mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-12-12 15:41:01 +00:00
396 lines
11 KiB
Python
396 lines
11 KiB
Python
import numpy as np
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from scipy import optimize
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from .utils import area_pad
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class Points:
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def __init__(self, points):
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if points is None or len(points) == 0:
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self._bool = False
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self.points = None
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else:
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self._bool = True
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self.points = np.array(points)
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if len(self.points.shape) == 1:
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self.points = np.array([self.points])
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self.x, self.y = self.points.T
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def __str__(self):
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return str(self.points)
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__repr__ = __str__
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def __iter__(self):
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return iter(self.points)
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def __getitem__(self, item):
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return self.points[item]
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def __len__(self):
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if self:
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return len(self.points)
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else:
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return 0
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def __bool__(self):
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return self._bool
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def link(self, point, is_horizontal=False):
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if is_horizontal:
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lines = [[y, np.pi / 2] for y in self.y]
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return Lines(lines, is_horizontal=True)
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else:
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x, y = point
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theta = -np.arctan((self.x - x) / (self.y - y))
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rho = self.x * np.cos(theta) + self.y * np.sin(theta)
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lines = np.array([rho, theta]).T
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return Lines(lines, is_horizontal=False)
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def mean(self):
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if not self:
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return None
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return np.round(np.mean(self.points, axis=0)).astype(int)
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def group(self, threshold=3):
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if not self:
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return np.array([])
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groups = []
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points = self.points
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if len(points) == 1:
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return np.array([points[0]])
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while len(points):
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p0, p1 = points[0], points[1:]
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distance = np.sum(np.abs(p1 - p0), axis=1)
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new = Points(np.append(p1[distance <= threshold], [p0], axis=0)).mean().tolist()
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groups.append(new)
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points = p1[distance > threshold]
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return np.array(groups)
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class Lines:
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MID_Y = 360
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def __init__(self, lines, is_horizontal):
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if lines is None or len(lines) == 0:
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self._bool = False
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self.lines = None
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else:
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self._bool = True
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self.lines = np.array(lines)
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if len(self.lines.shape) == 1:
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self.lines = np.array([self.lines])
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self.rho, self.theta = self.lines.T
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self.is_horizontal = is_horizontal
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def __str__(self):
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return str(self.lines)
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__repr__ = __str__
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def __iter__(self):
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return iter(self.lines)
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def __getitem__(self, item):
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return Lines(self.lines[item], is_horizontal=self.is_horizontal)
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def __len__(self):
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if self:
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return len(self.lines)
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else:
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return 0
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def __bool__(self):
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return self._bool
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@property
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def sin(self):
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return np.sin(self.theta)
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@property
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def cos(self):
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return np.cos(self.theta)
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@property
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def mean(self):
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if not self:
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return None
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if self.is_horizontal:
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return np.mean(self.lines, axis=0)
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else:
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x = np.mean(self.mid)
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theta = np.mean(self.theta)
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rho = x * np.cos(theta) + self.MID_Y * np.sin(theta)
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return np.array((rho, theta))
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@property
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def mid(self):
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if not self:
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return np.array([])
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if self.is_horizontal:
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return self.rho
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else:
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return (self.rho - self.MID_Y * self.sin) / self.cos
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def get_x(self, y):
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return (self.rho - y * self.sin) / self.cos
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def get_y(self, x):
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return (self.rho - x * self.cos) / self.sin
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def add(self, other):
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if not other:
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return self
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if not self:
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return other
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lines = np.append(self.lines, other.lines, axis=0)
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return Lines(lines, is_horizontal=self.is_horizontal)
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def move(self, x, y):
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if not self:
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return self
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if self.is_horizontal:
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self.lines[:, 0] += y
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else:
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self.lines[:, 0] += x * self.cos + y * self.sin
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return Lines(self.lines, is_horizontal=self.is_horizontal)
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def sort(self):
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if not self:
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return self
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lines = self.lines[np.argsort(self.mid)]
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return Lines(lines, is_horizontal=self.is_horizontal)
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def group(self, threshold=3):
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if not self:
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return self
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lines = self.sort()
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prev = 0
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regrouped = []
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group = []
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for mid, line in zip(lines.mid, lines.lines):
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line = line.tolist()
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if mid - prev > threshold:
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if len(regrouped) == 0:
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if len(group) != 0:
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regrouped = [group]
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else:
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regrouped += [group]
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group = [line]
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else:
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group.append(line)
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prev = mid
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regrouped += [group]
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regrouped = np.vstack([Lines(r, is_horizontal=self.is_horizontal).mean for r in regrouped])
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return Lines(regrouped, is_horizontal=self.is_horizontal)
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def distance_to_point(self, point):
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x, y = point
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return self.rho - x * self.cos - y * self.sin
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@staticmethod
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def cross_two_lines(lines1, lines2):
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for rho1, sin1, cos1 in zip(lines1.rho, lines1.sin, lines1.cos):
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for rho2, sin2, cos2 in zip(lines2.rho, lines2.sin, lines2.cos):
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a = np.array([[cos1, sin1], [cos2, sin2]])
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b = np.array([rho1, rho2])
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yield np.linalg.solve(a, b)
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def cross(self, other):
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points = np.vstack(self.cross_two_lines(self, other))
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points = Points(points)
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return points
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def delete(self, other, threshold=3):
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if not self:
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return self
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other_mid = other.mid
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lines = []
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for mid, line in zip(self.mid, self.lines):
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if np.any(np.abs(other_mid - mid) < threshold):
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continue
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lines.append(line)
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return Lines(lines, is_horizontal=self.is_horizontal)
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def area2corner(area):
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"""
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Args:
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area: (x1, y1, x2, y2)
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Returns:
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np.ndarray: [upper-left, upper-right, bottom-left, bottom-right]
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"""
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return np.array([[area[0], area[1]], [area[2], area[1]], [area[0], area[3]], [area[2], area[3]]])
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def corner2area(corner):
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"""
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Args:
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corner: [upper-left, upper-right, bottom-left, bottom-right]
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Returns:
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np.ndarray: (x1, y1, x2, y2)
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"""
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x, y = np.array(corner).T
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return np.rint([np.min(x), np.min(y), np.max(x), np.max(y)]).astype(int)
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def corner2inner(corner):
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"""
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The largest rectangle inscribed in trapezoid.
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Args:
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corner: ((x0, y0), (x1, y1), (x2, y2), (x3, y3))
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Returns:
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tuple[int]: (upper_left_x, upper_left_y, bottom_right_x, bottom_right_y).
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"""
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x0, y0, x1, y1, x2, y2, x3, y3 = np.array(corner).flatten()
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area = tuple(np.rint((max(x0, x2), max(y0, y1), min(x1, x3), min(y2, y3))).astype(int))
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return area
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def corner2outer(corner):
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"""
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The smallest rectangle circumscribed by the trapezoid.
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Args:
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corner: ((x0, y0), (x1, y1), (x2, y2), (x3, y3))
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Returns:
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tuple[int]: (upper_left_x, upper_left_y, bottom_right_x, bottom_right_y).
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"""
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x0, y0, x1, y1, x2, y2, x3, y3 = np.array(corner).flatten()
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area = tuple(np.rint((min(x0, x2), min(y0, y1), max(x1, x3), max(y2, y3))).astype(int))
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return area
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def trapezoid2area(corner, pad=0):
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"""
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Convert corners of a trapezoid to area.
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Args:
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corner: ((x0, y0), (x1, y1), (x2, y2), (x3, y3))
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pad (int):
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Positive value for inscribed area.
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Negative value and 0 for circumscribed area.
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Returns:
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tuple[int]: (upper_left_x, upper_left_y, bottom_right_x, bottom_right_y).
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"""
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if pad > 0:
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return area_pad(corner2inner(corner), pad=pad)
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elif pad < 0:
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return area_pad(corner2outer(corner), pad=pad)
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else:
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return area_pad(corner2area(corner), pad=pad)
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def points_to_area_generator(points, shape):
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"""
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Args:
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points (np.ndarray): N x 2 array.
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shape (tuple): (x, y).
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Yields:
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tuple, np.ndarray: (x, y), [upper-left, upper-right, bottom-left, bottom-right]
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"""
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points = points.reshape(*shape[::-1], 2)
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for y in range(shape[1] - 1):
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for x in range(shape[0] - 1):
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area = np.array([points[y, x], points[y, x + 1], points[y + 1, x], points[y + 1, x + 1]])
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yield ((x, y), area)
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def get_map_inner(points):
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"""
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Args:
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points (np.ndarray): N x 2 array.
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Yields:
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np.ndarray: (x, y).
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"""
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points = np.array(points)
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if len(points.shape) == 1:
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points = np.array([points])
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return np.mean(points, axis=0)
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def separate_edges(edges, inner):
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"""
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Args:
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edges: A iterate object which contains float ot integer.
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inner (float, int): A inner point to separate edges.
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Returns:
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float, float: Lower edge and upper edge. if not found, return None
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"""
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if len(edges) == 0:
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return None, None
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elif len(edges) == 1:
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edge = edges[0]
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return (None, edge) if edge > inner else (edge, None)
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else:
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lower = [edge for edge in edges if edge < inner]
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upper = [edge for edge in edges if edge > inner]
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lower = lower[0] if len(lower) else None
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upper = upper[-1] if len(upper) else None
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return lower, upper
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def perspective_transform(points, data):
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"""
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Args:
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points: A 2D array with shape (n, 2)
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data: Perspective data, a 2D array with shape (3, 3),
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see https://web.archive.org/web/20150222120106/xenia.media.mit.edu/~cwren/interpolator/
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Returns:
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np.ndarray: 2D array with shape (n, 2)
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"""
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points = np.pad(np.array(points), ((0, 0), (0, 1)), mode='constant', constant_values=1)
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matrix = data.dot(points.T)
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x, y = matrix[0] / matrix[2], matrix[1] / matrix[2]
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points = np.array([x, y]).T
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return points
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def fit_points(points, mod, encourage=1):
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"""
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Get a closet point in a group of points with common difference.
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Will ignore points in the distance.
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Args:
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points: Points on image, a 2D array with shape (n, 2)
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mod: Common difference of points, (x, y).
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encourage (int, float): Describe how close to fit a group of points, in pixel.
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Smaller means closer to local minimum, larger means closer to global minimum.
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Returns:
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np.ndarray: (x, y)
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"""
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encourage = np.square(encourage)
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mod = np.array(mod)
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points = np.array(points) % mod
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points = np.append(points - mod, points, axis=0)
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def cal_distance(point):
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distance = np.linalg.norm(points - point, axis=1)
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return np.sum(1 / (1 + np.exp(encourage / distance) / distance))
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# Fast local minimizer
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# result = optimize.minimize(cal_distance, np.mean(points, axis=0), method='SLSQP')
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# return result['x'] % mod
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# Brute-force global minimizer
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area = np.append(-mod - 10, mod + 10)
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result = optimize.brute(cal_distance, ((area[0], area[2]), (area[1], area[3])))
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return result % mod
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