Initial commit: FishServer monorepo (FishAction, FishMeasure, fish_api)

Made-with: Cursor

FishMeasure/run_predict_from_svo2.sh

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+#!/usr/bin/env bash
+# Fish step flags mirror run_fish_evaluation_simple.sh; weight = test_dgcnn via this script.
+# Iterates each fishXX folder under SESSION_ROOT (top-level *.svo2 per folder).
+# Layout: output_weight_estimator/<fish_name>/<svo_stem>/{cloud,images,weight_prediction.json,...}
+#
+# Weight aggregation (test_dgcnn_weight_estimator): per-PLY length = PCA major axis on raw points (mm);
+# final weight = mean of predicted_weight_g over the K longest PLYs (--weight-top-by-length --weight-top-k 5).
+# Without --weight-top-by-length, top-K would be by predicted weight instead of length.
+#
+# Requires bash (arrays, shopt, [[). If you run `sh this.sh`, dash will re-exec bash:
+if [ -z "${BASH_VERSION:-}" ]; then
+  exec bash "$0" "$@"
+fi
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+SESSION_ROOT="/home/ubuntu/data/fish/2016-1-22-last"
+OUT_PARENT="output_weight_estimator"
+
+shopt -s nullglob
+for fish_dir in "$SESSION_ROOT"/fish*/; do
+  [[ -d "$fish_dir" ]] || continue
+  svos=("$fish_dir"*.svo2)
+  [[ ${#svos[@]} -gt 0 ]] || continue
+
+  fish_name="$(basename "${fish_dir%/}")"
+  save_out="$OUT_PARENT/$fish_name"
+
+  echo ""
+  echo "============================================================"
+  echo " $fish_name  (${#svos[@]} .svo2)  →  $save_out/<svo_stem>/"
+  echo "============================================================"
+
+  python3 predict_weigth_from_svo2.py \
+    --batch-svo-folder "$fish_dir" \
+    --weight-checkpoint weight_estimator/runs/dgcnn_20260312_171043/best.pt \
+    --save-output "$save_out" \
+    --yolo-model "/home/ubuntu/projects/FishMeasure/runs/train/fish_detection_20251127_104658/weights/best.pt" \
+    --conf 0.5 \
+    --imgsz 640 \
+    --sam-device cuda \
+    --max-frames 0 \
+    --filter-pointcloud \
+    --use-density-filter \
+    --pointcloud-classifier "/home/ubuntu/projects/FishMeasure/pointcloud_classifier/Pointnet_Pointnet2_pytorch/log/classification/fish_pointnet2_finetune/checkpoints/best_model.pth" \
+    --use-pointcloud-classifier \
+    --pointcloud-classifier-threshold 0.7 \
+    --use-flatness-filter \
+    --flatness-threshold 70.0 \
+    --frame-stride 1 \
+    --weight-top-k 5
+    #--weight-top-by-length
+done
+shopt -u nullglob