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https://github.com/PaddlePaddle/FastDeploy.git
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Improve PPOCR API docs
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@@ -30,6 +30,17 @@ from .c_lib_wrap import (
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is_built_with_trt,
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get_default_cuda_directory, )
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def set_logger(enable_info=True, enable_warning=True):
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"""Set behaviour of logger while using FastDeploy
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:param enable_info: (boolean)Whether to print out log level of INFO
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:param enable_warning: (boolean)Whether to print out log level of WARNING, recommend to set to True
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"""
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from .c_lib_wrap import set_logger
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set_logger(enable_info, enable_warning)
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from .runtime import Runtime, RuntimeOption
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from .model import FastDeployModel
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from . import c_lib_wrap as C
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@@ -46,7 +46,6 @@ class PaddleClasPreprocessor(ProcessorManager):
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When the initial operator is Resize, and input image size is large,
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maybe it's better to run resize on CPU, because the HostToDevice memcpy
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is time consuming. Set this True to run the initial resize on CPU.
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:param: v: True or False
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"""
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self._manager.initial_resize_on_cpu(v)
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@@ -39,50 +39,31 @@ class DBDetectorPreprocessor:
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@property
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def max_side_len(self):
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"""
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Return the max_side_len of DBDetectorPreprocessor
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"""Get max_side_len value.
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"""
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return self._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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"""Set the max_side_len for DBDetectorPreprocessor
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:param: value : the max_side_len 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._preprocessor.max_side_len = value
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@property
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def is_scale(self):
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return self._preprocessor.is_scale
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@is_scale.setter
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def is_scale(self, value):
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assert isinstance(
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value, bool), "The value to set `is_scale` must be type of bool."
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self._preprocessor.is_scale = value
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@property
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def scale(self):
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return self._preprocessor.scale
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@scale.setter
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def scale(self, value):
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assert isinstance(
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value, list), "The value to set `scale` must be type of list."
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self._preprocessor.scale = value
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@property
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def mean(self):
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return self._preprocessor.mean
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@mean.setter
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def mean(self, value):
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assert isinstance(
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value, list), "The value to set `mean` must be type of list."
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self._preprocessor.mean = value
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def set_normalize(self,
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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is_scale=True):
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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._preprocessor.set_normalize(mean, std, is_scale)
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class DBDetectorPostprocessor:
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@@ -251,18 +232,10 @@ class DBDetector(FastDeployModel):
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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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# Det Preprocessor Property
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@property
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def max_side_len(self):
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@@ -274,36 +247,6 @@ class DBDetector(FastDeployModel):
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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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@property
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def is_scale(self):
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return self._model.preprocessor.is_scale
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@is_scale.setter
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def is_scale(self, value):
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assert isinstance(
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value, bool), "The value to set `is_scale` must be type of bool."
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self._model.preprocessor.is_scale = value
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@property
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def scale(self):
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return self._model.preprocessor.scale
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@scale.setter
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def scale(self, value):
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assert isinstance(
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value, list), "The value to set `scale` must be type of list."
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self._model.preprocessor.scale = value
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@property
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def mean(self):
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return self._model.preprocessor.mean
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@mean.setter
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def mean(self, value):
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assert isinstance(
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value, list), "The value to set `mean` must be type of list."
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self._model.preprocessor.mean = 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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