def test_normalize_origin(self):
'Check that the origin joint is zero.'
center_joint = 'HipCenter'
for frame in normalize_origin(self.frames, center_joint):
self.assertEqual(
frame['jointPositions']['jointPositionDict'][center_joint],
(0.0, 0.0, 0.0))
split_items, load_option_info, load_skeleton_data, tweak)
from simanneal import Annealer
from normal import normalize_scale, normalize_origin
from copy import deepcopy
#if __name__ == '__main__': # Emacs doesn't like this.
OPTION_INFO = load_option_info('option.json')
## Build path to file.
EXPERIMENT_FILE_PATH = join(KINECT_EXPERIMENT_DIR, OPTION_INFO['dir_name'],
OPTION_INFO['file_name'])
## Open file and extract JSON frames data.
FRAMES = normalize_scale(
normalize_origin(load_skeleton_data(EXPERIMENT_FILE_PATH), 'HipCenter'),
'HipCenter', 'Head')
class KinectWeightsProblem(Annealer):
'A class with methods defined for generating weights for classifying joint data.'
def __init__(self, frames):
self.frames = frames
self.state = {
joint: random.uniform(0.0, 0.5)
for joint in self.frames[0]['jointPositions']
['jointPositionDict'].keys()
}
self.ideal_model = {
joint: (0, 0, 0)
from random import seed
from time import clock
#if __name__ == '__main__': # Emacs doesn't like this.
OPTION_INFO = load_option_info('option.json')
## Build path to file.
EXPERIMENT_FILE_PATH = join(
KINECT_EXPERIMENT_DIR,
OPTION_INFO['dir_name'],
OPTION_INFO['file_name']
)
## Open file and extract JSON frames data.
FRAMES = normalize_scale(normalize_origin(load_skeleton_data(EXPERIMENT_FILE_PATH), 'HipCenter'),
'HipCenter', 'Head')
## Get the joint distances from the frame data.
DISTANCES = map(generate_distances, FRAMES)
## Split the distance data. Use one half for
## training and the other half for testing.
seed(OPTION_INFO['shuffle_seed'])
shuffle(DISTANCES)
TRAINING_DISTANCES, TESTING_DISTANCES = split_items(DISTANCES, 0.5)
seed(OPTION_INFO['training_seed'])
## Train the weights.
WEIGHTS, _ = KinectWeightsProblem(TRAINING_DISTANCES).anneal()
