mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-11-24 01:21:25 +00:00
223 lines
8.4 KiB
Python
223 lines
8.4 KiB
Python
import numpy as np
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from PIL import Image
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from skimage.color import rgb2hsv
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from module.base.utils import color_similarity_2d
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from module.template.assets import *
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class GridPredictor:
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ENEMY_SCALE_IMAGE_SIZE = (50, 50)
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ENEMY_PERSPECTIVE_IMAGE_SIZE = (50, 50)
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RED_BORDER_IGNORE_TOP = 10
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def __init__(self, location, image, corner):
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"""
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Args:
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location:
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image:
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corner:
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"""
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self.location = location
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self.image = image
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self.corner = corner.flatten()
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x0, y0, x1, y1, x2, y2, x3, y3 = self.corner
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divisor = x0 - x1 + x2 - x3
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x = (x0 * x2 - x1 * x3) / divisor
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y = (x0 * y2 - x1 * y2 + x2 * y0 - x3 * y0) / divisor
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self._image_center = np.array([x, y, x, y])
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self._image_a = (-x0 * x2 + x0 * x3 + x1 * x2 - x1 * x3) / divisor
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self._perspective = (
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(-x0 + x1) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[0], # a
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(-x0 * x3 + x1 * x2) / (-x2 + x3) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1], # b
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x0, # c
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0, # d
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(-x0 * y2 + x1 * y2 + x2 * y0 - x3 * y0) / (-x2 + x3) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1], # e
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y0, # f
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0, # g
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((-x0 + x1) / (-x2 + x3) - 1) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE[1] # h
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)
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def predict(self):
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self.image_transform = self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective)
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# self.image_hole = self.get_relative_image((-1, -1, 1, 1))
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self.is_enemy = self.predict_static_red_border()
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self.enemy_scale = self.predict_enemy_scale()
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if self.enemy_scale > 0:
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self.is_enemy = True
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self.is_mystery = self.predict_mystery()
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if not self.is_enemy and not self.is_mystery:
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self.is_siren = self.predict_dynamic_red_border()
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self.is_fleet = self.predict_fleet()
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if self.is_fleet:
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self.is_current_fleet = self.predict_current_fleet()
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self.is_boss = self.predict_boss()
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# self.image_perspective = color_similarity_2d(
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# self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective)
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# , color=(255, 36, 82)
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# )
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def get_relative_image(self, relative_location, output_shape=None):
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"""
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Args:
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relative_location(tuple): upper_left_x, upper_left_y, bottom_right_x, bottom_right_y
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(-1, -1, 1, 1)
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output_shape(tuple): (x, y)
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Returns:
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PIL.Image.Image
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"""
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area = self._image_center + np.array(relative_location) * self._image_a
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area = tuple(np.rint(area).astype(int))
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image = self.image.crop(area)
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if output_shape is not None:
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image = image.resize(output_shape)
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return image
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def predict_enemy_scale(self):
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"""
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icon on the upperleft which shows enemy scale: Large Middle Small.
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Returns:
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int: 1: Small, 2: Middle, 3: Large.
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"""
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# if not self.is_enemy:
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# return 0
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image = self.get_relative_image((-0.415 - 0.7, -0.62 - 0.7, -0.415, -0.62))
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image = np.stack(
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[
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color_similarity_2d(image, (255, 130, 132)),
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color_similarity_2d(image, (255, 239, 148)),
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color_similarity_2d(image, (255, 235, 156))
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], axis=2
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)
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image = Image.fromarray(image).resize(self.ENEMY_SCALE_IMAGE_SIZE)
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if TEMPLATE_ENEMY_L.match(image):
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scale = 3
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elif TEMPLATE_ENEMY_M.match(image):
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scale = 2
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elif TEMPLATE_ENEMY_S.match(image):
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scale = 1
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else:
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scale = 0
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return scale
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def predict_static_red_border(self):
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# image = self.image.transform(self.ENEMY_PERSPECTIVE_IMAGE_SIZE, Image.PERSPECTIVE, self._perspective)
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image = color_similarity_2d(
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self.image_transform.crop((0, self.RED_BORDER_IGNORE_TOP, *self.ENEMY_PERSPECTIVE_IMAGE_SIZE)),
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color=(255, 36, 82))
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# Image.fromarray(np.array(image).astype('uint8'), mode='RGB').save(f'{self}.png')
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count = np.sum(image > 221)
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return count > 40
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def predict_dynamic_red_border(self, pad=4):
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image = np.array(
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self.image_transform.crop((0, self.RED_BORDER_IGNORE_TOP, *self.ENEMY_PERSPECTIVE_IMAGE_SIZE))
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).astype(float)
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r, b = image[:, :, 0], image[:, :, 2]
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image = r - b
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image[image < 0] = 0
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image[image > 255] = 255
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mask = np.ones(np.array(image.shape) - pad * 2) * -1
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mask = np.pad(mask, ((pad, pad), (pad, pad)), mode='constant', constant_values=1)
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image = image * mask
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image[r < 221] = 0
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# print(self, np.mean(image))
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return np.mean(image) > 2
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def screen_point_to_grid_location(self, point):
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a, b, c, d, e, f, g, h = self._perspective
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y = (point[1] - f) / (e - point[1] * h)
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x = (point[0] * (h * y + 1) - b * y - c) / a
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res = np.array((x, y)) / self.ENEMY_PERSPECTIVE_IMAGE_SIZE
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return res
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def _relative_image_color_count(self, area, color, output_shape=(50, 50), color_threshold=221):
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image = self.get_relative_image(area, output_shape=output_shape)
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image = color_similarity_2d(image, color=color)
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count = np.sum(image > color_threshold)
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return count
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def _relative_image_color_hue_count(self, area, h, s=None, v=None, output_shape=(50, 50)):
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image = self.get_relative_image(area, output_shape=output_shape)
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hsv = rgb2hsv(np.array(image) / 255)
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hue = hsv[:, :, 0]
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h = np.array([-1, 1]) + h
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count = (h[0] / 360 < hue) & (hue < h[1] / 360)
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if s:
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s = np.array([-1, 1]) + s
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saturation = hsv[:, :, 1]
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count &= (s[0] / 100 < saturation) & (saturation < s[1] / 100)
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if v:
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v = np.array([-1, 1]) + v
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value = hsv[:, :, 2]
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count &= (v[0] / 100 < value) & (value < v[1] / 100)
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count = np.sum(count)
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return count
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def predict_mystery(self):
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# if not self.may_mystery:
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# return False
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# cyan question mark
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if self._relative_image_color_count(
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area=(-0.3, -2, 0.3, -0.6), color=(148, 255, 247), output_shape=(20, 50)) > 50:
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return True
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# white background
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# if self._relative_image_color_count(
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# area=(-0.7, -1.7, 0.7, -0.3), color=(239, 239, 239), output_shape=(50, 50)) > 700:
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# return True
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return False
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def predict_fleet(self):
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# white ammo icon
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# return self._relative_image_color_count(
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# area=(-1, -2, -0.5, -1.5), color=(255, 255, 255), color_threshold=252) > 300
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# count = self._relative_image_color_hue_count(area=(-1, -2, -0.5, -1.5), h=(0, 360), s=(0, 5), v=(95, 100))
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# return count > 300
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image = self.get_relative_image((-1, -2, -0.5, -1.5), output_shape=self.ENEMY_SCALE_IMAGE_SIZE)
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image = color_similarity_2d(image, (255, 255, 255))
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return TEMPLATE_FLEET_AMMO.match(image)
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def predict_current_fleet(self):
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# Green arrow over head with hue around 141.
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# image = self.get_relative_image((-0.5, -3.5, 0.5, -2.5))
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# hue = rgb2hsv(np.array(image) / 255)[:, :, 0] * 360
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# count = np.sum((141 - 3 < hue) & (hue < 141 + 3))
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# return count > 1000
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count = self._relative_image_color_hue_count(
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area=(-0.5, -3.5, 0.5, -2.5), h=(141 - 3, 141 + 10), output_shape=(50, 50))
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return count > 600
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def predict_boss(self):
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# count = self._relative_image_color_count(
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# area=(-0.55, -0.2, 0.45, 0.2), color=(255, 77, 82), color_threshold=247)
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# return count > 100
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if TEMPLATE_ENEMY_BOSS.match(
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self.get_relative_image((-0.55, -0.2, 0.45, 0.2), output_shape=(50, 20)),
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similarity=0.75):
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return True
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# 微层混合 event_20200326_cn
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if self._relative_image_color_hue_count(
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area=(0.13, -0.05, 0.63, 0.15), h=(358 - 3, 358 + 3), v=(96, 100), output_shape=(50, 20)) > 100:
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if TEMPLATE_ENEMY_BOSS.match(
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self.get_relative_image((0.13, -0.05, 0.63, 0.15), output_shape=(50, 20)), similarity=0.4):
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return True
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return False
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