142 lines
6.6 KiB
Markdown
142 lines
6.6 KiB
Markdown
English | [简体中文](README_ch.md)
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# Getting Started
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[1. Install whl package](#Install)
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[2. Quick Start](#QuickStart)
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[3. PostProcess](#PostProcess)
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[4. Results](#Results)
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[5. Training](#Training)
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<a name="Install"></a>
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## 1. Install whl package
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```bash
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wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
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pip install -U layoutparser-0.0.0-py3-none-any.whl
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```
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<a name="QuickStart"></a>
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## 2. Quick Start
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Use LayoutParser to identify the layout of a document:
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```python
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import cv2
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import layoutparser as lp
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image = cv2.imread("doc/table/layout.jpg")
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image = image[..., ::-1]
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# load model
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model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
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threshold=0.5,
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label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
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enforce_cpu=False,
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enable_mkldnn=True)
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# detect
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layout = model.detect(image)
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# show result
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show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
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show_img.show()
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```
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The following figure shows the result, with different colored detection boxes representing different categories and displaying specific categories in the upper left corner of the box with `show_element_type`
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<div align="center">
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<img src="../../doc/table/result_all.jpg" width = "600" />
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</div>
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`PaddleDetectionLayoutModel`parameters are described as follows:
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| parameter | description | default | remark |
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| :------------: | :------------------------------------------------------: | :---------: | :----------------------------------------------------------: |
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| config_path | model config path | None | Specify config_ path will automatically download the model (only for the first time,the model will exist and will not be downloaded again) |
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| model_path | model path | None | local model path, config_ path and model_ path must be set to one, cannot be none at the same time |
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| threshold | threshold of prediction score | 0.5 | \ |
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| input_shape | picture size of reshape | [3,640,640] | \ |
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| batch_size | testing batch size | 1 | \ |
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| label_map | category mapping table | None | Setting config_ path, it can be none, and the label is automatically obtained according to the dataset name_ map |
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| enforce_cpu | whether to use CPU | False | False to use GPU, and True to force the use of CPU |
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| enforce_mkldnn | whether mkldnn acceleration is enabled in CPU prediction | True | \ |
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| thread_num | the number of CPU threads | 10 | \ |
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The following model configurations and label maps are currently supported, which you can use by modifying '--config_path' and '--label_map' to detect different types of content:
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| dataset | config_path | label_map |
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| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
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| [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"} |
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| TableBank latex | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"} |
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| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
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* TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
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* Download TableBank dataset contains both word and latex。
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<a name="PostProcess"></a>
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## 3. PostProcess
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Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:
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```python
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# follow the above code
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# filter areas for a specific text type
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text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
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figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
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# text areas may be detected within the image area, delete these areas
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text_blocks = lp.Layout([b for b in text_blocks \
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if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
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# sort text areas and assign ID
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h, w = image.shape[:2]
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left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
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left_blocks = text_blocks.filter_by(left_interval, center=True)
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left_blocks.sort(key = lambda b:b.coordinates[1])
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right_blocks = [b for b in text_blocks if b not in left_blocks]
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right_blocks.sort(key = lambda b:b.coordinates[1])
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# the two lists are merged and the indexes are added in order
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text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
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# display result
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show_img = lp.draw_box(image, text_blocks,
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box_width=3,
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show_element_id=True)
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show_img.show()
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```
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Displays results with only the "Text" category：
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<div align="center">
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<img src="../../doc/table/result_text.jpg" width = "600" />
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</div>
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<a name="Results"></a>
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## 4. Results
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| Dataset | mAP | CPU time cost | GPU time cost |
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| --------- | ---- | ------------- | ------------- |
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| PubLayNet | 93.6 | 1713.7ms | 66.6ms |
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| TableBank | 96.2 | 1968.4ms | 65.1ms |
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**Envrionment：**
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**CPU：** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
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**GPU：** a single NVIDIA Tesla P40
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<a name="Training"></a>
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## 5. Training
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The above model is based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). If you want to train your own layout parser model，please refer to：[train_layoutparser_model](train_layoutparser_model.md)
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