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import tensorflow as tf
# load model
MODEL = tf.keras.models.load_model('models/LSTM_model_20signs_7.h5')
#------------------------------
# PRE-PROCESSING CONFIGURATION
#------------------------------
#limit dataset for quick test
QUICK_TEST = True
QUICK_LIMIT = 500
#Define length of sequences for padding or cutting; 22 is the median length of all sequences
LENGTH = 22
#define min or max length of sequences; sequences too long/too short will be dropped
#max value of 92 was defined by calculating the interquartile range
MIN_LENGTH = 10
MAX_LENGTH = 92
#final data will be flattened, if false data will be 3 dimensional
FLATTEN = False
#define initialization of numpy array
ARRAY = False #(True=Zeros, False=empty values)
#Define padding mode
#1 = padding at start&end; 2 = padding at end; 3 = no padding, 4 = copy first/lastframe, 5 = copy last frame)
#Note: Mode 3 will give you an error due to different lengths, working on that
PADDING = 2
CONSTANT_VALUE = 0 #only required for mode 1 and 2; enter tf.constant(float('nan')) for NaN
#define if z coordinate will be dropped
DROP_Z = True
#mirror, flips x coordinate for data augmentation
MIRROR = True
#define if csv file should be filtered
CSV_FILTER = False
#define how many participants for test set
TEST_COUNT = 5 #5 participants account for ca 23% of dataset
#generate test or train dataset (True = Train dataset; False = Test dataset)
#TRAIN = True #only works if CSV_FILTER is activated
TRAIN = True
#filter for specific signs
SIGN_FILTER = True
sign_list = [0,1,5,8]
#define filenames for x and y:
feature_data = 'X' #x data
feature_labels = 'y' #y data
#use for test dataset
#feature_data = 'X_test_h6' #x data
#feature_labels = 'y_test_h6' #y data
RANDOM_STATE = 42
#Defining Landmarks
#index ranges for each landmark type
#dont change these landmarks
FACE = list(range(0, 468))
LEFT_HAND = list(range(468, 489))
POSE = list(range(489, 522))
POSE_UPPER = list(range(489, 510))
RIGHT_HAND = list(range(522, 543))
LIPS = [61, 185, 40, 39, 37, 0, 267, 269, 270, 409,
291,146, 91,181, 84, 17, 314, 405, 321, 375,
78, 191, 80, 81, 82, 13, 312, 311, 310, 415,
95, 88, 178, 87, 14,317, 402, 318, 324, 308]
lipsUpperOuter= [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291]
lipsLowerOuter= [146, 91, 181, 84, 17, 314, 405, 321, 375, 291]
lipsUpperInner= [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308]
lipsLowerInner= [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308]
#defining landmarks that will be merged
averaging_sets = []
#generating list with all landmarks selected for preprocessing
#change landmarks you want to use here:
point_landmarks_right = RIGHT_HAND + lipsUpperInner + lipsLowerInner
point_landmarks_left = LEFT_HAND + lipsUpperInner + lipsLowerInner
#calculating sum of total landmarks used
LANDMARKS = len(point_landmarks_right) + len(averaging_sets)
print(f'Total count of used landmarks: {LANDMARKS}')
#defining input shape for model
if DROP_Z:
INPUT_SHAPE = (LENGTH,LANDMARKS*2)
else:
INPUT_SHAPE = (LENGTH,LANDMARKS*3)
print(INPUT_SHAPE)
#------------------------------
# GAME MECHANICS
#------------------------------
COUNTDOWN = 0
LABEL_MAP = {'brown': 0, 'callonphone': 1, 'cow': 2, 'cry': 3, 'dad': 4, 'fireman': 5, 'frog': 6, 'gum': 7, 'icecream': 8, 'minemy': 9, 'nose': 10, 'owl': 11, 'please': 12, 'radio': 13, 'shhh': 14, 'shirt': 15, 'tomorrow': 16, 'uncle': 17, 'water': 18, 'who': 19}
SELECTED_SIGNS = list(LABEL_MAP.keys())
SELECTED_LABELS = [LABEL_MAP[x] for x in SELECTED_SIGNS]
#------------------------------
# VISUALIZATION
#------------------------------
TRANSITION_FRAMES = LENGTH |