TS05_PIR/Data_load_spec.py at master · ts05/TS05_PIR · GitHub

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import numpy as np
import os

def Data_load(weather, exist, sensor_use, X, Y):

    current_path = os.getcwd()

    classfication = Labeling_tag(weather, exist)
    print(classfication)
    os.chdir("labeled_data/" + weather + "/" + exist + "/" + str(sensor_use) + "/")

    print(os.getcwd())

    for filename in os.listdir("."):

        namelist = filename.split('_')
        name = namelist[3].split('-')

        if name[0] == "03" or (name[0] == "04" and namelist[5].startswith("None")):
            print(filename)
            data = np.loadtxt(filename, delimiter=",", dtype=np.float32)

            for i in range(0, data.__len__()):
                X.append(data[i])
                Y.append(classfication)
                if classfication[2] == 1 and i > 200 :
                    print("Cut.. ! (>200)")
                    break


    os.chdir(current_path)


def Data_find_load(weather, exist, sensor_use, X, Y):

    current_path = os.getcwd()


def Labeling_tag(weather, exist):
    if weather == "Sunny":
        if exist == "Animal":
            return [1, 0, 0, 1, 0, 0]
        elif exist == "Both":
            return [1, 0, 0, 0, 1, 0]
        elif exist == "Human":
            return [1, 0, 0, 0, 0, 1]
        elif exist == "None":
            return [1, 0, 1, 0, 0, 0]

    elif weather == "Rain":
        if exist == "Animal":
            return [0, 1, 0, 1, 0, 0]
        elif exist == "Both":
            return [0, 1, 0, 0, 1, 0]
        elif exist == "Human":
            return [0, 1, 0, 0, 0, 1]
        elif exist == "None":
            return [0, 1, 1, 0, 0, 0]