{ "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" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "target = 'non_scaled_transforms.json'\n", "\n", "with open(target, 'r') as file1:\n", " data1 = json.load(file1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "sorted_frames = sorted(data1[\"frames\"], key=lambda x: x[\"colmap_im_id\"])\n", "\n", "poses = []\n", "\n", "for frame in sorted_frames:\n", " poses.append(np.array(frame[\"transform_matrix\"]))\n", "\n", "poses_scaled = torch.from_numpy(np.array(poses).astype(np.float32))\n", "\n", "poses_scaled, transform_matrix = camera_utils.auto_orient_and_center_poses(\n", " poses_scaled,\n", " method=\"up\",\n", " center_method=\"poses\",\n", ")\n", "\n", "# print(poses_scaled)\n", "# print(transform_matrix)\n", "\n", "# Scale poses\n", "scale_factor = 1.0\n", "# auto_scale_poses:\n", "scale_factor /= float(torch.max(torch.abs(poses_scaled[:, :3, 3])))\n", "scale_factor *= 1.0\n", "\n", "poses_scaled[:, :3, 3] *= scale_factor" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[ 0.9951, 0.0040, -0.0988, 0.0537],\n", " [ 0.0040, 0.9968, 0.0800, 0.0412],\n", " [ 0.0988, -0.0800, 0.9919, 0.0352]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transform_matrix" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "for idx,frame in enumerate(sorted_frames):\n", " sorted_frames[idx][\"transform_matrix\"] = poses_scaled[idx]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'file_path': 'images/frame_00001.png',\n", " 'transform_matrix': tensor([[ 0.6294, -0.1369, 0.7649, -0.0277],\n", " [ 0.7726, 0.0046, -0.6349, 0.0161],\n", " [ 0.0834, 0.9906, 0.1086, 0.0155]]),\n", " 'colmap_im_id': 1},\n", " {'file_path': 'images/frame_00002.png',\n", " 'transform_matrix': tensor([[ 0.6274, -0.1397, 0.7660, -0.0260],\n", " [ 0.7740, 0.0047, -0.6331, 0.0184],\n", " [ 0.0849, 0.9902, 0.1111, 0.0161]]),\n", " 'colmap_im_id': 2},\n", " {'file_path': 'images/frame_00003.png',\n", " 'transform_matrix': tensor([[ 0.6562, -0.1577, 0.7379, -0.0223],\n", " [ 0.7493, 0.0207, -0.6619, 0.0203],\n", " [ 0.0891, 0.9873, 0.1317, 0.0156]]),\n", " 'colmap_im_id': 3},\n", " {'file_path': 'images/frame_00004.png',\n", " 'transform_matrix': tensor([[ 0.7186, -0.1504, 0.6790, -0.0156],\n", " [ 0.6906, 0.0390, -0.7222, 0.0283],\n", " [ 0.0822, 0.9879, 0.1319, 0.0154]]),\n", " 'colmap_im_id': 4},\n", " {'file_path': 'images/frame_00005.png',\n", " 'transform_matrix': tensor([[ 8.1225e-01, -1.3311e-01, 5.6792e-01, 9.8144e-04],\n", " [ 5.7897e-01, 6.5325e-02, -8.1273e-01, 3.9127e-02],\n", " [ 7.1081e-02, 9.8895e-01, 1.3013e-01, 1.5675e-02]]),\n", " 'colmap_im_id': 5},\n", " {'file_path': 'images/frame_00006.png',\n", " 'transform_matrix': tensor([[ 0.8761, -0.1215, 0.4666, 0.0184],\n", " [ 0.4774, 0.0837, -0.8747, 0.0440],\n", " [ 0.0672, 0.9891, 0.1313, 0.0164]]),\n", " 'colmap_im_id': 6},\n", " {'file_path': 'images/frame_00007.png',\n", " 'transform_matrix': tensor([[ 0.9289, -0.1149, 0.3522, 0.0335],\n", " [ 0.3637, 0.1029, -0.9258, 0.0425],\n", " [ 0.0702, 0.9880, 0.1374, 0.0173]]),\n", " 'colmap_im_id': 7},\n", " {'file_path': 'images/frame_00008.png',\n", " 'transform_matrix': tensor([[ 0.9776, -0.0819, 0.1938, 0.0568],\n", " [ 0.2026, 0.1176, -0.9722, 0.0429],\n", " [ 0.0568, 0.9897, 0.1316, 0.0190]]),\n", " 'colmap_im_id': 8},\n", " {'file_path': 'images/frame_00009.png',\n", " 'transform_matrix': tensor([[ 0.9923, -0.0661, 0.1043, 0.0830],\n", " [ 0.1105, 0.0980, -0.9890, 0.0461],\n", " [ 0.0551, 0.9930, 0.1046, 0.0231]]),\n", " 'colmap_im_id': 9},\n", " {'file_path': 'images/frame_00010.png',\n", " 'transform_matrix': tensor([[ 0.9994, -0.0338, 0.0055, 0.1144],\n", " [ 0.0083, 0.0832, -0.9965, 0.0434],\n", " [ 0.0332, 0.9960, 0.0834, 0.0263]]),\n", " 'colmap_im_id': 10},\n", " {'file_path': 'images/frame_00011.png',\n", " 'transform_matrix': tensor([[ 0.9925, -0.0389, -0.1161, 0.1528],\n", " [-0.1114, 0.1069, -0.9880, 0.0456],\n", " [ 0.0509, 0.9935, 0.1017, 0.0255]]),\n", " 'colmap_im_id': 11},\n", " {'file_path': 'images/frame_00012.png',\n", " 'transform_matrix': tensor([[ 0.9793, -0.0381, -0.1989, 0.1618],\n", " [-0.1933, 0.1180, -0.9740, 0.0517],\n", " [ 0.0606, 0.9923, 0.1082, 0.0247]]),\n", " 'colmap_im_id': 12},\n", " {'file_path': 'images/frame_00013.png',\n", " 'transform_matrix': tensor([[ 0.9246, 0.0048, -0.3810, 0.1467],\n", " [-0.3792, 0.1085, -0.9189, 0.0707],\n", " [ 0.0370, 0.9941, 0.1022, 0.0254]]),\n", " 'colmap_im_id': 13},\n", " {'file_path': 'images/frame_00014.png',\n", " 'transform_matrix': tensor([[ 0.8514, 0.0317, -0.5236, 0.1351],\n", " [-0.5235, 0.1137, -0.8444, 0.0844],\n", " [ 0.0327, 0.9930, 0.1134, 0.0248]]),\n", " 'colmap_im_id': 14},\n", " {'file_path': 'images/frame_00015.png',\n", " 'transform_matrix': tensor([[ 0.8031, 0.0552, -0.5933, 0.1605],\n", " [-0.5948, 0.1327, -0.7928, 0.0960],\n", " [ 0.0350, 0.9896, 0.1394, 0.0273]]),\n", " 'colmap_im_id': 15},\n", " {'file_path': 'images/frame_00016.png',\n", " 'transform_matrix': tensor([[ 0.7988, 0.0455, -0.5999, 0.2151],\n", " [-0.5997, 0.1395, -0.7879, 0.1080],\n", " [ 0.0479, 0.9892, 0.1387, 0.0321]]),\n", " 'colmap_im_id': 16},\n", " {'file_path': 'images/frame_00017.png',\n", " 'transform_matrix': tensor([[ 0.7803, 0.0694, -0.6216, 0.2633],\n", " [-0.6239, 0.1567, -0.7656, 0.1286],\n", " [ 0.0443, 0.9852, 0.1656, 0.0335]]),\n", " 'colmap_im_id': 17},\n", " {'file_path': 'images/frame_00018.png',\n", " 'transform_matrix': tensor([[ 0.7795, 0.0873, -0.6203, 0.2996],\n", " [-0.6258, 0.1514, 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22},\n", " {'file_path': 'images/frame_00023.png',\n", " 'transform_matrix': tensor([[ 0.6910, 0.0431, -0.7216, 0.4566],\n", " [-0.7214, 0.1049, -0.6845, 0.2751],\n", " [ 0.0462, 0.9935, 0.1036, 0.0610]]),\n", " 'colmap_im_id': 23},\n", " {'file_path': 'images/frame_00024.png',\n", " 'transform_matrix': tensor([[ 0.5919, 0.0798, -0.8021, 0.4995],\n", " [-0.8054, 0.0962, -0.5848, 0.2839],\n", " [ 0.0306, 0.9922, 0.1212, 0.0605]]),\n", " 'colmap_im_id': 24},\n", " {'file_path': 'images/frame_00025.png',\n", " 'transform_matrix': tensor([[ 0.4684, 0.1161, -0.8759, 0.5278],\n", " [-0.8834, 0.0755, -0.4624, 0.2948],\n", " [ 0.0125, 0.9904, 0.1379, 0.0580]]),\n", " 'colmap_im_id': 25},\n", " {'file_path': 'images/frame_00026.png',\n", " 'transform_matrix': tensor([[ 0.3089, 0.0966, -0.9462, 0.5258],\n", " [-0.9511, 0.0256, -0.3079, 0.3001],\n", " [-0.0055, 0.9950, 0.0998, 0.0637]]),\n", " 'colmap_im_id': 26},\n", " {'file_path': 'images/frame_00027.png',\n", " 'transform_matrix': tensor([[ 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-0.0483, 0.1112]]),\n", " 'colmap_im_id': 126},\n", " {'file_path': 'images/frame_00127.png',\n", " 'transform_matrix': tensor([[-0.9105, -0.0676, -0.4081, 0.6759],\n", " [-0.4092, 0.2917, 0.8646, 0.6920],\n", " [ 0.0606, 0.9541, -0.2932, 0.1321]]),\n", " 'colmap_im_id': 127}]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_frames" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Convert tensor to a list for JSON serialization\n", "for frame in data1['frames']:\n", " # Convert each tensor within 'transform_matrix' to a list\n", " frame['transform_matrix'] = frame['transform_matrix'].tolist()\n", "\n", "# Now data1 should be fully serializable\n", "with open('transforms_camera_path.json', 'w') as f:\n", " json.dump(data1, f)\n", "\n", "data1[\"frames\"] = sorted_frames" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'w': 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"execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Writing JSON data\n", "with open('normalized_transforms.json', 'w') as f:\n", " json.dump(data1, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "nerfstudio2", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.18" } }, "nbformat": 4, "nbformat_minor": 2 }