[Model] Support PP-StructureV2-Layout model (#1867)

* [Model] init pp-structurev2-layout code

* [Model] init pp-structurev2-layout code

* [Model] init pp-structurev2-layout code

* [Model] add structurev2_layout_preprocessor

* [PP-StructureV2] add postprocessor and layout detector class

* [PP-StructureV2] add postprocessor and layout detector class

* [PP-StructureV2] add postprocessor and layout detector class

* [PP-StructureV2] add postprocessor and layout detector class

* [PP-StructureV2] add postprocessor and layout detector class

* [pybind] add pp-structurev2-layout model pybind

* [pybind] add pp-structurev2-layout model pybind

* [Bug Fix] fixed code style

* [examples] add pp-structurev2-layout c++ examples

* [PP-StructureV2] add python example and docs

* [benchmark] add pp-structurev2-layout benchmark support

python/fastdeploy/vision/ocr/ppocr/__init__.py

@@ -650,7 +650,7 @@ class Recognizer(FastDeployModel):
 
 class StructureV2TablePreprocessor:
     def __init__(self):
-        """Create a preprocessor for StructureV2TableModel
+        """Create a preprocessor for StructureV2Table Model
         """
         self._preprocessor = C.vision.ocr.StructureV2TablePreprocessor()
 
@@ -664,12 +664,12 @@ class StructureV2TablePreprocessor:
 
 class StructureV2TablePostprocessor:
     def __init__(self):
-        """Create a postprocessor for StructureV2TableModel
+        """Create a postprocessor for StructureV2Table Model
         """
         self._postprocessor = C.vision.ocr.StructureV2TablePostprocessor()
 
     def run(self, runtime_results):
-        """Postprocess the runtime results for StructureV2TableModel
+        """Postprocess the runtime results for StructureV2Table Model
         :param: runtime_results: (list of FDTensor or list of pyArray)The output FDTensor results from runtime
         :return: list of Result(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
         """
@@ -683,10 +683,11 @@ class StructureV2Table(FastDeployModel):
                  table_char_dict_path="",
                  runtime_option=None,
                  model_format=ModelFormat.PADDLE):
-        """Load OCR StructureV2Table model provided by PaddleOCR.
+        """Load StructureV2Table model provided by PP-StructureV2.
 
         :param model_file: (str)Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
         :param params_file: (str)Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+        :param table_char_dict_path: (str)Path of table_char_dict file, e.g ../ppocr/utils/dict/table_structure_dict_ch.txt
         :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
         :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
         """
@@ -703,8 +704,8 @@ class StructureV2Table(FastDeployModel):
             self._runnable = True
 
     def clone(self):
-        """Clone OCR StructureV2Table model object
-        :return: a new OCR StructureV2Table model object
+        """Clone StructureV2Table model object
+        :return: a new StructureV2Table model object
         """
 
         class StructureV2TableClone(StructureV2Table):
@@ -749,6 +750,105 @@ class StructureV2Table(FastDeployModel):
         self._model.postprocessor = value
 
 
+class StructureV2LayoutPreprocessor:
+    def __init__(self):
+        """Create a preprocessor for StructureV2Layout Model
+        """
+        self._preprocessor = C.vision.ocr.StructureV2LayoutPreprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for StructureV2Layout Model
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims)
+
+
+class StructureV2LayoutPostprocessor:
+    def __init__(self):
+        """Create a postprocessor for StructureV2Layout Model
+        """
+        self._postprocessor = C.vision.ocr.StructureV2LayoutPostprocessor()
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for StructureV2Layout Model
+        :param: runtime_results: (list of FDTensor or list of pyArray)The output FDTensor results from runtime
+        :return: list of Result(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)
+
+
+class StructureV2Layout(FastDeployModel):
+    def __init__(self,
+                 model_file="",
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load StructureV2Layout model provided by PP-StructureV2.
+
+        :param model_file: (str)Path of model file, e.g ./picodet_lcnet_x1_0_fgd_layout_infer/model.pdmodel.
+        :param params_file: (str)Path of parameter file, e.g ./picodet_lcnet_x1_0_fgd_layout_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
+        """
+        super(StructureV2Layout, self).__init__(runtime_option)
+
+        if (len(model_file) == 0):
+            self._model = C.vision.ocr.StructureV2Layout()
+            self._runnable = False
+        else:
+            self._model = C.vision.ocr.StructureV2Layout(
+                model_file, params_file, self._runtime_option, model_format)
+            assert self.initialized, "StructureV2Layout model initialize failed."
+            self._runnable = True
+
+    def clone(self):
+        """Clone StructureV2Layout model object
+        :return: a new StructureV2Table model object
+        """
+
+        class StructureV2LayoutClone(StructureV2Layout):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = StructureV2LayoutClone(self._model.clone())
+        return clone_model
+
+    def predict(self, input_image):
+        """Predict an input image
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: bboxes
+        """
+        if self._runnable:
+            return self._model.predict(input_image)
+        return False
+
+    def batch_predict(self, images):
+        """Predict a batch of input image
+        :param images: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return: list of bboxes list
+        """
+        if self._runnable:
+            return self._model.batch_predict(images)
+        return False
+
+    @property
+    def preprocessor(self):
+        return self._model.preprocessor
+
+    @preprocessor.setter
+    def preprocessor(self, value):
+        self._model.preprocessor = value
+
+    @property
+    def postprocessor(self):
+        return self._model.postprocessor
+
+    @postprocessor.setter
+    def postprocessor(self, value):
+        self._model.postprocessor = value
+
+
 class PPOCRv3(FastDeployModel):
     def __init__(self, det_model=None, cls_model=None, rec_model=None):
         """Consruct a pipeline with text detector, direction classifier and text recognizer models