import module.config.server as server from module.base.decorator import cached_property, del_cached_property from module.base.resource import Resource from module.base.utils import * from module.exception import ScriptError class Button(Resource): def __init__(self, file, area, search, color, button): """ Args: file: Filepath to an assets area: Area to crop template search: Area to search from, 20px larger than `area` by default color: Average color of assets button: Area to click if assets appears on the image """ self.file: str = file self.area: t.Tuple[int, int, int, int] = area self.search: t.Tuple[int, int, int, int] = search self.color: t.Tuple[int, int, int] = color self._button: t.Tuple[int, int, int, int] = button self.resource_add(self.file) self._button_offset: t.Tuple[int, int] = (0, 0) @property def button(self): return area_offset(self._button, self._button_offset) def load_offset(self, button): self._button_offset = button._button_offset def clear_offset(self): self._button_offset = (0, 0) @cached_property def image(self): return load_image(self.file, self.area) def resource_release(self): del_cached_property(self, 'image') self.clear_offset() def __str__(self): return self.file __repr__ = __str__ def __eq__(self, other): return str(self) == str(other) def __hash__(self): return hash(self.file) def __bool__(self): return True def match_color(self, image, threshold=10) -> bool: """ Check if the button appears on the image, using average color Args: image (np.ndarray): Screenshot. threshold (int): Default to 10. Returns: bool: True if button appears on screenshot. """ color = get_color(image, self.area) return color_similar( color1=color, color2=self.color, threshold=threshold ) def match_template(self, image, similarity=0.85) -> bool: """ Detects assets by template matching. To Some buttons, its location may not be static, `_button_offset` will be set. Args: image: Screenshot. similarity (float): 0-1. Returns: bool. """ image = crop(image, self.search, copy=False) res = cv2.matchTemplate(self.image, image, cv2.TM_CCOEFF_NORMED) _, sim, _, point = cv2.minMaxLoc(res) self._button_offset = np.array(point) + self.search[:2] - self.area[:2] return sim > similarity def match_template_color(self, image, similarity=0.85, threshold=30) -> bool: """ Template match first, color match then Args: image: Screenshot. similarity (float): 0-1. threshold (int): Default to 10. Returns: """ matched = self.match_template(image, similarity=similarity) if not matched: return False area = area_offset(self.area, offset=self._button_offset) color = get_color(image, area) return color_similar( color1=color, color2=self.color, threshold=threshold ) class ButtonWrapper(Resource): def __init__(self, name='MULTI_ASSETS', **kwargs): self.name = name self.data_buttons = kwargs self._matched_button: t.Optional[Button] = None self.resource_add(f'{name}:{next(self.iter_buttons(), None)}') def resource_release(self): del_cached_property(self, 'buttons') self._matched_button = None def __str__(self): return self.name __repr__ = __str__ def __eq__(self, other): return str(self) == str(other) def __hash__(self): return hash(self.name) def __bool__(self): return True def iter_buttons(self) -> t.Iterator[Button]: for _, assets in self.data_buttons.items(): if isinstance(assets, Button): yield assets elif isinstance(assets, list): for asset in assets: yield asset @cached_property def buttons(self) -> t.List[Button]: for trial in [server.lang, 'share', 'cn']: try: assets = self.data_buttons[trial] if isinstance(assets, Button): return [assets] elif isinstance(assets, list): return assets except KeyError: pass raise ScriptError(f'ButtonWrapper({self}) on server {server.lang} has no fallback button') def match_color(self, image, threshold=10) -> bool: for assets in self.buttons: if assets.match_color(image, threshold=threshold): self._matched_button = assets return True return False def match_template(self, image, similarity=0.85) -> bool: for assets in self.buttons: if assets.match_template(image, similarity=similarity): self._matched_button = assets return True return False def match_template_color(self, image, similarity=0.85, threshold=30) -> bool: for assets in self.buttons: if assets.match_template_color(image, similarity=similarity, threshold=threshold): self._matched_button = assets return True return False @property def matched_button(self) -> Button: if self._matched_button is None: return self.buttons[0] else: return self._matched_button @property def area(self) -> tuple[int, int, int, int]: return self.matched_button.area @property def search(self) -> tuple[int, int, int, int]: return self.matched_button.search @property def color(self) -> tuple[int, int, int]: return self.matched_button.color @property def button(self) -> tuple[int, int, int, int]: return self.matched_button.button @property def button_offset(self) -> tuple[int, int]: return self.matched_button._button_offset @property def width(self) -> int: return area_size(self.area)[0] @property def height(self) -> int: return area_size(self.area)[1] def load_offset(self, button): """ Load offset from another button. Args: button (Button, ButtonWrapper): """ if isinstance(button, ButtonWrapper): button = button.matched_button for b in self.iter_buttons(): b.load_offset(button) def clear_offset(self): for b in self.iter_buttons(): b.clear_offset() def load_search(self, area): """ Set `search` attribute. Note that this method is irreversible. Args: area: """ for b in self.iter_buttons(): b.search = area class ClickButton: def __init__(self, button, name='CLICK_BUTTON'): self.area = button self.button = button self.name = name def __str__(self): return self.name __repr__ = __str__ def __eq__(self, other): return str(self) == str(other) def __hash__(self): return hash(self.name) def __bool__(self): return True def match_template(image, template, similarity=0.85): """ Args: image (np.ndarray): Screenshot template (np.ndarray): area (tuple): Crop area of image. offset (int, tuple): Detection area offset. similarity (float): 0-1. Similarity. Lower than this value will return float(0). Returns: bool: """ res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED) _, sim, _, point = cv2.minMaxLoc(res) return sim > similarity