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[Model] change ocr pre and post (#568)

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* change ocr pre and post

* add pybind

* change ocr

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix copy bug

* fix code style

* fix bug

* add new function

* fix windows ci bug
This commit is contained in:
Thomas Young
2022-11-18 13:17:42 +08:00
committed by GitHub
parent 1609ce1bab
commit 143506b654
31 changed files with 1402 additions and 569 deletions
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172
python/fastdeploy/vision/ocr/ppocr/__init__.py Normal file → Executable file
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@@ -41,40 +41,11 @@ class DBDetector(FastDeployModel):
assert self.initialized, "DBDetector initialize failed."
# 一些跟DBDetector模型有关的属性封装
@property
def max_side_len(self):
return self._model.max_side_len
'''
@property
def det_db_thresh(self):
return self._model.det_db_thresh
@property
def det_db_box_thresh(self):
return self._model.det_db_box_thresh
@property
def det_db_unclip_ratio(self):
return self._model.det_db_unclip_ratio
@property
def det_db_score_mode(self):
return self._model.det_db_score_mode
@property
def use_dilation(self):
return self._model.use_dilation
@property
def is_scale(self):
return self._model.max_wh
@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.max_side_len = value
@det_db_thresh.setter
def det_db_thresh(self, value):
assert isinstance(
@@ -82,6 +53,10 @@ class DBDetector(FastDeployModel):
float), "The value to set `det_db_thresh` must be type of float."
self._model.det_db_thresh = value
@property
def det_db_box_thresh(self):
return self._model.det_db_box_thresh
@det_db_box_thresh.setter
def det_db_box_thresh(self, value):
assert isinstance(
@@ -89,6 +64,10 @@ class DBDetector(FastDeployModel):
), "The value to set `det_db_box_thresh` must be type of float."
self._model.det_db_box_thresh = value
@property
def det_db_unclip_ratio(self):
return self._model.det_db_unclip_ratio
@det_db_unclip_ratio.setter
def det_db_unclip_ratio(self, value):
assert isinstance(
@@ -96,6 +75,10 @@ class DBDetector(FastDeployModel):
), "The value to set `det_db_unclip_ratio` must be type of float."
self._model.det_db_unclip_ratio = value
@property
def det_db_score_mode(self):
return self._model.det_db_score_mode
@det_db_score_mode.setter
def det_db_score_mode(self, value):
assert isinstance(
@@ -103,6 +86,10 @@ class DBDetector(FastDeployModel):
str), "The value to set `det_db_score_mode` must be type of str."
self._model.det_db_score_mode = value
@property
def use_dilation(self):
return self._model.use_dilation
@use_dilation.setter
def use_dilation(self, value):
assert isinstance(
@@ -110,11 +97,26 @@ class DBDetector(FastDeployModel):
bool), "The value to set `use_dilation` must be type of bool."
self._model.use_dilation = value
@property
def max_side_len(self):
return self._model.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.max_side_len = value
@property
def is_scale(self):
return self._model.max_wh
@is_scale.setter
def is_scale(self, value):
assert isinstance(
value, bool), "The value to set `is_scale` must be type of bool."
self._model.is_scale = value
'''
class Classifier(FastDeployModel):
@@ -139,6 +141,7 @@ class Classifier(FastDeployModel):
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "Classifier initialize failed."
'''
@property
def cls_thresh(self):
return self._model.cls_thresh
@@ -170,6 +173,7 @@ class Classifier(FastDeployModel):
value,
int), "The value to set `cls_batch_num` must be type of int."
self._model.cls_batch_num = value
'''
class Recognizer(FastDeployModel):
@@ -197,6 +201,7 @@ class Recognizer(FastDeployModel):
model_format)
assert self.initialized, "Recognizer initialize failed."
'''
@property
def rec_img_h(self):
return self._model.rec_img_h
@@ -227,6 +232,7 @@ class Recognizer(FastDeployModel):
value,
int), "The value to set `rec_batch_num` must be type of int."
self._model.rec_batch_num = value
'''
class PPOCRv3(FastDeployModel):
@@ -253,6 +259,14 @@ class PPOCRv3(FastDeployModel):
"""
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)
class PPOCRSystemv3(PPOCRv3):
def __init__(self, det_model=None, cls_model=None, rec_model=None):
@@ -289,6 +303,14 @@ class PPOCRv2(FastDeployModel):
"""
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)
class PPOCRSystemv2(PPOCRv2):
def __init__(self, det_model=None, cls_model=None, rec_model=None):
@@ -299,3 +321,93 @@ class PPOCRSystemv2(PPOCRv2):
def predict(self, input_image):
return super(PPOCRSystemv2, self).predict(input_image)
class DBDetectorPreprocessor:
def __init__(self):
"""Create a preprocessor for DBDetectorModel
"""
self._preprocessor = C.vision.ocr.DBDetectorPreprocessor()
def run(self, input_ims):
"""Preprocess input images for DBDetectorModel
:param: input_ims: (list of numpy.ndarray) The input image
:return: pair(list of FDTensor, list of std::array<int, 4>)
"""
return self._preprocessor.run(input_ims)
class DBDetectorPostprocessor:
def __init__(self):
"""Create a postprocessor for DBDetectorModel
"""
self._postprocessor = C.vision.ocr.DBDetectorPostprocessor()
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)
class RecognizerPreprocessor:
def __init__(self):
"""Create a preprocessor for RecognizerModel
"""
self._preprocessor = C.vision.ocr.RecognizerPreprocessor()
def run(self, input_ims):
"""Preprocess input images for RecognizerModel
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
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 ClassifierPreprocessor:
def __init__(self):
"""Create a preprocessor for ClassifierModel
"""
self._preprocessor = C.vision.ocr.ClassifierPreprocessor()
def run(self, input_ims):
"""Preprocess input images for ClassifierModel
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
class ClassifierPostprocessor:
def __init__(self):
"""Create a postprocessor for ClassifierModel
"""
self._postprocessor = C.vision.ocr.ClassifierPostprocessor()
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)
def sort_boxes(boxes):
return C.vision.ocr.sort_boxes(boxes)