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https://github.com/LmeSzinc/StarRailCopilot.git
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321 lines
10 KiB
Python
321 lines
10 KiB
Python
import cv2
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import numpy as np
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from scipy import signal
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from module.base.base import ModuleBase
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from module.base.button import ButtonWrapper
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from module.base.decorator import cached_property, del_cached_property
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from module.base.timer import Timer
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from module.base.utils import Lines, area_center, area_offset, color_similarity_2d
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from module.exception import ScriptError
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from module.logger import logger
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def xywh2xyxy(area):
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"""
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Convert (x, y, width, height) to (x1, y1, x2, y2)
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"""
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x, y, w, h = area
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return x, y, x + w, y + h
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def xyxy2xywh(area):
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"""
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Convert (x1, y1, x2, y2) to (x, y, width, height)
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"""
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x1, y1, x2, y2 = area
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return min(x1, x2), min(y1, y2), abs(x2 - x1), abs(y2 - y1)
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class InventoryItem:
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def __init__(self, main: ModuleBase, loca: tuple[int, int], point: tuple[int, int]):
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self.main = main
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self.loca = loca
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self.point = point
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def __str__(self):
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return f'Item({self.loca})'
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__repr__ = __str__
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def crop(self, area, copy=False):
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area = area_offset(area, offset=self.point)
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return self.main.image_crop(area, copy=copy)
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@cached_property
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def button(self):
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area = area_offset((-40, -20, 40, 20), offset=self.point)
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return area
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@cached_property
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def is_selected(self):
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image = self.crop((-60, -100, 60, 40))
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image = color_similarity_2d(image, (255, 255, 255))
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param = {
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'height': 160,
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}
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hori = cv2.reduce(image, 1, cv2.REDUCE_AVG).flatten()
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peaks, _ = signal.find_peaks(hori, **param)
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if len(peaks) != 2:
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return False
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vert = cv2.reduce(image, 0, cv2.REDUCE_AVG).flatten()
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peaks, _ = signal.find_peaks(vert, **param)
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if len(peaks) != 2:
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return False
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return True
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class InventoryManager:
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GRID_DELTA = (104, 124)
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ERROR_LINES_TOLERANCE = (-10, 10)
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COINCIDENT_POINT_ENCOURAGE_DISTANCE = 1.
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def __init__(self, main: ModuleBase, inventory: ButtonWrapper):
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"""
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max_count: expected max count of this inventory page
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"""
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self.main = main
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self.inventory = inventory
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self.items: dict[tuple[int, int], InventoryItem] = {}
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self.selected: InventoryItem | None = None
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def mid_cleanse(self, mids, mid_diff_range, edge_range):
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"""
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Args:
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mids:
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mid_diff_range:
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edge_range:
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Returns:
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"""
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count = len(mids)
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if count == 1:
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return mids
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elif count == 2:
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return mids
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# print(mids)
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encourage = self.COINCIDENT_POINT_ENCOURAGE_DISTANCE ** 2
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# Drawing lines
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def iter_lines():
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for index, mid in enumerate(mids):
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for n in range(self.ERROR_LINES_TOLERANCE[0], self.ERROR_LINES_TOLERANCE[1] + 1):
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theta = np.arctan(index + n)
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rho = mid * np.cos(theta)
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yield [rho, theta]
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def coincident_point_value(point):
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"""Value that measures how close a point to the coincident point. The smaller the better.
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Coincident point may be many.
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Use an activation function to encourage a group of coincident lines and ignore wrong lines.
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"""
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x, y = point
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# Do not use:
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# distance = coincident.distance_to_point(point)
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distance = np.abs(x - coincident.get_x(y))
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# print((distance * 1).astype(int).reshape(len(mids), np.diff(self.config.ERROR_LINES_TOLERANCE)[0]+1))
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# Activation function
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# distance = 1 / (1 + np.exp(16 / distance - distance))
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distance = 1 / (1 + np.exp(encourage / distance) / distance)
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distance = np.sum(distance)
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return distance
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# Fitting mid
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coincident = Lines(np.vstack(list(iter_lines())), is_horizontal=False)
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coincident_point_range = (
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(
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-abs(self.ERROR_LINES_TOLERANCE[0]) * mid_diff_range[1] + edge_range[0],
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abs(self.ERROR_LINES_TOLERANCE[1]) * mid_diff_range[1] + edge_range[1]
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),
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mid_diff_range
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)
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from scipy import optimize
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coincident_point = optimize.brute(coincident_point_value, coincident_point_range)
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# print(coincident_point)
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# Filling mid
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left, right = edge_range
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mids = np.arange(-25, 25) * coincident_point[1] + coincident_point[0]
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mids = mids[(mids > left) & (mids < right)]
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# print(mids)
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return mids
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def update(self):
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image = self.main.image_crop(self.inventory, copy=False)
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image = color_similarity_2d(image, color=(252, 200, 109))
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# Search rarity stars
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, dst=image)
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# image_star = cv2.inRange(image, 221, 255)
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# Close rarity stars as item
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 3))
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cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, dst=image)
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image_item = cv2.inRange(image, 221, 255)
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# from PIL import Image
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# Image.fromarray(image_star).show()
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def iter_area(im):
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# Iter matched area from given image
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contours, _ = cv2.findContours(im, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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for cont in contours:
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rect = cv2.boundingRect(cv2.convexHull(cont).astype(np.float32))
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# width < 5stars and height < 1star
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if not (65 > rect[2] >= 5 and 10 > rect[3]):
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continue
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rect = xywh2xyxy(rect)
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rect = area_center(rect)
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yield rect
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area_item = list(iter_area(image_item))
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# Re-generate a correct xy array
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points = np.array(area_item)
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points += self.inventory.area[:2]
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area = self.inventory.area
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x_list = np.unique(np.sort(points[:, 0]))
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y_list = np.unique(np.sort(points[:, 1]))
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x_list = self.mid_cleanse(
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x_list,
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mid_diff_range=(self.GRID_DELTA[0] - 3, self.GRID_DELTA[0] + 3),
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edge_range=(area[0], area[2])
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)
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y_list = self.mid_cleanse(
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y_list,
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mid_diff_range=(self.GRID_DELTA[1] - 3, self.GRID_DELTA[1] + 3),
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edge_range=(area[1], area[3])
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)
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def is_near_existing(p):
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diff = np.linalg.norm(points - p, axis=1)
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return np.any(diff < 3)
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def iter_items():
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y_max = -1
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for y in y_list:
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for x in x_list:
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if is_near_existing((x, y)):
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y_max = y
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break
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for yi, y in enumerate(y_list):
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if y < y_max:
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# Fill items
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for xi, x in enumerate(x_list):
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yield InventoryItem(main=self.main, loca=(xi, yi), point=(int(x), int(y)))
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elif y == y_max:
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# Fill until the last item
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for xi, x in enumerate(x_list):
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if is_near_existing((x, y)):
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yield InventoryItem(main=self.main, loca=(xi, yi), point=(int(x), int(y)))
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else:
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break
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# Re-generate items
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self.items = {}
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selected = []
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for item in iter_items():
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self.items[item.loca] = item
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if item.is_selected:
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selected.append(item)
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# Check selected
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self.selected = None
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count = len(selected)
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if count == 0:
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# logger.warning('Inventory has no item selected')
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pass
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elif count > 1:
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logger.warning(f'Inventory has multiple items selected: {selected}')
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self.selected = selected[0]
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else:
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self.selected = selected[0]
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logger.info(f'Inventory: {len(self.items)} items, selected {self.selected}')
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def get_row_first(self, row=1, first=0) -> InventoryItem | None:
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"""
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Get the first item of the next row
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Args:
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row: 1 for next row, -1 for prev row
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first: 0 for the first_item
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"""
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if self.selected == None:
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return None
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loca = self.selected.loca
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loca = (first, loca[1] + row)
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try:
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return self.items[loca]
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except KeyError:
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return None
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def get_right(self) -> InventoryItem | None:
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"""
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Get the right item of the selected
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"""
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if self.selected == None:
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return None
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loca = self.selected.loca
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loca = (loca[0] + 1, loca[1])
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try:
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return self.items[loca]
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except KeyError:
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return None
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def get_first(self) -> InventoryItem | None:
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"""
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Get the first item of inventory
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"""
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try:
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return self.items[(0, 0)]
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except KeyError:
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return None
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def select(self, item, skip_first_screenshot=True):
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logger.info(f'Inventory select {item}')
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if isinstance(item, InventoryItem):
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pass
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else:
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try:
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item = self.items[item]
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except KeyError:
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raise ScriptError(f'Inventory select {item} but is not in current items')
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interval = Timer(2, count=6)
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while 1:
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if skip_first_screenshot:
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skip_first_screenshot = False
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else:
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self.main.device.screenshot()
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# End
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del_cached_property(item, 'is_selected')
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if item.is_selected:
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logger.info('Inventory item selected')
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break
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# Click
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if interval.reached():
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self.main.device.click(item)
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interval.reset()
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self.update()
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def wait_selected(self, skip_first_screenshot=True):
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timeout = Timer(2, count=6).start()
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while 1:
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if skip_first_screenshot:
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skip_first_screenshot = False
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else:
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self.main.device.screenshot()
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self.update()
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if self.selected is not None:
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break
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if timeout.reached():
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logger.warning('Wait inventory selected timeout')
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break
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