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README.md

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@@ -84,7 +84,7 @@ placeholders for paths to input and output directories, respectively.
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If you have a small dataset with less than 10 subjects, then it might be easiest to run the defacing algorithm serially.
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```bash
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python dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR}
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python dsst-defacing-pipeline/src/dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR}
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```
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#### Option 2: In parallel at subject level
@@ -102,7 +102,7 @@ a. Assuming these scripts are run on the NIH HPC system, the first step would be
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for i in `ls -d ${INPUT_DIR}/sub-*`; do \
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SUBJ=$(echo $i | sed "s|${INPUT_DIR}/||g" ); \
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echo "python src/dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR} -p ${SUBJ}"; \
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echo "python dsst-defacing-pipeline/src/dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR} -p ${SUBJ}"; \
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done > defacing_parallel_subject_level.swarm
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```
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@@ -126,7 +126,7 @@ for i in `ls -d ${INPUT_DIR}/sub-*`; do
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SUBJ=$(echo $i | sed "s|${INPUT_DIR}/||g" );
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for j in `ls -d ${INPUT_DIR}/${SUBJ}/ses-*`; do
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SESS=$(echo $j | sed "s|${INPUT_DIR}/${SUBJ}/||g" )
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echo "python src/dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR} -p ${SUBJ} -s ${SESS}";
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echo "python dsst-defacing-pipeline/src/dsst_defacing_wf.py -i ${INPUT_DIR} -o ${OUTPUT_DIR} -p ${SUBJ} -s ${SESS}";
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done;
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done > defacing_parallel_session_level.swarm
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```
@@ -140,7 +140,7 @@ swarm -f defacing_parallel_session_level.swarm --merge-output --logdir ${OUTPUT_
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Generate 3D renders for every defaced image in the output directory.
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```bash
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python src/generate_renders.py -o ${OUTPUT_DIR}
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python dsst-defacing-pipeline/src/generate_renders.py -o ${OUTPUT_DIR}
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```
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### Visual Inspection

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