DBNet.pytorch: 添加連結描述
系統:windows 10
1、資料集預處理:
(1)把訓練資料train和測試資料test的img和gt,放到datasets檔案夾下
(2)将訓練資料和測試資料生成如下圖的格式:
生成train.txt和test.txt,儲存到datasets檔案夾下(必須也把test.txt也生成)。
生成檔案的代碼如下:
import os
def get_images(img_path):
'''
find image files in data path
:return: list of files found
'''
img_path = os.path.abspath(img_path)
files = []
exts = ['jpg', 'png', 'jpeg', 'JPG', 'PNG']
for parent, dirnames, filenames in os.walk(img_path):
for filename in filenames:
for ext in exts:
if filename.endswith(ext):
files.append(os.path.join(parent, filename))
break
print('Find {} images'.format(len(files)))
return sorted(files)
def get_txts(txt_path):
'''
find gt files in data path
:return: list of files found
'''
txt_path = os.path.abspath(txt_path)
files = []
exts = ['txt']
for parent, dirnames, filenames in os.walk(txt_path):
for filename in filenames:
for ext in exts:
if filename.endswith(ext):
files.append(os.path.join(parent, filename))
break
print('Find {} txts'.format(len(files)))
return sorted(files)
if __name__ == '__main__':
import json
#img_path = './data/ch4_training_images'
#img_path = './train/img'
img_path = './test/img'
files = get_images(img_path)
#txt_path = './data/ch4_training_localization_transcription_gt'
#txt_path = './train/gt'
txt_path = './test/gt'
txts = get_txts(txt_path)
n = len(files)
assert len(files) == len(txts)
with open('test.txt', 'w') as f:
for i in range(n):
line = files[i] + '\t' + txts[i] + '\n'
#line = files[i] + ' ' + txts[i] + '\n'
f.write(line)
print('dataset generated ^_^ ')
參考:添加連結描述
2、配置檔案的修改
(1)把data_path的路徑改為:- E:\ZhuoZhuangOCR\Paper\Latest\DB-Resnet\DBNet.pytorch\datasets\train.txt (使用絕對路徑)
dataset:
train:
dataset:
args:
data_path:
- E:\ZhuoZhuangOCR\Paper\Latest\DB-Resnet\DBNet.pytorch\datasets\train.txt
img_mode: RGB
(2)把base的路徑由相對路徑改為絕對路徑,