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250 lines
9.5 KiB
Bash
250 lines
9.5 KiB
Bash
#!/usr/bin/env bash
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set -euo pipefail
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usage() {
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cat >&2 <<'EOF'
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Usage:
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REAL_RASTER_PATH=/path/to/orthophoto.tif \
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REAL_REFERENCE_VECTOR_PATH=/path/to/reference-buildings.geojson \
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CALIBRATION_THRESHOLDS="0.50 0.35 0.25 0.15" \
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bash scripts/run_detection_calibration_sweep.sh [base_url]
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or:
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bash scripts/run_detection_calibration_sweep.sh [base_url] /path/to/orthophoto.tif /path/to/reference-buildings.geojson
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Required inputs:
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REAL_RASTER_PATH Georeferenced .tif/.tiff/.geotiff raster.
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REAL_REFERENCE_VECTOR_PATH EPSG-aware .geojson/.json reference vector with building polygons.
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Optional environment:
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CALIBRATION_THRESHOLDS Space/comma separated confidence thresholds, default: 0.50 0.35 0.25 0.15.
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CALIBRATION_OUTPUT_DIR Output directory, default: artifacts/detection-calibration/<timestamp>.
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REAL_MODEL_ASSET_ID Specific /api/v1/detection/model-assets id to use.
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REAL_TILE_SIZE Raster tile size passed to the underlying real-data workflow.
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REAL_TILE_OVERLAP Raster tile overlap passed to the underlying real-data workflow.
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REAL_IOU_THRESHOLD QA IoU threshold, default inherited by the underlying workflow.
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EOF
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}
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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
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cd "$ROOT"
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BASE_URL="${1:-${GE_INTEL_BASE_URL:-http://localhost:1202}}"
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REAL_RASTER_PATH="${2:-${REAL_RASTER_PATH:-}}"
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REAL_REFERENCE_VECTOR_PATH="${3:-${REAL_REFERENCE_VECTOR_PATH:-}}"
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CALIBRATION_THRESHOLDS="${CALIBRATION_THRESHOLDS:-0.50 0.35 0.25 0.15}"
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CALIBRATION_OUTPUT_DIR="${CALIBRATION_OUTPUT_DIR:-artifacts/detection-calibration/$(date -u +%Y%m%dT%H%M%SZ)}"
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if [ "${BASE_URL}" = "-h" ] || [ "${BASE_URL}" = "--help" ]; then
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usage
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exit 0
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fi
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if [ -z "${REAL_RASTER_PATH}" ] || [ -z "${REAL_REFERENCE_VECTOR_PATH}" ]; then
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usage
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exit 2
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fi
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if ! command -v curl >/dev/null 2>&1; then
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echo "curl is required for detection calibration sweep verification" >&2
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exit 1
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fi
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if [ -n "${PYTHON_BIN:-}" ]; then
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PYTHON_BIN="${PYTHON_BIN}"
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else
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PYTHON_BIN=""
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for candidate in python3 python.exe python; do
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if command -v "${candidate}" >/dev/null 2>&1 && "${candidate}" -c "import json, sys" >/dev/null 2>&1; then
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PYTHON_BIN="${candidate}"
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break
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fi
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done
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fi
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if [ -z "${PYTHON_BIN}" ]; then
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echo "A Python interpreter is required for JSON parsing" >&2
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exit 1
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fi
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thresholds_normalized="$(printf '%s' "${CALIBRATION_THRESHOLDS}" | tr ',' ' ')"
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mkdir -p "${CALIBRATION_OUTPUT_DIR}"
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"${PYTHON_BIN}" - "${thresholds_normalized}" <<'PY'
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import sys
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raw = sys.argv[1].split()
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if not raw:
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raise SystemExit("CALIBRATION_THRESHOLDS must contain at least one threshold")
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for value in raw:
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try:
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threshold = float(value)
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except ValueError as exc:
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raise SystemExit(f"Invalid confidence threshold: {value}") from exc
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if threshold < 0.0 or threshold > 1.0:
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raise SystemExit(f"Confidence threshold must be between 0 and 1: {value}")
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PY
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echo "== GeoIntel detection calibration sweep =="
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echo "Base URL: ${BASE_URL}"
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echo "Raster: ${REAL_RASTER_PATH}"
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echo "Reference vector: ${REAL_REFERENCE_VECTOR_PATH}"
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echo "Thresholds: ${thresholds_normalized}"
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echo "Output: ${CALIBRATION_OUTPUT_DIR}"
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run_index=0
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for threshold in ${thresholds_normalized}; do
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run_index=$((run_index + 1))
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threshold_label="$(printf '%s' "${threshold}" | tr '.-' 'pm')"
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run_log="${CALIBRATION_OUTPUT_DIR}/threshold_${threshold_label}.log"
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quality_response="${CALIBRATION_OUTPUT_DIR}/threshold_${threshold_label}_quality_checks.json"
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detection_run_response="${CALIBRATION_OUTPUT_DIR}/threshold_${threshold_label}_detection_run.json"
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run_summary="${CALIBRATION_OUTPUT_DIR}/threshold_${threshold_label}_summary.json"
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echo "-- Threshold ${threshold} (${run_index}) --"
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if ! REAL_PROJECT_NAME="GeoIntel Detection Calibration ${threshold}" \
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REAL_CONFIDENCE_THRESHOLD="${threshold}" \
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bash scripts/verify_real_data_detection_qa_workflow.sh "${BASE_URL}" "${REAL_RASTER_PATH}" "${REAL_REFERENCE_VECTOR_PATH}" \
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>"${run_log}" 2>&1; then
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echo "Calibration threshold ${threshold} failed. Log: ${run_log}" >&2
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tail -n 80 "${run_log}" >&2 || true
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exit 1
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fi
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project_id="$(sed -n 's/^Project: //p' "${run_log}" | tail -n 1)"
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analysis_run_id="$(sed -n 's/^Analysis run: //p' "${run_log}" | tail -n 1)"
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quality_check_id="$(sed -n 's/^Quality check: //p' "${run_log}" | tail -n 1)"
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detection_count="$(sed -n 's/^Detections: //p' "${run_log}" | tail -n 1)"
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if [ -z "${project_id}" ] || [ -z "${analysis_run_id}" ] || [ -z "${quality_check_id}" ]; then
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echo "Calibration threshold ${threshold} did not print project/run/quality ids. Log: ${run_log}" >&2
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exit 1
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fi
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curl -fsS "${BASE_URL%/}/api/v1/projects/${project_id}/quality-checks?limit=200" > "${quality_response}"
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curl -fsS "${BASE_URL%/}/api/v1/detection/runs/${analysis_run_id}" > "${detection_run_response}"
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"${PYTHON_BIN}" - \
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"${quality_response}" \
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"${detection_run_response}" \
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"${run_summary}" \
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"${threshold}" \
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"${project_id}" \
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"${analysis_run_id}" \
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"${quality_check_id}" \
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"${detection_count}" \
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"${run_log}" <<'PY'
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import json
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import sys
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quality_path, detection_run_path, output_path, threshold, project_id, analysis_run_id, quality_check_id, detection_count, run_log = sys.argv[1:10]
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with open(quality_path, "r", encoding="utf-8") as handle:
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payload = json.load(handle)
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if "data" not in payload:
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raise SystemExit("Quality-check list is not a canonical GeoIntel data envelope")
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with open(detection_run_path, "r", encoding="utf-8") as handle:
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detection_run_payload = json.load(handle)
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if "data" not in detection_run_payload:
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raise SystemExit("Detection run detail is not a canonical GeoIntel data envelope")
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detection_run = detection_run_payload["data"]
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run_result = detection_run.get("result_json") or {}
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items = payload["data"].get("items") or []
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quality_check = next((item for item in items if str(item.get("id")) == quality_check_id), None)
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if quality_check is None:
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raise SystemExit(f"Quality check not found in project list: {quality_check_id}")
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metrics = {
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metric.get("metric_key"): metric.get("metric_value")
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for metric in quality_check.get("metrics", [])
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if metric.get("metric_key")
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}
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findings = quality_check.get("findings_json") or {}
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summary = {
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"threshold": float(threshold),
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"project_id": project_id,
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"analysis_run_id": analysis_run_id,
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"quality_check_id": quality_check_id,
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"detection_count": int(detection_count),
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"quality_status": quality_check.get("status"),
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"quality_score": quality_check.get("score"),
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"raw_detection_count": run_result.get("raw_detection_count", int(detection_count)),
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"suppressed_detection_count": run_result.get("suppressed_detection_count", 0),
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"duplicate_iou_threshold": run_result.get("duplicate_iou_threshold"),
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"precision": metrics.get("precision"),
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"recall": metrics.get("recall"),
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"f1_score": metrics.get("f1_score", metrics.get("f1")),
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"mean_iou": metrics.get("mean_iou"),
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"matches": findings.get("matches"),
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"false_positives": findings.get("false_positives"),
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"false_negatives": findings.get("false_negatives"),
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"run_log": run_log,
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}
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with open(output_path, "w", encoding="utf-8") as handle:
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json.dump(summary, handle, indent=2, sort_keys=True)
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print(
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"threshold={threshold} detections={detections} raw={raw} suppressed={suppressed} score={score} "
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"precision={precision} recall={recall} f1={f1} matches={matches} fp={fp} fn={fn}".format(
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threshold=summary["threshold"],
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detections=summary["detection_count"],
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raw=summary["raw_detection_count"],
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suppressed=summary["suppressed_detection_count"],
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score=summary["quality_score"],
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precision=summary["precision"],
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recall=summary["recall"],
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f1=summary["f1_score"],
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matches=summary["matches"],
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fp=summary["false_positives"],
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fn=summary["false_negatives"],
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)
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)
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PY
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done
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"${PYTHON_BIN}" - "${CALIBRATION_OUTPUT_DIR}" "${BASE_URL}" "${thresholds_normalized}" <<'PY'
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import glob
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import json
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import os
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import sys
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from datetime import datetime, timezone
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output_dir, base_url, thresholds = sys.argv[1:4]
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items = []
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for path in sorted(glob.glob(os.path.join(output_dir, "threshold_*_summary.json"))):
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with open(path, "r", encoding="utf-8") as handle:
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items.append(json.load(handle))
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ranked = [
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item
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for item in items
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if item.get("quality_score") is not None
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]
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best_by_score = max(ranked, key=lambda item: item["quality_score"], default=None)
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summary = {
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"generated_at": datetime.now(timezone.utc).isoformat(),
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"base_url": base_url,
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"thresholds": [float(value) for value in thresholds.split()],
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"best_by_score": best_by_score,
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"items": items,
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}
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summary_path = os.path.join(output_dir, "calibration_summary.json")
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with open(summary_path, "w", encoding="utf-8") as handle:
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json.dump(summary, handle, indent=2, sort_keys=True)
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print("")
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print("Detection calibration summary")
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print("threshold\tdetections\traw\tsuppressed\tscore\tprecision\trecall\tf1\tmatches\tfp\tfn")
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for item in items:
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print(
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"{threshold:.2f}\t{detection_count}\t{raw_detection_count}\t{suppressed_detection_count}\t{quality_score}\t{precision}\t{recall}\t{f1_score}\t{matches}\t{false_positives}\t{false_negatives}".format(
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**item
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)
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)
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print("")
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print(f"Summary: {summary_path}")
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if best_by_score:
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print(
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"best_by_score threshold={threshold:.2f} score={quality_score} f1={f1_score} detections={detection_count}".format(
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**best_by_score
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)
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)
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PY
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