flan
105099c819
Previously, assets that passed embedding-phase selection but failed the faces API confidence check in execute_jobs were silently skipped with no tracker entry. They appeared as valid candidates on every future run, were re-selected, and re-skipped in an endless cycle. Now they are marked rejected so they are excluded from future runs. RETRY_REJECTED=true clears them if Immich later re-processes the image.
684 lines
32 KiB
Python
684 lines
32 KiB
Python
"""Execution phase: image processing and Frigate upload."""
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import logging
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import os
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import shutil
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import time
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from io import BytesIO
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from urllib.parse import quote
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import requests
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from PIL import Image
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from rich import print as rprint
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from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
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from .config import Config, get_headers
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from .frigate_api import (
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delete_frigate_person_files,
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get_all_frigate_person_files,
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get_frigate_person_files,
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recognize_face,
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)
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from .image_processing import process_face_mode, process_full_mode, process_object_mode
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from .immich_api import fetch_face_data, fetch_full_image
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from .log_config import console
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from .quality import assess_quality
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from .upload_tracker import (
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get_lowest_quality_mapped_file,
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get_most_redundant_mapped_file,
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get_tracked_frigate_file_count,
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get_tracked_frigate_filenames,
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has_frigate_scores,
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mark_rejected,
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mark_uploaded,
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record_frigate_files_batch,
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remove_frigate_file,
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)
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logger = logging.getLogger(__name__)
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def _safe_person_dir(output_dir: str, person_name: str) -> str:
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"""Return the output subdirectory for a person, raising ValueError on path traversal.
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os.path.join silently discards output_dir when person_name is absolute,
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and '../..' sequences resolve outside the tree. Both are rejected here.
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"""
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candidate = os.path.realpath(os.path.join(output_dir, person_name))
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base = os.path.realpath(output_dir)
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# Use the base path as its own prefix when it's the filesystem root ("/"),
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# otherwise append os.sep — avoids the false "//" double-slash when base == "/".
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base_prefix = base if base == os.sep else base + os.sep
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if not candidate.startswith(base_prefix) and candidate != base:
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raise ValueError(f"Person name {person_name!r} escapes output directory — skipping")
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return candidate
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def _reconcile_frigate_mappings(
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person_name: str,
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known_files_before: set[str],
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uploaded: list[tuple[str, str | None]],
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) -> None:
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"""Map Frigate filenames to asset IDs after a batch of uploads.
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Polls until all expected new files appear in the Frigate API, then maps
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them to asset IDs by filename timestamp order (Frigate processes the
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upload queue in FIFO order, so earlier uploads get earlier timestamps).
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KNOWN LIMITATION — race condition with external uploads:
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If another client uploads a face file for this person concurrently, the
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count of new files will exceed `len(uploaded)` and we bail out entirely
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(the "> target" branch). That's safe — we never record a wrong mapping —
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but those uploads become permanently unmapped (they won't be eligible for
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quality replacement). The right fix is a Frigate API that returns the
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filename in the upload response, removing the need for any post-upload
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diffing. Until then, the external-upload guard keeps mappings correct at
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the cost of occasionally missing them when another client is active.
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"""
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target = len(uploaded)
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current_files: set[str] = set()
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for delay in (1, 2, 4, 8):
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time.sleep(delay)
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fresh = get_frigate_person_files(person_name)
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if fresh is None:
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logger.warning(
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f"{person_name}: Frigate API unreachable during mapping reconciliation"
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" — quality replacement won't target these files"
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)
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return
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current_files = set(fresh)
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if len(current_files - known_files_before) >= target:
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break
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new_files = current_files - known_files_before
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if len(new_files) == target:
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def _ts(fname: str) -> float:
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try:
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return float(fname.rsplit("_", 1)[-1].replace(".webp", ""))
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except (ValueError, IndexError):
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return 0.0
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mappings = {
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frigate_file: asset_id
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for (_, asset_id), frigate_file in zip(uploaded, sorted(new_files, key=_ts))
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if asset_id
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}
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record_frigate_files_batch(person_name, mappings)
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elif len(new_files) > target:
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logger.info(
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f"{person_name}: {len(new_files)} new Frigate files for {target} uploads"
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" (external upload detected) — skipping file mapping"
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)
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else:
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logger.warning(
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f"{person_name}: only {len(new_files)} of {target} expected Frigate files"
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" appeared after reconciliation — mapping skipped"
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)
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def _enrich_asset_with_face_data(asset: dict, person: dict) -> dict:
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"""Enrich an asset dict with face bounding box data from the Immich faces API.
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The search/metadata endpoint does not include face bounding box data,
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so we fetch it from GET /api/faces?id={asset_id} and inject it into
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the asset's "people" field so process_face_mode can find it.
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Returns the enriched asset dict (modifies in place and returns it).
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"""
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person_id = person["id"]
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face_data = fetch_face_data(asset["id"], person_id=person_id)
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if face_data is None:
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logger.debug(f"No face data returned for {person.get('name')} in asset {asset.get('id')}")
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# Clean any None entries from the people list (can come from Immich API)
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if "people" in asset:
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asset["people"] = [p for p in asset["people"] if p is not None]
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return asset
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# Skip zero-area bounding boxes (face detection failed or no face found)
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if face_data.bbox == (0, 0, 0, 0):
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logger.debug(f"Zero-area bounding box for {person.get('name')} in asset {asset.get('id')}")
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# Clean any None entries from the people list (can come from Immich API)
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if "people" in asset:
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asset["people"] = [p for p in asset["people"] if p is not None]
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return asset
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face_info = {
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"boundingBoxX1": face_data.bbox[0],
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"boundingBoxY1": face_data.bbox[1],
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"boundingBoxX2": face_data.bbox[2],
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"boundingBoxY2": face_data.bbox[3],
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"imageWidth": face_data.image_width,
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"imageHeight": face_data.image_height,
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}
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# Inject into asset so process_face_mode can find it via asset["people"]
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asset["people"] = [{"id": person_id, "faces": [face_info]}]
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asset["face_confidence"] = face_data.confidence
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return asset
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def execute_jobs(jobs: list[dict]) -> None:
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"""Download and process images for all jobs.
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Builds an asset_map per job (filename → Immich asset ID) so that
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upload_to_frigate() can mark assets as uploaded after success.
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"""
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if not jobs:
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return
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console.rule("[bold blue]Execution Phase")
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use_full_res = Config.USE_FULL_RESOLUTION
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# Load InsightFace app for landmark-based crop alignment (face mode only).
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# The model is already resident from the diversity/embedding phase, so this
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# is just a singleton lookup — no load cost.
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insightface_app = None
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if any(j["config"].get("mode", "face") == "face" for j in jobs) and Config.ENABLE_FACE_ALIGNMENT:
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try:
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from .embeddings import get_insightface_app
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insightface_app = get_insightface_app()
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except Exception as e:
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logger.debug(f"InsightFace unavailable for crop alignment: {e}")
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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console=console,
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) as progress:
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grand_total = sum(j["limit"] for j in jobs)
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overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
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for job in jobs:
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person, assets, config = job["person"], job["assets"], job["config"]
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name, mode = person["name"], config.get("mode", "face")
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job_task = progress.add_task(f"Processing {name}...", total=len(assets))
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try:
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person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
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except ValueError as e:
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logger.error(str(e))
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continue
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# Face crops are transient (uploaded then discarded); wipe before each run.
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# Object crops are the deliverable; preserve them across runs.
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if mode == "face" and os.path.isdir(person_dir):
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shutil.rmtree(person_dir)
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os.makedirs(person_dir, exist_ok=True)
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# Track filename → asset_id, filename → confidence score, filename → crop dims
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asset_map: dict[str, str] = {}
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score_map: dict[str, float | None] = {}
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dims_map: dict[str, tuple[int, int]] = {}
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count = 0
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for asset in assets:
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try:
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# For face mode, enrich the asset with face bounding box data
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# from the Immich faces API (not included in search/metadata results)
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if mode == "face":
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asset = _enrich_asset_with_face_data(asset, person)
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# Skip download if detection confidence already disqualifies
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# the asset — avoids fetching a large image we'll discard.
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conf = asset.get("face_confidence")
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if conf is not None and conf < Config.MIN_CONFIDENCE:
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progress.console.print(
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f"[yellow]Skipped {asset['id']}"
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f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
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)
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mark_rejected(asset["id"], person_name=name)
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progress.advance(job_task)
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progress.advance(overall_task)
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continue
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# Use full-resolution for final output when configured
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if use_full_res:
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img = fetch_full_image(asset["id"])
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else:
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resp = requests.get(
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f"{Config.IMMICH_URL}/api/assets/{asset['id']}/thumbnail?size=preview&format=JPEG",
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headers=get_headers(),
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timeout=30,
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)
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img = Image.open(BytesIO(resp.content)) if resp.ok else None
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if img is None:
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progress.console.print(f"[red]Failed download {asset['id']}[/red]")
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else:
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saved = (
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process_face_mode(img, asset, person, person_dir, count, insightface_app=insightface_app)
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if mode == "face"
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else process_object_mode(img, config, person_dir, count)
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if mode == "object"
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else process_full_mode(img, person_dir, count)
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)
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if saved:
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# Record which asset produced which output file
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filename = f"{count}.jpg"
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asset_map[filename] = asset["id"]
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score_map[filename] = asset.get("quality_score")
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if mode == "face" and isinstance(saved, tuple):
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dims_map[filename] = saved
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# Time-spread path: compute blur score from the downloaded
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# image. Cap at 1440px so the scale matches the preview
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# thumbnails the embedding path uses for scoring — Laplacian
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# variance grows with resolution, making full-res and
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# thumbnail scores incomparable if left uncapped.
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if mode == "face" and score_map[filename] is None:
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try:
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score_img = img.convert("RGB") if img.mode != "RGB" else img
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if score_img.width > 1440 or score_img.height > 1440:
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score_img = score_img.copy()
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score_img.thumbnail((1440, 1440), Image.LANCZOS)
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score_map[filename] = assess_quality(score_img).blur_score
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except Exception as exc:
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logger.debug(f"Quality score fallback for {asset['id']}: {exc}")
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score_map[filename] = 0.0 # unknown quality — treat as lowest
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# Also record object-mode variant filenames
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if mode == "object":
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for f in sorted(os.listdir(person_dir)):
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if f.startswith(f"{count}_") and f not in asset_map:
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asset_map[f] = asset["id"]
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score_map[f] = asset.get("face_confidence")
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count += 1
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else:
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progress.console.print(
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f"[yellow]Skipped {asset['id']} (no usable face data)[/yellow]"
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)
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except Exception as e:
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logger.error(f"Failed to process asset {asset['id']}: {e}")
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progress.advance(job_task)
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progress.advance(overall_task)
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# Store maps on the job so upload_to_frigate can use them
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job["asset_map"] = asset_map
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job["score_map"] = score_map
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job["dims_map"] = dims_map
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progress.remove_task(job_task)
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# Log how many images were actually saved vs selected
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if count < len(assets):
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logger.info(f"{name}: saved {count}/{len(assets)} selected images")
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def upload_to_frigate(jobs: list[dict]) -> None:
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"""Upload processed face crops to Frigate via API with detailed logging.
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Only runs for face-mode jobs. Object-mode crops are saved to the output
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directory as the deliverable and must be copied to Frigate manually.
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After each successful upload, records the Immich asset ID in the
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upload tracker so it is skipped on future runs.
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"""
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face_jobs = [j for j in jobs if j["config"].get("mode", "face") == "face"]
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if not face_jobs:
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rprint("[dim]No face-mode jobs to upload.[/dim]")
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return
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# Notify user about object-mode jobs that were skipped
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object_jobs = [j for j in jobs if j["config"].get("mode") == "object"]
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for job in object_jobs:
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name = job["person"]["name"]
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try:
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person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
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except ValueError as e:
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logger.error(str(e))
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continue
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rprint(f" [dim]📁 {name} (object): crops saved to {person_dir} — copy to Frigate manually[/dim]")
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frigate_url = os.environ.get("FRIGATE_URL", "")
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if not frigate_url:
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rprint("[yellow]⚠️ FRIGATE_URL not set, skipping upload.[/yellow]")
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return
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rprint("\n[bold cyan]📤 Uploading to Frigate[/bold cyan]")
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rprint(f" Target: [dim]{frigate_url}[/dim]")
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# Build a mapping of output filenames → Immich asset IDs
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# from the asset_map stored on each job during execute_jobs()
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filename_to_asset_id: dict[str, dict[str, str]] = {}
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total_files = 0
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for job in face_jobs:
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name = job["person"]["name"]
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asset_map = job.get("asset_map", {})
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filename_to_asset_id[name] = asset_map
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total_files += len(asset_map)
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if total_files == 0:
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rprint(" [yellow]No images found to upload.[/yellow]")
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return
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rprint(f" People: [bold]{len(face_jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
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uploaded, failed = 0, 0
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max_retries = 2
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# Fetch all Frigate training files once — avoids one GET /api/faces per person.
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# Falls back to per-person calls inside the loop if this fetch fails.
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all_frigate_files = get_all_frigate_person_files()
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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console=console,
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) as progress:
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upload_task = progress.add_task("[green]Uploading to Frigate", total=total_files)
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for job in face_jobs:
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name = job["person"]["name"]
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# URL-encode the name for the API (handles spaces, special chars)
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encoded_name = quote(name, safe="")
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if " " in name:
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progress.console.print(f" ℹ️ URL-encoded name for Frigate API: '{name}' → '{encoded_name}'")
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try:
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person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
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except ValueError as e:
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logger.error(str(e))
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continue
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if not os.path.isdir(person_dir):
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progress.console.print(f" [dim]⏭️ {name}: no output directory, skipping[/dim]")
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continue
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asset_map = filename_to_asset_id.get(name, {})
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score_map = job.get("score_map", {})
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dims_map = job.get("dims_map", {})
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person_files = sorted(asset_map.keys())
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if not person_files:
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progress.console.print(f" [dim]⏭️ {name}: no images found[/dim]")
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continue
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progress.console.print(f" 📁 {name}: uploading {len(person_files)} image(s)...")
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person_uploaded = 0
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person_failed = 0
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# Snapshot live Frigate files for post-upload reconciliation diff only.
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# effective_count is sourced from the tracker (mapped files) so that
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# manually-added Frigate files don't consume winnow's managed quota.
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# Replacement targets also come exclusively from the tracker, so manually
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# added files are never selected for deletion — only winnow-uploaded ones.
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_snapshot = (
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all_frigate_files.get(name, []) if all_frigate_files is not None
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else get_frigate_person_files(name)
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)
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if _snapshot is None:
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# Frigate GET is down; fall back to the tracker's mapped filenames
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# as the pre-upload baseline. reconciliation will still work unless
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# there are concurrent manual uploads (handled by >target guard).
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logger.warning(
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f"{name}: Frigate API unreachable at upload start"
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" — using tracker baseline for post-upload reconciliation"
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)
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known_frigate_files_at_start: set[str] = get_tracked_frigate_filenames(name)
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else:
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known_frigate_files_at_start: set[str] = set(_snapshot)
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# Remove tracker mappings for files that no longer exist in Frigate
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# (manually deleted, or cleaned up outside winnow). This corrects the
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# effective_count so those slots are available for new uploads.
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stale = get_tracked_frigate_filenames(name) - known_frigate_files_at_start
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for stale_fn in stale:
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remove_frigate_file(name, stale_fn)
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if stale:
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progress.console.print(
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f" [dim]{name}: cleared {len(stale)} stale mapping(s)"
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" (file(s) no longer in Frigate)[/dim]"
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)
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effective_count = get_tracked_frigate_file_count(name)
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pre_run_count = effective_count
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quality_replacement = job.get("config", {}).get("quality_replacement", False)
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if Config.ENABLE_FRIGATE_SCORES and pre_run_count == 0:
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progress.console.print(
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f" [dim]{name}: first run — Frigate diversity scoring will apply from the next run[/dim]"
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)
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actually_uploaded: list[tuple[str, str | None]] = []
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failed_deletes: set[str] = set()
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min_quality_score_for_slot: float | None = None
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person_has_fscores: bool = has_frigate_scores(name)
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for fname in person_files:
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fpath = os.path.join(person_dir, fname)
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# If a previous replacement delete succeeded but that upload failed,
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# require the next candidate to beat the deleted file's score so the
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# freed slot isn't filled with something worse than what we removed.
|
||
if min_quality_score_for_slot is not None:
|
||
file_score = score_map.get(fname)
|
||
if file_score is None or file_score <= min_quality_score_for_slot:
|
||
score_str = f"{file_score:.3f}" if file_score is not None else "N/A"
|
||
progress.console.print(
|
||
f" [dim]⏭ {fname}: score {score_str} ≤ freed slot floor"
|
||
f" {min_quality_score_for_slot:.3f}, skipping[/dim]"
|
||
)
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
at_cap = effective_count >= Config.MAX_AUTO_IMAGES
|
||
|
||
# Pre-upload Frigate score — clean measurement (image not yet in training set).
|
||
# Called for all below-cap uploads (seeds frigate_scores for future at-cap
|
||
# replacement) and for at-cap uploads when scores already exist. Skipped on
|
||
# the first run (pre_run_count == 0) since Frigate has no model yet.
|
||
# recognize_face returns (face_name, score); we only use the score when the
|
||
# best match is for the correct person. Mismatches (or "unknown") are treated
|
||
# as None so a wrong-person score never drives a ceiling skip or replacement.
|
||
# Frigate rebuilds its model asynchronously after any delete (clear + background
|
||
# thread), so the first recognize call after a deletion returns None — our code
|
||
# handles this conservatively by skipping that candidate until the next run.
|
||
pre_fscore: float | None = None
|
||
if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0:
|
||
if not at_cap or person_has_fscores:
|
||
_result = recognize_face(fpath)
|
||
if _result is not None and (_result[0] or "").casefold() == name.casefold():
|
||
pre_fscore = _result[1]
|
||
|
||
# Below-cap novelty gate: skip candidates already covered by the Frigate model,
|
||
# including conditions learned from manually-added images winnow can't track.
|
||
# pre_fscore is None on the first run (pre_run_count == 0 skips recognize_face
|
||
# above), so this block never fires on the first run without an extra guard.
|
||
if not at_cap and pre_fscore is not None:
|
||
_ceiling = Config.FRIGATE_SCORE_CEILING
|
||
if _ceiling is None:
|
||
# Dynamic default: bar = most-redundant tracked file's Frigate score.
|
||
# Falls back to uploading freely when no tracked scores exist yet.
|
||
_bar = get_most_redundant_mapped_file(name)
|
||
_skip = _bar is not None and pre_fscore > _bar[2]
|
||
_bar_str = f"most redundant tracked {_bar[2]:.2f}" if _bar else ""
|
||
elif _ceiling == 0.0:
|
||
_skip = False # explicitly disabled
|
||
_bar_str = ""
|
||
else:
|
||
_skip = pre_fscore > _ceiling
|
||
_bar_str = f"ceiling {_ceiling:.2f}"
|
||
if _skip:
|
||
progress.console.print(
|
||
f" [dim]⏭ {fname}: Frigate score {pre_fscore:.2f}"
|
||
f" > {_bar_str}, already covered[/dim]"
|
||
)
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
if at_cap:
|
||
if not quality_replacement:
|
||
progress.console.print(f" [dim]⏭ {fname}: at cap, quality replacement disabled[/dim]")
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
|
||
if using_fscore:
|
||
candidate_score = pre_fscore
|
||
get_target = get_most_redundant_mapped_file
|
||
score_label, better_note = "frigate", " (more novel)"
|
||
no_score_msg = "Frigate recognize unavailable, skipping replacement"
|
||
else:
|
||
candidate_score = score_map.get(fname)
|
||
get_target = get_lowest_quality_mapped_file
|
||
score_label, better_note = "blur", ""
|
||
no_score_msg = "no quality score, skipping replacement"
|
||
|
||
if candidate_score is None:
|
||
progress.console.print(f" [dim]⏭ {fname}: {no_score_msg}[/dim]")
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
target = get_target(name, exclude=failed_deletes)
|
||
not_better = target is None or (
|
||
candidate_score >= target[2] if using_fscore else candidate_score <= target[2]
|
||
)
|
||
if not_better:
|
||
target_str = f"{target[2]:.3f}" if target is not None else "N/A"
|
||
op = "<" if using_fscore else ">"
|
||
progress.console.print(
|
||
f" [dim]⏭ {fname}: {score_label} {candidate_score:.3f}"
|
||
f" not {op} {target_str}, skipping[/dim]"
|
||
)
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
target_frigate_file, _target_asset_id, target_score = target
|
||
op = "<" if using_fscore else ">"
|
||
progress.console.print(
|
||
f" 🔄 {fname}: {score_label} {candidate_score:.3f} {op} {target_score:.3f},"
|
||
f" replacing {target_frigate_file}{better_note}"
|
||
)
|
||
if delete_frigate_person_files(name, [target_frigate_file]):
|
||
remove_frigate_file(name, target_frigate_file)
|
||
person_has_fscores = has_frigate_scores(name)
|
||
effective_count -= 1
|
||
min_quality_score_for_slot = None if using_fscore else candidate_score
|
||
else:
|
||
logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
|
||
failed_deletes.add(target_frigate_file)
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
for attempt in range(1, max_retries + 1):
|
||
try:
|
||
with open(fpath, "rb") as f:
|
||
resp = requests.post(
|
||
f"{frigate_url}/api/faces/{encoded_name}/register",
|
||
files={"file": (fname, f, "image/jpeg")},
|
||
timeout=30,
|
||
)
|
||
if resp.status_code == 200:
|
||
uploaded += 1
|
||
person_uploaded += 1
|
||
effective_count += 1
|
||
min_quality_score_for_slot = None
|
||
|
||
asset_id = asset_map.get(fname)
|
||
if asset_id:
|
||
mark_uploaded(
|
||
asset_id,
|
||
person_name=name,
|
||
score=score_map.get(fname),
|
||
crop_dims=dims_map.get(fname),
|
||
frigate_score=pre_fscore,
|
||
)
|
||
if pre_fscore is not None:
|
||
person_has_fscores = True
|
||
actually_uploaded.append((fname, asset_id))
|
||
|
||
break
|
||
else:
|
||
if attempt < max_retries:
|
||
logger.warning(
|
||
f"Upload attempt {attempt}/{max_retries} for {fname}:"
|
||
f" HTTP {resp.status_code}, retrying..."
|
||
)
|
||
continue
|
||
failed += 1
|
||
person_failed += 1
|
||
progress.console.print(
|
||
f" [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
|
||
)
|
||
full_body = resp.text
|
||
try:
|
||
error_detail = resp.json().get("message", full_body[:100])
|
||
except Exception:
|
||
error_detail = full_body[:100]
|
||
if resp.status_code == 400:
|
||
progress.console.print(f" [dim]{error_detail}[/dim]")
|
||
else:
|
||
logger.debug(f"{fname} HTTP {resp.status_code}: {error_detail}")
|
||
if resp.status_code == 400 and "face" in full_body.lower():
|
||
asset_id = asset_map.get(fname)
|
||
if asset_id:
|
||
mark_rejected(asset_id, person_name=name)
|
||
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
|
||
if attempt < max_retries:
|
||
logger.warning(
|
||
f"Upload attempt {attempt}/{max_retries} for {fname}:"
|
||
f" {type(exc).__name__}, retrying..."
|
||
)
|
||
continue
|
||
failed += 1
|
||
person_failed += 1
|
||
label = (
|
||
"Connection refused"
|
||
if isinstance(exc, requests.exceptions.ConnectionError)
|
||
else "Request timed out (30s)"
|
||
)
|
||
progress.console.print(
|
||
f" [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
|
||
)
|
||
except Exception as e:
|
||
if attempt < max_retries:
|
||
logger.warning(
|
||
f"Upload attempt {attempt}/{max_retries} for {fname}:"
|
||
f" {type(e).__name__}, retrying..."
|
||
)
|
||
continue
|
||
failed += 1
|
||
person_failed += 1
|
||
progress.console.print(
|
||
f" [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
|
||
)
|
||
|
||
progress.advance(upload_task)
|
||
|
||
if min_quality_score_for_slot is not None:
|
||
logger.warning(
|
||
f"{name}: freed replacement slot (floor {min_quality_score_for_slot:.3f})"
|
||
" was not filled this run — will be available next run"
|
||
)
|
||
|
||
# Batch-map Frigate filenames to asset IDs now that all uploads are done.
|
||
if actually_uploaded:
|
||
_reconcile_frigate_mappings(name, known_frigate_files_at_start, actually_uploaded)
|
||
|
||
# Per-person summary
|
||
if person_failed == 0:
|
||
progress.console.print(
|
||
f" ✅ {name}: {person_uploaded}/{person_uploaded} uploaded"
|
||
)
|
||
else:
|
||
progress.console.print(
|
||
f" ⚠️ {name}: {person_uploaded} succeeded, {person_failed} failed"
|
||
)
|
||
|
||
# Grand summary
|
||
rprint("\n [bold]Frigate Upload Summary:[/bold]")
|
||
rprint(f" ✅ Succeeded: [green]{uploaded}[/green]")
|
||
if failed:
|
||
rprint(f" ❌ Failed: [red]{failed}[/red]")
|
||
else:
|
||
rprint(" ❌ Failed: 0")
|
||
|
||
if failed > 0:
|
||
rprint(" [yellow]Check logs above for per-file error details.[/yellow]")
|
||
|
||
if failed == total_files and total_files > 0:
|
||
rprint(" [bold red]All uploads failed. Verify FRIGATE_URL is reachable and API is enabled.[/bold red]")
|