-
Notifications
You must be signed in to change notification settings - Fork 116
/
utils.py
332 lines (278 loc) · 14 KB
/
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
import os
import shutil
from glob import glob
import cv2
import numpy as np
import csv
import pandas as pd
from typing import Tuple
from imageio import imread, imsave
from tqdm import tqdm
from dh_segment.io import PAGE
# Constant definitions
TARGET_HEIGHT = 1100
DRAWING_COLOR_BASELINES = (255, 0, 0)
DRAWING_COLOR_LINES = (0, 255, 0)
DRAWING_COLOR_POINTS = (0, 0, 255)
RANDOM_SEED = 0
np.random.seed(RANDOM_SEED)
def get_page_filename(image_filename: str) -> str:
"""
Given an path to a .jpg or .png file, get the corresponding .xml file.
:param image_filename: filename of the image
:return: the filename of the corresponding .xml file, raises exception if .xml file does not exist
"""
page_filename = os.path.join(os.path.dirname(image_filename),
'page',
'{}.xml'.format(os.path.basename(image_filename)[:-4]))
if os.path.exists(page_filename):
return page_filename
else:
raise FileNotFoundError
def get_image_label_basename(image_filename: str) -> str:
"""
Creates a new filename composed of the begining of the folder/collection (ex. EPFL, ABP) and the original filename
:param image_filename: path of the image filename
:return:
"""
# Get acronym followed by name of file
directory, basename = os.path.split(image_filename)
acronym = directory.split(os.path.sep)[-1].split('_')[0]
return '{}_{}'.format(acronym, basename.split('.')[0])
def save_and_resize(img: np.array,
filename: str,
size=None,
nearest: bool=False) -> None:
"""
Resizes the image if necessary and saves it. The resizing will keep the image ratio
:param img: the image to resize and save (numpy array)
:param filename: filename of the saved image
:param size: size of the image after resizing (in pixels). The ratio of the original image will be kept
:param nearest: whether to use nearest interpolation method (default to False)
:return:
"""
if size is not None:
h, w = img.shape[:2]
ratio = float(np.sqrt(size/(h*w)))
resized = cv2.resize(img, (int(w*ratio), int(h*ratio)),
interpolation=cv2.INTER_NEAREST if nearest else cv2.INTER_LINEAR)
imsave(filename, resized)
else:
imsave(filename, img)
def annotate_one_page(image_filename: str,
output_dir: str,
size: int=None,
draw_baselines: bool=True,
draw_lines: bool=False,
draw_endpoints: bool=False,
baseline_thickness: float=0.2,
diameter_endpoint: int=20) -> Tuple[str, str]:
"""
Creates an annotated mask and corresponding original image and saves it in 'labels' and 'images' folders.
Also copies the corresponding .xml file into 'gt' folder.
:param image_filename: filename of the image to process
:param output_dir: directory to output the annotated label image
:param size: Size of the resized image (# pixels)
:param draw_baselines: Draws the baselines (boolean)
:param draw_lines: Draws the polygon's lines (boolean)
:param draw_endpoints: Predict beginning and end of baselines (True, False)
:param baseline_thickness: Thickness of annotated baseline (percentage of the line's height)
:param diameter_endpoint: Diameter of annotated start/end points
:return: (output_image_path, output_label_path)
"""
page_filename = get_page_filename(image_filename)
# Parse xml file and get TextLines
page = PAGE.parse_file(page_filename)
text_lines = [tl for tr in page.text_regions for tl in tr.text_lines]
img = imread(image_filename, pilmode='RGB')
# Create empty mask
gt = np.zeros_like(img)
if text_lines:
if draw_baselines:
# Thickness : should be a percentage of the line height, for example 0.2
# First, get the mean line height.
mean_line_height, _, _ = _compute_statistics_line_height(page)
absolute_baseline_thickness = int(max(gt.shape[0]*0.002, baseline_thickness*mean_line_height))
# Draw the baselines
gt_baselines = np.zeros_like(img[:, :, 0])
gt_baselines = cv2.polylines(gt_baselines,
[PAGE.Point.list_to_cv2poly(tl.baseline) for tl in
text_lines],
isClosed=False, color=255,
thickness=absolute_baseline_thickness)
gt[:, :, np.argmax(DRAWING_COLOR_BASELINES)] = gt_baselines
if draw_lines:
# Draw the lines
gt_lines = np.zeros_like(img[:, :, 0])
for tl in text_lines:
gt_lines = cv2.fillPoly(gt_lines,
[PAGE.Point.list_to_cv2poly(tl.coords)],
color=255)
gt[:, :, np.argmax(DRAWING_COLOR_LINES)] = gt_lines
if draw_endpoints:
# Draw endpoints of baselines
gt_points = np.zeros_like(img[:, :, 0])
for tl in text_lines:
try:
gt_points = cv2.circle(gt_points, (tl.baseline[0].x, tl.baseline[0].y),
radius=int((diameter_endpoint / 2 * (gt_points.shape[0] / TARGET_HEIGHT))),
color=255, thickness=-1)
gt_points = cv2.circle(gt_points, (tl.baseline[-1].x, tl.baseline[-1].y),
radius=int((diameter_endpoint / 2 * (gt_points.shape[0] / TARGET_HEIGHT))),
color=255, thickness=-1)
except IndexError:
print('Length of baseline is {}'.format(len(tl.baseline)))
gt[:, :, np.argmax(DRAWING_COLOR_POINTS)] = gt_points
# Make output filenames
image_label_basename = get_image_label_basename(image_filename)
output_image_path = os.path.join(output_dir, 'images', '{}.jpg'.format(image_label_basename))
output_label_path = os.path.join(output_dir, 'labels', '{}.png'.format(image_label_basename))
# Resize (if necessary) and save image and label
save_and_resize(img, output_image_path, size=size)
save_and_resize(gt, output_label_path, size=size, nearest=True)
# Copy XML file to 'gt' folder
shutil.copy(page_filename, os.path.join(output_dir, 'gt', '{}.xml'.format(image_label_basename)))
return os.path.abspath(output_image_path), os.path.abspath(output_label_path)
def cbad_set_generator(input_dir: str,
output_dir: str,
img_size: int,
multilabel: bool=False,
draw_baselines: bool=True,
draw_lines: bool=False,
line_thickness: float=0.2,
draw_endpoints: bool=False,
circle_thickness: int =20) -> None:
"""
Creates a set with 'images', 'labels', 'gt' folders, classes.txt file and .csv data
:param input_dir: Input directory containing images and PAGE files
:param output_dir: Output directory to save images and labels
:param img_size: Size of the resized image (# pixels)
:param multilabel: whether the training will have the MULTILABEL prediction type
:param draw_baselines: Draws the baselines (boolean)
:param draw_lines: Draws the polygon's lines (boolean)
:param line_thickness: Thickness of annotated baseline (percentage of the line's height)
:param draw_endpoints: Predict beginning and end of baselines (True, False)
:param circle_thickness: Diameter of annotated start/end points
:return:
"""
# Get image filenames to process
image_filenames_list = glob('{}/**/*.jpg'.format(input_dir))
# set
os.makedirs(os.path.join('{}'.format(output_dir), 'images'))
os.makedirs(os.path.join('{}'.format(output_dir), 'labels'))
os.makedirs(os.path.join('{}'.format(output_dir), 'gt'))
tuples_images_labels = list()
for image_filename in tqdm(image_filenames_list):
output_image_path, output_label_path = annotate_one_page(image_filename,
output_dir, img_size, draw_baselines=draw_baselines,
draw_lines=draw_lines,
baseline_thickness=line_thickness,
draw_endpoints=draw_endpoints,
diameter_endpoint=circle_thickness)
tuples_images_labels.append((output_image_path, output_label_path))
# Create classes.txt file
classes = [(0, 0, 0)]
if draw_baselines:
classes.append(DRAWING_COLOR_BASELINES)
if draw_lines:
classes.append(DRAWING_COLOR_LINES)
if draw_endpoints:
classes.append(DRAWING_COLOR_POINTS)
if draw_baselines and draw_lines:
classes.append(tuple(np.array(DRAWING_COLOR_BASELINES) + np.array(DRAWING_COLOR_LINES)))
if draw_baselines and draw_endpoints:
classes.append(tuple(np.array(DRAWING_COLOR_BASELINES) + np.array(DRAWING_COLOR_POINTS)))
if draw_lines and draw_endpoints:
classes.append(tuple(np.array(DRAWING_COLOR_LINES) + np.array(DRAWING_COLOR_POINTS)))
if draw_baselines and draw_lines and draw_endpoints:
classes.append(tuple(np.array(DRAWING_COLOR_BASELINES) + np.array(DRAWING_COLOR_LINES) + np.array(DRAWING_COLOR_POINTS)))
# Deal with multiclassification
if multilabel:
multiclass_codes = np.greater(classes, len(classes) * [[0, 0, 0]]).astype(int)
final_classes = np.hstack((classes, multiclass_codes))
else:
final_classes = classes
np.savetxt(os.path.join(output_dir, 'classes.txt'), final_classes, fmt='%d')
with open(os.path.join(output_dir, 'set_data.csv'), 'w') as f:
writer = csv.writer(f)
for row in tuples_images_labels:
writer.writerow(row)
def split_set_for_eval(csv_filename: str) -> None:
"""
Splits set into two sets (0.15 and 0.85).
:param csv_filename: path to csv file containing in each row image_filename,label_filename
:return:
"""
df_data = pd.read_csv(csv_filename, header=None)
# take 15% for eval
df_eval = df_data.sample(frac=0.15, random_state=42)
indexes = df_data.index.difference(df_eval.index)
df_train = df_data.loc[indexes]
# save CSVs
saving_dir = os.path.dirname(csv_filename)
df_eval.to_csv(os.path.join(saving_dir, 'eval_data.csv'), header=False, index=False, encoding='utf8')
df_train.to_csv(os.path.join(saving_dir, 'train_data.csv'), header=False, index=False, encoding='utf8')
# def draw_lines_fn(xml_filename: str, output_dir: str):
# """
# Given an XML PAGE file, draws the corresponding lines in the original image.
#
# :param xml_filename:
# :param output_dir:
# :return:
# """
# basename = os.path.basename(xml_filename).split('.')[0]
# generated_page = PAGE.parse_file(xml_filename)
# drawing_img = generated_page.image_filename
# generated_page.draw_baselines(drawing_img, color=(0, 0, 255))
# imsave(os.path.join(output_dir, '{}.jpg'.format(basename)), drawing_img)
def _compute_statistics_line_height(page_class: PAGE.Page, verbose: bool=False) -> Tuple[float, float, float]:
"""
Function to compute mean and std of line height in a page.
:param page_class: PAGE.Page object
:param verbose: either to print computational info or not
:return: tuple (mean, standard deviation, median)
"""
y_lines_coords = [[c.y for c in tl.coords] for tr in page_class.text_regions for tl in tr.text_lines if tl.coords]
line_heights = np.array([np.max(y_line_coord) - np.min(y_line_coord) for y_line_coord in y_lines_coords])
# Remove outliers
if len(line_heights) > 3:
outliers = _is_outlier(np.array(line_heights))
line_heights_filtered = line_heights[~outliers]
else:
line_heights_filtered = line_heights
if verbose:
print('Considering {}/{} lines to compute line height statistics'.format(len(line_heights_filtered),
len(line_heights)))
# Compute mean, std, median
mean = np.mean(line_heights_filtered)
median = np.median(line_heights_filtered)
standard_deviation = np.std(line_heights_filtered)
return mean, standard_deviation, median
def _is_outlier(points, thresh=3.5):
"""
Returns a boolean array with True if points are outliers and False
otherwise. Used to find outliers in 1D data.
https://stackoverflow.com/questions/22354094/pythonic-way-of-detecting-outliers-in-one-dimensional-observation-data
References:
Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
Handle Outliers", The ASQC Basic References in Quality Control:
Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
:param points : An numobservations by numdimensions array of observations
:param thresh : The modified z-score to use as a threshold. Observations with
a modified z-score (based on the median absolute deviation) greater
than this value will be classified as outliers.
:return: mask : A num_observations-length boolean array.
"""
if len(points.shape) == 1:
points = points[:, None]
median = np.median(points, axis=0)
diff = np.sum((points - median)**2, axis=-1)
diff = np.sqrt(diff)
med_abs_deviation = np.median(diff)
# Replace zero values by epsilon
if not isinstance(med_abs_deviation, float):
med_abs_deviation = np.maximum(med_abs_deviation, len(med_abs_deviation)*[1e-10])
else:
med_abs_deviation = np.maximum(med_abs_deviation, 1e-10)
modified_z_score = 0.6745 * diff / med_abs_deviation
return modified_z_score > thresh