UNISURF Preprocessing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "saNBv0dY-Eef"
      },
      "source": [
        "# UNISURF Dataset Processing.\n",
        "\n",
        "**Author**: [Keunhong Park](https://keunhong.com)\n",
        "\n",
        "[[Project Page](https://nerfies.github.io)]\n",
        "[[Paper](https://storage.googleapis.com/nerfies-public/videos/nerfies_paper.pdf)]\n",
        "[[Video](https://www.youtube.com/watch?v=MrKrnHhk8IA)]\n",
        "[[GitHub](https://github.com/google/nerfies)]\n",
        "\n",
        "This notebook contains an example workflow for converting a video file to a Nerfies dataset.\n",
        "\n",
        "### Instructions\n",
        "\n",
        "1. Convert a video into our dataset format using this notebook. OR Convert a directory of images into a video that can then be converted into our dataset format.\n",
        "2. Train a NLR using the [training notebook](https://colab.sandbox.google.com/github/google/nerfies/blob/main/notebooks/Nerfies_Training.ipynb).\n",
        "\n",
        "NOTE: UNISURF preprocessing, we require masks to be provided. \n",
        "\n",
        "\n",
        "### Notes\n",
        "* While this will work for small datasets in a Colab runtime, larger datasets will require more compute power.\n",
        "* If you would like to train a model on a serious dataset, you should consider copying this to your own workstation and running it there. Some minor modifications will be required, and you will have to install the dependencies separately.\n",
        "* Please report issues on the [GitHub issue tracker](https://github.com/google/nerfies/issues).\n",
        "\n",
        "If you find this work useful, please consider citing:\n",
        "```bibtex\n",
        "@article{park2021nerfies\n",
        "  author    = {Park, Keunhong \n",
        "               and Sinha, Utkarsh \n",
        "               and Barron, Jonathan T. \n",
        "               and Bouaziz, Sofien \n",
        "               and Goldman, Dan B \n",
        "               and Seitz, Steven M. \n",
        "               and Martin-Brualla, Ricardo},\n",
        "  title     = {Nerfies: Deformable Neural Radiance Fields},\n",
        "  journal   = {ICCV},\n",
        "  year      = {2021},\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['000021.json', '000013.json', '000004.json', '000040.json', '000002.json', '000041.json', '000032.json', '000009.json', '000008.json', '000016.json', '000048.json', '000019.json', '000007.json', '000034.json', '000000.json', '000042.json', '000024.json', '000014.json', '000045.json', '000017.json', '000049.json', '000047.json', '000039.json', '000022.json', '000003.json', '000012.json', '000044.json', '000005.json', '000001.json', '000006.json', '000020.json', '000050.json', '000033.json', '000018.json', '000023.json', '000031.json', '000015.json', '000043.json']\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "folder = \"/ubc/cs/research/kmyi/svsamban/research/data-nerfies/curls50/camera\"\n",
        "\n",
        "# Iterate\n",
        "for file in os.listdir(folder):\n",
        "\t# Checking if the file is present in the list\n",
        "\tif 'left1_' in file:\n",
        "\t\toldName = os.path.join(folder, file)\n",
        "\t\tnewName = os.path.join(folder, file[6:])\n",
        "\n",
        "\t\t# Rename the file\n",
        "\t\tos.rename(oldName, newName)\n",
        "\n",
        "res = os.listdir(folder)\n",
        "print(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbXoNhFF-D8Q"
      },
      "source": [
        "## Install dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8QlvguTr92ko"
      },
      "outputs": [],
      "source": [
        "!apt-get install colmap ffmpeg\n",
        "\n",
        "!pip install numpy==1.19.3\n",
        "!pip install mediapipe\n",
        "!pip install tensorflow_graphics\n",
        "!pip install git+https://github.com/google/nerfies.git@v2\n",
        "!pip install \"git+https://github.com/google/nerfies.git#egg=pycolmap&subdirectory=third_party/pycolmap\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Z-ASlgBUPXJ"
      },
      "source": [
        "## Make a Video.\n",
        "\n",
        "Creates a video out of images in a given folder at 1 fps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import cv2\n",
        "import os\n",
        "\n",
        "# @markdown The directory containing all the RGB PNG images of your dataset.\n",
        "image_folder = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/dtu_v3/processed/scan55-og' # @param {type: 'string'}\n",
        "# @markdown The directory containing all the mask PNG images of your dataset.\n",
        "mask_folder = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/dtu_v3/processed/scan55-og' # @param {type: 'string'}\n",
        "dim = (800,600) # w by h\n",
        "avi_file_path = os.path.join(image_folder, 'video.avi')\n",
        "mp4_name = os.path.join(image_folder, 'video')\n",
        "\n",
        "images = [img for img in sorted(os.listdir(image_folder)) if img.endswith(\"_rgb.png\")]\n",
        "if len(images) > 100:\n",
        "    images = images[:100]\n",
        "frame = cv2.imread(os.path.join(image_folder, images[0]))\n",
        "\n",
        "video = cv2.VideoWriter(avi_file_path, 0, 1, dim)\n",
        "\n",
        "for image in images:\n",
        "    video.write(cv2.resize(cv2.imread(os.path.join(image_folder, image)), dim, interpolation = cv2.INTER_AREA))\n",
        "\n",
        "# cv2.destroyAllWindows()\n",
        "video.release()\n",
        "\n",
        "# def convert_avi_to_mp4(avi_file_path, output_name):\n",
        "os.popen(\"ffmpeg -i '{input}' -ac 2 -b:v 2000k -c:a aac -c:v libx264 -b:a 160k -vprofile high -bf 0 -strict experimental -f mp4 '{output}.mp4'\".format(input = avi_file_path, output = mp4_name))\n",
        "mp4_name = mp4_name + '.mp4'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "5NR5OGyeUOKU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Directories configured:\n",
            "  root_dir = /ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs/scan55\n",
            "  rgb_raw_dir = /ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs/scan55/rgb-raw\n",
            "  rgb_dir = /ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs/scan55/rgb\n",
            "  colmap_dir = /ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs/scan55/colmap\n",
            "  mask_dir = /ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs/scan55/rgb/mask\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# @title Configure dataset directories\n",
        "from pathlib import Path\n",
        "\n",
        "# @markdown The base directory for all captures. This can be anything if you're running this notebook on your own Jupyter runtime.\n",
        "save_dir = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs'  # @param {type: 'string'}\n",
        "# @markdown The name of this capture. The working directory will be `$save_dir/$capture_name` in which the Nerfies version of the dataset will be stored. **Make sure you change this** when processing a new video.\n",
        "capture_name = 'scan55'  # @param {type: 'string'}\n",
        "# The root directory for this capture.\n",
        "root_dir = Path(save_dir, capture_name)\n",
        "# Where to save RGB images.\n",
        "rgb_dir = root_dir / 'rgb'\n",
        "rgb_raw_dir = root_dir / 'rgb-raw'\n",
        "# Where to save the COLMAP outputs.\n",
        "colmap_dir = root_dir / 'colmap'\n",
        "colmap_db_path = colmap_dir / 'database.db'\n",
        "colmap_out_path = colmap_dir / 'sparse'\n",
        "# Where to save masks.\n",
        "mask_dir = root_dir / 'rgb' / 'mask'\n",
        "\n",
        "colmap_out_path.mkdir(exist_ok=True, parents=True)\n",
        "rgb_raw_dir.mkdir(exist_ok=True, parents=True)\n",
        "mask_dir.mkdir(exist_ok=True, parents=True)\n",
        "\n",
        "print(f\"\"\"Directories configured:\n",
        "  root_dir = {root_dir}\n",
        "  rgb_raw_dir = {rgb_raw_dir}\n",
        "  rgb_dir = {rgb_dir}\n",
        "  colmap_dir = {colmap_dir}\n",
        "  mask_dir = {mask_dir}\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "to4QpKLFHf2s"
      },
      "source": [
        "## Dataset Processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SFzPpUoM99nd"
      },
      "outputs": [],
      "source": [
        "# @title Upload video file.\n",
        "# @markdown Select a video file (.mp4, .mov, etc.) from your disk. This will upload it to the local Colab working directory.\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nscgY8DW-DHk"
      },
      "source": [
        "### Move Masks.\n",
        "\n",
        "In this step we copy and resize exisiting mask PNG images to the mask directory under RGB."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# @title reshape and move pre-existing masks.\n",
        "from PIL import Image\n",
        " \n",
        "i = 1\n",
        "for file in sorted(os.listdir(mask_folder)):\n",
        "    f_img = mask_folder+\"/\"+file\n",
        "\n",
        "    if os.path.isfile(f_img) and file[-9:] == '.png':\n",
        "        # print(file)\n",
        "        img = Image.open(f_img)\n",
        "        img = img.resize(dim)\n",
        "        img.save(str(mask_dir)+'/'+str(i).zfill(6)+'.png')\n",
        "        i = i + 1\n",
        "        \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nscgY8DW-DHk"
      },
      "source": [
        "### Load Video.\n",
        "\n",
        "In this step we load the video file and flatten it into PNG files using ffmpeg. If starting with images, use 1 fps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "rjnL6FdlCGhE"
      },
      "outputs": [],
      "source": [
        "# @title Flatten into images.\n",
        "\n",
        "import cv2\n",
        "\n",
        "\n",
        "# @markdown Flattens the video into images. The results will be saved to `rgb_raw_dir`.\n",
        "video_path = mp4_name\n",
        "\n",
        "# @markdown Adjust `max_scale` to something smaller for faster processing.\n",
        "max_scale = 1 #0.9803921568627451  # @param {type:'number'}\n",
        "# @markdown A smaller FPS will be much faster for bundle adjustment, but at the expensive of a lower sampling density for training. For the paper we used ~15 fps but we default to something lower here to get you started faster.\n",
        "# @markdown If given an fps of -1 we will try to auto-compute it.\n",
        "fps = 1  # @param {type:'number'}\n",
        "target_num_frames = 49 # @param {type: 'number'}\n",
        "\n",
        "cap = cv2.VideoCapture(video_path)\n",
        "input_fps = cap.get(cv2.CAP_PROP_FPS)\n",
        "num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "\n",
        "if num_frames < target_num_frames:\n",
        "  raise RuntimeError(\n",
        "      'The video is too short and has fewer frames than the target.')\n",
        "\n",
        "if fps == -1:\n",
        "  fps = int(target_num_frames / num_frames * input_fps)\n",
        "  print(f\"Auto-computed FPS = {fps}\")\n",
        "\n",
        "# @markdown Check this if you want to reprocess the frames.\n",
        "overwrite = True  # @param {type:'boolean'}\n",
        "\n",
        "if (rgb_dir / '1x').exists() and not overwrite:\n",
        "  raise RuntimeError(\n",
        "      f'The RGB frames have already been processed. Check `overwrite` and run again if you really meant to do this.')\n",
        "else:\n",
        "  filters = f\"mpdecimate,setpts=N/FRAME_RATE/TB,scale=iw*{max_scale}:ih*{max_scale}\"\n",
        "  tmp_rgb_raw_dir = 'rgb-raw'\n",
        "  out_pattern = str('rgb-raw/%06d.png')\n",
        "  !mkdir -p \"$tmp_rgb_raw_dir\"\n",
        "  !rm -rf $tmp_rgb_raw_dir/*\n",
        "  !rm -rf $rgb_raw_dir/*\n",
        "  !ffmpeg -i \"$video_path\" -r $fps -vf $filters \"$out_pattern\"\n",
        "  !mkdir -p \"$rgb_raw_dir\"\n",
        "  !rsync -av \"$tmp_rgb_raw_dir/\" \"$rgb_raw_dir/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "5YsXeX4ckaKJ"
      },
      "outputs": [],
      "source": [
        "# @title Resize images into different scales.\n",
        "# @markdown Here we save the input images at various resolutions (downsample by a factor of 1, 2, 4, 8). We use area relation interpolation to prevent moire artifacts.\n",
        "import concurrent.futures\n",
        "import numpy as np\n",
        "import cv2\n",
        "import imageio\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "def save_image(path, image: np.ndarray) -> None:\n",
        "  print(f'Saving {path}')\n",
        "  if not path.parent.exists():\n",
        "    path.parent.mkdir(exist_ok=True, parents=True)\n",
        "  with path.open('wb') as f:\n",
        "    image = Image.fromarray(np.asarray(image))\n",
        "    image.save(f, format=path.suffix.lstrip('.'))\n",
        "\n",
        "\n",
        "def image_to_uint8(image: np.ndarray) -> np.ndarray:\n",
        "  \"\"\"Convert the image to a uint8 array.\"\"\"\n",
        "  if image.dtype == np.uint8:\n",
        "    return image\n",
        "  if not issubclass(image.dtype.type, np.floating):\n",
        "    raise ValueError(\n",
        "        f'Input image should be a floating type but is of type {image.dtype!r}')\n",
        "  return (image * 255).clip(0.0, 255).astype(np.uint8)\n",
        "\n",
        "\n",
        "def make_divisible(image: np.ndarray, divisor: int) -> np.ndarray:\n",
        "  \"\"\"Trim the image if not divisible by the divisor.\"\"\"\n",
        "  height, width = image.shape[:2]\n",
        "  if height % divisor == 0 and width % divisor == 0:\n",
        "    return image\n",
        "\n",
        "  new_height = height - height % divisor\n",
        "  new_width = width - width % divisor\n",
        "\n",
        "  return image[:new_height, :new_width]\n",
        "\n",
        "\n",
        "def downsample_image(image: np.ndarray, scale: int) -> np.ndarray:\n",
        "  \"\"\"Downsamples the image by an integer factor to prevent artifacts.\"\"\"\n",
        "  if scale == 1:\n",
        "    return image\n",
        "\n",
        "  height, width = image.shape[:2]\n",
        "  if height % scale > 0 or width % scale > 0:\n",
        "    raise ValueError(f'Image shape ({height},{width}) must be divisible by the'\n",
        "                     f' scale ({scale}).')\n",
        "  out_height, out_width = height // scale, width // scale\n",
        "  resized = cv2.resize(image, (out_width, out_height), cv2.INTER_AREA)\n",
        "  return resized\n",
        "\n",
        "\n",
        "\n",
        "image_scales = \"1\"  # @param {type: \"string\"}\n",
        "image_scales = [int(x) for x in image_scales.split(',')]\n",
        "\n",
        "tmp_rgb_dir = Path('rgb')\n",
        "!rm -rf $tmp_rgb_dir/*\n",
        "\n",
        "for image_path in Path(tmp_rgb_raw_dir).glob('*.png'):\n",
        "  image = make_divisible(imageio.imread(image_path), max(image_scales))\n",
        "  for scale in image_scales:\n",
        "    save_image(\n",
        "        tmp_rgb_dir / f'{scale}x/{image_path.stem}.png',\n",
        "        image_to_uint8(downsample_image(image, scale)))\n",
        "\n",
        "!rsync -av \"$tmp_rgb_dir/\" \"$rgb_dir/\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "ql9r4rufLQue"
      },
      "outputs": [],
      "source": [
        "# @title Example frame.\n",
        "# @markdown Make sure that the video was processed correctly.\n",
        "# @markdown If this gives an exception, try running the preceding cell one more time--sometimes uploading to Google Drive can fail.\n",
        "\n",
        "from pathlib import Path\n",
        "import imageio\n",
        "from PIL import Image\n",
        "\n",
        "image_paths = list((rgb_dir / '1x').iterdir())\n",
        "Image.open(image_paths[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YnhY66zOShI"
      },
      "source": [
        "### Camera registration with COLMAP."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T2xqbzxILqZO"
      },
      "outputs": [],
      "source": [
        "# @title Extract features.\n",
        "# @markdown Computes SIFT features and saves them to the COLMAP DB.\n",
        "share_intrinsics = True  # @param {type: 'boolean'}\n",
        "assume_upright_cameras = True  # @param {type: 'boolean'}\n",
        "\n",
        "# @markdown This sets the scale at which we will run COLMAP. A scale of 1 will be more accurate but will be slow.\n",
        "colmap_image_scale = 1  # @param {type: 'number'}\n",
        "colmap_rgb_dir = rgb_dir / f'{colmap_image_scale}x'\n",
        "\n",
        "# @markdown Check this if you want to re-process SfM.\n",
        "overwrite = False  # @param {type: 'boolean'}\n",
        "\n",
        "if overwrite and colmap_db_path.exists():\n",
        "  colmap_db_path.unlink()\n",
        "print(str(colmap_rgb_dir))\n",
        "!colmap feature_extractor \\\n",
        "--SiftExtraction.use_gpu 1 \\\n",
        "--SiftExtraction.upright {int(assume_upright_cameras)} \\\n",
        "--ImageReader.camera_model OPENCV \\\n",
        "--ImageReader.single_camera {int(share_intrinsics)} \\\n",
        "--database_path \"{str(colmap_db_path)}\" \\\n",
        "--image_path \"{str(colmap_rgb_dir)}\" \\\n",
        "--ImageReader.mask_path \"{str(mask_dir)}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "7f_n95abLqw6"
      },
      "outputs": [],
      "source": [
        "# @title Match features.\n",
        "# @markdown Match the SIFT features between images. Use `exhaustive` if you only have a few images and use `vocab_tree` if you have a lot of images.\n",
        "\n",
        "match_method = 'vocab_tree'  # @param [\"exhaustive\", \"vocab_tree\"]\n",
        "\n",
        "if match_method == 'exhaustive':\n",
        "  !colmap exhaustive_matcher \\\n",
        "    --SiftMatching.use_gpu 0 \\\n",
        "    --database_path \"{str(colmap_db_path)}\"\n",
        "else:\n",
        "  # Use this if you have lots of frames.\n",
        "  !wget https://demuc.de/colmap/vocab_tree_flickr100K_words32K.bin\n",
        "  !colmap vocab_tree_matcher \\\n",
        "    --VocabTreeMatching.vocab_tree_path vocab_tree_flickr100K_words32K.bin \\\n",
        "    --SiftMatching.use_gpu 0 \\\n",
        "    --database_path \"{str(colmap_db_path)}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aR52ZlXJOAn3"
      },
      "outputs": [],
      "source": [
        "# @title Reconstruction.\n",
        "# @markdown Run structure-from-motion to compute camera parameters.\n",
        "\n",
        "refine_principal_point = True  #@param {type:\"boolean\"}\n",
        "min_num_matches =   32# @param {type: 'number'}\n",
        "filter_max_reproj_error = 2  # @param {type: 'number'}\n",
        "tri_complete_max_reproj_error = 2  # @param {type: 'number'}\n",
        "\n",
        "!colmap mapper \\\n",
        "  --Mapper.ba_refine_principal_point {int(refine_principal_point)} \\\n",
        "  --Mapper.filter_max_reproj_error $filter_max_reproj_error \\\n",
        "  --Mapper.tri_complete_max_reproj_error $tri_complete_max_reproj_error \\\n",
        "  --Mapper.min_num_matches $min_num_matches \\\n",
        "  --database_path \"{str(colmap_db_path)}\" \\\n",
        "  --image_path \"{str(colmap_rgb_dir)}\" \\\n",
        "  --output_path \"{str(colmap_out_path)}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "1ckBrtc9O4s4"
      },
      "outputs": [],
      "source": [
        "# @title Verify that SfM worked.\n",
        "\n",
        "if not colmap_db_path.exists():\n",
        "  raise RuntimeError(f'The COLMAP DB does not exist, did you run the reconstruction?')\n",
        "elif not (colmap_dir / 'sparse/0/cameras.bin').exists():\n",
        "  raise RuntimeError(\"\"\"\n",
        "SfM seems to have failed. Try some of the following options:\n",
        " - Increase the FPS when flattenting to images. There should be at least 50-ish images.\n",
        " - Decrease `min_num_matches`.\n",
        " - If you images aren't upright, uncheck `assume_upright_cameras`.\n",
        "\"\"\")\n",
        "else:\n",
        "  print(\"Everything looks good!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqpRdhDBdRjT"
      },
      "source": [
        "## Parse Data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "5LuJwJawdXKw"
      },
      "outputs": [],
      "source": [
        "# @title Define Scene Manager.\n",
        "from absl import logging\n",
        "from typing import Dict\n",
        "import numpy as np\n",
        "from nerfies.camera import Camera\n",
        "import pycolmap\n",
        "from pycolmap import Quaternion\n",
        "\n",
        "\n",
        "def convert_colmap_camera(colmap_camera, colmap_image):\n",
        "  \"\"\"Converts a pycolmap `image` to an SFM camera.\"\"\"\n",
        "  camera_rotation = colmap_image.R()\n",
        "  camera_position = -(colmap_image.t @ camera_rotation)\n",
        "  new_camera = Camera(\n",
        "      orientation=camera_rotation,\n",
        "      position=camera_position,\n",
        "      focal_length=colmap_camera.fx,\n",
        "      pixel_aspect_ratio=colmap_camera.fx / colmap_camera.fx,\n",
        "      principal_point=np.array([colmap_camera.cx, colmap_camera.cy]),\n",
        "      radial_distortion=np.array([colmap_camera.k1, colmap_camera.k2, 0.0]),\n",
        "      tangential_distortion=np.array([colmap_camera.p1, colmap_camera.p2]),\n",
        "      skew=0.0,\n",
        "      image_size=np.array([colmap_camera.width, colmap_camera.height])\n",
        "  )\n",
        "  return new_camera\n",
        "\n",
        "\n",
        "def filter_outlier_points(points, inner_percentile):\n",
        "  \"\"\"Filters outlier points.\"\"\"\n",
        "  outer = 1.0 - inner_percentile\n",
        "  lower = outer / 2.0\n",
        "  upper = 1.0 - lower\n",
        "  centers_min = np.quantile(points, lower, axis=0)\n",
        "  centers_max = np.quantile(points, upper, axis=0)\n",
        "  result = points.copy()\n",
        "\n",
        "  too_near = np.any(result < centers_min[None, :], axis=1)\n",
        "  too_far = np.any(result > centers_max[None, :], axis=1)\n",
        "\n",
        "  return result[~(too_near | too_far)]\n",
        "\n",
        "\n",
        "def average_reprojection_errors(points, pixels, cameras):\n",
        "  \"\"\"Computes the average reprojection errors of the points.\"\"\"\n",
        "  cam_errors = []\n",
        "  for i, camera in enumerate(cameras):\n",
        "    cam_error = reprojection_error(points, pixels[:, i], camera)\n",
        "    cam_errors.append(cam_error)\n",
        "  cam_error = np.stack(cam_errors)\n",
        "\n",
        "  return cam_error.mean(axis=1)\n",
        "\n",
        "\n",
        "def _get_camera_translation(camera):\n",
        "  \"\"\"Computes the extrinsic translation of the camera.\"\"\"\n",
        "  rot_mat = camera.orientation\n",
        "  return -camera.position.dot(rot_mat.T)\n",
        "\n",
        "\n",
        "def _transform_camera(camera, transform_mat):\n",
        "  \"\"\"Transforms the camera using the given transformation matrix.\"\"\"\n",
        "  # The determinant gives us volumetric scaling factor.\n",
        "  # Take the cube root to get the linear scaling factor.\n",
        "  scale = np.cbrt(linalg.det(transform_mat[:, :3]))\n",
        "  quat_transform = ~Quaternion.FromR(transform_mat[:, :3] / scale)\n",
        "\n",
        "  translation = _get_camera_translation(camera)\n",
        "  rot_quat = Quaternion.FromR(camera.orientation)\n",
        "  rot_quat *= quat_transform\n",
        "  translation = scale * translation - rot_quat.ToR().dot(transform_mat[:, 3])\n",
        "  new_transform = np.eye(4)\n",
        "  new_transform[:3, :3] = rot_quat.ToR()\n",
        "  new_transform[:3, 3] = translation\n",
        "\n",
        "  rotation = rot_quat.ToR()\n",
        "  new_camera = camera.copy()\n",
        "  new_camera.orientation = rotation\n",
        "  new_camera.position = -(translation @ rotation)\n",
        "  return new_camera\n",
        "\n",
        "\n",
        "def _pycolmap_to_sfm_cameras(manager: pycolmap.SceneManager) -> Dict[int, Camera]:\n",
        "  \"\"\"Creates SFM cameras.\"\"\"\n",
        "  # Use the original filenames as indices.\n",
        "  # This mapping necessary since COLMAP uses arbitrary numbers for the\n",
        "  # image_id.\n",
        "  image_id_to_colmap_id = {\n",
        "      image.name.split('.')[0]: image_id\n",
        "      for image_id, image in manager.images.items()\n",
        "  }\n",
        "\n",
        "  sfm_cameras = {}\n",
        "  for image_id in image_id_to_colmap_id:\n",
        "    colmap_id = image_id_to_colmap_id[image_id]\n",
        "    image = manager.images[colmap_id]\n",
        "    camera = manager.cameras[image.camera_id]\n",
        "    sfm_cameras[image_id] = convert_colmap_camera(camera, image)\n",
        "\n",
        "  return sfm_cameras\n",
        "\n",
        "\n",
        "class SceneManager:\n",
        "  \"\"\"A thin wrapper around pycolmap.\"\"\"\n",
        "\n",
        "  @classmethod\n",
        "  def from_pycolmap(cls, colmap_path, image_path, min_track_length=10):\n",
        "    \"\"\"Create a scene manager using pycolmap.\"\"\"\n",
        "    manager = pycolmap.SceneManager(str(colmap_path))\n",
        "    manager.load_cameras()\n",
        "    manager.load_images()\n",
        "    manager.load_points3D()\n",
        "    manager.filter_points3D(min_track_len=min_track_length)\n",
        "    sfm_cameras = _pycolmap_to_sfm_cameras(manager)\n",
        "    return cls(sfm_cameras, manager.get_filtered_points3D(), image_path)\n",
        "\n",
        "  def __init__(self, cameras, points, image_path):\n",
        "    self.image_path = Path(image_path)\n",
        "    self.camera_dict = cameras\n",
        "    self.points = points\n",
        "\n",
        "    logging.info('Created scene manager with %d cameras', len(self.camera_dict))\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.camera_dict)\n",
        "\n",
        "  @property\n",
        "  def image_ids(self):\n",
        "    return sorted(self.camera_dict.keys())\n",
        "\n",
        "  @property\n",
        "  def camera_list(self):\n",
        "    return [self.camera_dict[i] for i in self.image_ids]\n",
        "\n",
        "  @property\n",
        "  def camera_positions(self):\n",
        "    \"\"\"Returns an array of camera positions.\"\"\"\n",
        "    return np.stack([camera.position for camera in self.camera_list])\n",
        "\n",
        "  def load_image(self, image_id):\n",
        "    \"\"\"Loads the image with the specified image_id.\"\"\"\n",
        "    path = self.image_path / f'{image_id}.png'\n",
        "    with path.open('rb') as f:\n",
        "      return imageio.imread(f)\n",
        "\n",
        "  def triangulate_pixels(self, pixels):\n",
        "    \"\"\"Triangulates the pixels across all cameras in the scene.\n",
        "\n",
        "    Args:\n",
        "      pixels: the pixels to triangulate. There must be the same number of pixels\n",
        "        as cameras in the scene.\n",
        "\n",
        "    Returns:\n",
        "      The 3D points triangulated from the pixels.\n",
        "    \"\"\"\n",
        "    if pixels.shape != (len(self), 2):\n",
        "      raise ValueError(\n",
        "          f'The number of pixels ({len(pixels)}) must be equal to the number '\n",
        "          f'of cameras ({len(self)}).')\n",
        "\n",
        "    return triangulate_pixels(pixels, self.camera_list)\n",
        "\n",
        "  def change_basis(self, axes, center):\n",
        "    \"\"\"Change the basis of the scene.\n",
        "\n",
        "    Args:\n",
        "      axes: the axes of the new coordinate frame.\n",
        "      center: the center of the new coordinate frame.\n",
        "\n",
        "    Returns:\n",
        "      A new SceneManager with transformed points and cameras.\n",
        "    \"\"\"\n",
        "    transform_mat = np.zeros((3, 4))\n",
        "    transform_mat[:3, :3] = axes.T\n",
        "    transform_mat[:, 3] = -(center @ axes)\n",
        "    return self.transform(transform_mat)\n",
        "\n",
        "  def transform(self, transform_mat):\n",
        "    \"\"\"Transform the scene using a transformation matrix.\n",
        "\n",
        "    Args:\n",
        "      transform_mat: a 3x4 transformation matrix representation a\n",
        "        transformation.\n",
        "\n",
        "    Returns:\n",
        "      A new SceneManager with transformed points and cameras.\n",
        "    \"\"\"\n",
        "    if transform_mat.shape != (3, 4):\n",
        "      raise ValueError('transform_mat should be a 3x4 transformation matrix.')\n",
        "\n",
        "    points = None\n",
        "    if self.points is not None:\n",
        "      points = self.points.copy()\n",
        "      points = points @ transform_mat[:, :3].T + transform_mat[:, 3]\n",
        "\n",
        "    new_cameras = {}\n",
        "    for image_id, camera in self.camera_dict.items():\n",
        "      new_cameras[image_id] = _transform_camera(camera, transform_mat)\n",
        "\n",
        "    return SceneManager(new_cameras, points, self.image_path)\n",
        "\n",
        "  def filter_images(self, image_ids):\n",
        "    num_filtered = 0\n",
        "    for image_id in image_ids:\n",
        "      if self.camera_dict.pop(image_id, None) is not None:\n",
        "        num_filtered += 1\n",
        "\n",
        "    return num_filtered\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "HdAegiHVWdY9"
      },
      "outputs": [],
      "source": [
        "# @title Load COLMAP scene.\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "scene_manager = SceneManager.from_pycolmap(\n",
        "    colmap_dir / 'sparse/0', \n",
        "    rgb_dir / f'1x', \n",
        "    min_track_length=5)\n",
        "\n",
        "if colmap_image_scale > 1:\n",
        "  print(f'Scaling COLMAP cameras back to 1x from {colmap_image_scale}x.')\n",
        "  for item_id in scene_manager.image_ids:\n",
        "    camera = scene_manager.camera_dict[item_id]\n",
        "    scene_manager.camera_dict[item_id] = camera.scale(colmap_image_scale)\n",
        "\n",
        "np.save(str(root_dir)+'/points.npy', scene_manager.points, allow_pickle=True)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter3d(\n",
        "    x=scene_manager.points[:, 0],\n",
        "    y=scene_manager.points[:, 1],\n",
        "    z=scene_manager.points[:, 2],\n",
        "    mode='markers',\n",
        "    marker=dict(size=2),\n",
        "))\n",
        "fig.add_trace(go.Scatter3d(\n",
        "    x=scene_manager.camera_positions[:, 0],\n",
        "    y=scene_manager.camera_positions[:, 1],\n",
        "    z=scene_manager.camera_positions[:, 2],\n",
        "    mode='markers',\n",
        "    marker=dict(size=2),\n",
        "))\n",
        "fig.update_layout(scene_dragmode='orbit')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "e92Kcuoa5i9h"
      },
      "outputs": [],
      "source": [
        "# @title Filter blurry frames.\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "def variance_of_laplacian(image: np.ndarray) -> np.ndarray:\n",
        "  \"\"\"Compute the variance of the Laplacian which measure the focus.\"\"\"\n",
        "  gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
        "  return cv2.Laplacian(gray, cv2.CV_64F).var()\n",
        "\n",
        "\n",
        "blur_filter_perc = 95.0 # @param {type: 'number'}\n",
        "blur_filter_perc = 100.0 # @param {type: 'number'}\n",
        "if blur_filter_perc > 0.0:\n",
        "  image_paths = sorted(rgb_dir.iterdir())\n",
        "  print('Loading images.')\n",
        "  images = list(map(scene_manager.load_image, scene_manager.image_ids))\n",
        "  print('Computing blur scores.')\n",
        "  blur_scores = np.array([variance_of_laplacian(im) for im in images])\n",
        "  blur_thres = np.percentile(blur_scores, blur_filter_perc)\n",
        "  blur_filter_inds = np.where(blur_scores >= blur_thres)[0]\n",
        "  blur_filter_scores = [blur_scores[i] for i in blur_filter_inds]\n",
        "  blur_filter_inds = blur_filter_inds[np.argsort(blur_filter_scores)]\n",
        "  blur_filter_scores = np.sort(blur_filter_scores)\n",
        "  blur_filter_image_ids = [scene_manager.image_ids[i] for i in blur_filter_inds]\n",
        "  print(f'Filtering {len(blur_filter_image_ids)} IDs: {blur_filter_image_ids}')\n",
        "  num_filtered = scene_manager.filter_images(blur_filter_image_ids)\n",
        "  print(f'Filtered {num_filtered} images')\n",
        "\n",
        "  plt.figure(figsize=(15, 10))\n",
        "  plt.subplot(121)\n",
        "  plt.title('Least blurry')\n",
        "  plt.imshow(images[blur_filter_inds[-1]])\n",
        "  plt.subplot(122)\n",
        "  plt.title('Most blurry')\n",
        "  plt.imshow(images[blur_filter_inds[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtSV7C5y3Yuv"
      },
      "source": [
        "### Face Processing.\n",
        "\n",
        "This section runs the optional step of computing facial landmarks for the purpose of test camera generation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "lDOphUXt5AQ-"
      },
      "outputs": [],
      "source": [
        "import jax\n",
        "from jax import numpy as jnp\n",
        "from tensorflow_graphics.geometry.representation.ray import triangulate as ray_triangulate\n",
        "\n",
        "!pip install --upgrade tensorflow-graphics-gpu\n",
        "use_face = False  # @param {type: 'boolean'}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "hVjyA5sW3AVZ"
      },
      "outputs": [],
      "source": [
        "# @title Compute 2D landmarks.\n",
        "\n",
        "import imageio\n",
        "import mediapipe as mp\n",
        "from PIL import Image\n",
        "\n",
        "if use_face:\n",
        "  mp_face_mesh = mp.solutions.face_mesh\n",
        "  mp_drawing = mp.solutions.drawing_utils \n",
        "  drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)\n",
        "  \n",
        "  # Initialize MediaPipe Face Mesh.\n",
        "  face_mesh = mp_face_mesh.FaceMesh(\n",
        "      static_image_mode=True,\n",
        "      max_num_faces=2,\n",
        "      min_detection_confidence=0.5)\n",
        "  \n",
        "  \n",
        "  def compute_landmarks(image):\n",
        "    height, width = image.shape[:2]\n",
        "    results = face_mesh.process(image)\n",
        "    if results.multi_face_landmarks is None:\n",
        "      return None\n",
        "    # Choose first face found.\n",
        "    landmarks = results.multi_face_landmarks[0].landmark\n",
        "    landmarks = np.array(\n",
        "        [(o.x * width, o.y * height) for o in landmarks],\n",
        "        dtype=np.uint32)\n",
        "    return landmarks\n",
        "\n",
        "  landmarks_dict = {}\n",
        "  for item_id in scene_manager.image_ids:\n",
        "    image = scene_manager.load_image(item_id)\n",
        "    landmarks = compute_landmarks(image)\n",
        "    if landmarks is not None:\n",
        "      landmarks_dict[item_id] = landmarks\n",
        "  \n",
        "  landmark_item_ids = sorted(landmarks_dict)\n",
        "  landmarks_pixels = np.array([landmarks_dict[i] for i in landmark_item_ids])\n",
        "  landmarks_cameras = [scene_manager.camera_dict[i] for i in landmark_item_ids]\n",
        "  \n",
        "  from matplotlib import pyplot as plt\n",
        "  plt.imshow(image)\n",
        "  plt.scatter(x=landmarks[..., 0], y=landmarks[..., 1], s=1);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "axRj1ItALAuC"
      },
      "outputs": [],
      "source": [
        "# @title Triangulate landmarks in 3D.\n",
        "\n",
        "if use_face:\n",
        "  def compute_camera_rays(points, camera):\n",
        "    origins = np.broadcast_to(camera.position[None, :], (points.shape[0], 3))\n",
        "    directions = camera.pixels_to_rays(points.astype(jnp.float32))\n",
        "    endpoints = origins + directions\n",
        "    return origins, endpoints\n",
        "  \n",
        "  \n",
        "  def triangulate_landmarks(landmarks, cameras):\n",
        "    all_origins = []\n",
        "    all_endpoints = []\n",
        "    nan_inds = []\n",
        "    for i, (camera_landmarks, camera) in enumerate(zip(landmarks, cameras)):\n",
        "      origins, endpoints = compute_camera_rays(camera_landmarks, camera)\n",
        "      if np.isnan(origins).sum() > 0.0 or np.isnan(endpoints).sum() > 0.0:\n",
        "        continue\n",
        "      all_origins.append(origins)\n",
        "      all_endpoints.append(endpoints)\n",
        "    all_origins = np.stack(all_origins, axis=-2).astype(np.float32)\n",
        "    all_endpoints = np.stack(all_endpoints, axis=-2).astype(np.float32)\n",
        "    weights = np.ones(all_origins.shape[:2], dtype=np.float32)\n",
        "    points = np.array(ray_triangulate(all_origins, all_endpoints, weights))\n",
        "  \n",
        "    return points\n",
        "  \n",
        "\n",
        "  landmark_points = triangulate_landmarks(landmarks_pixels, landmarks_cameras)\n",
        "else:\n",
        "  landmark_points = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "gRU-bJ8NYzR_"
      },
      "outputs": [],
      "source": [
        "# @title Normalize scene based on landmarks.\n",
        "from scipy import linalg\n",
        "\n",
        "DEFAULT_IPD = 0.06\n",
        "NOSE_TIP_IDX = 1\n",
        "FOREHEAD_IDX = 10\n",
        "CHIN_IDX = 152\n",
        "RIGHT_EYE_IDX = 145\n",
        "LEFT_EYE_IDX = 385\n",
        "RIGHT_TEMPLE_IDX = 162\n",
        "LEFT_TEMPLE_IDX = 389\n",
        "\n",
        "\n",
        "def _normalize(x):\n",
        "  return x / linalg.norm(x)\n",
        "\n",
        "\n",
        "def fit_plane_normal(points):\n",
        "  \"\"\"Fit a plane to the points and return the normal.\"\"\"\n",
        "  centroid = points.sum(axis=0) / points.shape[0]\n",
        "  _, _, vh = linalg.svd(points - centroid)\n",
        "  return vh[2, :]\n",
        "\n",
        "\n",
        "def metric_scale_from_ipd(landmark_points, reference_ipd):\n",
        "  \"\"\"Infer the scene-to-metric conversion ratio from facial landmarks.\"\"\"\n",
        "  left_eye = landmark_points[LEFT_EYE_IDX]\n",
        "  right_eye = landmark_points[RIGHT_EYE_IDX]\n",
        "  model_ipd = linalg.norm(left_eye - right_eye)\n",
        "  return reference_ipd / model_ipd\n",
        "\n",
        "\n",
        "def basis_from_landmarks(landmark_points):\n",
        "  \"\"\"Computes an orthonormal basis from facial landmarks.\"\"\"\n",
        "  # Estimate Z by fitting a plane\n",
        "  # This works better than trusting the chin to forehead vector, especially in\n",
        "  # full body captures.\n",
        "  face_axis_z = _normalize(fit_plane_normal(landmark_points))\n",
        "  face_axis_y = _normalize(landmark_points[FOREHEAD_IDX] -\n",
        "                           landmark_points[CHIN_IDX])\n",
        "  face_axis_x = _normalize(landmark_points[LEFT_TEMPLE_IDX] -\n",
        "                           landmark_points[RIGHT_TEMPLE_IDX])\n",
        "\n",
        "  # Fitted plane normal might be flipped. Check using a heuristic and flip it if\n",
        "  # it's flipped.\n",
        "  z_flipped = np.dot(np.cross(face_axis_x, face_axis_y), face_axis_z)\n",
        "  if z_flipped < 0.0:\n",
        "    face_axis_z *= -1\n",
        "\n",
        "  # Ensure axes are orthogonal, with the Z axis being fixed.\n",
        "  face_axis_y = np.cross(face_axis_z, face_axis_x)\n",
        "  face_axis_x = np.cross(face_axis_y, face_axis_z)\n",
        "\n",
        "  return np.stack([face_axis_x, face_axis_y, face_axis_z]).T\n",
        "\n",
        "\n",
        "if use_face:\n",
        "  face_basis = basis_from_landmarks(landmark_points)\n",
        "  new_scene_manager = scene_manager.change_basis(\n",
        "      face_basis, landmark_points[NOSE_TIP_IDX])\n",
        "  new_cameras = [new_scene_manager.camera_dict[i] for i in landmark_item_ids]\n",
        "  new_landmark_points = triangulate_landmarks(landmarks_pixels, new_cameras)\n",
        "  face_basis = basis_from_landmarks(landmark_points)\n",
        "  scene_to_metric = metric_scale_from_ipd(landmark_points, DEFAULT_IPD)\n",
        "  \n",
        "  print(f'Computed basis: {face_basis}')\n",
        "  print(f'Estimated metric scale = {scene_to_metric:.02f}')\n",
        "else:\n",
        "  new_scene_manager = scene_manager"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPuR5MKk6Ubh"
      },
      "source": [
        "## Compute scene information.\n",
        "\n",
        "This section computes the scene information necessary for NeRF training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "klgXn8BQ8uH9"
      },
      "outputs": [],
      "source": [
        "# @title Compute near/far planes.\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "def estimate_near_far_for_image(scene_manager, image_id):\n",
        "  \"\"\"Estimate near/far plane for a single image based via point cloud.\"\"\"\n",
        "  points = filter_outlier_points(scene_manager.points, 0.95)\n",
        "  points = np.concatenate([\n",
        "      points,\n",
        "      scene_manager.camera_positions,\n",
        "  ], axis=0)\n",
        "  camera = scene_manager.camera_dict[image_id]\n",
        "  pixels = camera.project(points)\n",
        "  depths = camera.points_to_local_points(points)[..., 2]\n",
        "\n",
        "  # in_frustum = camera.ArePixelsInFrustum(pixels)\n",
        "  in_frustum = (\n",
        "      (pixels[..., 0] >= 0.0)\n",
        "      & (pixels[..., 0] <= camera.image_size_x)\n",
        "      & (pixels[..., 1] >= 0.0)\n",
        "      & (pixels[..., 1] <= camera.image_size_y))\n",
        "  depths = depths[in_frustum]\n",
        "\n",
        "  in_front_of_camera = depths > 0\n",
        "  depths = depths[in_front_of_camera]\n",
        "\n",
        "  near = np.quantile(depths, 0.001)\n",
        "  far = np.quantile(depths, 0.999)\n",
        "\n",
        "  return near, far\n",
        "\n",
        "\n",
        "def estimate_near_far(scene_manager):\n",
        "  \"\"\"Estimate near/far plane for a set of randomly-chosen images.\"\"\"\n",
        "  # image_ids = sorted(scene_manager.images.keys())\n",
        "  image_ids = scene_manager.image_ids\n",
        "  rng = np.random.RandomState(0)\n",
        "  image_ids = rng.choice(\n",
        "      image_ids, size=len(scene_manager.camera_list), replace=False)\n",
        "  \n",
        "  result = []\n",
        "  for image_id in image_ids:\n",
        "    near, far = estimate_near_far_for_image(scene_manager, image_id)\n",
        "    result.append({'image_id': image_id, 'near': near, 'far': far})\n",
        "  result = pd.DataFrame.from_records(result)\n",
        "  return result\n",
        "\n",
        "\n",
        "near_far = estimate_near_far(new_scene_manager)\n",
        "print('Statistics for near/far computation:')\n",
        "print(near_far.describe())\n",
        "print()\n",
        "\n",
        "near = near_far['near'].quantile(0.001) / 0.8\n",
        "far = near_far['far'].quantile(0.999) * 1.2\n",
        "print('Selected near/far values:')\n",
        "print(f'Near = {near:.04f}')\n",
        "print(f'Far = {far:.04f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "kOgCoT62ArbD"
      },
      "outputs": [],
      "source": [
        "# @title Compute scene center and scale.\n",
        "\n",
        "def get_bbox_corners(points):\n",
        "  lower = points.min(axis=0)\n",
        "  upper = points.max(axis=0)\n",
        "  return np.stack([lower, upper])\n",
        "\n",
        "\n",
        "points = filter_outlier_points(new_scene_manager.points, 0.95)\n",
        "bbox_corners = get_bbox_corners(\n",
        "    np.concatenate([points, new_scene_manager.camera_positions], axis=0))\n",
        "\n",
        "scene_center = np.mean(bbox_corners, axis=0)\n",
        "scene_scale = 1.0 / np.sqrt(np.sum((bbox_corners[1] - bbox_corners[0]) ** 2))\n",
        "\n",
        "print(f'Scene Center: {scene_center}')\n",
        "print(f'Scene Scale: {scene_scale}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "6Q1KC4xw6Til"
      },
      "outputs": [],
      "source": [
        "# @title Visualize scene.\n",
        "\n",
        "def scatter_points(points, size=2):\n",
        "  return go.Scatter3d(\n",
        "    x=points[:, 0],\n",
        "    y=points[:, 1],\n",
        "    z=points[:, 2],\n",
        "    mode='markers',\n",
        "    marker=dict(size=size),\n",
        "  )\n",
        "\n",
        "camera = new_scene_manager.camera_list[0]\n",
        "near_points = camera.pixels_to_points(\n",
        "    camera.get_pixel_centers()[::8, ::8], jnp.array(near)).reshape((-1, 3))\n",
        "far_points = camera.pixels_to_points(\n",
        "    camera.get_pixel_centers()[::8, ::8], jnp.array(far)).reshape((-1, 3))\n",
        "\n",
        "data = [\n",
        "  scatter_points(new_scene_manager.points),\n",
        "  scatter_points(new_scene_manager.camera_positions),\n",
        "  scatter_points(bbox_corners),\n",
        "  scatter_points(near_points),\n",
        "  scatter_points(far_points),\n",
        "]\n",
        "if use_face:\n",
        "  data.append(scatter_points(new_landmark_points))\n",
        "fig = go.Figure(data=data)\n",
        "fig.update_layout(scene_dragmode='orbit')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtOTEI_Tbpt_"
      },
      "source": [
        "## Generate test cameras."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "WvvOLabUeJUX"
      },
      "outputs": [],
      "source": [
        "# @title Define Utilities.\n",
        "_EPSILON = 1e-5\n",
        "\n",
        "\n",
        "def points_bound(points):\n",
        "  \"\"\"Computes the min and max dims of the points.\"\"\"\n",
        "  min_dim = np.min(points, axis=0)\n",
        "  max_dim = np.max(points, axis=0)\n",
        "  return np.stack((min_dim, max_dim), axis=1)\n",
        "\n",
        "\n",
        "def points_centroid(points):\n",
        "  \"\"\"Computes the centroid of the points from the bounding box.\"\"\"\n",
        "  return points_bound(points).mean(axis=1)\n",
        "\n",
        "\n",
        "def points_bounding_size(points):\n",
        "  \"\"\"Computes the bounding size of the points from the bounding box.\"\"\"\n",
        "  bounds = points_bound(points)\n",
        "  return np.linalg.norm(bounds[:, 1] - bounds[:, 0])\n",
        "\n",
        "\n",
        "def look_at(camera,\n",
        "            camera_position: np.ndarray,\n",
        "            look_at_position: np.ndarray,\n",
        "            up_vector: np.ndarray):\n",
        "  look_at_camera = camera.copy()\n",
        "  optical_axis = look_at_position - camera_position\n",
        "  norm = np.linalg.norm(optical_axis)\n",
        "  if norm < _EPSILON:\n",
        "    raise ValueError('The camera center and look at position are too close.')\n",
        "  optical_axis /= norm\n",
        "\n",
        "  right_vector = np.cross(optical_axis, up_vector)\n",
        "  norm = np.linalg.norm(right_vector)\n",
        "  if norm < _EPSILON:\n",
        "    raise ValueError('The up-vector is parallel to the optical axis.')\n",
        "  right_vector /= norm\n",
        "\n",
        "  # The three directions here are orthogonal to each other and form a right\n",
        "  # handed coordinate system.\n",
        "  camera_rotation = np.identity(3)\n",
        "  camera_rotation[0, :] = right_vector\n",
        "  camera_rotation[1, :] = np.cross(optical_axis, right_vector)\n",
        "  camera_rotation[2, :] = optical_axis\n",
        "\n",
        "  look_at_camera.position = camera_position\n",
        "  look_at_camera.orientation = camera_rotation\n",
        "  return look_at_camera\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "e5cHTuhP9Dgp"
      },
      "outputs": [],
      "source": [
        "# @title Generate camera trajectory.\n",
        "\n",
        "import math\n",
        "from scipy import interpolate\n",
        "from plotly.offline import iplot\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "\n",
        "def compute_camera_rays(points, camera):\n",
        "  origins = np.broadcast_to(camera.position[None, :], (points.shape[0], 3))\n",
        "  directions = camera.pixels_to_rays(points.astype(jnp.float32))\n",
        "  endpoints = origins + directions\n",
        "  return origins, endpoints\n",
        "\n",
        "\n",
        "def triangulate_rays(origins, directions):\n",
        "  origins = origins[np.newaxis, ...].astype('float32')\n",
        "  directions = directions[np.newaxis, ...].astype('float32')\n",
        "  weights = np.ones(origins.shape[:2], dtype=np.float32)\n",
        "  points = np.array(ray_triangulate(origins, origins + directions, weights))\n",
        "  return points.squeeze()\n",
        "\n",
        "\n",
        "ref_cameras = [c for c in new_scene_manager.camera_list]\n",
        "origins = np.array([c.position for c in ref_cameras])\n",
        "directions = np.array([c.optical_axis for c in ref_cameras])\n",
        "look_at = triangulate_rays(origins, directions)\n",
        "print('look_at', look_at)\n",
        "\n",
        "avg_position = np.mean(origins, axis=0)\n",
        "print('avg_position', avg_position)\n",
        "\n",
        "up = -np.mean([c.orientation[..., 1] for c in ref_cameras], axis=0)\n",
        "print('up', up)\n",
        "\n",
        "bounding_size = points_bounding_size(origins) / 2\n",
        "x_scale =   0.75# @param {type: 'number'}\n",
        "y_scale = 0.75  # @param {type: 'number'}\n",
        "xs = x_scale * bounding_size\n",
        "ys = y_scale * bounding_size\n",
        "radius = 0.75  # @param {type: 'number'}\n",
        "num_frames = 100  # @param {type: 'number'}\n",
        "\n",
        "\n",
        "origin = np.zeros(3)\n",
        "\n",
        "ref_camera = ref_cameras[0]\n",
        "print(ref_camera.position)\n",
        "z_offset = -0.1\n",
        "\n",
        "angles = np.linspace(0, 2*math.pi, num=num_frames)\n",
        "positions = []\n",
        "for angle in angles:\n",
        "  x = np.cos(angle) * radius * xs\n",
        "  y = np.sin(angle) * radius * ys\n",
        "  # x = xs * radius * np.cos(angle) / (1 + np.sin(angle) ** 2)\n",
        "  # y = ys * radius * np.sin(angle) * np.cos(angle) / (1 + np.sin(angle) ** 2)\n",
        "\n",
        "  position = np.array([x, y, z_offset])\n",
        "  # Make distance to reference point constant.\n",
        "  position = avg_position + position\n",
        "  positions.append(position)\n",
        "\n",
        "positions = np.stack(positions)\n",
        "\n",
        "orbit_cameras = []\n",
        "for position in positions:\n",
        "  camera = ref_camera.look_at(position, look_at, up)\n",
        "  orbit_cameras.append(camera)\n",
        "\n",
        "camera_paths = {'orbit-mild': orbit_cameras}\n",
        "\n",
        "traces = [\n",
        "  scatter_points(new_scene_manager.points),\n",
        "  scatter_points(new_scene_manager.camera_positions),\n",
        "  scatter_points(bbox_corners),\n",
        "  scatter_points(near_points),\n",
        "  scatter_points(far_points),\n",
        "\n",
        "  scatter_points(positions),\n",
        "  scatter_points(origins),\n",
        "]\n",
        "fig = go.Figure(traces)\n",
        "fig.update_layout(scene_dragmode='orbit')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYJ6aI45IIwd"
      },
      "source": [
        "## Save data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "aDFYTpGB6_Gl"
      },
      "outputs": [],
      "source": [
        "# @title Save scene information to `scene.json`.\n",
        "from pprint import pprint\n",
        "import json\n",
        "\n",
        "scene_json_path = root_dir /  'scene.json'\n",
        "with scene_json_path.open('w') as f:\n",
        "  json.dump({\n",
        "      'scale': scene_scale,\n",
        "      'center': scene_center.tolist(),\n",
        "      'bbox': bbox_corners.tolist(),\n",
        "      'near': near * scene_scale,\n",
        "      'far': far * scene_scale,\n",
        "  }, f, indent=2)\n",
        "\n",
        "print(f'Saved scene information to {scene_json_path}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "k_oQ-4MTGFpz"
      },
      "outputs": [],
      "source": [
        "# @title Save dataset split to `dataset.json`.\n",
        "\n",
        "all_ids = scene_manager.image_ids\n",
        "val_ids = all_ids[::20]\n",
        "train_ids = sorted(set(all_ids) - set(val_ids))\n",
        "dataset_json = {\n",
        "    'count': len(scene_manager),\n",
        "    'num_exemplars': len(train_ids),\n",
        "    'ids': scene_manager.image_ids,\n",
        "    'train_ids': train_ids,\n",
        "    'val_ids': val_ids,\n",
        "}\n",
        "\n",
        "dataset_json_path = root_dir / 'dataset.json'\n",
        "with dataset_json_path.open('w') as f:\n",
        "    json.dump(dataset_json, f, indent=2)\n",
        "\n",
        "print(f'Saved dataset information to {dataset_json_path}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "3PWkPkBVGnSl"
      },
      "outputs": [],
      "source": [
        "# @title Save metadata information to `metadata.json`.\n",
        "import bisect\n",
        "\n",
        "metadata_json = {}\n",
        "for i, image_id in enumerate(train_ids):\n",
        "  metadata_json[image_id] = {\n",
        "      'warp_id': i,\n",
        "      'appearance_id': i,\n",
        "      'camera_id': 0,\n",
        "  }\n",
        "for i, image_id in enumerate(val_ids):\n",
        "  i = bisect.bisect_left(train_ids, image_id)\n",
        "  metadata_json[image_id] = {\n",
        "      'warp_id': i,\n",
        "      'appearance_id': i,\n",
        "      'camera_id': 0,\n",
        "  }\n",
        "\n",
        "metadata_json_path = root_dir / 'metadata.json'\n",
        "with metadata_json_path.open('w') as f:\n",
        "    json.dump(metadata_json, f, indent=2)\n",
        "\n",
        "print(f'Saved metadata information to {metadata_json_path}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "4Uxu0yKlGs3V"
      },
      "outputs": [],
      "source": [
        "# @title Save cameras.\n",
        "camera_dir = root_dir / 'camera'\n",
        "camera_dir.mkdir(exist_ok=True, parents=True)\n",
        "for item_id, camera in new_scene_manager.camera_dict.items():\n",
        "  camera_path = camera_dir / f'{item_id}.json'\n",
        "  print(f'Saving camera to {camera_path!s}')\n",
        "  with camera_path.open('w') as f:\n",
        "    json.dump(camera.to_json(), f, indent=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WA_Icz5_Ia4h"
      },
      "outputs": [],
      "source": [
        "# @title Save test cameras.\n",
        "\n",
        "import json\n",
        "\n",
        "test_camera_dir = root_dir / 'camera-paths'\n",
        "for test_path_name, test_cameras in camera_paths.items():\n",
        "  out_dir = test_camera_dir / test_path_name\n",
        "  out_dir.mkdir(exist_ok=True, parents=True)\n",
        "  for i, camera in enumerate(test_cameras):\n",
        "    camera_path = out_dir / f'{i:06d}.json'\n",
        "    print(f'Saving camera to {camera_path!s}')\n",
        "    with camera_path.open('w') as f:\n",
        "      json.dump(camera.to_json(), f, indent=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "def project(points, cam):\n",
        "    points = np.array(points)\n",
        "    points = points.reshape((-1, 3))\n",
        "    translated_points = points - cam['position']\n",
        "    local_points = (np.matmul(cam['orientation'], translated_points.T)).T\n",
        "    x = local_points[..., 0] / local_points[..., 2]\n",
        "    y = local_points[..., 1] / local_points[..., 2]\n",
        "    r2 = x**2 + y**2\n",
        "    distortion = 1.0 + r2 * (\n",
        "        cam['radial_distortion'][0] + r2 *\n",
        "        (cam['radial_distortion'][1] + cam['radial_distortion'][2] * r2))\n",
        "    x_times_y = x * y\n",
        "    x = (\n",
        "        x * distortion + 2.0 * cam['tangential_distortion'][0] * x_times_y +\n",
        "        cam['tangential_distortion'][1] * (r2 + 2.0 * x**2))\n",
        "    y = (\n",
        "        y * distortion + 2.0 * cam['tangential_distortion'][1] * x_times_y +\n",
        "        cam['tangential_distortion'][0] * (r2 + 2.0 * y**2))\n",
        "    pixel_x = cam['focal_length'] * x + cam['skew'] * y + cam['principal_point'][0]\n",
        "    pixel_y = cam['focal_length'] * cam['pixel_aspect_ratio'] * y + cam['principal_point'][1]\n",
        "\n",
        "    pixels = np.stack([pixel_x, pixel_y], axis=-1)\n",
        "    return pixels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iV-YLB_TEMq"
      },
      "source": [
        "## Converting COLMAP Outputs into View Projection and Model Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'red' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn [104], line 18\u001b[0m\n\u001b[1;32m     16\u001b[0m save_dir \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m./data/DIY/\u001b[39m\u001b[39m'\u001b[39m\u001b[39m+\u001b[39mcapture_name\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m/scan\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m     17\u001b[0m config_dir \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m./configs/DIY\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m---> 18\u001b[0m red\n\u001b[1;32m     19\u001b[0m directory \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(root_dir)\n\u001b[1;32m     20\u001b[0m directory_cam \u001b[39m=\u001b[39m directory\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m/camera\u001b[39m\u001b[39m'\u001b[39m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'red' is not defined"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import argparse\n",
        "import os\n",
        "import json\n",
        "import shutil\n",
        "from pathlib import Path\n",
        "import matplotlib.image as mpimg\n",
        "import preprocess_cameras\n",
        "\n",
        "# Arguments\n",
        "\n",
        "capture_name = 'toby'\n",
        "root_dir = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs' + '/' + capture_name\n",
        "# root_dir = '/ubc/cs/research/kmyi/svsamban/research/data-nerfies' + '/' + capture_name\n",
        "\n",
        "save_dir = './data/DIY/'+capture_name+'/scan'\n",
        "config_dir = './configs/DIY'\n",
        "\n",
        "directory = str(root_dir)\n",
        "directory_cam = directory+'/camera'\n",
        "Path(save_dir+'/image').mkdir(parents=True, exist_ok=True)\n",
        "Path(save_dir+'/mask').mkdir(parents=True, exist_ok=True)\n",
        "scene = json.load(open(directory+'/scene.json'))\n",
        "\n",
        "scale = 1.\n",
        "if 'data-nerfies' in root_dir:\n",
        "    scale = 2.\n",
        "\n",
        "i = 0\n",
        "savez_dict = dict()\n",
        "# iterate over files in that directory\n",
        "for filename in sorted(os.listdir(directory_cam)):\n",
        "    f = os.path.join(directory_cam, filename)\n",
        "    if os.path.isfile(f):\n",
        "        file = open(f)\n",
        "        cam = json.load(file)\n",
        "        \n",
        "        T = np.eye(4)\n",
        "        T[:3,3] = -1*np.array(cam['position'])\n",
        "        R = np.eye(4)\n",
        "        R[:3,:3] = np.array(cam['orientation'])\n",
        "        V = np.matmul(R,T)\n",
        "        K = np.eye(4)\n",
        "        K[0][0] = cam['focal_length']/scale\n",
        "        K[0][1] = cam['skew']\n",
        "        K[0][2] = cam['principal_point'][0]/scale\n",
        "        K[1][1] = cam['focal_length'] * cam['pixel_aspect_ratio']/scale\n",
        "        K[1][2] = cam['principal_point'][1]/scale\n",
        "\n",
        "        savez_dict['world_mat_'  + str(i)] = K @ V\n",
        "        \n",
        "        i = i + 1\n",
        "    if i > 100:\n",
        "        break\n",
        "## Create config file\n",
        "import yaml\n",
        "with open(\"./configs/DTU/scan0024.yaml\", \"r\") as stream:\n",
        "    try:\n",
        "        data = yaml.safe_load(stream)\n",
        "    except yaml.YAMLError as exc:\n",
        "        print(exc)\n",
        "data['dataloading']['path'] = 'data/DIY/'\n",
        "data['dataloading']['classes'] = '[{}]'.format(capture_name)\n",
        "data['dataloading']['n_views'] = i\n",
        "shape = cam['image_size']\n",
        "vis_resolution = '[{0}, {1}]'.format(str(int(shape[0]/scale)),str(int(shape[1]/scale)))\n",
        "scheduler_milestones = '[{0}, {1}]'.format(str(data['training']['scheduler_milestones'][0]),str(data['training']['scheduler_milestones'][1]))\n",
        "print(i,'total views of size ', vis_resolution)\n",
        "data['training']['out_dir'] = './out/DIY/' + capture_name + '/'\n",
        "data['training']['vis_resolution'] = vis_resolution\n",
        "data['training']['scheduler_milestones'] = scheduler_milestones\n",
        "\n",
        "with open(config_dir+'/'+capture_name+'.yaml', 'w') as outfile:\n",
        "    yaml.dump(data, outfile, default_flow_style=False)\n",
        "\n",
        "## Moving Masks and RGBs\n",
        "i = 0\n",
        "for filename in sorted(os.listdir(directory_cam)):\n",
        "    f = os.path.join(directory_cam, filename)\n",
        "    if os.path.isfile(f):\n",
        "        file = open(f)\n",
        "        cam = json.load(file)\n",
        "        \n",
        "        if 'data-nerfies' in root_dir:\n",
        "            shutil.copy(directory+'/rgb/2x/'+filename[:-5]+'.png', os.path.join(save_dir,'image',str(i).zfill(6)+'.png'))\n",
        "            shutil.copy(directory+'/mask/2x/'+filename[:-5]+'.png', os.path.join(save_dir,'mask',str(i).zfill(6)+'.png'))\n",
        "        else:\n",
        "            shutil.copy(directory+'/rgb/1x/'+filename[:-5]+'.png', os.path.join(save_dir,'image',str(i).zfill(6)+'.png'))\n",
        "            shutil.copy(directory+'/rgb/mask/'+filename[:-5]+'.png', os.path.join(save_dir,'mask',str(i).zfill(6)+'.png'))\n",
        "        i = i + 1\n",
        "    if i > 100:\n",
        "        break\n",
        "\n",
        "np.savez(save_dir+'/cameras.npz', ** savez_dict)\n",
        "for name in ['train','test','val']:\n",
        "    if not os.path.isfile(save_dir[:-5]+\"/\"+name+\".lst\"):\n",
        "        with open(save_dir[:-5]+\"/\"+name+\".lst\", \"a\") as f:\n",
        "            f.write('scan')\n",
        "            print(name)\n",
        "    else:\n",
        "        print(name,'.lst already exists. if you would like to overwrite please delete files manually first')\n",
        "\n",
        "## SAVE MATRICES\n",
        "np.savez(save_dir+\"/cameras.npz\", **savez_dict)\n",
        "preprocess_cameras.preprocess_cameras(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Extract Background points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from PIL import Image\n",
        "import numpy as np \n",
        "import json\n",
        "\n",
        "capture_name = 'toby'\n",
        "root_dir = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs' + '/' + capture_name\n",
        "# root_dir = '/ubc/cs/research/kmyi/svsamban/research/data-nerfies' + '/' + capture_name\n",
        "\n",
        "directory = root_dir \n",
        "directory_cam = directory + '/camera'\n",
        "coords_og = np.load(directory+'/points.npy',allow_pickle=True)\n",
        "coords_filtered = coords_og\n",
        "coords_thrownout = []\n",
        "masks_used = 1\n",
        "max_masks_used = 30\n",
        "# iterate over files in that directory\n",
        "for filename in sorted(os.listdir(directory_cam)):\n",
        "    f = os.path.join(directory_cam, filename)\n",
        "    if os.path.isfile(f):\n",
        "        file = open(f)\n",
        "        key = int(filename[:6])\n",
        "        cam = json.load(file)\n",
        "\n",
        "        # load COLMAP points\n",
        "        points = np.array(coords_filtered)\n",
        "        pixels = project(points, cam)\n",
        "        coords_filtered = []\n",
        "        coords_thrownout = []\n",
        "\n",
        "        # load rgb\n",
        "        im = mpimg.imread(directory+'/rgb/1x/'+str(key).zfill(6)+'.png')\n",
        "        shape = im.shape\n",
        "        scale = 1.0\n",
        "        new_shape = (int(shape[1]/scale),int(shape[0]/scale))\n",
        "        im = np.array(Image.fromarray((im*255).astype(np.uint8)).resize(new_shape))\n",
        "        imgog = im.copy()\n",
        "        \n",
        "        # load mask \n",
        "        if capture_name == 'curls':\n",
        "            mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'_mask.png')\n",
        "        else:\n",
        "            mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'.png')\n",
        "            mask = np.sum(mask,axis=-1)/3.0\n",
        "        mask = np.array(Image.fromarray((mask*255).astype(np.uint8)).resize(new_shape))\n",
        "        \n",
        "        bad = 0\n",
        "        for i in range(len(pixels)):\n",
        "            x1 = int(pixels[i,1]/scale)\n",
        "            x2 = int(pixels[i,0]/scale)\n",
        "            dot_size = 20\n",
        "            if x1 < new_shape[1] and x2 < new_shape[0] and x1 > -1 and x2 > -1:\n",
        "                if mask[x1,x2] > 100 :\n",
        "                    im[x1-dot_size:x1,x2-dot_size:x2,:] = [0,255,0]\n",
        "                    # print('in')\n",
        "                    coords_filtered.append(points[i])\n",
        "                else:\n",
        "                    im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "                    coords_thrownout.append(points[i])\n",
        "                    # print('out')\n",
        "            else :\n",
        "                bad = bad+1\n",
        "                coords_filtered.append(points[i])\n",
        "\n",
        "        # pixels_thrownout = project(coords_thrownout, cam)\n",
        "        # for k in range(len(pixels_thrownout)):\n",
        "        #     x2,x1 = pixels_thrownout[k]\n",
        "        #     x1 = int(x1)\n",
        "        #     x2 = int(x2)\n",
        "        #     im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "        # plt.imshow(mask)\n",
        "        # plt.imshow(im)\n",
        "\n",
        "        # plt.imshow(img)\n",
        "        fig=plt.figure()\n",
        "        fig.add_subplot(1,3,3)\n",
        "        plt.imshow(imgog)\n",
        "        fig.add_subplot(1,3,2)\n",
        "        plt.imshow(im)\n",
        "        fig.add_subplot(1,3,1)\n",
        "        plt.imshow(mask)\n",
        "        plt.show()\n",
        "        \n",
        "        print('points preserved:', len(coords_filtered), '... throwing out ', len(coords_thrownout), 'points and ', bad, ' points out of range')\n",
        "        masks_used = masks_used + 1\n",
        "        if masks_used > max_masks_used:\n",
        "            pixels_thrownout = project(coords_thrownout, cam)\n",
        "            for k in range(len(pixels_thrownout)):\n",
        "                x2,x1 = pixels_thrownout[k]\n",
        "                x1 = int(x1)\n",
        "                x2 = int(x2)\n",
        "                im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "            # plt.imshow(mask)\n",
        "            # plt.imshow(im)\n",
        "\n",
        "            # # plt.imshow(img)\n",
        "            # fig=plt.figure()\n",
        "            # fig.add_subplot(1,3,3)\n",
        "            # plt.imshow(imgog)\n",
        "            # fig.add_subplot(1,3,2)\n",
        "            # plt.imshow(im)\n",
        "            # fig.add_subplot(1,3,1)\n",
        "            # plt.imshow(mask)\n",
        "            # plt.show()\n",
        "            break\n",
        "obj_points = coords_filtered\n",
        "bg_points = coords_og[:,0].copy()\n",
        "i = 0\n",
        "for x in coords_og:\n",
        "    seen = False\n",
        "    for y in obj_points:\n",
        "        if (x==y).all():\n",
        "            seen = True\n",
        "            break\n",
        "    bg_points[i] = seen\n",
        "    i += 1\n",
        "    \n",
        "background_points = coords_og[~bg_points.astype(bool)]\n",
        "np.save(save_dir+'/points.npy',background_points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "np.save(root_dir+'/points_bg.npy',background_points)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iV-YLB_TEMq"
      },
      "source": [
        "## Visualizing COLMAP Points "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys([18, 23, 35])\n",
            "18\n",
            "bad pts:  11198\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "23\n",
            "bad pts:  11214\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "35\n",
            "bad pts:  11153\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 3D PTS Projection from IPYNB nerfies projection\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import numpy as np \n",
        "import json\n",
        "\n",
        "capture_name = 'toby'\n",
        "root_dir = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs' + '/' + capture_name\n",
        "# root_dir = '/ubc/cs/research/kmyi/svsamban/research/data-nerfies' + '/' + capture_name\n",
        "\n",
        "directory = root_dir \n",
        "scale = 1.\n",
        "if 'data-nerfies' in root_dir:\n",
        "    scale = 2.\n",
        "# points = np.array(coords_filtered) #np.load(directory+'/points.npy',allow_pickle=True)\n",
        "points = np.load(save_dir+'/points.npy') ##BG POINTS\n",
        "y = json.load(open(directory+'/scene.json'))\n",
        "directory_cam = directory+'/camera'\n",
        "\n",
        "blue = {}\n",
        "i = 0\n",
        "# iterate over files in that directory\n",
        "for key in [18,23,35]:\n",
        "    file = open(directory_cam+'/'+str(key).zfill(6)+'.json')\n",
        "    cam = json.load(file)\n",
        "\n",
        "    points = points.reshape((-1, 3))\n",
        "    points_homog = np.concatenate((points.T, np.ones_like(points)[:,0].reshape(1,-1)), 0)\n",
        "\n",
        "\n",
        "    T = np.eye(4)\n",
        "    T[:3,3] = -1*np.array(cam['position'])\n",
        "    R = np.eye(4)\n",
        "    R[:3,:3] = np.array(cam['orientation'])\n",
        "    V = np.matmul(R,T)\n",
        "\n",
        "    K = np.eye(4)\n",
        "    K[0][0] = cam['focal_length']/scale\n",
        "    K[0][1] = cam['skew']\n",
        "    K[0][2] = cam['principal_point'][0]/scale\n",
        "    K[1][1] = cam['focal_length'] * cam['pixel_aspect_ratio']/scale\n",
        "    K[1][2] = cam['principal_point'][1]/scale\n",
        "\n",
        "    P = K @ V\n",
        "\n",
        "    pixels =  P @ points_homog\n",
        "    pixels = pixels.T\n",
        "\n",
        "    # Get normalized local pixel positions.\n",
        "    pixels = pixels / np.expand_dims(pixels[..., 3],axis=1)\n",
        "    pixels = pixels / np.expand_dims(pixels[..., 2],axis=1)\n",
        "\n",
        "    # pixels = project(points,cam)\n",
        "    blue[key] = pixels\n",
        "\n",
        "\n",
        "ii=0\n",
        "print(blue.keys())\n",
        "for id in sorted(blue.keys()):\n",
        "    b = np.array(blue[id])\n",
        "\n",
        "    # im2 = directory+'/rgb/2x/left1_'+str(id).zfill(6)+'.png'\n",
        "    im2 = directory+'/rgb/1x/'+str(id).zfill(6)+'.png'\n",
        "    img2 = mpimg.imread(im2)\n",
        "    shape = img2.shape[:2]\n",
        "    # print(shape)\n",
        "    new_shape = np.array([shape[1],shape[0]])\n",
        "    img2 = np.array(Image.fromarray((img2*255).astype(np.uint8)).resize(new_shape))\n",
        "    img2og = img2.copy()\n",
        "    bad = 0\n",
        "    for i in range(len(b)):\n",
        "        x1 = int(b[i,1]/(shape[0]/new_shape[1]))\n",
        "        x2 = int(b[i,0]/(shape[0]/new_shape[1]))\n",
        "        if x1 < 120 and x2 < 160 and x1 > -1 and x2 > -1:\n",
        "            n = 2\n",
        "            img2[x1-n:x1+n,x2-n:x2+n,:] = [0,0.,255.]\n",
        "            # img2[x1,x2,:] = [0,0.,255.]\n",
        "        else:\n",
        "            bad = bad + 1\n",
        "            pass\n",
        "\n",
        "    print(id)\n",
        "    print('bad pts: ', bad)\n",
        "    fig=plt.figure()\n",
        "    fig.add_subplot(1,2,ii+2)\n",
        "    plt.imshow(img2)\n",
        "    fig.add_subplot(1,2,ii+1)\n",
        "    plt.imshow(img2og)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Extract Background points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "points = np.array(background_points)\n",
        "pixels = project(points, cam)\n",
        "coords_filtered = []\n",
        "coords_thrownout = []\n",
        "\n",
        "# load rgb\n",
        "im = mpimg.imread(directory+'/rgb/1x/'+str(key).zfill(6)+'.png')\n",
        "shape = im.shape\n",
        "scale = 1.0\n",
        "new_shape = (int(shape[1]/scale),int(shape[0]/scale))\n",
        "im = np.array(Image.fromarray((im*255).astype(np.uint8)).resize(new_shape))\n",
        "imgog = im.copy()\n",
        "\n",
        "# load mask \n",
        "if capture_name == 'curls':\n",
        "    mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'_mask.png')\n",
        "else:\n",
        "    mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'.png')\n",
        "    mask = np.sum(mask,axis=-1)/3.0\n",
        "mask = np.array(Image.fromarray((mask*255).astype(np.uint8)).resize(new_shape))\n",
        "\n",
        "bad = 0\n",
        "for i in range(len(pixels)):\n",
        "    x1 = int(pixels[i,1]/scale)\n",
        "    x2 = int(pixels[i,0]/scale)\n",
        "    dot_size = 20\n",
        "    if x1 < new_shape[1] and x2 < new_shape[0] and x1 > -1 and x2 > -1:\n",
        "        if mask[x1,x2] > 100 :\n",
        "            im[x1-dot_size:x1,x2-dot_size:x2,:] = [0,255,0]\n",
        "            # print('in')\n",
        "            # coords_filtered.append(points[i])\n",
        "        else:\n",
        "            im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "\n",
        "fig=plt.figure()\n",
        "fig.add_subplot(1,3,3)\n",
        "plt.imshow(imgog)\n",
        "fig.add_subplot(1,3,2)\n",
        "plt.imshow(im)\n",
        "fig.add_subplot(1,3,1)\n",
        "plt.imshow(mask)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "len(background_points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# bg_points = [x for x in coords_og for y in obj_points if not (x==y).all()]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "len()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "sum(bg_points.astype(int))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "len(coords_og)#-len(coords_filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "len(coords_filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from PIL import Image\n",
        "import numpy as np \n",
        "import json\n",
        "\n",
        "capture_name = 'toby'\n",
        "root_dir = '/ubc/cs/research/kmyi/svsamban/research/metanlrpp/my_data/my_dtu/colmap_outputs' + '/' + capture_name\n",
        "# root_dir = '/ubc/cs/research/kmyi/svsamban/research/data-nerfies' + '/' + capture_name\n",
        "\n",
        "directory = root_dir \n",
        "directory_cam = directory + '/camera'\n",
        "coords_og = np.load(directory+'/points.npy',allow_pickle=True)\n",
        "# coords_filtered = coords_og\n",
        "coords_thrownout = []\n",
        "masks_used = 1\n",
        "max_masks_used = 3\n",
        "# iterate over files in that directory\n",
        "for filename in sorted(os.listdir(directory_cam)):\n",
        "    f = os.path.join(directory_cam, filename)\n",
        "    if os.path.isfile(f):\n",
        "        file = open(f)\n",
        "        key = int(filename[:6])\n",
        "        cam = json.load(file)\n",
        "\n",
        "        # load COLMAP points\n",
        "        points = np.array(coords_filtered_og)\n",
        "        pixels = project(points, cam)\n",
        "        coords_filtered = []\n",
        "        # coords_thrownout = []\n",
        "\n",
        "        # load rgb\n",
        "        im = mpimg.imread(directory+'/rgb/1x/'+str(key).zfill(6)+'.png')\n",
        "        shape = im.shape\n",
        "        scale = 1.0\n",
        "        new_shape = (int(shape[1]/scale),int(shape[0]/scale))\n",
        "        im = np.array(Image.fromarray((im*255).astype(np.uint8)).resize(new_shape))\n",
        "        imgog = im.copy()\n",
        "        \n",
        "        # load mask \n",
        "        if capture_name == 'curls':\n",
        "            mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'_mask.png')\n",
        "        else:\n",
        "            mask = mpimg.imread(directory+'/rgb/mask/'+str(key).zfill(6)+'.png')\n",
        "            mask = np.sum(mask,axis=-1)/3.0\n",
        "        mask = np.array(Image.fromarray((mask*255).astype(np.uint8)).resize(new_shape))\n",
        "        \n",
        "        bad = 0\n",
        "        for i in range(len(pixels)):\n",
        "            x1 = int(pixels[i,1]/scale)\n",
        "            x2 = int(pixels[i,0]/scale)\n",
        "            dot_size = 20\n",
        "            if x1 < new_shape[1] and x2 < new_shape[0] and x1 > -1 and x2 > -1:\n",
        "                if mask[x1,x2] < 100 :\n",
        "                    im[x1-dot_size:x1,x2-dot_size:x2,:] = [0,255,0]\n",
        "                    # print('in')\n",
        "                    # coords_filtered.append(points[i])\n",
        "                else:\n",
        "                    im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "                    # coords_thrownout.append(points[i])\n",
        "                    # print('out')\n",
        "            else :\n",
        "                bad = bad+1\n",
        "                # coords_filtered.append(points[i])\n",
        "\n",
        "        # pixels_thrownout = project(coords_thrownout, cam)\n",
        "        # for k in range(len(pixels_thrownout)):\n",
        "        #     x2,x1 = pixels_thrownout[k]\n",
        "        #     x1 = int(x1)\n",
        "        #     x2 = int(x2)\n",
        "        #     im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "        # plt.imshow(mask)\n",
        "        # plt.imshow(im)\n",
        "\n",
        "        # plt.imshow(img)\n",
        "        fig=plt.figure()\n",
        "        fig.add_subplot(1,3,3)\n",
        "        plt.imshow(imgog)\n",
        "        fig.add_subplot(1,3,2)\n",
        "        plt.imshow(im)\n",
        "        fig.add_subplot(1,1,1)\n",
        "        plt.imshow(mask)\n",
        "        plt.show()\n",
        "        \n",
        "        print('points preserved:', len(coords_filtered), '... throwing out ', len(coords_thrownout), 'points and ', bad, ' points out of range')\n",
        "        masks_used = masks_used + 1\n",
        "        if masks_used > max_masks_used:\n",
        "            pixels_thrownout = project(coords_thrownout, cam)\n",
        "            for k in range(len(pixels_thrownout)):\n",
        "                x2,x1 = pixels_thrownout[k]\n",
        "                x1 = int(x1)\n",
        "                x2 = int(x2)\n",
        "                im[x1-dot_size:x1,x2-dot_size:x2,:] = [255,0,0]\n",
        "\n",
        "            # plt.imshow(mask)\n",
        "            # plt.imshow(im)\n",
        "\n",
        "            # # plt.imshow(img)\n",
        "            # fig=plt.figure()\n",
        "            # fig.add_subplot(1,3,3)\n",
        "            # plt.imshow(imgog)\n",
        "            # fig.add_subplot(1,3,2)\n",
        "            # plt.imshow(im)\n",
        "            # fig.add_subplot(1,3,1)\n",
        "            # plt.imshow(mask)\n",
        "            # plt.show()\n",
        "            break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "len(coords_filtered_og)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "coords_filtered_og = coords_filtered\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iV-YLB_TEMq"
      },
      "source": [
        "## Training\n",
        "\n",
        " * You are now ready to train a Nerfie or a MetaNLR++!\n",
        " * Head over to the [training Colab](https://colab.sandbox.google.com/github/google/nerfies/blob/main/notebooks/Nerfies_Training.ipynb) for a basic demo."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "THE Nerfies Capture Processing v2.ipynb",
      "private_outputs": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.13 ('nerfies')",
      "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": "3.8.13"
    },
    "vscode": {
      "interpreter": {
        "hash": "3d826e6bb019bccda207a38670479af3e69d7f44b890edc7d8de0cf9055ead28"
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