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clemson.py
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clemson.py
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"""Clemson dataset"""
from collections import Counter
import csv
import os
import datetime as dt
import logging
import glob
import xlrd
import xml.etree.cElementTree as etree
import tensorflow as tf
import numpy as np
FREQUENCY = 15
ACC_SENSITIVITY = 660.0
GYRO_SENSITIVITY = 2.5
DEFAULT_LABEL = "idle"
FLIP_ACC = [-1., 1., 1.]
FLIP_GYRO = [1., -1., -1.]
TIME_FACTOR = 1000000
TRAIN_IDS = ['p005','p006','p007','p013','p015','p016','p020','p021','p022',
'p025','p026','p027','p030','p031','p033','p036','p037','p038','p043',
'p044','p045','p048','p050','p051','p054','p055','p056','p059','p060',
'p061','p065','p066','p067','p070','p071','p072','p077','p078','p079',
'p082','p083','p084','p087','p088','p089','p092','p093','p095','p099',
'p100','p101','p104','p105','p106','p109','p110','p111','p115','p116',
'p117','p120','p121','p122','p129','p130','p131','p136','p137','p138',
'p142','p143','p144','p148','p150','p151','p157','p158','p159','p162',
'p164','p165','p170','p171','p172','p175','p176','p177','p180','p181',
'p182','p186','p187','p188','p192','p194','p195','p201','p202','p204',
'p207','p208','p209','p218','p219','p220','p226','p229','p230','p233',
'p234','p235','p241','p242','p244','p247','p248','p251','p256','p257',
'p259','p263','p264','p265','p268','p269','p270','p273','p274','p275',
'p278','p279','p280','p283','p284','p285','p291','p292','p293','p309',
'p311','p312','p320','p322','p324','p331','p332','p334','p338','p341',
'p343','p353','p361','p368','p384','p392','p396','p406','p410','p411']
VALID_IDS = ['p011','p017','p023','p028','p034','p039','p046','p052',
'p057','p062','p068','p074','p080','p085','p090','p096','p102','p107',
'p113','p118','p123','p132','p139','p145','p153','p160','p166','p173',
'p178','p184','p189','p198','p205','p215','p221','p231','p236','p245',
'p252','p260','p266','p271','p276','p281','p289','p297','p315','p326',
'p336','p347','p372','p397','p413']
TEST_IDS = ['p012','p019','p024','p029','p035','p042','p047','p053','p058',
'p064','p069','p075','p081','p086','p091','p098','p103','p108','p114',
'p119','p125','p133','p140','p146','p154','p161','p169','p174','p179',
'p185','p190','p199','p206','p217','p224','p232','p237','p246','p253',
'p262','p267','p272','p277','p282','p290','p298','p318','p329','p337',
'p352','p377','p401']
DESSERTS_FOODS = ['ice_cream', 'yogurt_and_ice_cream', 'cupcake',
'rice_krispie_treat', 'fruit_loop_and_rice_krispie_treat', 'custom_cake',
'chunky_chocolate_chip_cookie', 'strawberry_shortcake',
'cereal_lucky_charms_and_cocoa_puffs', 'sweetzza_chocolate_peanut_butter',
'cherry_cobbler', 'brownie', 'chocolate_cake', 'peach_cobbler',
'cereal_apple_jacks', 'vanilla_pudding', 'mini_donut', 'frozen_yogurt',
'custom_desert_pink_cake', 'ice_cream_cone', 'mousse',
'marshmallow_on_a_stick', 'glazed_donut', 'apple_pie',
'waffle_and_ice_cream', 'brownie_and_yogurt', 'yellow_cake', 'cheesecake',
'blueberry_cobbler', 'oatmeal_cookie', 'peanut_butter_chocolate_fudge',
'm_and_m_cookie', 'caribbean_pie', 'custom_muffin', 'custom_cookie',
'sugar_cookie', 'banana_pudding', 'pound_cake', 'pudding_cake',
'double_oatmeal_cookie_with_frosting_filling', 'toffee',
'snickerdoodle_cookie', 'custom_chocolate_peanut_butter_bars',
'bran_and_raisin_muffin', 'blueberry_cobbler_and_ice_cream',
'cinnamon_apples_and_ice_cream', 'waffle_bar', 'chocolate_pudding',
'sweetzza_cinnamon_pecan', 'sweetzza_apple', 'pancakes', 'spice_cake',
'cinnamon_bread', 'candied_sweet_potatoes', 'bread_pudding', 'oatmeal',
'yogurt', 'cereal_rice_krispies', 'cereal_trix_and_honey_nut_cheerios',
'cereal_reeses_puffs', 'granola_cereal_with_raisins_and_strawberry_yogurt',
'cereal_trix', 'cereal_life', 'cereal_apple_jacks', 'cereal_lucky_charms',
'cereal_corn_pops']
DRINKS_FOODS = ['kiwi_juice', 'sprite_zero', 'sweet_tea', 'water', 'apple_juice',
'whole_milk', 'cranberry_juice', 'coffee', 'kiwi_strawberry_juice', 'sprite',
'skim_milk', 'unsweet_tea', 'sweet_and_unsweet_tea_mix',
'mr_pibb_cherry_coke_mellow_yellow_lemonade_powerade_mix', 'diet_coke',
'coke_zero', 'lemonade_and_sweet_tea_mix', 'powerade', 'chocolate_milk',
'orange_juice_and_apple_berry', 'coca_cola', 'cherry_coke', 'dr_pepper',
'lemonade', 'lowfat_milk', 'orange_fanta', 'pink_lemonade', 'vitamin_water',
'apple_juice_and_water', 'water_and_sweet_tea_mix', 'lowfat_chocolate_milk',
'grape_juice', 'soy_milk', 'green_tea', 'cranberry_juice_water_mix',
'root_beer', 'mellow_yellow', 'orange_juice',
'cranberry_grape_and_sprite_mix', 'mr_pibb', 'chocolate_skim_milk_mix',
'cranberry_juice_cocktail']
FRUIT_VEG_FOODS = ['custom_fruit_bowl', 'apple', 'banana', 'cantaloupe',
'pineapple', 'grapefruit', 'orange', 'olives', 'pear_slices', 'melon',
'blueberries_with_whipped_cream', 'peaches', 'banana_with_peanut_butter',
'ambrosia_fruit_with_yogurt', 'collard_greens', 'green_beans',
'steamed_carrots', 'pickle', 'cauliflower', 'peas', 'broccoli',
'fresh_carrots', 'edamame', 'pears_and_cottage_cheese']
MEAT_DISHES_FOODS = ['hash_sweet_potato_and_bacon', 'roast_beef', 'corn_dog',
'roast_pork_loin', 'biscuits_and_sausage_gravy', 'spicy_bbq_pork_spare_ribs',
'roll', 'smokehouse_bbq_station', 'chicken_enchiladas',
'bbq_turkey_london_broil', 'latin_spiced_pork_roast', 'turkey_meatloaf',
'sausage_links', 'sausage_strata', 'spice_pork_and_vegetables', 'picadillo',
'turkey_sliced', 'hush_puppies', 'buffalo_tenders',
'grilled_italian_sausage_with_onions_and_peppers', 'cajun_roasted_pork_loin',
'roasted_turkey_breast_with_herbed_gravy', 'hot_dog', 'turkey_bacon',
'kielbasa', 'fresca_chicken_quesadilla', 'char_sui_braised_pork',
'grits_and_sausage_links_and_scrambled_eggs', 'sauteed_pollock',
'fried_shrimp', 'shrimp_masala_and_peas', 'pad_thai_shrimp_station',
'blackened_tilapia', 'seafood_newburg', 'vegetable_shrimp_sautee',
'shrimp_masala', 'grilled_ham_steak', 'country_fried_steak',
'chinese_beef_and_green_pepper_steak', 'grilled_chicken',
'grilled_jerk_chicken', 'oven_fried_chicken', 'hunan_chicken',
'baked_honey_bbq_lemon_chicken', 'soy_chicken', 'popcorn_chicken',
'sweet_and_spicy_chicken_with_asian_vegetables', 'baked_rotisserie_chicken',
'sweet_and_spicy_chicken', 'hunters_chicken', 'meat_lasagna',
'southern_frito_pie']
PIZZA_FOODS = ['pepperoni_pizza', 'eggplant_and_broccoli_pizza', 'cheese_pizza',
'margherita_pizza', 'snickers_pizza', 'pineapple_upside_down_pizza',
'chicken_bacon_pesto_pizza', 'chicken,_bacon_and_chipotle_ranch_pizza',
'sausage_pizza', 'bbq_chicken_pizza',
'chicken,_black_bean,_jalapeno,_and_pico_pizza', 'taco_pizza',
'mushroom,_caramelized_onion,_and_pepperoni_pizza',
'strawberry_shortcake_pizza', 'mushroom,_red_pepper_and_pesto_pizza',
'veggie_pizza', 'mushroom,_red_pepper_and_spinach_pizza',
'pepperoni_and_sausage_pizza']
RICE_DISHES_FOODS = ['black_beans_and_rice', 'coconut_rice',
'hunan_chicken_and_rice', 'white_rice', 'mexican_rice', 'cilantro_lime_rice',
'pork_chop_suey_with_white_rice', 'jasmine_curried_balinese_rice',
'yellow_rice', 'jasmine_rice', 'brown_rice',
'shrimp_masala_and_peas_and_rice', 'stir_fry_with_jasmine_rice', 'stir_fry',
'stir_fry_with_edamame', 'vegetable_stir_fry_with_black_bean_sauce',
'vegetable_stir_fry_and_rice_noodles']
SALAD_FOODS = ['salad_bar', 'coleslaw_cowboy', 'custom_spinach_salad',
'caesar_salad', 'mango_salad', 'potato_salad',
'penne_spinach_and_balsamic_salad', 'coleslaw', 'pasta_salad',
'wild_rice_and_barley_salad', 'panzanella_crostini_salad', 'rowdys_coleslaw',
'caesar_salad_station_with_chicken', 'custom_coleslaw', 'vegetable_salad',
'baked_beans_and_carrots_and_coleslaw', 'caprese']
SANDWICHES_FOODS = ['bbq_brisket_on_kaiser_roll', 'custom_sandwich',
'custom_whole_wheat_chicken_salad_sandwich', 'homestyle_chicken_sandwich',
'grilled_cheese_and_tomato_sandwich', 'custom_sandwich_chicken',
'chicken_sandwich_with_pepper_jack_and_pic', 'chicken_sandwich',
'grilled_ham_and_cheese_sandwich', 'custom_spinach_wrap_with_chicken_and_ham',
'israeli_couscous_salad', 'custom_turkey_sub', 'custom_turkey_sandwich',
'custom_pita_grilled_sandwich', 'pepper_jack_ranch_chicken_wrap',
'custom_sandwich_italian', 'buffalo_blue_chicken_wrap',
'custom_sandwich_chicken_salad', 'sloppy_joe',
'custom_chicken_salad_sandwich',
'peanut_butter_and_jelly_wrap_with_rice_krispies', 'monte_cristo_sandwich',
'custom_grilled_chicken_wrap', 'bbq_turkey_sandwich',
'buffalo_chicken_sandwich', 'grilled_veggie_sub', 'custom_chicken_wrap',
'chicken_caesar_wrap', 'custom_wrap', 'bbq_pork_sandwich',
'custom_wrap_spinach', 'california_chicken_wrap',
'italian_sausage_sandwich_with_peppers_and_onions', 'custom_sandwich_turkey',
'chicken_sandwich_with_chipotle_mayo', 'custom_sandwich_fried_peanut_butter',
'reuben_melt', 'vietnamese_pork_sandwich_on_baguette', 'grilled_baguette',
'grilled_cheese_and_bacon_on_texas_toast', 'hamburger', 'garden_burger',
'cheeseburger', 'patty_melt', 'vegetable_egg_roll',
'bacon,_grilled_apple,_and_blue_cheese_bruschetta',
'soft_shell_pork_carnitas_tacos', 'taco', 'soft_chicken_taco', 'bread',
'pita_bread', 'ham,_egg,_cheese,_and_salsa_burrito', 'burrito_station']
SNACKS_FOODS = ['signature_chips', 'garlic_breadsticks', 'pretzels',
'shoestring_french_fries', 'diced_hashbrowns', 'popcorn',
'french_toast_sticks', 'croutons', 'goldfish']
SOUPS_STEWS_FOODS = ['veggie_gumbo_soup', 'corn_soup', 'seafood_bisque_soup',
'lintel_soup', 'vegetable_soup', 'squash_soup', 'tomato_basil_soup',
'chicken_and_corn_soup', 'manhattan_clam_chowder',
'pho_chicken_broth_bowl_station', 'coconut_curry_soup', 'pork_cadillo_stew',
'stew_beef', 'polenta_with_broccoli_rabe_and_mushrooms', 'beans_borracho',
'sweet_creamed_corn']
VEG_DISHES_FOODS = ['overstuffed_potato_station',
'portobella_mushroom_with_bbq_onions', 'capri_blend_vegetables',
'eggplant_parmesan', 'ginger_orange_glazed_steamed_carrots', 'refried_beans',
'baked_potato', 'marinated_tomatoes', 'cauliflower_au_gratin',
'roasted_garlic_potatoes', 'steamed_spinach_with_lemon_pepper',
'mashed_potatoes_and_peas', 'asian_vegetables', 'glazed_baby_carrots',
'steamed_and_seasoned_veggies', 'oven_roasted_red_potatoes',
'steamed_california_blend_vegetables', 'peas_and_carrots', 'corn_on_the_cob',
'roasted_sweet_potato', 'seasoned_yellow_squash', 'black_beans_cumin',
'seasoned_corn', 'african_spiced_sweet_potato',
'chesapeake_corn_and_tomatoes', 'lyonnaise_potatoes', 'corn_salsa',
'mashed_potatoes_and_corn', 'fried_plantains', 'baked_beans',
'steamed_capri_blend_vegetables', 'mashed_red_potatoes', 'chickpeas',
'russet_mashed_potatoes_and_onions', 'wasabi_mashed_potatoes',
'succotash_ancho', 'sauteed_tomatoes_and_zucchini', 'calabacitas',
'broccoli_florets_steamed_with_lemon_zest', 'seasoned_dry_limas',
'cornbread', 'bread_with_refried_beans', 'pasta_tour_of_italy',
'macaroni_and_cheese', 'fiesta_pasta',
'farfalle_pasta_with_broccoli_and_ricotta', 'rotini_with_marinara',
'veggie_indian_curry', 'tofu_grilled_sesame_seed', 'grilled_bbq_tofu',
'scrambled_eggs', 'cottage_cheese', 'spinach_and_cheese_quiche', 'falafel',
'black_bean_cakes', 'eggplant_and_bean_casserole', 'potato_pancakes',
'hard_boiled_eggs']
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _floats_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
class Dataset():
def __init__(self, src_dir, exp_dir, dom_hand_spec, label_spec,
label_spec_inherit, exp_uniform, exp_format):
self.src_dir = src_dir
self.exp_dir = exp_dir
self.dom_hand_spec = dom_hand_spec
self.label_spec = label_spec
self.label_spec_inherit = label_spec_inherit
self.exp_uniform = exp_uniform
self.exp_format = exp_format
# Class names
self.names_1, self.names_2, self.names_3, self.names_4, self.names_5, self.names_6 = \
self.__class_names()
# Class counters
self.counts_1, self.counts_2, self.counts_3, self.counts_4, self.counts_5, self.counts_6 = \
Counter(), Counter(), Counter(), Counter(), Counter(), Counter()
def __class_names(self):
"""Get class names from label master file"""
assert os.path.isfile(self.label_spec), "Couldn't find label_spec file at {}".format(self.label_spec)
names_1, names_2, names_3, names_4, names_5, names_6 = [], [], [], [], [], []
tree = etree.parse(self.label_spec)
categories = tree.getroot()
for tag in categories[0]:
names_1.append(tag.attrib['name'])
for tag in categories[1]:
names_2.append(tag.attrib['name'])
for tag in categories[2]:
names_3.append(tag.attrib['name'])
for tag in categories[3]:
names_4.append(tag.attrib['name'])
for tag in categories[4]:
names_5.append(tag.attrib['name'])
for tag in categories[5]:
names_6.append(tag.attrib['name'])
return names_1, names_2, names_3, names_4, names_5, names_6
def __add_to_class_counts(self, class_counts, labels):
"""Add increment to class counts"""
unique, counts = np.unique(labels, return_counts=True)
new_class_counts = Counter(dict(zip(unique, counts)))
return class_counts + new_class_counts
def __get_food_class(self, food):
if food in DESSERTS_FOODS:
return 'dessert'
elif food in DRINKS_FOODS:
return 'drink'
elif food in FRUIT_VEG_FOODS:
return 'fruit_veg'
elif food in MEAT_DISHES_FOODS:
return 'meat_dish'
elif food in PIZZA_FOODS:
return 'pizza'
elif food in RICE_DISHES_FOODS:
return 'rice_dish'
elif food in SALAD_FOODS:
return 'salad'
elif food in SANDWICHES_FOODS:
return 'sandwich_wrap'
elif food in SNACKS_FOODS:
return 'snack'
elif food in SOUPS_STEWS_FOODS:
return 'soup_stew'
elif food in VEG_DISHES_FOODS:
return 'veg_dish'
else:
return None
def ids(self):
data_dir = os.path.join(self.src_dir, "all-data")
subject_ids = [x for x in next(os.walk(data_dir))[1]]
ids = []
for subject_id in subject_ids:
subject_dir = os.path.join(data_dir, subject_id)
session_ids = [x for x in next(os.walk(subject_dir))[1]]
for session_id in session_ids:
ids.append((subject_id, session_id))
return ids
def check(self, id):
# Path of gesture annotations
gesture_dir = os.path.join(self.src_dir, "all-gt-gestures", id[0],
id[1], "gesture_union.txt")
if not os.path.isfile(gesture_dir):
logging.warn("No gesture annotations found. Skipping {}_{}.".format(
id[0], id[1]))
return False
# Path of bite annotations
bite_dir = os.path.join(self.src_dir, "all-gt-bites", id[0],
id[1], "gt_union.txt")
if not os.path.isfile(bite_dir):
logging.warn("No bite annotations found. Skipping {}_{}.".format(
id[0], id[1]))
return False
return True
def data(self, _, id):
logging.info("Reading raw data from txt")
# Read acc and gyro
dir = os.path.join(self.src_dir, "all-data", id[0], id[1])
files = glob.glob(os.path.join(dir, "*.txt"))
assert files, "No raw data found for {} {}".format(id[0], id[1])
acc = []
gyro = []
with open(files[0]) as dest_f:
# Read voltage values
v_acc_x = []; v_acc_y = []; v_acc_z = []
v_gyro_x = []; v_gyro_y = []; v_gyro_z = []
for row in csv.reader(dest_f, delimiter='\t'):
v_acc_x.append(float(row[0]))
v_acc_y.append(float(row[1]))
v_acc_z.append(float(row[2]))
v_gyro_x.append(float(row[3]))
v_gyro_y.append(float(row[4]))
v_gyro_z.append(float(row[5]))
# Calculate voltage averages for gyroscope
v_gyro_x_avg = np.average(v_gyro_x)
v_gyro_y_avg = np.average(v_gyro_y)
v_gyro_z_avg = np.average(v_gyro_z)
# Derive acceleration in g and rotational velocity in deg/s
for i, vals in enumerate(zip(
v_acc_x, v_acc_y, v_acc_z, v_gyro_x, v_gyro_y, v_gyro_z)):
acc_x = (vals[0] - 1.65) * 1000.0 / ACC_SENSITIVITY
acc_y = (vals[1] - 1.65) * 1000.0 / ACC_SENSITIVITY
acc_z = (vals[2] - 1.65) * 1000.0 / ACC_SENSITIVITY
acc.append([acc_x, acc_y, acc_z])
gyro_x = (vals[3] - v_gyro_x_avg) * 1000.0 / GYRO_SENSITIVITY
gyro_y = (vals[4] - v_gyro_y_avg) * 1000.0 / GYRO_SENSITIVITY
gyro_z = (vals[5] - v_gyro_z_avg) * 1000.0 / GYRO_SENSITIVITY
gyro.append([gyro_x, gyro_y, gyro_z])
dt = TIME_FACTOR // FREQUENCY # In microseconds
timestamps = range(0, len(acc)*dt, dt)
return timestamps, {"hand": (acc, gyro)}
def dominant(self, id):
"""Read handedness, which is the hand sensor was placed on"""
file_path = os.path.join(self.src_dir, "demographics.xlsx")
workbook = xlrd.open_workbook(file_path)
sheet = workbook.sheet_by_index(0)
for rowx in range(sheet.nrows):
cols = sheet.row_values(rowx)
if cols[0].lower() == id[0]:
return cols[4].lower()
return None
def labels(self, _, id, timestamps):
def _index_to_ms(index):
dt = TIME_FACTOR // FREQUENCY
return index * dt
# Read gesture ground truth
gesture_dir = os.path.join(self.src_dir, "all-gt-gestures", id[0],
id[1], "gesture_union.txt")
label_1, label_2, start_time, end_time = [], [], [], []
with open(gesture_dir) as dest_f:
for row in csv.reader(dest_f, delimiter='\t'):
if row[0].lower() in self.names_2:
label_1.append("intake")
label_2.append(row[0].lower())
start_time.append(_index_to_ms(int(row[1])))
end_time.append(_index_to_ms(int(row[2])))
# Read bite ground truth by matching with gestures
bite_dir = os.path.join(self.src_dir, "all-gt-bites", id[0],
id[1], "gt_union.txt")
num = len(timestamps)
labels_1 = np.empty(num, dtype='U25'); labels_1.fill(DEFAULT_LABEL)
labels_2 = np.empty(num, dtype='U25'); labels_2.fill(DEFAULT_LABEL)
labels_3 = np.empty(num, dtype='U25'); labels_3.fill(DEFAULT_LABEL)
labels_4 = np.empty(num, dtype='U25'); labels_4.fill(DEFAULT_LABEL)
labels_5 = np.empty(num, dtype='U25'); labels_5.fill(DEFAULT_LABEL)
labels_6 = np.empty(num, dtype='U25'); labels_6.fill(DEFAULT_LABEL)
for l1, l2, start, end in zip(label_1, label_2, start_time, end_time):
start_frame = np.argmax(np.array(timestamps) >= start)
end_frame = np.argmax(np.array(timestamps) > end)
match_found = False
with open(bite_dir) as dest_f:
for row in csv.reader(dest_f, delimiter='\t'):
time = _index_to_ms(int(row[1]))
if time >= start and time <= end:
if row[2].lower() in self.names_3:
l3 = row[2].lower()
if row[3].lower() in self.names_4:
l4 = row[3].lower()
if row[4].lower() in self.names_5:
l5 = row[4].lower()
food = self.__get_food_class(row[5].lower())
if food in self.names_6:
l6 = food
else:
l6 = "NA"
logging.warn("No food class identified for {}".format(food))
match_found = True
break
if not match_found:
l3 = "NA"; l4 = "NA"; l5 = "NA"; l6 = "NA"
labels_1[start_frame:end_frame] = l1
labels_2[start_frame:end_frame] = l2
if l3 in self.names_3:
labels_3[start_frame:end_frame] = l3
if l4 in self.names_4:
labels_4[start_frame:end_frame] = l4
if l5 in self.names_5:
labels_5[start_frame:end_frame] = l5
if l6 in self.names_6:
labels_6[start_frame:end_frame] = l6
# Update class names
self.counts_1 = self.__add_to_class_counts(self.counts_1, labels_1)
self.counts_2 = self.__add_to_class_counts(self.counts_2, labels_2)
self.counts_3 = self.__add_to_class_counts(self.counts_3, labels_3)
self.counts_4 = self.__add_to_class_counts(self.counts_4, labels_4)
self.counts_5 = self.__add_to_class_counts(self.counts_5, labels_5)
self.counts_6 = self.__add_to_class_counts(self.counts_6, labels_6)
return (labels_1, labels_2, labels_3, labels_4, labels_5, labels_6)
def write(self, path, id, timestamps, data, dominant_hand, labels):
frame_ids = range(0, len(timestamps))
id = '_'.join(id)
def _format_time(t):
return (dt.datetime.min + dt.timedelta(microseconds=t)).time().strftime('%H:%M:%S.%f')
timestamps = [_format_time(t) for t in timestamps]
acc = np.asarray(data["hand"][0])
gyro = np.asarray(data["hand"][1])
assert len(timestamps) == len(acc), \
"Number timestamps and acc readings must be equal"
assert len(timestamps) == len(gyro), \
"Number timestamps and acc readings must be equal"
if self.exp_format == 'csv':
with open(path, 'w', newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(["id", "frame_id", "timestamp", "acc_x", "acc_y",
"acc_z", "gyro_x", "gyro_y", "gyro_z", "hand",
"label_1", "label_2", "label_3", "label_4", "label_5", "label_6"])
for i in range(0, len(timestamps)):
writer.writerow([id, frame_ids[i], timestamps[i],
acc[i][0], acc[i][1], acc[i][2], gyro[i][0], gyro[i][1],
gyro[i][2], dominant_hand, labels[0][i], labels[1][i],
labels[2][i], labels[3][i], labels[4][i], labels[5][i]])
elif self.exp_format == 'tfrecord':
with tf.io.TFRecordWriter(path) as tfrecord_writer:
for i in range(0, len(timestamps)):
example = tf.train.Example(features=tf.train.Features(feature={
'example/subject_id': _bytes_feature(id.encode()),
'example/frame_id': _int64_feature(frame_ids[i]),
'example/timestamp': _bytes_feature(timestamps[i].encode()),
'example/acc': _floats_feature(acc[i].ravel()),
'example/gyro': _floats_feature(gyro[i].ravel()),
'example/label_1': _bytes_feature(labels[0][i].encode()),
'example/label_2': _bytes_feature(labels[1][i].encode()),
'example/label_3': _bytes_feature(labels[2][i].encode()),
'example/label_4': _bytes_feature(labels[3][i].encode()),
'example/label_5': _bytes_feature(labels[4][i].encode()),
'example/label_6': _bytes_feature(labels[5][i].encode())
}))
tfrecord_writer.write(example.SerializeToString())
def done(self):
logging.info("Done")
if not (self.counts_1[DEFAULT_LABEL] == self.counts_2[DEFAULT_LABEL] == self.counts_3[DEFAULT_LABEL] == self.counts_4[DEFAULT_LABEL] == self.counts_5[DEFAULT_LABEL] == self.counts_6[DEFAULT_LABEL]):
logging.warning("Idle counts are not equal for all classes. " +
"Please check label spec and/or label files.")
logging.info("Final number of frames for category 1: {0}.".format(self.counts_1))
logging.info("Final number of frames for category 2: {0}.".format(self.counts_2))
logging.info("Final number of frames for category 3: {0}.".format(self.counts_3))
logging.info("Final number of frames for category 4: {0}.".format(self.counts_4))
logging.info("Final number of frames for category 5: {0}.".format(self.counts_5))
logging.info("Final number of frames for category 6: {0}.".format(self.counts_6))
def get_flip_signs(self):
return FLIP_ACC, FLIP_GYRO
def get_frequency(self):
return FREQUENCY
def get_time_factor(self):
return TIME_FACTOR
def get_train_ids(self):
return TRAIN_IDS
def get_valid_ids(self):
return VALID_IDS
def get_test_ids(self):
return TEST_IDS