transform3 = CenteringTransform()
transform5 = ResampleInSpaceTransform(samples=80)
# Apply transforms
dataset.addTransform(transform1)
dataset.addTransform(transform2)
dataset.addTransform(transform3)
dataset.addTransform(transform5)
for file in dataset.applyTransforms():
sequences = file[0]
buffer = []
res = []
for item in sequences:
buffer.append(item)
res.append(Test.compare(buffer, models))
print(res)
################### Advanced Version ###################
class CsvDatasetRealTime(CsvDataset):
"""
Class for firing frame like real time:
Once it reads the sequence, it fires the frame one by one.
"""
def __init__(self, dir, maxlen=20, num_samples=20):
"""
:param dir: dataset's path
:param maxlen: indicates the dimension of the circular buffer
(its size should be big enough to contain all the frame of a gesture)
"""
# confusion matrix
confusion_matrix = ConfusionMatrix(directories)
# neighbor
best_hmm = 1
for directory in directories:
# original kalman + resampled + parsing
dataset = CsvDatasetExtended(base_dir + 'parsed/' + directory + '/',
type=str)
# apply transforms
for sequence in dataset.readDataset():
# get log_probabilities obtained from sequence
points = [
item for sublist in (sequence.getPoints(columns=[0]))
for item in sublist
]
probabilities = Test.compare(points, models)
# verify if row_label is contained in the firsts x elements (default, x=1)
keys = []
for key, value in sorted(probabilities.items(),
key=lambda kv: kv[1],
reverse=True)[:best_hmm]:
if isinstance(key, tuple):
keys = keys + list(itertools.chain(key))
else:
keys.append(key)
index_label = directory if directory in keys else keys[0]
if directory not in keys:
print('\n')
print(points)
print(probabilities)
