"override_transition_enabled\"] = False\n",
    "    temp_dic[\"override_transition_sec\"] = None\n",
    "\n",
    "    keyframes.append(temp_dic)\n",
    "\n",
    "camera_path = []\n",
    "for matrix in stretched_matrix:\n",
    "    temp_dic = {}\n",
    "    temp_dic[\"camera_to_world\"] = list(matrix)\n",
    "    temp_dic[\"fov\"] = 75.0\n",
    "    temp_dic[\"aspect\"] = 0.5625\n",
    "\n",
    "    camera_path.append(temp_dic)\n",
    "\n",
    "tmp[\"fps\"] = fps\n",
    "tmp[\"seconds\"] = seconds\n",
    "tmp[\"keyframes\"] = keyframes\n",
    "tmp[\"camera_path\"] = camera_path\n",
    "\n",
    "tmp[\"render_height\"] = 1920\n",
    "tmp[\"render_width\"] = 1080"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the updated dictionary back to the JSON file\n",
    "nerf_template = './site1_large/nerf_camera_path.json'\n",
    "with open(nerf_template, 'w') as f:\n",
    "    json.dump(tmp, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reverse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NeRF Render(Normed) to Camera-World "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load transformation matrices\n",
    "transform_matrix = './site1_large/transform_matrix.json'\n",
    "with open(transform_matrix, 'r') as file:\n",
    "    t_matrix = json.load(file)\n",
    "\n",
    "# Load NeRF Render\n",
    "normed_camera_world_transforms = '/home/worker/yt/autocon/dataset/site1_large/camera_paths/final_col.json'\n",
    "with open(normed_camera_world_transforms, 'r') as file:\n",
    "    normed_path = json.load(file)\n",
    "\n",
    "normed_path_raw = copy.deepcopy(normed_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Norm to Camera\n",
    "camera_world_transforms = []\n",
    "\n",
    "for frame in normed_path['camera_path']:\n",
    "    normed_transform_4x4 = np.array(frame['camera_to_world']).reshape(4,4)\n",
    "    normed_transform_4x4[:3, 3] /= t_matrix['norm_scale']\n",
    "\n",
    "    # normed_transform_4x4 = np.vstack([normed_transform_3x4, [0., 0., 0., 1.0]])\n",
    "\n",
    "    matrix_1 = np.linalg.inv(np.array(t_matrix['c2n_transform']))\n",
    "    matrix_2 = normed_transform_4x4\n",
    "\n",
    "    camera_world_transform_4x4 = np.dot(matrix_1, matrix_2)\n",
    "    camera_world_transforms.append(list(list(row) for row in camera_world_transform_4x4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[-0.14323084465315403,\n",
       "   0.15568792529783063,\n",
       "   -0.9773669947000363,\n",
       "   109.37991865094716],\n",
       "  [-0.9896683659049774,\n",
       "   -0.02895193798366831,\n",
       "   0.14042173468922403,\n",
       "   31.982859718923518],\n",
       "  [-0.006434699593758372,\n",
       "   0.9873819201760229,\n",
       "   0.15822622530979755,\n",
       "   -4.89083185328284],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432308387710771,\n",
       "   0.15568792540785717,\n",
       "   -0.9773669955445147,\n",
       "   109.39048695915534],\n",
       "  [-0.9896683667551337,\n",
       "   -0.028951937223428592,\n",
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       "   31.978886636074524],\n",
       "  [-0.006434699768164712,\n",
       "   0.9873819201809662,\n",
       "   0.1582262252718588,\n",
       "   -4.515034715481193],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323083288899963,\n",
       "   0.15568792551788357,\n",
       "   -0.9773669963889926,\n",
       "   109.40105526736352],\n",
       "  [-0.9896683676052895,\n",
       "   -0.028951936463188197,\n",
       "   0.14042172301920536,\n",
       "   31.97491355322553],\n",
       "  [-0.006434699942570719,\n",
       "   0.987381920185909,\n",
       "   0.15822622523392024,\n",
       "   -4.139237577679544],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323082700692255,\n",
       "   0.1556879256279106,\n",
       "   -0.9773669972334705,\n",
       "   109.41162357557171],\n",
       "  [-0.9896683684554454,\n",
       "   -0.028951935702948243,\n",
       "   0.14042171718419622,\n",
       "   31.970940470376537],\n",
       "  [-0.006434700116976941,\n",
       "   0.9873819201908517,\n",
       "   0.15822622519598226,\n",
       "   -3.7634404398778973],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323082112484584,\n",
       "   0.15568792573793697,\n",
       "   -0.9773669980779489,\n",
       "   109.42219188377989],\n",
       "  [-0.9896683693056016,\n",
       "   -0.028951934942708635,\n",
       "   0.14042171134918702,\n",
       "   31.966967387527543],\n",
       "  [-0.0064347002913832775,\n",
       "   0.9873819201957951,\n",
       "   0.1582262251580433,\n",
       "   -3.387643302076249],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432308152427685,\n",
       "   0.1556879258479638,\n",
       "   -0.9773669989224262,\n",
       "   109.43276019198808],\n",
       "  [-0.9896683701557569,\n",
       "   -0.028951934182468234,\n",
       "   0.14042170551417799,\n",
       "   31.96299430467855],\n",
       "  [-0.006434700465789271,\n",
       "   0.9873819202007376,\n",
       "   0.1582262251201055,\n",
       "   -3.0118461642746013],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323080936069132,\n",
       "   0.15568792595798986,\n",
       "   -0.9773669997669047,\n",
       "   109.44332850019627],\n",
       "  [-0.9896683710059131,\n",
       "   -0.0289519334222284,\n",
       "   0.14042169967916845,\n",
       "   31.959021221829556],\n",
       "  [-0.006434700640195559,\n",
       "   0.9873819202056807,\n",
       "   0.15822622508216638,\n",
       "   -2.636049026472954],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323080347861403,\n",
       "   0.1556879260680165,\n",
       "   -0.9773670006113824,\n",
       "   109.45389680840445],\n",
       "  [-0.9896683718560688,\n",
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       "   0.14042169384415903,\n",
       "   31.955048138980562],\n",
       "  [-0.006434700814601777,\n",
       "   0.9873819202106237,\n",
       "   0.15822622504422812,\n",
       "   -2.2602518886713057],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323079759653728,\n",
       "   0.15568792617804297,\n",
       "   -0.9773670014558603,\n",
       "   109.46446511661262],\n",
       "  [-0.9896683727062245,\n",
       "   -0.028951931901748173,\n",
       "   0.14042168800915028,\n",
       "   31.951075056131568],\n",
       "  [-0.006434700989007662,\n",
       "   0.9873819202155668,\n",
       "   0.15822622500628944,\n",
       "   -1.8844547508696585],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323079171445996,\n",
       "   0.15568792628806988,\n",
       "   -0.977367002300338,\n",
       "   109.47503342482081],\n",
       "  [-0.9896683735563803,\n",
       "   -0.028951931141508444,\n",
       "   0.14042168217414064,\n",
       "   31.947101973282575],\n",
       "  [-0.006434701163414162,\n",
       "   0.9873819202205096,\n",
       "   0.15822622496835145,\n",
       "   -1.508657613068011],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323078583238302,\n",
       "   0.1556879263980963,\n",
       "   -0.9773670031448162,\n",
       "   109.485601733029],\n",
       "  [-0.9896683744065363,\n",
       "   -0.02895193038126861,\n",
       "   0.14042167633913133,\n",
       "   31.94312889043358],\n",
       "  [-0.006434701337820165,\n",
       "   0.9873819202254528,\n",
       "   0.15822622493041255,\n",
       "   -1.1328604752663631],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432307799503055,\n",
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       "   -0.9773670039892938,\n",
       "   109.49617004123718],\n",
       "  [-0.9896683752566919,\n",
       "   -0.02895192962102811,\n",
       "   0.14042167050412194,\n",
       "   31.939155807584594],\n",
       "  [-0.006434701512225999,\n",
       "   0.9873819202303957,\n",
       "   0.15822622489247393,\n",
       "   -0.7570633374647153],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432307740682284,\n",
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       "   -0.9773670048337715,\n",
       "   109.50673834944537],\n",
       "  [-0.9896683761068474,\n",
       "   -0.02895192886078805,\n",
       "   0.14042166466911277,\n",
       "   31.9351827247356],\n",
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       "   0.1582262235266873,\n",
       "   12.771633623394601],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323056231344747,\n",
       "   0.15568793057910063,\n",
       "   -0.9773670352349471,\n",
       "   109.88719744493999],\n",
       "  [-0.9896684067124271,\n",
       "   -0.02895190149214825,\n",
       "   0.14042145460877475,\n",
       "   31.79215174217184],\n",
       "  [-0.0064347079652545824,\n",
       "   0.9873819204132862,\n",
       "   0.1582262234887484,\n",
       "   13.14743076119625],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432305564313708,\n",
       "   0.15568793068912729,\n",
       "   -0.9773670360794235,\n",
       "   109.89776575314816],\n",
       "  [-0.9896684075625815,\n",
       "   -0.028951900731908863,\n",
       "   0.14042144877376578,\n",
       "   31.788178659322842],\n",
       "  [-0.006434708139660995,\n",
       "   0.9873819204182293,\n",
       "   0.15822622345081017,\n",
       "   13.523227898997899],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323055054929307,\n",
       "   0.15568793079915352,\n",
       "   -0.9773670369238998,\n",
       "   109.90833406135637],\n",
       "  [-0.9896684084127356,\n",
       "   -0.02895189997166836,\n",
       "   0.14042144293875614,\n",
       "   31.78420557647386],\n",
       "  [-0.006434708314067027,\n",
       "   0.9873819204231722,\n",
       "   0.15822622341287162,\n",
       "   13.899025036799545],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323054466721621,\n",
       "   0.1556879309091798,\n",
       "   -0.9773670377683764,\n",
       "   109.91890236956455],\n",
       "  [-0.9896684092628899,\n",
       "   -0.028951899211428968,\n",
       "   0.14042143710374674,\n",
       "   31.78023249362486],\n",
       "  [-0.006434708488473495,\n",
       "   0.9873819204281153,\n",
       "   0.15822622337493286,\n",
       "   14.274822174601193],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323053878513858,\n",
       "   0.15568793101920647,\n",
       "   -0.9773670386128525,\n",
       "   109.92947067777271],\n",
       "  [-0.9896684101130443,\n",
       "   -0.02895189845118835,\n",
       "   0.14042143126873732,\n",
       "   31.776259410775864],\n",
       "  [-0.006434708662879249,\n",
       "   0.9873819204330582,\n",
       "   0.15822622333699465,\n",
       "   14.650619312402844],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323053290306137,\n",
       "   0.15568793112923263,\n",
       "   -0.9773670394573291,\n",
       "   109.94003898598089],\n",
       "  [-0.9896684109631986,\n",
       "   -0.02895189769094885,\n",
       "   0.14042142543372774,\n",
       "   31.772286327926874],\n",
       "  [-0.00643470883728561,\n",
       "   0.9873819204380013,\n",
       "   0.15822622329905578,\n",
       "   15.02641645020449],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432305270209843,\n",
       "   0.1556879312392592,\n",
       "   -0.9773670403018053,\n",
       "   109.9506072941891],\n",
       "  [-0.9896684118133527,\n",
       "   -0.028951896930708567,\n",
       "   0.1404214195987185,\n",
       "   31.768313245077884],\n",
       "  [-0.0064347090116914675,\n",
       "   0.9873819204429443,\n",
       "   0.1582262232611173,\n",
       "   15.40221358800614],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432305211389068,\n",
       "   0.15568793134928555,\n",
       "   -0.9773670411462816,\n",
       "   109.96117560239729],\n",
       "  [-0.9896684126635068,\n",
       "   -0.028951896170468727,\n",
       "   0.14042141376370895,\n",
       "   31.764340162228887],\n",
       "  [-0.006434709186097991,\n",
       "   0.9873819204478872,\n",
       "   0.15822622322317909,\n",
       "   15.778010725807786],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323051525682903,\n",
       "   0.15568793145931153,\n",
       "   -0.9773670419907577,\n",
       "   109.97174391060545],\n",
       "  [-0.9896684135136607,\n",
       "   -0.028951895410228225,\n",
       "   0.14042140792869923,\n",
       "   31.76036707937989],\n",
       "  [-0.006434709360503796,\n",
       "   0.9873819204528301,\n",
       "   0.15822622318524024,\n",
       "   16.15380786360943],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323050937475196,\n",
       "   0.1556879315693376,\n",
       "   -0.9773670428352338,\n",
       "   109.98231221881363],\n",
       "  [-0.9896684143638148,\n",
       "   -0.028951894649988614,\n",
       "   0.14042140209368995,\n",
       "   31.7563939965309],\n",
       "  [-0.006434709534910261,\n",
       "   0.9873819204577732,\n",
       "   0.15822622314730142,\n",
       "   16.52960500141108],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.14323050349267422,\n",
       "   0.1556879316793642,\n",
       "   -0.9773670436797101,\n",
       "   109.99288052702184],\n",
       "  [-0.9896684152139689,\n",
       "   -0.028951893889748108,\n",
       "   0.1404213962586801,\n",
       "   31.752420913681902],\n",
       "  [-0.006434709709316067,\n",
       "   0.9873819204627161,\n",
       "   0.1582262231093631,\n",
       "   16.90540213921273],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[-0.1432304976105968,\n",
       "   0.15568793178939014,\n",
       "   -0.9773670445241862,\n",
       "   110.00344883523002],\n",
       "  [-0.989668416064123,\n",
       "   -0.028951893129508164,\n",
       "   0.14042139042367066,\n",
       "   31.748447830832912],\n",
       "  [-0.006434709883722317,\n",
       "   0.9873819204676593,\n",
       "   0.15822622307142417,\n",
       "   17.281199277014377],\n",
       "  [0.0, 0.0, 0.0, 1.0]]]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camera_world_transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Reverse Using Transfrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load transformation matrices\n",
    "transform_matrix = './site1_large/transform_matrix.json'\n",
    "with open(transform_matrix, 'r') as file:\n",
    "    t_matrix = json.load(file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pix4d to BIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "camera_world_transforms = []\n",
    "\n",
    "train_transform = './site1_large/transforms.json'\n",
    "with open(train_transform, 'r') as file:\n",
    "    nerf_path = json.load(file)\n",
    "\n",
    "for frame in nerf_path['frames']:\n",
    "    nerf_transform_4x4 = frame['transform_matrix']\n",
    "\n",
    "    camera_world_transforms.append(list(list(row) for row in nerf_transform_4x4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Camera to BIM\n",
    "bim_transforms = []\n",
    "\n",
    "for transform in camera_world_transforms:\n",
    "    camera_world_transform_4x4 = np.array(transform)\n",
    "\n",
    "    # # Extract rotation and translation components\n",
    "    # reversed_transform_matrix = np.eye(4)\n",
    "    t = camera_world_transform_4x4[:3, 3]\n",
    "\n",
    "    # reversed_transform_matrix[0, 3] = float(-t[0])\n",
    "    # reversed_transform_matrix[1, 3] = float(-t[1])\n",
    "    # reversed_transform_matrix[2, 3] = float(t[2])\n",
    "\n",
    "    # camera_world_transform_4x4[0, 0] *= -1\n",
    "    # camera_world_transform_4x4[1, 0] *= -1\n",
    "    # camera_world_transform_4x4[2, 1] *= -1\n",
    "    # camera_world_transform_4x4[2, 2] *= -1\n",
    "\n",
    "    camera_world_transform_4x4[0, 3] = float(-t[0])\n",
    "    camera_world_transform_4x4[1, 3] = float(-t[1])\n",
    "    camera_world_transform_4x4[2, 3] = float(t[2])\n",
    "\n",
    "    # reversed_transform_matrix[:3, :3] = camera_world_transform_4x4[:3, :3].T\n",
    "\n",
    "    # Compute the inverse of b2c_transform\n",
    "    b2c_transform_inverse = np.linalg.inv(np.array(t_matrix['b2c_transform']))\n",
    "\n",
    "    # Apply the inverse of b2c_transform\n",
    "    bim_transform_4x4_reversed = np.dot(b2c_transform_inverse, camera_world_transform_4x4)\n",
    "\n",
    "    # Append the reversed transformation for verification\n",
    "    bim_transforms.append(list(list(row) for row in bim_transform_4x4_reversed))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1893, 4, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(bim_transforms).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the filename\n",
    "bim_path_temp_name = 'bim_path_template.json'\n",
    "\n",
    "# Write the updated dictionary back to the JSON file\n",
    "with open(bim_path_temp_name, 'r') as f:\n",
    "    bim_path_temp = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_frames = []\n",
    "\n",
    "for matrix in bim_transforms:\n",
    "    temp_dic = {}\n",
    "    \n",
    "    temp_dic[\"transform_matrix\"] = matrix\n",
    "\n",
    "    new_frames.append(temp_dic)\n",
    "\n",
    "bim_path_temp[\"frames\"] = new_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the updated dictionary back to the JSON file\n",
    "bim_path_name = './site1_large/bim_camera_path.json'\n",
    "with open(bim_path_name, 'w') as f:\n",
    "    json.dump(bim_path_temp, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pix4d to NeRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "camera_world_transforms = []\n",
    "\n",
    "train_transform = './site1_large/transforms.json'\n",
    "with open(train_transform, 'r') as file:\n",
    "    nerf_path = json.load(file)\n",
    "\n",
    "for frame in nerf_path['frames']:\n",
    "    nerf_transform_4x4 = frame['transform_matrix']\n",
    "\n",
    "    camera_world_transforms.append(list(list(row) for row in nerf_transform_4x4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_camera_world_transforms = []\n",
    "\n",
    "for transform in camera_world_transforms:\n",
    "\n",
    "    matrix_1 = np.array(transform)\n",
    "    matrix_2 = np.array(t_matrix['c2n_transform'])\n",
    "\n",
    "    normed_camera_world_transform_4x4 = np.dot(matrix_2, matrix_1)\n",
    "    normed_camera_world_transform_4x4[:3, 3] *= t_matrix['norm_scale']\n",
    "\n",
    "    normed_camera_world_transforms.append(list(list(row) for row in normed_camera_world_transform_4x4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nerf_template = './nerf_template.json'\n",
    "# with open(nerf_template, 'r') as file:\n",
    "#     tmp = json.load(file)\n",
    "\n",
    "# # Write the updated dictionary back to the JSON file\n",
    "# with open(nerf_template, 'w') as f:\n",
    "#     json.dump(tmp, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "nerf_template = './nerf_template.json'\n",
    "with open(nerf_template, 'r') as file:\n",
    "    tmp = json.load(file)\n",
    "\n",
    "\n",
    "stretched_matrix = np.array(normed_camera_world_transforms).reshape(-1,16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps = 1.0\n",
    "seconds = math.ceil(len(stretched_matrix) / fps)\n",
    "\n",
    "\n",
    "keyframes = []\n",
    "for matrix in stretched_matrix:\n",
    "    temp_dic = {}\n",
    "    temp_dic[\"matrix\"] = list(matrix)\n",
    "    temp_dic[\"fov\"] = 75.0\n",
    "    temp_dic[\"aspect\"] = 0.5625\n",
    "    temp_dic[\"override_transition_enabled\"] = False\n",
    "    temp_dic[\"override_transition_sec\"] = None\n",
    "\n",
    "    keyframes.append(temp_dic)\n",
    "\n",
    "camera_path = []\n",
    "for matrix in stretched_matrix:\n",
    "    temp_dic = {}\n",
    "    temp_dic[\"camera_to_world\"] = list(matrix)\n",
    "    temp_dic[\"fov\"] = 75.0\n",
    "    temp_dic[\"aspect\"] = 0.5625\n",
    "\n",
    "    camera_path.append(temp_dic)\n",
    "\n",
    "tmp[\"fps\"] = fps\n",
    "tmp[\"seconds\"] = seconds\n",
    "tmp[\"keyframes\"] = keyframes\n",
    "tmp[\"camera_path\"] = camera_path\n",
    "\n",
    "tmp[\"render_height\"] = 1920\n",
    "tmp[\"render_width\"] = 1080"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the updated dictionary back to the JSON file\n",
    "nerf_template = './site1_large/nerf_camera_path.json'\n",
    "with open(nerf_template, 'w') as f:\n",
    "    json.dump(tmp, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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