mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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709ba51612
* WIP, add VI-LayoutXLM * fix pybind * update the dir of ser_vi_layoutxlm model * update dir and name of ser_vi_layoutxlm model * update model name to StructureV2SerViLayoutXLMModel * fix import paddle bug --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
1338 lines
48 KiB
Python
Executable File
1338 lines
48 KiB
Python
Executable File
# # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# #
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# # Licensed under the Apache License, Version 2.0 (the "License");
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# # you may not use this file except in compliance with the License.
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# # You may obtain a copy of the License at
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# #
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# # http://www.apache.org/licenses/LICENSE-2.0
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# #
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# # Unless required by applicable law or agreed to in writing, software
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# # distributed under the License is distributed on an "AS IS" BASIS,
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# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# # See the License for the specific language governing permissions and
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# # limitations under the License.
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from __future__ import absolute_import
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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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from ...common import ProcessorManager
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from .utils.ser_vi_layoutxlm.vqa_utils import *
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from .utils.ser_vi_layoutxlm.transforms import *
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from .utils.ser_vi_layoutxlm.operators import *
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from pathlib import Path
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def sort_boxes(boxes):
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return C.vision.ocr.sort_boxes(boxes)
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class DBDetectorPreprocessor(ProcessorManager):
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def __init__(self):
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"""
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Create a preprocessor for DBDetectorModel
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"""
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super(DBDetectorPreprocessor, self).__init__()
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self._manager = C.vision.ocr.DBDetectorPreprocessor()
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@property
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def max_side_len(self):
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"""Get max_side_len value.
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"""
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return self._manager.max_side_len
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@max_side_len.setter
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def max_side_len(self, value):
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"""Set max_side_len value.
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:param: value: (int) max_side_len value
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"""
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assert isinstance(
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value, int), "The value to set `max_side_len` must be type of int."
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self._manager.max_side_len = value
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def set_normalize(self, mean, std, is_scale):
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"""Set preprocess normalize parameters, please call this API to
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customize the normalize parameters, otherwise it will use the default
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normalize parameters.
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:param: mean: (list of float) mean values
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:param: std: (list of float) std values
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:param: is_scale: (boolean) whether to scale
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"""
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self._manager.set_normalize(mean, std, is_scale)
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@property
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def static_shape_infer(self):
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return self._manager.static_shape_infer
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@static_shape_infer.setter
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def static_shape_infer(self, value):
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assert isinstance(
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value,
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bool), "The value to set `static_shape_infer` must be type of bool."
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self._manager.static_shape_infer = value
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def disable_normalize(self):
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"""
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This function will disable normalize in preprocessing step.
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"""
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self._manager.disable_normalize()
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def disable_permute(self):
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"""
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This function will disable hwc2chw in preprocessing step.
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"""
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self._manager.disable_permute()
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class DBDetectorPostprocessor:
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def __init__(self):
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"""
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Create a postprocessor for DBDetectorModel
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"""
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self._postprocessor = C.vision.ocr.DBDetectorPostprocessor()
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def run(self, runtime_results, batch_det_img_info):
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"""Postprocess the runtime results for DBDetectorModel
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:param: runtime_results: (list of FDTensor or list of pyArray)The output FDTensor results from runtime
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:param: batch_det_img_info: (list of std::array<int, 4>)The output of det_preprocessor
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:return: list of Result(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
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"""
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return self._postprocessor.run(runtime_results, batch_det_img_info)
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@property
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def det_db_thresh(self):
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"""
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Return the det_db_thresh of DBDetectorPostprocessor
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"""
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return self._postprocessor.det_db_thresh
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@det_db_thresh.setter
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def det_db_thresh(self, value):
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"""Set the det_db_thresh for DBDetectorPostprocessor
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:param: value : the det_db_thresh value
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"""
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assert isinstance(
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value,
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float), "The value to set `det_db_thresh` must be type of float."
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self._postprocessor.det_db_thresh = value
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@property
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def det_db_box_thresh(self):
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"""
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Return the det_db_box_thresh of DBDetectorPostprocessor
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"""
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return self._postprocessor.det_db_box_thresh
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@det_db_box_thresh.setter
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def det_db_box_thresh(self, value):
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"""Set the det_db_box_thresh for DBDetectorPostprocessor
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:param: value : the det_db_box_thresh value
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"""
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assert isinstance(
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value, float
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), "The value to set `det_db_box_thresh` must be type of float."
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self._postprocessor.det_db_box_thresh = value
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@property
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def det_db_unclip_ratio(self):
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"""
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Return the det_db_unclip_ratio of DBDetectorPostprocessor
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"""
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return self._postprocessor.det_db_unclip_ratio
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@det_db_unclip_ratio.setter
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def det_db_unclip_ratio(self, value):
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"""Set the det_db_unclip_ratio for DBDetectorPostprocessor
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:param: value : the det_db_unclip_ratio value
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"""
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assert isinstance(
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value, float
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), "The value to set `det_db_unclip_ratio` must be type of float."
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self._postprocessor.det_db_unclip_ratio = value
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@property
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def det_db_score_mode(self):
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"""
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Return the det_db_score_mode of DBDetectorPostprocessor
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"""
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return self._postprocessor.det_db_score_mode
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@det_db_score_mode.setter
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def det_db_score_mode(self, value):
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"""Set the det_db_score_mode for DBDetectorPostprocessor
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:param: value : the det_db_score_mode value
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"""
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assert isinstance(
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value,
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str), "The value to set `det_db_score_mode` must be type of str."
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self._postprocessor.det_db_score_mode = value
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@property
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def use_dilation(self):
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"""
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Return the use_dilation of DBDetectorPostprocessor
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"""
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return self._postprocessor.use_dilation
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@use_dilation.setter
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def use_dilation(self, value):
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"""Set the use_dilation for DBDetectorPostprocessor
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:param: value : the use_dilation value
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"""
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assert isinstance(
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value,
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bool), "The value to set `use_dilation` must be type of bool."
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self._postprocessor.use_dilation = value
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class DBDetector(FastDeployModel):
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def __init__(self,
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model_file="",
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load OCR detection model provided by PaddleOCR.
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:param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
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:param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
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"""
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super(DBDetector, self).__init__(runtime_option)
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if (len(model_file) == 0):
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self._model = C.vision.ocr.DBDetector()
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self._runnable = False
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else:
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self._model = C.vision.ocr.DBDetector(
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model_file, params_file, self._runtime_option, model_format)
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assert self.initialized, "DBDetector initialize failed."
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self._runnable = True
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def clone(self):
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"""Clone OCR detection model object
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:return: a new OCR detection model object
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"""
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class DBDetectorClone(DBDetector):
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def __init__(self, model):
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self._model = model
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clone_model = DBDetectorClone(self._model.clone())
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return clone_model
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def predict(self, input_image):
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: boxes
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"""
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if self._runnable:
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return self._model.predict(input_image)
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return False
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def batch_predict(self, images):
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"""Predict a batch of input image
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:param images: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return: batch_boxes
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"""
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if self._runnable:
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return self._model.batch_predict(images)
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return False
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@property
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def preprocessor(self):
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return self._model.preprocessor
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@property
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def postprocessor(self):
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return self._model.postprocessor
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# Det Preprocessor Property
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@property
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def max_side_len(self):
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return self._model.preprocessor.max_side_len
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@max_side_len.setter
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def max_side_len(self, value):
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assert isinstance(
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value, int), "The value to set `max_side_len` must be type of int."
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self._model.preprocessor.max_side_len = value
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# Det Ppstprocessor Property
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@property
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def det_db_thresh(self):
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return self._model.postprocessor.det_db_thresh
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@det_db_thresh.setter
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def det_db_thresh(self, value):
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assert isinstance(
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value,
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float), "The value to set `det_db_thresh` must be type of float."
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self._model.postprocessor.det_db_thresh = value
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@property
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def det_db_box_thresh(self):
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return self._model.postprocessor.det_db_box_thresh
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@det_db_box_thresh.setter
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def det_db_box_thresh(self, value):
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assert isinstance(
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value, float
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), "The value to set `det_db_box_thresh` must be type of float."
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self._model.postprocessor.det_db_box_thresh = value
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@property
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def det_db_unclip_ratio(self):
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return self._model.postprocessor.det_db_unclip_ratio
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@det_db_unclip_ratio.setter
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def det_db_unclip_ratio(self, value):
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assert isinstance(
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value, float
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), "The value to set `det_db_unclip_ratio` must be type of float."
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self._model.postprocessor.det_db_unclip_ratio = value
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@property
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def det_db_score_mode(self):
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return self._model.postprocessor.det_db_score_mode
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@det_db_score_mode.setter
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def det_db_score_mode(self, value):
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assert isinstance(
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value,
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str), "The value to set `det_db_score_mode` must be type of str."
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self._model.postprocessor.det_db_score_mode = value
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@property
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def use_dilation(self):
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return self._model.postprocessor.use_dilation
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@use_dilation.setter
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def use_dilation(self, value):
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assert isinstance(
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value,
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bool), "The value to set `use_dilation` must be type of bool."
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self._model.postprocessor.use_dilation = value
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class ClassifierPreprocessor(ProcessorManager):
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def __init__(self):
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"""Create a preprocessor for ClassifierModel
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"""
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super(ClassifierPreprocessor, self).__init__()
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self._manager = C.vision.ocr.ClassifierPreprocessor()
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def set_normalize(self, mean, std, is_scale):
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"""Set preprocess normalize parameters, please call this API to
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customize the normalize parameters, otherwise it will use the default
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normalize parameters.
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:param: mean: (list of float) mean values
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:param: std: (list of float) std values
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:param: is_scale: (boolean) whether to scale
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"""
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self._manager.set_normalize(mean, std, is_scale)
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@property
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def cls_image_shape(self):
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return self._manager.cls_image_shape
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@cls_image_shape.setter
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def cls_image_shape(self, value):
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assert isinstance(
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value,
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list), "The value to set `cls_image_shape` must be type of list."
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self._manager.cls_image_shape = value
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def disable_normalize(self):
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"""
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This function will disable normalize in preprocessing step.
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"""
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self._manager.disable_normalize()
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def disable_permute(self):
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"""
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This function will disable hwc2chw in preprocessing step.
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"""
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self._manager.disable_permute()
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class ClassifierPostprocessor:
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def __init__(self):
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"""Create a postprocessor for ClassifierModel
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"""
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self._postprocessor = C.vision.ocr.ClassifierPostprocessor()
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def run(self, runtime_results):
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"""Postprocess the runtime results for ClassifierModel
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:param: runtime_results: (list of FDTensor or list of pyArray)The output FDTensor results from runtime
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:return: list of Result(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
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"""
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return self._postprocessor.run(runtime_results)
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@property
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def cls_thresh(self):
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"""
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Return the cls_thresh of ClassifierPostprocessor
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"""
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return self._postprocessor.cls_thresh
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@cls_thresh.setter
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def cls_thresh(self, value):
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"""Set the cls_thresh for ClassifierPostprocessor
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:param: value: the value of cls_thresh
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"""
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assert isinstance(
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value,
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float), "The value to set `cls_thresh` must be type of float."
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self._postprocessor.cls_thresh = value
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class Classifier(FastDeployModel):
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def __init__(self,
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model_file="",
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load OCR classification model provided by PaddleOCR.
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:param model_file: (str)Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
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: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.
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
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"""
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super(Classifier, self).__init__(runtime_option)
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if (len(model_file) == 0):
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self._model = C.vision.ocr.Classifier()
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self._runnable = False
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else:
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self._model = C.vision.ocr.Classifier(
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model_file, params_file, self._runtime_option, model_format)
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assert self.initialized, "Classifier initialize failed."
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self._runnable = True
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def clone(self):
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"""Clone OCR classification model object
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:return: a new OCR classification model object
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"""
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class ClassifierClone(Classifier):
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def __init__(self, model):
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self._model = model
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clone_model = ClassifierClone(self._model.clone())
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return clone_model
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def predict(self, input_image):
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: cls_label, cls_score
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"""
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if self._runnable:
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return self._model.predict(input_image)
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return False
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def batch_predict(self, images):
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"""Predict a batch of input image
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:param images: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return: list of cls_label, list of cls_score
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"""
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if self._runnable:
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return self._model.batch_predict(images)
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return False
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@property
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def preprocessor(self):
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return self._model.preprocessor
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@preprocessor.setter
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def preprocessor(self, value):
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self._model.preprocessor = value
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@property
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def postprocessor(self):
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return self._model.postprocessor
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@postprocessor.setter
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def postprocessor(self, value):
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self._model.postprocessor = value
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@property
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def cls_image_shape(self):
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return self._model.preprocessor.cls_image_shape
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@cls_image_shape.setter
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def cls_image_shape(self, value):
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assert isinstance(
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value,
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list), "The value to set `cls_image_shape` must be type of list."
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self._model.preprocessor.cls_image_shape = value
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# Cls Postprocessor Property
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@property
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def cls_thresh(self):
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return self._model.postprocessor.cls_thresh
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@cls_thresh.setter
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def cls_thresh(self, value):
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assert isinstance(
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value,
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float), "The value to set `cls_thresh` must be type of float."
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self._model.postprocessor.cls_thresh = value
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class RecognizerPreprocessor(ProcessorManager):
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def __init__(self):
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"""Create a preprocessor for RecognizerModel
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"""
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super(RecognizerPreprocessor, self).__init__()
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self._manager = C.vision.ocr.RecognizerPreprocessor()
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@property
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def static_shape_infer(self):
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return self._manager.static_shape_infer
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@static_shape_infer.setter
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def static_shape_infer(self, value):
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assert isinstance(
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value,
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bool), "The value to set `static_shape_infer` must be type of bool."
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self._manager.static_shape_infer = value
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|
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def set_normalize(self, mean, std, is_scale):
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"""Set preprocess normalize parameters, please call this API to
|
|
customize the normalize parameters, otherwise it will use the default
|
|
normalize parameters.
|
|
:param: mean: (list of float) mean values
|
|
:param: std: (list of float) std values
|
|
:param: is_scale: (boolean) whether to scale
|
|
"""
|
|
self._manager.set_normalize(mean, std, is_scale)
|
|
|
|
@property
|
|
def rec_image_shape(self):
|
|
return self._manager.rec_image_shape
|
|
|
|
@rec_image_shape.setter
|
|
def rec_image_shape(self, value):
|
|
assert isinstance(
|
|
value,
|
|
list), "The value to set `rec_image_shape` must be type of list."
|
|
self._manager.rec_image_shape = value
|
|
|
|
def disable_normalize(self):
|
|
"""
|
|
This function will disable normalize in preprocessing step.
|
|
"""
|
|
self._manager.disable_normalize()
|
|
|
|
def disable_permute(self):
|
|
"""
|
|
This function will disable hwc2chw in preprocessing step.
|
|
"""
|
|
self._manager.disable_permute()
|
|
|
|
|
|
class RecognizerPostprocessor:
|
|
def __init__(self, label_path):
|
|
"""Create a postprocessor for RecognizerModel
|
|
:param label_path: (str)Path of label file
|
|
"""
|
|
self._postprocessor = C.vision.ocr.RecognizerPostprocessor(label_path)
|
|
|
|
def run(self, runtime_results):
|
|
"""Postprocess the runtime results for RecognizerModel
|
|
: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 Recognizer(FastDeployModel):
|
|
def __init__(self,
|
|
model_file="",
|
|
params_file="",
|
|
label_path="",
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load OCR recognition model provided by PaddleOCR
|
|
|
|
:param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
|
|
:param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
|
|
:param label_path: (str)Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.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.
|
|
"""
|
|
super(Recognizer, self).__init__(runtime_option)
|
|
|
|
if (len(model_file) == 0):
|
|
self._model = C.vision.ocr.Recognizer()
|
|
self._runnable = False
|
|
else:
|
|
self._model = C.vision.ocr.Recognizer(
|
|
model_file, params_file, label_path, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "Recognizer initialize failed."
|
|
self._runnable = True
|
|
|
|
def clone(self):
|
|
"""Clone OCR recognition model object
|
|
:return: a new OCR recognition model object
|
|
"""
|
|
|
|
class RecognizerClone(Recognizer):
|
|
def __init__(self, model):
|
|
self._model = model
|
|
|
|
clone_model = RecognizerClone(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: rec_text, rec_score
|
|
"""
|
|
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 rec_text, list of rec_score
|
|
"""
|
|
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
|
|
|
|
@property
|
|
def static_shape_infer(self):
|
|
return self._model.preprocessor.static_shape_infer
|
|
|
|
@static_shape_infer.setter
|
|
def static_shape_infer(self, value):
|
|
assert isinstance(
|
|
value,
|
|
bool), "The value to set `static_shape_infer` must be type of bool."
|
|
self._model.preprocessor.static_shape_infer = value
|
|
|
|
@property
|
|
def rec_image_shape(self):
|
|
return self._model.preprocessor.rec_image_shape
|
|
|
|
@rec_image_shape.setter
|
|
def rec_image_shape(self, value):
|
|
assert isinstance(
|
|
value,
|
|
list), "The value to set `rec_image_shape` must be type of list."
|
|
self._model.preprocessor.rec_image_shape = value
|
|
|
|
|
|
class StructureV2TablePreprocessor:
|
|
def __init__(self):
|
|
"""Create a preprocessor for StructureV2Table Model
|
|
"""
|
|
self._preprocessor = C.vision.ocr.StructureV2TablePreprocessor()
|
|
|
|
def run(self, input_ims):
|
|
"""Preprocess input images for StructureV2TableModel
|
|
:param: input_ims: (list of numpy.ndarray)The input image
|
|
:return: list of FDTensor
|
|
"""
|
|
return self._preprocessor.run(input_ims)
|
|
|
|
|
|
class StructureV2TablePostprocessor:
|
|
def __init__(self):
|
|
"""Create a postprocessor for StructureV2Table Model
|
|
"""
|
|
self._postprocessor = C.vision.ocr.StructureV2TablePostprocessor()
|
|
|
|
def run(self, runtime_results):
|
|
"""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)
|
|
"""
|
|
return self._postprocessor.run(runtime_results)
|
|
|
|
|
|
class StructureV2Table(FastDeployModel):
|
|
def __init__(self,
|
|
model_file="",
|
|
params_file="",
|
|
table_char_dict_path="",
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""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.
|
|
"""
|
|
super(StructureV2Table, self).__init__(runtime_option)
|
|
|
|
if (len(model_file) == 0):
|
|
self._model = C.vision.ocr.StructureV2Table()
|
|
self._runnable = False
|
|
else:
|
|
self._model = C.vision.ocr.StructureV2Table(
|
|
model_file, params_file, table_char_dict_path,
|
|
self._runtime_option, model_format)
|
|
assert self.initialized, "Classifier initialize failed."
|
|
self._runnable = True
|
|
|
|
def clone(self):
|
|
"""Clone StructureV2Table model object
|
|
:return: a new StructureV2Table model object
|
|
"""
|
|
|
|
class StructureV2TableClone(StructureV2Table):
|
|
def __init__(self, model):
|
|
self._model = model
|
|
|
|
clone_model = StructureV2TableClone(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: bbox, structure
|
|
"""
|
|
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 bbox list, list of structure
|
|
"""
|
|
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 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 PPOCRv4(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
|
|
|
|
:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
|
|
:param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
|
|
:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
|
|
"""
|
|
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
|
|
if cls_model is None:
|
|
self.system_ = C.vision.ocr.PPOCRv4(det_model._model,
|
|
rec_model._model)
|
|
else:
|
|
self.system_ = C.vision.ocr.PPOCRv4(
|
|
det_model._model, cls_model._model, rec_model._model)
|
|
|
|
def clone(self):
|
|
"""Clone PPOCRv4 pipeline object
|
|
:return: a new PPOCRv4 pipeline object
|
|
"""
|
|
|
|
class PPOCRv4Clone(PPOCRv4):
|
|
def __init__(self, system):
|
|
self.system_ = system
|
|
|
|
clone_model = PPOCRv4Clone(self.system_.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: OCRResult
|
|
"""
|
|
return self.system_.predict(input_image)
|
|
|
|
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: OCRBatchResult
|
|
"""
|
|
return self.system_.batch_predict(images)
|
|
|
|
@property
|
|
def cls_batch_size(self):
|
|
return self.system_.cls_batch_size
|
|
|
|
@cls_batch_size.setter
|
|
def cls_batch_size(self, value):
|
|
assert isinstance(
|
|
value,
|
|
int), "The value to set `cls_batch_size` must be type of int."
|
|
self.system_.cls_batch_size = value
|
|
|
|
@property
|
|
def rec_batch_size(self):
|
|
return self.system_.rec_batch_size
|
|
|
|
@rec_batch_size.setter
|
|
def rec_batch_size(self, value):
|
|
assert isinstance(
|
|
value,
|
|
int), "The value to set `rec_batch_size` must be type of int."
|
|
self.system_.rec_batch_size = value
|
|
|
|
|
|
class PPOCRSystemv4(PPOCRv4):
|
|
def __init__(self, det_model=None, cls_model=None, rec_model=None):
|
|
logging.warning(
|
|
"DEPRECATED: fd.vision.ocr.PPOCRSystemv4 is deprecated, "
|
|
"please use fd.vision.ocr.PPOCRv4 instead.")
|
|
super(PPOCRSystemv4, self).__init__(det_model, cls_model, rec_model)
|
|
|
|
def predict(self, input_image):
|
|
return super(PPOCRSystemv4, self).predict(input_image)
|
|
|
|
|
|
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
|
|
|
|
:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
|
|
:param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
|
|
:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
|
|
"""
|
|
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
|
|
if cls_model is None:
|
|
self.system_ = C.vision.ocr.PPOCRv3(det_model._model,
|
|
rec_model._model)
|
|
else:
|
|
self.system_ = C.vision.ocr.PPOCRv3(
|
|
det_model._model, cls_model._model, rec_model._model)
|
|
|
|
def clone(self):
|
|
"""Clone PPOCRv3 pipeline object
|
|
:return: a new PPOCRv3 pipeline object
|
|
"""
|
|
|
|
class PPOCRv3Clone(PPOCRv3):
|
|
def __init__(self, system):
|
|
self.system_ = system
|
|
|
|
clone_model = PPOCRv3Clone(self.system_.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: OCRResult
|
|
"""
|
|
return self.system_.predict(input_image)
|
|
|
|
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: OCRBatchResult
|
|
"""
|
|
return self.system_.batch_predict(images)
|
|
|
|
@property
|
|
def cls_batch_size(self):
|
|
return self.system_.cls_batch_size
|
|
|
|
@cls_batch_size.setter
|
|
def cls_batch_size(self, value):
|
|
assert isinstance(
|
|
value,
|
|
int), "The value to set `cls_batch_size` must be type of int."
|
|
self.system_.cls_batch_size = value
|
|
|
|
@property
|
|
def rec_batch_size(self):
|
|
return self.system_.rec_batch_size
|
|
|
|
@rec_batch_size.setter
|
|
def rec_batch_size(self, value):
|
|
assert isinstance(
|
|
value,
|
|
int), "The value to set `rec_batch_size` must be type of int."
|
|
self.system_.rec_batch_size = value
|
|
|
|
|
|
class PPOCRSystemv3(PPOCRv3):
|
|
def __init__(self, det_model=None, cls_model=None, rec_model=None):
|
|
logging.warning(
|
|
"DEPRECATED: fd.vision.ocr.PPOCRSystemv3 is deprecated, "
|
|
"please use fd.vision.ocr.PPOCRv3 instead.")
|
|
super(PPOCRSystemv3, self).__init__(det_model, cls_model, rec_model)
|
|
|
|
def predict(self, input_image):
|
|
return super(PPOCRSystemv3, self).predict(input_image)
|
|
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class PPOCRv2(FastDeployModel):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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"""Consruct a pipeline with text detector, direction classifier and text recognizer models
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:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
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:param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
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:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
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"""
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assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
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if cls_model is None:
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self.system_ = C.vision.ocr.PPOCRv2(det_model._model,
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rec_model._model)
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else:
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self.system_ = C.vision.ocr.PPOCRv2(
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det_model._model, cls_model._model, rec_model._model)
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def clone(self):
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"""Clone PPOCRv3 pipeline object
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:return: a new PPOCRv3 pipeline object
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"""
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class PPOCRv2Clone(PPOCRv2):
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def __init__(self, system):
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self.system_ = system
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clone_model = PPOCRv2Clone(self.system_.clone())
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return clone_model
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def predict(self, input_image):
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: OCRResult
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"""
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return self.system_.predict(input_image)
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def batch_predict(self, images):
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"""Predict a batch of input image
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:param images: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return: OCRBatchResult
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"""
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return self.system_.batch_predict(images)
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@property
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def cls_batch_size(self):
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return self.system_.cls_batch_size
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@cls_batch_size.setter
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def cls_batch_size(self, value):
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assert isinstance(
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value,
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int), "The value to set `cls_batch_size` must be type of int."
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self.system_.cls_batch_size = value
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@property
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def rec_batch_size(self):
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return self.system_.rec_batch_size
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@rec_batch_size.setter
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def rec_batch_size(self, value):
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assert isinstance(
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value,
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int), "The value to set `rec_batch_size` must be type of int."
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self.system_.rec_batch_size = value
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class PPOCRSystemv2(PPOCRv2):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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logging.warning(
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"DEPRECATED: fd.vision.ocr.PPOCRSystemv2 is deprecated, "
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"please use fd.vision.ocr.PPOCRv2 instead.")
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super(PPOCRSystemv2, self).__init__(det_model, cls_model, rec_model)
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def predict(self, input_image):
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return super(PPOCRSystemv2, self).predict(input_image)
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class PPStructureV2Table(FastDeployModel):
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def __init__(self, det_model=None, rec_model=None, table_model=None):
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"""Consruct a pipeline with text detector, text recognizer and table recognizer models
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:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
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:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
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:param table_model: (FastDeployModel) The table recognition model object created by fastdeploy.vision.ocr.Table.
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"""
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assert det_model is not None and rec_model is not None and table_model is not None, "The det_model, rec_model and table_model cannot be None."
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self.system_ = C.vision.ocr.PPStructureV2Table(
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det_model._model,
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rec_model._model,
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table_model._model, )
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def clone(self):
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"""Clone PPStructureV2Table pipeline object
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:return: a new PPStructureV2Table pipeline object
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"""
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class PPStructureV2TableClone(PPStructureV2Table):
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def __init__(self, system):
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self.system_ = system
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clone_model = PPStructureV2TableClone(self.system_.clone())
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return clone_model
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def predict(self, input_image):
|
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: OCRResult
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"""
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return self.system_.predict(input_image)
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def batch_predict(self, images):
|
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"""Predict a batch of input image
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:param images: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return: OCRBatchResult
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"""
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return self.system_.batch_predict(images)
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class PPStructureV2TableSystem(PPStructureV2Table):
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def __init__(self, det_model=None, rec_model=None, table_model=None):
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logging.warning(
|
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"DEPRECATED: fd.vision.ocr.PPStructureV2TableSystem is deprecated, "
|
|
"please use fd.vision.ocr.PPStructureV2Table instead.")
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super(PPStructureV2TableSystem, self).__init__(det_model, rec_model,
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table_model)
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def predict(self, input_image):
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return super(PPStructureV2TableSystem, self).predict(input_image)
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class StructureV2SERViLayoutXLMModelPreprocessor():
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def __init__(self, ser_dict_path, use_gpu=True):
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"""Create a preprocessor for Ser-Vi-LayoutXLM model.
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:param: ser_dict_path: (str) class file path
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:param: use_gpu: (bool) whether use gpu to OCR process
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"""
|
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self._manager = None
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from paddleocr import PaddleOCR
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self.ocr_engine = PaddleOCR(
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use_angle_cls=False,
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det_model_dir=None,
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rec_model_dir=None,
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show_log=False,
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use_gpu=use_gpu)
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pre_process_list = [{
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'VQATokenLabelEncode': {
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'class_path': ser_dict_path,
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'contains_re': False,
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'ocr_engine': self.ocr_engine,
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'order_method': "tb-yx"
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}
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}, {
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'VQATokenPad': {
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'max_seq_len': 512,
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'return_attention_mask': True
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}
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}, {
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'VQASerTokenChunk': {
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'max_seq_len': 512,
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'return_attention_mask': True
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}
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}, {
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'Resize': {
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'size': [224, 224]
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}
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}, {
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'NormalizeImage': {
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'std': [58.395, 57.12, 57.375],
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'mean': [123.675, 116.28, 103.53],
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'scale': '1',
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'order': 'hwc'
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}
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}, {
|
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'ToCHWImage': None
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}, {
|
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'KeepKeys': {
|
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'keep_keys': [
|
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'input_ids', 'bbox', 'attention_mask', 'token_type_ids',
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'image', 'labels', 'segment_offset_id', 'ocr_info',
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'entities'
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]
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}
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}]
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self.preprocess_op = create_operators(pre_process_list,
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{'infer_mode': True})
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|
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def _transform(self, data, ops=None):
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""" transform """
|
|
if ops is None:
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ops = []
|
|
for op in ops:
|
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data = op(data)
|
|
if data is None:
|
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return None
|
|
return data
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|
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def run(self, input_im):
|
|
"""Run preprocess of Ser-Vi-LayoutXLM model
|
|
:param: input_ims: (numpy.ndarray) input image
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"""
|
|
ori_im = input_im.copy()
|
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data = {'image': input_im}
|
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data = transform(data, self.preprocess_op)
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|
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for idx in range(len(data)):
|
|
if isinstance(data[idx], np.ndarray):
|
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data[idx] = np.expand_dims(data[idx], axis=0)
|
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else:
|
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data[idx] = [data[idx]]
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|
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return data
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|
|
|
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class StructureV2SERViLayoutXLMModelPostprocessor():
|
|
def __init__(self, class_path):
|
|
"""Create a postprocessor for Ser-Vi-LayoutXLM model.
|
|
:param: class_path: (string) class file path
|
|
"""
|
|
self.postprocessor_op = VQASerTokenLayoutLMPostProcess(class_path)
|
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|
|
def run(self, preds, batch=None, *args, **kwargs):
|
|
"""Run postprocess of Ser-Vi-LayoutXLM model.
|
|
:param: preds: (list) results of infering
|
|
"""
|
|
return self.postprocessor_op(preds, batch, *args, **kwargs)
|
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|
|
|
|
class StructureV2SERViLayoutXLMModel(FastDeployModel):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
ser_dict_path,
|
|
class_path,
|
|
config_file="",
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load SERViLayoutXLM model provided by PP-StructureV2.
|
|
|
|
:param model_file: (str)Path of model file, e.g ./ser_vi_layout_xlm/model.pdmodel.
|
|
:param params_file: (str)Path of parameter file, e.g ./ser_vi_layout_xlm/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
|
|
:param ser_dict_path: (str) class file path
|
|
:param class_path: (str) class file path
|
|
: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(StructureV2SERViLayoutXLMModel, self).__init__(runtime_option)
|
|
|
|
assert self._runtime_option.backend != 0, \
|
|
"Runtime Option required backend setting."
|
|
self._model = C.vision.ocr.StructureV2SERViLayoutXLMModel(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
|
|
assert self.initialized, "SERViLayoutXLM model initialize failed."
|
|
|
|
self.preprocessor = StructureV2SERViLayoutXLMModelPreprocessor(
|
|
ser_dict_path)
|
|
self.postprocesser = StructureV2SERViLayoutXLMModelPostprocessor(
|
|
class_path)
|
|
|
|
self.input_name_0 = self._model.get_input_info(0).name
|
|
self.input_name_1 = self._model.get_input_info(1).name
|
|
self.input_name_2 = self._model.get_input_info(2).name
|
|
self.input_name_3 = self._model.get_input_info(3).name
|
|
|
|
def predict(self, image):
|
|
assert isinstance(image,
|
|
np.ndarray), "predict recives numpy.ndarray(BGR)"
|
|
|
|
data = self.preprocessor.run(image)
|
|
infer_input = {
|
|
self.input_name_0: data[0],
|
|
self.input_name_1: data[1],
|
|
self.input_name_2: data[2],
|
|
self.input_name_3: data[3],
|
|
}
|
|
|
|
infer_result = self._model.infer(infer_input)
|
|
infer_result = infer_result[0]
|
|
|
|
post_result = self.postprocesser.run(infer_result,
|
|
segment_offset_ids=data[6],
|
|
ocr_infos=data[7])
|
|
|
|
return post_result
|
|
|
|
def batch_predict(self, image_list):
|
|
assert isinstance(image_list, list) and \
|
|
isinstance(image_list[0], np.ndarray), \
|
|
"batch_predict recives list of numpy.ndarray(BGR)"
|
|
|
|
# reading and preprocessing images
|
|
datas = None
|
|
for image in image_list:
|
|
data = self.preprocessor.run(image)
|
|
|
|
# concatenate data to batch
|
|
if datas == None:
|
|
datas = data
|
|
else:
|
|
for idx in range(len(data)):
|
|
if isinstance(data[idx], np.ndarray):
|
|
datas[idx] = np.concatenate(
|
|
(datas[idx], data[idx]), axis=0)
|
|
else:
|
|
datas[idx].extend(data[idx])
|
|
|
|
# infer
|
|
infer_inputs = {
|
|
self.input_name_0: datas[0],
|
|
self.input_name_1: datas[1],
|
|
self.input_name_2: datas[2],
|
|
self.input_name_3: datas[3],
|
|
}
|
|
|
|
infer_results = self._model.infer(infer_inputs)
|
|
infer_results = infer_results[0]
|
|
|
|
# postprocessing
|
|
post_results = self.postprocesser.run(infer_results,
|
|
segment_offset_ids=datas[6],
|
|
ocr_infos=datas[7])
|
|
|
|
return post_results
|