flanandClaude Sonnet 4.6
759579fc30
- Store Immich face confidence scores per asset in frigate_uploaded_ids.json
- Add frigate_api.py: query GET /api/faces to count trained images per person
- Record Frigate training count as frigate_count in tracker for offline fallback
- MAX_AUTO_IMAGES cap now uses live Frigate count → cached frigate_count → local uploaded count
- Startup summary shows last known Frigate training count per person
- Migrate by_person entries from flat list to {asset_ids, scores, frigate_count} dict
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
346 lines
15 KiB
Python
346 lines
15 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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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 .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 .logging import console
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from .upload_tracker import mark_rejected, mark_uploaded
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logger = logging.getLogger(__name__)
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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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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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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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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 and filename → confidence score
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asset_map: dict[str, str] = {}
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score_map: dict[str, float | None] = {}
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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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# 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)
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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("face_confidence")
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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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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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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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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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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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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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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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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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for fname in person_files:
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fpath = os.path.join(person_dir, fname)
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for attempt in range(1, max_retries + 1):
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try:
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with open(fpath, "rb") as f:
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resp = requests.post(
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f"{frigate_url}/api/faces/{encoded_name}/register",
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files={"file": (fname, f, "image/jpeg")},
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timeout=30,
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)
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if resp.status_code == 200:
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uploaded += 1
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person_uploaded += 1
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# Mark this asset as uploaded so it's skipped on future runs
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asset_id = asset_map.get(fname)
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if asset_id:
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mark_uploaded(asset_id, person_name=name, score=score_map.get(fname))
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break
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else:
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if attempt < max_retries:
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logger.warning(
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f"Upload attempt {attempt}/{max_retries} for {fname}:"
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f" HTTP {resp.status_code}, retrying..."
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)
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continue
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failed += 1
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person_failed += 1
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progress.console.print(
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f" [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
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)
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try:
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error_detail = resp.json().get("message", resp.text[:100])
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progress.console.print(f" [dim]{error_detail}[/dim]")
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except Exception:
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error_detail = resp.text[:100]
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progress.console.print(f" [dim]{error_detail}[/dim]")
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if resp.status_code == 400 and "face" in error_detail.lower():
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asset_id = asset_map.get(fname)
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if asset_id:
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mark_rejected(asset_id, person_name=name)
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except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
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if attempt < max_retries:
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logger.warning(
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f"Upload attempt {attempt}/{max_retries} for {fname}:"
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f" {type(exc).__name__}, retrying..."
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)
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continue
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failed += 1
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person_failed += 1
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label = (
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"Connection refused"
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if isinstance(exc, requests.exceptions.ConnectionError)
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else "Request timed out (30s)"
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)
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progress.console.print(
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f" [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
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)
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except Exception as e:
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if attempt < max_retries:
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logger.warning(
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f"Upload attempt {attempt}/{max_retries} for {fname}:"
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f" {type(e).__name__}, retrying..."
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)
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continue
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failed += 1
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person_failed += 1
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progress.console.print(
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f" [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
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)
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progress.advance(upload_task)
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# Per-person summary
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if person_failed == 0:
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progress.console.print(
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f" ✅ {name}: {person_uploaded}/{person_uploaded} uploaded"
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)
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else:
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progress.console.print(
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f" ⚠️ {name}: {person_uploaded} succeeded, {person_failed} failed"
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)
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# Grand summary
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rprint("\n [bold]Frigate Upload Summary:[/bold]")
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rprint(f" ✅ Succeeded: [green]{uploaded}[/green]")
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if failed:
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rprint(f" ❌ Failed: [red]{failed}[/red]")
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else:
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rprint(" ❌ Failed: 0")
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if failed > 0:
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rprint(" [yellow]Check logs above for per-file error details.[/yellow]")
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if failed == total_files and total_files > 0:
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rprint(" [bold red]All uploads failed. Verify FRIGATE_URL is reachable and API is enabled.[/bold red]")
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