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from __future__ import print_function
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import savgol_filter
from scipy.interpolate import interp1d
alphabet = [
'\x00', ' ', '!', '"', '#', "'", '(', ')', ',', '-', '.',
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';',
'?', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',
'L', 'M', 'N', 'O', 'P', 'R', 'S', 'T', 'U', 'V', 'W', 'Y',
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x',
'y', 'z'
]
alphabet_ord = list(map(ord, alphabet))
alpha_to_num = defaultdict(int, list(map(reversed, enumerate(alphabet))))
num_to_alpha = dict(enumerate(alphabet_ord))
MAX_STROKE_LEN = 1200
MAX_CHAR_LEN = 75
def align(coords):
"""
corrects for global slant/offset in handwriting strokes
"""
coords = np.copy(coords)
X, Y = coords[:, 0].reshape(-1, 1), coords[:, 1].reshape(-1, 1)
X = np.concatenate([np.ones([X.shape[0], 1]), X], axis=1)
offset, slope = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y).squeeze()
theta = np.arctan(slope)
rotation_matrix = np.array(
[[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]]
)
coords[:, :2] = np.dot(coords[:, :2], rotation_matrix) - offset
return coords
def skew(coords, degrees):
"""
skews strokes by given degrees
"""
coords = np.copy(coords)
theta = degrees * np.pi/180
A = np.array([[np.cos(-theta), 0], [np.sin(-theta), 1]])
coords[:, :2] = np.dot(coords[:, :2], A)
return coords
def stretch(coords, x_factor, y_factor):
"""
stretches strokes along x and y axis
"""
coords = np.copy(coords)
coords[:, :2] *= np.array([x_factor, y_factor])
return coords
def add_noise(coords, scale):
"""
adds gaussian noise to strokes
"""
coords = np.copy(coords)
coords[1:, :2] += np.random.normal(loc=0.0, scale=scale, size=coords[1:, :2].shape)
return coords
def encode_ascii(ascii_string):
"""
encodes ascii string to array of ints
"""
return np.array(list(map(lambda x: alpha_to_num[x], ascii_string)) + [0])
def denoise(coords):
"""
smoothing filter to mitigate some artifacts of the data collection
"""
coords = np.split(coords, np.where(coords[:, 2] == 1)[0] + 1, axis=0)
new_coords = []
for stroke in coords:
if len(stroke) != 0:
x_new = savgol_filter(stroke[:, 0], 7, 3, mode='nearest')
y_new = savgol_filter(stroke[:, 1], 7, 3, mode='nearest')
xy_coords = np.hstack([x_new.reshape(-1, 1), y_new.reshape(-1, 1)])
stroke = np.concatenate([xy_coords, stroke[:, 2].reshape(-1, 1)], axis=1)
new_coords.append(stroke)
coords = np.vstack(new_coords)
return coords
def interpolate(coords, factor=2):
"""
interpolates strokes using cubic spline
"""
coords = np.split(coords, np.where(coords[:, 2] == 1)[0] + 1, axis=0)
new_coords = []
for stroke in coords:
if len(stroke) == 0:
continue
xy_coords = stroke[:, :2]
if len(stroke) > 3:
f_x = interp1d(np.arange(len(stroke)), stroke[:, 0], kind='cubic')
f_y = interp1d(np.arange(len(stroke)), stroke[:, 1], kind='cubic')
xx = np.linspace(0, len(stroke) - 1, factor*(len(stroke)))
yy = np.linspace(0, len(stroke) - 1, factor*(len(stroke)))
x_new = f_x(xx)
y_new = f_y(yy)
xy_coords = np.hstack([x_new.reshape(-1, 1), y_new.reshape(-1, 1)])
stroke_eos = np.zeros([len(xy_coords), 1])
stroke_eos[-1] = 1.0
stroke = np.concatenate([xy_coords, stroke_eos], axis=1)
new_coords.append(stroke)
coords = np.vstack(new_coords)
return coords
def normalize(offsets):
"""
normalizes strokes to median unit norm
"""
offsets = np.copy(offsets)
offsets[:, :2] /= np.median(np.linalg.norm(offsets[:, :2], axis=1))
return offsets
def coords_to_offsets(coords):
"""
convert from coordinates to offsets
"""
offsets = np.concatenate([coords[1:, :2] - coords[:-1, :2], coords[1:, 2:3]], axis=1)
offsets = np.concatenate([np.array([[0, 0, 1]]), offsets], axis=0)
return offsets
def offsets_to_coords(offsets):
"""
convert from offsets to coordinates
"""
return np.concatenate([np.cumsum(offsets[:, :2], axis=0), offsets[:, 2:3]], axis=1)
def draw(
offsets,
ascii_seq=None,
align_strokes=True,
denoise_strokes=True,
interpolation_factor=None,
save_file=None
):
strokes = offsets_to_coords(offsets)
if denoise_strokes:
strokes = denoise(strokes)
if interpolation_factor is not None:
strokes = interpolate(strokes, factor=interpolation_factor)
if align_strokes:
strokes[:, :2] = align(strokes[:, :2])
fig, ax = plt.subplots(figsize=(12, 3))
stroke = []
for x, y, eos in strokes:
stroke.append((x, y))
if eos == 1:
coords = zip(*stroke)
ax.plot(coords[0], coords[1], 'k')
stroke = []
if stroke:
coords = zip(*stroke)
ax.plot(coords[0], coords[1], 'k')
stroke = []
ax.set_xlim(-50, 600)
ax.set_ylim(-40, 40)
ax.set_aspect('equal')
plt.tick_params(
axis='both',
left='off',
top='off',
right='off',
bottom='off',
labelleft='off',
labeltop='off',
labelright='off',
labelbottom='off'
)
if ascii_seq is not None:
if not isinstance(ascii_seq, str):
ascii_seq = ''.join(list(map(chr, ascii_seq)))
plt.title(ascii_seq)
if save_file is not None:
plt.savefig(save_file)
print('saved to {}'.format(save_file))
else:
plt.show()
plt.close('all')
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