# Getting Started [1. Install whl package](#Install whl package) [2. Quick Start](#Quick Start) [3. PostProcess](#PostProcess) [4. Results](#Results) [5. Training](#Training) ## 1. Install whl package ```bash wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl pip install -U layoutparser-0.0.0-py3-none-any.whl ``` ## 2. Quick Start Use LayoutParser to identify the layout of a given document: ```python import cv2 import layoutparser as lp image = cv2.imread("doc/table/layout.png") image = image[..., ::-1] # load model model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config", threshold=0.5, label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}, enforce_cpu=False, enable_mkldnn=True) # detect layout = model.detect(image) # show result show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True) show_img.show() ``` 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`
`PaddleDetectionLayoutModel`parameters are described as follows: | parameter | description | default | remark | | :------------: | :------------------------------------------------------: | :---------: | :----------------------------------------------------------: | | 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) | | 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 | | threshold | threshold of prediction score | 0.5 | \ | | input_shape | picture size of reshape | [3,640,640] | \ | | batch_size | testing batch size | 1 | \ | | 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 | | enforce_cpu | whether to use CPU | False | False to use GPU, and True to force the use of CPU | | enforce_mkldnn | whether mkldnn acceleration is enabled in CPU prediction | True | \ | | thread_num | the number of CPU threads | 10 | \ | 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: | dataset | config_path | label_map | | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- | | [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"} | | TableBank latex | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"} | | [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"} | * TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively; * Download TableBank dataset contains both word and latex。 ## 3. PostProcess 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: ```python # follow the above code # filter areas for a specific text type text_blocks = lp.Layout([b for b in layout if b.type=='Text']) figure_blocks = lp.Layout([b for b in layout if b.type=='Figure']) # text areas may be detected within the image area, delete these areas text_blocks = lp.Layout([b for b in text_blocks \ if not any(b.is_in(b_fig) for b_fig in figure_blocks)]) # sort text areas and assign ID h, w = image.shape[:2] left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image) left_blocks = text_blocks.filter_by(left_interval, center=True) left_blocks.sort(key = lambda b:b.coordinates[1]) right_blocks = [b for b in text_blocks if b not in left_blocks] right_blocks.sort(key = lambda b:b.coordinates[1]) # the two lists are merged and the indexes are added in order text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)]) # display result show_img = lp.draw_box(image, text_blocks, box_width=3, show_element_id=True) show_img.show() ``` Displays results with only the "Text" category:
## 4. Results | Dataset | mAP | CPU time cost | GPU time cost | | --------- | ---- | ------------- | ------------- | | PubLayNet | 93.6 | 1713.7ms | 66.6ms | | TableBank | 96.2 | 1968.4ms | 65.1ms | **Envrionment:** ​ **CPU:** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz,24core ​ **GPU:** a single NVIDIA Tesla P40 ## 5. Training 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_en.md)