-
Notifications
You must be signed in to change notification settings - Fork 2
/
gen_ocr_train_val_test.py
203 lines (176 loc) · 8.41 KB
/
gen_ocr_train_val_test.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
# coding:utf8
import os
import csv
import json
import shutil
import random
import argparse
# 删除划分的训练集、验证集、测试集文件夹，重新创建一个空的文件夹
def isCreateOrDeleteFolder(path, flag):
flagPath = os.path.join(path, flag)
if os.path.exists(flagPath):
shutil.rmtree(flagPath)
os.makedirs(flagPath)
flagAbsPath = os.path.abspath(flagPath)
return flagAbsPath
def splitTrainVal(root, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
# 按照指定的比例划分训练集、验证集、测试集
dataAbsPath = os.path.abspath(root)
if flag == "det":
labelFilePath = os.path.join(dataAbsPath, args.detLabelFileName)
elif flag == "rec":
labelFilePath = os.path.join(dataAbsPath, args.recLabelFileName)
labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
labelFileContent = labelFileRead.readlines()
random.shuffle(labelFileContent)
labelRecordLen = len(labelFileContent)
for index, labelRecordInfo in enumerate(labelFileContent):
imageRelativePath = labelRecordInfo.split('\t')[0]
imageLabel = labelRecordInfo.split('\t')[1]
imageName = os.path.basename(imageRelativePath)
if flag == "det":
imagePath = os.path.join(dataAbsPath, imageName)
elif flag == "rec":
imagePath = os.path.join(dataAbsPath, "{}/{}".format(args.recImageDirName, imageName))
# 按预设的比例划分训练集、验证集、测试集
trainValTestRatio = args.trainValTestRatio.split(":")
trainRatio = eval(trainValTestRatio[0]) / 10
valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
curRatio = index / labelRecordLen
if curRatio < trainRatio:
imageCopyPath = os.path.join(absTrainRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
if flag=='rec':
newCopyPath = imageCopyPath.split('/')[-1]
trainTxt.writerow([newCopyPath, imageLabel.replace('\n', '')])
else:
imageLabelList = json.loads(imageLabel)
detPath = os.path.join((absTrainRootPath+'_gt'), ('gt_'+imageName.replace('.jpg', '.txt')))
removeFile(detPath)
detTxt = open(detPath, "a", encoding="UTF-8")
for label in imageLabelList:
point_list = sum(label['points'], [])
before_idx = len(point_list)
point_list.insert(before_idx, label['transcription'])
txt = ",".join([str(i) for i in point_list])
detTxt.write(txt)
detTxt.write('\n')
elif curRatio >= trainRatio and curRatio < valRatio:
imageCopyPath = os.path.join(absValRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
if flag=='rec':
newCopyPath = imageCopyPath.split('/')[-1]
valTxt.writerow([newCopyPath, imageLabel.replace('\n', '')])
else:
imageLabelList = json.loads(imageLabel)
detPath = os.path.join((absValRootPath+'_gt'), ('gt_'+imageName.replace('.jpg', '.txt')))
removeFile(detPath)
detTxt = open(detPath, "a", encoding="UTF-8")
for label in imageLabelList:
point_list = sum(label['points'], [])
before_idx = len(point_list)
point_list.insert(before_idx, label['transcription'])
txt = ",".join([str(i) for i in point_list])
detTxt.write(txt)
detTxt.write('\n')
else:
imageCopyPath = os.path.join(absTestRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
if flag=='rec':
newCopyPath = imageCopyPath.split('/')[-1]
testTxt.writerow([newCopyPath, imageLabel.replace('\n', '')])
else:
imageLabelList = json.loads(imageLabel)
detPath = os.path.join((absTestRootPath+'_gt'), ('gt_'+imageName.replace('.jpg', '.txt')))
removeFile(detPath)
detTxt = open(detPath, "a", encoding="UTF-8")
for label in imageLabelList:
point_list = sum(label['points'], [])
before_idx = len(point_list)
point_list.insert(before_idx, label['transcription'])
txt = ",".join([str(i) for i in point_list])
detTxt.write(txt)
detTxt.write('\n')
# 删掉存在的文件
def removeFile(path):
if os.path.exists(path):
os.remove(path)
def genDetRecTrainVal(args):
detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
detAbsTrainRootPathGt = isCreateOrDeleteFolder(args.detRootPath, "train_gt")
detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
detAbsValRootPathGt = isCreateOrDeleteFolder(args.detRootPath, "val_gt")
detAbsTestRootPath = isCreateOrDeleteFolder(args.detRootPath, "test")
detAbsTestRootPathGt = isCreateOrDeleteFolder(args.detRootPath, "test_gt")
recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
recAbsTestRootPath = isCreateOrDeleteFolder(args.recRootPath, "test")
removeFile(os.path.join(args.recRootPath, "train.csv"))
removeFile(os.path.join(args.recRootPath, "val.csv"))
removeFile(os.path.join(args.recRootPath, "test.csv"))
recTrainCsv = open(os.path.join(args.recRootPath+'/train', "labels.csv"), "a", encoding="UTF-8", newline='')
recValCsv= open(os.path.join(args.recRootPath+'/val', "labels.csv"), "a", encoding="UTF-8", newline='')
recTestCsv = open(os.path.join(args.recRootPath+'/test', "labels.csv"), "a", encoding="UTF-8", newline='')
header = ['filename', 'words']
recTrainTxt = csv.writer(recTrainCsv)
recTrainTxt.writerow(header)
recValTxt = csv.writer(recValCsv)
recValTxt.writerow(header)
recTestTxt = csv.writer(recTestCsv)
recTestTxt.writerow(header)
splitTrainVal(args.datasetRootPath, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, 'detTrainTxt', 'detValTxt',
'detTestTxt', "det")
for root, dirs, files in os.walk(args.datasetRootPath):
for dir in dirs:
if dir == 'crop_img':
splitTrainVal(root, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
recTestTxt, "rec")
else:
continue
break
if __name__ == "__main__":
# Function description: divide the training set, verification set and test set of detection and recognition respectively
# Description: You can adjust the parameters according to your own path and needs. Image data is often marked by multiple people in batches. Each batch of image data is placed in a folder and marked with EasyOCRLabel.
# In this way, there will be multiple labeled image folders to summarize and divide the training set, verification set, and test set requirements
parser = argparse.ArgumentParser()
parser.add_argument(
"--trainValTestRatio",
type=str,
default="7:0:3",
help="ratio of trainset:valset:testset")
parser.add_argument(
"--datasetRootPath",
type=str,
default="train_data/",
help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
)
parser.add_argument(
"--detRootPath",
type=str,
default="train_data/det",
help="the path where the divided detection dataset is placed")
parser.add_argument(
"--recRootPath",
type=str,
default="train_data/rec",
help="the path where the divided recognition dataset is placed"
)
parser.add_argument(
"--detLabelFileName",
type=str,
default="Label.txt",
help="the name of the detection annotation file")
parser.add_argument(
"--recLabelFileName",
type=str,
default="rec_gt.txt",
help="the name of the recognition annotation file"
)
parser.add_argument(
"--recImageDirName",
type=str,
default="crop_img",
help="the name of the folder where the cropped recognition dataset is located"
)
args = parser.parse_args()
genDetRecTrainVal(args)