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"""
作者:林泽毅
建这个开源库的起源呢,是因为在做onnx推理的时候,需要将原来的tensor转换成numpy.array
问题是Tensor和Numpy的矩阵排布逻辑不同
包括Tensor推理经常会进行Transform,比如ToTensor,Normalize等
就想做一些等价转换的函数。
"""
import numpy as np
def NTo_Tensor(array):
"""
:param array: opencv/PIL读取的numpy矩阵
:return:返回一个形如Tensor的numpy矩阵
Example:
Inputs:array.shape = (512,512,3)
Outputs:output.shape = (3,512,512)
"""
output = array.transpose((2, 0, 1))
return output
def NNormalize(array, mean=np.array([0.5, 0.5, 0.5]), std=np.array([0.5, 0.5, 0.5]), dtype=np.float32):
"""
:param array: opencv/PIL读取的numpy矩阵
mean: 归一化均值,np.array格式
std: 归一化标准差,np.array格式
dtype：输出的numpy数据格式,一般onnx需要float32
:return:numpy矩阵
Example:
Inputs:array为opencv/PIL读取的一张图片
mean=np.array([0.5,0.5,0.5])
std=np.array([0.5,0.5,0.5])
dtype=np.float32
Outputs:output为归一化后的numpy矩阵
"""
im = array / 255.0
im = np.divide(np.subtract(im, mean), std)
output = np.asarray(im, dtype=dtype)
return output
def NUnsqueeze(array, axis=0):
"""
:param array: opencv/PIL读取的numpy矩阵
axis：要增加的维度
:return:numpy矩阵
Example:
Inputs:array为opencv/PIL读取的一张图片,array.shape为[512,512,3]
axis=0
Outputs:output为array在第0维增加一个维度,shape转为[1,512,512,3]
"""
if axis == 0:
output = array[None, :, :, :]
elif axis == 1:
output = array[:, None, :, :]
elif axis == 2:
output = array[:, :, None, :]
else:
output = array[:, :, :, None]
return output
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