def test_normalize_scale(self):
ratios = []
check_frame = self.frames[0]['jointPositions']['jointPositionDict']
other_check_frame = normalize_scale(self.frames[:1], 'HipCenter', 'Head')[0]['jointPositions']['jointPositionDict']
for first_joint in check_frame.keys():
for second_joint in other_check_frame.keys():
if first_joint == second_joint:
continue
first_check_vector = check_frame[first_joint]
second_check_vector = check_frame[second_joint]
first_other_check_vector = other_check_frame[first_joint]
second_other_check_vector = other_check_frame[second_joint]
check_ratio = distance(
first_check_vector,
second_check_vector)
other_check_ratio = distance(
first_other_check_vector,
second_other_check_vector)
ratios.append(check_ratio / other_check_ratio)
head = ratios[0]
for i, ratio in enumerate(ratios):
self.assertTrue(abs(ratio - head) < 0.0000001, (ratio, head))
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()
## Test the weights and display info on how well they work.