{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RDD reconstruction example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sfm import match_rdd, extract_rdd\n",
    "from hloc import (\n",
    "    extract_features,\n",
    "    reconstruction,\n",
    "    visualization,\n",
    "    pairs_from_retrieval,\n",
    "    pairs_from_exhaustive,\n",
    ")\n",
    "from pathlib import Path\n",
    "import os\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_dir = Path('./assets/mapping')\n",
    "device = torch.cuda.is_available()\n",
    "images = [image for image in os.listdir(images_dir) if image.endswith('.jpg') or image.endswith('.png')]\n",
    "outputs = Path('./outputs/reconstruction')\n",
    "if not outputs.exists():\n",
    "    outputs.mkdir(parents=True)\n",
    "sfm_pairs = outputs / 'sfm_pairs.txt'\n",
    "retrieval_conf = extract_features.confs[\"netvlad\"]\n",
    "feature_conf = extract_rdd.confs[\"rdd\"]\n",
    "matcher_conf = match_rdd.confs[\"rdd+lightglue\"]\n",
    "exhaustive_if_less = 30\n",
    "num_matched = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# image_retrieval\n",
    "if len(images) < exhaustive_if_less:\n",
    "    pairs_from_exhaustive.main(sfm_pairs, images)\n",
    "else:\n",
    "    retrieval_path = extract_features.main(retrieval_conf, images_dir, outputs)\n",
    "    pairs_from_retrieval.main(retrieval_path, sfm_pairs, num_matched=num_matched)\n",
    "    \n",
    "# feature_extraction\n",
    "feature_path = extract_rdd.main(feature_conf, images_dir, outputs)\n",
    "# matching\n",
    "match_path = match_rdd.main(matcher_conf, sfm_pairs, feature_conf['output'], outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reconstruction\n",
    "image_options = {}\n",
    "mapper_options = {}\n",
    "model = reconstruction.main(outputs, images_dir, sfm_pairs, feature_path, \n",
    "            match_path, verbose=True, camera_mode='PER_IMAGE', image_options=image_options, mapper_options=mapper_options,\n",
    "            min_match_score = 0.2, skip_geometric_verification=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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