添加方向分类器inference文档

doc/doc_ch/inference.md

@@ -11,6 +11,7 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
 - [一、训练模型转inference模型](#训练模型转inference模型)
     - [检测模型转inference模型](#检测模型转inference模型)
     - [识别模型转inference模型](#识别模型转inference模型)  
+    - [方向分类模型转inference模型](#方向模型转inference模型)  
 
 - [二、文本检测模型推理](#文本检测模型推理)
     - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
@@ -24,7 +25,10 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
     - [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)
     - [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)
 
-- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
+- [四、方向分类模型推理](#方向识别模型推理)
+    - [1. 方向分类模型推理](#方向分类模型推理)
+
+- [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)
     - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
     - [2. 其他模型推理](#其他模型推理)
 
@@ -84,6 +88,32 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa
   └─  params    识别inference模型的参数文件
 ```
 
+<a name="方向分类模型转inference模型"></a>
+### 方向分类模型转inference模型
+
+下载方向分类模型：
+```
+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/
+```
+
+方向分类模型转inference模型与检测的方式相同，如下：
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# 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。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
+        Global.save_inference_dir=./inference/cls/
+```
+
+转换成功后，在目录下有两个文件：
+```
+/inference/cls/
+  └─  model     识别inference模型的program文件
+  └─  params    识别inference模型的参数文件
+```
+
 <a name="文本检测模型推理"></a>
 ## 二、文本检测模型推理
 
@@ -275,15 +305,36 @@ dict_character = list(self.character_str)
 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"
 ```
 
-<a name="文本检测、识别串联推理"></a>
-## 四、文本检测、识别串联推理
+
+<a name="方向分类模型推理"></a>
+## 四、方向分类模型推理
+
+下面将介绍方向分类模型推理。
+
+<a name="方向分类模型推理"></a>
+### 1. 方向分类模型推理
+
+方向分类模型推理，可以执行如下命令：
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+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：
+
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]
+
+<a name="文本检测、方向分类和文字识别串联推理"></a>
+## 五、文本检测、方向分类和文字识别串联推理
 <a name="超轻量中文OCR模型推理"></a>
 ### 1. 超轻量中文OCR模型推理
 
-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。
+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。
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
+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
 ```
 
 执行命令后，识别结果图像如下：

doc/doc_en/inference_en.md

@@ -12,6 +12,7 @@ Next, we first introduce how to convert a trained model into an inference model,
 - [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
     - [Convert detection model to inference model](#Convert_detection_model)
     - [Convert recognition model to inference model](#Convert_recognition_model)
+    - [Convert angle classification model to inference model](#Convert_angle_class_model)
 
 
 - [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
@@ -26,8 +27,10 @@ Next, we first introduce how to convert a trained model into an inference model,
     - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
     - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
 
+- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
+    - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
 
-- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
+- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
     - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
     - [2. OTHER MODELS](#OTHER_MODELS)
 
@@ -87,6 +90,33 @@ After the conversion is successful, there are two files in the directory:
   └─  params    Identify the parameter files of the inference model
 ```
 
+<a name="Convert_angle_class_model"></a>
+### Convert angle classification model to inference model
+
+Download the angle classification model:
+```
+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/
+```
+
+The angle classification model is converted to the inference model in the same way as the detection, as follows:
+```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+#  Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
+        Global.save_inference_dir=./inference/cls/
+```
+
+After the conversion is successful, there are two files in the directory:
+```
+/inference/cls/
+  └─  model     Identify the saved model files
+  └─  params    Identify the parameter files of the inference model
+```
+
+
 <a name="DETECTION_MODEL_INFERENCE"></a>
 ## TEXT DETECTION MODEL INFERENCE
 
@@ -276,16 +306,39 @@ If the chars dictionary is modified during training, you need to specify the new
 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"
 ```
 
+
+<a name="ANGLE_CLASSIFICATION_MODEL_INFERENCE"></a>
+## ANGLE CLASSIFICATION MODEL INFERENCE
+
+The following will introduce the angle classification model inference.
+
+
+<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
+### 1.ANGLE CLASSIFICATION MODEL INFERENCE
+
+For angle classification model inference, you can execute the following commands:
+
+```
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+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/ch/word_4.jpg:['0', 0.9999963]
+
+
 <a name="CONCATENATION"></a>
-## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
+## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
 
 <a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
 ### 1. LIGHTWEIGHT CHINESE MODEL
 
-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.
+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.
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"  --rec_model_dir="./inference/rec_crnn/"
+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
 ```
 
 After executing the command, the recognition result image is as follows: