def __init__(self,
data_dir,
n_triplets,
transform=None,
transform_parameter=np.array([1.0, 1.0, 1.0]),
eval=False,
ratio=0.8):
super(TripletDataset, self).__init__()
self.dir = data_dir
self.n_triplets = n_triplets
self.transform = transform
self.transform_parameter = transform_parameter
self.eval = eval
with open('LabelEncoder.pkl', 'rb') as f:
self.le = pkl.load(f)
self.train_csv = pd.read_csv('/data1/whale/train.csv').as_matrix()
np.random.shuffle(self.train_csv)
self.data = os.listdir(self.dir)
print('Generating {} triplets'.format(self.n_triplets))
self.triplets = generate_triplets(self.train_csv,
self.le, self.n_triplets,
len(self.le.classes_))
#!/usr/bin/env python3
from utils import load_images, load_csv, generate_triplets
import numpy as np
import matplotlib.pyplot as plt
images, filenames = load_images('HBTNaligned', as_array=True)
triplet_names = load_csv('FVTriplets.csv')
print("triplet_names", len(triplet_names))
A, P, N = generate_triplets(images, filenames, triplet_names)
print("A ", A.shape)
print("P ", P.shape)
print("N ", N.shape)
plt.subplot(1, 3, 1)
plt.title('Anchor:' + triplet_names[0][0])
plt.imshow(A[0])
plt.subplot(1, 3, 2)
plt.title('Positive:' + triplet_names[0][1])
plt.imshow(P[0])
plt.subplot(1, 3, 3)
plt.title('Negative:' + triplet_names[0][2])
plt.imshow(N[0])
plt.tight_layout()
plt.show()