-
Notifications
You must be signed in to change notification settings - Fork 458
/
egohands_dataset_clean_4classes.py
244 lines (201 loc) · 8.8 KB
/
egohands_dataset_clean_4classes.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
import scipy.io as sio
import numpy as np
import os
import gc
import six.moves.urllib as urllib
import cv2
import time
import xml.etree.cElementTree as ET
import random
import shutil as sh
from shutil import copyfile
import zipfile
import csv
def save_csv(csv_path, csv_content):
with open(csv_path, 'w') as csvfile:
wr = csv.writer(csvfile)
for i in range(len(csv_content)):
wr.writerow(csv_content[i])
def get_bbox_visualize(base_path, dir):
image_path_array = []
for root, dirs, filenames in os.walk(base_path + dir):
for f in filenames:
if (f.split(".")[1] == "jpg"):
img_path = base_path + dir + "/" + f
image_path_array.append(img_path)
# sort image_path_array to ensure its in the low to high order expected in polygon.mat
image_path_array.sort()
boxes = sio.loadmat(
base_path + dir + "/polygons.mat")
# there are 100 of these per folder in the egohands dataset
polygons = boxes["polygons"][0] # polygons는 100개, boxes는 4개 , polygons는 100 *1 이어서 뒤에 인덱스를 1로 바꾸면 달라지 ㅁ
pointindex = 0
# classLabel
hands = ['myleft', 'myright', 'yourleft', 'yourright']
for first in polygons:
font = cv2.FONT_HERSHEY_SIMPLEX
img_id = image_path_array[pointindex]
img = cv2.imread(img_id)
img_params = {}
img_params["width"] = np.size(img, 1)
img_params["height"] = np.size(img, 0)
head, tail = os.path.split(img_id)
img_params["filename"] = tail
img_params["path"] = os.path.abspath(img_id)
img_params["type"] = "train"
pointindex += 1
boxarray = []
csvholder = []
cindex = 0 # for hands(label list) index
for pointlist in first:
# print(len(first)) : #four
pst = np.empty((0, 2), int)
# print(np.shape(pst)) #(0,2)
max_x = max_y = min_x = min_y = height = width = 0
findex = 0
for point in pointlist:
if (len(point) == 2):
x = int(point[0])
y = int(point[1])
# print('x', x)
# print('y', y)
if (findex == 0):
min_x = x
min_y = y
findex += 1
max_x = x if (x > max_x) else max_x
min_x = x if (x < min_x) else min_x
max_y = y if (y > max_y) else max_y
min_y = y if (y < min_y) else min_y
# print(index, "====", len(point))
appeno = np.array([[x, y]])
pst = np.append(pst, appeno, axis=0)
# white dot hand edge
cv2.putText(img, ".", (x, y), font, 0.7,
(255, 255, 255), 2, cv2.LINE_AA)
# hold : for making hand blob
hold = {}
hold['minx'] = min_x
hold['miny'] = min_y
hold['maxx'] = max_x
hold['maxy'] = max_y
if (min_x > 0 and min_y > 0 and max_x > 0 and max_y > 0):
boxarray.append(hold)
# in canse of just hands class 4th parameters 'hands', but if myleft, myright, yourleft,yourright 4 classes : hands[cindex]
labelrow = [tail,
np.size(img, 1), np.size(img, 0), hands[cindex], min_x, min_y, max_x, max_y]
csvholder.append(labelrow)
cv2.polylines(img, [pst], True, (0, 255, 255), 1)
cv2.rectangle(img, (min_x, max_y),
(max_x, min_y), (0, 255, 0), 1)
cindex += 1 # for class change
csv_path = img_id.split(".")[0]
if not os.path.exists(csv_path + ".csv"):
cv2.putText(img, "DIR : " + dir + " - " + tail, (20, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (77, 255, 9), 2)
cv2.imshow('Verifying annotation ', img)
save_csv(csv_path + ".csv", csvholder)
print("===== saving csv file for ", tail)
cv2.waitKey(2) # close window when a key press is detected
def create_directory(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# combine all individual csv files for each image into a single csv file per folder.
def generate_label_files(image_dir):
header = ['filename', 'width', 'height',
'class', 'xmin', 'ymin', 'xmax', 'ymax']
for root, dirs, filenames in os.walk(image_dir):
for dir in dirs:
csvholder = []
csvholder.append(header)
loop_index = 0
for f in os.listdir(image_dir + dir):
if (f.split(".")[1] == "csv"):
loop_index += 1
# print(loop_index, f)
csv_file = open(image_dir + dir + "/" + f, 'r')
reader = csv.reader(csv_file)
for row in reader:
csvholder.append(row)
csv_file.close()
os.remove(image_dir + dir + "/" + f)
save_csv(image_dir + dir + "/" + dir + "_labels.csv", csvholder)
print("Saved label csv for ", dir, image_dir +
dir + "/" + dir + "_labels.csv")
# Split data, copy to train/test folders
def split_data_test_eval_train(image_dir):
create_directory("images")
create_directory("images/train")
create_directory("images/test")
data_size = 4000
loop_index = 0
data_sampsize = int(0.1 * data_size)
test_samp_array = random.sample(range(data_size), k=data_sampsize)
for root, dirs, filenames in os.walk(image_dir):
for dir in dirs:
for f in os.listdir(image_dir + dir):
if (f.split(".")[1] == "jpg"):
loop_index += 1
print(loop_index, f)
if loop_index in test_samp_array:
os.rename(image_dir + dir +
"/" + f, "images/test/" + f)
os.rename(image_dir + dir +
"/" + f.split(".")[0] + ".csv", "images/test/" + f.split(".")[0] + ".csv")
else:
os.rename(image_dir + dir +
"/" + f, "images/train/" + f)
os.rename(image_dir + dir +
"/" + f.split(".")[0] + ".csv", "images/train/" + f.split(".")[0] + ".csv")
print(loop_index, image_dir + f)
print("> done scanning director ", dir)
os.remove(image_dir + dir + "/polygons.mat")
os.rmdir(image_dir + dir)
print("Train/test content generation complete!")
generate_label_files("images/")
def generate_csv_files(image_dir):
for root, dirs, filenames in os.walk(image_dir):
for dir in dirs:
get_bbox_visualize(image_dir, dir)
print("CSV generation complete!\nGenerating train/test/eval folders")
split_data_test_eval_train("egohands/_LABELLED_SAMPLES/")
# rename image files so we can have them all in a train/test/eval folder.
def rename_files(image_dir):
print("Renaming files")
loop_index = 0
for root, dirs, filenames in os.walk(image_dir):
for dir in dirs:
for f in os.listdir(image_dir + dir):
if (dir not in f):
if (f.split(".")[1] == "jpg"):
loop_index += 1
os.rename(image_dir + dir +
"/" + f, image_dir + dir +
"/" + dir + "_" + f)
else:
break
generate_csv_files("egohands/_LABELLED_SAMPLES/")
def extract_folder(dataset_path):
print("Egohands dataset already downloaded.\nGenerating CSV files")
if not os.path.exists("egohands"):
zip_ref = zipfile.ZipFile(dataset_path, 'r')
print("> Extracting Dataset files")
zip_ref.extractall("egohands")
print("> Extraction complete")
zip_ref.close()
rename_files("egohands/_LABELLED_SAMPLES/")
def download_egohands_dataset(dataset_url, dataset_path):
is_downloaded = os.path.exists(dataset_path)
if not is_downloaded:
print(
"> downloading egohands dataset. This may take a while (1.3GB, say 3-5mins). Coffee break?")
opener = urllib.request.URLopener()
opener.retrieve(dataset_url, dataset_path)
print("> download complete")
extract_folder(dataset_path);
else:
extract_folder(dataset_path)
EGOHANDS_DATASET_URL = "http://vision.soic.indiana.edu/egohands_files/egohands_data.zip"
EGO_HANDS_FILE = "egohands_data.zip"
# download_egohands_dataset(EGOHANDS_DATASET_URL, EGO_HANDS_FILE)
extract_folder(EGO_HANDS_FILE)