136 lines
6.7 KiB
Markdown
Executable File
136 lines
6.7 KiB
Markdown
Executable File
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# Python Inference for PP-OCR Model Library
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This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
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- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
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- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
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- [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
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- [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
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- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
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- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
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<a name="DETECTION_MODEL_INFERENCE"></a>
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## Text Detection Model Inference
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The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
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```
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# download DB text detection inference model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
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tar xf ch_ppocr_mobile_v2.0_det_infer.tar
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# predict
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
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```
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The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
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You can use the parameters `limit_type` and `det_limit_side_len` to limit the size of the input image,
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The optional parameters of `limit_type` are [`max`, `min`], and
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`det_limit_size_len` is a positive integer, generally set to a multiple of 32, such as 960.
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The default setting of the parameters is `limit_type='max', det_limit_side_len=960`. Indicates that the longest side of the network input image cannot exceed 960,
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If this value is exceeded, the image will be resized with the same width ratio to ensure that the longest side is `det_limit_side_len`.
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Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest side of the image is limited to 960.
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If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
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```
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
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```
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If you want to use the CPU for prediction, execute the command as follows
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```
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
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```
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<a name="RECOGNITION_MODEL_INFERENCE"></a>
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## Text Recognition Model Inference
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<a name="LIGHTWEIGHT_RECOGNITION"></a>
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### 1. Lightweight Chinese Recognition Model Inference
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For lightweight Chinese recognition model inference, you can execute the following commands:
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```
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# download CRNN text recognition inference model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
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tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
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```
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After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
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```bash
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Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
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```
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<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
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### 2. Multilingaul Model Inference
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If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
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You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
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```
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After executing the command, the prediction result of the above figure is:
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``` text
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Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
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```
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<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
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## Angle Classification Model Inference
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For angle classification model inference, you can execute the following commands:
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```
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# download text angle class inference model：
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
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tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
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python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
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```
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After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
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```
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Predicts of ./doc/imgs_words_en/word_10.png:['0', 0.9999995]
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```
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<a name="CONCATENATION"></a>
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## Text Detection Angle Classification and Recognition Inference Concatenation
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When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
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```shell
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# use direction classifier
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
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# not use use direction classifier
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
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# use multi-process
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
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```
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After executing the command, the recognition result image is as follows:
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