"""Serve the local GeoAI Workbench console and its narrow local-run APIs.""" from __future__ import annotations import argparse import base64 import binascii import hashlib import json import os import re import shutil import subprocess import threading from datetime import UTC, datetime from http import HTTPStatus from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer from pathlib import Path, PurePosixPath from typing import Any from urllib.parse import parse_qs, unquote, urlsplit from uuid import uuid4 DEFAULT_HOST = "127.0.0.1" DEFAULT_PORT = 6173 # Uploads are sent as Base64 JSON. Keep the request limit above two 1 GiB # files after encoding while retaining a per-file bound for local experiments. MAX_REQUEST_BYTES = 3072 * 1024 * 1024 MAX_FILE_BYTES = 1024 * 1024 * 1024 MAX_IMAGES_PER_RUN = 12 MAX_SEGMENTATION_IMAGES_PER_RUN = 6 MAX_MEASUREMENT_RASTERS_PER_RUN = 4 MAX_POINTCLOUDS_PER_RUN = 2 MAX_PHOTO_RECONSTRUCTION_IMAGES_PER_RUN = 30 PHOTO_RECONSTRUCTION_TIMEOUT = 7200 RISK_RULE_REQUIRED_FILES = {"observations", "zones", "rules"} MAX_ANOMALY_IMAGES_PER_ROLE = 6 CHANGE_THRESHOLD_DEFAULT = 0.5 CHANGE_THRESHOLD_MIN = 0.01 CHANGE_THRESHOLD_MAX = 0.99 CHANGE_MAX_DIMENSION_DEFAULT = 1024 CHANGE_MAX_DIMENSION_AUTO = 0 CHANGE_MAX_DIMENSION_MIN = 512 CHANGE_MAX_DIMENSION_MAX = 4096 CHANGE_PROCESSING_MODE_DEFAULT = "auto" CHANGE_PROCESSING_MODES = {"auto", "image", "geotiff"} SCAN_DEFAULT_THRESHOLDS = [0.3, 0.4, 0.5] SCAN_DEFAULT_AREAS = [64, 256, 686] SCAN_MAX_THRESHOLDS = 6 SCAN_MAX_AREAS = 6 SCAN_MAX_COMBINATIONS = 24 SCAN_JOB_TIMEOUT = 1800 ALLOWED_PATH_PREFIXES = ( "apps/workbench-console", "shared/outputs", "shared/data/raw/00-change-detection", "shared/data/raw/01-object-detection", "shared/data/raw/02-semantic-mapping", "shared/data/raw/09-anomaly-detection", "shared/data/raw/05-3d-pointcloud", ) SAFE_FILE_NAME = re.compile(r"[^\w.-]+", re.UNICODE) SAFE_UPLOAD_ID = re.compile(r"^[0-9a-f]{32}$") SAFE_SCAN_ID = re.compile(r"^[A-Za-z0-9._-]{1,100}$") SAFE_SCAN_RESULT_ID = re.compile(r"^threshold-\d+(?:\.\d+)?_area-\d+$") RUN_LOCK = threading.Lock() SCAN_JOBS: dict[str, dict[str, Any]] = {} SCAN_JOBS_LOCK = threading.Lock() ANOMALY_JOB_LOCK = threading.Lock() ANOMALY_JOBS: dict[str, dict[str, Any]] = {} PHOTO_RECONSTRUCTION_JOBS: dict[str, dict[str, Any]] = {} PHOTO_RECONSTRUCTION_JOBS_LOCK = threading.Lock() POINTCLOUD_TRAINING_JOBS: dict[str, dict[str, Any]] = {} POINTCLOUD_TRAINING_JOBS_LOCK = threading.Lock() POINTCLOUD_INFERENCE_JOBS: dict[str, dict[str, Any]] = {} POINTCLOUD_INFERENCE_JOBS_LOCK = threading.Lock() MAX_ANNOTATION_LABELS = 400_000 POINTCLOUD_CLASS_CODES = {1, 2, 5, 6, 15, 16} class ApiError(ValueError): """A request error that can be shown to the local console user.""" def safe_file_name(value: str, expected_suffixes: set[str]) -> str: name = Path(value).name suffix = Path(name).suffix.lower() if suffix not in expected_suffixes: raise ApiError(f"Unsupported file type: {suffix or '(none)'}.") stem = SAFE_FILE_NAME.sub("_", Path(name).stem).strip("._") or "upload" return f"{stem[:80]}{suffix}" def decode_upload(payload: dict[str, Any], expected_suffixes: set[str]) -> tuple[str, bytes]: if not isinstance(payload, dict) or not isinstance(payload.get("name"), str) or not isinstance(payload.get("content"), str): raise ApiError("Each uploaded file must include name and Base64 content.") name = safe_file_name(payload["name"], expected_suffixes) try: content = base64.b64decode(payload["content"], validate=True) except (binascii.Error, ValueError) as exc: raise ApiError(f"Invalid Base64 file content for {name}.") from exc if not content: raise ApiError(f"Uploaded file is empty: {name}.") if len(content) > MAX_FILE_BYTES: raise ApiError(f"Uploaded file exceeds {MAX_FILE_BYTES // (1024 * 1024)} MB: {name}.") return name, content def make_run_id(prefix: str) -> str: return f"{prefix}-{datetime.now(UTC):%Y%m%d-%H%M%S}-{uuid4().hex[:6]}" def relative_path(root: Path, path: Path) -> str: return path.relative_to(root).as_posix() def load_json(path: Path) -> dict[str, Any]: try: payload = json.loads(path.read_text(encoding="utf-8")) except (OSError, json.JSONDecodeError): return {} return payload if isinstance(payload, dict) else {} def file_sha256(path: Path) -> str: digest = hashlib.sha256() with path.open("rb") as stream: for chunk in iter(lambda: stream.read(8 * 1024 * 1024), b""): digest.update(chunk) return digest.hexdigest() def trajectory_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "15-trajectory-analysis" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent if not (artifact / "trajectory_summary.csv").is_file() or not (artifact / "events.json").is_file(): continue metadata = load_json(metadata_path) case_id = str(metadata.get("case_id") or artifact.name) is_real = case_id == "tian-dun-flight-19578" records.append( { "id": case_id, "label": "田墩实飞" if is_real else case_id, "note": "区域相交是来源数据的空间结果，不是违规结论。" if is_real else "本地实验运行结果，可继续查看结构化输出。", "artifactRoot": relative_path(root, artifact), "showSpatialContext": (artifact / "zones.geojson").is_file() and (artifact / "reference_routes.geojson").is_file(), "showFlyableZones": (artifact / "flyable_zones.geojson").is_file(), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def detection_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "01-object-detection" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent if not (artifact / "detections.json").is_file(): continue metadata = load_json(metadata_path) input_value = str(metadata.get("input_dir") or "") input_dir = Path(input_value) if input_value else root / "shared" / "data" / "raw" / "01-object-detection" try: input_root = relative_path(root, input_dir.resolve()) except ValueError: continue run_id = artifact.name if artifact != output_root else "baseline" records.append( { "id": run_id, "label": "既有基线结果" if run_id == "baseline" else run_id, "note": "CPU 基线：人员与常见车辆；树木不在当前模型有效类别内。", "artifactRoot": relative_path(root, artifact), "inputRoot": input_root, "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def change_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "00-change-detection" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) artifacts = metadata.get("artifacts") if metadata.get("capability") != "00-change-detection" or metadata.get("schema_version") != 1 or metadata.get("kind") == "parameter-scan-inference" or not isinstance(artifacts, dict): continue if not (artifact / str(artifacts.get("overlay") or "")).is_file() or not (artifact / str(artifacts.get("vector") or "")).is_file(): continue raw_root_value = str(metadata.get("raw_input_dir") or "shared/data/raw/00-change-detection/validation-20260817") raw_root = root / Path(raw_root_value) input_files = metadata.get("input_files") if not isinstance(input_files, list) or len(input_files) != 2: continue before_value = str(metadata.get("raw_before") or (Path(raw_root_value) / str(input_files[0])).as_posix()) after_value = str(metadata.get("raw_after") or (Path(raw_root_value) / str(input_files[1])).as_posix()) try: before_path = (root / before_value).resolve() after_path = (root / after_value).resolve() allowed_raw = (root / "shared" / "data" / "raw" / "00-change-detection").resolve() before_path.relative_to(allowed_raw) after_path.relative_to(allowed_raw) except ValueError: continue if not before_path.is_file() or not after_path.is_file(): continue registered_before_name = str(artifacts.get("before_processed_preview") or "") registered_after_name = str(artifacts.get("after_registered_preview") or "") registered_before_path = artifact / registered_before_name if registered_before_name else None registered_after_path = artifact / registered_after_name if registered_after_name else None run_id = artifact.name record = { "id": run_id, "label": run_id, "note": "ChangeStar CPU 变化栅格与 GeoAI 像素坐标图斑；结果需人工复核。", "artifactRoot": relative_path(root, artifact), "beforeImage": relative_path(root, before_path), "afterImage": relative_path(root, after_path), "createdAt": str(metadata.get("created_at") or ""), } if registered_before_path and registered_after_path and registered_before_path.is_file() and registered_after_path.is_file(): record["registeredBeforeImage"] = relative_path(root, registered_before_path) record["registeredAfterImage"] = relative_path(root, registered_after_path) # Existing console consumers use afterImage as the vector-overlay # base. Point it at the registered grid so polygons and highlights # share the same pixel coordinates as the model output. record["rawAfterImage"] = record["afterImage"] record["afterImage"] = record["registeredAfterImage"] records.append(record) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def change_parameter_scans(root: Path) -> list[dict[str, Any]]: """Discover read-only parameter scans produced from an existing change run.""" output_root = root / "shared" / "outputs" / "00-change-detection" records: list[dict[str, Any]] = [] for summary_path in output_root.rglob("scan_summary.json"): scan_root = summary_path.parent summary = load_json(summary_path) results: list[dict[str, Any]] = [] for item in summary.get("results", []): if not isinstance(item, dict) or not isinstance(item.get("directory"), str): continue directory = scan_root / item["directory"] overlay = directory / "overlay_preview.jpg" mask = directory / "change_mask.tif" regions = directory / "regions.json" if not overlay.is_file() or not mask.is_file() or not regions.is_file(): continue results.append( { "id": item["directory"], "label": f"T={float(item.get('threshold', 0.5)):.2f} / 面积={int(item.get('minimum_area_pixels', 0))} px", "threshold": item.get("threshold"), "minimumAreaPixels": item.get("minimum_area_pixels"), "cleanedComponents": item.get("cleaned_components"), "changedPixels": item.get("changed_pixels"), "changedPixelRatio": item.get("changed_pixel_ratio"), "vectorFeatureCount": item.get("vector_feature_count"), "fullVectorFeatureCount": (load_json(directory / "full_result.json").get("full_vector_feature_count") if (directory / "full_result.json").is_file() else None), "rectangleFeatureCount": ( load_json(directory / "full_result.json").get("rectangle_vector_feature_count") if (directory / "full_result.json").is_file() and load_json(directory / "full_result.json").get("rectangle_vector_feature_count") is not None else len(load_json(directory / "changes_rectangles.geojson").get("features", [])) if (directory / "changes_rectangles.geojson").is_file() else None ), "overlay": relative_path(root, overlay), "mask": relative_path(root, mask), "regions": relative_path(root, regions), "vector": (relative_path(root, directory / "changes.geojson") if (directory / "changes.geojson").is_file() else None), "rectangleVector": (relative_path(root, directory / "changes_rectangles.geojson") if (directory / "changes_rectangles.geojson").is_file() else None), "rectangleVectorWgs84": (relative_path(root, directory / "changes_rectangles_wgs84.geojson") if (directory / "changes_rectangles_wgs84.geojson").is_file() else None), } ) # A failed job may have been repaired or materialized later. Keep it # discoverable whenever at least one complete candidate exists; only # hide scans that still have no usable result. if not results: continue scan_metadata = load_json(scan_root / "scan_metadata.json") contact_sheet = scan_root / "parameter_scan_contact_sheet.jpg" records.append( { "id": scan_root.name, "label": f"低成本参数扫描 · {scan_root.name}", "note": f"复用已有变化概率结果，不重新运行 ChangeStar；源运行：{summary.get('source_run', '未知')}", "artifactRoot": relative_path(root, scan_root), "sourceRun": summary.get("source_run"), "userSubmitted": bool(scan_metadata), "contactSheet": relative_path(root, contact_sheet) if contact_sheet.is_file() else None, "results": results, } ) return sorted(records, key=lambda item: item["id"], reverse=True) def semantic_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "02-semantic-mapping" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) if metadata.get("capability") != "02-semantic-mapping" or not isinstance(metadata.get("images"), list): continue run_id = artifact.name if artifact != output_root else "baseline" records.append( { "id": run_id, "label": "语义分割基线" if run_id == "baseline" else run_id, "note": f"{metadata.get('task_name') or '通用颜色规则基线'}，输出栅格掩膜与 GeoAI 矢量结果。", "artifactRoot": relative_path(root, artifact), "inputRoot": str(metadata.get("input_dir") or "shared/data/processed/02-semantic-mapping"), "rawInputRoot": str(metadata.get("raw_input_dir") or "shared/data/raw/02-semantic-mapping"), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def measurement_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "04-spatial-measurement" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) if metadata.get("capability") != "04-spatial-measurement" or not isinstance(metadata.get("images"), list): continue run_id = artifact.name records.append( { "id": run_id, "label": run_id, "note": "GeoAI 栅格转矢量后进行对象计数、面积和周长测量。", "artifactRoot": relative_path(root, artifact), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def pointcloud_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "05-3d-pointcloud" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) dense_photo_reconstruction = metadata.get("dense_photo_reconstruction") if metadata.get("capability") == "05-3d-pointcloud" and isinstance(dense_photo_reconstruction, dict): textured_model = dense_photo_reconstruction.get("textured_model_file") dense_point_cloud = dense_photo_reconstruction.get("dense_point_cloud_file") mesh = dense_photo_reconstruction.get("mesh_file") if all(isinstance(item, str) and (artifact / item).is_file() for item in (textured_model, dense_point_cloud, mesh)): run_id = artifact.name records.append( { "id": run_id, "label": str(metadata.get("display_name") or run_id), "note": "CPU 稠密 MVS：显示经过深度融合、网格化和纹理化的局部模型；不是测绘级坐标、DSM、正射图或语义识别结论。", "artifactRoot": relative_path(root, artifact), "createdAt": str(metadata.get("created_at") or ""), } ) continue photo_reconstruction = metadata.get("photo_reconstruction") if metadata.get("capability") == "05-3d-pointcloud" and isinstance(photo_reconstruction, dict): preview = photo_reconstruction.get("preview_file") point_cloud = photo_reconstruction.get("point_cloud_file") if isinstance(preview, str) and isinstance(point_cloud, str) and (artifact / preview).is_file() and (artifact / point_cloud).is_file(): run_id = artifact.name records.append( { "id": run_id, "label": str(metadata.get("display_name") or run_id), "note": "CPU 稀疏 SfM：显示可复核点云、相机位姿与误差；不是稠密重建、DSM、语义识别或测绘精度结论。", "artifactRoot": relative_path(root, artifact), "createdAt": str(metadata.get("created_at") or ""), } ) continue point_clouds = metadata.get("point_clouds") if metadata.get("capability") != "05-3d-pointcloud" or not isinstance(point_clouds, list) or not point_clouds: continue if any(not isinstance(item, dict) or not (artifact / str(item.get("preview_file") or "")).is_file() or not (artifact / str(item.get("vector_file") or "")).is_file() for item in point_clouds): continue run_id = artifact.name records.append( { "id": run_id, "label": str(metadata.get("display_name") or run_id), "note": "CPU 语义规则基线：地面、植被、构筑物以及电线/杆塔候选，需要人工复核；不提供测绘精度或资产台账结论。" if any(isinstance(item, dict) and item.get("semantic_summary_file") for item in point_clouds) else "CPU 几何基线：地面/高出地物分离、DSM、近似网格和 GeoAI 足迹；不提供语义类别或测绘精度结论。", "artifactRoot": relative_path(root, artifact), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def pointcloud_annotation_sources(root: Path) -> list[dict[str, Any]]: """Expose only generated, fixed preview PLYs suitable for manual labels.""" sources: list[dict[str, Any]] = [] for case in pointcloud_runs(root): artifact = root / str(case["artifactRoot"]) metadata = load_json(artifact / "run_metadata.json") for cloud in metadata.get("point_clouds", []): if not isinstance(cloud, dict): continue name = cloud.get("semantic_annotation_source_point_cloud") if not isinstance(name, str) or Path(name).name != name: continue path = artifact / name if not path.is_file() or path.suffix.lower() != ".ply": continue sources.append({ "id": f"{case['id']}:{name}", "runId": case["id"], "label": f"{case['label']} / {cloud.get('file', name)}", "artifactRoot": case["artifactRoot"], "file": name, "url": f"/{case['artifactRoot']}/{name}", "sha256": file_sha256(path), "pointCount": int(cloud.get("semantic_preview_points") or 0), "sourceKind": str(cloud.get("semantic_annotation_source_kind") or "generated point-cloud preview"), }) return sources def pointcloud_annotations(root: Path) -> list[dict[str, Any]]: output = root / "shared" / "outputs" / "05-3d-pointcloud" / "annotations" records: list[dict[str, Any]] = [] for path in output.glob("*/annotation.json"): data = load_json(path) if data.get("schema_version") != 1 or not isinstance(data.get("id"), str): continue records.append({"id": data["id"], "sourceId": data.get("source_id"), "createdAt": data.get("created_at"), "labelCount": len(data.get("labels", [])), "classCounts": data.get("class_counts", {}), "path": relative_path(root, path)}) return sorted(records, key=lambda item: (str(item["createdAt"]), str(item["id"])), reverse=True) def pointcloud_training_job(job_id: str) -> dict[str, Any] | None: with POINTCLOUD_TRAINING_JOBS_LOCK: value = POINTCLOUD_TRAINING_JOBS.get(job_id) return dict(value) if value else None def execute_pointcloud_training_job(root: Path, job_id: str, annotation: Path, output: Path, device: str) -> None: with POINTCLOUD_TRAINING_JOBS_LOCK: POINTCLOUD_TRAINING_JOBS[job_id].update({"status": "running", "stage": "training", "startedAt": datetime.now(UTC).isoformat()}) python = root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" command = [str(python), str(root / "capabilities" / "05-3d-pointcloud" / "train_pointcloud_semantic_model.py"), "--annotation", str(annotation), "--output", str(output), "--device", device] try: with RUN_LOCK: completed = subprocess.run(command, cwd=root, capture_output=True, text=True, timeout=14_400, check=False) if completed.returncode: message = (completed.stderr or completed.stdout or "Unknown training error.").strip().splitlines()[-1] raise ApiError(message[:600]) metrics = output / "metrics.json" model = output / "model.pt" preview = output / "predicted-semantic-preview.ply" if not all(path.is_file() for path in (metrics, model, preview)): raise ApiError("Training finished without model, metrics, and predicted preview artifacts.") with POINTCLOUD_TRAINING_JOBS_LOCK: POINTCLOUD_TRAINING_JOBS[job_id].update({"status": "complete", "stage": "complete", "completedAt": datetime.now(UTC).isoformat(), "artifactRoot": relative_path(root, output), "metrics": relative_path(root, metrics), "model": relative_path(root, model), "preview": relative_path(root, preview)}) except Exception as exc: with POINTCLOUD_TRAINING_JOBS_LOCK: POINTCLOUD_TRAINING_JOBS[job_id].update({"status": "failed", "stage": "failed", "completedAt": datetime.now(UTC).isoformat(), "error": str(exc)[:700]}) def pointcloud_semantic_models(root: Path) -> list[dict[str, Any]]: """Expose only complete locally trained models, never arbitrary model paths.""" output_root = root / "shared" / "outputs" / "05-3d-pointcloud" / "training-runs" records: list[dict[str, Any]] = [] for model_path in output_root.glob("*/model.pt"): metrics_path = model_path.with_name("metrics.json") metrics = load_json(metrics_path) classes = metrics.get("classes") if metrics.get("capability") != "05-3d-pointcloud" or metrics.get("classification") != "B" or not isinstance(classes, dict): continue class_codes = sorted(str(code) for code in classes if str(code).isdigit()) if len(class_codes) < 2: continue test = metrics.get("test") if isinstance(metrics.get("test"), dict) else {} report = test.get("report") if isinstance(test.get("report"), dict) else {} summary: dict[str, float] = {} for code in class_codes: definition = classes.get(code) key = definition.get("key") if isinstance(definition, dict) else None score = report.get(key) if isinstance(key, str) else None if isinstance(score, dict) and isinstance(score.get("f1-score"), (int, float)): summary[key] = round(float(score["f1-score"]), 3) records.append({"id": model_path.parent.name, "label": model_path.parent.name, "artifactRoot": relative_path(root, model_path.parent), "model": relative_path(root, model_path), "metrics": relative_path(root, metrics_path), "createdAt": str(metrics.get("created_at") or ""), "classes": classes, "testF1": summary}) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def pointcloud_inference_job(job_id: str) -> dict[str, Any] | None: with POINTCLOUD_INFERENCE_JOBS_LOCK: value = POINTCLOUD_INFERENCE_JOBS.get(job_id) return dict(value) if value else None def execute_pointcloud_inference_job(root: Path, job_id: str, model: Path, source: Path, output: Path) -> None: with POINTCLOUD_INFERENCE_JOBS_LOCK: POINTCLOUD_INFERENCE_JOBS[job_id].update({"status": "running", "stage": "inference", "startedAt": datetime.now(UTC).isoformat()}) python = root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" command = [str(python), str(root / "capabilities" / "05-3d-pointcloud" / "apply_pointcloud_semantic_model.py"), "--model", str(model), "--input", str(source), "--output", str(output), "--device", "cpu"] try: with RUN_LOCK: completed = subprocess.run(command, cwd=root, capture_output=True, text=True, timeout=14_400, check=False) if completed.returncode: message = (completed.stderr or completed.stdout or "Unknown model inference error.").strip().splitlines()[-1] raise ApiError(message[:600]) metadata = output / "run_metadata.json" preview = output / "predicted-semantic-preview.ply" classified_las = output / "predicted-semantic-classified.las" counts = output / "class-counts.csv" summary = output / "prediction-summary.json" if not all(path.is_file() for path in (metadata, preview, classified_las, counts, summary)): raise ApiError("Model inference finished without all expected prediction artifacts.") with POINTCLOUD_INFERENCE_JOBS_LOCK: POINTCLOUD_INFERENCE_JOBS[job_id].update({"status": "complete", "stage": "complete", "completedAt": datetime.now(UTC).isoformat(), "artifactRoot": relative_path(root, output), "metadata": relative_path(root, metadata), "preview": relative_path(root, preview), "classifiedLas": relative_path(root, classified_las), "classCounts": relative_path(root, counts), "summary": relative_path(root, summary)}) except Exception as exc: with POINTCLOUD_INFERENCE_JOBS_LOCK: POINTCLOUD_INFERENCE_JOBS[job_id].update({"status": "failed", "stage": "failed", "completedAt": datetime.now(UTC).isoformat(), "error": str(exc)[:700]}) def risk_rule_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "07-risk-rule-engine" records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) artifacts = metadata.get("artifacts") if metadata.get("capability") != "07-risk-rule-engine" or not isinstance(artifacts, dict): continue required = ("risk_raster", "risk_preview", "risk_vector", "risk_scores_csv", "summary") if any(not isinstance(artifacts.get(key), str) or not (artifact / artifacts[key]).is_file() for key in required): continue run_id = artifact.name records.append( { "id": run_id, "label": str(metadata.get("display_name") or run_id), "note": "可审计空间规则评分，仅供人工复核，不构成事件或处置结论。", "artifactRoot": relative_path(root, artifact), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def anomaly_runs(root: Path) -> list[dict[str, Any]]: output_root = root / "shared" / "outputs" / "09-anomaly-detection" allowed_raw = (root / "shared" / "data" / "raw" / "09-anomaly-detection").resolve() records: list[dict[str, Any]] = [] for metadata_path in output_root.rglob("run_metadata.json"): artifact = metadata_path.parent metadata = load_json(metadata_path) images = metadata.get("images") if metadata.get("capability") != "09-anomaly-detection" or not isinstance(images, list): continue raw_input_value = str(metadata.get("raw_input_dir") or "") raw_reference_value = str(metadata.get("raw_reference_dir") or "") if not raw_input_value or not raw_reference_value: continue try: raw_input = (root / raw_input_value).resolve() raw_reference = (root / raw_reference_value).resolve() raw_input.relative_to(allowed_raw) raw_reference.relative_to(allowed_raw) except ValueError: continue if not raw_input.is_dir() or not raw_reference.is_dir(): continue if any(not (artifact / str(item.get("overlay_file") or "")).is_file() for item in images if isinstance(item, dict)): continue run_id = artifact.name records.append( { "id": run_id, "label": str(metadata.get("display_name") or run_id), "note": str(metadata.get("case_note") or "规则基线与 Isolation Forest 的视觉离群候选，只供人工复核。"), "artifactRoot": relative_path(root, artifact), "inputRoot": relative_path(root, raw_input), "referenceRoot": relative_path(root, raw_reference), "createdAt": str(metadata.get("created_at") or ""), } ) return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True) def anomaly_job(job_id: str) -> dict[str, Any] | None: with ANOMALY_JOB_LOCK: value = ANOMALY_JOBS.get(job_id) return dict(value) if value else None def photo_reconstruction_job(job_id: str) -> dict[str, Any] | None: with PHOTO_RECONSTRUCTION_JOBS_LOCK: value = PHOTO_RECONSTRUCTION_JOBS.get(job_id) return dict(value) if value else None def run_background_command(command: list[str], root: Path, timeout: int) -> None: completed = subprocess.run(command, cwd=root, capture_output=True, text=True, timeout=timeout, check=False) if completed.returncode: message = (completed.stderr or completed.stdout or "Unknown script error.").strip().splitlines()[-1] raise ApiError(f"Processing failed: {message[:600]}") def execute_photo_reconstruction_job( root: Path, job_id: str, run_id: str, raw_root: Path, processed_root: Path, sparse_output: Path, output: Path, source_sha256: dict[str, str], source_bytes: dict[str, int], use_position_priors: bool, ) -> None: python = root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" sparse_command = [ str(python), str(root / "capabilities" / "05-3d-pointcloud" / "run_photo_reconstruction.py"), "--input", str(processed_root), "--output", str(sparse_output), "--max-image-size", "2000", "--max-features", "18000", "--camera-model", "OPENCV", ] if use_position_priors: sparse_command.extend(["--matching-mode", "spatial", "--matching-neighbors", "4", "--use-position-priors", "--prior-position-loss-scale-m", "0.05"]) else: sparse_command.extend(["--matching-mode", "exhaustive"]) dense_command = [ str(python), str(root / "capabilities" / "05-3d-pointcloud" / "run_cpu_dense_reconstruction.py"), "--input", str(processed_root), "--sparse-model", str(sparse_output / "sparse_model" / "0"), "--output", str(output), "--openmvs-bin", str(root / "shared" / "tools" / "openmvs-2.4.0" / "vc17" / "x64" / "Release"), "--threads", "12", "--max-resolution", "2400", "--dense-resolution-level", "0", "--dense-number-views", "8", "--dense-number-views-fuse", "2", "--target-faces", "800000", ] try: with PHOTO_RECONSTRUCTION_JOBS_LOCK: PHOTO_RECONSTRUCTION_JOBS[job_id].update({"status": "running", "stage": "sparse_sfm", "startedAt": datetime.now(UTC).isoformat()}) with RUN_LOCK: run_background_command(sparse_command, root, 1800) sparse_metadata = load_json(sparse_output / "run_metadata.json") if not (sparse_output / "sparse_model" / "0").is_dir(): raise ApiError("Sparse photo reconstruction finished without the expected COLMAP model.") with PHOTO_RECONSTRUCTION_JOBS_LOCK: PHOTO_RECONSTRUCTION_JOBS[job_id].update({"stage": "dense_mvs"}) run_background_command(dense_command, root, PHOTO_RECONSTRUCTION_TIMEOUT) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("CPU dense reconstruction finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["photo_reconstruction"] = sparse_metadata.get("photo_reconstruction", {}) metadata["input_dir"] = relative_path(root, processed_root) metadata["raw_input_dir"] = relative_path(root, raw_root) metadata["source_sha256"] = source_sha256 metadata["source_bytes"] = source_bytes metadata["console_photo_reconstruction"] = {"use_position_priors": use_position_priors, "matching_mode": "spatial" if use_position_priors else "exhaustive"} metadata["display_name"] = f"用户照片 CPU 稠密重建（{len(source_sha256)} 图）" metadata["case_note"] = "用户上传的同架次 JPG/JPEG 照片经 CPU SfM/MVS 重建；需要人工检查几何与纹理质量，不是测绘级成果。" metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") definition = next(item for item in pointcloud_runs(root) if item["id"] == run_id) with PHOTO_RECONSTRUCTION_JOBS_LOCK: PHOTO_RECONSTRUCTION_JOBS[job_id].update({"status": "complete", "stage": "complete", "run": definition, "finishedAt": datetime.now(UTC).isoformat()}) except Exception as exc: # pragma: no cover - background boundary with PHOTO_RECONSTRUCTION_JOBS_LOCK: PHOTO_RECONSTRUCTION_JOBS[job_id].update({"status": "failed", "stage": "failed", "error": str(exc), "finishedAt": datetime.now(UTC).isoformat()}) def validate_anomaly_parameters(payload: dict[str, Any]) -> tuple[int, int, float, int]: tile_size = payload.get("tileSize", 256) stride = payload.get("stride", 128) threshold_quantile = payload.get("thresholdQuantile", 0.995) random_state = payload.get("randomState", 42) if isinstance(tile_size, bool) or not isinstance(tile_size, int) or not 128 <= tile_size <= 1024: raise ApiError("Tile size must be an integer between 128 and 1024.") if isinstance(stride, bool) or not isinstance(stride, int) or not 32 <= stride <= tile_size: raise ApiError("Stride must be an integer between 32 and tile size.") if isinstance(threshold_quantile, bool) or not isinstance(threshold_quantile, (int, float)) or not 0.9 <= float(threshold_quantile) <= 0.9999: raise ApiError("Threshold quantile must be between 0.9 and 0.9999.") if isinstance(random_state, bool) or not isinstance(random_state, int) or not 0 <= random_state <= 2_147_483_647: raise ApiError("Random state must be a non-negative integer.") return tile_size, stride, float(threshold_quantile), random_state def execute_anomaly_job( root: Path, job_id: str, run_id: str, raw_reference: Path, raw_input: Path, processed_reference: Path, processed_input: Path, output: Path, tile_size: int, stride: int, threshold_quantile: float, random_state: int, ) -> None: with ANOMALY_JOB_LOCK: ANOMALY_JOBS[job_id]["status"] = "running" python = root / ".venvs" / "09-anomaly-detection" / "Scripts" / "python.exe" command = [ str(python), str(root / "capabilities" / "09-anomaly-detection" / "run_anomaly_detection.py"), "--reference", str(processed_reference), "--input", str(processed_input), "--output", str(output), "--tile-size", str(tile_size), "--stride", str(stride), "--threshold-quantile", f"{threshold_quantile:.6f}", "--random-state", str(random_state), "--spatial-mode", "auto", ] try: with RUN_LOCK: completed = subprocess.run(command, cwd=root, capture_output=True, text=True, timeout=1800, check=False) if completed.returncode: message = (completed.stderr or completed.stdout or "Unknown script error.").strip().splitlines()[-1] raise ApiError(f"Processing failed: {message[:600]}") metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("Anomaly-detection script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["raw_input_dir"] = relative_path(root, raw_input) metadata["raw_reference_dir"] = relative_path(root, raw_reference) metadata["processed_input_dir"] = relative_path(root, processed_input) metadata["processed_reference_dir"] = relative_path(root, processed_reference) metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") definition = next(item for item in anomaly_runs(root) if item["id"] == run_id) with ANOMALY_JOB_LOCK: ANOMALY_JOBS[job_id].update({"status": "complete", "run": definition, "finishedAt": datetime.now(UTC).isoformat()}) except Exception as exc: # pragma: no cover - background boundary with ANOMALY_JOB_LOCK: ANOMALY_JOBS[job_id].update({"status": "failed", "error": str(exc), "finishedAt": datetime.now(UTC).isoformat()}) def semantic_tasks(root: Path) -> list[dict[str, Any]]: catalog = load_json(root / "capabilities" / "02-semantic-mapping" / "configs" / "task-catalog.json") tasks = catalog.get("tasks") if not isinstance(tasks, list): return [] return [item for item in tasks if isinstance(item, dict) and isinstance(item.get("id"), str)] class WorkbenchConsoleHandler(SimpleHTTPRequestHandler): """Static UI plus fixed, local-only ingestion and experiment commands.""" server_version = "GeoAIWorkbench/1.0" @property def root(self) -> Path: return Path(self.directory).resolve() def do_GET(self) -> None: # noqa: N802 - inherited standard-library method name path = urlsplit(self.path).path if path == "/api/change-detection/runs": self.send_json(HTTPStatus.OK, {"runs": change_runs(self.root)}) return if path == "/api/change-detection/scans": self.send_json(HTTPStatus.OK, {"scans": change_parameter_scans(self.root)}) return if path.startswith("/api/change-detection/scan-jobs/"): job_id = path.rstrip("/").rsplit("/", 1)[-1] with SCAN_JOBS_LOCK: job = dict(SCAN_JOBS.get(job_id, {})) if not job: self.send_json(HTTPStatus.NOT_FOUND, {"error": "Unknown change-detection scan job."}) else: self.send_json(HTTPStatus.OK, {"job": job}) return if path == "/api/trajectory/runs": self.send_json(HTTPStatus.OK, {"runs": trajectory_runs(self.root)}) return if path == "/api/object-detection/runs": self.send_json(HTTPStatus.OK, {"runs": detection_runs(self.root)}) return if path == "/api/semantic-mapping/runs": self.send_json(HTTPStatus.OK, {"runs": semantic_runs(self.root)}) return if path == "/api/semantic-mapping/tasks": self.send_json(HTTPStatus.OK, {"tasks": semantic_tasks(self.root)}) return if path == "/api/spatial-measurement/runs": self.send_json(HTTPStatus.OK, {"runs": measurement_runs(self.root)}) return if path == "/api/3d-pointcloud/runs": self.send_json(HTTPStatus.OK, {"runs": pointcloud_runs(self.root)}) return if path == "/api/3d-pointcloud/annotation-sources": self.send_json(HTTPStatus.OK, {"sources": pointcloud_annotation_sources(self.root)}) return if path == "/api/3d-pointcloud/annotations": self.send_json(HTTPStatus.OK, {"annotations": pointcloud_annotations(self.root)}) return if path == "/api/3d-pointcloud/semantic-models": self.send_json(HTTPStatus.OK, {"models": pointcloud_semantic_models(self.root)}) return if path.startswith("/api/3d-pointcloud/model-inference-jobs/"): job_id = path.rstrip("/").rsplit("/", 1)[-1] job = pointcloud_inference_job(job_id) self.send_json(HTTPStatus.OK if job else HTTPStatus.NOT_FOUND, {"job": job} if job else {"error": "Unknown point-cloud model inference job."}) return if path.startswith("/api/3d-pointcloud/training-jobs/"): job_id = path.rstrip("/").rsplit("/", 1)[-1] job = pointcloud_training_job(job_id) self.send_json(HTTPStatus.OK if job else HTTPStatus.NOT_FOUND, {"job": job} if job else {"error": "Unknown point-cloud training job."}) return if path.startswith("/api/3d-pointcloud/photo-reconstruction-jobs/"): job_id = path.rstrip("/").rsplit("/", 1)[-1] job = photo_reconstruction_job(job_id) self.send_json(HTTPStatus.OK if job else HTTPStatus.NOT_FOUND, {"job": job} if job else {"error": "Unknown photo-reconstruction job."}) return if path == "/api/risk-rule-engine/runs": self.send_json(HTTPStatus.OK, {"runs": risk_rule_runs(self.root)}) return if path == "/api/anomaly-detection/runs": self.send_json(HTTPStatus.OK, {"runs": anomaly_runs(self.root)}) return if path.startswith("/api/anomaly-detection/jobs/"): job_id = path.rstrip("/").rsplit("/", 1)[-1] job = anomaly_job(job_id) self.send_json(HTTPStatus.OK if job else HTTPStatus.NOT_FOUND, {"job": job} if job else {"error": "Unknown anomaly-detection job."}) return if path == "/": self.send_response(HTTPStatus.FOUND) self.send_header("Location", "/apps/workbench-console/") self.end_headers() return super().do_GET() def do_POST(self) -> None: # noqa: N802 - inherited standard-library method name path = urlsplit(self.path).path try: payload = self.read_json_body() if path == "/api/change-detection/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_change_run(payload)}) return if path == "/api/change-detection/scans": self.send_json(HTTPStatus.ACCEPTED, {"job": self.create_change_scan(payload)}) return if path.startswith("/api/change-detection/scans/") and path.endswith("/promote"): scan_id = path.split("/")[-2] self.send_json(HTTPStatus.CREATED, {"run": self.promote_change_scan(scan_id, payload)}) return if path == "/api/trajectory/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_trajectory_run(payload)}) return if path == "/api/object-detection/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_detection_run(payload)}) return if path == "/api/semantic-mapping/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_semantic_run(payload)}) return if path == "/api/spatial-measurement/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_measurement_run(payload)}) return if path == "/api/3d-pointcloud/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_pointcloud_run(payload)}) return if path == "/api/3d-pointcloud/annotations": self.send_json(HTTPStatus.CREATED, {"annotation": self.create_pointcloud_annotation(payload)}) return if path == "/api/3d-pointcloud/training-runs": self.send_json(HTTPStatus.ACCEPTED, {"job": self.create_pointcloud_training_run(payload)}) return if path == "/api/3d-pointcloud/model-inference-runs": self.send_json(HTTPStatus.ACCEPTED, {"job": self.create_pointcloud_model_inference_run(payload)}) return if path == "/api/3d-pointcloud/photo-reconstruction-runs": self.send_json(HTTPStatus.ACCEPTED, {"job": self.create_photo_reconstruction_run(payload)}) return if path == "/api/risk-rule-engine/runs": self.send_json(HTTPStatus.CREATED, {"run": self.create_risk_rule_run(payload)}) return if path == "/api/anomaly-detection/runs": self.send_json(HTTPStatus.ACCEPTED, {"job": self.create_anomaly_run(payload)}) return self.send_json(HTTPStatus.NOT_FOUND, {"error": "Unknown local API endpoint."}) except ApiError as exc: self.send_json(HTTPStatus.BAD_REQUEST, {"error": str(exc)}) except subprocess.TimeoutExpired: self.send_json(HTTPStatus.GATEWAY_TIMEOUT, {"error": "The local run exceeded its time limit; no existing result was overwritten."}) except Exception as exc: # pragma: no cover - defensive server boundary self.log_error("local run failed: %s", exc) self.send_json(HTTPStatus.INTERNAL_SERVER_ERROR, {"error": "Local run failed. Check the console terminal for details."}) def do_DELETE(self) -> None: # noqa: N802 - annotation revisions are explicitly user-removable path = urlsplit(self.path).path prefix = "/api/3d-pointcloud/annotations/" if not path.startswith(prefix): self.send_json(HTTPStatus.NOT_FOUND, {"error": "Unknown local API endpoint."}) return try: annotation_id = path[len(prefix):] if not annotation_id or "/" in annotation_id or SAFE_FILE_NAME.search(annotation_id) or len(annotation_id) > 120: raise ApiError("Invalid annotation id.") record = next((item for item in pointcloud_annotations(self.root) if item["id"] == annotation_id), None) if not record: raise ApiError("The selected annotation revision is unavailable.") annotation_root = (self.root / "shared" / "outputs" / "05-3d-pointcloud" / "annotations").resolve() location = (annotation_root / annotation_id).resolve() location.relative_to(annotation_root) if not (location / "annotation.json").is_file(): raise ApiError("The selected annotation revision is incomplete.") shutil.rmtree(location) self.send_json(HTTPStatus.OK, {"deletedId": annotation_id}) except ApiError as exc: self.send_json(HTTPStatus.BAD_REQUEST, {"error": str(exc)}) except ValueError: self.send_json(HTTPStatus.BAD_REQUEST, {"error": "Invalid annotation location."}) except Exception as exc: # pragma: no cover - defensive server boundary self.log_error("annotation deletion failed: %s", exc) self.send_json(HTTPStatus.INTERNAL_SERVER_ERROR, {"error": "Annotation deletion failed. Check the console terminal for details."}) def do_PUT(self) -> None: # noqa: N802 - binary upload endpoint path = urlsplit(self.path).path if not path.startswith("/api/change-detection/uploads/") and not path.startswith("/api/anomaly-detection/uploads/") and not path.startswith("/api/3d-pointcloud/photo-uploads/") and not path.startswith("/api/3d-pointcloud/pointcloud-uploads/"): self.send_json(HTTPStatus.NOT_FOUND, {"error": "Unknown local API endpoint."}) return try: if path.startswith("/api/anomaly-detection/uploads/"): result = self.receive_anomaly_upload(path) elif path.startswith("/api/3d-pointcloud/photo-uploads/"): result = self.receive_photo_reconstruction_upload(path) elif path.startswith("/api/3d-pointcloud/pointcloud-uploads/"): result = self.receive_pointcloud_upload(path) else: result = self.receive_change_upload(path) self.send_json(HTTPStatus.CREATED, result) except ApiError as exc: self.send_json(HTTPStatus.BAD_REQUEST, {"error": str(exc)}) except Exception as exc: # pragma: no cover - defensive server boundary self.log_error("binary upload failed: %s", exc) self.send_json(HTTPStatus.INTERNAL_SERVER_ERROR, {"error": "Binary upload failed. Check the console terminal for details."}) def do_OPTIONS(self) -> None: # noqa: N802 self.send_response(HTTPStatus.NO_CONTENT) self.send_header("Allow", "GET, POST, PUT, DELETE, OPTIONS") self.end_headers() def read_json_body(self) -> dict[str, Any]: content_length = self.headers.get("Content-Length") if content_length is None or not content_length.isdigit(): raise ApiError("A JSON request body with Content-Length is required.") size = int(content_length) if size <= 0 or size > MAX_REQUEST_BYTES: raise ApiError(f"Request must be between 1 byte and {MAX_REQUEST_BYTES // (1024 * 1024)} MB.") if "application/json" not in self.headers.get("Content-Type", ""): raise ApiError("Content-Type must be application/json.") try: payload = json.loads(self.rfile.read(size).decode("utf-8")) except (UnicodeDecodeError, json.JSONDecodeError) as exc: raise ApiError("Request body is not valid UTF-8 JSON.") from exc if not isinstance(payload, dict): raise ApiError("JSON request body must be an object.") return payload def run_command(self, command: list[str], timeout: int) -> None: completed = subprocess.run(command, cwd=self.root, capture_output=True, text=True, timeout=timeout, check=False) if completed.returncode: message = (completed.stderr or completed.stdout or "Unknown script error.").strip().splitlines()[-1] raise ApiError(f"Processing failed: {message[:600]}") def receive_change_upload(self, path: str) -> dict[str, Any]: return self.receive_binary_upload(path, "00-change-detection", {"before", "after"}, {".jpg", ".jpeg", ".png", ".tif", ".tiff"}, "change-detection") def receive_anomaly_upload(self, path: str) -> dict[str, Any]: return self.receive_binary_upload(path, "09-anomaly-detection", {"reference", "input"}, {".jpg", ".jpeg", ".png", ".tif", ".tiff"}, "anomaly-detection") def receive_photo_reconstruction_upload(self, path: str) -> dict[str, Any]: return self.receive_binary_upload(path, "05-3d-pointcloud", {"photo"}, {".jpg", ".jpeg"}, "photo reconstruction") def receive_pointcloud_upload(self, path: str) -> dict[str, Any]: return self.receive_binary_upload(path, "05-3d-pointcloud", {"pointcloud"}, {".ply", ".pcd", ".xyz", ".xyzn", ".xyzrgb", ".las", ".laz"}, "point-cloud") def receive_binary_upload( self, path: str, capability: str, allowed_roles: set[str], suffixes: set[str], label: str, ) -> dict[str, Any]: upload_id = path.rstrip("/").rsplit("/", 1)[-1] if not SAFE_UPLOAD_ID.fullmatch(upload_id): raise ApiError(f"Invalid {label} upload id.") query = parse_qs(urlsplit(self.path).query) role = query.get("role", [""])[0] if role not in allowed_roles: raise ApiError(f"Invalid {label} upload role.") encoded_name = self.headers.get("X-Upload-Name", "") if len(encoded_name) > 2048: raise ApiError("Encoded upload name is too long.") try: name = unquote(encoded_name, encoding="utf-8", errors="strict") except UnicodeError as exc: raise ApiError("Upload name is not valid UTF-8 percent encoding.") from exc safe_name = safe_file_name(name, suffixes) content_length = self.headers.get("Content-Length") if content_length is None or not content_length.isdigit(): raise ApiError("Binary upload requires a Content-Length header.") size = int(content_length) if size <= 0 or size > MAX_FILE_BYTES: raise ApiError(f"Uploaded file must be between 1 byte and {MAX_FILE_BYTES // (1024 * 1024)} MB: {safe_name}.") staging = self.root / "shared" / "data" / "raw" / capability / "uploads" / upload_id staging.mkdir(parents=True, exist_ok=False) part = staging / f"{role}.part" target = staging / f"{role}{Path(safe_name).suffix.lower()}" remaining = size digest = hashlib.sha256() try: with part.open("wb") as stream: while remaining: chunk = self.rfile.read(min(8 * 1024 * 1024, remaining)) if not chunk: raise ApiError("Binary upload ended before Content-Length was reached.") stream.write(chunk) digest.update(chunk) remaining -= len(chunk) part.replace(target) (staging / f"{role}.json").write_text(json.dumps({"role": role, "name": safe_name, "size": size, "sha256": digest.hexdigest()}), encoding="utf-8") except Exception: part.unlink(missing_ok=True) target.unlink(missing_ok=True) raise return {"uploadId": upload_id, "role": role, "name": safe_name, "size": size, "sha256": digest.hexdigest()} def resolve_change_upload(self, payload: Any, role: str) -> tuple[str, Path]: return self.resolve_binary_upload(payload, role, "00-change-detection", "change-detection") def resolve_anomaly_upload(self, payload: Any, role: str) -> tuple[str, Path, str]: name, path = self.resolve_binary_upload(payload, role, "09-anomaly-detection", "anomaly-detection") manifest = load_json(path.parent / f"{role}.json") return name, path, str(manifest.get("sha256") or "") def resolve_photo_reconstruction_upload(self, payload: Any) -> tuple[str, Path, str]: name, path = self.resolve_binary_upload(payload, "photo", "05-3d-pointcloud", "photo reconstruction") manifest = load_json(path.parent / "photo.json") return name, path, str(manifest.get("sha256") or "") def resolve_pointcloud_upload(self, payload: Any) -> tuple[str, Path, str]: name, path = self.resolve_binary_upload(payload, "pointcloud", "05-3d-pointcloud", "point-cloud") manifest = load_json(path.parent / "pointcloud.json") return name, path, str(manifest.get("sha256") or "") def resolve_binary_upload(self, payload: Any, role: str, capability: str, label: str) -> tuple[str, Path]: if not isinstance(payload, dict) or not isinstance(payload.get("uploadId"), str): raise ApiError(f"{label} uploads must include a {role} uploadId.") upload_id = payload["uploadId"] if not SAFE_UPLOAD_ID.fullmatch(upload_id): raise ApiError(f"Invalid {label} upload id.") staging = self.root / "shared" / "data" / "raw" / capability / "uploads" / upload_id manifest = load_json(staging / f"{role}.json") name = str(manifest.get("name") or "") path = staging / f"{role}{Path(name).suffix.lower()}" if manifest.get("role") != role or not name or not path.is_file(): raise ApiError(f"The staged {role} upload is unavailable or incomplete.") return name, path def create_trajectory_run(self, payload: dict[str, Any]) -> dict[str, Any]: files = payload.get("files") if not isinstance(files, dict): raise ApiError("Trajectory request must contain a files object.") required = { "flight": {".xlsx"}, "route": {".kmz"}, "restricted": {".geojson"}, } decoded = {key: decode_upload(files.get(key), suffixes) for key, suffixes in required.items()} flyable = decode_upload(files["flyable"], {".gzip"}) if files.get("flyable") else None run_id = make_run_id("trajectory") raw_root = self.root / "shared" / "data" / "raw" / "15-trajectory-analysis" / "runs" / run_id paths = {"flight": raw_root / "tracks" / decoded["flight"][0], "route": raw_root / "routes" / decoded["route"][0], "restricted": raw_root / "areas" / decoded["restricted"][0]} for key, path in paths.items(): path.parent.mkdir(parents=True, exist_ok=True) path.write_bytes(decoded[key][1]) if flyable: flyable_path = raw_root / "areas" / flyable[0] flyable_path.write_bytes(flyable[1]) processed = self.root / "shared" / "data" / "processed" / "15-trajectory-analysis" / run_id output_parent = self.root / "shared" / "outputs" / "15-trajectory-analysis" / "runs" / run_id python = self.root / ".venvs" / "15-trajectory-analysis" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Trajectory virtual environment is unavailable. Run the capability setup first.") with RUN_LOCK: self.run_command([str(python), str(self.root / "capabilities" / "15-trajectory-analysis" / "prepare_real_flight.py"), "--raw-dir", str(raw_root), "--output", str(processed), "--case-id", run_id], 300) self.run_command([str(python), str(self.root / "capabilities" / "15-trajectory-analysis" / "run_trajectory_analysis.py"), "--input", str(processed / f"{run_id}.case.json"), "--output", str(output_parent)], 300) artifact = output_parent / run_id if not (artifact / "run_metadata.json").is_file(): raise ApiError("Trajectory script finished without the expected result metadata.") return next(item for item in trajectory_runs(self.root) if item["id"] == run_id) def create_detection_run(self, payload: dict[str, Any]) -> dict[str, Any]: uploads = payload.get("images") if not isinstance(uploads, list) or not uploads: raise ApiError("Object-detection request must include at least one image.") if len(uploads) > MAX_IMAGES_PER_RUN: raise ApiError(f"A local run accepts at most {MAX_IMAGES_PER_RUN} images.") decoded = [decode_upload(item, {".jpg", ".jpeg", ".png"}) for item in uploads] if len({name.casefold() for name, _ in decoded}) != len(decoded): raise ApiError("Uploaded image names must be unique within one run.") run_id = make_run_id("detection") raw_root = self.root / "shared" / "data" / "raw" / "01-object-detection" / "runs" / run_id raw_root.mkdir(parents=True, exist_ok=False) for name, content in decoded: (raw_root / name).write_bytes(content) output = self.root / "shared" / "outputs" / "01-object-detection" / "runs" / run_id python = self.root / ".venvs" / "01-object-detection" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Object-detection virtual environment is unavailable. Run the capability setup first.") with RUN_LOCK: self.run_command([str(python), str(self.root / "capabilities" / "01-object-detection" / "run_detection.py"), "--input", str(raw_root), "--output", str(output)], 1200) if not (output / "run_metadata.json").is_file(): raise ApiError("Detection script finished without the expected result metadata.") return next(item for item in detection_runs(self.root) if item["id"] == run_id) def create_change_run(self, payload: dict[str, Any]) -> dict[str, Any]: files = payload.get("files") uploads = payload.get("uploads") staged: dict[str, tuple[str, Path]] = {} if isinstance(uploads, dict): staged["before"] = self.resolve_change_upload(uploads.get("before"), "before") staged["after"] = self.resolve_change_upload(uploads.get("after"), "after") elif isinstance(files, dict): decoded_before = decode_upload(files.get("before"), {".jpg", ".jpeg", ".png", ".tif", ".tiff"}) decoded_after = decode_upload(files.get("after"), {".jpg", ".jpeg", ".png", ".tif", ".tiff"}) else: raise ApiError("Change-detection request must contain before and after files or uploads.") threshold_value = payload.get("threshold", CHANGE_THRESHOLD_DEFAULT) if isinstance(threshold_value, bool) or not isinstance(threshold_value, (int, float)): raise ApiError("Change-detection threshold must be a number between 0.01 and 0.99.") threshold = float(threshold_value) if not CHANGE_THRESHOLD_MIN <= threshold <= CHANGE_THRESHOLD_MAX: raise ApiError("Change-detection threshold must be between 0.01 and 0.99.") processing_mode = payload.get("processingMode", CHANGE_PROCESSING_MODE_DEFAULT) if not isinstance(processing_mode, str) or processing_mode not in CHANGE_PROCESSING_MODES: raise ApiError("Change-detection processing mode must be auto, image, or geotiff.") max_dimension_value = payload.get("maxDimension", CHANGE_MAX_DIMENSION_AUTO) if isinstance(max_dimension_value, bool) or not isinstance(max_dimension_value, int): raise ApiError("Change-detection resolution must be an integer: 0 or between 512 and 4096.") max_dimension = int(max_dimension_value) if max_dimension != CHANGE_MAX_DIMENSION_AUTO and not CHANGE_MAX_DIMENSION_MIN <= max_dimension <= CHANGE_MAX_DIMENSION_MAX: raise ApiError("Change-detection resolution must be 0 or between 512 and 4096.") if staged: before_name, after_name = staged["before"][0], staged["after"][0] else: before_name, after_name = decoded_before[0], decoded_after[0] run_id = make_run_id("change") raw_root = self.root / "shared" / "data" / "raw" / "00-change-detection" / "runs" / run_id before_path = raw_root / "before" / before_name after_path = raw_root / "after" / after_name before_path.parent.mkdir(parents=True, exist_ok=False) after_path.parent.mkdir(parents=True, exist_ok=False) if staged: shutil.copyfile(staged["before"][1], before_path) shutil.copyfile(staged["after"][1], after_path) else: before_path.write_bytes(decoded_before[1]) after_path.write_bytes(decoded_after[1]) processed_root = self.root / "shared" / "data" / "processed" / "00-change-detection" / run_id output = self.root / "shared" / "outputs" / "00-change-detection" / "runs" / run_id python = self.root / ".venvs" / "00-change-detection" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Change-detection virtual environment is unavailable. Run the capability setup first.") with RUN_LOCK: self.run_command( [ str(python), str(self.root / "capabilities" / "00-change-detection" / "run_change_detection.py"), "--before", str(before_path), "--after", str(after_path), "--threshold", f"{threshold:.4f}", "--max-dimension", str(max_dimension), "--processing-mode", processing_mode, "--processed-output", str(processed_root), "--output", str(output), ], 1200, ) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("Change-detection script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["raw_input_dir"] = relative_path(self.root, raw_root) metadata["processed_input_dir"] = relative_path(self.root, processed_root) metadata["raw_before"] = relative_path(self.root, before_path) metadata["raw_after"] = relative_path(self.root, after_path) metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") return next(item for item in change_runs(self.root) if item["id"] == run_id) def _scan_parameters(self, payload: dict[str, Any]) -> tuple[list[float], list[int]]: raw_thresholds = payload.get("thresholds", SCAN_DEFAULT_THRESHOLDS) raw_areas = payload.get("minimumAreas", SCAN_DEFAULT_AREAS) if not isinstance(raw_thresholds, list) or not raw_thresholds or len(raw_thresholds) > SCAN_MAX_THRESHOLDS: raise ApiError(f"Parameter scan thresholds must contain 1-{SCAN_MAX_THRESHOLDS} values.") if not isinstance(raw_areas, list) or not raw_areas or len(raw_areas) > SCAN_MAX_AREAS: raise ApiError(f"Parameter scan minimum areas must contain 1-{SCAN_MAX_AREAS} values.") thresholds: list[float] = [] for value in raw_thresholds: if isinstance(value, bool) or not isinstance(value, (int, float)): raise ApiError("Each scan threshold must be a number between 0.01 and 0.99.") number = round(float(value), 4) if not CHANGE_THRESHOLD_MIN <= number <= CHANGE_THRESHOLD_MAX: raise ApiError("Each scan threshold must be between 0.01 and 0.99.") if number not in thresholds: thresholds.append(number) areas: list[int] = [] for value in raw_areas: if isinstance(value, bool) or not isinstance(value, int) or not 16 <= value <= 200000: raise ApiError("Each scan minimum area must be an integer between 16 and 200000 pixels.") if value not in areas: areas.append(value) if len(thresholds) * len(areas) > SCAN_MAX_COMBINATIONS: raise ApiError(f"A parameter scan accepts at most {SCAN_MAX_COMBINATIONS} combinations.") return thresholds, areas def _scan_root(self, scan_id: str) -> Path: if not SAFE_SCAN_ID.fullmatch(scan_id): raise ApiError("Invalid parameter-scan id.") scan_root = self.root / "shared" / "outputs" / "00-change-detection" / "parameter-scans" / scan_id if not scan_root.is_dir() or not (scan_root / "scan_summary.json").is_file(): raise ApiError("The parameter-scan result is unavailable.") return scan_root def promote_change_scan(self, scan_id: str, payload: dict[str, Any]) -> dict[str, Any]: scan_root = self._scan_root(scan_id) result_id = payload.get("resultId") if not isinstance(result_id, str) or not SAFE_SCAN_RESULT_ID.fullmatch(result_id): raise ApiError("A valid parameter-scan resultId is required.") result_dir = scan_root / result_id summary = load_json(result_dir / "summary.json") full_result = load_json(result_dir / "full_result.json") inference_run_id = str(load_json(scan_root / "scan_summary.json").get("source_run") or "") inference_output = self.root / "shared" / "outputs" / "00-change-detection" / "runs" / inference_run_id inference_metadata = load_json(inference_output / "run_metadata.json") if not result_dir.is_dir() or not (result_dir / "changes.geojson").is_file() or not inference_metadata: raise ApiError("The selected scan result is incomplete and cannot be promoted.") run_id = make_run_id("change") output = self.root / "shared" / "outputs" / "00-change-detection" / "runs" / run_id output.mkdir(parents=True, exist_ok=False) for source_name, destination_name in ( ("change_probability.tif", "change_probability.tif"), ("change_mask.tif", "change_mask.tif"), ("generic_difference_mask.tif", "generic_difference_mask.tif"), ("change_model_overlay.jpg", "change_model_overlay.jpg"), ("before_processed_preview.jpg", "before_processed_preview.jpg"), ("after_registered_preview.jpg", "after_registered_preview.jpg"), ("changes.geojson", "changes.geojson"), ("changes_rectangles.geojson", "changes_rectangles.geojson"), ("changes_rectangles_wgs84.geojson", "changes_rectangles_wgs84.geojson"), ): source = result_dir / source_name if source_name.startswith("change_mask") or source_name.startswith("changes") else inference_output / source_name if source.is_file(): shutil.copyfile(source, output / destination_name) overlay_source = result_dir / "overlay_preview.jpg" if (inference_output / "change_overlay.jpg").is_file() and float(summary.get("threshold", 0.5)) == float(inference_metadata.get("thresholds", {}).get("change_probability", 0.5)): overlay_source = inference_output / "change_overlay.jpg" if not overlay_source.is_file(): raise ApiError("The selected scan preview is unavailable.") shutil.copyfile(overlay_source, output / "change_overlay.jpg") metadata = dict(inference_metadata) scan_raw_root = self.root / "shared" / "data" / "raw" / "00-change-detection" / "runs" / scan_id before_raw = scan_raw_root / "before" / str(inference_metadata.get("input_files", ["before.tif", "after.tif"])[0]) after_raw = scan_raw_root / "after" / str(inference_metadata.get("input_files", ["before.tif", "after.tif"])[1]) rectangle_count = int(full_result.get("rectangle_vector_feature_count") or 0) if rectangle_count == 0 and (result_dir / "changes_rectangles.geojson").is_file(): rectangle_count = len(load_json(result_dir / "changes_rectangles.geojson").get("features", [])) metadata.update( { "kind": "formal-change-run", "created_at": datetime.now(UTC).isoformat(), "thresholds": {"change_probability": float(summary.get("threshold", 0.5)), "minimum_component_pixels": int(summary.get("minimum_area_pixels", 16))}, "raw_changed_pixels": int(summary.get("raw_changed_pixels", 0)), "changed_pixels": int(summary.get("changed_pixels", 0)), "changed_pixel_ratio": float(summary.get("changed_pixel_ratio", 0)), "vector_feature_count": int(full_result.get("full_vector_feature_count", summary.get("vector_feature_count", 0))), "rectangle_feature_count": rectangle_count, "promoted_from_scan": scan_id, "promoted_result": result_id, "raw_input_dir": relative_path(self.root, scan_raw_root), "raw_before": relative_path(self.root, before_raw), "raw_after": relative_path(self.root, after_raw), "processed_input_dir": relative_path(self.root, self.root / "shared" / "data" / "processed" / "00-change-detection" / scan_id), "generic_difference": inference_metadata.get("generic_difference"), "artifacts": { "probability_raster": "change_probability.tif", "raw_mask_raster": "change_mask.tif", "mask_raster": "change_mask.tif", "generic_difference_mask": "generic_difference_mask.tif" if (output / "generic_difference_mask.tif").is_file() else None, "overlay": "change_overlay.jpg", "model_overlay": "change_model_overlay.jpg" if (output / "change_model_overlay.jpg").is_file() else None, "before_processed_preview": "before_processed_preview.jpg" if (output / "before_processed_preview.jpg").is_file() else None, "after_registered_preview": "after_registered_preview.jpg" if (output / "after_registered_preview.jpg").is_file() else None, "vector": "changes.geojson", "rectangle_vector": "changes_rectangles.geojson", "rectangle_vector_wgs84": "changes_rectangles_wgs84.geojson" if (output / "changes_rectangles_wgs84.geojson").is_file() else None, "features": "full_result.json", }, } ) (output / "full_result.json").write_text(json.dumps(full_result, ensure_ascii=False, indent=2), encoding="utf-8") (output / "run_metadata.json").write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") return next(item for item in change_runs(self.root) if item["id"] == run_id) def create_change_scan(self, payload: dict[str, Any]) -> dict[str, Any]: uploads = payload.get("uploads") if not isinstance(uploads, dict): raise ApiError("Parameter scan must contain staged before and after uploads.") staged = { "before": self.resolve_change_upload(uploads.get("before"), "before"), "after": self.resolve_change_upload(uploads.get("after"), "after"), } thresholds, areas = self._scan_parameters(payload) processing_mode = payload.get("processingMode", CHANGE_PROCESSING_MODE_DEFAULT) if not isinstance(processing_mode, str) or processing_mode not in CHANGE_PROCESSING_MODES: raise ApiError("Change-detection processing mode must be auto, image, or geotiff.") max_dimension_value = payload.get("maxDimension", CHANGE_MAX_DIMENSION_AUTO) if isinstance(max_dimension_value, bool) or not isinstance(max_dimension_value, int): raise ApiError("Change-detection resolution must be an integer: 0 or between 512 and 4096.") max_dimension = int(max_dimension_value) if max_dimension != CHANGE_MAX_DIMENSION_AUTO and not CHANGE_MAX_DIMENSION_MIN <= max_dimension <= CHANGE_MAX_DIMENSION_MAX: raise ApiError("Change-detection resolution must be 0 or between 512 and 4096.") run_id = make_run_id("scan") raw_root = self.root / "shared" / "data" / "raw" / "00-change-detection" / "runs" / run_id before_path = raw_root / "before" / staged["before"][0] after_path = raw_root / "after" / staged["after"][0] before_path.parent.mkdir(parents=True, exist_ok=False) after_path.parent.mkdir(parents=True, exist_ok=False) shutil.copyfile(staged["before"][1], before_path) shutil.copyfile(staged["after"][1], after_path) processed_root = self.root / "shared" / "data" / "processed" / "00-change-detection" / run_id inference_output = self.root / "shared" / "outputs" / "00-change-detection" / "runs" / run_id scan_output = self.root / "shared" / "outputs" / "00-change-detection" / "parameter-scans" / run_id with SCAN_JOBS_LOCK: SCAN_JOBS[run_id] = { "id": run_id, "status": "queued", "createdAt": datetime.now(UTC).isoformat(), "thresholds": thresholds, "minimumAreas": areas, "processingMode": processing_mode, "maxDimension": max_dimension, } thread = threading.Thread( target=self._run_change_scan, args=(run_id, before_path, after_path, processed_root, inference_output, scan_output, thresholds, areas, processing_mode, max_dimension), daemon=True, name=f"change-scan-{run_id}", ) thread.start() return dict(SCAN_JOBS[run_id]) def _update_scan_job(self, job_id: str, **values: Any) -> None: with SCAN_JOBS_LOCK: if job_id in SCAN_JOBS: SCAN_JOBS[job_id].update(values) def _run_change_scan( self, run_id: str, before_path: Path, after_path: Path, processed_root: Path, inference_output: Path, scan_output: Path, thresholds: list[float], areas: list[int], processing_mode: str, max_dimension: int, ) -> None: python = self.root / ".venvs" / "00-change-detection" / "Scripts" / "python.exe" try: if not python.is_file(): raise ApiError("Change-detection virtual environment is unavailable. Run the capability setup first.") self._update_scan_job(run_id, status="running", phase="inference") with RUN_LOCK: self.run_command( [ str(python), str(self.root / "capabilities" / "00-change-detection" / "run_change_detection.py"), "--before", str(before_path), "--after", str(after_path), "--threshold", "0.5000", "--max-dimension", str(max_dimension), "--processing-mode", processing_mode, "--processed-output", str(processed_root), "--output", str(inference_output), ], SCAN_JOB_TIMEOUT, ) inference_metadata_path = inference_output / "run_metadata.json" inference_metadata = load_json(inference_metadata_path) inference_metadata["kind"] = "parameter-scan-inference" inference_metadata["scan_job_id"] = run_id inference_metadata_path.write_text(json.dumps(inference_metadata, ensure_ascii=False, indent=2), encoding="utf-8") self._update_scan_job(run_id, phase="parameter-scan") command = [ str(python), str(self.root / "capabilities" / "00-change-detection" / "scan_change_detection_parameters.py"), "--run-dir", str(inference_output), "--output", str(scan_output), ] for threshold in thresholds: command.extend(["--threshold", f"{threshold:.4f}"]) for area in areas: command.extend(["--minimum-area", str(area)]) self.run_command(command, SCAN_JOB_TIMEOUT) self._update_scan_job(run_id, phase="vectorization") vector_command = [ str(python), str(self.root / "capabilities" / "00-change-detection" / "materialize_parameter_scan_candidates.py"), "--scan-dir", str(scan_output), ] for threshold in thresholds: for area in areas: vector_command.extend(["--candidate", f"threshold-{threshold:.2f}_area-{area}"]) self.run_command(vector_command, SCAN_JOB_TIMEOUT) metadata = { "capability": "00-change-detection", "kind": "parameter-scan", "source_run": run_id, "created_at": datetime.now(UTC).isoformat(), "thresholds": thresholds, "minimum_areas": areas, "processing_mode": processing_mode, "max_dimension": max_dimension, "raw_input_dir": relative_path(self.root, before_path.parent.parent), "processed_input_dir": relative_path(self.root, processed_root), } scan_output.mkdir(parents=True, exist_ok=True) (scan_output / "scan_metadata.json").write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") self._update_scan_job(run_id, status="completed", phase="done", scanId=run_id) except Exception as exc: # background errors are returned through polling scan_output.mkdir(parents=True, exist_ok=True) (scan_output / "scan_failed.json").write_text(json.dumps({"job_id": run_id, "error": str(exc)[:600]}, ensure_ascii=False, indent=2), encoding="utf-8") self._update_scan_job(run_id, status="failed", phase="error", error=str(exc)[:600]) def create_semantic_run(self, payload: dict[str, Any]) -> dict[str, Any]: task_id = str(payload.get("taskId") or "color_baseline") task = next((item for item in semantic_tasks(self.root) if item["id"] == task_id), None) if task is None: raise ApiError(f"Unknown semantic-mapping task: {task_id}.") if task.get("selectable") is not True: raise ApiError(f"Semantic-mapping task is not runnable yet: {task_id}.") uploads = payload.get("images") if not isinstance(uploads, list) or not uploads: raise ApiError("Semantic-mapping request must include at least one image.") if len(uploads) > MAX_SEGMENTATION_IMAGES_PER_RUN: raise ApiError(f"A semantic-mapping run accepts at most {MAX_SEGMENTATION_IMAGES_PER_RUN} images.") decoded = [decode_upload(item, {".jpg", ".jpeg", ".png", ".tif", ".tiff"}) for item in uploads] if len({name.casefold() for name, _ in decoded}) != len(decoded): raise ApiError("Uploaded image names must be unique within one run.") run_id = make_run_id("semantic") raw_root = self.root / "shared" / "data" / "raw" / "02-semantic-mapping" / "runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "02-semantic-mapping" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) for name, content in decoded: (raw_root / name).write_bytes(content) (processed_root / name).write_bytes(content) output = self.root / "shared" / "outputs" / "02-semantic-mapping" / "runs" / run_id python = self.root / ".venvs" / "02-semantic-mapping" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Semantic-mapping virtual environment is unavailable. Run the capability setup first.") with RUN_LOCK: self.run_command([str(python), str(self.root / "capabilities" / "02-semantic-mapping" / "run_semantic_segmentation.py"), "--input", str(processed_root), "--output", str(output)], 900) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("Semantic-mapping script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["task_id"] = task_id metadata["task_name"] = str(task.get("name") or task_id) metadata["input_dir"] = relative_path(self.root, processed_root) metadata["raw_input_dir"] = relative_path(self.root, raw_root) metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") return next(item for item in semantic_runs(self.root) if item["id"] == run_id) def create_anomaly_run(self, payload: dict[str, Any]) -> dict[str, Any]: tile_size, stride, threshold_quantile, random_state = validate_anomaly_parameters(payload) uploads = payload.get("uploads") if not isinstance(uploads, dict): raise ApiError("Anomaly-detection request must contain reference and input uploads.") reference_values = uploads.get("reference") input_values = uploads.get("input") if not isinstance(reference_values, list) or not reference_values: raise ApiError("Select at least one normal reference image.") if not isinstance(input_values, list) or not input_values: raise ApiError("Select at least one image to inspect.") if len(reference_values) > MAX_ANOMALY_IMAGES_PER_ROLE or len(input_values) > MAX_ANOMALY_IMAGES_PER_ROLE: raise ApiError(f"An anomaly-detection run accepts at most {MAX_ANOMALY_IMAGES_PER_ROLE} images in each group.") references = [self.resolve_anomaly_upload(value, "reference") for value in reference_values] inputs = [self.resolve_anomaly_upload(value, "input") for value in input_values] if len({name.casefold() for name, _, _ in references}) != len(references): raise ApiError("Normal reference image names must be unique within one run.") if len({name.casefold() for name, _, _ in inputs}) != len(inputs): raise ApiError("Input image names must be unique within one run.") python = self.root / ".venvs" / "09-anomaly-detection" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Anomaly-detection virtual environment is unavailable. Run the capability setup first.") run_id = make_run_id("anomaly") job_id = uuid4().hex raw_root = self.root / "shared" / "data" / "raw" / "09-anomaly-detection" / "runs" / run_id raw_reference = raw_root / "reference" raw_input = raw_root / "input" processed_root = self.root / "shared" / "data" / "processed" / "09-anomaly-detection" / run_id processed_reference = processed_root / "reference" processed_input = processed_root / "input" output = self.root / "shared" / "outputs" / "09-anomaly-detection" / "runs" / run_id for directory in (raw_reference, raw_input, processed_reference, processed_input): directory.mkdir(parents=True, exist_ok=False) for group, raw_dir, processed_dir in ((references, raw_reference, processed_reference), (inputs, raw_input, processed_input)): for name, staged_path, expected_sha256 in group: raw_path = raw_dir / name processed_path = processed_dir / name shutil.copyfile(staged_path, raw_path) if expected_sha256 and file_sha256(raw_path) != expected_sha256: raise ApiError(f"Uploaded file checksum changed while staging: {name}.") shutil.copyfile(raw_path, processed_path) for _, staged_path, _ in references + inputs: shutil.rmtree(staged_path.parent) created_at = datetime.now(UTC).isoformat() job = {"id": job_id, "runId": run_id, "status": "queued", "createdAt": created_at} with ANOMALY_JOB_LOCK: ANOMALY_JOBS[job_id] = job thread = threading.Thread( target=execute_anomaly_job, args=(self.root, job_id, run_id, raw_reference, raw_input, processed_reference, processed_input, output, tile_size, stride, threshold_quantile, random_state), daemon=True, name=f"anomaly-{run_id}", ) thread.start() return dict(job) def create_measurement_run(self, payload: dict[str, Any]) -> dict[str, Any]: uploads = payload.get("rasters") if not isinstance(uploads, list) or not uploads: raise ApiError("Spatial-measurement request must include at least one label raster.") if len(uploads) > MAX_MEASUREMENT_RASTERS_PER_RUN: raise ApiError(f"A spatial-measurement run accepts at most {MAX_MEASUREMENT_RASTERS_PER_RUN} rasters.") decoded = [decode_upload(item, {".png", ".tif", ".tiff"}) for item in uploads] if len({name.casefold() for name, _ in decoded}) != len(decoded): raise ApiError("Uploaded raster names must be unique within one run.") run_id = make_run_id("measurement") raw_root = self.root / "shared" / "data" / "raw" / "04-spatial-measurement" / "runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "04-spatial-measurement" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) for name, content in decoded: (raw_root / name).write_bytes(content) (processed_root / name).write_bytes(content) output = self.root / "shared" / "outputs" / "04-spatial-measurement" / "runs" / run_id python = self.root / ".venvs" / "04-spatial-measurement" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Spatial-measurement virtual environment is unavailable. Run the capability setup first.") with RUN_LOCK: self.run_command([str(python), str(self.root / "capabilities" / "04-spatial-measurement" / "run_spatial_measurement.py"), "--input", str(processed_root), "--output", str(output)], 900) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("Spatial-measurement script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["input_dir"] = relative_path(self.root, processed_root) metadata["raw_input_dir"] = relative_path(self.root, raw_root) metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") return next(item for item in measurement_runs(self.root) if item["id"] == run_id) def create_pointcloud_run(self, payload: dict[str, Any]) -> dict[str, Any]: uploads = payload.get("pointClouds") source_dense_run_id = payload.get("sourceDenseRunId") if uploads is None and not isinstance(source_dense_run_id, str): raise ApiError("3D point-cloud request must include at least one PLY, PCD, XYZ, LAS, or LAZ file.") if uploads is not None and (not isinstance(uploads, list) or not uploads): raise ApiError("3D point-cloud request must include at least one PLY, PCD, XYZ, LAS, or LAZ file.") if isinstance(uploads, list) and len(uploads) > MAX_POINTCLOUDS_PER_RUN: raise ApiError(f"A 3D point-cloud run accepts at most {MAX_POINTCLOUDS_PER_RUN} files.") suffixes = {".ply", ".pcd", ".xyz", ".xyzn", ".xyzrgb", ".las", ".laz"} staged_upload_dirs: list[Path] = [] if isinstance(source_dense_run_id, str): if SAFE_FILE_NAME.search(source_dense_run_id) or len(source_dense_run_id) > 120: raise ApiError("Invalid dense point-cloud source run id.") source_case = next((item for item in pointcloud_runs(self.root) if item["id"] == source_dense_run_id), None) if not source_case: raise ApiError("The selected dense point-cloud source is unavailable.") source_artifact = self.root / str(source_case["artifactRoot"]) source_metadata = load_json(source_artifact / "run_metadata.json") dense = source_metadata.get("dense_photo_reconstruction") source_file = dense.get("dense_point_cloud_file") if isinstance(dense, dict) else None source_path = source_artifact / str(source_file or "") if not isinstance(source_file, str) or source_path.suffix.lower() != ".ply" or not source_path.is_file(): raise ApiError("The selected run has no available dense PLY output.") decoded = [(f"{source_dense_run_id}-dense.ply", source_path, file_sha256(source_path))] elif all(isinstance(item, dict) and isinstance(item.get("content"), str) for item in uploads): decoded = [(name, content, "") for name, content in (decode_upload(item, suffixes) for item in uploads)] else: decoded = [self.resolve_pointcloud_upload(item) for item in uploads] staged_upload_dirs = [path.parent for _, path, _ in decoded] if len({name.casefold() for name, _, _ in decoded}) != len(decoded): raise ApiError("Uploaded point-cloud names must be unique within one run.") run_id = make_run_id("pointcloud") raw_root = self.root / "shared" / "data" / "raw" / "05-3d-pointcloud" / "runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "05-3d-pointcloud" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) source_sha256: dict[str, str] = {} source_bytes: dict[str, int] = {} for name, staged_or_content, expected_sha256 in decoded: raw_path = raw_root / name if isinstance(staged_or_content, bytes): raw_path.write_bytes(staged_or_content) else: shutil.copyfile(staged_or_content, raw_path) if expected_sha256 and file_sha256(raw_path) != expected_sha256: raise ApiError(f"Uploaded point-cloud checksum changed while staging: {name}.") source_sha256[name] = file_sha256(raw_path) source_bytes[name] = raw_path.stat().st_size shutil.copyfile(raw_path, processed_root / name) output = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "runs" / run_id python = self.root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("3D point-cloud virtual environment is unavailable. Run the capability setup first.") command = [str(python), str(self.root / "capabilities" / "05-3d-pointcloud" / "run_pointcloud_understanding.py"), "--input", str(processed_root), "--output", str(output), "--ground-up-axis", "z"] with RUN_LOCK: self.run_command(command, 900) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("3D point-cloud script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["input_dir"] = relative_path(self.root, processed_root) metadata["raw_input_dir"] = relative_path(self.root, raw_root) metadata["source_sha256"] = source_sha256 metadata["source_bytes"] = source_bytes metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") for staging in staged_upload_dirs: shutil.rmtree(staging) return next(item for item in pointcloud_runs(self.root) if item["id"] == run_id) def create_pointcloud_annotation(self, payload: dict[str, Any]) -> dict[str, Any]: source_id = payload.get("sourceId") labels = payload.get("labels") if not isinstance(source_id, str) or not isinstance(labels, list): raise ApiError("Annotation request must include a sourceId and labels array.") source = next((item for item in pointcloud_annotation_sources(self.root) if item["id"] == source_id), None) if not source: raise ApiError("The selected generated annotation source is unavailable.") if len(labels) > MAX_ANNOTATION_LABELS: raise ApiError(f"An annotation revision accepts at most {MAX_ANNOTATION_LABELS} labelled points.") compact: dict[int, int] = {} for item in labels: if not isinstance(item, list) or len(item) != 2 or not all(isinstance(value, int) for value in item): raise ApiError("Each annotation label must be [pointIndex, classCode].") index, code = item if index < 0 or index >= int(source["pointCount"]) or code not in POINTCLOUD_CLASS_CODES: raise ApiError("Annotation contains an out-of-range point index or unsupported class code.") compact[index] = code if not compact: raise ApiError("Save at least one user-confirmed point label.") annotation_id = make_run_id("annotation") location = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "annotations" / annotation_id location.mkdir(parents=True, exist_ok=False) source_path = self.root / str(source["artifactRoot"]) / str(source["file"]) class_counts = {str(code): sum(value == code for value in compact.values()) for code in sorted(POINTCLOUD_CLASS_CODES)} document = { "schema_version": 1, "id": annotation_id, "created_at": datetime.now(UTC).isoformat(), "source_id": source_id, "source_path": str(source_path.resolve()), "source_sha256": source["sha256"], "source_run_id": source["runId"], "point_count": int(source["pointCount"]), "labels": [[index, code] for index, code in sorted(compact.items())], "class_counts": class_counts, "class_schema": {str(code): {"code": code} for code in sorted(POINTCLOUD_CLASS_CODES)}, "provenance": "human_confirmed_point_labels_only", } path = location / "annotation.json" path.write_text(json.dumps(document, ensure_ascii=False, indent=2), encoding="utf-8") return {"id": annotation_id, "sourceId": source_id, "path": relative_path(self.root, path), "labelCount": len(compact), "classCounts": class_counts, "createdAt": document["created_at"]} def create_pointcloud_training_run(self, payload: dict[str, Any]) -> dict[str, Any]: annotation_id = payload.get("annotationId") device = payload.get("device", "auto") if not isinstance(annotation_id, str) or SAFE_FILE_NAME.search(annotation_id) or len(annotation_id) > 120: raise ApiError("Invalid annotation id.") if device not in {"auto", "cpu", "cuda"}: raise ApiError("Training device must be auto, cpu, or cuda.") annotation = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "annotations" / annotation_id / "annotation.json" record = load_json(annotation) if record.get("schema_version") != 1: raise ApiError("The selected annotation revision is unavailable.") python = self.root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("3D point-cloud virtual environment is unavailable. Run the capability setup first.") job_id = uuid4().hex output = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "training-runs" / make_run_id("semantic-model") job = {"id": job_id, "annotationId": annotation_id, "status": "queued", "stage": "queued", "device": device, "createdAt": datetime.now(UTC).isoformat()} with POINTCLOUD_TRAINING_JOBS_LOCK: POINTCLOUD_TRAINING_JOBS[job_id] = job thread = threading.Thread(target=execute_pointcloud_training_job, args=(self.root, job_id, annotation, output, device), daemon=True, name=f"pointcloud-training-{job_id[:8]}") thread.start() return dict(job) def create_pointcloud_model_inference_run(self, payload: dict[str, Any]) -> dict[str, Any]: model_id = payload.get("modelId") upload = payload.get("pointCloud") if not isinstance(model_id, str) or SAFE_FILE_NAME.search(model_id) or len(model_id) > 120: raise ApiError("Invalid trained model id.") model_record = next((item for item in pointcloud_semantic_models(self.root) if item["id"] == model_id), None) if not model_record: raise ApiError("The selected trained model is unavailable or incomplete.") name, staged_path, expected_sha256 = self.resolve_pointcloud_upload(upload) suffixes = {".ply", ".pcd", ".xyz", ".xyzn", ".xyzrgb", ".las", ".laz"} if Path(name).suffix.lower() not in suffixes: raise ApiError("Model inference requires a PLY, PCD, XYZ, LAS, or LAZ point cloud.") python = self.root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("3D point-cloud virtual environment is unavailable. Run the capability setup first.") run_id = make_run_id("semantic-inference") raw_root = self.root / "shared" / "data" / "raw" / "05-3d-pointcloud" / "model-inference-runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "05-3d-pointcloud" / "model-inference-runs" / run_id output = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "model-inference-runs" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) raw_path = raw_root / name shutil.copyfile(staged_path, raw_path) actual_sha256 = file_sha256(raw_path) if expected_sha256 and actual_sha256 != expected_sha256: raise ApiError("Uploaded point-cloud checksum changed while staging.") processed_path = processed_root / name shutil.copyfile(raw_path, processed_path) # Only remove the staging copy after its immutable raw copy was verified. shutil.rmtree(staged_path.parent) model_path = self.root / str(model_record["model"]) training_root = (self.root / "shared" / "outputs" / "05-3d-pointcloud" / "training-runs").resolve() try: model_path.resolve().relative_to(training_root) except ValueError as exc: raise ApiError("Selected model is outside the allowed training output directory.") from exc job_id = uuid4().hex job = {"id": job_id, "runId": run_id, "modelId": model_id, "inputName": name, "status": "queued", "stage": "queued", "device": "cpu", "createdAt": datetime.now(UTC).isoformat(), "sourceSha256": actual_sha256, "rawInput": relative_path(self.root, raw_path), "processedInput": relative_path(self.root, processed_path)} with POINTCLOUD_INFERENCE_JOBS_LOCK: POINTCLOUD_INFERENCE_JOBS[job_id] = job thread = threading.Thread(target=execute_pointcloud_inference_job, args=(self.root, job_id, model_path, processed_path, output), daemon=True, name=f"pointcloud-inference-{job_id[:8]}") thread.start() return dict(job) def create_photo_reconstruction_run(self, payload: dict[str, Any]) -> dict[str, Any]: uploads = payload.get("photos") if not isinstance(uploads, list) or len(uploads) < 3: raise ApiError("Photo reconstruction needs at least three JPG/JPEG photos from one coherent flight or camera sequence.") if len(uploads) > MAX_PHOTO_RECONSTRUCTION_IMAGES_PER_RUN: raise ApiError(f"A photo reconstruction run accepts at most {MAX_PHOTO_RECONSTRUCTION_IMAGES_PER_RUN} photos.") use_position_priors = payload.get("usePositionPriors", False) if not isinstance(use_position_priors, bool): raise ApiError("Photo reconstruction usePositionPriors must be true or false.") photos = [self.resolve_photo_reconstruction_upload(value) for value in uploads] if len({name.casefold() for name, _, _ in photos}) != len(photos): raise ApiError("Uploaded photo names must be unique within one run.") python = self.root / ".venvs" / "05-3d-pointcloud" / "Scripts" / "python.exe" openmvs = self.root / "shared" / "tools" / "openmvs-2.4.0" / "vc17" / "x64" / "Release" if not python.is_file() or not (openmvs / "DensifyPointCloud.exe").is_file(): raise ApiError("Photo-reconstruction CPU environment is unavailable. Run the capability setup first.") run_id = make_run_id("photo-reconstruction") job_id = uuid4().hex raw_root = self.root / "shared" / "data" / "raw" / "05-3d-pointcloud" / "runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "05-3d-pointcloud" / run_id sparse_output = processed_root / "sparse_sfm" output = self.root / "shared" / "outputs" / "05-3d-pointcloud" / "runs" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) source_sha256: dict[str, str] = {} source_bytes: dict[str, int] = {} for name, staged_path, expected_sha256 in photos: raw_path = raw_root / name shutil.copyfile(staged_path, raw_path) actual_sha256 = file_sha256(raw_path) if expected_sha256 and actual_sha256 != expected_sha256: raise ApiError(f"Uploaded file checksum changed while staging: {name}.") shutil.copyfile(raw_path, processed_root / name) source_sha256[name] = actual_sha256 source_bytes[name] = raw_path.stat().st_size for _, staged_path, _ in photos: shutil.rmtree(staged_path.parent) job = {"id": job_id, "runId": run_id, "status": "queued", "stage": "queued", "createdAt": datetime.now(UTC).isoformat(), "inputImages": len(photos), "usePositionPriors": use_position_priors} with PHOTO_RECONSTRUCTION_JOBS_LOCK: PHOTO_RECONSTRUCTION_JOBS[job_id] = job thread = threading.Thread( target=execute_photo_reconstruction_job, args=(self.root, job_id, run_id, raw_root, processed_root, sparse_output, output, source_sha256, source_bytes, use_position_priors), daemon=True, name=f"photo-reconstruction-{run_id}", ) thread.start() return dict(job) def create_risk_rule_run(self, payload: dict[str, Any]) -> dict[str, Any]: files = payload.get("files") if not isinstance(files, dict) or set(files) != RISK_RULE_REQUIRED_FILES: raise ApiError("Risk-rule request must contain observations, zones and rules files.") observations_name, observations_bytes = decode_upload(files["observations"], {".geojson"}) zones_name, zones_bytes = decode_upload(files["zones"], {".geojson"}) rules_name, rules_bytes = decode_upload(files["rules"], {".json"}) if len({observations_name.casefold(), zones_name.casefold(), rules_name.casefold()}) != 3: raise ApiError("Risk-rule uploaded file names must be unique.") run_id = make_run_id("risk") raw_root = self.root / "shared" / "data" / "raw" / "07-risk-rule-engine" / "runs" / run_id processed_root = self.root / "shared" / "data" / "processed" / "07-risk-rule-engine" / run_id raw_root.mkdir(parents=True, exist_ok=False) processed_root.mkdir(parents=True, exist_ok=False) staged = ((observations_name, observations_bytes), (zones_name, zones_bytes), (rules_name, rules_bytes)) for name, content in staged: (raw_root / name).write_bytes(content) (processed_root / name).write_bytes(content) output = self.root / "shared" / "outputs" / "07-risk-rule-engine" / "runs" / run_id python = self.root / ".venvs" / "07-risk-rule-engine" / "Scripts" / "python.exe" if not python.is_file(): raise ApiError("Risk-rule virtual environment is unavailable. Run the capability setup first.") command = [ str(python), str(self.root / "capabilities" / "07-risk-rule-engine" / "run_risk_rule_engine.py"), "--observations", str(processed_root / observations_name), "--zones", str(processed_root / zones_name), "--rules", str(processed_root / rules_name), "--output", str(output), ] with RUN_LOCK: self.run_command(command, 600) metadata_path = output / "run_metadata.json" if not metadata_path.is_file(): raise ApiError("Risk-rule script finished without the expected result metadata.") metadata = load_json(metadata_path) metadata["input_dir"] = relative_path(self.root, processed_root) metadata["raw_input_dir"] = relative_path(self.root, raw_root) metadata["source_bytes"] = {name: len(content) for name, content in staged} metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") return next(item for item in risk_rule_runs(self.root) if item["id"] == run_id) def send_json(self, status: HTTPStatus, payload: dict[str, Any]) -> None: body = json.dumps(payload, ensure_ascii=False).encode("utf-8") self.send_response(status) self.send_header("Content-Type", "application/json; charset=utf-8") self.send_header("Content-Length", str(len(body))) self.end_headers() self.wfile.write(body) def translate_path(self, path: str) -> str: """Expose only the static UI and artifacts required by the local console.""" decoded_path = unquote(urlsplit(path).path).lstrip("/") requested = PurePosixPath(decoded_path) is_allowed = any(decoded_path == prefix or decoded_path.startswith(f"{prefix}/") for prefix in ALLOWED_PATH_PREFIXES) if ".." in requested.parts or not is_allowed: return os.fspath(Path(self.directory) / ".console-forbidden") if decoded_path == "apps/workbench-console" or decoded_path.startswith("apps/workbench-console/"): console_relative = requested.parts[2:] return os.fspath(Path(self.directory) / "apps" / "workbench-console" / "dist" / Path(*console_relative)) return os.fspath(Path(self.directory).joinpath(*requested.parts)) def end_headers(self) -> None: self.send_header("Cache-Control", "no-store") self.send_header("X-Content-Type-Options", "nosniff") super().end_headers() def parse_args() -> argparse.Namespace: root = Path(__file__).resolve().parents[1] parser = argparse.ArgumentParser(description="Serve the local GeoAI Workbench console.") parser.add_argument("--host", default=DEFAULT_HOST, help="Bind address. Defaults to loopback only.") parser.add_argument("--port", type=int, default=DEFAULT_PORT, help="TCP port in the 6000-6999 range.") parser.add_argument("--root", type=Path, default=root, help="Workbench repository root to serve.") return parser.parse_args() def main() -> int: args = parse_args() if not 6000 <= args.port <= 6999: raise SystemExit("Port must be in the 6000-6999 range.") if args.host not in {"127.0.0.1", "localhost", "::1"}: raise SystemExit("This console is local-only. Use 127.0.0.1, localhost, or ::1.") root = args.root.resolve() app_dir = root / "apps" / "workbench-console" if not (root / "shared").is_dir() or not (app_dir / "dist" / "index.html").is_file(): raise SystemExit(f"Not a GeoAI Workbench root: {root}") handler = lambda *handler_args, **handler_kwargs: WorkbenchConsoleHandler(*handler_args, directory=os.fspath(root), **handler_kwargs) # noqa: E731 server = ThreadingHTTPServer((args.host, args.port), handler) print(f"GeoAI Workbench console: http://{args.host}:{args.port}") print("Local runs use fixed capability scripts and create a new run directory.") try: server.serve_forever() except KeyboardInterrupt: print("\nConsole stopped.") finally: server.server_close() return 0 if __name__ == "__main__": raise SystemExit(main())