# # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# #
# # Licensed under the Apache License, Version 2.0 (the "License");
# # you may not use this file except in compliance with the License.
# # You may obtain a copy of the License at
# #
# # http://www.apache.org/licenses/LICENSE-2.0
# #
# # Unless required by applicable law or agreed to in writing, software
# # distributed under the License is distributed on an "AS IS" BASIS,
# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# # See the License for the specific language governing permissions and
# # limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
from ...common import ProcessorManager
def sort_boxes(boxes):
return C.vision.ocr.sort_boxes(boxes)
[docs]class DBDetectorPreprocessor(ProcessorManager):
def __init__(self):
"""
Create a preprocessor for DBDetectorModel
"""
super(DBDetectorPreprocessor, self).__init__()
self._manager = C.vision.ocr.DBDetectorPreprocessor()
@property
def max_side_len(self):
"""Get max_side_len value.
"""
return self._manager.max_side_len
@max_side_len.setter
def max_side_len(self, value):
"""Set max_side_len value.
:param: value: (int) max_side_len value
"""
assert isinstance(
value, int), "The value to set `max_side_len` must be type of int."
self._manager.max_side_len = value
[docs] def set_normalize(self, mean, std, is_scale):
"""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 static_shape_infer(self):
return self._manager.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._manager.static_shape_infer = value
[docs] def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._manager.disable_normalize()
[docs] def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._manager.disable_permute()
[docs]class DBDetectorPostprocessor:
def __init__(self):
"""
Create a postprocessor for DBDetectorModel
"""
self._postprocessor = C.vision.ocr.DBDetectorPostprocessor()
[docs] def run(self, runtime_results, batch_det_img_info):
"""Postprocess the runtime results for DBDetectorModel
:param: runtime_results: (list of FDTensor or list of pyArray)The output FDTensor results from runtime
:param: batch_det_img_info: (list of std::array<int, 4>)The output of det_preprocessor
: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, batch_det_img_info)
@property
def det_db_thresh(self):
"""
Return the det_db_thresh of DBDetectorPostprocessor
"""
return self._postprocessor.det_db_thresh
@det_db_thresh.setter
def det_db_thresh(self, value):
"""Set the det_db_thresh for DBDetectorPostprocessor
:param: value : the det_db_thresh value
"""
assert isinstance(
value,
float), "The value to set `det_db_thresh` must be type of float."
self._postprocessor.det_db_thresh = value
@property
def det_db_box_thresh(self):
"""
Return the det_db_box_thresh of DBDetectorPostprocessor
"""
return self._postprocessor.det_db_box_thresh
@det_db_box_thresh.setter
def det_db_box_thresh(self, value):
"""Set the det_db_box_thresh for DBDetectorPostprocessor
:param: value : the det_db_box_thresh value
"""
assert isinstance(
value, float
), "The value to set `det_db_box_thresh` must be type of float."
self._postprocessor.det_db_box_thresh = value
@property
def det_db_unclip_ratio(self):
"""
Return the det_db_unclip_ratio of DBDetectorPostprocessor
"""
return self._postprocessor.det_db_unclip_ratio
@det_db_unclip_ratio.setter
def det_db_unclip_ratio(self, value):
"""Set the det_db_unclip_ratio for DBDetectorPostprocessor
:param: value : the det_db_unclip_ratio value
"""
assert isinstance(
value, float
), "The value to set `det_db_unclip_ratio` must be type of float."
self._postprocessor.det_db_unclip_ratio = value
@property
def det_db_score_mode(self):
"""
Return the det_db_score_mode of DBDetectorPostprocessor
"""
return self._postprocessor.det_db_score_mode
@det_db_score_mode.setter
def det_db_score_mode(self, value):
"""Set the det_db_score_mode for DBDetectorPostprocessor
:param: value : the det_db_score_mode value
"""
assert isinstance(
value,
str), "The value to set `det_db_score_mode` must be type of str."
self._postprocessor.det_db_score_mode = value
@property
def use_dilation(self):
"""
Return the use_dilation of DBDetectorPostprocessor
"""
return self._postprocessor.use_dilation
@use_dilation.setter
def use_dilation(self, value):
"""Set the use_dilation for DBDetectorPostprocessor
:param: value : the use_dilation value
"""
assert isinstance(
value,
bool), "The value to set `use_dilation` must be type of bool."
self._postprocessor.use_dilation = value
[docs]class DBDetector(FastDeployModel):
def __init__(self,
model_file="",
params_file="",
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load OCR detection model provided by PaddleOCR.
:param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
: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.
: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(DBDetector, self).__init__(runtime_option)
if (len(model_file) == 0):
self._model = C.vision.ocr.DBDetector()
self._runnable = False
else:
self._model = C.vision.ocr.DBDetector(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "DBDetector initialize failed."
self._runnable = True
[docs] def clone(self):
"""Clone OCR detection model object
:return: a new OCR detection model object
"""
class DBDetectorClone(DBDetector):
def __init__(self, model):
self._model = model
clone_model = DBDetectorClone(self._model.clone())
return clone_model
[docs] 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: boxes
"""
if self._runnable:
return self._model.predict(input_image)
return False
[docs] 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: batch_boxes
"""
if self._runnable:
return self._model.batch_predict(images)
return False
@property
def preprocessor(self):
return self._model.preprocessor
@property
def postprocessor(self):
return self._model.postprocessor
# Det Preprocessor Property
@property
def max_side_len(self):
return self._model.preprocessor.max_side_len
@max_side_len.setter
def max_side_len(self, value):
assert isinstance(
value, int), "The value to set `max_side_len` must be type of int."
self._model.preprocessor.max_side_len = value
# Det Ppstprocessor Property
@property
def det_db_thresh(self):
return self._model.postprocessor.det_db_thresh
@det_db_thresh.setter
def det_db_thresh(self, value):
assert isinstance(
value,
float), "The value to set `det_db_thresh` must be type of float."
self._model.postprocessor.det_db_thresh = value
@property
def det_db_box_thresh(self):
return self._model.postprocessor.det_db_box_thresh
@det_db_box_thresh.setter
def det_db_box_thresh(self, value):
assert isinstance(
value, float
), "The value to set `det_db_box_thresh` must be type of float."
self._model.postprocessor.det_db_box_thresh = value
@property
def det_db_unclip_ratio(self):
return self._model.postprocessor.det_db_unclip_ratio
@det_db_unclip_ratio.setter
def det_db_unclip_ratio(self, value):
assert isinstance(
value, float
), "The value to set `det_db_unclip_ratio` must be type of float."
self._model.postprocessor.det_db_unclip_ratio = value
@property
def det_db_score_mode(self):
return self._model.postprocessor.det_db_score_mode
@det_db_score_mode.setter
def det_db_score_mode(self, value):
assert isinstance(
value,
str), "The value to set `det_db_score_mode` must be type of str."
self._model.postprocessor.det_db_score_mode = value
@property
def use_dilation(self):
return self._model.postprocessor.use_dilation
@use_dilation.setter
def use_dilation(self, value):
assert isinstance(
value,
bool), "The value to set `use_dilation` must be type of bool."
self._model.postprocessor.use_dilation = value
[docs]class ClassifierPreprocessor(ProcessorManager):
def __init__(self):
"""Create a preprocessor for ClassifierModel
"""
super(ClassifierPreprocessor, self).__init__()
self._manager = C.vision.ocr.ClassifierPreprocessor()
[docs] def set_normalize(self, mean, std, is_scale):
"""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 cls_image_shape(self):
return self._manager.cls_image_shape
@cls_image_shape.setter
def cls_image_shape(self, value):
assert isinstance(
value,
list), "The value to set `cls_image_shape` must be type of list."
self._manager.cls_image_shape = value
[docs] def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._manager.disable_normalize()
[docs] def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._manager.disable_permute()
[docs]class ClassifierPostprocessor:
def __init__(self):
"""Create a postprocessor for ClassifierModel
"""
self._postprocessor = C.vision.ocr.ClassifierPostprocessor()
[docs] def run(self, runtime_results):
"""Postprocess the runtime results for ClassifierModel
: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)
@property
def cls_thresh(self):
"""
Return the cls_thresh of ClassifierPostprocessor
"""
return self._postprocessor.cls_thresh
@cls_thresh.setter
def cls_thresh(self, value):
"""Set the cls_thresh for ClassifierPostprocessor
:param: value: the value of cls_thresh
"""
assert isinstance(
value,
float), "The value to set `cls_thresh` must be type of float."
self._postprocessor.cls_thresh = value
[docs]class Classifier(FastDeployModel):
def __init__(self,
model_file="",
params_file="",
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load OCR classification model provided by PaddleOCR.
: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 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(Classifier, self).__init__(runtime_option)
if (len(model_file) == 0):
self._model = C.vision.ocr.Classifier()
self._runnable = False
else:
self._model = C.vision.ocr.Classifier(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "Classifier initialize failed."
self._runnable = True
[docs] def clone(self):
"""Clone OCR classification model object
:return: a new OCR classification model object
"""
class ClassifierClone(Classifier):
def __init__(self, model):
self._model = model
clone_model = ClassifierClone(self._model.clone())
return clone_model
[docs] 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: cls_label, cls_score
"""
if self._runnable:
return self._model.predict(input_image)
return False
[docs] 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 cls_label, list of cls_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 cls_image_shape(self):
return self._model.preprocessor.cls_image_shape
@cls_image_shape.setter
def cls_image_shape(self, value):
assert isinstance(
value,
list), "The value to set `cls_image_shape` must be type of list."
self._model.preprocessor.cls_image_shape = value
# Cls Postprocessor Property
@property
def cls_thresh(self):
return self._model.postprocessor.cls_thresh
@cls_thresh.setter
def cls_thresh(self, value):
assert isinstance(
value,
float), "The value to set `cls_thresh` must be type of float."
self._model.postprocessor.cls_thresh = value
[docs]class RecognizerPreprocessor(ProcessorManager):
def __init__(self):
"""Create a preprocessor for RecognizerModel
"""
self._manager = C.vision.ocr.RecognizerPreprocessor()
@property
def static_shape_infer(self):
return self._manager.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._manager.static_shape_infer = value
[docs] def set_normalize(self, mean, std, is_scale):
"""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
[docs] def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._manager.disable_normalize()
[docs] def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._manager.disable_permute()
[docs]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)
[docs] 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)
[docs]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
[docs] 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
[docs] 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
[docs] 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
[docs]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)
[docs] 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
[docs] 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)
[docs] 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)
[docs]class PPOCRv2(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.PPOCRv2(det_model._model,
rec_model._model)
else:
self.system_ = C.vision.ocr.PPOCRv2(
det_model._model, cls_model._model, rec_model._model)
[docs] def clone(self):
"""Clone PPOCRv3 pipeline object
:return: a new PPOCRv3 pipeline object
"""
class PPOCRv2Clone(PPOCRv2):
def __init__(self, system):
self.system_ = system
clone_model = PPOCRv2Clone(self.system_.clone())
return clone_model
[docs] 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)
[docs] 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 PPOCRSystemv2(PPOCRv2):
def __init__(self, det_model=None, cls_model=None, rec_model=None):
logging.warning(
"DEPRECATED: fd.vision.ocr.PPOCRSystemv2 is deprecated, "
"please use fd.vision.ocr.PPOCRv2 instead.")
super(PPOCRSystemv2, self).__init__(det_model, cls_model, rec_model)
def predict(self, input_image):
return super(PPOCRSystemv2, self).predict(input_image)