import numpy as np from PIL import Image from skimage.color import rgb2hsv from module.base.utils import color_similarity_2d from module.template.assets import * class GridPredictor: ENEMY_SCALE_IMAGE_SIZE = (50, 50) ENEMY_PERSPECTIVE_IMAGE_SIZE = (50, 50) def __init__(self, location, image, corner): """ Args: location: image: corner: """ self.location = location self.image = image self.corner = corner.flatten() x0, y0, x1, y1, x2, y2, x3, y3 = self.corner divisor = x0 - x1 + x2 - x3 x = (x0 * x2 - x1 * x3) / divisor y = (x0 * y2 - x1 * y2 + x2 * y0 - x3 * y0) / divisor self._image_center = np.array([x, y, x, y]) self._image_a = (-x0 * x2 + x0 * x3 + x1 * x2 - x1 * x3) / divisor self._perspective = ( (-x0 + x1) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[0], # a (-x0 * x3 + x1 * x2) / (-x2 + x3) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1], # b x0, # c 0, # d (-x0 * y2 + x1 * y2 + x2 * y0 - x3 * y0) / (-x2 + x3) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1], # e y0, # f 0, # g ((-x0 + x1) / (-x2 + x3) - 1) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1] # h ) def predict(self): self.image_transform = self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective) # self.image_hole = self.get_relative_image((-1, -1, 1, 1)) self.is_enemy = self.predict_static_red_border() self.enemy_scale = self.predict_enemy_scale() if self.enemy_scale > 0: self.is_enemy = True self.is_mystery = self.predict_mystery() if not self.is_enemy and not self.is_mystery: self.is_siren = self.predict_dynamic_red_border() self.is_fleet = self.predict_fleet() if self.is_fleet: self.is_current_fleet = self.predict_current_fleet() self.is_boss = self.predict_boss() # self.image_perspective = color_similarity_2d( # self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective) # , color=(255, 36, 82) # ) def get_relative_image(self, relative_location, output_shape=None): """ Args: relative_location(tuple): upper_left_x, upper_left_y, bottom_right_x, bottom_right_y (-1, -1, 1, 1) output_shape(tuple): (x, y) Returns: PIL.Image.Image """ area = self._image_center + np.array(relative_location) * self._image_a area = tuple(np.rint(area).astype(int)) image = self.image.crop(area) if output_shape is not None: image = image.resize(output_shape) return image def predict_enemy_scale(self): """ icon on the upperleft which shows enemy scale: Large Middle Small. Returns: int: 1: Small, 2: Middle, 3: Large. """ # if not self.is_enemy: # return 0 image = self.get_relative_image((-0.415 - 0.7, -0.62 - 0.7, -0.415, -0.62)) image = np.stack( [ color_similarity_2d(image, (255, 130, 132)), color_similarity_2d(image, (255, 239, 148)), color_similarity_2d(image, (255, 235, 156)) ], axis=2 ) image = Image.fromarray(image).resize(self.ENEMY_SCALE_IMAGE_SIZE) if TEMPLATE_ENEMY_L.match(image): scale = 3 elif TEMPLATE_ENEMY_M.match(image): scale = 2 elif TEMPLATE_ENEMY_S.match(image): scale = 1 else: scale = 0 return scale def predict_static_red_border(self): # image = self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective) image = color_similarity_2d(self.image_transform, color=(255, 36, 82)) # Image.fromarray(np.array(image).astype('uint8'), mode='RGB').save(f'{self}.png') count = np.sum(image > 221) return count > 40 def predict_dynamic_red_border(self, pad=4): image = np.array(self.image_transform).astype(float) r, b = image[:, :, 0], image[:, :, 2] image = r - b image[image < 0] = 0 image[image > 255] = 255 mask = np.ones(np.array(image.shape) - pad * 2) * -1 mask = np.pad(mask, ((pad, pad), (pad, pad)), mode='constant', constant_values=1) image = image * mask image[r < 221] = 0 # print(self, np.mean(image)) return np.mean(image) > 2 def screen_point_to_grid_location(self, point): a, b, c, d, e, f, g, h = self._perspective y = (point[1] - f) / (e - point[1] * h) x = (point[0] * (h * y + 1) - b * y - c) / a res = np.array((x, y)) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE return res def _relative_image_color_count(self, area, color, output_shape=(50, 50), color_threshold=221): image = self.get_relative_image(area, output_shape=output_shape) image = color_similarity_2d(image, color=color) count = np.sum(image > color_threshold) return count def _relative_image_color_hue_count(self, area, h, s=None, v=None, output_shape=(50, 50)): image = self.get_relative_image(area, output_shape=output_shape) hsv = rgb2hsv(np.array(image) / 255) hue = hsv[:, :, 0] h = np.array([-1, 1]) + h count = (h[0] / 360 < hue) & (hue < h[1] / 360) if s: s = np.array([-1, 1]) + s saturation = hsv[:, :, 1] count &= (s[0] / 100 < saturation) & (saturation < s[1] / 100) if v: v = np.array([-1, 1]) + v value = hsv[:, :, 2] count &= (v[0] / 100 < value) & (value < v[1] / 100) count = np.sum(count) return count def predict_mystery(self): # if not self.may_mystery: # return False # cyan question mark if self._relative_image_color_count( area=(-0.3, -2, 0.3, -0.6), color=(148, 255, 247), output_shape=(20, 50)) > 50: return True # white background # if self._relative_image_color_count( # area=(-0.7, -1.7, 0.7, -0.3), color=(239, 239, 239), output_shape=(50, 50)) > 700: # return True return False def predict_fleet(self): # white ammo icon # return self._relative_image_color_count( # area=(-1, -2, -0.5, -1.5), color=(255, 255, 255), color_threshold=252) > 300 # count = self._relative_image_color_hue_count(area=(-1, -2, -0.5, -1.5), h=(0, 360), s=(0, 5), v=(95, 100)) # return count > 300 image = self.get_relative_image((-1, -2, -0.5, -1.5), output_shape=self.ENEMY_SCALE_IMAGE_SIZE) image = color_similarity_2d(image, (255, 255, 255)) return TEMPLATE_FLEET_AMMO.match(image) def predict_current_fleet(self): # Green arrow over head with hue around 141. # image = self.get_relative_image((-0.5, -3.5, 0.5, -2.5)) # hue = rgb2hsv(np.array(image) / 255)[:, :, 0] * 360 # count = np.sum((141 - 3 < hue) & (hue < 141 + 3)) # return count > 1000 count = self._relative_image_color_hue_count( area=(-0.5, -3.5, 0.5, -2.5), h=(141 - 3, 141 + 10), output_shape=(50, 50)) return count > 600 def predict_boss(self): # count = self._relative_image_color_count( # area=(-0.55, -0.2, 0.45, 0.2), color=(255, 77, 82), color_threshold=247) # return count > 100 if TEMPLATE_ENEMY_BOSS.match( self.get_relative_image((-0.55, -0.2, 0.45, 0.2), output_shape=(50, 20)), similarity=0.75): return True # 微层混合 event_20200326_cn if self._relative_image_color_hue_count( area=(0.13, -0.05, 0.63, 0.15), h=(358 - 3, 358 + 3), v=(96, 100), output_shape=(50, 20)) > 100: if TEMPLATE_ENEMY_BOSS.match( self.get_relative_image((0.13, -0.05, 0.63, 0.15), output_shape=(50, 20)), similarity=0.4): return True return False