diff --git a/MANIFEST.in b/MANIFEST.in
index e16f157d6e9dd249d6c6a14ae54313759a6752c4..cd1c9636d4d23cc4d0f745403ec8ca407d1cc1a8 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,7 +1,7 @@
-include LICENSE.txt
+include LICENSE
include README.md
-recursive-include ppocr/utils *.txt utility.py logging.py
+recursive-include ppocr/utils *.txt utility.py logging.py network.py
recursive-include ppocr/data/ *.py
recursive-include ppocr/postprocess *.py
recursive-include tools/infer *.py
diff --git a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java
index 1c83e2184fe55aedd5022da839ab294b6bbe475c..b4ea34e2a38f91f3ecb1001c6bff3b71496b8f91 100644
--- a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java
+++ b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java
@@ -465,8 +465,12 @@ public class MainActivity extends AppCompatActivity {
}
public void btn_load_model_click(View view) {
- tvStatus.setText("STATUS: load model ......");
- loadModel();
+ if (predictor.isLoaded()){
+ tvStatus.setText("STATUS: model has been loaded");
+ }else{
+ tvStatus.setText("STATUS: load model ......");
+ loadModel();
+ }
}
public void btn_run_model_click(View view) {
diff --git a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
index 1c294995c25b7eb3fa6ded17f41f193bddfc3886..b474d8886a10746b8ac181085c62481dfe7a4229 100644
--- a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
+++ b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
@@ -194,26 +194,25 @@ public class Predictor {
"supported!");
return false;
}
- int[] channelStride = new int[]{width * height, width * height * 2};
- int p = scaleImage.getPixel(scaleImage.getWidth() - 1, scaleImage.getHeight() - 1);
- for (int y = 0; y < height; y++) {
- for (int x = 0; x < width; x++) {
- int color = scaleImage.getPixel(x, y);
- float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
- (float) blue(color) / 255.0f};
- inputData[y * width + x] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
- inputData[y * width + x + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
- inputData[y * width + x + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
- }
+ int[] channelStride = new int[]{width * height, width * height * 2};
+ int[] pixels=new int[width*height];
+ scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
+ for (int i = 0; i < pixels.length; i++) {
+ int color = pixels[i];
+ float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
+ (float) blue(color) / 255.0f};
+ inputData[i] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
+ inputData[i + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
+ inputData[i+ channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
}
} else if (channels == 1) {
- for (int y = 0; y < height; y++) {
- for (int x = 0; x < width; x++) {
- int color = inputImage.getPixel(x, y);
- float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
- inputData[y * width + x] = (gray - inputMean[0]) / inputStd[0];
- }
+ int[] pixels=new int[width*height];
+ scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
+ for (int i = 0; i < pixels.length; i++) {
+ int color = pixels[i];
+ float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
+ inputData[i] = (gray - inputMean[0]) / inputStd[0];
}
} else {
Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ", only channel 1 and 3 is " +
diff --git a/doc/doc_ch/config.md b/doc/doc_ch/config.md
index 3f8f7ff1674d63e721d7ad2ced31bf771b0183eb..74cd238134d1999a6fbd96d0ad053d0304231a0b 100644
--- a/doc/doc_ch/config.md
+++ b/doc/doc_ch/config.md
@@ -111,9 +111,9 @@
| 字段 | 用途 | 默认值 | 备注 |
| :---------------------: | :---------------------: | :--------------: | :--------------------: |
| **dataset** | 每次迭代返回一个样本 | - | - |
-| name | dataset类名 | SimpleDataSet | 目前支持`SimpleDataSet`和`LMDBDateSet` |
+| name | dataset类名 | SimpleDataSet | 目前支持`SimpleDataSet`和`LMDBDataSet` |
| data_dir | 数据集图片存放路径 | ./train_data | \ |
-| label_file_list | 数据标签路径 | ["./train_data/train_list.txt"] | dataset为LMDBDateSet时不需要此参数 |
+| label_file_list | 数据标签路径 | ["./train_data/train_list.txt"] | dataset为LMDBDataSet时不需要此参数 |
| ratio_list | 数据集的比例 | [1.0] | 若label_file_list中有两个train_list，且ratio_list为[0.4,0.6]，则从train_list1中采样40%，从train_list2中采样60%组合整个dataset |
| transforms | 对图片和标签进行变换的方法列表 | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | 见[ppocr/data/imaug](../../ppocr/data/imaug) |
| **loader** | dataloader相关 | - | |
diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index 8a7c341cf24738b8af8c974a6da41bcb1b51ce48..0f860065bef9eff8f90c18f120e43dcf0c2a47aa 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -243,7 +243,7 @@ Optimizer:
Train:
dataset:
- # 数据集格式，支持LMDBDateSet以及SimpleDataSet
+ # 数据集格式，支持LMDBDataSet以及SimpleDataSet
name: SimpleDataSet
# 数据集路径
data_dir: ./train_data/
@@ -263,7 +263,7 @@ Train:
Eval:
dataset:
- # 数据集格式，支持LMDBDateSet以及SimpleDataSet
+ # 数据集格式，支持LMDBDataSet以及SimpleDataSet
name: SimpleDataSet
# 数据集路径
data_dir: ./train_data
@@ -393,7 +393,7 @@ Global:
Train:
dataset:
- # 数据集格式，支持LMDBDateSet以及SimpleDataSet
+ # 数据集格式，支持LMDBDataSet以及SimpleDataSet
name: SimpleDataSet
# 数据集路径
data_dir: ./train_data/
@@ -403,7 +403,7 @@ Train:
Eval:
dataset:
- # 数据集格式，支持LMDBDateSet以及SimpleDataSet
+ # 数据集格式，支持LMDBDataSet以及SimpleDataSet
name: SimpleDataSet
# 数据集路径
data_dir: ./train_data
diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md
index 28ebb6e830369447395c661cbcc76aaf067a91d9..5e5847c4b298553b2d376b90196b61b7e0286efe 100644
--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
@@ -110,9 +110,9 @@ In ppocr, the network is divided into four stages: Transform, Backbone, Neck and
| Parameter | Use | Defaults | Note |
| :---------------------: | :---------------------: | :--------------: | :--------------------: |
| **dataset** | Return one sample per iteration | - | - |
-| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDateSet` |
+| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDataSet` |
| data_dir | Image folder path | ./train_data | \ |
-| label_file_list | Groundtruth file path | ["./train_data/train_list.txt"] | This parameter is not required when dataset is LMDBDateSet |
+| label_file_list | Groundtruth file path | ["./train_data/train_list.txt"] | This parameter is not required when dataset is LMDBDataSet |
| ratio_list | Ratio of data set | [1.0] | If there are two train_lists in label_file_list and ratio_list is [0.4,0.6], 40% will be sampled from train_list1, and 60% will be sampled from train_list2 to combine the entire dataset |
| transforms | List of methods to transform images and labels | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | see[ppocr/data/imaug](../../ppocr/data/imaug) |
| **loader** | dataloader related | - | |
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index 0b3db6a235bdbfeb930d6cf3f7d086829fd32c43..e23166e0caef4f6a246502fa12101f86d61e4eac 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -237,7 +237,7 @@ Optimizer:
Train:
dataset:
- # Type of dataset，we support LMDBDateSet and SimpleDataSet
+ # Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data/
@@ -257,7 +257,7 @@ Train:
Eval:
dataset:
- # Type of dataset，we support LMDBDateSet and SimpleDataSet
+ # Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data
@@ -394,7 +394,7 @@ Global:
Train:
dataset:
- # Type of dataset，we support LMDBDateSet and SimpleDataSet
+ # Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data/
@@ -404,7 +404,7 @@ Train:
Eval:
dataset:
- # Type of dataset，we support LMDBDateSet and SimpleDataSet
+ # Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data
diff --git a/doc/joinus.PNG b/doc/joinus.PNG
index 6e299a2ebe0eb52aa799ba9fa924bd685cd248de..4a274e631d8516789fca47e2db66bc0ac2d0f223 100644
Binary files a/doc/joinus.PNG and b/doc/joinus.PNG differ
diff --git a/doc/table/1.png b/doc/table/1.png
new file mode 100644
index 0000000000000000000000000000000000000000..47df618ab1bef431a5dd94418c01be16b09d31aa
Binary files /dev/null and b/doc/table/1.png differ
diff --git a/paddleocr.py b/paddleocr.py
index 1e4d94ff4e72da951e1ffb92edb50715482581ae..48c8c9c6523dc3f813189477e641f0e51b740885 100644
--- a/paddleocr.py
+++ b/paddleocr.py
@@ -19,17 +19,16 @@ __dir__ = os.path.dirname(__file__)
sys.path.append(os.path.join(__dir__, ''))
import cv2
+import logging
import numpy as np
from pathlib import Path
-import tarfile
-import requests
-from tqdm import tqdm
from tools.infer import predict_system
from ppocr.utils.logging import get_logger
logger = get_logger()
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+from ppocr.utils.network import maybe_download, download_with_progressbar
from tools.infer.utility import draw_ocr, init_args, str2bool
__all__ = ['PaddleOCR']
@@ -37,84 +36,84 @@ __all__ = ['PaddleOCR']
model_urls = {
'det': {
'ch':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+ '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'
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
},
'rec': {
'ch': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
}
},
'cls':
- 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
}
SUPPORT_DET_MODEL = ['DB']
@@ -123,50 +122,6 @@ SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
-def download_with_progressbar(url, save_path):
- response = requests.get(url, stream=True)
- total_size_in_bytes = int(response.headers.get('content-length', 0))
- block_size = 1024 # 1 Kibibyte
- progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
- with open(save_path, 'wb') as file:
- for data in response.iter_content(block_size):
- progress_bar.update(len(data))
- file.write(data)
- progress_bar.close()
- if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
- logger.error("Something went wrong while downloading models")
- sys.exit(0)
-
-
-def maybe_download(model_storage_directory, url):
- # using custom model
- tar_file_name_list = [
- 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
- ]
- if not os.path.exists(
- os.path.join(model_storage_directory, 'inference.pdiparams')
- ) or not os.path.exists(
- os.path.join(model_storage_directory, 'inference.pdmodel')):
- tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
- print('download {} to {}'.format(url, tmp_path))
- os.makedirs(model_storage_directory, exist_ok=True)
- download_with_progressbar(url, tmp_path)
- with tarfile.open(tmp_path, 'r') as tarObj:
- for member in tarObj.getmembers():
- filename = None
- for tar_file_name in tar_file_name_list:
- if tar_file_name in member.name:
- filename = tar_file_name
- if filename is None:
- continue
- file = tarObj.extractfile(member)
- with open(
- os.path.join(model_storage_directory, filename),
- 'wb') as f:
- f.write(file.read())
- os.remove(tmp_path)
-
-
def parse_args(mMain=True):
import argparse
parser = init_args()
@@ -194,10 +149,12 @@ class PaddleOCR(predict_system.TextSystem):
args:
**kwargs: other params show in paddleocr --help
"""
- postprocess_params = parse_args(mMain=False)
- postprocess_params.__dict__.update(**kwargs)
- self.use_angle_cls = postprocess_params.use_angle_cls
- lang = postprocess_params.lang
+ params = parse_args(mMain=False)
+ params.__dict__.update(**kwargs)
+ if params.show_log:
+ logger.setLevel(logging.DEBUG)
+ self.use_angle_cls = params.use_angle_cls
+ lang = params.lang
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
@@ -223,46 +180,46 @@ class PaddleOCR(predict_system.TextSystem):
lang = "devanagari"
assert lang in model_urls[
'rec'], 'param lang must in {}, but got {}'.format(
- model_urls['rec'].keys(), lang)
+ 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:
+ if params.rec_char_dict_path is None:
use_inner_dict = True
- postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
+ params.rec_char_dict_path = model_urls['rec'][lang][
'dict_path']
# init model dir
- if postprocess_params.det_model_dir is None:
- postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION,
+ if params.det_model_dir is None:
+ 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, VERSION,
+ if params.rec_model_dir is None:
+ 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')
- print(postprocess_params)
+ if params.cls_model_dir is None:
+ params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
# download model
- maybe_download(postprocess_params.det_model_dir,
+ maybe_download(params.det_model_dir,
model_urls['det'][det_lang])
- maybe_download(postprocess_params.rec_model_dir,
+ maybe_download(params.rec_model_dir,
model_urls['rec'][lang]['url'])
- maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
+ maybe_download(params.cls_model_dir, model_urls['cls'])
- if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
+ if params.det_algorithm not in SUPPORT_DET_MODEL:
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
sys.exit(0)
- if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
+ if params.rec_algorithm not in SUPPORT_REC_MODEL:
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
sys.exit(0)
if use_inner_dict:
- postprocess_params.rec_char_dict_path = str(
- Path(__file__).parent / postprocess_params.rec_char_dict_path)
+ params.rec_char_dict_path = str(
+ Path(__file__).parent / params.rec_char_dict_path)
+ print(params)
# init det_model and rec_model
- super().__init__(postprocess_params)
+ super().__init__(params)
def ocr(self, img, det=True, rec=True, cls=True):
"""
diff --git a/ppocr/data/imaug/operators.py b/ppocr/data/imaug/operators.py
index 9c48b09647527cf718113ea1b5df152ff7befa04..2535b4420c503f2e9e9cc5a677ef70c4dd9c36be 100644
--- a/ppocr/data/imaug/operators.py
+++ b/ppocr/data/imaug/operators.py
@@ -81,7 +81,7 @@ class NormalizeImage(object):
assert isinstance(img,
np.ndarray), "invalid input 'img' in NormalizeImage"
data['image'] = (
- img.astype('float32') * self.scale - self.mean) / self.std
+ img.astype('float32') * self.scale - self.mean) / self.std
return data
@@ -163,7 +163,7 @@ class DetResizeForTest(object):
img, (ratio_h, ratio_w)
"""
limit_side_len = self.limit_side_len
- h, w, _ = img.shape
+ h, w, c = img.shape
# limit the max side
if self.limit_type == 'max':
@@ -174,7 +174,7 @@ class DetResizeForTest(object):
ratio = float(limit_side_len) / w
else:
ratio = 1.
- else:
+ elif self.limit_type == 'min':
if min(h, w) < limit_side_len:
if h < w:
ratio = float(limit_side_len) / h
@@ -182,6 +182,10 @@ class DetResizeForTest(object):
ratio = float(limit_side_len) / w
else:
ratio = 1.
+ elif self.limit_type == 'resize_long':
+ ratio = float(limit_side_len) / max(h,w)
+ else:
+ raise Exception('not support limit type, image ')
resize_h = int(h * ratio)
resize_w = int(w * ratio)
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index d353391c9af2b85bd01ba659f541fa1791461f68..85ce580f95b13539c6aeea32b188bfd3b435d140 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -44,16 +44,16 @@ class BaseRecLabelDecode(object):
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
- self.character_str = ""
+ 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:
line = line.decode('utf-8').strip("\n").strip("\r\n")
- self.character_str += line
+ self.character_str.append(line)
if use_space_char:
- self.character_str += " "
+ self.character_str.append(" ")
dict_character = list(self.character_str)
else:
@@ -288,3 +288,156 @@ class SRNLabelDecode(BaseRecLabelDecode):
assert False, "unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
+
+
+class TableLabelDecode(object):
+ """ """
+
+ def __init__(self,
+ max_text_length,
+ max_elem_length,
+ max_cell_num,
+ character_dict_path,
+ **kwargs):
+ self.max_text_length = max_text_length
+ self.max_elem_length = max_elem_length
+ self.max_cell_num = max_cell_num
+ list_character, list_elem = self.load_char_elem_dict(character_dict_path)
+ list_character = self.add_special_char(list_character)
+ list_elem = self.add_special_char(list_elem)
+ self.dict_character = {}
+ self.dict_idx_character = {}
+ for i, char in enumerate(list_character):
+ self.dict_idx_character[i] = char
+ self.dict_character[char] = i
+ self.dict_elem = {}
+ self.dict_idx_elem = {}
+ for i, elem in enumerate(list_elem):
+ self.dict_idx_elem[i] = elem
+ self.dict_elem[elem] = i
+
+ def load_char_elem_dict(self, character_dict_path):
+ list_character = []
+ list_elem = []
+ with open(character_dict_path, "rb") as fin:
+ lines = fin.readlines()
+ substr = lines[0].decode('utf-8').strip("\n").split("\t")
+ character_num = int(substr[0])
+ elem_num = int(substr[1])
+ for cno in range(1, 1 + character_num):
+ character = lines[cno].decode('utf-8').strip("\n")
+ list_character.append(character)
+ for eno in range(1 + character_num, 1 + character_num + elem_num):
+ elem = lines[eno].decode('utf-8').strip("\n")
+ list_elem.append(elem)
+ return list_character, list_elem
+
+ def add_special_char(self, list_character):
+ self.beg_str = "sos"
+ self.end_str = "eos"
+ list_character = [self.beg_str] + list_character + [self.end_str]
+ return list_character
+
+ def get_sp_tokens(self):
+ char_beg_idx = self.get_beg_end_flag_idx('beg', 'char')
+ char_end_idx = self.get_beg_end_flag_idx('end', 'char')
+ elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
+ elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
+ elem_char_idx1 = self.dict_elem['']
+ elem_char_idx2 = self.dict_elem['', ' 0 and tmp_elem_idx == end_idx:
+ break
+ if tmp_elem_idx in ignored_tokens:
+ continue
+
+ char_list.append(current_dict[tmp_elem_idx])
+ elem_pos_list.append(idx)
+ score_list.append(structure_probs[batch_idx, idx])
+ elem_idx_list.append(tmp_elem_idx)
+ result_list.append(char_list)
+ result_pos_list.append(elem_pos_list)
+ result_score_list.append(score_list)
+ result_elem_idx_list.append(elem_idx_list)
+ return result_list, result_pos_list, result_score_list, result_elem_idx_list
+
+ def get_ignored_tokens(self, char_or_elem):
+ beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem)
+ end_idx = self.get_beg_end_flag_idx("end", char_or_elem)
+ return [beg_idx, end_idx]
+
+ def get_beg_end_flag_idx(self, beg_or_end, char_or_elem):
+ if char_or_elem == "char":
+ if beg_or_end == "beg":
+ idx = self.dict_character[self.beg_str]
+ elif beg_or_end == "end":
+ idx = self.dict_character[self.end_str]
+ else:
+ assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \
+ % beg_or_end
+ elif char_or_elem == "elem":
+ if beg_or_end == "beg":
+ idx = self.dict_elem[self.beg_str]
+ elif beg_or_end == "end":
+ idx = self.dict_elem[self.end_str]
+ else:
+ assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
+ % beg_or_end
+ else:
+ assert False, "Unsupport type %s in char_or_elem" \
+ % char_or_elem
+ return idx
diff --git a/ppocr/utils/dict/table_dict.txt b/ppocr/utils/dict/table_dict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ef028c786cbce6d1e25856c62986d757b31f93b
--- /dev/null
+++ b/ppocr/utils/dict/table_dict.txt
@@ -0,0 +1,277 @@
+←
+
+☆
+─
+α
+
+
+⋅
+$
+ω
+ψ
+χ
+(
+υ
+≥
+σ
+,
+ρ
+ε
+0
+■
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+<
+✓
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+║
+
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+Φ
+Χ
+θ
+P
+κ
+λ
+μ
+T
+ξ
+X
+β
+γ
+δ
+\
+ζ
+η
+`
+d
+
+h
+f
+l
+Θ
+p
+√
+t
+
+x
+Β
+Γ
+Δ
+|
+ǂ
+ɛ
+j
+̧
+➢
+
+̌
+′
+«
+△
+▲
+#
+
+'
+Ι
++
+¶
+/
+▼
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+7
+▪
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+?
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+B
+≈
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+̄
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+♯
+R
+V
+
+―
+Z
+♣
+^
+¤
+¥
+§
+
+¢
+£
+≦
+
+≤
+‖
+Λ
+©
+n
+↓
+→
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+r
+°
+±
+v
+
+♂
+k
+♀
+~
+ᅟ
+̇
+@
+”
+♦
+ł
+®
+⊕
+„
+!
+
+%
+⇓
+)
+-
+1
+5
+9
+=
+А
+A
+‰
+⋆
+Σ
+E
+◦
+I
+※
+M
+m
+̨
+⩾
+†
+
+•
+U
+Y
+
+]
+̸
+2
+‐
+–
+‒
+̂
+—
+̀
+́
+’
+‘
+⋮
+⋯
+̊
+“
+̈
+≧
+q
+u
+ı
+y
+
+
+̃
+}
+ν
diff --git a/ppocr/utils/dict/table_structure_dict.txt b/ppocr/utils/dict/table_structure_dict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c4531e5f3b8c498e70d3c2ea0471e5e746a2c30
--- /dev/null
+++ b/ppocr/utils/dict/table_structure_dict.txt
@@ -0,0 +1,2759 @@
+277 28 1267 1186
+
+V
+a
+r
+i
+b
+l
+e
+
+H
+z
+d
+
+t
+o
+9
+5
+%
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+
+p
+
+v
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+-
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++
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+[
+]
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+−
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+μ
+±
+J
+j
+W
+_
+Δ
+B
+“
+:
+Y
+α
+λ
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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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+Γ
+↔
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+
+∣
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+ι
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+
+◦
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+◊
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+ł
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+∈
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+✔
+̇
+æ
+ʹ
+ˆ
+♣
+⇓
+∩
+⊕
+⇒
+⇑
+̨
+Ι
+Λ
+⋯
+А
+⋮
+
+
+|
+ |
+
+
+
+
+
+ colspan="2"
+ colspan="3"
+ rowspan="2"
+ colspan="4"
+ colspan="6"
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+ rowspan="4"
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+ rowspan="10"
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diff --git a/ppocr/utils/network.py b/ppocr/utils/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..453abb693d4c0ed370c1031b677d5bf51661add9
--- /dev/null
+++ b/ppocr/utils/network.py
@@ -0,0 +1,82 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
+import os
+import sys
+import tarfile
+import requests
+from tqdm import tqdm
+
+from ppocr.utils.logging import get_logger
+
+
+def download_with_progressbar(url, save_path):
+ logger = get_logger()
+ response = requests.get(url, stream=True)
+ total_size_in_bytes = int(response.headers.get('content-length', 0))
+ block_size = 1024 # 1 Kibibyte
+ progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+ with open(save_path, 'wb') as file:
+ for data in response.iter_content(block_size):
+ progress_bar.update(len(data))
+ file.write(data)
+ progress_bar.close()
+ if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
+ logger.error("Something went wrong while downloading models")
+ sys.exit(0)
+
+
+def maybe_download(model_storage_directory, url):
+ # using custom model
+ tar_file_name_list = [
+ 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
+ ]
+ if not os.path.exists(
+ os.path.join(model_storage_directory, 'inference.pdiparams')
+ ) or not os.path.exists(
+ os.path.join(model_storage_directory, 'inference.pdmodel')):
+ assert url.endswith('.tar'), 'Only supports tar compressed package'
+ tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
+ print('download {} to {}'.format(url, tmp_path))
+ os.makedirs(model_storage_directory, exist_ok=True)
+ download_with_progressbar(url, tmp_path)
+ with tarfile.open(tmp_path, 'r') as tarObj:
+ for member in tarObj.getmembers():
+ filename = None
+ for tar_file_name in tar_file_name_list:
+ if tar_file_name in member.name:
+ filename = tar_file_name
+ if filename is None:
+ continue
+ file = tarObj.extractfile(member)
+ with open(
+ os.path.join(model_storage_directory, filename),
+ 'wb') as f:
+ f.write(file.read())
+ os.remove(tmp_path)
+
+
+def is_link(s):
+ return s is not None and s.startswith('http')
+
+
+def confirm_model_dir_url(model_dir, default_model_dir, default_url):
+ url = default_url
+ if model_dir is None or is_link(model_dir):
+ if is_link(model_dir):
+ url = model_dir
+ file_name = url.split('/')[-1][:-4]
+ model_dir = default_model_dir
+ model_dir = os.path.join(model_dir, file_name)
+ return model_dir, url
diff --git a/ppstructure/MANIFEST.in b/ppstructure/MANIFEST.in
new file mode 100644
index 0000000000000000000000000000000000000000..2961e722b7cebe8e1912be2dd903fcdecb694019
--- /dev/null
+++ b/ppstructure/MANIFEST.in
@@ -0,0 +1,9 @@
+include LICENSE
+include README.md
+
+recursive-include ppocr/utils *.txt utility.py logging.py network.py
+recursive-include ppocr/data/ *.py
+recursive-include ppocr/postprocess *.py
+recursive-include tools/infer *.py
+recursive-include ppstructure *.py
+
diff --git a/ppstructure/README_ch.md b/ppstructure/README_ch.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..22505ad83c6dc58adf472f3db94cbf608b9bbd01 100644
--- a/ppstructure/README_ch.md
+++ b/ppstructure/README_ch.md
@@ -0,0 +1,30 @@
+# TableStructurer
+
+1. 代码使用
+```python
+import cv2
+from paddlestructure import PaddleStructure,draw_result
+
+table_engine = PaddleStructure(
+ output='./output/table',
+ show_log=True)
+
+img_path = '../doc/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+for line in result:
+ print(line)
+
+from PIL import Image
+
+font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
+image = Image.open(img_path).convert('RGB')
+im_show = draw_result(image, result,font_path=font_path)
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+2. 命令行使用
+```bash
+paddlestructure --image_dir=../doc/table/1.png
+```
diff --git a/ppstructure/__init__.py b/ppstructure/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7055bee443fb86648b80bcb892778a114bc47d71
--- /dev/null
+++ b/ppstructure/__init__.py
@@ -0,0 +1,17 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .paddlestructure import PaddleStructure, draw_result, to_excel
+
+__all__ = ['PaddleStructure', 'draw_result', 'to_excel']
diff --git a/ppstructure/layout/README.md b/ppstructure/layout/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/ppstructure/layout/README_ch.md b/ppstructure/layout/README_ch.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/ppstructure/paddlestructure.py b/ppstructure/paddlestructure.py
new file mode 100644
index 0000000000000000000000000000000000000000..57a53d6496f66771f1f6f7628751b4f0ac0fc3b5
--- /dev/null
+++ b/ppstructure/paddlestructure.py
@@ -0,0 +1,148 @@
+# Copyright (c) 2020 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.
+import logging
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+import cv2
+import numpy as np
+from pathlib import Path
+
+from ppocr.utils.logging import get_logger
+from ppstructure.predict_system import OCRSystem, save_res
+from ppstructure.table.predict_table import to_excel
+from ppstructure.utility import init_args, draw_result
+
+logger = get_logger()
+from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+from ppocr.utils.network import maybe_download, download_with_progressbar, confirm_model_dir_url, is_link
+
+__all__ = ['PaddleStructure', 'draw_result', 'to_excel']
+
+VERSION = '2.1'
+BASE_DIR = os.path.expanduser("~/.paddlestructure/")
+
+model_urls = {
+ 'det': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar',
+ 'rec': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
+ 'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar'
+
+}
+
+
+def parse_args(mMain=True):
+ import argparse
+ parser = init_args()
+ parser.add_help = mMain
+
+ for action in parser._actions:
+ if action.dest in ['rec_char_dict_path', 'structure_char_dict_path']:
+ action.default = None
+ if mMain:
+ return parser.parse_args()
+ else:
+ inference_args_dict = {}
+ for action in parser._actions:
+ inference_args_dict[action.dest] = action.default
+ return argparse.Namespace(**inference_args_dict)
+
+
+class PaddleStructure(OCRSystem):
+ def __init__(self, **kwargs):
+ params = parse_args(mMain=False)
+ params.__dict__.update(**kwargs)
+ if params.show_log:
+ logger.setLevel(logging.DEBUG)
+ params.use_angle_cls = False
+ # init model dir
+ params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'det'),
+ model_urls['det'])
+ params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'rec'),
+ model_urls['rec'])
+ params.structure_model_dir, structure_url = confirm_model_dir_url(params.structure_model_dir,
+ os.path.join(BASE_DIR, VERSION, 'structure'),
+ model_urls['structure'])
+ # download model
+ maybe_download(params.det_model_dir, det_url)
+ maybe_download(params.rec_model_dir, rec_url)
+ maybe_download(params.structure_model_dir, structure_url)
+
+ if params.rec_char_dict_path is None:
+ params.rec_char_type = 'EN'
+ if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')):
+ params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')
+ else:
+ params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt')
+ if params.structure_char_dict_path is None:
+ if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')):
+ params.structure_char_dict_path = str(
+ Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
+ else:
+ params.structure_char_dict_path = str(
+ Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
+
+ print(params)
+ super().__init__(params)
+
+ def __call__(self, img):
+ if isinstance(img, str):
+ # download net image
+ if img.startswith('http'):
+ download_with_progressbar(img, 'tmp.jpg')
+ img = 'tmp.jpg'
+ image_file = img
+ img, flag = check_and_read_gif(image_file)
+ if not flag:
+ with open(image_file, 'rb') as f:
+ np_arr = np.frombuffer(f.read(), dtype=np.uint8)
+ img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+ if img is None:
+ logger.error("error in loading image:{}".format(image_file))
+ return None
+ if isinstance(img, np.ndarray) and len(img.shape) == 2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+ res = super().__call__(img)
+ return res
+
+
+def main():
+ # for cmd
+ args = parse_args(mMain=True)
+ image_dir = args.image_dir
+ save_folder = args.output
+ if image_dir.startswith('http'):
+ download_with_progressbar(image_dir, 'tmp.jpg')
+ image_file_list = ['tmp.jpg']
+ else:
+ image_file_list = get_image_file_list(args.image_dir)
+ if len(image_file_list) == 0:
+ logger.error('no images find in {}'.format(args.image_dir))
+ return
+
+ structure_engine = PaddleStructure(**(args.__dict__))
+ for img_path in image_file_list:
+ img_name = os.path.basename(img_path).split('.')[0]
+ logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
+ result = structure_engine(img_path)
+ for item in result:
+ logger.info(item['res'])
+ save_res(result, save_folder, img_name)
+ logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
\ No newline at end of file
diff --git a/ppstructure/predict_system.py b/ppstructure/predict_system.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..2cdfcce2eb3ad4abe4407f781eb99e3591ecebde 100644
--- a/ppstructure/predict_system.py
+++ b/ppstructure/predict_system.py
@@ -0,0 +1,132 @@
+# Copyright (c) 2020 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.
+
+import os
+import sys
+import subprocess
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+import cv2
+import numpy as np
+import time
+
+import layoutparser as lp
+
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.utils.logging import get_logger
+from tools.infer.predict_system import TextSystem
+from ppstructure.table.predict_table import TableSystem, to_excel
+from ppstructure.utility import parse_args,draw_result
+
+logger = get_logger()
+
+
+class OCRSystem(object):
+ def __init__(self, args):
+ args.det_limit_type = 'resize_long'
+ args.drop_score = 0
+ self.text_system = TextSystem(args)
+ self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
+ self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
+ threshold=0.5, enable_mkldnn=args.enable_mkldnn,
+ enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads)
+ self.use_angle_cls = args.use_angle_cls
+ self.drop_score = args.drop_score
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ layout_res = self.table_layout.detect(img[..., ::-1])
+ res_list = []
+ for region in layout_res:
+ x1, y1, x2, y2 = region.coordinates
+ x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+ roi_img = ori_im[y1:y2, x1:x2, :]
+ if region.type == 'Table':
+ res = self.table_system(roi_img)
+ elif region.type == 'Figure':
+ continue
+ else:
+ filter_boxes, filter_rec_res = self.text_system(roi_img)
+ filter_boxes = [x + [x1, y1] for x in filter_boxes]
+ filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
+
+ res = (filter_boxes, filter_rec_res)
+ res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res})
+ return res_list
+
+def save_res(res, save_folder, img_name):
+ excel_save_folder = os.path.join(save_folder, img_name)
+ os.makedirs(excel_save_folder, exist_ok=True)
+ # save res
+ for region in res:
+ if region['type'] == 'Table':
+ excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
+ to_excel(region['res'], excel_path)
+ elif region['type'] == 'Figure':
+ pass
+ else:
+ with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f:
+ for box, rec_res in zip(region['res'][0], region['res'][1]):
+ f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
+
+
+def main(args):
+ image_file_list = get_image_file_list(args.image_dir)
+ image_file_list = image_file_list
+ image_file_list = image_file_list[args.process_id::args.total_process_num]
+ save_folder = args.output
+ os.makedirs(save_folder, exist_ok=True)
+
+ structure_sys = OCRSystem(args)
+ img_num = len(image_file_list)
+ for i, image_file in enumerate(image_file_list):
+ logger.info("[{}/{}] {}".format(i, img_num, image_file))
+ img, flag = check_and_read_gif(image_file)
+ img_name = os.path.basename(image_file).split('.')[0]
+
+ if not flag:
+ img = cv2.imread(image_file)
+ if img is None:
+ logger.error("error in loading image:{}".format(image_file))
+ continue
+ starttime = time.time()
+ res = structure_sys(img)
+ save_res(res, save_folder, img_name)
+ draw_img = draw_result(img,res, args.vis_font_path)
+ cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
+ logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
+ elapse = time.time() - starttime
+ logger.info("Predict time : {:.3f}s".format(elapse))
+
+
+if __name__ == "__main__":
+ args = parse_args()
+ if args.use_mp:
+ p_list = []
+ total_process_num = args.total_process_num
+ for process_id in range(total_process_num):
+ cmd = [sys.executable, "-u"] + sys.argv + [
+ "--process_id={}".format(process_id),
+ "--use_mp={}".format(False)
+ ]
+ p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+ p_list.append(p)
+ for p in p_list:
+ p.wait()
+ else:
+ main(args)
diff --git a/ppstructure/setup.py b/ppstructure/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e68b2e44140f6ad5a13661349666d17cfe45524
--- /dev/null
+++ b/ppstructure/setup.py
@@ -0,0 +1,72 @@
+# Copyright (c) 2020 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.
+import os
+
+from setuptools import setup
+from io import open
+import shutil
+
+with open('../requirements.txt', encoding="utf-8-sig") as f:
+ requirements = f.readlines()
+ requirements.append('tqdm')
+ requirements.append('layoutparser')
+ requirements.append('iopath')
+
+
+def readme():
+ with open('README_ch.md', encoding="utf-8-sig") as f:
+ README = f.read()
+ return README
+
+
+shutil.copytree('../ppstructure/table', './ppstructure/table')
+shutil.copyfile('../ppstructure/predict_system.py', './ppstructure/predict_system.py')
+shutil.copyfile('../ppstructure/utility.py', './ppstructure/utility.py')
+shutil.copytree('../ppocr', './ppocr')
+shutil.copytree('../tools', './tools')
+shutil.copyfile('../LICENSE', './LICENSE')
+
+setup(
+ name='paddlestructure',
+ packages=['paddlestructure'],
+ package_dir={'paddlestructure': ''},
+ include_package_data=True,
+ entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]},
+ version='1.0',
+ 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',
+ long_description=readme(),
+ long_description_content_type='text/markdown',
+ url='https://github.com/PaddlePaddle/PaddleOCR',
+ download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
+ keywords=[
+ 'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
+ ],
+ classifiers=[
+ 'Intended Audience :: Developers', 'Operating System :: OS Independent',
+ 'Natural Language :: Chinese (Simplified)',
+ 'Programming Language :: Python :: 3',
+ 'Programming Language :: Python :: 3.2',
+ 'Programming Language :: Python :: 3.3',
+ 'Programming Language :: Python :: 3.4',
+ 'Programming Language :: Python :: 3.5',
+ 'Programming Language :: Python :: 3.6',
+ 'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
+ ], )
+
+shutil.rmtree('ppocr')
+shutil.rmtree('tools')
+shutil.rmtree('ppstructure')
+os.remove('LICENSE')
diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..105231068a99eb6c012a125ba3fb65934c5d4ac6 100644
--- a/ppstructure/table/README_ch.md
+++ b/ppstructure/table/README_ch.md
@@ -0,0 +1,15 @@
+# 表格结构和内容预测
+
+先cd到PaddleOCR/ppstructure目录下
+
+预测
+```python
+python3 table/predict_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs/PMC3006023_004_00.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --table_output ../output/table
+```
+运行完成后，每张图片的excel表格会保存到table_output字段指定的目录下
+
+评估
+
+```python
+python3 table/eval_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
+```
diff --git a/ppstructure/table/__init__.py b/ppstructure/table/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d11e265597c7c8e39098a228108da3bb954b892
--- /dev/null
+++ b/ppstructure/table/__init__.py
@@ -0,0 +1,13 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
diff --git a/ppstructure/table/eval_table.py b/ppstructure/table/eval_table.py
new file mode 100755
index 0000000000000000000000000000000000000000..1bcbaa8d0d0b2669828dc6b19c3370a30c522ede
--- /dev/null
+++ b/ppstructure/table/eval_table.py
@@ -0,0 +1,69 @@
+# Copyright (c) 2020 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.
+import os
+import sys
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+import cv2
+import json
+from tqdm import tqdm
+from ppstructure.table.table_metric import TEDS
+from ppstructure.table.predict_table import TableSystem
+from ppstructure.utility import init_args
+
+
+def parse_args():
+ parser = init_args()
+ parser.add_argument("--gt_path", type=str)
+ return parser.parse_args()
+
+def main(gt_path, img_root, args):
+ teds = TEDS(n_jobs=16)
+
+ text_sys = TableSystem(args)
+ jsons_gt = json.load(open(gt_path)) # gt
+ pred_htmls = []
+ gt_htmls = []
+ for img_name in tqdm(jsons_gt):
+ # read image
+ img = cv2.imread(os.path.join(img_root,img_name))
+ pred_html = text_sys(img)
+ pred_htmls.append(pred_html)
+
+ gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name]
+ gt_html, gt = get_gt_html(gt_structures, contents_with_block)
+ gt_htmls.append(gt_html)
+ scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
+ print('teds:', sum(scores) / len(scores))
+
+
+def get_gt_html(gt_structures, contents_with_block):
+ end_html = []
+ td_index = 0
+ for tag in gt_structures:
+ if ' | ' in tag:
+ if contents_with_block[td_index] != []:
+ end_html.extend(contents_with_block[td_index])
+ end_html.append(tag)
+ td_index += 1
+ else:
+ end_html.append(tag)
+ return ''.join(end_html), end_html
+
+
+if __name__ == '__main__':
+ args = parse_args()
+ main(args.gt_path,args.image_dir, args)
diff --git a/ppstructure/table/matcher.py b/ppstructure/table/matcher.py
new file mode 100755
index 0000000000000000000000000000000000000000..c3b56384403f5fd92a8db4b4bb378a6d55e5a76c
--- /dev/null
+++ b/ppstructure/table/matcher.py
@@ -0,0 +1,192 @@
+import json
+def distance(box_1, box_2):
+ x1, y1, x2, y2 = box_1
+ x3, y3, x4, y4 = box_2
+ dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2)
+ dis_2 = abs(x3 - x1) + abs(y3 - y1)
+ dis_3 = abs(x4- x2) + abs(y4 - y2)
+ return dis + min(dis_2, dis_3)
+
+def compute_iou(rec1, rec2):
+ """
+ computing IoU
+ :param rec1: (y0, x0, y1, x1), which reflects
+ (top, left, bottom, right)
+ :param rec2: (y0, x0, y1, x1)
+ :return: scala value of IoU
+ """
+ # computing area of each rectangles
+ S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
+ S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
+
+ # computing the sum_area
+ sum_area = S_rec1 + S_rec2
+
+ # find the each edge of intersect rectangle
+ left_line = max(rec1[1], rec2[1])
+ right_line = min(rec1[3], rec2[3])
+ top_line = max(rec1[0], rec2[0])
+ bottom_line = min(rec1[2], rec2[2])
+
+ # judge if there is an intersect
+ if left_line >= right_line or top_line >= bottom_line:
+ return 0.0
+ else:
+ intersect = (right_line - left_line) * (bottom_line - top_line)
+ return (intersect / (sum_area - intersect))*1.0
+
+
+
+def matcher_merge(ocr_bboxes, pred_bboxes):
+ all_dis = []
+ ious = []
+ matched = {}
+ for i, gt_box in enumerate(ocr_bboxes):
+ distances = []
+ for j, pred_box in enumerate(pred_bboxes):
+ # compute l1 distence and IOU between two boxes
+ distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box)))
+ sorted_distances = distances.copy()
+ # select nearest cell
+ sorted_distances = sorted(sorted_distances, key = lambda item: (item[1], item[0]))
+ if distances.index(sorted_distances[0]) not in matched.keys():
+ matched[distances.index(sorted_distances[0])] = [i]
+ else:
+ matched[distances.index(sorted_distances[0])].append(i)
+ return matched#, sum(ious) / len(ious)
+
+def complex_num(pred_bboxes):
+ complex_nums = []
+ for bbox in pred_bboxes:
+ distances = []
+ temp_ious = []
+ for pred_bbox in pred_bboxes:
+ if bbox != pred_bbox:
+ distances.append(distance(bbox, pred_bbox))
+ temp_ious.append(compute_iou(bbox, pred_bbox))
+ complex_nums.append(temp_ious[distances.index(min(distances))])
+ return sum(complex_nums) / len(complex_nums)
+
+def get_rows(pred_bboxes):
+ pre_bbox = pred_bboxes[0]
+ res = []
+ step = 0
+ for i in range(len(pred_bboxes)):
+ bbox = pred_bboxes[i]
+ if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0:
+ break
+ else:
+ res.append(bbox)
+ step += 1
+ for i in range(step):
+ pred_bboxes.pop(0)
+ return res, pred_bboxes
+def refine_rows(pred_bboxes): # 微调整行的框，使在一条水平线上
+ ys_1 = []
+ ys_2 = []
+ for box in pred_bboxes:
+ ys_1.append(box[1])
+ ys_2.append(box[3])
+ min_y_1 = sum(ys_1) / len(ys_1)
+ min_y_2 = sum(ys_2) / len(ys_2)
+ re_boxes = []
+ for box in pred_bboxes:
+ box[1] = min_y_1
+ box[3] = min_y_2
+ re_boxes.append(box)
+ return re_boxes
+
+def matcher_refine_row(gt_bboxes, pred_bboxes):
+ before_refine_pred_bboxes = pred_bboxes.copy()
+ pred_bboxes = []
+ while(len(before_refine_pred_bboxes) != 0):
+ row_bboxes, before_refine_pred_bboxes = get_rows(before_refine_pred_bboxes)
+ print(row_bboxes)
+ pred_bboxes.extend(refine_rows(row_bboxes))
+ all_dis = []
+ ious = []
+ matched = {}
+ for i, gt_box in enumerate(gt_bboxes):
+ distances = []
+ #temp_ious = []
+ for j, pred_box in enumerate(pred_bboxes):
+ distances.append(distance(gt_box, pred_box))
+ #temp_ious.append(compute_iou(gt_box, pred_box))
+ #all_dis.append(min(distances))
+ #ious.append(temp_ious[distances.index(min(distances))])
+ if distances.index(min(distances)) not in matched.keys():
+ matched[distances.index(min(distances))] = [i]
+ else:
+ matched[distances.index(min(distances))].append(i)
+ return matched#, sum(ious) / len(ious)
+
+
+
+#先挑选出一行，再进行匹配
+def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes):
+ gt_box_index = 0
+ delete_gt_bboxes = gt_bboxes.copy()
+ match_bboxes_ready = []
+ matched = {}
+ while(len(delete_gt_bboxes) != 0):
+ row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes)
+ row_bboxes = sorted(row_bboxes, key = lambda key: key[0])
+ if len(pred_bboxes_rows) > 0:
+ match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+ print(row_bboxes)
+ for i, gt_box in enumerate(row_bboxes):
+ #print(gt_box)
+ pred_distances = []
+ distances = []
+ for pred_bbox in pred_bboxes:
+ pred_distances.append(distance(gt_box, pred_bbox))
+ for j, pred_box in enumerate(match_bboxes_ready):
+ distances.append(distance(gt_box, pred_box))
+ index = pred_distances.index(min(distances))
+ #print('index', index)
+ if index not in matched.keys():
+ matched[index] = [gt_box_index]
+ else:
+ matched[index].append(gt_box_index)
+ gt_box_index += 1
+ return matched
+
+def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes):
+ '''
+ gt_bboxes: 排序后
+ pred_bboxes:
+ '''
+ pre_bbox = gt_bboxes[0]
+ matched = {}
+ match_bboxes_ready = []
+ match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+ for i, gt_box in enumerate(gt_bboxes):
+
+ pred_distances = []
+ for pred_bbox in pred_bboxes:
+ pred_distances.append(distance(gt_box, pred_bbox))
+ distances = []
+ gap_pre = gt_box[1] - pre_bbox[1]
+ gap_pre_1 = gt_box[0] - pre_bbox[2]
+ #print(gap_pre, len(pred_bboxes_rows))
+ if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0):
+ match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+ if len(pred_bboxes_rows) == 1:
+ match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+ if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0:
+ match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
+ if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0:
+ break
+ #print(match_bboxes_ready)
+ for j, pred_box in enumerate(match_bboxes_ready):
+ distances.append(distance(gt_box, pred_box))
+ index = pred_distances.index(min(distances))
+ #print(gt_box, index)
+ #match_bboxes_ready.pop(distances.index(min(distances)))
+ print(gt_box, match_bboxes_ready[distances.index(min(distances))])
+ if index not in matched.keys():
+ matched[index] = [i]
+ else:
+ matched[index].append(i)
+ pre_bbox = gt_box
+ return matched
diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py
new file mode 100755
index 0000000000000000000000000000000000000000..6e680b3574ba28b439acad34424b51dfdc02078c
--- /dev/null
+++ b/ppstructure/table/predict_structure.py
@@ -0,0 +1,141 @@
+# Copyright (c) 2020 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.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import math
+import time
+import traceback
+import paddle
+
+import tools.infer.utility as utility
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+
+logger = get_logger()
+
+
+class TableStructurer(object):
+ def __init__(self, args):
+ pre_process_list = [{
+ 'ResizeTableImage': {
+ 'max_len': args.structure_max_len
+ }
+ }, {
+ 'NormalizeImage': {
+ 'std': [0.229, 0.224, 0.225],
+ 'mean': [0.485, 0.456, 0.406],
+ 'scale': '1./255.',
+ 'order': 'hwc'
+ }
+ }, {
+ 'PaddingTableImage': None
+ }, {
+ 'ToCHWImage': None
+ }, {
+ 'KeepKeys': {
+ 'keep_keys': ['image']
+ }
+ }]
+ postprocess_params = {
+ 'name': 'TableLabelDecode',
+ "character_type": args.structure_char_type,
+ "character_dict_path": args.structure_char_dict_path,
+ "max_text_length": args.structure_max_text_length,
+ "max_elem_length": args.structure_max_elem_length,
+ "max_cell_num": args.structure_max_cell_num
+ }
+
+ self.preprocess_op = create_operators(pre_process_list)
+ self.postprocess_op = build_post_process(postprocess_params)
+ self.predictor, self.input_tensor, self.output_tensors = \
+ utility.create_predictor(args, 'structure', logger)
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ data = {'image': img}
+ data = transform(data, self.preprocess_op)
+ img = data[0]
+ if img is None:
+ return None, 0
+ img = np.expand_dims(img, axis=0)
+ img = img.copy()
+ starttime = time.time()
+
+ self.input_tensor.copy_from_cpu(img)
+ self.predictor.run()
+ outputs = []
+ for output_tensor in self.output_tensors:
+ output = output_tensor.copy_to_cpu()
+ outputs.append(output)
+
+ preds = {}
+ preds['structure_probs'] = outputs[1]
+ preds['loc_preds'] = outputs[0]
+
+ post_result = self.postprocess_op(preds)
+
+ structure_str_list = post_result['structure_str_list']
+ res_loc = post_result['res_loc']
+ imgh, imgw = ori_im.shape[0:2]
+ res_loc_final = []
+ for rno in range(len(res_loc[0])):
+ x0, y0, x1, y1 = res_loc[0][rno]
+ left = max(int(imgw * x0), 0)
+ top = max(int(imgh * y0), 0)
+ right = min(int(imgw * x1), imgw - 1)
+ bottom = min(int(imgh * y1), imgh - 1)
+ res_loc_final.append([left, top, right, bottom])
+
+ structure_str_list = structure_str_list[0][:-1]
+ structure_str_list = ['', '', ''] + structure_str_list + [' ', '', '']
+
+ elapse = time.time() - starttime
+ return (structure_str_list, res_loc_final), elapse
+
+
+def main(args):
+ image_file_list = get_image_file_list(args.image_dir)
+ table_structurer = TableStructurer(args)
+ count = 0
+ total_time = 0
+ for image_file in image_file_list:
+ img, flag = check_and_read_gif(image_file)
+ if not flag:
+ img = cv2.imread(image_file)
+ if img is None:
+ logger.info("error in loading image:{}".format(image_file))
+ continue
+ structure_res, elapse = table_structurer(img)
+
+ logger.info("result: {}".format(structure_res))
+
+ if count > 0:
+ total_time += elapse
+ count += 1
+ logger.info("Predict time of {}: {}".format(image_file, elapse))
+
+
+if __name__ == "__main__":
+ main(utility.parse_args())
diff --git a/ppstructure/table/predict_table.py b/ppstructure/table/predict_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4edd22c3de4df5f0ba3e0a1e28a8c346a48d4ee
--- /dev/null
+++ b/ppstructure/table/predict_table.py
@@ -0,0 +1,221 @@
+# Copyright (c) 2020 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.
+
+import os
+import sys
+import subprocess
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+import cv2
+import copy
+import numpy as np
+import time
+import tools.infer.predict_rec as predict_rec
+import tools.infer.predict_det as predict_det
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.utils.logging import get_logger
+from ppstructure.table.matcher import distance, compute_iou
+from ppstructure.utility import parse_args
+import ppstructure.table.predict_structure as predict_strture
+
+logger = get_logger()
+
+
+def expand(pix, det_box, shape):
+ x0, y0, x1, y1 = det_box
+ # print(shape)
+ h, w, c = shape
+ tmp_x0 = x0 - pix
+ tmp_x1 = x1 + pix
+ tmp_y0 = y0 - pix
+ tmp_y1 = y1 + pix
+ x0_ = tmp_x0 if tmp_x0 >= 0 else 0
+ x1_ = tmp_x1 if tmp_x1 <= w else w
+ y0_ = tmp_y0 if tmp_y0 >= 0 else 0
+ y1_ = tmp_y1 if tmp_y1 <= h else h
+ return x0_, y0_, x1_, y1_
+
+
+class TableSystem(object):
+ def __init__(self, args, text_detector=None, text_recognizer=None):
+ self.text_detector = predict_det.TextDetector(args) if text_detector is None else text_detector
+ self.text_recognizer = predict_rec.TextRecognizer(args) if text_recognizer is None else text_recognizer
+ self.table_structurer = predict_strture.TableStructurer(args)
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ structure_res, elapse = self.table_structurer(copy.deepcopy(img))
+ dt_boxes, elapse = self.text_detector(copy.deepcopy(img))
+ dt_boxes = sorted_boxes(dt_boxes)
+
+ r_boxes = []
+ for box in dt_boxes:
+ x_min = box[:, 0].min() - 1
+ x_max = box[:, 0].max() + 1
+ y_min = box[:, 1].min() - 1
+ y_max = box[:, 1].max() + 1
+ box = [x_min, y_min, x_max, y_max]
+ r_boxes.append(box)
+ dt_boxes = np.array(r_boxes)
+
+ logger.debug("dt_boxes num : {}, elapse : {}".format(
+ len(dt_boxes), elapse))
+ if dt_boxes is None:
+ return None, None
+ img_crop_list = []
+
+ for i in range(len(dt_boxes)):
+ det_box = dt_boxes[i]
+ x0, y0, x1, y1 = expand(2, det_box, ori_im.shape)
+ text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :]
+ img_crop_list.append(text_rect)
+ rec_res, elapse = self.text_recognizer(img_crop_list)
+ logger.debug("rec_res num : {}, elapse : {}".format(
+ len(rec_res), elapse))
+
+ pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res)
+ return pred_html
+
+ def rebuild_table(self, structure_res, dt_boxes, rec_res):
+ pred_structures, pred_bboxes = structure_res
+ matched_index = self.match_result(dt_boxes, pred_bboxes)
+ pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res)
+ return pred_html, pred
+
+ def match_result(self, dt_boxes, pred_bboxes):
+ matched = {}
+ for i, gt_box in enumerate(dt_boxes):
+ # gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])]
+ distances = []
+ for j, pred_box in enumerate(pred_bboxes):
+ distances.append(
+ (distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) # 获取两两cell之间的L1距离和 1- IOU
+ sorted_distances = distances.copy()
+ # 根据距离和IOU挑选最"近"的cell
+ sorted_distances = sorted(sorted_distances, key=lambda item: (item[1], item[0]))
+ if distances.index(sorted_distances[0]) not in matched.keys():
+ matched[distances.index(sorted_distances[0])] = [i]
+ else:
+ matched[distances.index(sorted_distances[0])].append(i)
+ return matched
+
+ def get_pred_html(self, pred_structures, matched_index, ocr_contents):
+ end_html = []
+ td_index = 0
+ for tag in pred_structures:
+ if ' | ' in tag:
+ if td_index in matched_index.keys():
+ b_with = False
+ if '' in ocr_contents[matched_index[td_index][0]] and len(matched_index[td_index]) > 1:
+ b_with = True
+ end_html.extend('')
+ for i, td_index_index in enumerate(matched_index[td_index]):
+ content = ocr_contents[td_index_index][0]
+ if len(matched_index[td_index]) > 1:
+ if len(content) == 0:
+ continue
+ if content[0] == ' ':
+ content = content[1:]
+ if '' in content:
+ content = content[3:]
+ if '' in content:
+ content = content[:-4]
+ if len(content) == 0:
+ continue
+ if i != len(matched_index[td_index]) - 1 and ' ' != content[-1]:
+ content += ' '
+ end_html.extend(content)
+ if b_with:
+ end_html.extend('')
+
+ end_html.append(tag)
+ td_index += 1
+ else:
+ end_html.append(tag)
+ return ''.join(end_html), end_html
+
+
+def sorted_boxes(dt_boxes):
+ """
+ Sort text boxes in order from top to bottom, left to right
+ args:
+ dt_boxes(array):detected text boxes with shape [4, 2]
+ return:
+ sorted boxes(array) with shape [4, 2]
+ """
+ num_boxes = dt_boxes.shape[0]
+ sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
+ _boxes = list(sorted_boxes)
+
+ for i in range(num_boxes - 1):
+ if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
+ (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+ tmp = _boxes[i]
+ _boxes[i] = _boxes[i + 1]
+ _boxes[i + 1] = tmp
+ return _boxes
+
+
+def to_excel(html_table, excel_path):
+ from tablepyxl import tablepyxl
+ tablepyxl.document_to_xl(html_table, excel_path)
+
+
+def main(args):
+ image_file_list = get_image_file_list(args.image_dir)
+ image_file_list = image_file_list[args.process_id::args.total_process_num]
+ os.makedirs(args.output, exist_ok=True)
+
+ text_sys = TableSystem(args)
+ img_num = len(image_file_list)
+ for i, image_file in enumerate(image_file_list):
+ logger.info("[{}/{}] {}".format(i, img_num, image_file))
+ img, flag = check_and_read_gif(image_file)
+ excel_path = os.path.join(args.table_output, os.path.basename(image_file).split('.')[0] + '.xlsx')
+ if not flag:
+ img = cv2.imread(image_file)
+ if img is None:
+ logger.error("error in loading image:{}".format(image_file))
+ continue
+ starttime = time.time()
+ pred_html = text_sys(img)
+
+ to_excel(pred_html, excel_path)
+ logger.info('excel saved to {}'.format(excel_path))
+ logger.info(pred_html)
+ elapse = time.time() - starttime
+ logger.info("Predict time : {:.3f}s".format(elapse))
+
+
+if __name__ == "__main__":
+ args = parse_args()
+ if args.use_mp:
+ p_list = []
+ total_process_num = args.total_process_num
+ for process_id in range(total_process_num):
+ cmd = [sys.executable, "-u"] + sys.argv + [
+ "--process_id={}".format(process_id),
+ "--use_mp={}".format(False)
+ ]
+ p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+ p_list.append(p)
+ for p in p_list:
+ p.wait()
+ else:
+ main(args)
diff --git a/ppstructure/table/table_metric/__init__.py b/ppstructure/table/table_metric/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..de2d307430f68881ece1e41357d3b2f423e07ddd
--- /dev/null
+++ b/ppstructure/table/table_metric/__init__.py
@@ -0,0 +1,16 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
+__all__ = ['TEDS']
+from .table_metric import TEDS
\ No newline at end of file
diff --git a/ppstructure/table/table_metric/parallel.py b/ppstructure/table/table_metric/parallel.py
new file mode 100755
index 0000000000000000000000000000000000000000..f7326a1f506ca5fb7b3e97b0d077dc016e7eb7c7
--- /dev/null
+++ b/ppstructure/table/table_metric/parallel.py
@@ -0,0 +1,51 @@
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+
+def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0):
+ """
+ A parallel version of the map function with a progress bar.
+ Args:
+ array (array-like): An array to iterate over.
+ function (function): A python function to apply to the elements of array
+ n_jobs (int, default=16): The number of cores to use
+ use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of
+ keyword arguments to function
+ front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job.
+ Useful for catching bugs
+ Returns:
+ [function(array[0]), function(array[1]), ...]
+ """
+ # We run the first few iterations serially to catch bugs
+ if front_num > 0:
+ front = [function(**a) if use_kwargs else function(a)
+ for a in array[:front_num]]
+ else:
+ front = []
+ # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging.
+ if n_jobs == 1:
+ return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])]
+ # Assemble the workers
+ with ProcessPoolExecutor(max_workers=n_jobs) as pool:
+ # Pass the elements of array into function
+ if use_kwargs:
+ futures = [pool.submit(function, **a) for a in array[front_num:]]
+ else:
+ futures = [pool.submit(function, a) for a in array[front_num:]]
+ kwargs = {
+ 'total': len(futures),
+ 'unit': 'it',
+ 'unit_scale': True,
+ 'leave': True
+ }
+ # Print out the progress as tasks complete
+ for f in tqdm(as_completed(futures), **kwargs):
+ pass
+ out = []
+ # Get the results from the futures.
+ for i, future in tqdm(enumerate(futures)):
+ try:
+ out.append(future.result())
+ except Exception as e:
+ out.append(e)
+ return front + out
diff --git a/ppstructure/table/table_metric/table_metric.py b/ppstructure/table/table_metric/table_metric.py
new file mode 100755
index 0000000000000000000000000000000000000000..9aca98ad785d4614a803fa5a277a6e4a27b3b078
--- /dev/null
+++ b/ppstructure/table/table_metric/table_metric.py
@@ -0,0 +1,247 @@
+# Copyright 2020 IBM
+# Author: peter.zhong@au1.ibm.com
+#
+# This is free software; you can redistribute it and/or modify
+# it under the terms of the Apache 2.0 License.
+#
+# This software is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# Apache 2.0 License for more details.
+
+import distance
+from apted import APTED, Config
+from apted.helpers import Tree
+from lxml import etree, html
+from collections import deque
+from .parallel import parallel_process
+from tqdm import tqdm
+
+
+class TableTree(Tree):
+ def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
+ self.tag = tag
+ self.colspan = colspan
+ self.rowspan = rowspan
+ self.content = content
+ self.children = list(children)
+
+ def bracket(self):
+ """Show tree using brackets notation"""
+ if self.tag == 'td':
+ result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \
+ (self.tag, self.colspan, self.rowspan, self.content)
+ else:
+ result = '"tag": %s' % self.tag
+ for child in self.children:
+ result += child.bracket()
+ return "{{{}}}".format(result)
+
+
+class CustomConfig(Config):
+ @staticmethod
+ def maximum(*sequences):
+ """Get maximum possible value
+ """
+ return max(map(len, sequences))
+
+ def normalized_distance(self, *sequences):
+ """Get distance from 0 to 1
+ """
+ return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+ def rename(self, node1, node2):
+ """Compares attributes of trees"""
+ #print(node1.tag)
+ if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+ return 1.
+ if node1.tag == 'td':
+ if node1.content or node2.content:
+ #print(node1.content, )
+ return self.normalized_distance(node1.content, node2.content)
+ return 0.
+
+
+
+class CustomConfig_del_short(Config):
+ @staticmethod
+ def maximum(*sequences):
+ """Get maximum possible value
+ """
+ return max(map(len, sequences))
+
+ def normalized_distance(self, *sequences):
+ """Get distance from 0 to 1
+ """
+ return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+ def rename(self, node1, node2):
+ """Compares attributes of trees"""
+ if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+ return 1.
+ if node1.tag == 'td':
+ if node1.content or node2.content:
+ #print('before')
+ #print(node1.content, node2.content)
+ #print('after')
+ node1_content = node1.content
+ node2_content = node2.content
+ if len(node1_content) < 3:
+ node1_content = ['####']
+ if len(node2_content) < 3:
+ node2_content = ['####']
+ return self.normalized_distance(node1_content, node2_content)
+ return 0.
+
+class CustomConfig_del_block(Config):
+ @staticmethod
+ def maximum(*sequences):
+ """Get maximum possible value
+ """
+ return max(map(len, sequences))
+
+ def normalized_distance(self, *sequences):
+ """Get distance from 0 to 1
+ """
+ return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+ def rename(self, node1, node2):
+ """Compares attributes of trees"""
+ if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+ return 1.
+ if node1.tag == 'td':
+ if node1.content or node2.content:
+
+ node1_content = node1.content
+ node2_content = node2.content
+ while ' ' in node1_content:
+ print(node1_content.index(' '))
+ node1_content.pop(node1_content.index(' '))
+ while ' ' in node2_content:
+ print(node2_content.index(' '))
+ node2_content.pop(node2_content.index(' '))
+ return self.normalized_distance(node1_content, node2_content)
+ return 0.
+
+class TEDS(object):
+ ''' Tree Edit Distance basead Similarity
+ '''
+
+ def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None):
+ assert isinstance(n_jobs, int) and (
+ n_jobs >= 1), 'n_jobs must be an integer greather than 1'
+ self.structure_only = structure_only
+ self.n_jobs = n_jobs
+ self.ignore_nodes = ignore_nodes
+ self.__tokens__ = []
+
+ def tokenize(self, node):
+ ''' Tokenizes table cells
+ '''
+ self.__tokens__.append('<%s>' % node.tag)
+ if node.text is not None:
+ self.__tokens__ += list(node.text)
+ for n in node.getchildren():
+ self.tokenize(n)
+ if node.tag != 'unk':
+ self.__tokens__.append('' % node.tag)
+ if node.tag != 'td' and node.tail is not None:
+ self.__tokens__ += list(node.tail)
+
+ def load_html_tree(self, node, parent=None):
+ ''' Converts HTML tree to the format required by apted
+ '''
+ global __tokens__
+ if node.tag == 'td':
+ if self.structure_only:
+ cell = []
+ else:
+ self.__tokens__ = []
+ self.tokenize(node)
+ cell = self.__tokens__[1:-1].copy()
+ new_node = TableTree(node.tag,
+ int(node.attrib.get('colspan', '1')),
+ int(node.attrib.get('rowspan', '1')),
+ cell, *deque())
+ else:
+ new_node = TableTree(node.tag, None, None, None, *deque())
+ if parent is not None:
+ parent.children.append(new_node)
+ if node.tag != 'td':
+ for n in node.getchildren():
+ self.load_html_tree(n, new_node)
+ if parent is None:
+ return new_node
+
+ def evaluate(self, pred, true):
+ ''' Computes TEDS score between the prediction and the ground truth of a
+ given sample
+ '''
+ if (not pred) or (not true):
+ return 0.0
+ parser = html.HTMLParser(remove_comments=True, encoding='utf-8')
+ pred = html.fromstring(pred, parser=parser)
+ true = html.fromstring(true, parser=parser)
+ if pred.xpath('body/table') and true.xpath('body/table'):
+ pred = pred.xpath('body/table')[0]
+ true = true.xpath('body/table')[0]
+ if self.ignore_nodes:
+ etree.strip_tags(pred, *self.ignore_nodes)
+ etree.strip_tags(true, *self.ignore_nodes)
+ n_nodes_pred = len(pred.xpath(".//*"))
+ n_nodes_true = len(true.xpath(".//*"))
+ n_nodes = max(n_nodes_pred, n_nodes_true)
+ tree_pred = self.load_html_tree(pred)
+ tree_true = self.load_html_tree(true)
+ distance = APTED(tree_pred, tree_true,
+ CustomConfig()).compute_edit_distance()
+ return 1.0 - (float(distance) / n_nodes)
+ else:
+ return 0.0
+
+ def batch_evaluate(self, pred_json, true_json):
+ ''' Computes TEDS score between the prediction and the ground truth of
+ a batch of samples
+ @params pred_json: {'FILENAME': 'HTML CODE', ...}
+ @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...}
+ @output: {'FILENAME': 'TEDS SCORE', ...}
+ '''
+ samples = true_json.keys()
+ if self.n_jobs == 1:
+ scores = [self.evaluate(pred_json.get(
+ filename, ''), true_json[filename]['html']) for filename in tqdm(samples)]
+ else:
+ inputs = [{'pred': pred_json.get(
+ filename, ''), 'true': true_json[filename]['html']} for filename in samples]
+ scores = parallel_process(
+ inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
+ scores = dict(zip(samples, scores))
+ return scores
+
+ def batch_evaluate_html(self, pred_htmls, true_htmls):
+ ''' Computes TEDS score between the prediction and the ground truth of
+ a batch of samples
+ '''
+ if self.n_jobs == 1:
+ scores = [self.evaluate(pred_html, true_html) for (
+ pred_html, true_html) in zip(pred_htmls, true_htmls)]
+ else:
+ inputs = [{"pred": pred_html, "true": true_html} for(
+ pred_html, true_html) in zip(pred_htmls, true_htmls)]
+
+ scores = parallel_process(
+ inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
+ return scores
+
+
+if __name__ == '__main__':
+ import json
+ import pprint
+ with open('sample_pred.json') as fp:
+ pred_json = json.load(fp)
+ with open('sample_gt.json') as fp:
+ true_json = json.load(fp)
+ teds = TEDS(n_jobs=4)
+ scores = teds.batch_evaluate(pred_json, true_json)
+ pp = pprint.PrettyPrinter()
+ pp.pprint(scores)
diff --git a/ppstructure/table/tablepyxl/__init__.py b/ppstructure/table/tablepyxl/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc0085071cf4497b01fc648e7c38f2e8d9d173d0
--- /dev/null
+++ b/ppstructure/table/tablepyxl/__init__.py
@@ -0,0 +1,13 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
\ No newline at end of file
diff --git a/ppstructure/table/tablepyxl/style.py b/ppstructure/table/tablepyxl/style.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebd794b1b47d7f9e4f9294dde7330f592d613656
--- /dev/null
+++ b/ppstructure/table/tablepyxl/style.py
@@ -0,0 +1,283 @@
+# This is where we handle translating css styles into openpyxl styles
+# and cascading those from parent to child in the dom.
+
+from openpyxl.cell import cell
+from openpyxl.styles import Font, Alignment, PatternFill, NamedStyle, Border, Side, Color
+from openpyxl.styles.fills import FILL_SOLID
+from openpyxl.styles.numbers import FORMAT_CURRENCY_USD_SIMPLE, FORMAT_PERCENTAGE
+from openpyxl.styles.colors import BLACK
+
+FORMAT_DATE_MMDDYYYY = 'mm/dd/yyyy'
+
+
+def colormap(color):
+ """
+ Convenience for looking up known colors
+ """
+ cmap = {'black': BLACK}
+ return cmap.get(color, color)
+
+
+def style_string_to_dict(style):
+ """
+ Convert css style string to a python dictionary
+ """
+ def clean_split(string, delim):
+ return (s.strip() for s in string.split(delim))
+ styles = [clean_split(s, ":") for s in style.split(";") if ":" in s]
+ return dict(styles)
+
+
+def get_side(style, name):
+ return {'border_style': style.get('border-{}-style'.format(name)),
+ 'color': colormap(style.get('border-{}-color'.format(name)))}
+
+known_styles = {}
+
+
+def style_dict_to_named_style(style_dict, number_format=None):
+ """
+ Change css style (stored in a python dictionary) to openpyxl NamedStyle
+ """
+
+ style_and_format_string = str({
+ 'style_dict': style_dict,
+ 'parent': style_dict.parent,
+ 'number_format': number_format,
+ })
+
+ if style_and_format_string not in known_styles:
+ # Font
+ font = Font(bold=style_dict.get('font-weight') == 'bold',
+ color=style_dict.get_color('color', None),
+ size=style_dict.get('font-size'))
+
+ # Alignment
+ alignment = Alignment(horizontal=style_dict.get('text-align', 'general'),
+ vertical=style_dict.get('vertical-align'),
+ wrap_text=style_dict.get('white-space', 'nowrap') == 'normal')
+
+ # Fill
+ bg_color = style_dict.get_color('background-color')
+ fg_color = style_dict.get_color('foreground-color', Color())
+ fill_type = style_dict.get('fill-type')
+ if bg_color and bg_color != 'transparent':
+ fill = PatternFill(fill_type=fill_type or FILL_SOLID,
+ start_color=bg_color,
+ end_color=fg_color)
+ else:
+ fill = PatternFill()
+
+ # Border
+ border = Border(left=Side(**get_side(style_dict, 'left')),
+ right=Side(**get_side(style_dict, 'right')),
+ top=Side(**get_side(style_dict, 'top')),
+ bottom=Side(**get_side(style_dict, 'bottom')),
+ diagonal=Side(**get_side(style_dict, 'diagonal')),
+ diagonal_direction=None,
+ outline=Side(**get_side(style_dict, 'outline')),
+ vertical=None,
+ horizontal=None)
+
+ name = 'Style {}'.format(len(known_styles) + 1)
+
+ pyxl_style = NamedStyle(name=name, font=font, fill=fill, alignment=alignment, border=border,
+ number_format=number_format)
+
+ known_styles[style_and_format_string] = pyxl_style
+
+ return known_styles[style_and_format_string]
+
+
+class StyleDict(dict):
+ """
+ It's like a dictionary, but it looks for items in the parent dictionary
+ """
+ def __init__(self, *args, **kwargs):
+ self.parent = kwargs.pop('parent', None)
+ super(StyleDict, self).__init__(*args, **kwargs)
+
+ def __getitem__(self, item):
+ if item in self:
+ return super(StyleDict, self).__getitem__(item)
+ elif self.parent:
+ return self.parent[item]
+ else:
+ raise KeyError('{} not found'.format(item))
+
+ def __hash__(self):
+ return hash(tuple([(k, self.get(k)) for k in self._keys()]))
+
+ # Yielding the keys avoids creating unnecessary data structures
+ # and happily works with both python2 and python3 where the
+ # .keys() method is a dictionary_view in python3 and a list in python2.
+ def _keys(self):
+ yielded = set()
+ for k in self.keys():
+ yielded.add(k)
+ yield k
+ if self.parent:
+ for k in self.parent._keys():
+ if k not in yielded:
+ yielded.add(k)
+ yield k
+
+ def get(self, k, d=None):
+ try:
+ return self[k]
+ except KeyError:
+ return d
+
+ def get_color(self, k, d=None):
+ """
+ Strip leading # off colors if necessary
+ """
+ color = self.get(k, d)
+ if hasattr(color, 'startswith') and color.startswith('#'):
+ color = color[1:]
+ if len(color) == 3: # Premailers reduces colors like #00ff00 to #0f0, openpyxl doesn't like that
+ color = ''.join(2 * c for c in color)
+ return color
+
+
+class Element(object):
+ """
+ Our base class for representing an html element along with a cascading style.
+ The element is created along with a parent so that the StyleDict that we store
+ can point to the parent's StyleDict.
+ """
+ def __init__(self, element, parent=None):
+ self.element = element
+ self.number_format = None
+ parent_style = parent.style_dict if parent else None
+ self.style_dict = StyleDict(style_string_to_dict(element.get('style', '')), parent=parent_style)
+ self._style_cache = None
+
+ def style(self):
+ """
+ Turn the css styles for this element into an openpyxl NamedStyle.
+ """
+ if not self._style_cache:
+ self._style_cache = style_dict_to_named_style(self.style_dict, number_format=self.number_format)
+ return self._style_cache
+
+ def get_dimension(self, dimension_key):
+ """
+ Extracts the dimension from the style dict of the Element and returns it as a float.
+ """
+ dimension = self.style_dict.get(dimension_key)
+ if dimension:
+ if dimension[-2:] in ['px', 'em', 'pt', 'in', 'cm']:
+ dimension = dimension[:-2]
+ dimension = float(dimension)
+ return dimension
+
+
+class Table(Element):
+ """
+ The concrete implementations of Elements are semantically named for the types of elements we are interested in.
+ This defines a very concrete tree structure for html tables that we expect to deal with. I prefer this compared to
+ allowing Element to have an arbitrary number of children and dealing with an abstract element tree.
+ """
+ def __init__(self, table):
+ """
+ takes an html table object (from lxml)
+ """
+ super(Table, self).__init__(table)
+ table_head = table.find('thead')
+ self.head = TableHead(table_head, parent=self) if table_head is not None else None
+ table_body = table.find('tbody')
+ self.body = TableBody(table_body if table_body is not None else table, parent=self)
+
+
+class TableHead(Element):
+ """
+ This class maps to the `` element of the html table.
+ """
+ def __init__(self, head, parent=None):
+ super(TableHead, self).__init__(head, parent=parent)
+ self.rows = [TableRow(tr, parent=self) for tr in head.findall('tr')]
+
+
+class TableBody(Element):
+ """
+ This class maps to the ` | ` element of the html table.
+ """
+ def __init__(self, body, parent=None):
+ super(TableBody, self).__init__(body, parent=parent)
+ self.rows = [TableRow(tr, parent=self) for tr in body.findall('tr')]
+
+
+class TableRow(Element):
+ """
+ This class maps to the `` element of the html table.
+ """
+ def __init__(self, tr, parent=None):
+ super(TableRow, self).__init__(tr, parent=parent)
+ self.cells = [TableCell(cell, parent=self) for cell in tr.findall('th') + tr.findall('td')]
+
+
+def element_to_string(el):
+ return _element_to_string(el).strip()
+
+
+def _element_to_string(el):
+ string = ''
+
+ for x in el.iterchildren():
+ string += '\n' + _element_to_string(x)
+
+ text = el.text.strip() if el.text else ''
+ tail = el.tail.strip() if el.tail else ''
+
+ return text + string + '\n' + tail
+
+
+class TableCell(Element):
+ """
+ This class maps to the `| ` element of the html table.
+ """
+ CELL_TYPES = {'TYPE_STRING', 'TYPE_FORMULA', 'TYPE_NUMERIC', 'TYPE_BOOL', 'TYPE_CURRENCY', 'TYPE_PERCENTAGE',
+ 'TYPE_NULL', 'TYPE_INLINE', 'TYPE_ERROR', 'TYPE_FORMULA_CACHE_STRING', 'TYPE_INTEGER'}
+
+ def __init__(self, cell, parent=None):
+ super(TableCell, self).__init__(cell, parent=parent)
+ self.value = element_to_string(cell)
+ self.number_format = self.get_number_format()
+
+ def data_type(self):
+ cell_types = self.CELL_TYPES & set(self.element.get('class', '').split())
+ if cell_types:
+ if 'TYPE_FORMULA' in cell_types:
+ # Make sure TYPE_FORMULA takes precedence over the other classes in the set.
+ cell_type = 'TYPE_FORMULA'
+ elif cell_types & {'TYPE_CURRENCY', 'TYPE_INTEGER', 'TYPE_PERCENTAGE'}:
+ cell_type = 'TYPE_NUMERIC'
+ else:
+ cell_type = cell_types.pop()
+ else:
+ cell_type = 'TYPE_STRING'
+ return getattr(cell, cell_type)
+
+ def get_number_format(self):
+ if 'TYPE_CURRENCY' in self.element.get('class', '').split():
+ return FORMAT_CURRENCY_USD_SIMPLE
+ if 'TYPE_INTEGER' in self.element.get('class', '').split():
+ return '#,##0'
+ if 'TYPE_PERCENTAGE' in self.element.get('class', '').split():
+ return FORMAT_PERCENTAGE
+ if 'TYPE_DATE' in self.element.get('class', '').split():
+ return FORMAT_DATE_MMDDYYYY
+ if self.data_type() == cell.TYPE_NUMERIC:
+ try:
+ int(self.value)
+ except ValueError:
+ return '#,##0.##'
+ else:
+ return '#,##0'
+
+ def format(self, cell):
+ cell.style = self.style()
+ data_type = self.data_type()
+ if data_type:
+ cell.data_type = data_type
\ No newline at end of file
diff --git a/ppstructure/table/tablepyxl/tablepyxl.py b/ppstructure/table/tablepyxl/tablepyxl.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba3cc0fc499fccd93ffe3993a99296bc6603ed8a
--- /dev/null
+++ b/ppstructure/table/tablepyxl/tablepyxl.py
@@ -0,0 +1,118 @@
+# Do imports like python3 so our package works for 2 and 3
+from __future__ import absolute_import
+
+from lxml import html
+from openpyxl import Workbook
+from openpyxl.utils import get_column_letter
+from premailer import Premailer
+from tablepyxl.style import Table
+
+
+def string_to_int(s):
+ if s.isdigit():
+ return int(s)
+ return 0
+
+
+def get_Tables(doc):
+ tree = html.fromstring(doc)
+ comments = tree.xpath('//comment()')
+ for comment in comments:
+ comment.drop_tag()
+ return [Table(table) for table in tree.xpath('//table')]
+
+
+def write_rows(worksheet, elem, row, column=1):
+ """
+ Writes every tr child element of elem to a row in the worksheet
+ returns the next row after all rows are written
+ """
+ from openpyxl.cell.cell import MergedCell
+
+ initial_column = column
+ for table_row in elem.rows:
+ for table_cell in table_row.cells:
+ cell = worksheet.cell(row=row, column=column)
+ while isinstance(cell, MergedCell):
+ column += 1
+ cell = worksheet.cell(row=row, column=column)
+
+ colspan = string_to_int(table_cell.element.get("colspan", "1"))
+ rowspan = string_to_int(table_cell.element.get("rowspan", "1"))
+ if rowspan > 1 or colspan > 1:
+ worksheet.merge_cells(start_row=row, start_column=column,
+ end_row=row + rowspan - 1, end_column=column + colspan - 1)
+
+ cell.value = table_cell.value
+ table_cell.format(cell)
+ min_width = table_cell.get_dimension('min-width')
+ max_width = table_cell.get_dimension('max-width')
+
+ if colspan == 1:
+ # Initially, when iterating for the first time through the loop, the width of all the cells is None.
+ # As we start filling in contents, the initial width of the cell (which can be retrieved by:
+ # worksheet.column_dimensions[get_column_letter(column)].width) is equal to the width of the previous
+ # cell in the same column (i.e. width of A2 = width of A1)
+ width = max(worksheet.column_dimensions[get_column_letter(column)].width or 0, len(table_cell.value) + 2)
+ if max_width and width > max_width:
+ width = max_width
+ elif min_width and width < min_width:
+ width = min_width
+ worksheet.column_dimensions[get_column_letter(column)].width = width
+ column += colspan
+ row += 1
+ column = initial_column
+ return row
+
+
+def table_to_sheet(table, wb):
+ """
+ Takes a table and workbook and writes the table to a new sheet.
+ The sheet title will be the same as the table attribute name.
+ """
+ ws = wb.create_sheet(title=table.element.get('name'))
+ insert_table(table, ws, 1, 1)
+
+
+def document_to_workbook(doc, wb=None, base_url=None):
+ """
+ Takes a string representation of an html document and writes one sheet for
+ every table in the document.
+ The workbook is returned
+ """
+ if not wb:
+ wb = Workbook()
+ wb.remove(wb.active)
+
+ inline_styles_doc = Premailer(doc, base_url=base_url, remove_classes=False).transform()
+ tables = get_Tables(inline_styles_doc)
+
+ for table in tables:
+ table_to_sheet(table, wb)
+
+ return wb
+
+
+def document_to_xl(doc, filename, base_url=None):
+ """
+ Takes a string representation of an html document and writes one sheet for
+ every table in the document. The workbook is written out to a file called filename
+ """
+ wb = document_to_workbook(doc, base_url=base_url)
+ wb.save(filename)
+
+
+def insert_table(table, worksheet, column, row):
+ if table.head:
+ row = write_rows(worksheet, table.head, row, column)
+ if table.body:
+ row = write_rows(worksheet, table.body, row, column)
+
+
+def insert_table_at_cell(table, cell):
+ """
+ Inserts a table at the location of an openpyxl Cell object.
+ """
+ ws = cell.parent
+ column, row = cell.column, cell.row
+ insert_table(table, ws, column, row)
\ No newline at end of file
diff --git a/ppstructure/utility.py b/ppstructure/utility.py
new file mode 100644
index 0000000000000000000000000000000000000000..8112b9efd2155d69784ebc9915d9c3ec30e94f9c
--- /dev/null
+++ b/ppstructure/utility.py
@@ -0,0 +1,59 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 PIL import Image
+import numpy as np
+from tools.infer.utility import draw_ocr_box_txt, init_args as infer_args
+
+
+def init_args():
+ parser = infer_args()
+
+ # params for output
+ parser.add_argument("--output", type=str, default='./output/table')
+ # params for table structure
+ parser.add_argument("--structure_max_len", type=int, default=488)
+ parser.add_argument("--structure_max_text_length", type=int, default=100)
+ parser.add_argument("--structure_max_elem_length", type=int, default=800)
+ parser.add_argument("--structure_max_cell_num", type=int, default=500)
+ parser.add_argument("--structure_model_dir", type=str)
+ parser.add_argument("--structure_char_type", type=str, default='en')
+ parser.add_argument("--structure_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
+
+ # params for layout detector
+ parser.add_argument("--layout_model_dir", type=str)
+ return parser
+
+
+def parse_args():
+ parser = init_args()
+ return parser.parse_args()
+
+
+def draw_result(image, result, font_path):
+ if isinstance(image, np.ndarray):
+ image = Image.fromarray(image)
+ boxes, txts, scores = [], [], []
+ for region in result:
+ if region['type'] == 'Table':
+ pass
+ elif region['type'] == 'Figure':
+ pass
+ else:
+ for box, rec_res in zip(region['res'][0], region['res'][1]):
+ boxes.append(np.array(box).reshape(-1, 2))
+ txts.append(rec_res[0])
+ scores.append(rec_res[1])
+ im_show = draw_ocr_box_txt(image, boxes, txts, scores, font_path=font_path,drop_score=0)
+ return im_show
\ No newline at end of file
diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py
index f5bade36315fbe321927df82cdd7cd8bf40b2ae5..baa89be130084d98628656fe4e309728a0e9f661 100755
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@@ -43,7 +43,7 @@ class TextDetector(object):
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
- 'limit_type': args.det_limit_type
+ 'limit_type': args.det_limit_type,
}
}, {
'NormalizeImage': {
diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py
index 8f1b8f4812a6ae47a2456b7da9d456befa374183..58a363e3e8fa852ce37cd5a44a19e460da00c2bc 100755
--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -88,6 +88,8 @@ class TextSystem(object):
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
+ logger.debug("dt_boxes num : {}, elapse : {}".format(
+ len(dt_boxes), elapse))
if dt_boxes is None:
return None, None
img_crop_list = []
@@ -101,9 +103,13 @@ class TextSystem(object):
if self.use_angle_cls and cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
+ logger.debug("cls num : {}, elapse : {}".format(
+ len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
-
+ logger.debug("rec_res num : {}, elapse : {}".format(
+ len(rec_res), elapse))
+ # self.print_draw_crop_rec_res(img_crop_list, rec_res)
filter_boxes, filter_rec_res = [], []
for box, rec_reuslt in zip(dt_boxes, rec_res):
text, score = rec_reuslt
@@ -149,7 +155,7 @@ def main(args):
if not flag:
img = cv2.imread(image_file)
if img is None:
- logger.info("error in loading image:{}".format(image_file))
+ logger.error("error in loading image:{}".format(image_file))
continue
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 20fcfe4bc7475822a5e382f8908b289b614a9fe1..9210c45783029996f8d6fd105c8413d01c768806 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -109,10 +109,12 @@ def init_args():
parser.add_argument("--use_mp", type=str2bool, default=False)
parser.add_argument("--total_process_num", type=int, default=1)
parser.add_argument("--process_id", type=int, default=0)
-
+
parser.add_argument("--benchmark", type=bool, default=False)
parser.add_argument("--save_log_path", type=str, default="./log_output/")
+ parser.add_argument("--show_log", type=str2bool, default=True)
+
return parser
@@ -198,6 +200,8 @@ def create_predictor(args, mode, logger):
model_dir = args.cls_model_dir
elif mode == 'rec':
model_dir = args.rec_model_dir
+ elif mode == 'structure':
+ model_dir = args.structure_model_dir
else:
model_dir = args.e2e_model_dir
@@ -327,7 +331,9 @@ def create_predictor(args, mode, logger):
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
-
+ config.switch_ir_optim(True)
+ if mode == 'structure':
+ config.switch_ir_optim(False)
# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
|