# 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']