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{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "RDD",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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