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PaddleOCR
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add windows doc

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revert-162-develop
tink2123 5 years ago
parent 8d9324cab1
commit b55b8eda22
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5
README.md
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@ -36,6 +36,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
#### 2.inference模型下载
*windows 环境下没有如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下*
#### (1)超轻量级中文OCR模型下载
```
mkdir inference && cd inference
@ -63,6 +65,9 @@ cd ..
# 设置PYTHONPATH环境变量
export PYTHONPATH=.
# windows下设置环境变量
SET PYTHONPATH=.
# 预测image_dir指定的单张图像
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/"

1
configs/rec/rec_chinese_lite_train.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 320]
max_text_length: 25
character_type: ch

3
configs/rec/rec_icdar15_train.yml
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@ -9,13 +9,12 @@ Global:
eval_batch_step: 500
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_icdar15_reader.yml
pretrain_weights:
pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
checkpoints:
save_inference_dir:
infer_img:

1
configs/rec/rec_mv3_none_bilstm_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_mv3_none_none_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_mv3_tps_bilstm_attn.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_mv3_tps_bilstm_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_r34_vd_none_none_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_r34_vd_tps_bilstm_attn.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

1
configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
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@ -9,7 +9,6 @@ Global:
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
drop_last: false
image_shape: [3, 32, 100]
max_text_length: 25
character_type: en

4
doc/inference.md
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@ -166,6 +166,10 @@ STAR-Net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/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```
### 3.基于Attention损失的识别模型推理
基于Attention损失的识别模型与ctc不同，需要额外设置识别算法参数 --rec_algorithm="RARE"
RARE 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/sare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"

6
doc/installation.md
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@ -8,6 +8,8 @@ PaddleOCR 工作环境
建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
*如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。*
1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。
```
# 切换到工作目录下
@ -54,6 +56,10 @@ python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsing
如果您的机器安装的是CUDA10，请运行以下命令安装
python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple
如果您的机器是CPU，请运行以下命令安装
python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
```

7
doc/recognition.md
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@ -41,6 +41,8 @@ PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
# 测试集标签
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```
最终训练集应有如下文件结构：
@ -168,10 +170,11 @@ Global:
评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml` 修改EvalReader中的 `label_file_path` 设置。
*注意* 评估时必须确保配置文件中 infer_img 字段为空
```
export CUDA_VISIBLE_DEVICES=0
# GPU 评估， Global.checkpoints 为待测权重
python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
### 预测
@ -184,7 +187,7 @@ python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkp
```
# 预测英文结果
python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```
预测图片：

19
ppocr/data/rec/dataset_traversal.py
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@ -42,14 +42,15 @@ class LMDBReader(object):
self.max_text_length = params['max_text_length']
self.mode = params['mode']
self.drop_last = False
self.tps = False
self.use_tps = False
if "tps" in params:
self.tps = True
self.ues_tps = True
if params['mode'] == 'train':
self.batch_size = params['train_batch_size_per_card']
self.drop_last = params['drop_last']
self.drop_last = True
else:
self.batch_size = params['test_batch_size_per_card']
self.drop_last = False
self.infer_img = params['infer_img']
def load_hierarchical_lmdb_dataset(self):
@ -114,7 +115,7 @@ class LMDBReader(object):
img=img,
image_shape=self.image_shape,
char_ops=self.char_ops,
tps=self.tps,
tps=self.use_tps,
infer_mode=True)
yield norm_img
else:
@ -181,15 +182,15 @@ class SimpleReader(object):
self.max_text_length = params['max_text_length']
self.mode = params['mode']
self.infer_img = params['infer_img']
self.tps = False
self.use_tps = False
if "tps" in params:
self.tps = True
self.drop_last = False
self.ues_tps = True
if params['mode'] == 'train':
self.batch_size = params['train_batch_size_per_card']
self.drop_last = params['drop_last']
self.drop_last = True
else:
self.batch_size = params['test_batch_size_per_card']
self.drop_last = False
def __call__(self, process_id):
if self.mode != 'train':
@ -206,7 +207,7 @@ class SimpleReader(object):
img=img,
image_shape=self.image_shape,
char_ops=self.char_ops,
tps=self.tps,
tps=self.use_tps,
infer_mode=True)
yield norm_img
else:

8
ppocr/data/rec/img_tools.py
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@ -95,14 +95,10 @@ def process_image(img,
max_text_length=None,
tps=None,
infer_mode=False):
if not infer_mode or char_ops.character_type == "en":
if not infer_mode or char_ops.character_type == "en" or tps != None:
norm_img = resize_norm_img(img, image_shape)
else:
if tps != None and char_ops.character_type == "ch":
image_shape = [3, 32, 320]
norm_img = resize_norm_img(img, image_shape)
else:
norm_img = resize_norm_img_chinese(img, image_shape)
norm_img = resize_norm_img_chinese(img, image_shape)
norm_img = norm_img[np.newaxis, :]
if label is not None:
char_num = char_ops.get_char_num()

12
tools/infer/predict_rec.py
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@ -38,8 +38,10 @@ class TextRecognizer(object):
char_ops_params["character_dict_path"] = args.rec_char_dict_path
if self.rec_algorithm != "RARE":
char_ops_params['loss_type'] = 'ctc'
self.loss_type = 'ctc'
else:
char_ops_params['loss_type'] = 'attention'
self.loss_type = 'attention'
self.char_ops = CharacterOps(char_ops_params)
def resize_norm_img(self, img, max_wh_ratio):
@ -85,7 +87,7 @@ class TextRecognizer(object):
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
if self.rec_algorithm != "RARE":
if self.loss_type == "ctc":
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
rec_idx_lod = self.output_tensors[0].lod()[0]
predict_batch = self.output_tensors[1].copy_to_cpu()
@ -139,9 +141,13 @@ if __name__ == "__main__":
img_list.append(img)
try:
rec_res, predict_time = text_recognizer(img_list)
except:
except Exception as e:
print(e)
logger.info(
"ERROR!! \nInput image shape is not equal with config. TPS does not support variable shape.\n"
"ERROR!!!! \n"
"Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
"If your model has tps module: "
"TPS does not support variable shape.\n"
"Please set --rec_image_shape=input_shape and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):

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