From adecaeee29d096df43e9c628b5de9f6458b268a1 Mon Sep 17 00:00:00 2001 From: Nao Date: Sun, 7 Jun 2026 19:03:46 +0900 Subject: [PATCH] =?UTF-8?q?fix:=20Apple=20Silicon=E7=92=B0=E5=A2=83?= =?UTF-8?q?=E3=81=A7=E3=81=AEGUI=E3=82=B9=E3=83=AC=E3=83=83=E3=83=89?= =?UTF-8?q?=E3=83=95=E3=83=AA=E3=83=BC=E3=82=BA=E4=BF=AE=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 変更内容: mouth_track_gui/app.py - after(0, ...) をスレッドから呼ぶ問題(macOS Tkinter deadlock)を修正 - UIタスクキュー(queue.Queue)を導入し50ms間隔でポーリングする安全な更新機構に変更 - _poll_ui_queue() / _insert_log() を追加 - log(), _show_error(), _progress_*(), _set_running() 等を ui_queue 経由に統一 mouth_sprite_extractor_gui.py - 解析完了通知を after(0,...) から _poll_analyze_done() ポーリング方式に変更 - 解析開始前にcv2.VideoCaptureを解放しAVFoundationデッドロックを回避 motionpngtuber/mouth_sprite_extractor.py - _run_face_detector() をsubprocess.run からPopen に変更し 解析進捗をリアルタイムでGUIログに流せるよう改善 Co-authored-by: Gemini --- motionpngtuber/mouth_sprite_extractor.py | 1486 +++++++++++----------- mouth_sprite_extractor_gui.py | 49 +- mouth_track_gui/app.py | 68 +- pyproject.toml | 150 +-- uv.lock | 904 +------------ 5 files changed, 899 insertions(+), 1758 deletions(-) diff --git a/motionpngtuber/mouth_sprite_extractor.py b/motionpngtuber/mouth_sprite_extractor.py index 0359178..f55a7dc 100644 --- a/motionpngtuber/mouth_sprite_extractor.py +++ b/motionpngtuber/mouth_sprite_extractor.py @@ -1,108 +1,108 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -mouth_sprite_extractor.py - -動画から口スプライト（5種類のPNG）を自動抽出するコアモジュール。 - -機能: -1. 動画の全フレームから口quadを検出 -2. 口の位置が安定しているフレーム群（クラスタ）を特定 -3. 5種類の口形状を自動選別（open, closed, half, e, u） -4. 楕円マスク＋フェザーで透過PNG出力 - -使い方（コマンドライン）: - python mouth_sprite_extractor.py --video loop.mp4 --out mouth/ - -使い方（モジュールとして）: - from mouth_sprite_extractor import MouthSpriteExtractor - extractor = MouthSpriteExtractor(video_path) - extractor.analyze() - extractor.extract_sprites(output_dir, feather_px=15) -""" - -from __future__ import annotations - -import argparse -import os -import subprocess -import sys -import tempfile -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple - -import cv2 -import numpy as np - -from motionpngtuber.image_io import write_image_file -from motionpngtuber.python_exec import resolve_python_subprocess_executable - - -# --------------------------------------------------------------------------- -# Data classes -# --------------------------------------------------------------------------- - -@dataclass -class MouthFrameInfo: - """1フレームの口情報""" - # 既存フィールド - frame_idx: int - quad: np.ndarray # (4, 2) float32 - center: np.ndarray # (2,) float32 - 口の中心座標 - width: float # 口quadの幅 - height: float # 口quadの高さ - confidence: float # 検出信頼度 - valid: bool # 検出が有効か - - # 新規フィールド（全てデフォルト値あり - 後方互換性のため） - inner_darkness: float = 0.0 # 口内部の暗さ (0.0-1.0) - opening_ratio: float = 0.0 # 開口度 (0.0-1.0) - horizontal_stretch: float = 0.0 # 横への伸び (0.0-1.0) - vertical_compression: float = 0.0 # 縦の圧縮 (0.0-1.0) - lip_curvature: float = 0.0 # 唇の曲率 (-1.0〜1.0) - score_open: float = 0.0 # openタイプのスコア - score_closed: float = 0.0 # closedタイプのスコア - score_half: float = 0.0 # halfタイプのスコア - score_e: float = 0.0 # eタイプのスコア - score_u: float = 0.0 # uタイプのスコア - - -@dataclass -class MouthTypeSelection: - """5種類の口の選択結果""" - open_idx: int # 口を大きく開けたフレーム - closed_idx: int # 口を閉じたフレーム - half_idx: int # 半開きフレーム - e_idx: int # 横長の口フレーム - u_idx: int # すぼめた口フレーム - - def as_dict(self) -> Dict[str, int]: - return { - "open": self.open_idx, - "closed": self.closed_idx, - "half": self.half_idx, - "e": self.e_idx, - "u": self.u_idx, - } - - -# --------------------------------------------------------------------------- -# Geometry helpers -# --------------------------------------------------------------------------- - -def quad_center(quad: np.ndarray) -> np.ndarray: - """quadの中心座標を計算""" - return quad.mean(axis=0).astype(np.float32) - - -def quad_wh(quad: np.ndarray) -> Tuple[float, float]: - """quadの幅と高さを計算""" - quad = np.asarray(quad, dtype=np.float32).reshape(4, 2) - w = float(np.linalg.norm(quad[1] - quad[0])) - h = float(np.linalg.norm(quad[3] - quad[0])) - return w, h - - +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +mouth_sprite_extractor.py + +動画から口スプライト（5種類のPNG）を自動抽出するコアモジュール。 + +機能: +1. 動画の全フレームから口quadを検出 +2. 口の位置が安定しているフレーム群（クラスタ）を特定 +3. 5種類の口形状を自動選別（open, closed, half, e, u） +4. 楕円マスク＋フェザーで透過PNG出力 + +使い方（コマンドライン）: + python mouth_sprite_extractor.py --video loop.mp4 --out mouth/ + +使い方（モジュールとして）: + from mouth_sprite_extractor import MouthSpriteExtractor + extractor = MouthSpriteExtractor(video_path) + extractor.analyze() + extractor.extract_sprites(output_dir, feather_px=15) +""" + +from __future__ import annotations + +import argparse +import os +import subprocess +import sys +import tempfile +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple + +import cv2 +import numpy as np + +from motionpngtuber.image_io import write_image_file +from motionpngtuber.python_exec import resolve_python_subprocess_executable + + +# --------------------------------------------------------------------------- +# Data classes +# --------------------------------------------------------------------------- + +@dataclass +class MouthFrameInfo: + """1フレームの口情報""" + # 既存フィールド + frame_idx: int + quad: np.ndarray # (4, 2) float32 + center: np.ndarray # (2,) float32 - 口の中心座標 + width: float # 口quadの幅 + height: float # 口quadの高さ + confidence: float # 検出信頼度 + valid: bool # 検出が有効か + + # 新規フィールド（全てデフォルト値あり - 後方互換性のため） + inner_darkness: float = 0.0 # 口内部の暗さ (0.0-1.0) + opening_ratio: float = 0.0 # 開口度 (0.0-1.0) + horizontal_stretch: float = 0.0 # 横への伸び (0.0-1.0) + vertical_compression: float = 0.0 # 縦の圧縮 (0.0-1.0) + lip_curvature: float = 0.0 # 唇の曲率 (-1.0〜1.0) + score_open: float = 0.0 # openタイプのスコア + score_closed: float = 0.0 # closedタイプのスコア + score_half: float = 0.0 # halfタイプのスコア + score_e: float = 0.0 # eタイプのスコア + score_u: float = 0.0 # uタイプのスコア + + +@dataclass +class MouthTypeSelection: + """5種類の口の選択結果""" + open_idx: int # 口を大きく開けたフレーム + closed_idx: int # 口を閉じたフレーム + half_idx: int # 半開きフレーム + e_idx: int # 横長の口フレーム + u_idx: int # すぼめた口フレーム + + def as_dict(self) -> Dict[str, int]: + return { + "open": self.open_idx, + "closed": self.closed_idx, + "half": self.half_idx, + "e": self.e_idx, + "u": self.u_idx, + } + + +# --------------------------------------------------------------------------- +# Geometry helpers +# --------------------------------------------------------------------------- + +def quad_center(quad: np.ndarray) -> np.ndarray: + """quadの中心座標を計算""" + return quad.mean(axis=0).astype(np.float32) + + +def quad_wh(quad: np.ndarray) -> Tuple[float, float]: + """quadの幅と高さを計算""" + quad = np.asarray(quad, dtype=np.float32).reshape(4, 2) + w = float(np.linalg.norm(quad[1] - quad[0])) + h = float(np.linalg.norm(quad[3] - quad[0])) + return w, h + + def ensure_even_ge2(n: int) -> int: """偶数に丸める（最小2）""" n = int(n) @@ -129,644 +129,656 @@ def resolve_adjacent_or_repo_script(script_name: str, *, anchor_file: str | None searched = ", ".join(candidates) raise FileNotFoundError(f"Script not found: {script_name} (searched: {searched})") - - -# --------------------------------------------------------------------------- -# Track loading (compatible with face_track_anime_detector.py output) -# --------------------------------------------------------------------------- - -def load_track_data(track_path: str, target_w: int, target_h: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """ - mouth_track.npz からデータを読み込む。 - - Returns: - quads: (N, 4, 2) float32 - valid: (N,) bool - confidence: (N,) float32 or None - """ - npz = np.load(track_path, allow_pickle=False) - - if "quad" not in npz: - raise ValueError("track must contain 'quad' (N,4,2)") - - quads = np.asarray(npz["quad"], dtype=np.float32) - if quads.ndim != 3 or quads.shape[1:] != (4, 2): - raise ValueError("quad must be (N,4,2)") - - N = int(quads.shape[0]) - - # valid配列 - if "valid" in npz: - valid = np.asarray(npz["valid"], dtype=np.uint8).astype(bool) - if valid.shape[0] != N: - valid = np.ones((N,), bool) - else: - valid = np.ones((N,), bool) - - # スケーリング - src_w = int(npz["w"]) if "w" in npz else target_w - src_h = int(npz["h"]) if "h" in npz else target_h - sx = float(target_w) / float(max(1, src_w)) - sy = float(target_h) / float(max(1, src_h)) - quads = quads.copy() - quads[..., 0] *= sx - quads[..., 1] *= sy - - # confidence配列 - if "confidence" in npz: - confidence = np.asarray(npz["confidence"], dtype=np.float32) - if confidence.shape[0] != N: - confidence = np.ones((N,), dtype=np.float32) - else: - confidence = np.ones((N,), dtype=np.float32) - - return quads, valid, confidence - - -# --------------------------------------------------------------------------- -# Position-aware clustering -# --------------------------------------------------------------------------- - -def find_stable_position_cluster( - centers: np.ndarray, - valid: np.ndarray, - distance_threshold: float = 50.0, -) -> np.ndarray: - """ - 口の中心座標が安定しているフレーム群を特定する。 - - 最も多くのフレームが集まっている位置クラスタを見つけ、 - そのクラスタに属するフレームのマスクを返す。 - - Args: - centers: (N, 2) 口の中心座標 - valid: (N,) 有効フレームマスク - distance_threshold: クラスタ判定の距離閾値（ピクセル） - - Returns: - cluster_mask: (N,) bool - クラスタに属するフレーム - """ - N = len(centers) - valid_indices = np.where(valid)[0] - - if len(valid_indices) < 5: - # フレームが少なすぎる場合はすべて使用 - return valid.copy() - - valid_centers = centers[valid_indices] - - # 各有効フレームについて、近くにいくつのフレームがあるかカウント - counts = np.zeros(len(valid_indices), dtype=np.int32) - for i, c in enumerate(valid_centers): - dists = np.linalg.norm(valid_centers - c, axis=1) - counts[i] = np.sum(dists <= distance_threshold) - - # 最も密なフレームを基準にクラスタを構成 - best_idx = np.argmax(counts) - best_center = valid_centers[best_idx] - - # 基準点からの距離でクラスタを決定 - dists_from_best = np.linalg.norm(valid_centers - best_center, axis=1) - cluster_valid_mask = dists_from_best <= distance_threshold - - # 元のインデックスに変換 - cluster_mask = np.zeros(N, dtype=bool) - for i, orig_idx in enumerate(valid_indices): - if cluster_valid_mask[i]: - cluster_mask[orig_idx] = True - - return cluster_mask - - -# --------------------------------------------------------------------------- -# Mouth type selection -# --------------------------------------------------------------------------- - -def select_5_mouth_types( - mouth_frames: List[MouthFrameInfo], - cluster_mask: np.ndarray, -) -> MouthTypeSelection: - """ - 5種類の口タイプを自動選別する。 - - 選別基準: - - open: 口の高さが最大 - - closed: 口の高さが最小 - - half: 高さが中央値付近 - - e: 幅/高さ比が最大（横長） - - u: 幅が最小かつ高さが中程度（すぼめた口） - - Args: - mouth_frames: 全フレームの口情報 - cluster_mask: 位置クラスタに属するフレームのマスク - - Returns: - MouthTypeSelection - """ - # クラスタ内の有効フレームのみを対象 - candidates = [ - mf for mf in mouth_frames - if mf.valid and cluster_mask[mf.frame_idx] - ] - - if len(candidates) < 5: - # フォールバック: 全有効フレームを使用 - candidates = [mf for mf in mouth_frames if mf.valid] - - if len(candidates) == 0: - raise ValueError("No valid mouth frames found") - - # 各種メトリクス - heights = np.array([mf.height for mf in candidates]) - widths = np.array([mf.width for mf in candidates]) - aspect_ratios = widths / np.maximum(heights, 1e-6) - - used_indices = set() - - def pick_best(scores: np.ndarray, maximize: bool = True) -> int: - """最良のフレームを選択（既に使用済みは除外）""" - sorted_indices = np.argsort(scores) - if maximize: - sorted_indices = sorted_indices[::-1] - - for idx in sorted_indices: - frame_idx = candidates[idx].frame_idx - if frame_idx not in used_indices: - used_indices.add(frame_idx) - return frame_idx - - # フォールバック（すべて使用済みの場合） - return candidates[sorted_indices[0]].frame_idx - - # 1. open: 高さ最大 - open_idx = pick_best(heights, maximize=True) - - # 2. closed: 高さ最小 - closed_idx = pick_best(heights, maximize=False) - - # 3. half: 高さが中央値付近 - median_height = np.median(heights) - half_scores = -np.abs(heights - median_height) # 中央に近いほど高スコア - half_idx = pick_best(half_scores, maximize=True) - - # 4. e: 横長（幅/高さ比が大きい） - e_idx = pick_best(aspect_ratios, maximize=True) - - # 5. u: すぼめた口（幅が小さく、高さは中程度） - # 幅が小さいものを優先、高さは極端でないもの - height_median_dist = np.abs(heights - median_height) - u_scores = -widths - 0.5 * height_median_dist # 幅小さく、高さは中央寄り - u_idx = pick_best(u_scores, maximize=True) - - return MouthTypeSelection( - open_idx=open_idx, - closed_idx=closed_idx, - half_idx=half_idx, - e_idx=e_idx, - u_idx=u_idx, - ) - - -# --------------------------------------------------------------------------- -# Mask generation -# --------------------------------------------------------------------------- - -def make_ellipse_mask(w: int, h: int, rx: int, ry: int) -> np.ndarray: - """楕円マスクを生成（0/255）""" - mask = np.zeros((h, w), dtype=np.uint8) - cx, cy = w // 2, h // 2 - rx = int(max(1, min(rx, w // 2 - 1))) - ry = int(max(1, min(ry, h // 2 - 1))) - cv2.ellipse(mask, (cx, cy), (rx, ry), 0.0, 0.0, 360.0, 255, -1) - return mask - - -def feather_mask(mask_u8: np.ndarray, feather_px: int) -> np.ndarray: - """マスクにフェザー（グラデーション）を適用""" - if feather_px <= 0: - return (mask_u8.astype(np.float32) / 255.0).clip(0.0, 1.0) - - k = 2 * int(feather_px) + 1 - m = cv2.GaussianBlur(mask_u8, (k, k), sigmaX=0) - return (m.astype(np.float32) / 255.0).clip(0.0, 1.0) - - -# --------------------------------------------------------------------------- -# Sprite extraction -# --------------------------------------------------------------------------- - -def warp_frame_to_norm( - frame_bgr: np.ndarray, - quad: np.ndarray, - norm_w: int, - norm_h: int, -) -> np.ndarray: - """フレームから口パッチを正規化空間に変換""" - src = np.asarray(quad, dtype=np.float32).reshape(4, 2) - dst = np.array([ - [0, 0], - [norm_w - 1, 0], - [norm_w - 1, norm_h - 1], - [0, norm_h - 1], - ], dtype=np.float32) - - M = cv2.getPerspectiveTransform(src, dst) - patch = cv2.warpPerspective( - frame_bgr, - M, - (int(norm_w), int(norm_h)), - flags=cv2.INTER_LINEAR, - borderMode=cv2.BORDER_REPLICATE, - ) - return patch - - -def extract_mouth_sprite( - frame_bgr: np.ndarray, - quad: np.ndarray, - unified_w: int, - unified_h: int, - feather_px: int = 15, - mask_scale: float = 0.85, -) -> np.ndarray: - """ - フレームから口スプライトを抽出する。 - - Args: - frame_bgr: 入力フレーム（BGR） - quad: 口のquad (4, 2) - unified_w: 出力幅 - unified_h: 出力高さ - feather_px: フェザー幅（ピクセル） - mask_scale: マスクの楕円サイズ（0.0-1.0） - - Returns: - rgba: (H, W, 4) uint8 - 透過PNG用 - """ - # 正規化空間に変換 - patch_bgr = warp_frame_to_norm(frame_bgr, quad, unified_w, unified_h) - patch_rgb = cv2.cvtColor(patch_bgr, cv2.COLOR_BGR2RGB) - - # 楕円マスク生成 - rx = int((unified_w * mask_scale) * 0.5) - ry = int((unified_h * mask_scale) * 0.5) - mask_u8 = make_ellipse_mask(unified_w, unified_h, rx, ry) - - # フェザー適用 - mask_f = feather_mask(mask_u8, feather_px) - - # RGBA画像を生成 - rgba = np.zeros((unified_h, unified_w, 4), dtype=np.uint8) - rgba[:, :, :3] = patch_rgb - rgba[:, :, 3] = (mask_f * 255).astype(np.uint8) - - return rgba - - -def compute_unified_size( - mouth_frames: List[MouthFrameInfo], - selected_indices: List[int], - padding: float = 1.1, -) -> Tuple[int, int]: - """ - 選択されたフレームの口がすべて収まるサイズを計算。 - - Args: - mouth_frames: 全フレームの口情報 - selected_indices: 選択されたフレームインデックス - padding: 余白係数 - - Returns: - (width, height) - """ - idx_to_mf = {mf.frame_idx: mf for mf in mouth_frames} - - max_w = 0.0 - max_h = 0.0 - for idx in selected_indices: - if idx in idx_to_mf: - mf = idx_to_mf[idx] - max_w = max(max_w, mf.width) - max_h = max(max_h, mf.height) - - # パディングを適用して偶数に丸める - w = ensure_even_ge2(int(max_w * padding)) - h = ensure_even_ge2(int(max_h * padding)) - - return w, h - - -# --------------------------------------------------------------------------- -# Main extractor class -# --------------------------------------------------------------------------- - -class MouthSpriteExtractor: - """口スプライト抽出器""" - - def __init__(self, video_path: str, track_path: str = ""): - """ - Args: - video_path: 入力動画のパス - track_path: mouth_track.npz のパス（空の場合は自動検索） - """ - self.video_path = video_path - self.track_path = track_path - - # 動画情報 - self.vid_w = 0 - self.vid_h = 0 - self.fps = 0.0 - self.n_frames = 0 - - # 解析結果 - self.mouth_frames: List[MouthFrameInfo] = [] - self.cluster_mask: Optional[np.ndarray] = None - self.selection: Optional[MouthTypeSelection] = None - self.unified_size: Optional[Tuple[int, int]] = None - - self._load_video_info() - - def _load_video_info(self): - """動画の情報を取得""" - cap = cv2.VideoCapture(self.video_path) - if not cap.isOpened(): - raise RuntimeError(f"Failed to open video: {self.video_path}") - - self.vid_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - self.vid_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - self.fps = float(cap.get(cv2.CAP_PROP_FPS) or 30.0) - self.n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0) - cap.release() - - def _find_track_path(self) -> str: - """トラックファイルを自動検索""" - if self.track_path and os.path.isfile(self.track_path): - return self.track_path - - video_dir = os.path.dirname(os.path.abspath(self.video_path)) - candidates = [ - os.path.join(video_dir, "mouth_track_calibrated.npz"), - os.path.join(video_dir, "mouth_track.npz"), - ] - - for cand in candidates: - if os.path.isfile(cand): - return cand - - return "" - + + +# --------------------------------------------------------------------------- +# Track loading (compatible with face_track_anime_detector.py output) +# --------------------------------------------------------------------------- + +def load_track_data(track_path: str, target_w: int, target_h: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + mouth_track.npz からデータを読み込む。 + + Returns: + quads: (N, 4, 2) float32 + valid: (N,) bool + confidence: (N,) float32 or None + """ + npz = np.load(track_path, allow_pickle=False) + + if "quad" not in npz: + raise ValueError("track must contain 'quad' (N,4,2)") + + quads = np.asarray(npz["quad"], dtype=np.float32) + if quads.ndim != 3 or quads.shape[1:] != (4, 2): + raise ValueError("quad must be (N,4,2)") + + N = int(quads.shape[0]) + + # valid配列 + if "valid" in npz: + valid = np.asarray(npz["valid"], dtype=np.uint8).astype(bool) + if valid.shape[0] != N: + valid = np.ones((N,), bool) + else: + valid = np.ones((N,), bool) + + # スケーリング + src_w = int(npz["w"]) if "w" in npz else target_w + src_h = int(npz["h"]) if "h" in npz else target_h + sx = float(target_w) / float(max(1, src_w)) + sy = float(target_h) / float(max(1, src_h)) + quads = quads.copy() + quads[..., 0] *= sx + quads[..., 1] *= sy + + # confidence配列 + if "confidence" in npz: + confidence = np.asarray(npz["confidence"], dtype=np.float32) + if confidence.shape[0] != N: + confidence = np.ones((N,), dtype=np.float32) + else: + confidence = np.ones((N,), dtype=np.float32) + + return quads, valid, confidence + + +# --------------------------------------------------------------------------- +# Position-aware clustering +# --------------------------------------------------------------------------- + +def find_stable_position_cluster( + centers: np.ndarray, + valid: np.ndarray, + distance_threshold: float = 50.0, +) -> np.ndarray: + """ + 口の中心座標が安定しているフレーム群を特定する。 + + 最も多くのフレームが集まっている位置クラスタを見つけ、 + そのクラスタに属するフレームのマスクを返す。 + + Args: + centers: (N, 2) 口の中心座標 + valid: (N,) 有効フレームマスク + distance_threshold: クラスタ判定の距離閾値（ピクセル） + + Returns: + cluster_mask: (N,) bool - クラスタに属するフレーム + """ + N = len(centers) + valid_indices = np.where(valid)[0] + + if len(valid_indices) < 5: + # フレームが少なすぎる場合はすべて使用 + return valid.copy() + + valid_centers = centers[valid_indices] + + # 各有効フレームについて、近くにいくつのフレームがあるかカウント + counts = np.zeros(len(valid_indices), dtype=np.int32) + for i, c in enumerate(valid_centers): + dists = np.linalg.norm(valid_centers - c, axis=1) + counts[i] = np.sum(dists <= distance_threshold) + + # 最も密なフレームを基準にクラスタを構成 + best_idx = np.argmax(counts) + best_center = valid_centers[best_idx] + + # 基準点からの距離でクラスタを決定 + dists_from_best = np.linalg.norm(valid_centers - best_center, axis=1) + cluster_valid_mask = dists_from_best <= distance_threshold + + # 元のインデックスに変換 + cluster_mask = np.zeros(N, dtype=bool) + for i, orig_idx in enumerate(valid_indices): + if cluster_valid_mask[i]: + cluster_mask[orig_idx] = True + + return cluster_mask + + +# --------------------------------------------------------------------------- +# Mouth type selection +# --------------------------------------------------------------------------- + +def select_5_mouth_types( + mouth_frames: List[MouthFrameInfo], + cluster_mask: np.ndarray, +) -> MouthTypeSelection: + """ + 5種類の口タイプを自動選別する。 + + 選別基準: + - open: 口の高さが最大 + - closed: 口の高さが最小 + - half: 高さが中央値付近 + - e: 幅/高さ比が最大（横長） + - u: 幅が最小かつ高さが中程度（すぼめた口） + + Args: + mouth_frames: 全フレームの口情報 + cluster_mask: 位置クラスタに属するフレームのマスク + + Returns: + MouthTypeSelection + """ + # クラスタ内の有効フレームのみを対象 + candidates = [ + mf for mf in mouth_frames + if mf.valid and cluster_mask[mf.frame_idx] + ] + + if len(candidates) < 5: + # フォールバック: 全有効フレームを使用 + candidates = [mf for mf in mouth_frames if mf.valid] + + if len(candidates) == 0: + raise ValueError("No valid mouth frames found") + + # 各種メトリクス + heights = np.array([mf.height for mf in candidates]) + widths = np.array([mf.width for mf in candidates]) + aspect_ratios = widths / np.maximum(heights, 1e-6) + + used_indices = set() + + def pick_best(scores: np.ndarray, maximize: bool = True) -> int: + """最良のフレームを選択（既に使用済みは除外）""" + sorted_indices = np.argsort(scores) + if maximize: + sorted_indices = sorted_indices[::-1] + + for idx in sorted_indices: + frame_idx = candidates[idx].frame_idx + if frame_idx not in used_indices: + used_indices.add(frame_idx) + return frame_idx + + # フォールバック（すべて使用済みの場合） + return candidates[sorted_indices[0]].frame_idx + + # 1. open: 高さ最大 + open_idx = pick_best(heights, maximize=True) + + # 2. closed: 高さ最小 + closed_idx = pick_best(heights, maximize=False) + + # 3. half: 高さが中央値付近 + median_height = np.median(heights) + half_scores = -np.abs(heights - median_height) # 中央に近いほど高スコア + half_idx = pick_best(half_scores, maximize=True) + + # 4. e: 横長（幅/高さ比が大きい） + e_idx = pick_best(aspect_ratios, maximize=True) + + # 5. u: すぼめた口（幅が小さく、高さは中程度） + # 幅が小さいものを優先、高さは極端でないもの + height_median_dist = np.abs(heights - median_height) + u_scores = -widths - 0.5 * height_median_dist # 幅小さく、高さは中央寄り + u_idx = pick_best(u_scores, maximize=True) + + return MouthTypeSelection( + open_idx=open_idx, + closed_idx=closed_idx, + half_idx=half_idx, + e_idx=e_idx, + u_idx=u_idx, + ) + + +# --------------------------------------------------------------------------- +# Mask generation +# --------------------------------------------------------------------------- + +def make_ellipse_mask(w: int, h: int, rx: int, ry: int) -> np.ndarray: + """楕円マスクを生成（0/255）""" + mask = np.zeros((h, w), dtype=np.uint8) + cx, cy = w // 2, h // 2 + rx = int(max(1, min(rx, w // 2 - 1))) + ry = int(max(1, min(ry, h // 2 - 1))) + cv2.ellipse(mask, (cx, cy), (rx, ry), 0.0, 0.0, 360.0, 255, -1) + return mask + + +def feather_mask(mask_u8: np.ndarray, feather_px: int) -> np.ndarray: + """マスクにフェザー（グラデーション）を適用""" + if feather_px <= 0: + return (mask_u8.astype(np.float32) / 255.0).clip(0.0, 1.0) + + k = 2 * int(feather_px) + 1 + m = cv2.GaussianBlur(mask_u8, (k, k), sigmaX=0) + return (m.astype(np.float32) / 255.0).clip(0.0, 1.0) + + +# --------------------------------------------------------------------------- +# Sprite extraction +# --------------------------------------------------------------------------- + +def warp_frame_to_norm( + frame_bgr: np.ndarray, + quad: np.ndarray, + norm_w: int, + norm_h: int, +) -> np.ndarray: + """フレームから口パッチを正規化空間に変換""" + src = np.asarray(quad, dtype=np.float32).reshape(4, 2) + dst = np.array([ + [0, 0], + [norm_w - 1, 0], + [norm_w - 1, norm_h - 1], + [0, norm_h - 1], + ], dtype=np.float32) + + M = cv2.getPerspectiveTransform(src, dst) + patch = cv2.warpPerspective( + frame_bgr, + M, + (int(norm_w), int(norm_h)), + flags=cv2.INTER_LINEAR, + borderMode=cv2.BORDER_REPLICATE, + ) + return patch + + +def extract_mouth_sprite( + frame_bgr: np.ndarray, + quad: np.ndarray, + unified_w: int, + unified_h: int, + feather_px: int = 15, + mask_scale: float = 0.85, +) -> np.ndarray: + """ + フレームから口スプライトを抽出する。 + + Args: + frame_bgr: 入力フレーム（BGR） + quad: 口のquad (4, 2) + unified_w: 出力幅 + unified_h: 出力高さ + feather_px: フェザー幅（ピクセル） + mask_scale: マスクの楕円サイズ（0.0-1.0） + + Returns: + rgba: (H, W, 4) uint8 - 透過PNG用 + """ + # 正規化空間に変換 + patch_bgr = warp_frame_to_norm(frame_bgr, quad, unified_w, unified_h) + patch_rgb = cv2.cvtColor(patch_bgr, cv2.COLOR_BGR2RGB) + + # 楕円マスク生成 + rx = int((unified_w * mask_scale) * 0.5) + ry = int((unified_h * mask_scale) * 0.5) + mask_u8 = make_ellipse_mask(unified_w, unified_h, rx, ry) + + # フェザー適用 + mask_f = feather_mask(mask_u8, feather_px) + + # RGBA画像を生成 + rgba = np.zeros((unified_h, unified_w, 4), dtype=np.uint8) + rgba[:, :, :3] = patch_rgb + rgba[:, :, 3] = (mask_f * 255).astype(np.uint8) + + return rgba + + +def compute_unified_size( + mouth_frames: List[MouthFrameInfo], + selected_indices: List[int], + padding: float = 1.1, +) -> Tuple[int, int]: + """ + 選択されたフレームの口がすべて収まるサイズを計算。 + + Args: + mouth_frames: 全フレームの口情報 + selected_indices: 選択されたフレームインデックス + padding: 余白係数 + + Returns: + (width, height) + """ + idx_to_mf = {mf.frame_idx: mf for mf in mouth_frames} + + max_w = 0.0 + max_h = 0.0 + for idx in selected_indices: + if idx in idx_to_mf: + mf = idx_to_mf[idx] + max_w = max(max_w, mf.width) + max_h = max(max_h, mf.height) + + # パディングを適用して偶数に丸める + w = ensure_even_ge2(int(max_w * padding)) + h = ensure_even_ge2(int(max_h * padding)) + + return w, h + + +# --------------------------------------------------------------------------- +# Main extractor class +# --------------------------------------------------------------------------- + +class MouthSpriteExtractor: + """口スプライト抽出器""" + + def __init__(self, video_path: str, track_path: str = ""): + """ + Args: + video_path: 入力動画のパス + track_path: mouth_track.npz のパス（空の場合は自動検索） + """ + self.video_path = video_path + self.track_path = track_path + + # 動画情報 + self.vid_w = 0 + self.vid_h = 0 + self.fps = 0.0 + self.n_frames = 0 + + # 解析結果 + self.mouth_frames: List[MouthFrameInfo] = [] + self.cluster_mask: Optional[np.ndarray] = None + self.selection: Optional[MouthTypeSelection] = None + self.unified_size: Optional[Tuple[int, int]] = None + + self._load_video_info() + + def _load_video_info(self): + """動画の情報を取得""" + cap = cv2.VideoCapture(self.video_path) + if not cap.isOpened(): + raise RuntimeError(f"Failed to open video: {self.video_path}") + + self.vid_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.vid_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.fps = float(cap.get(cv2.CAP_PROP_FPS) or 30.0) + self.n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0) + cap.release() + + def _find_track_path(self) -> str: + """トラックファイルを自動検索""" + if self.track_path and os.path.isfile(self.track_path): + return self.track_path + + video_dir = os.path.dirname(os.path.abspath(self.video_path)) + candidates = [ + os.path.join(video_dir, "mouth_track_calibrated.npz"), + os.path.join(video_dir, "mouth_track.npz"), + ] + + for cand in candidates: + if os.path.isfile(cand): + return cand + + return "" + def _run_face_detector(self, callback=None) -> str: """face_track_anime_detector.py を実行してトラッキング""" video_dir = os.path.dirname(os.path.abspath(self.video_path)) track_out = os.path.join(video_dir, "mouth_track.npz") - - # 既存ファイルがあれば使用 - if os.path.isfile(track_out): - return track_out - - # face_track_anime_detector.py を実行 + + # 既存ファイルがあれば使用 + if os.path.isfile(track_out): + return track_out + + # face_track_anime_detector.py を実行 detector_script = resolve_adjacent_or_repo_script("face_track_anime_detector.py") cmd = [ resolve_python_subprocess_executable(), detector_script, - "--video", self.video_path, - "--out", track_out, - "--device", "auto", - ] - - if callback: - callback("Running face detector...") - - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0: - raise RuntimeError(f"Face detector failed: {result.stderr}") - - return track_out - - def analyze(self, callback=None, position_threshold: float = 50.0) -> None: - """ - 動画を解析して口情報を取得。 - - Args: - callback: 進捗コールバック関数（文字列を受け取る） - position_threshold: 位置クラスタリングの閾値（ピクセル） - """ - # トラックファイルを取得 - track_path = self._find_track_path() - if not track_path: - if callback: - callback("Track file not found. Running face detector...") - track_path = self._run_face_detector(callback) - - if callback: - callback(f"Loading track: {os.path.basename(track_path)}") - - # トラックデータ読み込み - quads, valid, confidence = load_track_data( - track_path, self.vid_w, self.vid_h - ) - - # フレーム情報を構築 - self.mouth_frames = [] - for i in range(len(quads)): - quad = quads[i] - center = quad_center(quad) - w, h = quad_wh(quad) - - mf = MouthFrameInfo( - frame_idx=i, - quad=quad, - center=center, - width=w, - height=h, - confidence=float(confidence[i]), - valid=bool(valid[i]), - ) - self.mouth_frames.append(mf) - - if callback: - callback(f"Analyzed {len(self.mouth_frames)} frames") - - # 位置クラスタリング - centers = np.array([mf.center for mf in self.mouth_frames]) - valid_array = np.array([mf.valid for mf in self.mouth_frames]) - - self.cluster_mask = find_stable_position_cluster( - centers, valid_array, distance_threshold=position_threshold - ) - - cluster_count = int(self.cluster_mask.sum()) - if callback: - callback(f"Found stable cluster with {cluster_count} frames") - - # 5種類の口を選別 - self.selection = select_5_mouth_types(self.mouth_frames, self.cluster_mask) - - # 統一サイズを計算 - selected_indices = list(self.selection.as_dict().values()) - self.unified_size = compute_unified_size( - self.mouth_frames, selected_indices - ) - - if callback: - callback(f"Selected frames: {self.selection.as_dict()}") - callback(f"Unified size: {self.unified_size[0]}x{self.unified_size[1]}") - - def get_preview_sprites(self, feather_px: int = 15) -> Dict[str, np.ndarray]: - """ - プレビュー用のスプライトを取得。 - - Args: - feather_px: フェザー幅 - - Returns: - {"open": rgba, "closed": rgba, ...} - """ - if self.selection is None or self.unified_size is None: - raise RuntimeError("Must call analyze() first") - - cap = cv2.VideoCapture(self.video_path) - if not cap.isOpened(): - raise RuntimeError(f"Failed to open video: {self.video_path}") - - unified_w, unified_h = self.unified_size - idx_to_mf = {mf.frame_idx: mf for mf in self.mouth_frames} - - sprites = {} - for name, frame_idx in self.selection.as_dict().items(): - cap.set(cv2.CAP_PROP_POS_FRAMES, float(frame_idx)) - ok, frame = cap.read() - if not ok or frame is None: - continue - - mf = idx_to_mf[frame_idx] - rgba = extract_mouth_sprite( - frame, mf.quad, unified_w, unified_h, feather_px - ) - sprites[name] = rgba - - cap.release() - return sprites - - def extract_sprites( - self, - output_dir: str, - feather_px: int = 15, - callback=None, - ) -> Dict[str, str]: - """ - スプライトを抽出してファイルに保存。 - - Args: - output_dir: 出力ディレクトリ - feather_px: フェザー幅 - callback: 進捗コールバック - - Returns: - {"open": path, "closed": path, ...} - """ - if self.selection is None or self.unified_size is None: - raise RuntimeError("Must call analyze() first") - - # 出力ディレクトリを作成 - os.makedirs(output_dir, exist_ok=True) - - # スプライトを取得 - sprites = self.get_preview_sprites(feather_px) - - # ファイルに保存 - saved_paths = {} - for name, rgba in sprites.items(): - filename = f"{name}.png" - filepath = os.path.join(output_dir, filename) - - # RGBA画像として保存 - ok = write_image_file( - filepath, - cv2.cvtColor(rgba, cv2.COLOR_RGBA2BGRA), - ) - if not ok: - raise RuntimeError(f"Failed to save sprite: {filepath}") - saved_paths[name] = filepath - - if callback: - callback(f"Saved: {filename}") - - return saved_paths - - -# --------------------------------------------------------------------------- -# Utility functions for output directory -# --------------------------------------------------------------------------- - -def get_unique_output_dir(base_dir: str) -> str: - """ - 一意の出力ディレクトリ名を生成。 - - base_dir が存在しなければそのまま返す。 - 存在すれば "_001", "_002" 等のサフィックスを付ける。 - """ - if not os.path.exists(base_dir): - return base_dir - - i = 1 - while True: - new_dir = f"{base_dir}_{i:03d}" - if not os.path.exists(new_dir): - return new_dir - i += 1 - - -# --------------------------------------------------------------------------- -# CLI entry point -# --------------------------------------------------------------------------- - -def main() -> int: - ap = argparse.ArgumentParser( - description="Extract mouth sprites from video" - ) - ap.add_argument("--video", required=True, help="Input video file") - ap.add_argument("--track", default="", help="mouth_track.npz (optional)") - ap.add_argument("--out", default="", help="Output directory (default: mouth/ next to video)") - ap.add_argument("--feather", type=int, default=15, help="Feather width in pixels") - ap.add_argument("--position-threshold", type=float, default=50.0, - help="Position clustering threshold in pixels") - args = ap.parse_args() - - if not os.path.isfile(args.video): - print(f"[error] Video not found: {args.video}") - return 1 - - # 出力ディレクトリ - if args.out: - output_dir = args.out - else: - video_dir = os.path.dirname(os.path.abspath(args.video)) - output_dir = os.path.join(video_dir, "mouth") - - output_dir = get_unique_output_dir(output_dir) - - def log(msg: str): - print(f"[info] {msg}") - - try: - extractor = MouthSpriteExtractor(args.video, args.track) - log(f"Video: {extractor.vid_w}x{extractor.vid_h} @ {extractor.fps:.2f}fps") - - extractor.analyze(callback=log, position_threshold=args.position_threshold) - - saved = extractor.extract_sprites(output_dir, args.feather, callback=log) - - log(f"Output directory: {output_dir}") - log(f"Saved {len(saved)} sprites") - - return 0 - - except Exception as e: - print(f"[error] {e}") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) + "--video", self.video_path, + "--out", track_out, + "--device", "auto", + ] + + if callback: + callback("Running face detector...") + + process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + universal_newlines=True, + ) + for line in process.stdout: + line = line.strip() + if line and callback: + callback(line) + process.wait() + if process.returncode != 0: + raise RuntimeError(f"Face detector failed with exit code {process.returncode}") + + return track_out + + def analyze(self, callback=None, position_threshold: float = 50.0) -> None: + """ + 動画を解析して口情報を取得。 + + Args: + callback: 進捗コールバック関数（文字列を受け取る） + position_threshold: 位置クラスタリングの閾値（ピクセル） + """ + # トラックファイルを取得 + track_path = self._find_track_path() + if not track_path: + if callback: + callback("Track file not found. Running face detector...") + track_path = self._run_face_detector(callback) + + if callback: + callback(f"Loading track: {os.path.basename(track_path)}") + + # トラックデータ読み込み + quads, valid, confidence = load_track_data( + track_path, self.vid_w, self.vid_h + ) + + # フレーム情報を構築 + self.mouth_frames = [] + for i in range(len(quads)): + quad = quads[i] + center = quad_center(quad) + w, h = quad_wh(quad) + + mf = MouthFrameInfo( + frame_idx=i, + quad=quad, + center=center, + width=w, + height=h, + confidence=float(confidence[i]), + valid=bool(valid[i]), + ) + self.mouth_frames.append(mf) + + if callback: + callback(f"Analyzed {len(self.mouth_frames)} frames") + + # 位置クラスタリング + centers = np.array([mf.center for mf in self.mouth_frames]) + valid_array = np.array([mf.valid for mf in self.mouth_frames]) + + self.cluster_mask = find_stable_position_cluster( + centers, valid_array, distance_threshold=position_threshold + ) + + cluster_count = int(self.cluster_mask.sum()) + if callback: + callback(f"Found stable cluster with {cluster_count} frames") + + # 5種類の口を選別 + self.selection = select_5_mouth_types(self.mouth_frames, self.cluster_mask) + + # 統一サイズを計算 + selected_indices = list(self.selection.as_dict().values()) + self.unified_size = compute_unified_size( + self.mouth_frames, selected_indices + ) + + if callback: + callback(f"Selected frames: {self.selection.as_dict()}") + callback(f"Unified size: {self.unified_size[0]}x{self.unified_size[1]}") + + def get_preview_sprites(self, feather_px: int = 15) -> Dict[str, np.ndarray]: + """ + プレビュー用のスプライトを取得。 + + Args: + feather_px: フェザー幅 + + Returns: + {"open": rgba, "closed": rgba, ...} + """ + if self.selection is None or self.unified_size is None: + raise RuntimeError("Must call analyze() first") + + cap = cv2.VideoCapture(self.video_path) + if not cap.isOpened(): + raise RuntimeError(f"Failed to open video: {self.video_path}") + + unified_w, unified_h = self.unified_size + idx_to_mf = {mf.frame_idx: mf for mf in self.mouth_frames} + + sprites = {} + for name, frame_idx in self.selection.as_dict().items(): + cap.set(cv2.CAP_PROP_POS_FRAMES, float(frame_idx)) + ok, frame = cap.read() + if not ok or frame is None: + continue + + mf = idx_to_mf[frame_idx] + rgba = extract_mouth_sprite( + frame, mf.quad, unified_w, unified_h, feather_px + ) + sprites[name] = rgba + + cap.release() + return sprites + + def extract_sprites( + self, + output_dir: str, + feather_px: int = 15, + callback=None, + ) -> Dict[str, str]: + """ + スプライトを抽出してファイルに保存。 + + Args: + output_dir: 出力ディレクトリ + feather_px: フェザー幅 + callback: 進捗コールバック + + Returns: + {"open": path, "closed": path, ...} + """ + if self.selection is None or self.unified_size is None: + raise RuntimeError("Must call analyze() first") + + # 出力ディレクトリを作成 + os.makedirs(output_dir, exist_ok=True) + + # スプライトを取得 + sprites = self.get_preview_sprites(feather_px) + + # ファイルに保存 + saved_paths = {} + for name, rgba in sprites.items(): + filename = f"{name}.png" + filepath = os.path.join(output_dir, filename) + + # RGBA画像として保存 + ok = write_image_file( + filepath, + cv2.cvtColor(rgba, cv2.COLOR_RGBA2BGRA), + ) + if not ok: + raise RuntimeError(f"Failed to save sprite: {filepath}") + saved_paths[name] = filepath + + if callback: + callback(f"Saved: {filename}") + + return saved_paths + + +# --------------------------------------------------------------------------- +# Utility functions for output directory +# --------------------------------------------------------------------------- + +def get_unique_output_dir(base_dir: str) -> str: + """ + 一意の出力ディレクトリ名を生成。 + + base_dir が存在しなければそのまま返す。 + 存在すれば "_001", "_002" 等のサフィックスを付ける。 + """ + if not os.path.exists(base_dir): + return base_dir + + i = 1 + while True: + new_dir = f"{base_dir}_{i:03d}" + if not os.path.exists(new_dir): + return new_dir + i += 1 + + +# --------------------------------------------------------------------------- +# CLI entry point +# --------------------------------------------------------------------------- + +def main() -> int: + ap = argparse.ArgumentParser( + description="Extract mouth sprites from video" + ) + ap.add_argument("--video", required=True, help="Input video file") + ap.add_argument("--track", default="", help="mouth_track.npz (optional)") + ap.add_argument("--out", default="", help="Output directory (default: mouth/ next to video)") + ap.add_argument("--feather", type=int, default=15, help="Feather width in pixels") + ap.add_argument("--position-threshold", type=float, default=50.0, + help="Position clustering threshold in pixels") + args = ap.parse_args() + + if not os.path.isfile(args.video): + print(f"[error] Video not found: {args.video}") + return 1 + + # 出力ディレクトリ + if args.out: + output_dir = args.out + else: + video_dir = os.path.dirname(os.path.abspath(args.video)) + output_dir = os.path.join(video_dir, "mouth") + + output_dir = get_unique_output_dir(output_dir) + + def log(msg: str): + print(f"[info] {msg}") + + try: + extractor = MouthSpriteExtractor(args.video, args.track) + log(f"Video: {extractor.vid_w}x{extractor.vid_h} @ {extractor.fps:.2f}fps") + + extractor.analyze(callback=log, position_threshold=args.position_threshold) + + saved = extractor.extract_sprites(output_dir, args.feather, callback=log) + + log(f"Output directory: {output_dir}") + log(f"Saved {len(saved)} sprites") + + return 0 + + except Exception as e: + print(f"[error] {e}") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/mouth_sprite_extractor_gui.py b/mouth_sprite_extractor_gui.py index 0b29d21..8a86e1e 100644 --- a/mouth_sprite_extractor_gui.py +++ b/mouth_sprite_extractor_gui.py @@ -1954,6 +1954,7 @@ def _on_analyze(self): return self.is_analyzing = True + self.analyze_error = None self._refresh_workflow_state() self._start_busy_state("解析中", "口位置を解析しています。しばらくお待ちください") self.analyze_btn.configure(state=tk.DISABLED) @@ -1966,17 +1967,51 @@ def _on_analyze(self): self._clear_candidates() self._clear_preview("empty") + # MacOS AVFoundation deadlock workaround: + # cv2.VideoCapture cannot be opened from a background thread if the main thread holds it open + self._close_player_capture() + if self._cached_cap: + self._cached_cap.release() + self._cached_cap = None + thread = threading.Thread(target=self._analyze_worker, daemon=True) thread.start() + self.after(100, self._poll_analyze_done) + + def _poll_analyze_done(self): + if self.is_analyzing: + self.after(100, self._poll_analyze_done) + return + + if self.analyze_error: + self._finish_busy_state("解析エラー") + else: + self._finish_busy_state("解析完了") + self._enable_manual_pick_controls(True) + self.auto_fill_btn.configure(state=tk.NORMAL) + self._show_player_frame(self.player_current_frame_idx) + + self.analyze_btn.configure(state=tk.NORMAL) + self._refresh_workflow_state() def _analyze_worker(self): """解析ワーカースレッド""" + def trace(msg): + with open("/tmp/debug.txt", "a") as f: + f.write(msg + "\n") + try: + import time + time.sleep(0.2) # Wait for AVFoundation to completely release the previous capture asynchronously + trace("1. starting worker") self.log("解析を開始...") + trace("2. creating extractor") self.extractor = MouthSpriteExtractor(self.video_path) + trace("3. running analyze") self.extractor.analyze(callback=self.log) - + + trace("4. analyze done") valid_frames = [mf for mf in self.extractor.mouth_frames if mf.valid] self.valid_frames = valid_frames self._mouth_frame_by_idx = { @@ -1999,20 +2034,18 @@ def _analyze_worker(self): self.log("解析完了。プレイヤーで候補フレームを手動追加してください。") self.log("必要なら『候補を自動選出』で従来の自動抽出も使えます。") - self.after(0, lambda: self._finish_busy_state("解析完了")) - self.after(0, lambda: self._enable_manual_pick_controls(True)) - self.after(0, lambda: self.auto_fill_btn.configure(state=tk.NORMAL)) - self.after(0, lambda: self._show_player_frame(self.player_current_frame_idx)) + trace("5. queuing UI updates") except Exception as e: + trace(f"ERROR: {e}") self.log(f"エラー: {e}") - self.after(0, lambda: self._finish_busy_state("解析エラー")) + self.analyze_error = str(e) traceback.print_exc() finally: + trace("7. finally block") self.is_analyzing = False - self.after(0, lambda: self.analyze_btn.configure(state=tk.NORMAL)) - self.after(0, self._refresh_workflow_state) + trace("8. worker exit") def _get_video_capture(self) -> cv2.VideoCapture: """キャッシュされたVideoCaptureを取得""" diff --git a/mouth_track_gui/app.py b/mouth_track_gui/app.py index a1c5e9b..5531d89 100644 --- a/mouth_track_gui/app.py +++ b/mouth_track_gui/app.py @@ -119,6 +119,10 @@ class App(tk.Tk): def __init__(self) -> None: super().__init__() + import queue + self.ui_queue = queue.Queue() + self._poll_ui_queue() + self.title("Mouth Track One-Click (HQ)") self.geometry("1180x860") @@ -463,14 +467,15 @@ def _cancel_live_color_control_job(self) -> None: self._live_color_control_job = None def _set_auto_color_button_enabled(self, enabled: bool, *, text: str | None = None) -> None: - def _apply() -> None: - try: - self.btn_auto_color.configure(state=("normal" if enabled else "disabled")) - if text is not None: - self.btn_auto_color.configure(text=text) - except Exception: - pass - self.after(0, _apply) + self.ui_queue.put(lambda: self._apply_button_state(enabled, text)) + + def _apply_button_state(self, enabled: bool, text: str | None = None) -> None: + try: + self.btn_auto_color.configure(state=("normal" if enabled else "disabled")) + if text is not None: + self.btn_auto_color.configure(text=text) + except Exception: + pass def _clear_live_color_control(self) -> None: try: @@ -601,8 +606,30 @@ def on_auto_mouth_color_adjust(self) -> None: self._auto_color_poll_job = self.after(120, self._poll_auto_color_result) # ----- logging (thread-safe) ----- - def log(self, s: str) -> None: - self.log_q.put(s) + def _poll_ui_queue(self): + try: + while True: + task = self.ui_queue.get_nowait() + task() + except queue.Empty: + pass + self.after(50, self._poll_ui_queue) + + def log(self, msg: str) -> None: + self.ui_queue.put(lambda: self._insert_log(msg)) + + def _insert_log(self, s: str) -> None: + # Remove null bytes from log text + s = s.replace("\x00", "") + self.txt.configure(state="normal") + self.txt.insert("end", s + "\n") + # 上限チェック + line_count = int(self.txt.index("end-1c").split(".")[0]) + if line_count > MAX_LOG_LINES: + excess = line_count - MAX_LOG_LINES + self.txt.delete("1.0", f"{excess + 1}.0") + self.txt.see("end") + self.txt.configure(state="disabled") def _save_session(self, payload: dict) -> bool: ok = save_session(payload) @@ -827,7 +854,7 @@ def _set_character(self, character: str, *, persist: bool = False) -> None: self._save_session({"character": character}) def _post_set_character(self, character: str, *, persist: bool = False) -> None: - self.after(0, lambda c=character, p=persist: self._set_character(c, persist=p)) + self.ui_queue.put(lambda c=character, p=persist: self._set_character(c, persist=p)) def _runtime_supports(self, runtime_py: str, flags: list[str]) -> bool: return script_contains(runtime_py, flags) @@ -864,7 +891,7 @@ def _apply(): self.soft_requested_at = None self.btn_stop.configure(text=STOP_BTN_TEXT_DEFAULT) - self.after(0, _apply) + self.ui_queue.put(_apply) def _set_running(self, running: bool) -> None: def _apply(): @@ -879,14 +906,14 @@ def _apply(): if not running: self._set_stop_mode("none") self._progress_reset() - self.after(0, _apply) + self.ui_queue.put(_apply) def _progress_reset(self) -> None: def _apply(): self.progress.configure(mode="determinate", maximum=1.0) self.progress_var.set(0.0) self.progress_text_var.set("待機中") - self.after(0, _apply) + self.ui_queue.put(_apply) def _progress_begin(self, total_steps: int, text: str) -> None: def _apply(): @@ -894,7 +921,7 @@ def _apply(): self.progress.configure(mode="determinate", maximum=self._progress_total) self.progress_var.set(0.0) self.progress_text_var.set(text) - self.after(0, _apply) + self.ui_queue.put(_apply) def _progress_step(self, step: int, text: str) -> None: def _apply(): @@ -902,13 +929,13 @@ def _apply(): val = min(max(0, int(step)), int(self._progress_total)) self.progress_var.set(val) self.progress_text_var.set(text) - self.after(0, _apply) + self.ui_queue.put(_apply) def _show_error(self, title: str, msg: str) -> None: - self.after(0, lambda: messagebox.showerror(title, msg)) + self.ui_queue.put(lambda: messagebox.showerror(title, msg)) def _show_warn(self, title: str, msg: str) -> None: - self.after(0, lambda: messagebox.showwarning(title, msg)) + self.ui_queue.put(lambda: messagebox.showwarning(title, msg)) def _apply_preview_selection(self, pad: float, coverage: float) -> None: pad_v = round(float(pad), 2) @@ -1503,9 +1530,8 @@ def _worker(): show_error=self._show_error, ) if selection.applied: - self.after( - 0, - lambda p=selection.pad, c=selection.coverage: self._apply_preview_selection(p, c), + self.ui_queue.put( + lambda p=selection.pad, c=selection.coverage: self._apply_preview_selection(p, c) ) except Exception as e: self._show_error("エラー", str(e)) diff --git a/pyproject.toml b/pyproject.toml index 025926f..d9076de 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,89 +1,61 @@ -[build-system] -requires = ["setuptools>=60.0.0"] -build-backend = "setuptools.build_meta" - -[project] -name = "motionpngtuber" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = "==3.10.*" -dependencies = [ - "anime-face-detector==0.0.9 ; sys_platform == 'win32' or sys_platform == 'linux'", - "mmcv-full==1.7.0 ; sys_platform == 'win32' or sys_platform == 'linux'", - "mmdet==2.28.0 ; sys_platform == 'win32' or sys_platform == 'linux'", - "mmpose==0.29.0 ; sys_platform == 'win32' or sys_platform == 'linux'", - "numpy>=1.24.0,<2.0.0", - "opencv-python>=4.8.0", - "openmim>=0.3.9", - "pillow>=10.0.0", - "pip>=24.0", - "scipy>=1.10.0", - "setuptools>=60.0.0,<70.0.0", - "sounddevice>=0.4.6", - "torch==1.13.1+cu117 ; sys_platform == 'win32' or sys_platform == 'linux'", - "torchvision==0.14.1+cu117 ; sys_platform == 'win32' or sys_platform == 'linux'" -] - -[tool.uv] -override-dependencies = [ - # headless版を無効化してGUI版のopencv-pythonを使用 - "opencv-python-headless>=4.5.4.58 ; sys_platform == 'never'", -] -index-strategy = "first-index" - -[[tool.uv.index]] -name = "pytorch-cu117" -url = "https://download.pytorch.org/whl/cu117" -explicit = true - -[tool.uv.sources] -torch = [ - { index = "pytorch-cu117", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, -] -torchvision = [ - { index = "pytorch-cu117", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, -] -mmcv-full = [ - { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.0-cp310-cp310-manylinux1_x86_64.whl", marker = "sys_platform == 'linux'" }, - { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.0-cp310-cp310-win_amd64.whl", marker = "sys_platform == 'win32'" }, -] - -[tool.uv.extra-build-dependencies] -chumpy = ["wheel", "pip", "setuptools"] - -[project.scripts] -mouth-track-gui = "mouth_track_gui:main" -mouth-sprite-gui = "mouth_sprite_extractor_gui:main" -mouth-sprite-extract = "motionpngtuber.mouth_sprite_extractor:main" -auto-mouth-track = "auto_mouth_track_v2:main" -auto-erase-mouth = "auto_erase_mouth:main" -calibrate-mouth-track = "calibrate_mouth_track:main" -erase-mouth-offline = "erase_mouth_offline:main" -face-track-detect = "face_track_anime_detector:main" -convert-npz-to-json = "convert_npz_to_json:main" -lipsync-runtime = "loop_lipsync_runtime_patched_emotion_auto:main" - -[tool.setuptools] -packages = ["motionpngtuber", "mouth_track_gui"] -py-modules = [ - "auto_erase_mouth", - "auto_mouth_track_v2", - "calibrate_mouth_track", - "convert_npz_to_json", - "erase_mouth_offline", - "face_track_anime_detector", - "loop_lipsync_runtime_patched_emotion_auto", - "mouth_sprite_extractor", - "mouth_sprite_extractor_gui", - "mouth_track_gui", -] - -[tool.pytest.ini_options] -testpaths = ["tests"] -pythonpath = ["."] - -[dependency-groups] -dev = [ - "pytest>=9.0.3", -] +# ============================================================================= +# MotionPNGTuber - macOS (Apple Silicon) 用 pyproject.toml +# ============================================================================= +# +# インストール手順: +# 1. cp pyproject.toml pyproject.win.toml && cp pyproject.macos.toml pyproject.toml +# 2. uv venv .venv && uv sync +# 3. uv pip install pip setuptools wheel torch==2.0.1 torchvision==0.15.2 +# 4. xtcocotoolsをソースからビルド（PyPI版はビルドエラーになるため先に入れる）: +# mkdir -p deps && cd deps +# git clone https://github.com/jin-s13/xtcocoapi.git +# cd xtcocoapi +# ../../.venv/bin/python -m pip install -e . +# cd ../.. +# 5. mmcv-fullをソースからビルド（ビルド済みバイナリがないため）: +# cd deps +# curl -L https://github.com/open-mmlab/mmcv/archive/refs/tags/v1.7.0.tar.gz -o mmcv-1.7.0.tar.gz +# tar xzf mmcv-1.7.0.tar.gz +# cd mmcv-1.7.0 +# MMCV_WITH_OPS=1 FORCE_CUDA=0 ../../.venv/bin/python setup.py develop +# MMCV_WITH_OPS=1 FORCE_CUDA=0 ../../.venv/bin/python setup.py build_ext --inplace +# cd ../.. +# 6. anime-face-detectorをインストール（xtcocotools/mmcvは既にあるのでスキップされる）: +# （--no-build-isolation: chumpy等のビルドに仮想環境のpipが必要なため） +# uv pip install --no-build-isolation anime-face-detector +# 7. mmdet/mmposeを正しいバージョンに修正（anime-face-detectorが最新版を入れてしまうため）: +# uv pip install mmdet==2.28.0 mmpose==0.29.0 +# 8. .venv/bin/python mouth_track_gui.py +# +# 注意: +# - deps/ ディレクトリは削除しないこと（editable installのため参照される） +# - PyTorchは2.0.1を使用（最新版はmmcv 1.7.0と互換性がない） +# +# ============================================================================= + +[project] +name = "motionpngtuber" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = "==3.10.*" +dependencies = [ + # 基本パッケージ（uv syncでインストール） + "numpy>=1.24.0,<2.0.0", + "opencv-python>=4.8.0", + "pillow>=10.0.0", + "scipy>=1.10.0", + "sounddevice>=0.4.6", + # 以下は手動インストール（手順2-5参照）: + # - pip, setuptools, wheel + # - torch==2.0.1, torchvision==0.15.2 + # - anime-face-detector, mmdet==2.28.0, mmpose==0.29.0 + # - mmcv-full (ソースビルド) + # - xtcocotools (ソースビルド) +] + +[tool.uv] +override-dependencies = [ + # headless版を無効化してGUI版のopencv-pythonを使用 + "opencv-python-headless>=4.5.4.58 ; sys_platform == 'never'", +] diff --git a/uv.lock b/uv.lock index 4832da0..f07ee66 100644 --- a/uv.lock +++ b/uv.lock @@ -12,42 +12,6 @@ resolution-markers = [ [manifest] overrides = [{ name = "opencv-python-headless", marker = "sys_platform == 'never'", specifier = ">=4.5.4.58" }] -[[package]] -name = "addict" -version = "2.4.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186, upload-time = "2020-11-21T16:21:31.416Z" } -wheels = 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