from postprocesslen import Processor
import csv
import pyaudio
from significancetest import Trendtest as Tt
sigtest = Tt()
k = open("streamtest/real_time.csv", "w")
writer_log = csv.writer(k, lineterminator="\n")
p = open("streamtest/real_time_report_silent.csv", "w")
writer_log_raw = csv.writer(p, lineterminator="\n")
filter = Processor(file_name="streamtest/predict_sig.csv")
ParseData = DP()
RunGraph = WG()
SetMaker = SM()
JUMPTIME = 2
FRAMERATE = 4096
CHUNK = 8192
OFFSET = 512
TIMEOUT = 4096
TIMEOUTSECS = TIMEOUT / FRAMERATE
ALPHALEVEL = 0.995
STREAMING_ALPHA_LEVEL = 0.90
FORMAT = pyaudio.paInt16
timex = time.clock()
time_zero = time.clock()
prediction_dictionary = {0: "inhale", 1: "exhale", 2: "unknown"}
x = -1
last_x = -1
time_last = time.clock()
class Hyperparameters:
INPUT_LAYER = 43
HIDDEN_LAYER = 75 #Modify??
OUTPUT_LAYER = 3
NUM_EPOCHS = 10000
#NUM_EPOCHS = 1
BATCH_NUMBER = 240
LEARNING_RATE = 0.1
VALIDATION_NUMBER = 30
TEST_NUMBER = 30
HYP = Hyperparameters()
set_maker = SM()
pbfilename = "GraphV5/GRAPHS/GraphV5_frozen.pb"
file_name = "sen_data/inhale/61.wav"
prediction_dictionary = {0: "inhale", 1: "exhale", 2: "unknown"}
with tf.gfile.GFile(pbfilename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
input = graph.get_tensor_by_name("placeholders/input_placeholder:0")
output = graph.get_tensor_by_name("prediction_and_loss/output:0")