RAM by Torch

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 트레인:
https://github.com/Element-Research/rnn/blob/master/examples/recurrent-visual-attention.lua

테스트:
 https://github.com/Element-Research/rnn/blob/master/scripts/evaluate-rva.lua

http://torch.ch/blog/2015/09/21/rmva.html

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  1. 제갈식2017년 9월 10일 PM 7:58

    맥에서도 돌만큼 무겁진 않은데 오래걸린다..

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파이썬으로 Homomorphic Filtering 하기

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import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2 . imread ( 'messi5.jpg' , 0 ) f = np . fft . fft2 ( img ) fshift = np . fft . fftshift ( f ) magnitude_spectrum = 20 * np . log ( np . abs ( fshift )) plt . subplot ( 121 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), plt . yticks ([]) plt . subplot ( 122 ), plt . imshow ( magnitude_spectrum , cmap = 'gray' ) plt . title ( 'Magnitude Spectrum' ), plt . xticks ([]), plt . yticks ([]) plt . show () rows , cols = img . shape crow , ccol = rows / 2 , cols / 2 fshift [ crow - 30 : crow + 30 , ccol - 30 : ccol + 30 ] = 0 f_ishift = np . fft . ifftshift ( fshift ) img_back = np . fft . ifft2 ( f_ishift ) img_back = np . abs ( img_back ) plt . subplot ( 131 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), ...
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