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34 lines
1.2 KiB
34 lines
1.2 KiB
#!/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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