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#!/bin/bash
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set -e
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# set -x
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if [ ! -f utils.py ]; then
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echo "Downloading utils.py from the SuperGlue repo."
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echo "We cannot provide this file directly due to its strict licence."
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wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py
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fi
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# Use webcam 0 as input source.
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input=0
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# or use a pre-recorded video given the path.
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# input=/home/sunjiaming/Downloads/scannet_test/$scene_name.mp4
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# Toggle indoor/outdoor model here.
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model_ckpt=../weights/indoor_ds.ckpt
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# model_ckpt=../weights/outdoor_ds.ckpt
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# Optionally assign the GPU ID.
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# export CUDA_VISIBLE_DEVICES=0
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echo "Running LoFTR demo.."
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eval "$(conda shell.bash hook)"
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conda activate loftr
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python demo_loftr.py --weight $model_ckpt --input $input
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# To save the input video and output match visualizations.
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# python demo_loftr.py --weight $model_ckpt --input $input --save_video --save_input
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# Running on remote GPU servers with no GUI.
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# Save images first.
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# python demo_loftr.py --weight $model_ckpt --input $input --no_display --output_dir="./demo_images/"
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# Then convert them to a video.
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# ffmpeg -framerate 15 -pattern_type glob -i '*.png' -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4
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