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Merge remote-tracking branch 'origin/android_demo' into android_demo

This commit is contained in:
WenmuZhou 2021-05-10 14:13:09 +08:00
parent 8601b8e9f7 2f2d152c7a
commit e296a968f6
48 changed files with 6510 additions and 5369 deletions
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1
MANIFEST.in
View File

@ -5,3 +5,4 @@ recursive-include ppocr/utils *.txt utility.py logging.py
recursive-include ppocr/data/ *.py
recursive-include ppocr/postprocess *.py
recursive-include tools/infer *.py
recursive-include ppocr/utils/e2e_utils/ *.py

20
PPOCRLabel/PPOCRLabel.py
View File

@ -147,6 +147,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.itemsToShapesbox = {}
self.shapesToItemsbox = {}
self.prevLabelText = getStr('tempLabel')
self.noLabelText = getStr('nullLabel')
self.model = 'paddle'
self.PPreader = None
self.autoSaveNum = 5
@ -1020,7 +1021,7 @@ class MainWindow(QMainWindow, WindowMixin):
item.setText(str([(int(p.x()), int(p.y())) for p in shape.points]))
self.updateComboBox()
def updateComboBox(self): # TODO：貌似没用
def updateComboBox(self):
# Get the unique labels and add them to the Combobox.
itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())]
@ -1040,7 +1041,7 @@ class MainWindow(QMainWindow, WindowMixin):
return dict(label=s.label, # str
line_color=s.line_color.getRgb(),
fill_color=s.fill_color.getRgb(),
points=[(p.x(), p.y()) for p in s.points], # QPonitF
points=[(int(p.x()), int(p.y())) for p in s.points], # QPonitF
# add chris
difficult=s.difficult) # bool
@ -1069,7 +1070,7 @@ class MainWindow(QMainWindow, WindowMixin):
# print('Image:{0} -> Annotation:{1}'.format(self.filePath, annotationFilePath))
return True
except:
self.errorMessage(u'Error saving label data')
self.errorMessage(u'Error saving label data', u'Error saving label data')
return False
def copySelectedShape(self):
@ -1802,7 +1803,11 @@ class MainWindow(QMainWindow, WindowMixin):
result.insert(0, box)
print('result in reRec is ', result)
self.result_dic.append(result)
if result[1][0] == shape.label:
else:
print('Can not recognise the box')
self.result_dic.append([box,(self.noLabelText,0)])
if self.noLabelText == shape.label or result[1][0] == shape.label:
print('label no change')
else:
rec_flag += 1
@ -1836,9 +1841,14 @@ class MainWindow(QMainWindow, WindowMixin):
print('label no change')
else:
shape.label = result[1][0]
else:
print('Can not recognise the box')
if self.noLabelText == shape.label:
print('label no change')
else:
shape.label = self.noLabelText
self.singleLabel(shape)
self.setDirty()
print(box)
def autolcm(self):
vbox = QVBoxLayout()

2
PPOCRLabel/libs/canvas.py
View File

@ -45,7 +45,7 @@ class Canvas(QWidget):
CREATE, EDIT = list(range(2))
_fill_drawing = False # draw shadows
epsilon = 11.0
epsilon = 5.0
def __init__(self, *args, **kwargs):
super(Canvas, self).__init__(*args, **kwargs)

9742
PPOCRLabel/libs/resources.py
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File diff suppressed because it is too large Load Diff

1
PPOCRLabel/resources/strings/strings-zh-CN.properties
View File

@ -87,6 +87,7 @@ creatPolygon=四点标注
drawSquares=正方形标注
saveRec=保存识别结果
tempLabel=待识别
nullLabel=无法识别
steps=操作步骤
choseModelLg=选择模型语言
cancel=取消

3
PPOCRLabel/resources/strings/strings.properties
View File

@ -77,7 +77,7 @@ IR=Image Resize
autoRecognition=Auto Recognition
reRecognition=Re-recognition
mfile=File
medit=Eidt
medit=Edit
mview=View
mhelp=Help
iconList=Icon List
@ -87,6 +87,7 @@ creatPolygon=Create Quadrilateral
drawSquares=Draw Squares
saveRec=Save Recognition Result
tempLabel=TEMPORARY
nullLabel=NULL
steps=Steps
choseModelLg=Choose Model Language
cancel=Cancel

3
README.md
View File

@ -32,7 +32,8 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
<div align="center">
<img src="doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
<img src="doc/imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
<img src="doc/imgs_results/multi_lang/img_01.jpg" width="800">
<img src="doc/imgs_results/multi_lang/img_02.jpg" width="800">
</div>
The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).

4
configs/e2e/e2e_r50_vd_pg.yml
View File

@ -59,8 +59,10 @@ Optimizer:
PostProcess:
name: PGPostProcess
score_thresh: 0.5
mode: fast # fast or slow two ways
Metric:
name: E2EMetric
gt_mat_dir: # the dir of gt_mat
character_dict_path: ppocr/utils/ic15_dict.txt
main_indicator: f_score_e2e
@ -106,7 +108,7 @@ Eval:
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
loader:
shuffle: False
drop_last: False

132
configs/rec/multi_language/generate_multi_language_configs.py
View File

@ -19,21 +19,56 @@ import logging
logging.basicConfig(level=logging.INFO)
support_list = {
'it':'italian', 'xi':'spanish', 'pu':'portuguese', 'ru':'russian', 'ar':'arabic',
'ta':'tamil', 'ug':'uyghur', 'fa':'persian', 'ur':'urdu', 'rs':'serbian latin',
'oc':'occitan', 'rsc':'serbian cyrillic', 'bg':'bulgarian', 'uk':'ukranian', 'be':'belarusian',
'te':'telugu', 'ka':'kannada', 'chinese_cht':'chinese tradition','hi':'hindi','mr':'marathi',
'it': 'italian',
'xi': 'spanish',
'pu': 'portuguese',
'ru': 'russian',
'ar': 'arabic',
'ta': 'tamil',
'ug': 'uyghur',
'fa': 'persian',
'ur': 'urdu',
'rs': 'serbian latin',
'oc': 'occitan',
'rsc': 'serbian cyrillic',
'bg': 'bulgarian',
'uk': 'ukranian',
'be': 'belarusian',
'te': 'telugu',
'ka': 'kannada',
'chinese_cht': 'chinese tradition',
'hi': 'hindi',
'mr': 'marathi',
'ne': 'nepali',
}
assert(
os.path.isfile("./rec_multi_language_lite_train.yml")
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
'sw', 'tl', 'tr', 'uz', 'vi', 'latin'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
'dar', 'inh', 'che', 'lbe', 'lez', 'tab', 'cyrillic'
]
devanagari_lang = [
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
'sa', 'bgc', 'devanagari'
]
multi_lang = latin_lang + arabic_lang + cyrillic_lang + devanagari_lang
assert (os.path.isfile("./rec_multi_language_lite_train.yml")
), "Loss basic configuration file rec_multi_language_lite_train.yml.\
You can download it from \
https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/configs/rec/multi_language/"
global_config = yaml.load(open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
global_config = yaml.load(
open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
project_path = os.path.abspath(os.path.join(os.getcwd(), "../../../"))
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
@ -41,15 +76,30 @@ class ArgsParser(ArgumentParser):
self.add_argument(
"-o", "--opt", nargs='+', help="set configuration options")
self.add_argument(
"-l", "--language", nargs='+', help="set language type, support {}".format(support_list))
"-l",
"--language",
nargs='+',
help="set language type, support {}".format(support_list))
self.add_argument(
"--train",type=str,help="you can use this command to change the train dataset default path")
"--train",
type=str,
help="you can use this command to change the train dataset default path"
)
self.add_argument(
"--val",type=str,help="you can use this command to change the eval dataset default path")
"--val",
type=str,
help="you can use this command to change the eval dataset default path"
)
self.add_argument(
"--dict",type=str,help="you can use this command to change the dictionary default path")
"--dict",
type=str,
help="you can use this command to change the dictionary default path"
)
self.add_argument(
"--data_dir",type=str,help="you can use this command to change the dataset default root path")
"--data_dir",
type=str,
help="you can use this command to change the dataset default root path"
)
def parse_args(self, argv=None):
args = super(ArgsParser, self).parse_args(argv)
@ -68,21 +118,37 @@ class ArgsParser(ArgumentParser):
return config
def _set_language(self, type):
lang = type[0]
assert (type), "please use -l or --language to choose language type"
assert(
type[0] in support_list.keys()
lang in support_list.keys() or lang in multi_lang
),"the sub_keys(-l or --language) can only be one of support list: \n{},\nbut get: {}, " \
"please check your running command".format(support_list, type)
global_config['Global']['character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(type[0])
global_config['Global']['save_model_dir'] = './output/rec_{}_lite'.format(type[0])
global_config['Train']['dataset']['label_file_list'] = ["train_data/{}_train.txt".format(type[0])]
global_config['Eval']['dataset']['label_file_list'] = ["train_data/{}_val.txt".format(type[0])]
global_config['Global']['character_type'] = type[0]
"please check your running command".format(multi_lang, type)
if lang in latin_lang:
lang = "latin"
elif lang in arabic_lang:
lang = "arabic"
elif lang in cyrillic_lang:
lang = "cyrillic"
elif lang in devanagari_lang:
lang = "devanagari"
global_config['Global'][
'character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(lang)
global_config['Global'][
'save_model_dir'] = './output/rec_{}_lite'.format(lang)
global_config['Train']['dataset'][
'label_file_list'] = ["train_data/{}_train.txt".format(lang)]
global_config['Eval']['dataset'][
'label_file_list'] = ["train_data/{}_val.txt".format(lang)]
global_config['Global']['character_type'] = lang
assert (
os.path.isfile(os.path.join(project_path,global_config['Global']['character_dict_path']))
os.path.isfile(
os.path.join(project_path, global_config['Global'][
'character_dict_path']))
), "Loss default dictionary file {}_dict.txt.You can download it from \
https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(type[0])
return type[0]
https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(
lang)
return lang
def merge_config(config):
@ -111,10 +177,12 @@ def merge_config(config):
else:
cur = cur[sub_key]
def loss_file(path):
assert (
os.path.exists(path)
),"There is no such file:{},Please do not forget to put in the specified file".format(path)
), "There is no such file:{},Please do not forget to put in the specified file".format(
path)
if __name__ == '__main__':
@ -143,10 +211,16 @@ if __name__ == '__main__':
loss_file(data_dir)
with open(save_file_path, 'w') as f:
yaml.dump(dict(global_config), f, default_flow_style=False, sort_keys=False)
yaml.dump(
dict(global_config), f, default_flow_style=False, sort_keys=False)
logging.info("Project path is :{}".format(project_path))
logging.info("Train list path set to :{}".format(global_config['Train']['dataset']['label_file_list'][0]))
logging.info("Eval list path set to :{}".format(global_config['Eval']['dataset']['label_file_list'][0]))
logging.info("Dataset root path set to :{}".format(global_config['Eval']['dataset']['data_dir']))
logging.info("Dict path set to :{}".format(global_config['Global']['character_dict_path']))
logging.info("Config file set to :configs/rec/multi_language/{}".format(save_file_path))
logging.info("Train list path set to :{}".format(global_config['Train'][
'dataset']['label_file_list'][0]))
logging.info("Eval list path set to :{}".format(global_config['Eval'][
'dataset']['label_file_list'][0]))
logging.info("Dataset root path set to :{}".format(global_config['Eval'][
'dataset']['data_dir']))
logging.info("Dict path set to :{}".format(global_config['Global'][
'character_dict_path']))
logging.info("Config file set to :configs/rec/multi_language/{}".
format(save_file_path))

111
configs/rec/multi_language/rec_arabic_lite_train.yml Normal file
View File

@ -0,0 +1,111 @@
Global:
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_arabic_lite
save_epoch_step: 3
eval_batch_step:
- 0
- 2000
cal_metric_during_train: true
pretrained_model: null
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: null
character_dict_path: ppocr/utils/dict/arabic_dict.txt
character_type: arabic
max_text_length: 25
infer_mode: false
use_space_char: true
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: L2
factor: 1.0e-05
Architecture:
model_type: rec
algorithm: CRNN
Transform: null
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride:
- 1
- 2
- 2
- 2
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 1.0e-05
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/arabic_train.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug: null
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 256
drop_last: true
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/arabic_val.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 256
num_workers: 8

111
configs/rec/multi_language/rec_cyrillic_lite_train.yml Normal file
View File

@ -0,0 +1,111 @@
Global:
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_cyrillic_lite
save_epoch_step: 3
eval_batch_step:
- 0
- 2000
cal_metric_during_train: true
pretrained_model: null
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: null
character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
character_type: cyrillic
max_text_length: 25
infer_mode: false
use_space_char: true
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: L2
factor: 1.0e-05
Architecture:
model_type: rec
algorithm: CRNN
Transform: null
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride:
- 1
- 2
- 2
- 2
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 1.0e-05
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/cyrillic_train.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug: null
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 256
drop_last: true
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/cyrillic_val.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 256
num_workers: 8

111
configs/rec/multi_language/rec_devanagari_lite_train.yml Normal file
View File

@ -0,0 +1,111 @@
Global:
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_devanagari_lite
save_epoch_step: 3
eval_batch_step:
- 0
- 2000
cal_metric_during_train: true
pretrained_model: null
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: null
character_dict_path: ppocr/utils/dict/devanagari_dict.txt
character_type: devanagari
max_text_length: 25
infer_mode: false
use_space_char: true
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: L2
factor: 1.0e-05
Architecture:
model_type: rec
algorithm: CRNN
Transform: null
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride:
- 1
- 2
- 2
- 2
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 1.0e-05
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/devanagari_train.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug: null
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 256
drop_last: true
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/devanagari_val.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 256
num_workers: 8

4
configs/rec/multi_language/rec_en_number_lite_train.yml
View File

@ -15,11 +15,11 @@ Global:
use_visualdl: False
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/en_dict.txt
character_dict_path: ppocr/utils/en_dict.txt
character_type: EN
max_text_length: 25
infer_mode: False
use_space_char: False
use_space_char: True
Optimizer:

111
configs/rec/multi_language/rec_latin_lite_train.yml Normal file
View File

@ -0,0 +1,111 @@
Global:
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_latin_lite
save_epoch_step: 3
eval_batch_step:
- 0
- 2000
cal_metric_during_train: true
pretrained_model: null
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: null
character_dict_path: ppocr/utils/dict/latin_dict.txt
character_type: latin
max_text_length: 25
infer_mode: false
use_space_char: true
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: L2
factor: 1.0e-05
Architecture:
model_type: rec
algorithm: CRNN
Transform: null
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride:
- 1
- 2
- 2
- 2
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 1.0e-05
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/latin_train.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug: null
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 256
drop_last: true
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: train_data/
label_file_list:
- train_data/latin_val.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode: null
- RecResizeImg:
image_shape:
- 3
- 32
- 320
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 256
num_workers: 8

11
deploy/cpp_infer/CMakeLists.txt
View File

@ -40,6 +40,7 @@ endif()
if (WIN32)
include_directories("${PADDLE_LIB}/paddle/fluid/inference")
include_directories("${PADDLE_LIB}/paddle/include")
link_directories("${PADDLE_LIB}/paddle/lib")
link_directories("${PADDLE_LIB}/paddle/fluid/inference")
find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
@ -140,22 +141,22 @@ else()
endif ()
endif()
# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
# Note: libpaddle_inference_api.so/a must put before libpaddle_inference.so/a
if(WITH_STATIC_LIB)
if(WIN32)
set(DEPS
${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
endif()
else()
if(WIN32)
set(DEPS
${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
endif(WITH_STATIC_LIB)

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@ -74,9 +74,10 @@ opencv3/
* 有2种方式获取Paddle预测库，下面进行详细介绍。
#### 1.2.1 直接下载安装
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本。
* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
* 下载之后使用下面的方法解压。
@ -130,8 +131,6 @@ build/paddle_inference_install_dir/
其中`paddle`就是C++预测所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
## 2 开始运行
### 2.1 将模型导出为inference model
@ -232,7 +231,7 @@ visualize 1 # 是否对结果进行可视化，为1时，会在当前文件夹
最终屏幕上会输出检测结果如下。
<div align="center">
<img src="../imgs/cpp_infer_pred_12.png" width="600">
<img src="./imgs/cpp_infer_pred_12.png" width="600">
</div>

6
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@ -91,8 +91,8 @@ tar -xf paddle_inference.tgz
Finally you can see the following files in the folder of `paddle_inference/`.
#### 1.2.2 Compile from the source code
* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code.
* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
```shell
@ -238,7 +238,7 @@ visualize 1 # Whether to visualize the results，when it is set as 1, The predic
The detection results will be shown on the screen, which is as follows.
<div align="center">
<img src="../imgs/cpp_infer_pred_12.png" width="600">
<img src="./imgs/cpp_infer_pred_12.png" width="600">
</div>

68
doc/doc_ch/inference.md
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@ -13,7 +13,6 @@ inference 模型（`paddle.jit.save`保存的模型）
- [检测模型转inference模型](#检测模型转inference模型)
- [识别模型转inference模型](#识别模型转inference模型)
- [方向分类模型转inference模型](#方向分类模型转inference模型)
- [端到端模型转inference模型](#端到端模型转inference模型)
- [二、文本检测模型推理](#文本检测模型推理)
- [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
@ -28,13 +27,10 @@ inference 模型（`paddle.jit.save`保存的模型）
- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [5. 多语言模型的推理](#多语言模型的推理)
- [四、端到端模型推理](#端到端模型推理)
- [1. PGNet端到端模型推理](#PGNet端到端模型推理)
- [五、方向分类模型推理](#方向识别模型推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
- [六、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
- [2. 其他模型推理](#其他模型推理)
@ -122,32 +118,6 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo
├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略
└── inference.pdmodel # 分类inference模型的program文件
```
<a name="端到端模型转inference模型"></a>
### 端到端模型转inference模型
下载端到端模型：
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
```
端到端模型转inference模型与检测的方式相同，如下：
```
# -c 后面设置训练算法的yml配置文件
# -o 配置可选参数
# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
# Global.load_static_weights 参数需要设置为 False。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e/
```
转换成功后，在目录下有三个文件：
```
/inference/e2e/
├── inference.pdiparams # 分类inference模型的参数文件
├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略
└── inference.pdmodel # 分类inference模型的program文件
```
<a name="文本检测模型推理"></a>
## 二、文本检测模型推理
@ -362,38 +332,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
```
<a name="端到端模型推理"></a>
## 四、端到端模型推理
端到端模型推理，默认使用PGNet模型的配置参数。当不使用PGNet模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
<a name="PGNet端到端模型推理"></a>
### 1. PGNet端到端模型推理
#### (1). 四边形文本检测模型（ICDAR2015）
首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar))，可以使用如下命令进行转换：
```
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img_10_pgnet.jpg)
#### (2). 弯曲文本检测模型（Total-Text）
和四边形文本检测模型共用一个推理模型
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
```
python3.7 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
```
可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img623_pgnet.jpg)
<a name="方向分类模型推理"></a>
## 五、方向分类模型推理
## 四、方向分类模型推理
下面将介绍方向分类模型推理。
@ -418,7 +358,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
```
<a name="文本检测、方向分类和文字识别串联推理"></a>
## 六、文本检测、方向分类和文字识别串联推理
## 五、文本检测、方向分类和文字识别串联推理
<a name="超轻量中文OCR模型推理"></a>
### 1. 超轻量中文OCR模型推理

23
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@ -104,27 +104,16 @@ python3 generate_multi_language_configs.py -l it \
| german_mobile_v2.0_rec |德文识别|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| it_mobile_v2.0_rec |意大利文识别|rec_it_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
| xi_mobile_v2.0_rec |西班牙文识别|rec_xi_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_train.tar) |
| pu_mobile_v2.0_rec |葡萄牙文识别|rec_pu_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_train.tar) |
| ru_mobile_v2.0_rec |俄罗斯文识别|rec_ru_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_train.tar) |
| ar_mobile_v2.0_rec |阿拉伯文识别|rec_ar_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_train.tar) |
| hi_mobile_v2.0_rec |印地文识别|rec_hi_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec |中文繁体识别|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| ug_mobile_v2.0_rec |维吾尔文识别|rec_ug_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_train.tar) |
| fa_mobile_v2.0_rec |波斯文识别|rec_fa_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_train.tar) |
| ur_mobile_v2.0_rec |乌尔都文识别|rec_ur_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_train.tar) |
| rs_mobile_v2.0_rec |塞尔维亚文（latin）识别|rec_rs_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_train.tar) |
| oc_mobile_v2.0_rec |欧西坦文识别|rec_oc_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_train.tar) |
| mr_mobile_v2.0_rec |马拉地文识别|rec_mr_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_train.tar) |
| ne_mobile_v2.0_rec |尼泊尔文识别|rec_ne_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_train.tar) |
| rsc_mobile_v2.0_rec |塞尔维亚文（cyrillic）识别|rec_rsc_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_train.tar) |
| bg_mobile_v2.0_rec |保加利亚文识别|rec_bg_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_train.tar) |
| uk_mobile_v2.0_rec |乌克兰文识别|rec_uk_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_train.tar) |
| be_mobile_v2.0_rec |白俄罗斯文识别|rec_be_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec |泰卢固文识别|rec_te_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec |卡纳达文识别|rec_ka_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec |泰米尔文识别|rec_ta_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | 拉丁文识别 | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | 阿拉伯字母 | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | 斯拉夫字母 | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | 梵文字母 | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
更多支持语种请参考: [多语言模型](./multi_languages.md)
<a name="文本方向分类模型"></a>

44
doc/doc_ch/multi_languages.md
View File

@ -5,6 +5,25 @@
- 2021.4.9 支持**80种**语言的检测和识别
- 2021.4.9 支持**轻量高精度**英文模型检测识别
PaddleOCR 旨在打造一套丰富、领先、且实用的OCR工具库，不仅提供了通用场景下的中英文模型，也提供了专门在英文场景下训练的模型，
和覆盖[80个语言](#语种缩写)的小语种模型。
其中英文模型支持，大小写字母和常见标点的检测识别，并优化了空格字符的识别：
<div align="center">
<img src="../imgs_results/multi_lang/en_1.jpg" width="400" height="600">
</div>
小语种模型覆盖了拉丁语系、阿拉伯语系、中文繁体、韩语、日语等等：
<div align="center">
<img src="../imgs_results/multi_lang/japan_2.jpg" width="600" height="300">
<img src="../imgs_results/multi_lang/french_0.jpg" width="300" height="300">
</div>
本文档将简要介绍小语种模型的使用方法。
- [1 安装](#安装)
- [1.1 paddle 安装](#paddle安装)
- [1.2 paddleocr package 安装](#paddleocr_package_安装)
@ -40,7 +59,7 @@ pip instll paddlepaddle-gpu
pip 安装
```
pip install "paddleocr>=2.0.4" # 推荐使用2.0.4版本
pip install "paddleocr>=2.0.6" # 推荐使用2.0.6版本
```
本地构建并安装
```
@ -68,7 +87,11 @@ Paddleocr目前支持80个语种，可以通过修改--lang参数进行切换，
paddleocr --image_dir doc/imgs/japan_2.jpg --lang=japan
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs/japan_2.jpg)
<div align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs/japan_2.jpg" width="800">
</div>
结果是一个list，每个item包含了文本框，文字和识别置信度
```text
@ -132,14 +155,16 @@ image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/korean.ttf')
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
结果可视化:
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/korean.jpg)
<div align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/korean.jpg" width="800">
</div>
* 识别预测
@ -152,7 +177,8 @@ for line in result:
print(line)
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/german/1.jpg)
![](../imgs_words/german/1.jpg)
结果是一个tuple，只包含识别结果和识别置信度
@ -187,7 +213,10 @@ im_show.save('result.jpg')
```
结果可视化 ：
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/whl/12_det.jpg)
<div align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/whl/12_det.jpg" width="800">
</div>
ppocr 还支持方向分类， 更多使用方式请参考：[whl包使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md)。
@ -211,7 +240,7 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
|德文|german|german|
|日文|japan|japan|
|韩文|korean|korean|
|中文繁体|chinese traditional |ch_tra|
|中文繁体|chinese traditional |chinese_cht|
|意大利文| Italian |it|
|西班牙文|Spanish |es|
|葡萄牙文| Portuguese|pt|
@ -230,7 +259,6 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
|乌克兰文|Ukranian|uk|
|白俄罗斯文|Belarusian|be|
|泰卢固文|Telugu |te|
|卡纳达文|Kannada |kn|
|泰米尔文|Tamil |ta|
|南非荷兰文 |Afrikaans |af|
|阿塞拜疆文 |Azerbaijani |az|

35
doc/doc_ch/pgnet.md
View File

@ -2,7 +2,7 @@
- [一、简介](#简介)
- [二、环境配置](#环境配置)
- [三、快速使用](#快速使用)
- [四、模型训练、评估、推理](#快速训练)
- [四、模型训练、评估、推理](#模型训练、评估、推理)
<a name="简介"></a>
## 一、简介
@ -16,14 +16,31 @@ OCR算法可以分为两阶段算法和端对端的算法。二阶段OCR算法
- 提出基于图的修正模块（GRM）来进一步提高模型识别性能
- 精度更高，预测速度更快
PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf)， 算法原理图如下所示：
PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) ,算法原理图如下所示：
![](../pgnet_framework.png)
输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
其检测识别效果图如下：
![](../imgs_results/e2e_res_img293_pgnet.png)
![](../imgs_results/e2e_res_img295_pgnet.png)
### 性能指标
测试集: Total Text
测试环境: NVIDIA Tesla V100-SXM2-16GB
|PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|下载|
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
|Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度*
<a name="环境配置"></a>
## 二、环境配置
请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
@ -49,19 +66,19 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.
### 单张图像或者图像集合预测
```bash
# 预测image_dir指定的单张图像
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
# 预测image_dir指定的图像集合
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
# 如果想使用CPU进行预测，需设置use_gpu参数为False
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True --use_gpu=False
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True --use_gpu=False
```
### 可视化结果
可视化文本检测结果默认保存到./inference_results文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img623_pgnet.jpg)
<a name="快速训练"></a>
<a name="模型训练、评估、推理"></a>
## 四、模型训练、评估、推理
本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
@ -135,12 +152,12 @@ python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{
### 模型预测
测试单张图像的端到端识别效果
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
```
测试文件夹下所有图像的端到端识别效果
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
```
### 预测推理
@ -148,7 +165,7 @@ python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img=
首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，以英文数据集训练的模型为例[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar) ，可以使用如下命令进行转换：
```
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
```

30
doc/doc_ch/recognition.md
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@ -138,7 +138,7 @@ PaddleOCR内置了一部分字典，可以按需使用。
`ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的德文字典
`ppocr/utils/dict/en_dict.txt` 是一个包含63个字符的英文字典
`ppocr/utils/en_dict.txt` 是一个包含96个字符的英文字典
@ -285,7 +285,7 @@ Eval:
<a name="小语种"></a>
#### 2.3 小语种
PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
您有两种方式创建所需的配置文件：
@ -368,26 +368,12 @@ PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 | german |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 | japan |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 | korean |
| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 意大利语 | it |
| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 西班牙语 | xi |
| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 葡萄牙语 | pu |
| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 俄罗斯语 | ru |
| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯语 | ar |
| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 印地语 | hi |
| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 维吾尔语 | ug |
| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 波斯语 | fa |
| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌尔都语 | ur |
| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(latin)语 | rs |
| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 欧西坦语 | oc |
| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 马拉地语 | mr |
| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 尼泊尔语 | ne |
| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 塞尔维亚(cyrillic)语 | rsc |
| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 保加利亚语 | bg |
| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 乌克兰语 | uk |
| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 白俄罗斯语 | be |
| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰卢固语 | te |
| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 卡纳达语 | ka |
| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 泰米尔语 | ta |
| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 拉丁字母 | latin |
| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 阿拉伯字母 | ar |
| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 斯拉夫字母 | cyrillic |
| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 梵文字母 | devanagari |
更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。

25
doc/doc_en/models_list_en.md
View File

@ -102,27 +102,16 @@ python3 generate_multi_language_configs.py -l it \
| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
| xi_mobile_v2.0_rec |Lightweight model for Spanish recognition|rec_xi_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_train.tar) |
| pu_mobile_v2.0_rec |Lightweight model for Portuguese recognition|rec_pu_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_train.tar) |
| ru_mobile_v2.0_rec |Lightweight model for Russia recognition|rec_ru_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_train.tar) |
| ar_mobile_v2.0_rec |Lightweight model for Arabic recognition|rec_ar_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_train.tar) |
| hi_mobile_v2.0_rec |Lightweight model for Hindi recognition|rec_hi_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec |Lightweight model for chinese traditional recognition|rec_chinese_cht_lite_train.yml|5.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| ug_mobile_v2.0_rec |Lightweight model for Uyghur recognition|rec_ug_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_train.tar) |
| fa_mobile_v2.0_rec |Lightweight model for Persian recognition|rec_fa_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_train.tar) |
| ur_mobile_v2.0_rec |Lightweight model for Urdu recognition|rec_ur_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_train.tar) |
| rs_mobile_v2.0_rec |Lightweight model for Serbian(latin) recognition|rec_rs_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_train.tar) |
| oc_mobile_v2.0_rec |Lightweight model for Occitan recognition|rec_oc_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_train.tar) |
| mr_mobile_v2.0_rec |Lightweight model for Marathi recognition|rec_mr_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_train.tar) |
| ne_mobile_v2.0_rec |Lightweight model for Nepali recognition|rec_ne_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_train.tar) |
| rsc_mobile_v2.0_rec |Lightweight model for Serbian(cyrillic) recognition|rec_rsc_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_train.tar) |
| bg_mobile_v2.0_rec |Lightweight model for Bulgarian recognition|rec_bg_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_train.tar) |
| uk_mobile_v2.0_rec |Lightweight model for Ukranian recognition|rec_uk_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_train.tar) |
| be_mobile_v2.0_rec |Lightweight model for Belarusian recognition|rec_be_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_train.tar) |
| chinese_cht_mobile_v2.0_rec |Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
| te_mobile_v2.0_rec |Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
| ka_mobile_v2.0_rec |Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
| ta_mobile_v2.0_rec |Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
| latin_mobile_v2.0_rec | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
| arabic_mobile_v2.0_rec | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
| cyrillic_mobile_v2.0_rec | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
| devanagari_mobile_v2.0_rec | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
For more supported languages, please refer to : [Multi-language model](./multi_languages_en.md)
<a name="Angle"></a>

37
doc/doc_en/multi_languages_en.md
View File

@ -5,6 +5,26 @@
- 2021.4.9 supports the detection and recognition of 80 languages
- 2021.4.9 supports **lightweight high-precision** English model detection and recognition
PaddleOCR aims to create a rich, leading, and practical OCR tool library, which not only provides
Chinese and English models in general scenarios, but also provides models specifically trained
in English scenarios. And multilingual models covering [80 languages](#language_abbreviations).
Among them, the English model supports the detection and recognition of uppercase and lowercase
letters and common punctuation, and the recognition of space characters is optimized:
<div align="center">
<img src="../imgs_results/multi_lang/en_1.jpg" width="400" height="600">
</div>
The multilingual models cover Latin, Arabic, Traditional Chinese, Korean, Japanese, etc.:
<div align="center">
<img src="../imgs_results/multi_lang/japan_2.jpg" width="600" height="300">
<img src="../imgs_results/multi_lang/french_0.jpg" width="300" height="300">
</div>
This document will briefly introduce how to use the multilingual model.
- [1 Installation](#Install)
- [1.1 paddle installation](#paddleinstallation)
- [1.2 paddleocr package installation](#paddleocr_package_install)
@ -40,7 +60,7 @@ pip instll paddlepaddle-gpu
pip install
```
pip install "paddleocr>=2.0.4" # 2.0.4 version is recommended
pip install "paddleocr>=2.0.6" # 2.0.6 version is recommended
```
Build and install locally
```
@ -69,7 +89,7 @@ The specific supported [language] (#language_abbreviations) can be viewed in the
paddleocr --image_dir doc/imgs/japan_2.jpg --lang=japan
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs/japan_2.jpg)
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs/japan_2.jpg)
The result is a list, each item contains a text box, text and recognition confidence
```text
@ -86,7 +106,7 @@ The result is a list, each item contains a text box, text and recognition confid
paddleocr --image_dir doc/imgs_words/japan/1.jpg --det false --lang=japan
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/japan/1.jpg)
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_words/japan/1.jpg)
The result is a tuple, which returns the recognition result and recognition confidence
@ -133,13 +153,13 @@ image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/korean.ttf')
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
Visualization of results:
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/korean.jpg)
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/korean.jpg)
* Recognition
@ -153,7 +173,7 @@ for line in result:
print(line)
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/german/1.jpg)
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_words/german/1.jpg)
The result is a tuple, which only contains the recognition result and recognition confidence
@ -188,7 +208,7 @@ The result is a list, each item contains only text boxes
```
Visualization of results:
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/whl/12_det.jpg)
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/whl/12_det.jpg)
ppocr also supports direction classification. For more usage methods, please refer to: [whl package instructions](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md).
@ -212,7 +232,7 @@ For functions such as data annotation, you can read the complete [Document Tutor
|german|german|
|japan|japan|
|korean|korean|
|chinese traditional |ch_tra|
|chinese traditional |chinese_cht|
| Italian |it|
|Spanish |es|
| Portuguese|pt|
@ -231,7 +251,6 @@ For functions such as data annotation, you can read the complete [Document Tutor
|Ukranian|uk|
|Belarusian|be|
|Telugu |te|
|Kannada |kn|
|Tamil |ta|
|Afrikaans |af|
|Azerbaijani |az|

22
doc/doc_en/pgnet_en.md
View File

@ -23,6 +23,16 @@ The output of TBO and TCL can get text detection results after post-processing,
The results of detection and recognition are as follows:
![](../imgs_results/e2e_res_img293_pgnet.png)
![](../imgs_results/e2e_res_img295_pgnet.png)
### Performance
####Test set: Total Text
####Test environment: NVIDIA Tesla V100-SXM2-16GB
|PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|download|
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
|Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*
<a name="Environment_Configuration"></a>
## 2. Environment Configuration
@ -49,13 +59,13 @@ After decompression, there should be the following file structure:
### Single image or image set prediction
```bash
# Prediction single image specified by image_dir
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
# Prediction the collection of images specified by image_dir
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
# If you want to use CPU for prediction, you need to set use_gpu parameter is false
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True --use_gpu=False
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True --use_gpu=False
```
### Visualization results
The visualized end-to-end results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
@ -141,12 +151,12 @@ python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{
### Model Test
Test the end-to-end result on a single image:
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
```
Test the end-to-end result on all images in the folder:
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
```
### Model inference
@ -154,7 +164,7 @@ python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img=
First, convert the model saved in the PGNet end-to-end training process into an inference model. In the first stage of training based on composite dataset, the model of English data set training is taken as an example[model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar), you can use the following command to convert:
```
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**For PGNet quadrangle end-to-end model inference, you need to set the parameter `--e2e_algorithm="PGNet"`**, run the following command:
```

29
doc/doc_en/recognition_en.md
View File

@ -131,7 +131,7 @@ PaddleOCR has built-in dictionaries, which can be used on demand.
`ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters
`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
`ppocr/utils/en_dict.txt` is a English dictionary with 96 characters
The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
@ -279,7 +279,7 @@ Eval:
<a name="Multi_language"></a>
#### 2.3 Multi-language
PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is
PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
There are two ways to create the required configuration file:：
@ -368,27 +368,12 @@ Currently, the multi-language algorithms supported by PaddleOCR are:
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean |
| rec_it_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Italian | it |
| rec_xi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Spanish | xi |
| rec_pu_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Portuguese | pu |
| rec_ru_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Russia | ru |
| rec_ar_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Arabic | ar |
| rec_hi_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Hindi | hi |
| rec_ug_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Uyghur | ug |
| rec_fa_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Persian(Farsi) | fa |
| rec_ur_ite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Urdu | ur |
| rec_rs_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(latin) | rs |
| rec_oc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Occitan | oc |
| rec_mr_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Marathi | mr |
| rec_ne_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Nepali | ne |
| rec_rsc_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Serbian(cyrillic) | rsc |
| rec_bg_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Bulgarian | bg |
| rec_uk_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Ukranian | uk |
| rec_be_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Belarusian | be |
| rec_te_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Telugu | te |
| rec_ka_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Kannada | ka |
| rec_ta_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Tamil | ta |
| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Latin | latin |
| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | arabic | ar |
| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | cyrillic | cyrillic |
| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | devanagari | devanagari |
For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.

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45
paddleocr.py
View File

@ -30,12 +30,17 @@ from ppocr.utils.logging import get_logger
logger = get_logger()
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
from tools.infer.utility import draw_ocr
__all__ = ['PaddleOCR']
model_urls = {
'det':
'det': {
'ch':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
'en':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
},
'rec': {
'ch': {
'url':
@ -45,7 +50,7 @@ model_urls = {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/en_dict.txt'
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
@ -113,7 +118,7 @@ model_urls = {
}
SUPPORT_DET_MODEL = ['DB']
VERSION = 2.0
VERSION = '2.1'
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
@ -199,7 +204,7 @@ def parse_args(mMain=True, add_help=True):
parser.add_argument("--rec_model_dir", type=str, default=None)
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
parser.add_argument("--rec_char_type", type=str, default='ch')
parser.add_argument("--rec_batch_num", type=int, default=30)
parser.add_argument("--rec_batch_num", type=int, default=6)
parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument("--rec_char_dict_path", type=str, default=None)
parser.add_argument("--use_space_char", type=bool, default=True)
@ -209,7 +214,7 @@ def parse_args(mMain=True, add_help=True):
parser.add_argument("--cls_model_dir", type=str, default=None)
parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
parser.add_argument("--label_list", type=list, default=['0', '180'])
parser.add_argument("--cls_batch_num", type=int, default=30)
parser.add_argument("--cls_batch_num", type=int, default=6)
parser.add_argument("--cls_thresh", type=float, default=0.9)
parser.add_argument("--enable_mkldnn", type=bool, default=False)
@ -243,7 +248,7 @@ def parse_args(mMain=True, add_help=True):
rec_model_dir=None,
rec_image_shape="3, 32, 320",
rec_char_type='ch',
rec_batch_num=30,
rec_batch_num=6,
max_text_length=25,
rec_char_dict_path=None,
use_space_char=True,
@ -251,7 +256,7 @@ def parse_args(mMain=True, add_help=True):
cls_model_dir=None,
cls_image_shape="3, 48, 192",
label_list=['0', '180'],
cls_batch_num=30,
cls_batch_num=6,
cls_thresh=0.9,
enable_mkldnn=False,
use_zero_copy_run=False,
@ -274,10 +279,10 @@ class PaddleOCR(predict_system.TextSystem):
self.use_angle_cls = postprocess_params.use_angle_cls
lang = postprocess_params.lang
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'en', 'es', 'et', 'fr',
'ga', 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi',
'ms', 'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin',
'sk', 'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
@ -299,6 +304,10 @@ class PaddleOCR(predict_system.TextSystem):
assert lang in model_urls[
'rec'], 'param lang must in {}, but got {}'.format(
model_urls['rec'].keys(), lang)
if lang == "ch":
det_lang = "ch"
else:
det_lang = "en"
use_inner_dict = False
if postprocess_params.rec_char_dict_path is None:
use_inner_dict = True
@ -307,17 +316,17 @@ class PaddleOCR(predict_system.TextSystem):
# init model dir
if postprocess_params.det_model_dir is None:
postprocess_params.det_model_dir = os.path.join(
BASE_DIR, '{}/det'.format(VERSION))
postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION,
'det', det_lang)
if postprocess_params.rec_model_dir is None:
postprocess_params.rec_model_dir = os.path.join(
BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
postprocess_params.rec_model_dir = os.path.join(BASE_DIR, VERSION,
'rec', lang)
if postprocess_params.cls_model_dir is None:
postprocess_params.cls_model_dir = os.path.join(
BASE_DIR, '{}/cls'.format(VERSION))
postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
print(postprocess_params)
# download model
maybe_download(postprocess_params.det_model_dir, model_urls['det'])
maybe_download(postprocess_params.det_model_dir,
model_urls['det'][det_lang])
maybe_download(postprocess_params.rec_model_dir,
model_urls['rec'][lang]['url'])
maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])

18
ppocr/data/imaug/label_ops.py
View File

@ -96,7 +96,7 @@ class BaseRecLabelEncode(object):
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
'mr', 'ne'
'mr', 'ne', 'latin', 'arabic', 'cyrillic', 'devanagari'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
@ -200,18 +200,16 @@ class E2ELabelEncode(BaseRecLabelEncode):
self.pad_num = len(self.dict) # the length to pad
def __call__(self, data):
text_label_index_list, temp_text = [], []
texts = data['strs']
temp_texts = []
for text in texts:
text = text.lower()
temp_text = []
for c_ in text:
if c_ in self.dict:
temp_text.append(self.dict[c_])
temp_text = temp_text + [self.pad_num] * (self.max_text_len -
len(temp_text))
text_label_index_list.append(temp_text)
data['strs'] = np.array(text_label_index_list)
text = self.encode(text)
if text is None:
return None
text = text + [self.pad_num] * (self.max_text_len - len(text))
temp_texts.append(text)
data['strs'] = np.array(temp_texts)
return data

11
ppocr/data/pgnet_dataset.py
View File

@ -64,9 +64,6 @@ class PGDataSet(Dataset):
for line in f.readlines():
poly_str, txt = line.strip().split('\t')
poly = list(map(float, poly_str.split(',')))
if self.mode.lower() == "eval":
while len(poly) < 100:
poly.append(-1)
text_polys.append(
np.array(
poly, dtype=np.float32).reshape(-1, 2))
@ -139,10 +136,6 @@ class PGDataSet(Dataset):
try:
if self.data_format == 'icdar':
im_path = os.path.join(data_path, 'rgb', data_line)
if self.mode.lower() == "eval":
poly_path = os.path.join(data_path, 'poly_gt',
data_line.split('.')[0] + '.txt')
else:
poly_path = os.path.join(data_path, 'poly',
data_line.split('.')[0] + '.txt')
text_polys, text_tags, text_strs = self.extract_polys(poly_path)
@ -150,12 +143,14 @@ class PGDataSet(Dataset):
image_dir = os.path.join(os.path.dirname(data_path), 'image')
im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
data_line, image_dir)
img_id = int(data_line.split(".")[0][3:])
data = {
'img_path': im_path,
'polys': text_polys,
'tags': text_tags,
'strs': text_strs
'strs': text_strs,
'img_id': img_id
}
with open(data['img_path'], 'rb') as f:
img = f.read()

41
ppocr/metrics/e2e_metric.py
View File

@ -19,57 +19,28 @@ from __future__ import print_function
__all__ = ['E2EMetric']
from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results
from ppocr.utils.e2e_utils.extract_textpoint import get_dict
from ppocr.utils.e2e_utils.extract_textpoint_slow import get_dict
class E2EMetric(object):
def __init__(self,
gt_mat_dir,
character_dict_path,
main_indicator='f_score_e2e',
**kwargs):
self.gt_mat_dir = gt_mat_dir
self.label_list = get_dict(character_dict_path)
self.max_index = len(self.label_list)
self.main_indicator = main_indicator
self.reset()
def __call__(self, preds, batch, **kwargs):
temp_gt_polyons_batch = batch[2]
temp_gt_strs_batch = batch[3]
ignore_tags_batch = batch[4]
gt_polyons_batch = []
gt_strs_batch = []
temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
for temp_list in temp_gt_polyons_batch:
t = []
for index in temp_list:
if index[0] != -1 and index[1] != -1:
t.append(index)
gt_polyons_batch.append(t)
temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
for temp_list in temp_gt_strs_batch:
t = ""
for index in temp_list:
if index < self.max_index:
t += self.label_list[index]
gt_strs_batch.append(t)
for pred, gt_polyons, gt_strs, ignore_tags in zip(
[preds], [gt_polyons_batch], [gt_strs_batch], ignore_tags_batch):
# prepare gt
gt_info_list = [{
'points': gt_polyon,
'text': gt_str,
'ignore': ignore_tag
} for gt_polyon, gt_str, ignore_tag in
zip(gt_polyons, gt_strs, ignore_tags)]
# prepare det
img_id = batch[5][0]
e2e_info_list = [{
'points': det_polyon,
'text': pred_str
} for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
result = get_socre(gt_info_list, e2e_info_list)
} for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
self.results.append(result)
def get_metric(self):

123
ppocr/postprocess/pg_postprocess.py
View File

@ -22,10 +22,7 @@ import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
from ppocr.utils.e2e_utils.extract_textpoint import *
from ppocr.utils.e2e_utils.visual import *
import paddle
from ppocr.utils.e2e_utils.pgnet_pp_utils import PGNet_PostProcess
class PGPostProcess(object):
@ -33,10 +30,12 @@ class PGPostProcess(object):
The post process for PGNet.
"""
def __init__(self, character_dict_path, valid_set, score_thresh, **kwargs):
self.Lexicon_Table = get_dict(character_dict_path)
def __init__(self, character_dict_path, valid_set, score_thresh, mode,
**kwargs):
self.character_dict_path = character_dict_path
self.valid_set = valid_set
self.score_thresh = score_thresh
self.mode = mode
# c++ la-nms is faster, but only support python 3.5
self.is_python35 = False
@ -44,112 +43,10 @@ class PGPostProcess(object):
self.is_python35 = True
def __call__(self, outs_dict, shape_list):
p_score = outs_dict['f_score']
p_border = outs_dict['f_border']
p_char = outs_dict['f_char']
p_direction = outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
post = PGNet_PostProcess(self.character_dict_path, self.valid_set,
self.score_thresh, outs_dict, shape_list)
if self.mode == 'fast':
data = post.pg_postprocess_fast()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = shape_list[0]
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list(
p_score,
p_char,
p_direction,
score_thresh=self.score_thresh,
is_backbone=True,
is_curved=is_curved)
p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
char_seq_idx_set = []
for i in range(len(instance_yxs_list)):
gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
feature_len = [len(feature_seq[0])]
featyre_seq = paddle.to_tensor(feature_seq)
feature_len = np.array([feature_len]).astype(np.int64)
length = paddle.to_tensor(feature_len)
seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
input=featyre_seq, blank=36, input_length=length)
seq_pred_str = seq_pred[0].numpy().tolist()[0]
seq_len = seq_pred[1].numpy()[0][0]
temp_t = []
for c in seq_pred_str[:seq_len]:
temp_t.append(c)
char_seq_idx_set.append(temp_t)
seq_strs = []
for char_idx_set in char_seq_idx_set:
pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
seq_strs.append(pr_str)
poly_list = []
keep_str_list = []
all_point_list = []
all_point_pair_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(yx_center_line) == 1:
yx_center_line.append(yx_center_line[-1])
offset_expand = 1.0
if self.valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for batch_id, y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
all_point_list.append([
int(round(x * 4.0 / ratio_w)),
int(round(y * 4.0 / ratio_h))
])
all_point_pair_list.append(point_pair.round().astype(np.int32)
.tolist())
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
if len(keep_str) < 2:
continue
keep_str_list.append(keep_str)
if self.valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif self.valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
data = {
'points': poly_list,
'strs': keep_str_list,
}
data = post.pg_postprocess_slow()
return data

2
ppocr/postprocess/rec_postprocess.py
View File

@ -28,7 +28,7 @@ class BaseRecLabelDecode(object):
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
'ne', 'EN'
'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)

33
ppocr/utils/e2e_metric/Deteval.py
View File

@ -13,10 +13,11 @@
# limitations under the License.
import numpy as np
import scipy.io as io
from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
def get_socre(gt_dict, pred_dict):
def get_socre(gt_dir, img_id, pred_dict):
allInputs = 1
def input_reading_mod(pred_dict):
@ -30,31 +31,9 @@ def get_socre(gt_dict, pred_dict):
det.append([point, text])
return det
def gt_reading_mod(gt_dict):
"""This helper reads groundtruths from mat files"""
gt = []
n = len(gt_dict)
for i in range(n):
points = gt_dict[i]['points']
h = len(points)
text = gt_dict[i]['text']
xx = [
np.array(
['x:'], dtype='<U2'), 0, np.array(
['y:'], dtype='<U2'), 0, np.array(
['#'], dtype='<U1'), np.array(
['#'], dtype='<U1')
]
t_x, t_y = [], []
for j in range(h):
t_x.append(points[j][0])
t_y.append(points[j][1])
xx[1] = np.array([t_x], dtype='int16')
xx[3] = np.array([t_y], dtype='int16')
if text != "" and "#" not in text:
xx[4] = np.array([text], dtype='U{}'.format(len(text)))
xx[5] = np.array(['c'], dtype='<U1')
gt.append(xx)
def gt_reading_mod(gt_dir, gt_id):
gt = io.loadmat('%s/poly_gt_img%s.mat' % (gt_dir, gt_id))
gt = gt['polygt']
return gt
def detection_filtering(detections, groundtruths, threshold=0.5):
@ -101,7 +80,7 @@ def get_socre(gt_dict, pred_dict):
input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
and (input_id != 'Deteval_result_non_curved.txt'):
detections = input_reading_mod(pred_dict)
groundtruths = gt_reading_mod(gt_dict)
groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
detections = detection_filtering(
detections,
groundtruths) # filters detections overlapping with DC area

457
ppocr/utils/e2e_utils/extract_textpoint_fast.py Normal file
View File

@ -0,0 +1,457 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains various CTC decoders."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import math
import numpy as np
from itertools import groupby
from skimage.morphology._skeletonize import thin
def get_dict(character_dict_path):
character_str = ""
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode('utf-8').strip("\n").strip("\r\n")
character_str += line
dict_character = list(character_str)
return dict_character
def softmax(logits):
"""
logits: N x d
"""
max_value = np.max(logits, axis=1, keepdims=True)
exp = np.exp(logits - max_value)
exp_sum = np.sum(exp, axis=1, keepdims=True)
dist = exp / exp_sum
return dist
def get_keep_pos_idxs(labels, remove_blank=None):
"""
Remove duplicate and get pos idxs of keep items.
The value of keep_blank should be [None, 95].
"""
duplicate_len_list = []
keep_pos_idx_list = []
keep_char_idx_list = []
for k, v_ in groupby(labels):
current_len = len(list(v_))
if k != remove_blank:
current_idx = int(sum(duplicate_len_list) + current_len // 2)
keep_pos_idx_list.append(current_idx)
keep_char_idx_list.append(k)
duplicate_len_list.append(current_len)
return keep_char_idx_list, keep_pos_idx_list
def remove_blank(labels, blank=0):
new_labels = [x for x in labels if x != blank]
return new_labels
def insert_blank(labels, blank=0):
new_labels = [blank]
for l in labels:
new_labels += [l, blank]
return new_labels
def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
"""
CTC greedy (best path) decoder.
"""
raw_str = np.argmax(np.array(probs_seq), axis=1)
remove_blank_in_pos = None if keep_blank_in_idxs else blank
dedup_str, keep_idx_list = get_keep_pos_idxs(
raw_str, remove_blank=remove_blank_in_pos)
dst_str = remove_blank(dedup_str, blank=blank)
return dst_str, keep_idx_list
def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
_, _, C = logits_map.shape
ys, xs = zip(*gather_info)
logits_seq = logits_map[list(ys), list(xs)]
probs_seq = logits_seq
labels = np.argmax(probs_seq, axis=1)
dst_str = [k for k, v_ in groupby(labels) if k != C - 1]
detal = len(gather_info) // (pts_num - 1)
keep_idx_list = [0] + [detal * (i + 1) for i in range(pts_num - 2)] + [-1]
keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
return dst_str, keep_gather_list
def ctc_decoder_for_image(gather_info_list,
logits_map,
Lexicon_Table,
pts_num=6):
"""
CTC decoder using multiple processes.
"""
decoder_str = []
decoder_xys = []
for gather_info in gather_info_list:
if len(gather_info) < pts_num:
continue
dst_str, xys_list = instance_ctc_greedy_decoder(
gather_info, logits_map, pts_num=pts_num)
dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
if len(dst_str_readable) < 2:
continue
decoder_str.append(dst_str_readable)
decoder_xys.append(xys_list)
return decoder_str, decoder_xys
def sort_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list, point_direction):
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point, np.array(sorted_direction)
def add_id(pos_list, image_id=0):
"""
Add id for gather feature, for inference.
"""
new_list = []
for item in pos_list:
new_list.append((image_id, item[0], item[1]))
return new_list
def sort_and_expand_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
left_list = []
right_list = []
for i in range(append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
left_list.append((ly, lx))
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
right_list.append((ry, rx))
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
binary_tcl_map: h x w
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
max_append_num = 2 * append_num
left_list = []
right_list = []
for i in range(max_append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
if binary_tcl_map[ly, lx] > 0.5:
left_list.append((ly, lx))
else:
break
for i in range(max_append_num):
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
if binary_tcl_map[ry, rx] > 0.5:
right_list.append((ry, rx))
else:
break
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def point_pair2poly(point_pair_list):
"""
Transfer vertical point_pairs into poly point in clockwise.
"""
point_num = len(point_pair_list) * 2
point_list = [0] * point_num
for idx, point_pair in enumerate(point_pair_list):
point_list[idx] = point_pair[0]
point_list[point_num - 1 - idx] = point_pair[1]
return np.array(point_list).reshape(-1, 2)
def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
"""
expand poly along width.
"""
point_num = poly.shape[0]
left_quad = np.array(
[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
right_quad = np.array(
[
poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]
],
dtype=np.float32)
right_ratio = 1.0 + shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
poly[0] = left_quad_expand[0]
poly[-1] = left_quad_expand[-1]
poly[point_num // 2 - 1] = right_quad_expand[1]
poly[point_num // 2] = right_quad_expand[2]
return poly
def restore_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w,
src_h, valid_set):
poly_list = []
keep_str_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(keep_str) < 2:
print('--> too short, {}'.format(keep_str))
continue
offset_expand = 1.0
if valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2) * offset_expand
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
detected_poly = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
keep_str_list.append(keep_str)
if valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
return poly_list, keep_str_list
def generate_pivot_list_fast(p_score,
p_char_maps,
f_direction,
Lexicon_Table,
score_thresh=0.5):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score = p_score[0]
f_direction = f_direction.transpose(1, 2, 0)
p_tcl_map = (p_score > score_thresh) * 1.0
skeleton_map = thin(p_tcl_map.astype(np.uint8))
instance_count, instance_label_map = cv2.connectedComponents(
skeleton_map.astype(np.uint8), connectivity=8)
# get TCL Instance
all_pos_yxs = []
if instance_count > 0:
for instance_id in range(1, instance_count):
pos_list = []
ys, xs = np.where(instance_label_map == instance_id)
pos_list = list(zip(ys, xs))
if len(pos_list) < 3:
continue
pos_list_sorted = sort_and_expand_with_direction_v2(
pos_list, f_direction, p_tcl_map)
all_pos_yxs.append(pos_list_sorted)
p_char_maps = p_char_maps.transpose([1, 2, 0])
decoded_str, keep_yxs_list = ctc_decoder_for_image(
all_pos_yxs, logits_map=p_char_maps, Lexicon_Table=Lexicon_Table)
return keep_yxs_list, decoded_str
def extract_main_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
pos_list = np.array(pos_list)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
average_direction = average_direction / (
np.linalg.norm(average_direction) + 1e-6)
return average_direction
def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
"""
pos_list_full = np.array(pos_list).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
return sorted_list
def sort_by_direction_with_image_id(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list_full, point_direction):
pos_list_full = np.array(pos_list_full).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 3)
point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point

60
ppocr/utils/e2e_utils/extract_textpoint.py → ppocr/utils/e2e_utils/extract_textpoint_slow.py
View File

@ -35,6 +35,64 @@ def get_dict(character_dict_path):
return dict_character
def point_pair2poly(point_pair_list):
"""
Transfer vertical point_pairs into poly point in clockwise.
"""
pair_length_list = []
for point_pair in point_pair_list:
pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
pair_length_list.append(pair_length)
pair_length_list = np.array(pair_length_list)
pair_info = (pair_length_list.max(), pair_length_list.min(),
pair_length_list.mean())
point_num = len(point_pair_list) * 2
point_list = [0] * point_num
for idx, point_pair in enumerate(point_pair_list):
point_list[idx] = point_pair[0]
point_list[point_num - 1 - idx] = point_pair[1]
return np.array(point_list).reshape(-1, 2), pair_info
def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
"""
Generate shrink_quad_along_width.
"""
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
"""
expand poly along width.
"""
point_num = poly.shape[0]
left_quad = np.array(
[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
right_quad = np.array(
[
poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]
],
dtype=np.float32)
right_ratio = 1.0 + \
shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
poly[0] = left_quad_expand[0]
poly[-1] = left_quad_expand[-1]
poly[point_num // 2 - 1] = right_quad_expand[1]
poly[point_num // 2] = right_quad_expand[2]
return poly
def softmax(logits):
"""
logits: N x d
@ -399,7 +457,7 @@ def generate_pivot_list_horizontal(p_score,
return center_pos_yxs, end_points_yxs
def generate_pivot_list(p_score,
def generate_pivot_list_slow(p_score,
p_char_maps,
f_direction,
score_thresh=0.5,

181
ppocr/utils/e2e_utils/pgnet_pp_utils.py Normal file
View File

@ -0,0 +1,181 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
from extract_textpoint_slow import *
from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
class PGNet_PostProcess(object):
# two different post-process
def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict,
shape_list):
self.Lexicon_Table = get_dict(character_dict_path)
self.valid_set = valid_set
self.score_thresh = score_thresh
self.outs_dict = outs_dict
self.shape_list = shape_list
def pg_postprocess_fast(self):
p_score = self.outs_dict['f_score']
p_border = self.outs_dict['f_border']
p_char = self.outs_dict['f_char']
p_direction = self.outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
instance_yxs_list, seq_strs = generate_pivot_list_fast(
p_score,
p_char,
p_direction,
self.Lexicon_Table,
score_thresh=self.score_thresh)
poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
p_border, ratio_w, ratio_h,
src_w, src_h, self.valid_set)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data
def pg_postprocess_slow(self):
p_score = self.outs_dict['f_score']
p_border = self.outs_dict['f_border']
p_char = self.outs_dict['f_char']
p_direction = self.outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list_slow(
p_score,
p_char,
p_direction,
score_thresh=self.score_thresh,
is_backbone=True,
is_curved=is_curved)
p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
char_seq_idx_set = []
for i in range(len(instance_yxs_list)):
gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
feature_len = [len(feature_seq[0])]
featyre_seq = paddle.to_tensor(feature_seq)
feature_len = np.array([feature_len]).astype(np.int64)
length = paddle.to_tensor(feature_len)
seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
input=featyre_seq, blank=36, input_length=length)
seq_pred_str = seq_pred[0].numpy().tolist()[0]
seq_len = seq_pred[1].numpy()[0][0]
temp_t = []
for c in seq_pred_str[:seq_len]:
temp_t.append(c)
char_seq_idx_set.append(temp_t)
seq_strs = []
for char_idx_set in char_seq_idx_set:
pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
seq_strs.append(pr_str)
poly_list = []
keep_str_list = []
all_point_list = []
all_point_pair_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(yx_center_line) == 1:
yx_center_line.append(yx_center_line[-1])
offset_expand = 1.0
if self.valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for batch_id, y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
all_point_list.append([
int(round(x * 4.0 / ratio_w)),
int(round(y * 4.0 / ratio_h))
])
all_point_pair_list.append(point_pair.round().astype(np.int32)
.tolist())
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
if len(keep_str) < 2:
continue
keep_str_list.append(keep_str)
detected_poly = np.round(detected_poly).astype('int32')
if self.valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif self.valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data

95
ppocr/utils/en_dict.txt Normal file
View File

@ -0,0 +1,95 @@
0
1
2
3
4
5
6
7
8
9
:
;
<
=
>
?
@
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
[
\
]
^
_
`
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
y
z
{
|
}
~
!
"
#
$
%
&
'
(
)
*
+
,
-
.
/

2
setup.py
View File

@ -32,7 +32,7 @@ setup(
package_dir={'paddleocr': ''},
include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='2.0.4',
version='2.0.6',
install_requires=requirements,
license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',

1
tools/infer/predict_e2e.py
View File

@ -66,6 +66,7 @@ class TextE2E(object):
postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
postprocess_params["character_dict_path"] = args.e2e_char_dict_path
postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
postprocess_params["mode"] = args.e2e_pgnet_mode
self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
else:
logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))

1
tools/infer/utility.py
View File

@ -86,6 +86,7 @@ def parse_args():
"--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
# params for text classifier
parser.add_argument("--use_angle_cls", type=str2bool, default=False)