def test_OmniglotClass():
import cv2
root = './data/'
dataset = Omniglot(root=root, download=True)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
idx = 0
idx_alphabet = 0
idx_character = 0
idx_sample = 0
sample = dataset.getSample(idx_alphabet, idx_character, idx_sample)
image_path = dataset.sampleSample4Character4Alphabet(
idx_alphabet, idx_character, idx_sample)
changed = False
while True:
#sample = dataset[idx]
img = np.array(sample[0])
cv2.imshow('test', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('n'):
idx += 1
print(image_path)
idx_sample += 1
changed = True
elif key == ord('a'):
idx += 1
print(image_path)
idx_alphabet += 1
idx_character = 0
idx_sample = 0
changed = True
elif key == ord('c'):
idx += 1
print(image_path)
idx_character += 1
idx_sample = 0
changed = True
if changed:
changed = False
sample = dataset.getSample(idx_alphabet, idx_character, idx_sample)
image_path = dataset.sampleSample4Character4Alphabet(
idx_alphabet, idx_character, idx_sample)
def test_Omniglot():
import cv2
root = './data/'
dataset = Omniglot(root=root, download=True)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
idx = 0
idx_alphabet = 0
idx_character = 0
idx_sample = 0
while True:
sample = dataset[idx]
image_path = dataset.sampleSample4Character4Alphabet(
idx_alphabet, idx_character, idx_sample)
img = np.array(sample[0])
cv2.imshow('test', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('n'):
idx += 1
#print(sample[1])
print(image_path)
idx_sample += 1
print("next image")
def test_OmniglotTask():
import cv2
root = './data/'
dataset = Omniglot(root=root, download=True)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
idx_alphabet = 0
idx_sample = 0
#task, nbrCharacter4Task, nbrSample4Task = dataset.generateFewShotLearningTask( alphabet_idx=idx_alphabet)
task, nbrCharacter4Task, nbrSample4Task = dataset.generateIterFewShotLearningTask(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(task[idx_sample]['alphabet'],
task[idx_sample]['character'],
task[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
task[idx_sample]['alphabet'], task[idx_sample]['character'],
task[idx_sample]['sample'])
changed = False
taskChanged = False
idx = 0
while True:
#sample = dataset[idx]
img = np.array(sample[0])
cv2.imshow('test', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('n'):
idx += 1
idx_sample = (idx_sample + 1) % len(task)
changed = True
elif key == ord('a'):
idx_alphabet += 1
idx_character = 0
idx_sample = 0
taskChanged = True
if changed:
changed = False
sample = dataset.getSample(task[idx_sample]['alphabet'],
task[idx_sample]['character'],
task[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
task[idx_sample]['alphabet'], task[idx_sample]['character'],
task[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))
print('Sample {} / {}'.format(idx, nbrSample4Task))
if taskChanged:
taskChanged = False
idx = 0
task, nbrCharacter4Task, nbrSample4Task = dataset.generateFewShotLearningTask(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(task[idx_sample]['alphabet'],
task[idx_sample]['character'],
task[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
task[idx_sample]['alphabet'], task[idx_sample]['character'],
task[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))
def test_OmniglotSeq():
import cv2
root = './data/'
h = 240
w = 240
dataset = Omniglot(root=root, h=h, w=w)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
idx_alphabet = 0
idx_sample = 0
seq, nbrCharacter4Task, nbrSample4Task = dataset.generateIterFewShotInputSequence(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
changed = False
seqChanged = False
idx = 0
while True:
#sample = dataset[idx]
img = (sample['image'].numpy() * 255).transpose((1, 2, 0))
cv2.imshow('test', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('n'):
idx += 1
idx_sample = (idx_sample + 1) % len(seq)
changed = True
elif key == ord('a'):
idx_alphabet += 1
idx_character = 0
idx_sample = 0
seqChanged = True
if changed:
changed = False
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))
print('Sample {} / {}'.format(idx, nbrSample4Task))
print('Target :{}'.format(seq[idx_sample]['target']))
if seqChanged:
seqChanged = False
idx = 0
seq, nbrCharacter4Task, nbrSample4Task = dataset.generateFewShotLearningTask(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))