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https://github.com/Yuki-Kokomi/OpenECAD_Project.git
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add Bethany tool
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Yuki-Kokomi
263
Bethany/lib/sketch.py
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263
Bethany/lib/sketch.py
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import numpy as np
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import matplotlib
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matplotlib.use('TkAgg')
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import matplotlib.pyplot as plt
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from .curves import *
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from .macro import *
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########################## base ###########################
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class SketchBase(object):
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"""Base class for sketch (a collection of curves). """
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def __init__(self, children, reorder=True):
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self.children = children
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if reorder:
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self.reorder()
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@staticmethod
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def from_dict(stat):
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"""construct sketch from json data
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Args:
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stat (dict): dict from json data
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"""
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raise NotImplementedError
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@staticmethod
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def from_vector(vec, start_point, is_numerical=True):
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"""construct sketch from vector representation
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Args:
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vec (np.array): (seq_len, n_args)
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start_point (np.array): (2, ). If none, implicitly defined as the last end point.
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"""
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raise NotImplementedError
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def reorder(self):
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"""rearrange the curves to follow counter-clockwise direction"""
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raise NotImplementedError
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@property
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def start_point(self):
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return self.children[0].start_point
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@property
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def end_point(self):
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return self.children[-1].end_point
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@property
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def bbox(self):
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"""compute bounding box (min/max points) of the sketch"""
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all_points = np.concatenate([child.bbox for child in self.children], axis=0)
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return np.stack([np.min(all_points, axis=0), np.max(all_points, axis=0)], axis=0)
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@property
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def bbox_size(self):
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"""compute bounding box size (max of height and width)"""
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bbox_min, bbox_max = self.bbox[0], self.bbox[1]
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bbox_size = np.max(np.abs(np.concatenate([bbox_max - self.start_point, bbox_min - self.start_point])))
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return bbox_size
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@property
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def global_trans(self):
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"""start point + sketch size (bbox_size)"""
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return np.concatenate([self.start_point, np.array([self.bbox_size])])
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def transform(self, translate, scale):
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"""linear transformation"""
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for child in self.children:
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child.transform(translate, scale)
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def flip(self, axis):
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for child in self.children:
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child.flip(axis)
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self.reorder()
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def numericalize(self, n=256):
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"""quantize curve parameters into integers"""
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for child in self.children:
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child.numericalize(n)
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def normalize(self, size=256):
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"""normalize within the given size, with start_point in the middle center"""
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cur_size = self.bbox_size
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scale = (size / 2 * NORM_FACTOR - 1) / cur_size # prevent potential overflow if data augmentation applied
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#self.transform(-self.start_point, scale)
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#self.transform(np.array((size / 2, size / 2)), 1)
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def denormalize(self, bbox_size, size=256):
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"""inverse procedure of normalize method"""
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scale = bbox_size / (size / 2 * NORM_FACTOR - 1)
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#self.transform(-np.array((size / 2, size / 2)), scale)
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def to_vector(self):
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"""convert to vector representation"""
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raise NotImplementedError
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def draw(self, ax):
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"""draw sketch on matplotlib ax"""
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raise NotImplementedError
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def to_image(self):
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"""convert to image"""
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fig, ax = plt.subplots()
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self.draw(ax)
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ax.axis('equal')
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fig.canvas.draw()
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X = np.array(fig.canvas.renderer.buffer_rgba())[:, :, :3]
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plt.close(fig)
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return X
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def sample_points(self, n=32):
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"""uniformly sample points from the sketch"""
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raise NotImplementedError
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####################### loop & profile #######################
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class Loop(SketchBase):
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"""Sketch loop, a sequence of connected curves."""
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@staticmethod
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def from_dict(stat):
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all_curves = [construct_curve_from_dict(item) for item in stat['profile_curves']]
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this_loop = Loop(all_curves)
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this_loop.is_outer = stat['is_outer']
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return this_loop
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def __str__(self):
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return "Loop:" + "\n -" + "\n -".join([str(curve) for curve in self.children])
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@staticmethod
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def from_vector(vec, start_point=None, is_numerical=True):
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all_curves = []
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if start_point is None:
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# FIXME: explicit for loop can be avoided here
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for i in range(vec.shape[0]):
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if vec[i][0] == EOS_IDX:
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start_point = vec[i - 1][1:3]
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break
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for i in range(vec.shape[0]):
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type = vec[i][0]
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if type == SOL_IDX:
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continue
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elif type == EOS_IDX:
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break
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else:
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curve = construct_curve_from_vector(vec[i], start_point, is_numerical=is_numerical)
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start_point = vec[i][1:3] # current curve's end_point serves as next curve's start_point
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all_curves.append(curve)
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return Loop(all_curves)
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def reorder(self):
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"""reorder by starting left most and counter-clockwise"""
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if len(self.children) <= 1:
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return
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start_curve_idx = -1
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sx, sy = 10000, 10000
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# correct start-end point order
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if np.allclose(self.children[0].start_point, self.children[1].start_point) or \
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np.allclose(self.children[0].start_point, self.children[1].end_point):
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self.children[0].reverse()
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# correct start-end point order and find left-most point
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for i, curve in enumerate(self.children):
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if i < len(self.children) - 1 and np.allclose(curve.end_point, self.children[i + 1].end_point):
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self.children[i + 1].reverse()
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if round(curve.start_point[0], 6) < round(sx, 6) or \
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(round(curve.start_point[0], 6) == round(sx, 6) and round(curve.start_point[1], 6) < round(sy, 6)):
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start_curve_idx = i
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sx, sy = curve.start_point
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self.children = self.children[start_curve_idx:] + self.children[:start_curve_idx]
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# ensure mostly counter-clock wise
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if isinstance(self.children[0], Circle) or isinstance(self.children[-1], Circle): # FIXME: hard-coded
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return
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start_vec = self.children[0].direction()
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end_vec = self.children[-1].direction(from_start=False)
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if np.cross(end_vec, start_vec) <= 0:
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for curve in self.children:
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curve.reverse()
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self.children.reverse()
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def to_vector(self, max_len=None, add_sol=True, add_eos=True):
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loop_vec = np.stack([curve.to_vector() for curve in self.children], axis=0)
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if add_sol:
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loop_vec = np.concatenate([SOL_VEC[np.newaxis], loop_vec], axis=0)
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if add_eos:
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loop_vec = np.concatenate([loop_vec, EOS_VEC[np.newaxis]], axis=0)
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if max_len is None:
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return loop_vec
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if loop_vec.shape[0] > max_len:
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return None
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elif loop_vec.shape[0] < max_len:
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pad_vec = np.tile(EOS_VEC, max_len - loop_vec.shape[0]).reshape((-1, len(EOS_VEC)))
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loop_vec = np.concatenate([loop_vec, pad_vec], axis=0) # (max_len, 1 + N_ARGS)
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return loop_vec
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def draw(self, ax):
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colors = ['red', 'blue', 'green', 'brown', 'pink', 'yellow', 'purple', 'black'] * 10
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for i, curve in enumerate(self.children):
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curve.draw(ax, colors[i])
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def sample_points(self, n=32):
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points = np.stack([curve.sample_points(n) for curve in self.children], axis=0) # (n_curves, n, 2)
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return points
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class Profile(SketchBase):
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"""Sketch profile，a closed region formed by one or more loops.
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The outer-most loop is placed at first."""
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@staticmethod
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def from_dict(stat):
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all_loops = [Loop.from_dict(item) for item in stat['loops']]
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return Profile(all_loops)
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def __str__(self):
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return "Profile:" + "\n -".join([str(loop) for loop in self.children])
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@staticmethod
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def from_vector(vec, start_point=None, is_numerical=True):
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all_loops = []
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command = vec[:, 0]
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end_idx = command.tolist().index(EOS_IDX)
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indices = np.where(command[:end_idx] == SOL_IDX)[0].tolist() + [end_idx]
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for i in range(len(indices) - 1):
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loop_vec = vec[indices[i]:indices[i + 1]]
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loop_vec = np.concatenate([loop_vec, EOS_VEC[np.newaxis]], axis=0)
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if loop_vec[0][0] == SOL_IDX and loop_vec[1][0] not in [SOL_IDX, EOS_IDX]:
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all_loops.append(Loop.from_vector(loop_vec, is_numerical=is_numerical))
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return Profile(all_loops)
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def reorder(self):
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if len(self.children) <= 1:
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return
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all_loops_bbox_min = np.stack([loop.bbox[0] for loop in self.children], axis=0).round(6)
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ind = np.lexsort(all_loops_bbox_min.transpose()[[1, 0]])
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self.children = [self.children[i] for i in ind]
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def draw(self, ax):
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for i, loop in enumerate(self.children):
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loop.draw(ax)
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ax.text(loop.start_point[0], loop.start_point[1], str(i))
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def to_vector(self, max_n_loops=None, max_len_loop=None, pad=True):
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loop_vecs = [loop.to_vector(None, add_eos=False) for loop in self.children]
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if max_n_loops is not None and len(loop_vecs) > max_n_loops:
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return None
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for vec in loop_vecs:
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if max_len_loop is not None and vec.shape[0] > max_len_loop:
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return None
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profile_vec = np.concatenate(loop_vecs, axis=0)
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profile_vec = np.concatenate([profile_vec, EOS_VEC[np.newaxis]], axis=0)
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if pad:
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pad_len = max_n_loops * max_len_loop - profile_vec.shape[0]
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profile_vec = np.concatenate([profile_vec, EOS_VEC[np.newaxis].repeat(pad_len, axis=0)], axis=0)
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return profile_vec
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def sample_points(self, n=32):
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points = np.concatenate([loop.sample_points(n) for loop in self.children], axis=0)
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return points
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