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- # main imports
- import os
- import numpy as np
- # image processing imports
- from ipfml.processing import transform
- from ipfml.processing import reconstruction
- from ipfml.filters import convolution, kernels
- from ipfml import utils
- from PIL import Image
- # Transformation class to store transformation method of image and get usefull information
- class Transformation():
- def __init__(self, _transformation, _param, _size):
- self.transformation = _transformation
- self.param = _param
- self.size = _size
- def getTransformedImage(self, img):
- if self.transformation == 'svd_reconstruction':
- begin, end = list(map(int, self.param.split(',')))
- data = reconstruction.svd(img, [begin, end])
- if self.transformation == 'ipca_reconstruction':
- n_components, batch_size = list(map(int, self.param.split(',')))
- data = reconstruction.ipca(img, n_components, batch_size)
- if self.transformation == 'fast_ica_reconstruction':
- n_components = self.param
- data = reconstruction.fast_ica(img, n_components)
- if self.transformation == 'min_diff_filter':
- w_size, h_size = list(map(int, self.param.split(',')))
- h, w = list(map(int, self.size.split(',')))
- # bilateral with window of size (`w_size`, `h_size`)
- lab_img = transform.get_LAB_L(img)
- lab_img = Image.fromarray(lab_img)
- lab_img.thumbnail((h, w))
- diff_img = convolution.convolution2D(lab_img, kernels.min_bilateral_diff, (w_size, h_size))
- data = np.array(diff_img*255, 'uint8')
-
- 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 == 'min_diff_filter':
- w_size, h_size = list(map(int, self.param.split(',')))
- w, h = list(map(int, self.size.split(',')))
- path = os.path.join(path, 'W_' + str(w_size)) + '_' + str(h_size) + '_S_' + str(w) + '_' + str(h)
- if self.transformation == 'static':
- # param contains image name to find for each scene
- path = self.param
- 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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