xhs-note-crawling/python/LoFTR/demo/run_demo.sh

#!/bin/bash
set -e
# set -x

if [ ! -f utils.py ]; then
    echo "Downloading utils.py from the SuperGlue repo."
    echo "We cannot provide this file directly due to its strict licence."
    wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py
fi

# Use webcam 0 as input source. 
input=0
# or use a pre-recorded video given the path.
# input=/home/sunjiaming/Downloads/scannet_test/$scene_name.mp4

# Toggle indoor/outdoor model here.
model_ckpt=../weights/indoor_ds.ckpt
# model_ckpt=../weights/outdoor_ds.ckpt

# Optionally assign the GPU ID.
# export CUDA_VISIBLE_DEVICES=0

echo "Running LoFTR demo.."
eval "$(conda shell.bash hook)"
conda activate loftr
python demo_loftr.py --weight $model_ckpt --input $input
# To save the input video and output match visualizations.
# python demo_loftr.py --weight $model_ckpt --input $input --save_video --save_input

# Running on remote GPU servers with no GUI.
# Save images first.
# python demo_loftr.py --weight $model_ckpt --input $input --no_display --output_dir="./demo_images/"
# Then convert them to a video.
# ffmpeg -framerate 15 -pattern_type glob -i '*.png' -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4