[Doc] Update PaddleSeg comments

docs/api_docs/python/semantic_segmentation.md

@@ -1,9 +1,26 @@
 # Semantic Segmentation(语义分割)
 
+
+## fastdeploy.vision.segmentation.PaddleSegPreprocessor
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.segmentation.PaddleSegPreprocessor
+    :members:
+    :inherited-members:
+```
+
 ## fastdeploy.vision.segmentation.PaddleSegModel
 
 ```{eval-rst}
 .. autoclass:: fastdeploy.vision.segmentation.PaddleSegModel
     :members:
     :inherited-members:
 ```
+
+## fastdeploy.vision.segmentation.PaddleSegPostprocessor
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.segmentation.PaddleSegPostprocessor
+    :members:
+    :inherited-members:
+```

fastdeploy/vision/segmentation/ppseg/postprocessor.h

@@ -20,7 +20,8 @@
 namespace fastdeploy {
 namespace vision {
 namespace segmentation {
-
+/*! @brief Postprocessor object for PaddleSeg serials model.
+ */
 class FASTDEPLOY_DECL PaddleSegPostprocessor {
  public:
   /** \brief Create a postprocessor instance for PaddleSeg serials model

fastdeploy/vision/segmentation/ppseg/preprocessor.h

@@ -18,7 +18,8 @@
 namespace fastdeploy {
 namespace vision {
 namespace segmentation {
-
+/*! @brief Preprocessor object for PaddleSeg serials model.
+  */
 class FASTDEPLOY_DECL PaddleSegPreprocessor {
  public:
   /** \brief Create a preprocessor instance for PaddleSeg serials model

python/fastdeploy/vision/segmentation/ppseg/__init__.py

@@ -50,38 +50,43 @@ def predict(self, image):
         return self._model.predict(image)
 
     def batch_predict(self, image_list):
-        """Predict the segmentation results for a batch of input image
+        """Predict the segmentation results for a batch of input images
+
         :param image_list: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
-        :return list of SegmentationResult
+        :return: list of SegmentationResult
         """
         return self._model.batch_predict(image_list)
 
     @property
     def preprocessor(self):
         """Get PaddleSegPreprocessor object of the loaded model
-        :return PaddleSegPreprocessor
+
+        :return: PaddleSegPreprocessor
         """
         return self._model.preprocessor
 
     @property
     def postprocessor(self):
         """Get PaddleSegPostprocessor object of the loaded model
-        :return PaddleSegPostprocessor
+
+        :return: PaddleSegPostprocessor
         """
         return self._model.postprocessor
 
 
 class PaddleSegPreprocessor:
     def __init__(self, config_file):
         """Create a preprocessor for PaddleSegModel from configuration file
+
         :param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
         """
         self._preprocessor = C.vision.segmentation.PaddleSegPreprocessor(
             config_file)
 
     def run(self, input_ims):
         """Preprocess input images for PaddleSegModel
-        :param: input_ims: (list of numpy.ndarray)The input image
+
+        :param input_ims: (list of numpy.ndarray)The input image
         :return: list of FDTensor
         """
         return self._preprocessor.run(input_ims)
@@ -114,15 +119,17 @@ def is_vertical_screen(self, value):
 class PaddleSegPostprocessor:
     def __init__(self, config_file):
         """Create a postprocessor for PaddleSegModel from configuration file
+
         :param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
         """
         self._postprocessor = C.vision.segmentation.PaddleSegPostprocessor(
             config_file)
 
     def run(self, runtime_results, imgs_info):
         """Postprocess the runtime results for PaddleSegModel
-        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
-        :param: imgs_info: The original input images shape info map, key is "shape_info", value is [[image_height, image_width]]
+
+        :param runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :param imgs_info: The original input images shape info map, key is "shape_info", value is [[image_height, image_width]]
         :return: list of SegmentationResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
         """
         return self._postprocessor.run(runtime_results, imgs_info)