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.
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:
# download DB text detection inference model
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
tar xf ch_PP-OCRv3_det_infer.tar
# run inference
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/"
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:
You can use the parameters limit_type and det_limit_side_len to limit the size of the input image,
The optional parameters of limit_type are [max, min], and
det_limit_size_len is a positive integer, generally set to a multiple of 32, such as 960.
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,
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.
Set as limit_type='min', det_limit_side_len=960, it means that the shortest side of the image is limited to 960.
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:
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --det_limit_type=max --det_limit_side_len=1216
If you want to use the CPU for prediction, execute the command as follows
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --use_gpu=False
Note: The input shape used by the recognition model of PP-OCRv3 is 3, 48, 320. If you use other recognition models, you need to set the parameter --rec_image_shape according to the model. In addition, the rec_algorithm used by the recognition model of PP-OCRv3 is SVTR_LCNet by default. Note the difference from the original SVTR.
For lightweight Chinese recognition model inference, you can execute the following commands:
# download CRNN text recognition inference model
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar xf ch_PP-OCRv3_rec_infer.tar
# run inference
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --rec_image_shape=3,48,320
After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671)For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by --rec_char_dict_path:
# download en model：
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
tar xf en_PP-OCRv3_det_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
After executing the command, the prediction result of the above figure is:
Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
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,
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:
wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
After executing the command, the prediction result of the above figure is:
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
For angle classification model inference, you can execute the following commands:
# download text angle class inference model：
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
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"
After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
Predicts of ./doc/imgs_words_en/word_10.png:['0', 0.9999995]
Note: The input shape used by the recognition model of PP-OCRv3 is 3, 48, 320. If you use other recognition models, you need to set the parameter --rec_image_shape according to the model. In addition, the rec_algorithm used by the recognition model of PP-OCRv3 is SVTR_LCNet by default. Note the difference from the original SVTR.
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.
# use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true
# not use use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false
# use multi-process
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6After executing the command, the recognition result image is as follows:
