添加方向分类器inference文档
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@ -11,24 +11,28 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
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- [一、训练模型转inference模型](#训练模型转inference模型)
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- [检测模型转inference模型](#检测模型转inference模型)
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- [识别模型转inference模型](#识别模型转inference模型)
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- [方向分类模型转inference模型](#方向模型转inference模型)
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- [二、文本检测模型推理](#文本检测模型推理)
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- [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
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- [2. DB文本检测模型推理](#DB文本检测模型推理)
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- [3. EAST文本检测模型推理](#EAST文本检测模型推理)
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- [4. SAST文本检测模型推理](#SAST文本检测模型推理)
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- [三、文本识别模型推理](#文本识别模型推理)
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- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
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- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
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- [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
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- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
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- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
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- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
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- [四、方向分类模型推理](#方向识别模型推理)
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- [1. 方向分类模型推理](#方向分类模型推理)
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- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
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- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
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- [2. 其他模型推理](#其他模型推理)
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<a name="训练模型转inference模型"></a>
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## 一、训练模型转inference模型
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<a name="检测模型转inference模型"></a>
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@ -84,6 +88,32 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
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└─ params 识别inference模型的参数文件
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```
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<a name="方向分类模型转inference模型"></a>
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### 方向分类模型转inference模型
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下载方向分类模型：
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```
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
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```
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方向分类模型转inference模型与检测的方式相同，如下：
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```
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# -c后面设置训练算法的yml配置文件
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# -o配置可选参数
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# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
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Global.save_inference_dir=./inference/cls/
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```
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转换成功后，在目录下有两个文件：
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```
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/inference/cls/
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└─ model 识别inference模型的program文件
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└─ params 识别inference模型的参数文件
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```
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<a name="文本检测模型推理"></a>
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## 二、文本检测模型推理
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@ -275,15 +305,36 @@ dict_character = list(self.character_str)
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
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```
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<a name="文本检测、识别串联推理"></a>
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## 四、文本检测、识别串联推理
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<a name="方向分类模型推理"></a>
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## 四、方向分类模型推理
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下面将介绍方向分类模型推理。
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<a name="方向分类模型推理"></a>
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### 1. 方向分类模型推理
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方向分类模型推理，可以执行如下命令：
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```
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python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
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```
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执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
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Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
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<a name="文本检测、方向分类和文字识别串联推理"></a>
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## 五、文本检测、方向分类和文字识别串联推理
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<a name="超轻量中文OCR模型推理"></a>
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### 1. 超轻量中文OCR模型推理
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在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。
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在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。可视化识别结果默认保存到 ./inference_results 文件夹里面。
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```
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.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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```
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执行命令后，识别结果图像如下：
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@ -12,25 +12,28 @@ Next, we first introduce how to convert a trained model into an inference model,
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- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
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- [Convert detection model to inference model](#Convert_detection_model)
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- [Convert recognition model to inference model](#Convert_recognition_model)
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- [Convert angle classification model to inference model](#Convert_angle_class_model)
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- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
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- [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
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- [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
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- [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
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- [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
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- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
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- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
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- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
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- [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
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- [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
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- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
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- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
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- [1. 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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- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
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- [2. OTHER MODELS](#OTHER_MODELS)
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<a name="CONVERT"></a>
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## CONVERT TRAINING MODEL TO INFERENCE MODEL
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<a name="Convert_detection_model"></a>
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@ -87,6 +90,33 @@ After the conversion is successful, there are two files in the directory:
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└─ params Identify the parameter files of the inference model
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```
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<a name="Convert_angle_class_model"></a>
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### Convert angle classification model to inference model
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Download the angle classification model:
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```
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
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```
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The angle classification model is converted to the inference model in the same way as the detection, as follows:
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```
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# -c Set the training algorithm yml configuration file
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# -o Set optional parameters
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# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
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# Global.save_inference_dir Set the address where the converted model will be saved.
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python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
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Global.save_inference_dir=./inference/cls/
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```
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After the conversion is successful, there are two files in the directory:
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```
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/inference/cls/
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└─ model Identify the saved model files
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└─ params Identify the parameter files of the inference model
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```
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<a name="DETECTION_MODEL_INFERENCE"></a>
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## TEXT DETECTION MODEL INFERENCE
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@ -276,16 +306,39 @@ If the chars dictionary is modified during training, you need to specify the new
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
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```
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<a name="ANGLE_CLASSIFICATION_MODEL_INFERENCE"></a>
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## ANGLE CLASSIFICATION MODEL INFERENCE
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The following will introduce the angle classification model inference.
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<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
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### 1.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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python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
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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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Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
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<a name="CONCATENATION"></a>
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## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
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## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
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<a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
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### 1. LIGHTWEIGHT CHINESE MODEL
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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, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
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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 visualized recognition results are saved to the `./inference_results` folder by default.
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```
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.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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```
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After executing the command, the recognition result image is as follows:
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