# フーリエ変換に使う変数
dt = 1.0 / fps
N = len(vector1)
t = np.arange(0, N * dt, dt)
fq = np.linspace(0, 1.0 / dt, N)
# 手法２を実行
hz_fft2, num_fft2 = class2(np_vector_normal, hz_fft2, num_fft2)
# 手法３を実行
hz_fft3, num_fft3 = class3(np_err_normal, hz_fft3, num_fft3)
# 手法１～３を実行
class1Data = class1_output(class1_err, zahyou)
class2Data = class2_output(hz_fft2, num_fft2)
class3Data = class3_output(hz_fft3, num_fft3)
classList = [class1Data, class2Data, class3Data]
return classList
if __name__ == '__main__':
from tkinter import filedialog
from pythonFile import click_pct, timestump
import glob
import os
# ファイルダイアログからファイル選択
time = timestump.get_time()
typ = [('', '*')]
dir = 'C:\\pg'
path = filedialog.askopenfilename(filetypes=typ, initialdir=dir)
todo(path, time)
from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
from pythonFile import click_pct, k_means, timestump, getVideoData
import math
from tkinter import filedialog
import scipy.stats
import os
import time
import pickle
# ファイルダイアログからファイル選択
typ = [('','*')]
dir = '/media/koshiba/Data/video'
path = filedialog.askopenfilename(filetypes = typ, initialdir = dir)
time_data = timestump.get_time()
start = time.time()
dirName = getVideoData.getDirName(path)
videoName = getVideoData.getVideoName(path)
f = open('/media/koshiba/Data/opticalflow/point_data/' + dirName + '/' + videoName + '/category.txt', 'rb')
noise = pickle.load(f)
#noise = clusteringPoint.todo(path)
classList = Make_wavedata.todo(path, time_data)
print(noise)
predList = [[],[],[]]
accuracy = ['-1', '-1', '-1']
precision = ['-1', '-1', '-1']