diff --git a/autowsgr/__init__.py b/autowsgr/__init__.py index 2f7b90db..c3ff52c8 100644 --- a/autowsgr/__init__.py +++ b/autowsgr/__init__.py @@ -1,3 +1,3 @@ """AutoWSGR - 战舰少女R 自动化框架(v2)""" -__version__ = '2.1.9.post5' +__version__ = '2.1.9.post6' diff --git a/autowsgr/ui/choose_ship_page.py b/autowsgr/ui/choose_ship_page.py index a57fe31f..e888545a 100644 --- a/autowsgr/ui/choose_ship_page.py +++ b/autowsgr/ui/choose_ship_page.py @@ -13,6 +13,7 @@ from __future__ import annotations +import re import time from typing import TYPE_CHECKING @@ -25,7 +26,7 @@ ) from .utils import wait_for_page, wait_leave_page -from .utils.ship_list import locate_ship_rows, read_ship_levels +from .utils.ship_list import LevelOCRRetryNeededError, locate_ship_rows, read_ship_levels if TYPE_CHECKING: @@ -56,6 +57,7 @@ _SCROLL_FROM_Y: float = 0.55 _SCROLL_TO_Y: float = 0.30 _OCR_MAX_ATTEMPTS: int = 3 +_SHIP_ALIAS_SUFFIX_RE = re.compile(r'\s*[（(][^（）()]*[)）]\s*$') PAGE_SIGNATURE = PixelSignature( name='choose_ship_page', @@ -329,7 +331,8 @@ def _click_ship_in_list( Parameters ---------- name: - 目标舰船名 (精确名称)。 + 目标舰船名。 + 匹配时会先做舰名归一化（如去除“·改”与尾部括号别名）后再比较。 Returns ------- @@ -337,6 +340,7 @@ def _click_ship_in_list( 匹配并点击成功时返回舰船名；失败返回 ``None``。 """ assert self._ctx.ocr is not None + normalized_target = self._normalize_ship_name(name) for attempt in range(_OCR_MAX_ATTEMPTS): screen = self._ctrl.screenshot() @@ -348,31 +352,47 @@ def _click_ship_in_list( deduplicate_by_name=False, include_row_key=True, ) - raw_levels = read_ship_levels( - self._ctx.ocr, - screen, - deduplicate_by_name=False, - include_row_key=True, - ) + try: + raw_levels = read_ship_levels( + self._ctx.ocr, + screen, + deduplicate_by_name=False, + include_row_key=True, + ) + except LevelOCRRetryNeededError as exc: + _log.warning( + '[UI] 等级 OCR 噪声过高，触发重新识别 (第 {}/{} 次)', + attempt + 1, + _OCR_MAX_ATTEMPTS, + ) + if attempt >= _OCR_MAX_ATTEMPTS - 1: + raise RuntimeError('等级 OCR 噪声过高，重试后仍失败') from exc + time.sleep(0.3) + continue else: raw_hits = locate_ship_rows(self._ctx.ocr, screen) raw_levels = [] hits = [self._normalize_hit_entry(hit) for hit in raw_hits] - level_map: dict[float, list[int | None]] = {} + level_map: dict[float, dict[str, list[int | None]]] = {} for entry in raw_levels: - _, level, row_key = self._normalize_level_entry(entry) - level_map.setdefault(row_key, []).append(level) + level_name, level, row_key = self._normalize_level_entry(entry) + normalized_level_name = self._normalize_ship_name(level_name) + row_levels = level_map.setdefault(row_key, {}) + row_levels.setdefault(normalized_level_name, []).append(level) for matched, cx, cy, row_key in hits: - if matched != name: + normalized_matched = self._normalize_ship_name(matched) + if normalized_matched != normalized_target: continue level = None if use_level_filter: row_levels = level_map.get(row_key) if row_levels: - level = row_levels.pop(0) + name_levels = row_levels.get(normalized_matched) + if name_levels: + level = name_levels.pop(0) if not self._is_level_in_range(level, min_level, max_level): _log.warning( "[UI] 命中 '{}', 但等级 {} 不满足范围 [{}, {}]", @@ -406,3 +426,10 @@ def _click_ship_in_list( time.sleep(0.5) return None + + @staticmethod + def _normalize_ship_name(name: str) -> str: + normalized = name.strip() + normalized = normalized.removesuffix('·改') + normalized = _SHIP_ALIAS_SUFFIX_RE.sub('', normalized) + return normalized.strip() diff --git a/autowsgr/ui/utils/ship_list.py b/autowsgr/ui/utils/ship_list.py index b415559d..c29363d9 100644 --- a/autowsgr/ui/utils/ship_list.py +++ b/autowsgr/ui/utils/ship_list.py @@ -31,7 +31,15 @@ #: Legacy 选船列表左侧裁剪宽度 (px@1280) LEGACY_LIST_WIDTH: int = 1048 -_LEVEL_PATTERN = re.compile(r'[Ll][Vv]\.?\s*(\d+)') +_LEVEL_PATTERN = re.compile(r'[Ll][Vv]\.?\s*([0-9ILilOo]{1,6})') +_LEVEL_NOISY_PATTERN = re.compile(r'(?:[LlIi1O0][VvYy])[\.:]?\s*([0-9ILilOo]{1,6})') +_MAX_LEVEL_VALUE = 200 +_MAX_LEVEL_NOISE_CHARS = 1 +_MAX_NOISY_LEVEL_HITS_BEFORE_RETRY = 1 + + +class LevelOCRRetryNeededError(RuntimeError): + """等级 OCR 噪声过高，需要重新截图识别。""" def to_legacy_format(screen: np.ndarray) -> tuple[np.ndarray, float, float]: @@ -169,15 +177,151 @@ def recognize_ships_in_list( def _parse_level(text: str) -> int | None: """从 OCR 文本中提取 ``Lv.XX`` 格式等级数字。""" - m = _LEVEL_PATTERN.search(text) + level, _need_retry = _parse_level_with_status(text) + return level + + +def _parse_level_with_status(text: str) -> tuple[int | None, bool]: + """解析等级并返回是否应触发重识别。""" + compact = text.strip().replace(' ', '') + + m = _LEVEL_PATTERN.search(compact) if m: - try: - return int(m.group(1)) - except ValueError: - return None + raw_digits = m.group(1) + if _noise_char_count(raw_digits) > _MAX_LEVEL_NOISE_CHARS: + return None, True + level = _coerce_level_digits(raw_digits) + if level is not None: + return level, False + + m2 = _LEVEL_NOISY_PATTERN.search(compact) + if m2: + raw_digits = m2.group(1) + if _noise_char_count(raw_digits) > _MAX_LEVEL_NOISE_CHARS: + return None, True + level = _coerce_level_digits(raw_digits) + if level is not None: + return level, False + + return None, False + + +def _noise_char_count(raw_digits: str) -> int: + return sum(1 for ch in raw_digits if ch in 'ILilOo') + + +def _coerce_level_digits(raw_digits: str) -> int | None: + """将 OCR 提取出的数字串映射为合法等级值。""" + trans = str.maketrans( + { + 'I': '1', + 'i': '1', + 'l': '1', + 'L': '1', + 'O': '0', + 'o': '0', + } + ) + normalized = raw_digits.translate(trans) + digits = ''.join(ch for ch in normalized if ch.isdigit()) + if not digits: + return None + + candidates: list[int] = [] + + # 先尝试前 3 位（常见误读: 1046 -> 104, 110544 -> 110） + if len(digits) >= 3: + candidates.append(int(digits[:3])) + if len(digits) >= 2: + candidates.append(int(digits[:2])) + candidates.append(int(digits[:1])) + + # 兼容前导 0 的场景（如 051 -> 51） + if digits.startswith('0') and len(digits) >= 3: + candidates.insert(0, int(digits[1:3])) + + seen_vals: set[int] = set() + for value in candidates: + if value in seen_vals: + continue + seen_vals.add(value) + if 1 <= value <= _MAX_LEVEL_VALUE: + return value + return None +def _center_x(bbox: tuple[int, int, int, int] | None, width: int) -> float: + if bbox is None: + return width / 2 + x1, _, x2, _ = bbox + return (x1 + x2) / 2 + + +def _probe_level_near_name( + ocr: OCREngine, + screen: np.ndarray, + *, + y_start: int, + y_end: int, + name_x: float, + max_x: int, +) -> int | None: + """在同一 y 行按舰名 x 位置裁剪区域，二次识别等级。""" + h, w = screen.shape[:2] + row_h = max(1, y_end - y_start) + + x_pad = max(70, int(w * 0.045)) + x0 = max(0, int(name_x - x_pad)) + x1 = min(max_x, int(name_x + x_pad)) + + y0 = max(0, y_start - int(row_h * 1.6)) + y1 = min(h, y_end + int(row_h * 0.4)) + + if x1 <= x0 or y1 <= y0: + return None + + roi = screen[y0:y1, x0:x1] + if roi.size == 0: + return None + + parsed_levels: list[int] = [] + noisy_level_hits = 0 + + def collect_levels(img: np.ndarray) -> None: + nonlocal noisy_level_hits + results = ocr.recognize(img, allowlist='LlVvIiYy0Oo1.:-/0123456789') + for r in results: + text = r.text.strip() + if not text: + continue + level, need_retry = _parse_level_with_status(text) + if need_retry: + noisy_level_hits += 1 + continue + if level is not None: + parsed_levels.append(level) + + collect_levels(roi) + + gray = cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY) + up = cv2.resize(gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC) + norm = cv2.normalize(up, None, 0, 255, cv2.NORM_MINMAX) + binary = cv2.threshold(norm, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] + binary_rgb = cv2.cvtColor(binary, cv2.COLOR_GRAY2RGB) + collect_levels(binary_rgb) + + if not parsed_levels and noisy_level_hits > _MAX_NOISY_LEVEL_HITS_BEFORE_RETRY: + raise LevelOCRRetryNeededError( + f'等级 OCR 噪声过高: {noisy_level_hits} 条异常等级文本 (阈值 {_MAX_NOISY_LEVEL_HITS_BEFORE_RETRY})', + ) + + if not parsed_levels: + return None + + return max(parsed_levels) + + def read_ship_levels( ocr: OCREngine, screen: np.ndarray, @@ -230,36 +374,73 @@ def read_ship_levels( for y_start_720, y_end_720 in rows: y_start = max(0, int((y_start_720 - 1) * scale_y)) y_end = min(h, int((y_end_720 + 1) * scale_y)) + row_key = round((y_start + y_end) / 2 / h, 4) row_img = list_area_native[y_start:y_end] results = ocr.recognize(row_img) - row_name: str | None = None - row_level: int | None = None + name_hits: list[tuple[str, float]] = [] + local_level_hits: list[tuple[int, float]] = [] for r in results: text = r.text.strip() if not text: continue - # 尝试匹配等级 - if row_level is None: - level = _parse_level(text) - if level is not None: - row_level = level + x_center = _center_x(r.bbox, row_img.shape[1]) - # 尝试匹配舰船名 - if row_name is None: - name = _fuzzy_match(text, SHIPNAMES) - if name is not None and name not in seen: - row_name = name + level = _parse_level(text) + if level is not None: + local_level_hits.append((level, x_center)) - if row_name is not None: + name = _fuzzy_match(text, SHIPNAMES) + if name is not None: + name_hits.append((name, x_center)) + + if not name_hits: + continue + + name_hits.sort(key=lambda item: item[1]) + local_level_hits.sort(key=lambda item: item[1]) + max_pair_dist = max(80.0, row_img.shape[1] * 0.12) + + for row_name, name_x in name_hits: if deduplicate_by_name and row_name in seen: continue + + row_level: int | None = None + + best_level: int | None = None + best_dist = float('inf') + for candidate_level, candidate_x in local_level_hits: + dist = abs(candidate_x - name_x) + if dist < best_dist: + best_dist = dist + best_level = candidate_level + + if best_level is not None and best_dist <= max_pair_dist: + row_level = best_level + + if row_level is None: + probe_level = _probe_level_near_name( + ocr, + screen, + y_start=y_start, + y_end=y_end, + name_x=name_x, + max_x=list_w_native, + ) + if probe_level is not None: + row_level = probe_level + if deduplicate_by_name: seen.add(row_name) - row_key = round((y_start + y_end) / 2 / h, 4) + _log.debug( + '[选船列表] 等级识别命中: name={} level={} row_key={}', + row_name, + row_level if row_level is not None else 'None', + row_key, + ) if include_row_key: found.append((row_name, row_level, row_key)) else: @@ -267,6 +448,6 @@ def read_ship_levels( _log.debug( '[选船列表] 等级识别: {}', - [(n, lv) for n, lv in found], + [(entry[0], entry[1]) for entry in found], ) return found