digital-garbageandClaude Sonnet 5.5
0fc080b057
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
249 lines
11 KiB
Python
249 lines
11 KiB
Python
"""Add a camera move to each `[Shot N]` block of an H3 prompt that has none, aimed at the
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most specific thing in that shot. No language model: a small spaCy tagger (rules-only
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fallback when it is not installed) picks the target, a few words pick the move.
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`[Shot N]` here is MiniMax's own in-prompt shot, never a timeline scene. `<Subject N>` tags
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are the subjects every shot is about, so they are never a target on their own; a part or
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belonging of one ("<Subject 1>'s hand") is. A thing named in most shots (or in the text before
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the first shot) is a constant, not a target. Sound text (music, score...) is left alone.
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"""
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import math
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import random
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import re
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SHOT = re.compile(r"\[\s*Shot\s+(\d+)\s*\]", re.I)
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SUBJECT = re.compile(r"<\s*Subject\s*(\d+)\s*>", re.I)
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# A `<Subject N>` tag confuses the tagger ("Subject1 bites" reads as a noun); a real-looking
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# rare name parses like a person. Swapped in before tagging, swapped back after.
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NAMES = ("Mariel", "Tobias", "Odette", "Rurik", "Sabine", "Teodor", "Ilsa", "Corwin", "Yvette")
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STAND_IN = re.compile(r"\b(" + "|".join(NAMES) + r")\b")
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AUDIO = re.compile(r"\b(music|soundtrack|score|melody|instrumental|beat|bassline|synth|"
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r"song|tempo|audio|ambient sound|sound of)\b", re.I)
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CAMERA = re.compile(r"\b(camera|zoom(?:s|ing)?|close-?up|pan(?:s|ning)?|dolly|tracking|"
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r"rack focus|focus(?:es|ing)? (?:on|at)|tilt(?:s|ing)?|rotate|orbit|"
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r"push[- ]in|pull[- ]back|wide shot|handheld|crane)\b", re.I)
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GENERIC = {"camera", "video", "scene", "shot", "frame", "background", "foreground", "view",
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"image", "screen", "moment", "time", "way", "side", "front", "middle", "one",
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"other", "something", "everything", "thing", "music", "sound", "style", "lighting",
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"light", "atmosphere", "mood", "setting"}
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PARTS = {"lip", "mouth", "tongue", "tooth", "teeth", "eye", "eyes", "face", "cheek", "hand",
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"finger", "hair", "neck", "shoulder", "waist", "hip", "leg", "foot", "feet", "arm",
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"skin", "expression", "smile", "gaze", "back", "chin", "brow"}
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PLACES = {"room", "street", "kitchen", "bedroom", "bed", "table", "floor", "stage", "hall",
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"garden", "beach", "forest", "city", "car", "office", "bathroom", "field"}
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MOVES_DETAIL = ("Camera moves into a close-up on {x}.", "Camera focuses at {x}.",
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"Camera zooms in at {x}.")
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MOVES_OTHER = ("Camera zooms in at {x}.", "Camera moves around {x}.",
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"Camera rotates to {x}.")
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MOVES_TRAVEL = ("Camera moves from {x} to {y}.", "Camera pans from {x} to {y}.",
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"Camera shifts focus from {x} to {y}.")
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# Chained moves (varied mode): a follow-up to a first move, and a closing pull-back.
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MOVES_THEN = ("Then the camera moves to {y}.", "Then the camera pans to {y}.",
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"Then the camera focuses at {y}.")
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MOVES_FINISH = ("Then the camera zooms out.", "Then the camera pulls back.",
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"Then the camera pulls back to a wider view.")
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COUNT_WEIGHTS = (6, 3, 1) # one move / two / three, in a shot that gets any
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TRAVEL_SHARE = 0.5 # of one-move shots with two topics, how many travel X -> Y
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DETERMINERS = {"the", "a", "an", "this", "that", "these", "those"}
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POSSESSIVE_PRONOUNS = {"his", "her", "their", "its", "my", "your", "our"}
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_nlp = None
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_tried = False
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def _spacy():
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"""The small English tagger, or None (said once) when it is not installed."""
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global _nlp, _tried
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if not _tried:
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_tried = True
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try:
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import spacy
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_nlp = spacy.load("en_core_web_sm")
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except Exception as e: # noqa: BLE001
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try:
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import funpack_log as _log
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except ImportError:
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from . import funpack_log as _log
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fix = ("spacy not installed, run `pip install spacy`" if isinstance(e, ImportError)
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else "model missing, run `python -m spacy download en_core_web_sm`")
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_log.failed("FunPackCameraMoves", "spaCy tagger", e, f"simple word rules used; {fix}")
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return _nlp
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def _hide(text):
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return SUBJECT.sub(lambda m: NAMES[(int(m.group(1)) - 1) % len(NAMES)], text)
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def _show(text):
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return STAND_IN.sub(lambda m: f"<Subject {NAMES.index(m.group(1)) + 1}>", text)
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def _is_subject(text):
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return bool(STAND_IN.fullmatch(re.sub(r"^(?:the\s+)", "", text.strip(), flags=re.I)))
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def _split_sound(body):
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"""-> (picture text, sound text): everything from the first sentence about sound on."""
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for m in re.finditer(r"[^.!?]+[.!?]?\s*", body):
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if AUDIO.search(m.group(0)):
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return body[:m.start()], body[m.start():]
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return body, ""
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def _phrase(tok):
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"""A chunk head with the words that name it ("Subject1's hand", "red lips")."""
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keep = {tok}
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for c in tok.children:
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if c.dep_ in ("poss", "compound", "amod") and c.i < tok.i:
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keep.add(c)
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if c.dep_ == "poss":
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keep.update(g for g in c.children if g.dep_ == "case")
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toks = sorted(keep, key=lambda t: t.i)
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text = " ".join(t.text for t in toks).strip().replace(" 's", "'s").replace(" ’s", "’s")
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return text, any(c.dep_ == "poss" for c in tok.children)
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def candidates(picture):
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"""[(lemma, phrase, owned, position, dep)] for the nouns of one shot's picture text."""
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hidden = _hide(picture)
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nlp = _spacy()
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out = []
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if nlp is not None:
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doc = nlp(hidden)
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for chunk in doc.noun_chunks:
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root = chunk.root
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prev = doc[root.i - 1] if root.i else None
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if prev is not None and STAND_IN.fullmatch(prev.text) and prev.dep_ != "poss":
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continue # "<Subject 1> laughs": the tagger misread a verb
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if root.pos_ in ("PRON", "NUM") or _is_subject(chunk.text):
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continue
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lemma = root.lemma_.lower()
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if root.dep_ == "pobj" and root.head.lower_ == "of":
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continue # "a glass of wine": the glass is the thing
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text, owned = _phrase(root)
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if _is_subject(text) or lemma in GENERIC or STAND_IN.fullmatch(root.text):
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continue
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out.append((lemma, text, owned, chunk.start, root.dep_))
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return out
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for m in re.finditer(rf"(?:({'|'.join(NAMES)})'s|\b(his|her|their|the|a|an))\s+((?:\w+\s+)?\w+)",
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hidden, re.I):
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words = m.group(3).split()
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lemma = words[-1].lower().rstrip("s") if words[-1].lower() not in PARTS else words[-1].lower()
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if lemma in GENERIC:
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continue
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owner = m.group(1) or (m.group(2) if (m.group(2) or "").lower() in POSSESSIVE_PRONOUNS else "")
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text = (f"{owner}'s " if m.group(1) else f"{owner} " if owner else "") + " ".join(words)
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out.append((lemma, text.strip(), bool(owner), m.start(), "obj"))
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return out
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def _score(c):
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lemma, _text, owned, pos, dep = c
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return ((2 if owned or lemma in PARTS else 0) + (1 if dep in ("dobj", "pobj") else 0)
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- pos * 1e-4)
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def _name(c):
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"""The phrase of candidate `c` as it reads in the prompt, tags restored, with an article."""
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text = _show(c[1])
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if not re.match(r"(?:the|a|an|<Subject|his|her|their)\b", text, re.I) and "'s " not in text:
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text = "the " + text
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return text
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def _topics(pool):
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"""The distinct things a shot dwells on, in the order the text reaches them. Only things
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with a role beyond being the doer (owned parts, objects of a verb or preposition)."""
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seen, out = set(), []
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for c in sorted(pool, key=lambda c: c[3]):
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if _score(c) > 0.5 and c[0] not in seen:
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seen.add(c[0])
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out.append(c)
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return out
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def _pick(options, last, rng):
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"""One of `options`, never the one the previous shot opened with when others exist."""
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pool = [o for o in options if o != last] or list(options)
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return rng.choice(pool) if rng else pool[0]
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def _plan(pool, last, rng):
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"""-> (moves text, target description, opening template). Fixed and minimal without
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`rng` (one move, X -> Y when the shot has two topics); with it, how many moves, whether
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they travel and which words are all drawn, so no shot pattern repeats by construction."""
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topics = _topics(pool)
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best = max(pool, key=_score)
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if rng is None:
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k, travel = 1, len(topics) >= 2
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else:
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k = rng.choices((1, 2, 3), COUNT_WEIGHTS)[0]
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travel = k == 1 and len(topics) >= 2 and rng.random() < TRAVEL_SHARE
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if travel:
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x, y = _name(topics[0]), _name(topics[-1])
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move = _pick(MOVES_TRAVEL, last, rng)
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return move.format(x=x, y=y), f"{x} -> {y}", move
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first = topics[0] if (k > 1 and topics) else best
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moves = MOVES_DETAIL if (first[2] or first[0] in PARTS) else MOVES_OTHER
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opening = _pick(moves, last, rng)
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target = _name(first)
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sentences = [opening.format(x=target)]
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desc = target
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if k > 1:
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if len(topics) >= 2:
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y = _name(topics[-1])
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sentences.append(_pick(MOVES_THEN, None, rng).format(y=y))
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desc += f" -> {y}"
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else:
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sentences.append(_pick(MOVES_FINISH, None, rng))
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if k > 2 and len(topics) >= 2:
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sentences.append(_pick(MOVES_FINISH, None, rng))
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return " ".join(sentences), desc, opening
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def add_camera_moves(text, seed=None, chance=1.0):
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"""-> (new prompt, [per-shot report dicts]). Unchanged when there is no `[Shot N]`.
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`seed=None`: one plain move per shot (deterministic). With a `seed`, each shot is left
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alone with probability 1 - `chance`, and otherwise gets one to three moves, drawn per
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shot from the seed, so the same prompt and seed always give the same text."""
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marks = list(SHOT.finditer(text or ""))
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if not marks:
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return text, []
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header = text[:marks[0].start()]
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bodies = [text[m.end():(marks[i + 1].start() if i + 1 < len(marks) else len(text))]
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for i, m in enumerate(marks)]
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parts = [_split_sound(b) for b in bodies]
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cands = [candidates(p[0]) for p in parts]
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header_lemmas = {c[0] for c in candidates(header)}
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need = max(2, math.ceil(0.6 * len(bodies))) if len(bodies) > 1 else 10 ** 9
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seen = {}
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for lst in cands:
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for lemma in {c[0] for c in lst}:
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seen[lemma] = seen.get(lemma, 0) + 1
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constant = {l for l, n in seen.items() if n >= need} | header_lemmas
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report, out, last = [], [header], None
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for i, m in enumerate(marks):
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picture, sound = parts[i]
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entry = {"shot": int(m.group(1)), "move": None, "target": None, "why": ""}
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rng = random.Random(f"{seed}:{i}") if seed is not None else None
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pool = [c for c in cands[i] if c[0] not in constant]
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if CAMERA.search(picture):
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entry["why"] = "already has a camera move"
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elif not pool:
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entry["why"] = "nothing specific to aim at"
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elif rng is not None and rng.random() >= chance:
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entry["why"] = "left as written by chance"
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else:
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moves, desc, opening = _plan(pool, last, rng)
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entry.update(move=moves, target=desc)
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last = opening
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core = picture.rstrip()
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gap = picture[len(core):] or (" " if sound else "")
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picture = core + ("" if core.endswith((".", "!", "?")) else ".") + " " \
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+ moves + gap
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out.append(m.group(0) + picture + sound)
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report.append(entry)
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return "".join(out), report
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