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- import numpy as np
- import PIL.Image as Image
- import patchreconst as pr
- import matplotlib.pyplot as plt
- PATCH_SIZE = 32
- COMPONENT_COUNT = 100
- PATCH_COUNT = 400000
- EPOCH_COUNT = 16
- CODE_COUNT = 800
- SPARSITY_PARAMETER = 4.0
- NON_NEGATIVE = True
- scBasisFile = 'basis/basisnn' + str(NON_NEGATIVE) + 'lamb' + str(SPARSITY_PARAMETER) + 'comps' + str(COMPONENT_COUNT) + 'codes' + str(CODE_COUNT) + 'patches' + str(PATCH_COUNT) + 'epochs' + str(EPOCH_COUNT) + '.npy'
- scBasis = np.load(scBasisFile)
- icaFilterFile = 'icabasis/basisluisicacomps' + str(COMPONENT_COUNT) + 'codes' + str(CODE_COUNT) + 'patches' + str(PATCH_COUNT) + '.npy'
- icaFilters = np.load(icaFilterFile)
- patches = np.load('patches_1000_mod.npy')
- patchesOrig = np.load('patches_1000.npy')[:, :, :, 0]
- rmBounds = np.load('patches_1000_rmbounds.npy')
- rmBounds = (rmBounds[0], rmBounds[1], rmBounds[2])
- scCodes, icaCodes, v1Simple, v1cMean, angles = pr.responses(patches, scBasis, SPARSITY_PARAMETER, NON_NEGATIVE, icaFilters)
- pcaTransformed, v1C = pr.responsesPCAV1C(patches)
- patchesReconstV1 = pr.reconstructV1(v1Simple)
- patchesReconstPCA = pr.reconstructPCA(pcaTransformed, v1cMean, angles)
- patchesReconstSC = pr.reconstruct(scBasis, scCodes, v1cMean, angles)
- patchesReconstICA = pr.reconstruct(icaFilters, icaCodes, v1cMean, angles)
- '''
- for i in range(patchesReconstV1.shape[0]):
- f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, sharey = True)
- ax1.imshow(patchesOrig[i], cmap = 'gray', interpolation = 'none')
- ax1.title.set_text('Original patch')
- ax2.imshow(patches[i], cmap = 'gray', interpolation = 'none')
- ax2.title.set_text('Modified patch')
- ax3.imshow(patchesReconstV1[i], cmap = 'gray', interpolation = 'none')
- ax3.title.set_text('V1')
- ax4.imshow(patchesReconstSC[i], cmap = 'gray', interpolation = 'none')
- ax4.title.set_text('SC')
- plt.savefig('pred/' + str(i) + '.png')
- plt.close()
- '''
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