flan
461ceb7af4
- diversity: fix hard_count regression from round 7 — revert to 'is not None and < 0.85' so only images that actually receive a FPS boost (confirmed low confidence) are counted as hard examples; None-confidence images use conf_array=1.0 (no boost) and should not appear in the hard-example log count - diversity: fix _scale_bbox_to_thumbnail to use explicit zero-guard for imageWidth/imageHeight (meta_w or 0; scale = img_w/meta_w if meta_w else 1.0) — mirrors image_processing.py pattern; prevents `or img_w` from silently treating imageWidth=0 as missing and returning scale=1.0 without surfacing the zero-metadata case - embeddings: wrap all three os.close calls in _suppress_output finally block with try/except OSError: pass so a failed close in one branch cannot abort the outer finally and leak devnull_fd or the saved_err/saved_out fds - upload_tracker: clear corrupt flat-list key (data[flat_key] = []) after the isinstance warning instead of leaving the corrupt value in place — prevents stale IDs persisting across reset_person calls and future load_uploaded_ids() from seeing a non-list value - executor: move 'if pre_fscore is not None: person_has_fscores = True' out of the try/except else branch so it fires even when mark_uploaded raises; Frigate scores exist once measured regardless of tracker write success, and replacement strategy should reflect that
664 lines
34 KiB
Python
664 lines
34 KiB
Python
"""Execution phase: image processing and Frigate upload."""
|
||
|
||
import logging
|
||
import operator
|
||
import os
|
||
import shutil
|
||
from io import BytesIO
|
||
from urllib.parse import quote
|
||
|
||
import PIL
|
||
import requests
|
||
from PIL import Image
|
||
from rich import print as rprint
|
||
from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
|
||
|
||
from .config import Config, get_headers
|
||
from .frigate_api import (
|
||
_get_frigate_url,
|
||
delete_frigate_person_files,
|
||
get_all_frigate_person_files,
|
||
get_frigate_person_files,
|
||
get_frigate_version,
|
||
recognize_face,
|
||
)
|
||
from .image_processing import process_face_mode
|
||
from .immich_api import fetch_full_image
|
||
from .log_config import console
|
||
from .quality import blur_score_from_image
|
||
from .reconcile import enrich_asset_with_face_data, reconcile_frigate_mappings
|
||
from .upload_tracker import (
|
||
REJECT_TRACKER_FILE,
|
||
UPLOAD_TRACKER_FILE,
|
||
begin_batch,
|
||
flush_batch,
|
||
get_lowest_quality_mapped_file,
|
||
get_most_redundant_mapped_file,
|
||
get_tracked_frigate_file_count,
|
||
get_tracked_frigate_filenames,
|
||
has_frigate_scores,
|
||
mark_rejected,
|
||
mark_uploaded,
|
||
remove_frigate_file,
|
||
remove_frigate_files_batch,
|
||
)
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
def _safe_person_dir(output_dir: str, person_name: str) -> str:
|
||
"""Return the output subdirectory for a person, raising ValueError on path traversal.
|
||
|
||
os.path.join silently discards output_dir when person_name is absolute,
|
||
and '../..' sequences resolve outside the tree. Both are rejected by the
|
||
realpath+startswith guard, which is the load-bearing security check.
|
||
The islink check below provides an earlier, cleaner error message for the
|
||
symlink sub-case; it is redundant with (not a replacement for) the
|
||
realpath+startswith traversal check.
|
||
"""
|
||
raw = os.path.join(output_dir, person_name)
|
||
if os.path.islink(raw):
|
||
raise ValueError(f"Person name {person_name!r} resolves to a symlink — skipping")
|
||
candidate = os.path.realpath(raw)
|
||
base = os.path.realpath(output_dir)
|
||
# Use the base path as its own prefix when it's the filesystem root ("/"),
|
||
# otherwise append os.sep — avoids the false "//" double-slash when base == "/".
|
||
base_prefix = base if base == os.sep else base + os.sep
|
||
if not candidate.startswith(base_prefix) and candidate != base:
|
||
raise ValueError(f"Person name {person_name!r} escapes output directory — skipping")
|
||
return candidate
|
||
|
||
|
||
def execute_jobs(jobs: list[dict]) -> None:
|
||
"""Download and process images for all jobs.
|
||
|
||
Builds an asset_map per job (filename → Immich asset ID) so that
|
||
upload_to_frigate() can mark assets as uploaded after success.
|
||
"""
|
||
if not jobs:
|
||
return
|
||
|
||
console.rule("[bold blue]Execution Phase")
|
||
|
||
use_full_res = Config.USE_FULL_RESOLUTION
|
||
|
||
# Load InsightFace app for landmark-based crop alignment.
|
||
# The model is already resident from the diversity/embedding phase, so this
|
||
# is just a singleton lookup — no load cost.
|
||
insightface_app = None
|
||
if Config.ENABLE_FACE_ALIGNMENT:
|
||
try:
|
||
from .embeddings import get_insightface_app
|
||
|
||
insightface_app = get_insightface_app()
|
||
except Exception as e:
|
||
logger.debug("InsightFace unavailable for crop alignment: %s", e)
|
||
|
||
with Progress(
|
||
SpinnerColumn(),
|
||
TextColumn("[progress.description]{task.description}"),
|
||
BarColumn(),
|
||
TaskProgressColumn(),
|
||
console=console,
|
||
) as progress:
|
||
grand_total = sum(j["limit"] for j in jobs)
|
||
overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
|
||
|
||
for job in jobs:
|
||
person, assets = job["person"], job["assets"]
|
||
name = person["name"]
|
||
|
||
job_task = progress.add_task(f"Processing {name}...", total=len(assets))
|
||
try:
|
||
try:
|
||
person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
|
||
except ValueError as e:
|
||
logger.error(str(e))
|
||
continue
|
||
# Face crops are transient (uploaded then discarded); wipe before each run.
|
||
# A symlink could appear here via a TOCTOU race after _safe_person_dir
|
||
# returned — writing through it would land crops outside output_dir.
|
||
if os.path.islink(person_dir):
|
||
logger.error("person_dir %s became a symlink after path check — skipping job", person_dir)
|
||
continue
|
||
try:
|
||
if os.path.isdir(person_dir):
|
||
shutil.rmtree(person_dir)
|
||
os.makedirs(person_dir, exist_ok=True)
|
||
except OSError as e:
|
||
logger.error("Failed to prepare output dir for %s: %s", name, e)
|
||
continue
|
||
|
||
# Track filename → asset_id, filename → confidence score, filename → crop dims
|
||
asset_map: dict[str, str] = {}
|
||
score_map: dict[str, float | None] = {}
|
||
dims_map: dict[str, tuple[int, int]] = {}
|
||
|
||
count = 0
|
||
for asset in assets:
|
||
try:
|
||
# Enrich the asset with face bounding box data from the Immich
|
||
# faces API (not included in search/metadata results).
|
||
asset = enrich_asset_with_face_data(asset, person)
|
||
# Skip download if detection confidence already disqualifies
|
||
# the asset — avoids fetching a large image we'll discard.
|
||
conf = asset.get("face_confidence")
|
||
if conf is not None and conf < Config.MIN_CONFIDENCE:
|
||
progress.console.print(
|
||
f"[yellow]Skipped {asset['id']}"
|
||
f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
|
||
)
|
||
mark_rejected(asset["id"], person_name=name)
|
||
progress.advance(job_task)
|
||
progress.advance(overall_task)
|
||
continue
|
||
|
||
# Use full-resolution for final output when configured
|
||
if use_full_res:
|
||
img = fetch_full_image(asset["id"])
|
||
if img is None:
|
||
# Full-res download failed — could be a transient network
|
||
# error, so don't mark rejected; it will be retried next run.
|
||
pass
|
||
else:
|
||
resp = requests.get(
|
||
f"{Config.IMMICH_URL}/api/assets/{asset['id']}/thumbnail?size=preview&format=JPEG",
|
||
headers=get_headers(),
|
||
timeout=30,
|
||
)
|
||
if resp.ok:
|
||
try:
|
||
img = Image.open(BytesIO(resp.content))
|
||
except (PIL.UnidentifiedImageError, OSError):
|
||
# Pillow cannot identify the format or the content is
|
||
# truncated. The download already succeeded (resp.ok),
|
||
# so this is a data problem, not a transient network
|
||
# error — mark rejected so it isn't retried forever.
|
||
logger.warning("Invalid image data for asset %s — marking rejected", asset["id"])
|
||
mark_rejected(asset["id"], person_name=name)
|
||
img = None
|
||
else:
|
||
img = None
|
||
|
||
if img is None:
|
||
progress.console.print(f"[red]Failed download {asset['id']}[/red]")
|
||
else:
|
||
saved = process_face_mode(
|
||
img, asset, person, person_dir, count, insightface_app=insightface_app
|
||
)
|
||
if saved:
|
||
filename = f"{count}.jpg"
|
||
asset_map[filename] = asset["id"]
|
||
score_map[filename] = asset.get("quality_score")
|
||
if isinstance(saved, tuple):
|
||
dims_map[filename] = saved
|
||
# Time-spread path: compute blur score from the downloaded
|
||
# image. Capped at 1440px via blur_score_from_image() so the
|
||
# scale matches the preview thumbnails the embedding path uses
|
||
# — Laplacian variance grows with resolution, making full-res
|
||
# and thumbnail scores incomparable if left uncapped.
|
||
if score_map[filename] is None:
|
||
score_map[filename] = blur_score_from_image(img)
|
||
|
||
count += 1
|
||
else:
|
||
progress.console.print(
|
||
f"[yellow]Skipped {asset['id']} (no usable face data)[/yellow]"
|
||
)
|
||
except Exception as e:
|
||
logger.error("Failed to process asset %s: %s", asset.get("id", "<unknown>"), e)
|
||
|
||
progress.advance(job_task)
|
||
progress.advance(overall_task)
|
||
|
||
# Store maps on the job so upload_to_frigate can use them
|
||
job["asset_map"] = asset_map
|
||
job["score_map"] = score_map
|
||
job["dims_map"] = dims_map
|
||
|
||
# Log how many images were actually saved vs selected
|
||
if count < len(assets):
|
||
logger.info("%s: saved %s/%s selected images", name, count, len(assets))
|
||
finally:
|
||
progress.remove_task(job_task)
|
||
|
||
|
||
def upload_to_frigate(jobs: list[dict]) -> None:
|
||
"""Upload processed face crops to Frigate via API with detailed logging.
|
||
|
||
After each successful upload, records the Immich asset ID in the
|
||
upload tracker so it is skipped on future runs.
|
||
"""
|
||
if not jobs:
|
||
rprint("[dim]No jobs to upload.[/dim]")
|
||
return
|
||
|
||
frigate_url = _get_frigate_url()
|
||
if not frigate_url:
|
||
rprint("[yellow]⚠️ FRIGATE_URL not set, skipping upload.[/yellow]")
|
||
return
|
||
|
||
_frigate_version = get_frigate_version()
|
||
if _frigate_version is not None:
|
||
try:
|
||
parts = [int(x) for x in _frigate_version.lstrip("v").split("-")[0].split(".") if x.isdigit()]
|
||
if len(parts) >= 2 and (parts[0], parts[1]) < (0, 16):
|
||
rprint(
|
||
f" [yellow]⚠ Frigate {_frigate_version} detected — "
|
||
"face training API requires v0.16+. Uploads may fail.[/yellow]"
|
||
)
|
||
except Exception:
|
||
pass
|
||
|
||
rprint("\n[bold cyan]📤 Uploading to Frigate[/bold cyan]")
|
||
rprint(f" Target: [dim]{frigate_url}[/dim]")
|
||
|
||
# Build a mapping of output filenames → Immich asset IDs
|
||
# from the asset_map stored on each job during execute_jobs()
|
||
filename_to_asset_id: dict[str, dict[str, str]] = {}
|
||
total_files = 0
|
||
for job in jobs:
|
||
name = job["person"]["name"]
|
||
asset_map = job.get("asset_map", {})
|
||
filename_to_asset_id[name] = asset_map
|
||
total_files += len(asset_map)
|
||
|
||
if total_files == 0:
|
||
rprint(" [yellow]No images found to upload.[/yellow]")
|
||
return
|
||
|
||
rprint(f" People: [bold]{len(jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
|
||
|
||
uploaded, failed = 0, 0
|
||
max_retries = 2
|
||
|
||
# Fetch all Frigate training files once — avoids one GET /api/faces per person.
|
||
# Falls back to per-person calls inside the loop if this fetch fails.
|
||
all_frigate_files = get_all_frigate_person_files()
|
||
|
||
with Progress(
|
||
SpinnerColumn(),
|
||
TextColumn("[progress.description]{task.description}"),
|
||
BarColumn(),
|
||
TaskProgressColumn(),
|
||
console=console,
|
||
) as progress:
|
||
upload_task = progress.add_task("[green]Uploading to Frigate", total=total_files)
|
||
|
||
for job in jobs:
|
||
name = job["person"]["name"]
|
||
# URL-encode the name for the API (handles spaces, special chars)
|
||
encoded_name = quote(name, safe="")
|
||
if " " in name:
|
||
progress.console.print(f" ℹ️ URL-encoded name for Frigate API: '{name}' → '{encoded_name}'")
|
||
|
||
try:
|
||
person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
|
||
except ValueError as e:
|
||
logger.error(str(e))
|
||
continue
|
||
if not os.path.isdir(person_dir):
|
||
progress.console.print(f" [dim]⏭️ {name}: no output directory, skipping[/dim]")
|
||
continue
|
||
|
||
asset_map = filename_to_asset_id.get(name, {})
|
||
score_map = job.get("score_map", {})
|
||
dims_map = job.get("dims_map", {})
|
||
person_files = sorted(asset_map.keys())
|
||
|
||
if not person_files:
|
||
progress.console.print(f" [dim]⏭️ {name}: no images found[/dim]")
|
||
continue
|
||
|
||
progress.console.print(f" 📁 {name}: uploading {len(person_files)} image(s)...")
|
||
person_uploaded = 0
|
||
person_failed = 0
|
||
|
||
# Snapshot live Frigate files for post-upload reconciliation diff only.
|
||
# effective_count is sourced from the tracker (mapped files) so that
|
||
# manually-added Frigate files don't consume winnow's managed quota.
|
||
# Replacement targets also come exclusively from the tracker, so manually
|
||
# added files are never selected for deletion — only winnow-uploaded ones.
|
||
# LIMITATION — manual files are invisible to diversity decisions: winnow
|
||
# can observe their effect on the Frigate score (indirectly, via recognize)
|
||
# but cannot measure their embedding distribution directly. If a user has
|
||
# 20 manually-added frontals and winnow has room for 20 more, winnow may
|
||
# add more frontals because it can't see that frontals are already covered.
|
||
# TODO(frigate-api): if Frigate exposes per-file embeddings, compute
|
||
# diversity against the full training set (tracked + manual) rather than
|
||
# relying solely on the Frigate score as a proxy signal.
|
||
_snapshot = (
|
||
all_frigate_files.get(name, []) if all_frigate_files is not None
|
||
else get_frigate_person_files(name)
|
||
)
|
||
if _snapshot is None:
|
||
# Frigate GET is down. The tracker only knows files winnow mapped
|
||
# previously — it is blind to manually-added Frigate files. Using
|
||
# the tracker as the baseline would make those unmapped files look
|
||
# like new uploads in reconcile, triggering the >target guard and
|
||
# silently dropping all mappings. Skip reconciliation entirely when
|
||
# we can't get a reliable live snapshot.
|
||
logger.warning(
|
||
"%s: Frigate API unreachable at upload start"
|
||
" — file mapping will be skipped for this batch", name
|
||
)
|
||
known_frigate_files_at_start: set[str] = set()
|
||
_skip_reconcile = True
|
||
else:
|
||
known_frigate_files_at_start: set[str] = set(_snapshot)
|
||
_skip_reconcile = False
|
||
# Remove tracker mappings for files that no longer exist in Frigate
|
||
# (manually deleted, or cleaned up outside winnow). This corrects the
|
||
# effective_count so those slots are available for new uploads.
|
||
stale = get_tracked_frigate_filenames(name) - known_frigate_files_at_start
|
||
if stale:
|
||
remove_frigate_files_batch(name, list(stale))
|
||
progress.console.print(
|
||
f" [dim]{name}: cleared {len(stale)} stale mapping(s)"
|
||
" (file(s) no longer in Frigate)[/dim]"
|
||
)
|
||
effective_count = get_tracked_frigate_file_count(name)
|
||
quality_replacement = job.get("config", {}).get("quality_replacement", False)
|
||
if Config.ENABLE_FRIGATE_SCORES and effective_count == 0:
|
||
progress.console.print(
|
||
f" [dim]{name}: first run — Frigate diversity scoring will apply from the next run[/dim]"
|
||
)
|
||
# Snapshot whether Frigate has a model before the upload loop starts.
|
||
# effective_count is incremented inside the loop on each successful upload,
|
||
# so using the live value would incorrectly trigger recognize_face calls
|
||
# mid-batch on the first run (after the first upload sets it to 1).
|
||
has_frigate_model = effective_count > 0
|
||
actually_uploaded: list[tuple[str, str | None]] = []
|
||
failed_deletes: set[str] = set()
|
||
min_quality_score_for_slot: float | None = None
|
||
person_has_fscores: bool = has_frigate_scores(name)
|
||
|
||
begin_batch(UPLOAD_TRACKER_FILE)
|
||
begin_batch(REJECT_TRACKER_FILE)
|
||
try:
|
||
for fname in person_files:
|
||
fpath = os.path.join(person_dir, fname)
|
||
|
||
# If a previous replacement delete succeeded but that upload failed,
|
||
# require the next candidate to beat the deleted file's score so the
|
||
# 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 not None and file_score <= min_quality_score_for_slot:
|
||
progress.console.print(
|
||
f" [dim]⏭ {fname}: score {file_score:.3f} ≤ 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 when has_frigate_model is False (effective_count was 0 before the loop).
|
||
# 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.
|
||
# LIMITATION — async rebuild during multi-replacement runs: each deletion in a
|
||
# single run triggers a background model rebuild in Frigate. Subsequent recognize
|
||
# calls in the same run may get None (rebuild in progress), causing later
|
||
# candidates to fall back to blur-score replacement or be skipped entirely.
|
||
# The more replacements that happen in one run, the worse the scoring gets.
|
||
# TODO(frigate-api): if Frigate exposes a model generation counter or a
|
||
# rebuild-complete signal, poll it between recognize calls during replacement
|
||
# sequences rather than accepting stale/None scores.
|
||
pre_fscore: float | None = None
|
||
if Config.ENABLE_FRIGATE_SCORES and has_frigate_model:
|
||
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 when effective_count == 0 (no Frigate model yet),
|
||
# 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"
|
||
is_better_than = operator.lt
|
||
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"
|
||
is_better_than = operator.gt
|
||
|
||
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 not is_better_than(candidate_score, target[2])
|
||
if not_better:
|
||
target_str = f"{target[2]:.3f}" if target is not None else "N/A"
|
||
cmp_op = "<" if using_fscore else ">"
|
||
progress.console.print(
|
||
f" [dim]⏭ {fname}: {score_label} {candidate_score:.3f}"
|
||
f" not {cmp_op} {target_str}, skipping[/dim]"
|
||
)
|
||
progress.advance(upload_task)
|
||
continue
|
||
|
||
target_frigate_file, _target_asset_id, target_score = target
|
||
cmp_op = "<" if using_fscore else ">"
|
||
progress.console.print(
|
||
f" 🔄 {fname}: {score_label} {candidate_score:.3f} {cmp_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 target_score
|
||
else:
|
||
logger.warning(
|
||
"Failed to delete %s for %s, skipping replacement",
|
||
target_frigate_file, name,
|
||
)
|
||
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:
|
||
try:
|
||
mark_uploaded(
|
||
asset_id,
|
||
person_name=name,
|
||
score=score_map.get(fname),
|
||
crop_dims=dims_map.get(fname),
|
||
frigate_score=pre_fscore,
|
||
)
|
||
except Exception as tracker_exc:
|
||
# Upload to Frigate succeeded — don't retry on tracker
|
||
# failure or we'd upload a duplicate to Frigate.
|
||
logger.error(
|
||
"Tracker write failed for %s — upload succeeded"
|
||
" but asset may be re-selected next run: %s",
|
||
fname, tracker_exc,
|
||
)
|
||
if pre_fscore is not None:
|
||
person_has_fscores = True
|
||
# Always record for reconcile so the Frigate filename→asset_id
|
||
# mapping is created even when the tracker write fails.
|
||
# Trade-off: if mark_uploaded failed, asset_id is absent from
|
||
# asset_ids and scores. Consequences: (1) re-selected next run
|
||
# → Frigate duplicate; (2) excluded from quality-replacement
|
||
# candidates (_pick_mapped_file requires a scores entry);
|
||
# (3) counted toward MAX_AUTO_IMAGES cap (via frigate_files).
|
||
# The alternative — not appending — leaves the file permanently
|
||
# unmapped (reconcile never creates the frigate_files entry),
|
||
# making (2) and (3) permanent. Frigate duplicate is lesser.
|
||
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("%s HTTP %s: %s", fname, resp.status_code, error_detail)
|
||
_is_permanent = (
|
||
(resp.status_code == 400 and "face" in full_body.lower())
|
||
or resp.status_code == 422
|
||
)
|
||
if _is_permanent:
|
||
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"
|
||
)
|
||
|
||
finally:
|
||
try:
|
||
flush_batch(UPLOAD_TRACKER_FILE)
|
||
except Exception as _flush_exc:
|
||
logger.warning(
|
||
"flush_batch failed during cleanup"
|
||
" — batch will be recovered on next begin_batch: %s",
|
||
_flush_exc,
|
||
)
|
||
try:
|
||
flush_batch(REJECT_TRACKER_FILE)
|
||
except Exception as _flush_exc:
|
||
logger.warning(
|
||
"flush_batch failed during cleanup"
|
||
" — batch will be recovered on next begin_batch: %s",
|
||
_flush_exc,
|
||
)
|
||
|
||
# Batch-map Frigate filenames to asset IDs now that all uploads are done.
|
||
if actually_uploaded and not _skip_reconcile:
|
||
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]")
|