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 ([]), ...
import torch
답글삭제import torch.nn as nn
#from __future__ import print_function
import argparse
from PIL import Image
import torchvision.models as models
import skimage.io
from torch.autograd import Variable as V
from torch.nn import functional as f
from torchvision import transforms as trn
# define image transformation
centre_crop = trn.Compose([
trn.ToPILImage(),
trn.Scale(256),
trn.CenterCrop(224),
trn.ToTensor(),
trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
filename=r'/home/scrapmetal/Documents/mw_data/val/malware/VirusShare_0c8ab85240a6bfcd12bdc4fae2437ed91.png'
img = skimage.io.imread(filename)
x = V(centre_crop(img).unsqueeze(0), volatile=True)
model = models.__dict__['resnet18']()
model = torch.nn.DataParallel(model).cuda()
model = torch.load('mw_model0831.pth')
#model.load_state_dict(checkpoint['state_dict'])
#best_prec1 = checkpoint['best_prec1']
logit = model(x)
print(logit)
print(len(logit))
h_x = f.softmax(logit).data.squeeze()