bangumi-frames — Bilibili Anime Frame & Character Organizer

Overview

Give a Bilibili video (a bangumi ep link, a UP-upload BV link/id, or a **local

video file**); it downloads → extracts scene-change keyframes → splits scenery vs

character frames → organizes the character crops. One pass, two modes:

• **no --ref (cluster mode)** — group every character crop by CCIP identity into

characters/char_NN/.

• **with --ref DIR (one-vs-rest mode)** — given ONE character's reference folder,

pull every crop in the video that matches it into matched/, filenames prefixed

with distance (closest first) so a tight threshold yields a pure set.

Models are anime-specific (deepghs anime person detection + CCIP character-identity

embeddings) — they do not work on live-action footage.

When to use / when NOT to use

• Use when the user wants to collect/extract/organize anime frames or screenshots

from a Bilibili video — by character, by scenery, or to pull out one specific person.

• Don't use for live-action video (needs an insightface-class face stack instead),

or for generic video editing/trimming/transcoding.

Bundled resources

Prerequisites

• ffmpeg on PATH; yt-dlp on PATH for downloads (a local-file input skips download).

• Python 3.9+, pip install dghs-imgutils (first run pulls ~300 MB of models from

HuggingFace, then cached locally).

• A Bilibili cookie (Netscape cookies.txt). Resolution order:

--cookies > $BILIBILI_COOKIES > ~/bb_up/bb_cookies/www.bilibili.com_cookies.txt.

1080p+ / premium episodes need a cookie with membership; a preview-only download means

the cookie lacks access to that episode. Local-file input needs no cookie.

• Run the CCIP step on CPU — do not set ONNX_MODE=CoreML (CCIP crashes; the script

pops it before clustering/matching). Person detection is fine on CoreML.

• (Only for --clean) pip install rapidocr-onnxruntime simple-lama-inpainting.

• (Only for --engine pyscenedetect) pip install scenedetect.

Usage

SKILL=skills/bangumi-frames/scripts/bangumi_frames.py

# Mode 1 — cluster everyone into char_NN groups

python3 $SKILL https://www.bilibili.com/video/BV15qVm68E2h --out ~/frames

python3 $SKILL ep1231575 --out ~/frames           # ep / BV id also accepted

python3 $SKILL ~/local.mp4 --out ~/frames          # local file, skips download

# Mode 2 — pull out ONE character (ref folder = ~200 crops of that character)

python3 $SKILL BV15qVm68E2h --ref ~/refs/紫灵 --ref-eps 0.04 --out ~/frames

# Optional: strip burned-in subtitles + watermark before analysis

python3 $SKILL ep1231575 --clean --out ~/frames

Stages are idempotent (a stage is skipped when its output already exists; clustering /

matching always re-runs since the CCIP features are cached). For every flag, the per-stage

trade-offs, and the threshold lore, read the two reference files above.

Agent-native output: stdout is a single JSON envelope ({"ok", "data", "next", "meta"}

on success, {"ok": false, "error"} on failure — JSON when piped, a human summary on a TTY;

force with --format), stderr carries human progress logs, and exit codes are stable

(0 ok · 1 runtime · 2 auth · 3 validation). Use --dry-run to preview the plan

without downloading, --schema to print the output contract. Details in references/pipeline.md.

Output

<out>/<id>/                     # id = BV id / ep id / local filename

├── frames/  frames.json        # keyframes + timestamps

├── scenery/                    # frames with no detected character

├── crops/  features.npy        # character crops + cached CCIP features

├── detect.json                 # frame -> person boxes / crops

├── characters/                 # MODE 1: char_NN_crop/ + char_NN_full/ (paired), _unsorted/, _montage.png

├── matched/                    # MODE 2: 0.012_<crop>.jpg (distance-prefixed) + index.json

├── matched_montage.png         # MODE 2 sample montage

└── index.json                  # MODE 1: char group -> {crop, frame, time}

After a run, look at characters/_montage.png (mode 1) or matched_montage.png (mode 2)

first to judge quality, then read index.json. See references/modes.md for what to

adjust when grouping/matching is off.

Limits

• Anime / 2.5D-render art only; live-action needs a different (face-recognition) stack.

• CCIP may split one character's different forms (outfit / transform) into separate groups

— usually fine for "group by visual appearance"; for mode 2, put each form in the ref.

• 1080p+ on Bilibili needs a membership cookie; download is for personal offline analysis

only and uploads nothing.