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maziggy
12d17bfbe7
Bambu's end-gcode lowers the bed at gcode_state=FINISH. Bambuddy's live-camera grab captured the bed already dropped, ruining the photo framing. Source the photo from a brief Bambu timelapse instead — firmware stops timelapse recording AFTER toolhead parks but BEFORE bed-drop runs, so the last frame frames the finished print correctly. When capture_finish_photo is on AND the user did not opt in to timelapse for this print, force timelapse=True at dispatch + mark the new PrintArchive.bambuddy_forced_timelapse column. After extraction (success or failure), cleanup deletes the locally-attached file, clears archive.timelapse_path, and walks the four scanner directories (/timelapse, /timelapse/video, /record, /recording) trying FTP DELE against the original filename. User-opted-in timelapses pass through unchanged. Resolver lives at services/background_dispatch.py::resolve_effective_timelapse (module-level so the print queue can reuse it). Both dispatch paths wired: background_dispatch.py (Print Now / Reprint) AND print_scheduler.py:_start_print (the queue). Field testing caught the scheduler gap on the first round — AST regression test now asserts start_print(timelapse=...) references effective_timelapse, not the raw item.timelapse, so a future refactor can't silently drop it. Extractor: ffmpeg -i input.mp4 -update 1 -q:v 2 out.jpg. Decoded frames overwrite the same output file, so the file left on disk is the literal last frame regardless of duration. Bambu records one frame per layer-change, so a 16-layer cube produces a 0.6 s timelapse — the original -sseof -1.0 approach seeked before the start of the file and returned frame 0 (empty bed). Decoding every frame is fine; Bambu timelapses are short by construction even on hours-long prints. Migration adds bambuddy_forced_timelapse branched on is_sqlite() (DEFAULT 0 / DEFAULT FALSE — PG rejects DEFAULT 0 for BOOLEAN). Verified live on postgres:16-alpine. Photo-task wait_for budget extends 45s -> 75s when timelapse_was_active so the notification carries the bed-up photo instead of falling back to the live-cam grab on slow links. Scope limit, documented in the camera wiki: prints started directly on the printer touchscreen / Bambu Handy / Bambu Studio Send bypass both dispatch paths, so the override doesn't fire there. Future option: mid-print M981 S1 P20000 MQTT toggle in on_print_start. Setting description rewritten in all 11 locales to drop the "only works when timelapse enabled" caveat (Bambuddy now forces it) and explain the kept-or-deleted behaviour.
107 lines
5.7 KiB
Python
107 lines
5.7 KiB
Python
from datetime import datetime
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from sqlalchemy import JSON, Boolean, DateTime, Float, ForeignKey, Integer, String, Text, func
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from backend.app.core.database import Base
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class PrintArchive(Base):
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__tablename__ = "print_archives"
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id: Mapped[int] = mapped_column(primary_key=True)
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printer_id: Mapped[int | None] = mapped_column(ForeignKey("printers.id"), nullable=True)
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project_id: Mapped[int | None] = mapped_column(ForeignKey("projects.id", ondelete="SET NULL"), nullable=True)
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# File info
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filename: Mapped[str] = mapped_column(String(255))
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file_path: Mapped[str] = mapped_column(String(500))
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file_size: Mapped[int] = mapped_column(Integer)
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content_hash: Mapped[str | None] = mapped_column(String(64)) # SHA256 hash for duplicate detection
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thumbnail_path: Mapped[str | None] = mapped_column(String(500))
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timelapse_path: Mapped[str | None] = mapped_column(String(500))
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# True when Bambuddy forced timelapse recording on for this print so the
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# finish-photo extractor (#1397) could pull the post-park-pre-drop frame.
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# The cleanup path uses this to know the timelapse should be deleted
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# both locally and on the printer's SD after extraction — the user
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# didn't opt in to a timelapse recording.
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bambuddy_forced_timelapse: Mapped[bool] = mapped_column(Boolean, default=False, server_default="0")
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source_3mf_path: Mapped[str | None] = mapped_column(String(500)) # Original project 3MF from slicer
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f3d_path: Mapped[str | None] = mapped_column(String(500)) # Fusion 360 design file
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# Print details from 3MF / printer
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print_name: Mapped[str | None] = mapped_column(String(255))
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print_time_seconds: Mapped[int | None] = mapped_column(Integer)
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filament_used_grams: Mapped[float | None] = mapped_column(Float)
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filament_type: Mapped[str | None] = mapped_column(String(50))
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filament_color: Mapped[str | None] = mapped_column(String(200))
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layer_height: Mapped[float | None] = mapped_column(Float)
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total_layers: Mapped[int | None] = mapped_column(Integer)
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nozzle_diameter: Mapped[float | None] = mapped_column(Float)
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bed_temperature: Mapped[int | None] = mapped_column(Integer)
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bed_type: Mapped[str | None] = mapped_column(String(64)) # e.g. "Cool Plate", "Textured PEI Plate"
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nozzle_temperature: Mapped[int | None] = mapped_column(Integer)
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# Printer model this file was sliced for (extracted from 3MF metadata)
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sliced_for_model: Mapped[str | None] = mapped_column(String(50), nullable=True)
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# Print result
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status: Mapped[str] = mapped_column(String(20), default="completed")
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started_at: Mapped[datetime | None] = mapped_column(DateTime)
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completed_at: Mapped[datetime | None] = mapped_column(DateTime)
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# Printer-assigned subtask identifier from MQTT. Used to resume the same
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# archive row across a backend restart during a long-running print (#972):
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# if the same subtask_id reappears after restart, we know it's the same
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# print and keep the original row instead of cancel-then-create.
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subtask_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
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# Extended metadata (JSON blob for flexibility)
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extra_data: Mapped[dict | None] = mapped_column(JSON)
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# MakerWorld info (auto-extracted from 3MF)
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makerworld_url: Mapped[str | None] = mapped_column(String(500))
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designer: Mapped[str | None] = mapped_column(String(255))
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# User-defined external link (Printables, Thingiverse, etc.)
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external_url: Mapped[str | None] = mapped_column(String(500))
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# User additions
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is_favorite: Mapped[bool] = mapped_column(Boolean, default=False)
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tags: Mapped[str | None] = mapped_column(Text)
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notes: Mapped[str | None] = mapped_column(Text)
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cost: Mapped[float | None] = mapped_column(Float)
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photos: Mapped[list | None] = mapped_column(JSON) # List of photo filenames
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failure_reason: Mapped[str | None] = mapped_column(String(100)) # For failed prints
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quantity: Mapped[int] = mapped_column(Integer, default=1) # Number of items printed
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# Energy tracking
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energy_kwh: Mapped[float | None] = mapped_column(Float) # Energy consumed in kWh
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energy_cost: Mapped[float | None] = mapped_column(Float) # Cost of energy consumed
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# Plug lifetime counter captured at print start; delta at print end becomes energy_kwh.
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# Persisted so per-print tracking survives backend restarts mid-print (#941).
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energy_start_kwh: Mapped[float | None] = mapped_column(Float)
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# Timestamps
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created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())
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# Soft-delete sentinel (#1343). When non-null, the UI hides this archive
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# from listings (its files have already been removed from disk) but the
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# stats endpoint keeps counting it — deleting nine of ten Benchies no
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# longer wipes their filament / time / cost contribution from Quick Stats.
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# The opt-in "Also remove from statistics" checkbox in the delete dialog
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# bypasses the soft-delete path and hard-deletes the row.
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deleted_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True, default=None, index=True)
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# User tracking (who uploaded/created this archive)
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created_by_id: Mapped[int | None] = mapped_column(ForeignKey("users.id", ondelete="SET NULL"), nullable=True)
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# Relationships
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printer: Mapped["Printer | None"] = relationship(back_populates="archives")
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project: Mapped["Project | None"] = relationship(back_populates="archives")
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created_by: Mapped["User | None"] = relationship()
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from backend.app.models.printer import Printer # noqa: E402, F811
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from backend.app.models.project import Project # noqa: E402, F811
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from backend.app.models.user import User # noqa: E402, F811
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