# -*- coding: utf-8 -*-
 
import os.path
 
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.autograd import Variable 
 
import numpy as np
from PIL import Image 
 
features_dir = './features'
 
img_path = "hymenoptera_data/train/ants/0013035.jpg"
file_name = img_path.split('/')[-1]
feature_path = os.path.join(features_dir, file_name + '.txt')
 
 
transform1 = transforms.Compose([
    transforms.Scale(256),
    transforms.CenterCrop(224),
    transforms.ToTensor()  ]
)
 
img = Image.open(img_path)
img1 = transform1(img)
 
#resnet18 = models.resnet18(pretrained = True)
resnet50_feature_extractor = models.resnet50(pretrained = True)
resnet50_feature_extractor.fc = nn.Linear(2048, 2048)
torch.nn.init.eye(resnet50_feature_extractor.fc.weight)
 
for param in resnet50_feature_extractor.parameters():
  param.requires_grad = False
#resnet152 = models.resnet152(pretrained = True)
#densenet201 = models.densenet201(pretrained = True) 
x = Variable(torch.unsqueeze(img1, dim=0).float(), requires_grad=False)
#y1 = resnet18(x)
y = resnet50_feature_extractor(x)
y = y.data.numpy()
np.savetxt(feature_path, y, delimiter=',')
#y3 = resnet152(x)
#y4 = densenet201(x)
 
y_ = np.loadtxt(feature_path, delimiter=',').reshape(1, 2048)

# -*- coding: utf-8 -*-
import os, torch, glob
import numpy as np
from torch.autograd import Variable
from PIL import Image 
from torchvision import models, transforms
import torch.nn as nn
import shutil
data_dir = './hymenoptera_data'
features_dir = './features'
shutil.copytree(data_dir, os.path.join(features_dir, data_dir[2:]))
 
 
def extractor(img_path, saved_path, net, use_gpu):
  transform = transforms.Compose([
      transforms.Scale(256),
      transforms.CenterCrop(224),
      transforms.ToTensor()  ]
  )
  
  img = Image.open(img_path)
  img = transform(img)
  
 
 
  x = Variable(torch.unsqueeze(img, dim=0).float(), requires_grad=False)
  if use_gpu:
    x = x.cuda()
    net = net.cuda()
  y = net(x).cpu()
  y = y.data.numpy()
  np.savetxt(saved_path, y, delimiter=',')
  
if __name__ == '__main__':
  extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
    
  files_list = []
  sub_dirs = [x[0] for x in os.walk(data_dir) ]
  sub_dirs = sub_dirs[1:]
  for sub_dir in sub_dirs:
    for extention in extensions:
      file_glob = os.path.join(sub_dir, '*.' + extention)
      files_list.extend(glob.glob(file_glob))
    
  resnet50_feature_extractor = models.resnet50(pretrained = True)
  resnet50_feature_extractor.fc = nn.Linear(2048, 2048)
  torch.nn.init.eye(resnet50_feature_extractor.fc.weight)
  for param in resnet50_feature_extractor.parameters():
    param.requires_grad = False  
    
  use_gpu = torch.cuda.is_available()
 
  for x_path in files_list:
    print(x_path)
    fx_path = os.path.join(features_dir, x_path[2:] + '.txt')
    extractor(x_path, fx_path, resnet50_feature_extractor, use_gpu)

    resnet = models.resnet152(pretrained=True)
    modules = list(resnet.children())[:-1]   # delete the last fc layer.
    convnet = nn.Sequential(*modules)

resnet = models.resnet152(pretrained=True)
del resnet.fc