rm rec_char_type

This commit is contained in:
tink2123 2021-10-12 14:29:00 +08:00
parent af0bac580f
commit 380dc6c27d
35 changed files with 161 additions and 168 deletions

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@ -14,7 +14,6 @@ Global:
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: false
use_space_char: true

View File

@ -14,7 +14,6 @@ Global:
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: false
use_space_char: true

View File

@ -14,7 +14,6 @@ Global:
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: false
use_space_char: true

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: False
use_space_char: True

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: ch
max_text_length: 25
infer_mode: False
use_space_char: True

View File

@ -15,7 +15,6 @@ Global:
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

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@ -15,7 +15,6 @@ Global:
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

View File

@ -15,7 +15,6 @@ Global:
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

View File

@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/en_dict.txt
character_type: EN
max_text_length: 25
infer_mode: False
use_space_char: True

View File

@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/french_dict.txt
character_type: french
max_text_length: 25
infer_mode: False
use_space_char: False

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@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/german_dict.txt
character_type: german
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/japan_dict.txt
character_type: japan
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -16,7 +16,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict/korean_dict.txt
character_type: korean
max_text_length: 25
infer_mode: False
use_space_char: False

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@ -15,7 +15,6 @@ Global:
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

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/en_dict.txt
character_type: EN
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: EN_symbol
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: True

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -15,7 +15,6 @@ Global:
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict90.txt
character_type: EN_symbol
max_text_length: 30
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -15,7 +15,6 @@ Global:
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
character_dict_path:
character_type: en
character_dict_path:
max_text_length: 25
num_heads: 8
infer_mode: False

View File

@ -14,8 +14,7 @@ Global:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: EN_symbol
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 100
infer_mode: False
use_space_char: False

View File

@ -273,7 +273,7 @@ python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o G
CRNN 文本识别模型推理,可以执行如下命令:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
![](../imgs_words_en/word_336.png)
@ -288,7 +288,7 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
- 训练时采用的图像分辨率不同,训练上述模型采用的图像分辨率是[332100],而中文模型训练时,为了保证长文本的识别效果,训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率,即[3, 32, 320]。因此这里推理上述英文模型时需要通过参数rec_image_shape设置识别图像的形状。
- 字符列表DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验总共36个字符。所有大小字符都转成了小写字符不在上面列表的字符都忽略认为是空格。因此这里没有输入字符字典而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_type指定为英文"en"。
- 字符列表DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验总共36个字符。所有大小字符都转成了小写字符不在上面列表的字符都忽略认为是空格。因此这里没有输入字符字典而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path指定为英文字典"./ppocr/utils/ic15_dict.txt"。
```
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
@ -303,15 +303,15 @@ dict_character = list(self.character_str)
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
--rec_model_dir="./inference/srn/" \
--rec_image_shape="1, 64, 256" \
--rec_char_type="en" \
--rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
--rec_algorithm="SRN"
```
### 4. 自定义文本识别字典的推理
如果训练时修改了文本的字典在使用inference模型预测时需要通过`--rec_char_dict_path`指定使用的字典路径,并且设置 `rec_char_type=ch`
如果训练时修改了文本的字典在使用inference模型预测时需要通过`--rec_char_dict_path`指定使用的字典路径
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
```
<a name="多语言模型的推理"></a>
@ -320,7 +320,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
需要通过 `--vis_font_path` 指定可视化的字体路径,`doc/fonts/` 路径下有默认提供的小语种字体,例如韩文识别:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
```
![](../imgs_words/korean/1.jpg)
@ -388,7 +388,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --de
下面给出基于EAST文本检测和STAR-Net文本识别执行命令
```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
执行命令后,识别结果图像如下:

View File

@ -21,7 +21,7 @@ Next, we first introduce how to convert a trained model into an inference model,
- [2.2 DB Text Detection Model Inference](#DB_DETECTION)
- [2.3 East Text Detection Model Inference](#EAST_DETECTION)
- [2.4 Sast Text Detection Model Inference](#SAST_DETECTION)
- [3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
- [3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION)
- [3.2 CTC-Based Text Recognition Model Inference](#CTC-BASED_RECOGNITION)
@ -281,7 +281,7 @@ python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o G
For CRNN text recognition model inference, execute the following commands:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
![](../imgs_words_en/word_336.png)
@ -314,7 +314,7 @@ with the training, such as: --rec_image_shape="1, 64, 256"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
--rec_model_dir="./inference/srn/" \
--rec_image_shape="1, 64, 256" \
--rec_char_type="en" \
--rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
--rec_algorithm="SRN"
```
@ -323,7 +323,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
```
<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
@ -333,7 +333,7 @@ If you need to predict other language models, when using inference model predict
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
```
![](../imgs_words/korean/1.jpg)
@ -399,7 +399,7 @@ If you want to try other detection algorithms or recognition algorithms, please
The following command uses the combination of the EAST text detection and STAR-Net text recognition:
```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
```
After executing the command, the recognition result image is as follows:

View File

@ -21,6 +21,8 @@ import numpy as np
import string
import json
from ppocr.utils.logging import get_logger
class ClsLabelEncode(object):
def __init__(self, label_list, **kwargs):
@ -92,31 +94,22 @@ class BaseRecLabelEncode(object):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False):
support_character_type = [
'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', 'latin', 'arabic', 'cyrillic', 'devanagari'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
self.max_text_len = max_text_length
self.beg_str = "sos"
self.end_str = "eos"
if character_type == "en":
if character_dict_path is None:
logger = get_logger()
logger.warning(
"The character_dict_path is None, model can only recognize number and lower letters"
)
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif character_type == "EN_symbol":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
self.lower = True
else:
self.character_str = ""
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
character_type)
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
@ -125,7 +118,6 @@ class BaseRecLabelEncode(object):
if use_space_char:
self.character_str += " "
dict_character = list(self.character_str)
self.character_type = character_type
dict_character = self.add_special_char(dict_character)
self.dict = {}
for i, char in enumerate(dict_character):
@ -147,7 +139,7 @@ class BaseRecLabelEncode(object):
"""
if len(text) == 0 or len(text) > self.max_text_len:
return None
if self.character_type == "en":
if self.lower:
text = text.lower()
text_list = []
for char in text:
@ -167,13 +159,11 @@ class NRTRLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='EN_symbol',
use_space_char=False,
**kwargs):
super(NRTRLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(NRTRLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def __call__(self, data):
text = data['label']
@ -200,12 +190,10 @@ class CTCLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(CTCLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(CTCLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def __call__(self, data):
text = data['label']
@ -231,12 +219,10 @@ class E2ELabelEncodeTest(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='EN',
use_space_char=False,
**kwargs):
super(E2ELabelEncodeTest,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(E2ELabelEncodeTest, self).__init__(
max_text_length, character_dict_path, use_space_char)
def __call__(self, data):
import json
@ -305,12 +291,10 @@ class AttnLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(AttnLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(AttnLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
@ -353,12 +337,10 @@ class SEEDLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(SEEDLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(SEEDLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def add_special_char(self, dict_character):
self.end_str = "eos"
@ -385,12 +367,10 @@ class SRNLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length=25,
character_dict_path=None,
character_type='en',
use_space_char=False,
**kwargs):
super(SRNLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(SRNLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def add_special_char(self, dict_character):
dict_character = dict_character + [self.beg_str, self.end_str]
@ -598,12 +578,10 @@ class SARLabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(SARLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
super(SARLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def add_special_char(self, dict_character):
beg_end_str = "<BOS/EOS>"

View File

@ -21,33 +21,16 @@ import re
class BaseRecLabelDecode(object):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False):
support_character_type = [
'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', 'latin', 'arabic', 'cyrillic', 'devanagari'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
def __init__(self, character_dict_path=None, use_space_char=False):
self.beg_str = "sos"
self.end_str = "eos"
if character_type == "en":
self.character_str = []
if character_dict_path is None:
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif character_type == "EN_symbol":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
self.character_str = []
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
character_type)
self.lower = True
else:
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
@ -57,9 +40,6 @@ class BaseRecLabelDecode(object):
self.character_str.append(" ")
dict_character = list(self.character_str)
else:
raise NotImplementedError
self.character_type = character_type
dict_character = self.add_special_char(dict_character)
self.dict = {}
for i, char in enumerate(dict_character):
@ -102,13 +82,10 @@ class BaseRecLabelDecode(object):
class CTCLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(CTCLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, tuple):
@ -136,13 +113,12 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
model_name=["student"],
key=None,
**kwargs):
super(DistillationCTCLabelDecode, self).__init__(
character_dict_path, character_type, use_space_char)
super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
use_space_char)
if not isinstance(model_name, list):
model_name = [model_name]
self.model_name = model_name
@ -162,13 +138,9 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
class NRTRLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='EN_symbol',
use_space_char=True,
**kwargs):
def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
@ -230,13 +202,10 @@ class NRTRLabelDecode(BaseRecLabelDecode):
class AttnLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(AttnLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
@ -313,13 +282,10 @@ class AttnLabelDecode(BaseRecLabelDecode):
class SEEDLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SEEDLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
@ -394,13 +360,10 @@ class SEEDLabelDecode(BaseRecLabelDecode):
class SRNLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='en',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SRNLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
self.max_text_length = kwargs.get('max_text_length', 25)
def __call__(self, preds, label=None, *args, **kwargs):
@ -616,13 +579,10 @@ class TableLabelDecode(object):
class SARLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SARLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
self.rm_symbol = kwargs.get('rm_symbol', False)

View File

@ -0,0 +1,94 @@
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
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"
#
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&
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)
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-
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<
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[
\
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^
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`
{
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}
~

View File

@ -131,14 +131,9 @@ def main(args):
img_list.append(img)
try:
img_list, cls_res, predict_time = text_classifier(img_list)
except:
except Exception as E:
logger.info(traceback.format_exc())
logger.info(
"ERROR!!!! \n"
"Please read the FAQhttps://github.com/PaddlePaddle/PaddleOCR#faq \n"
"If your model has tps module: "
"TPS does not support variable shape.\n"
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
logger.info(E)
exit()
for ino in range(len(img_list)):
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],

View File

@ -38,40 +38,34 @@ logger = get_logger()
class TextRecognizer(object):
def __init__(self, args):
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
postprocess_params = {
'name': 'CTCLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
if self.rec_algorithm == "SRN":
postprocess_params = {
'name': 'SRNLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
'name': 'AttnLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'NRTR':
postprocess_params = {
'name': 'NRTRLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "SAR":
postprocess_params = {
'name': 'SARLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}

View File

@ -74,7 +74,6 @@ def init_args():
parser.add_argument("--rec_algorithm", type=str, default='CRNN')
parser.add_argument("--rec_model_dir", type=str)
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=6)
parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument(