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- import os
- from ipfml.processing import svd_reconstruction, fast_ica_reconstruction, ipca_reconstruction
- # Transformation class to store transformation method of image and get usefull information
- class Transformation():
- def __init__(self, _transformation, _param):
- self.transformation = _transformation
- self.param = _param
- def getTransformedImage(self, img):
- if self.transformation == 'svd_reconstruction':
- begin, end = list(map(int, self.param.split(',')))
- data = svd_reconstruction(img, [begin, end])
- if self.transformation == 'ipca_reconstruction':
- n_components, batch_size = list(map(int, self.param.split(',')))
- data = ipca_reconstruction(img, n_components, batch_size)
- if self.transformation == 'fast_ica_reconstruction':
- n_components = self.param
- data = fast_ica_reconstruction(img, n_components)
- if self.transformation == 'static':
- # static content, we keep input as it is
- data = img
- return data
-
- def getTransformationPath(self):
- path = self.transformation
- if self.transformation == 'svd_reconstruction':
- begin, end = list(map(int, self.param.split(',')))
- path = os.path.join(path, str(begin) + '_' + str(end))
- if self.transformation == 'ipca_reconstruction':
- n_components, batch_size = list(map(int, self.param.split(',')))
- path = os.path.join(path, 'N' + str(n_components) + '_' + str(batch_size))
- if self.transformation == 'fast_ica_reconstruction':
- n_components = self.param
- path = os.path.join(path, 'N' + str(n_components))
- if self.transformation == 'static':
- # param contains the whole path of image
- path = os.path.join(self.param, self.transformation)
- return path
- def getName(self):
- return self.transformation
- def getParam(self):
- return self.param
- def __str__( self ):
- return self.transformation + ' transformation with parameter : ' + self.param
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