forked from yangyanli/PointCNN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathquick_draw_utils.py
174 lines (148 loc) · 6.81 KB
/
quick_draw_utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import os
import sys
import math
import random
import numpy as np
from datetime import datetime
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import data_utils
def _stoke_decoding(stoke):
lift_pen_padding = 2.0
lines = []
points = []
x_prev = 0
y_prev = 0
was_drawing = False
for i in range(len(stoke)):
x = x_prev + stoke[i, 0]
y = y_prev + stoke[i, 1]
lift_pen = stoke[i, 2]
if lift_pen == lift_pen_padding:
break
is_drawing = (lift_pen == 0.0)
if is_drawing:
points.append((x, y))
if was_drawing and is_drawing and x_prev != x and y_prev != y:
lines.append(((x_prev, y_prev), (x, y)))
x_prev = x
y_prev = y
was_drawing = is_drawing
return lines, points
def map_fn(stoke, label, point_num=512):
lines, points = _stoke_decoding(stoke)
points_array = np.zeros(shape=(point_num, 3), dtype=np.float32)
normals_array = np.zeros(shape=(point_num, 3), dtype=np.float32)
if len(lines) == 0 and len(points) == 0:
print('Empty stoke detected!')
elif len(lines) == 0:
print('Stoke without any line detected!')
for sample_idx in range(point_num):
sample_idx_float = sample_idx / (point_num - 1)
px, py = points[sample_idx % len(points)]
points_array[sample_idx] = (px, sample_idx_float, py)
else:
line_len_list = []
for ((x0, y0), (x1, y1)) in lines:
x_diff = x1 - x0
y_diff = y1 - y0
line_len_list.append(math.sqrt(x_diff * x_diff + y_diff * y_diff))
line_len_sum = sum(line_len_list)
factor = point_num / line_len_sum
sample_nums = [math.ceil(line_len * factor) for line_len in line_len_list]
sample_num_total = sum(sample_nums)
sample_nums_indices = [x for x, y in sorted(enumerate(sample_nums), key=lambda x: x[1])]
for i in range(sample_num_total - point_num):
ii = sample_nums_indices[i]
sample_nums[ii] = sample_nums[ii] - 1
assert (sum(sample_nums) == point_num)
sample_idx = 0
for idx_line, line_sample_num in enumerate(sample_nums):
if line_sample_num == 0:
continue
((x0, y0), (x1, y1)) = lines[idx_line]
nx = y1 - y0
ny = x0 - x1
n_len = math.sqrt(nx * nx + ny * ny)
nx /= n_len
ny /= n_len
if line_sample_num == 1:
sample_idx_float = sample_idx / (point_num - 1)
points_array[sample_idx] = ((x0 + x1) / 2, sample_idx_float, (y0 + y1) / 2)
normals_array[sample_idx] = (nx, random.random() * 1e-6, ny)
sample_idx += 1
elif line_sample_num > 1:
x_diff = x1 - x0
y_diff = y1 - y0
for alpha in np.linspace(0, 1, line_sample_num):
sample_idx_float = sample_idx / (point_num - 1)
points_array[sample_idx] = (x0 + alpha * x_diff, sample_idx_float, y0 + alpha * y_diff)
normals_array[sample_idx] = (nx, random.random() * 1e-6, ny)
sample_idx += 1
points_min = np.amin(points_array, axis=0)
points_max = np.amax(points_array, axis=0)
points_center = (points_min + points_max) / 2
scale = np.amax(points_max - points_min) / 2
points_array = (points_array - points_center) * (0.8 / scale, 0.4, 0.8 / scale)
return np.concatenate((points_array, normals_array), axis=-1).astype(np.float32), label
def _extract_padded_stokes(stokes, stoke_len_max, stoke_placeholder, ratio):
padded_stokes_list = []
for stoke in stokes:
if (len(stoke)) == 0: # bad data, ignore it!
continue
lines, points = _stoke_decoding(stoke)
if len(lines) == 0 or len(points) == 0: # bad data, ignore it!
continue
pad_len = stoke_len_max - len(stoke)
if pad_len == 0:
padded_stokes_list.append(stoke.astype(np.float32))
else:
padded_stokes_list.append(np.concatenate([stoke.astype(np.float32), stoke_placeholder[:pad_len]], axis=0))
if len(padded_stokes_list) > ratio * len(stokes): # The data is too big, only use a subset...
break
return np.stack(padded_stokes_list)
def load_fn(folder_npz, ratio, categories=None):
lift_pen_padding = 2.0
categories = [line.strip() for line in
open(os.path.join(folder_npz, 'categories.txt'), 'r')] if categories is None else categories
stoke_len_max = 0
stoke_len_sum = 0
stoke_num = 0
load_data_list = []
for idx_category, category in enumerate(categories):
print('{}-Loading category {} ({} of {})...'.format(datetime.now(), category, idx_category+1, len(categories)))
sys.stdout.flush()
filename_category = os.path.join(folder_npz, category + '.npz')
load_data = np.load(filename_category, encoding='bytes')
load_data_list.append(load_data)
for tag in load_data:
for stoke in load_data[tag]:
stoke_len_max = max(stoke_len_max, stoke.shape[0])
stoke_len_sum += stoke.shape[0]
stoke_num += len(load_data[tag])
print('{}-Max stoke length: {}, average stoke length: {}.'.format(datetime.now(), stoke_len_max,
stoke_len_sum / stoke_num))
sys.stdout.flush()
stoke_placeholder = np.array([(0.0, 0.0, lift_pen_padding)] * stoke_len_max).astype(np.float32)
raw_train_list = []
label_train_list = []
raw_val_list = []
label_val_list = []
for idx_category, category in enumerate(categories):
print('{}-Extracting category {} ({} of {})...'.format(datetime.now(), category, idx_category+1, len(categories)))
sys.stdout.flush()
load_data = load_data_list[idx_category]
raw_train_list.append(_extract_padded_stokes(load_data['train'], stoke_len_max, stoke_placeholder, ratio))
label_train_list += [idx_category] * len(raw_train_list[-1])
raw_val_list.append(_extract_padded_stokes(load_data['valid'], stoke_len_max, stoke_placeholder, ratio))
label_val_list += [idx_category] * len(raw_val_list[-1])
raw_train = np.concatenate(raw_train_list, axis=0)
label_train = np.array(label_train_list)
raw_val = np.concatenate(raw_val_list, axis=0)
label_val = np.array(label_val_list)
print('{}-Shuffling data...'.format(datetime.now()))
sys.stdout.flush()
raw_train, label_train = data_utils.grouped_shuffle([raw_train, label_train])
raw_val, label_val = data_utils.grouped_shuffle([raw_val, label_val])
print('{}-Quick Draw data loaded!'.format(datetime.now()))
sys.stdout.flush()
return raw_train, label_train, raw_val, label_val