def doGet(path, videoName, savePath):
import Make_wavedata, clusteringPoint, make_figure, make_fft
from pythonFile import make_dirs
import os
import pickle
import shutil
print(path)
#保存ディレクトリの作成
'''
if not os.path.isdir(savePath + '/cat1/pict'):
for i in range(4):
os.makedirs(savePath + '/cat' + str(i+1) + '/pict')
if not os.path.isdir(savePath + '/fft'):
os.makedirs(savePath + '/fft')
else:
shutil.rmtree(savePath + '/fft')
os.makedirs(savePath + '/fft')
'''
make_dirs.makeDir(savePath)
zahyou = Make_wavedata.todo(path) #オプティカルフローで各特徴点の移動を推定
print(zahyou)
zahyou = clusteringPoint.todo(path, zahyou) #手動で分類する
make_figure.todo(zahyou, savePath, videoName) #取得した特徴点の動きをグラフにする
fft_savepaht = savePath + '/fft'
make_fft.doGet(path, zahyou, fft_savepaht, videoName) #取得した特徴点の動きをFFT変換する
f = open(savePath + '/pointData_' + videoName + '.txt', 'wb')
pickle.dump(zahyou, f)
def doGet(path, videoName, savePath):
import Make_wavedata, clusteringPoint, make_figure, make_fft, make_dirs
import os
import pickle
import shutil
print(path, videoName, savePath)
#保存ディレクトリの作成
make_dirs.makeDir(savePath)
zahyou = Make_wavedata.todo(path) #オプティカルフローで各特徴点の移動を推定
print(zahyou)
zahyou = clusteringPoint.todo(path, zahyou) #手動で分類する
make_figure.todo(zahyou, savePath, videoName) #取得した特徴点の動きをグラフにする
f = open(savePath + '/pointData_' + videoName + '.txt', 'wb')
pickle.dump(zahyou, f)
fft_savepaht = savePath + '/fft'
make_fft.doGet(path, fft_savepaht, videoName) #取得した特徴点の動きをFFT変換する
def doGet(path, videoName, savePath):
import Make_wavedata, clusteringPoint, make_figure, make_fft
from pythonFile import make_dirs, getVideoData
import os
import pickle
import shutil
print(path, videoName, savePath)
dirName = getVideoData.getDirName(path)
videoName = getVideoData.getVideoName(path)
#保存ディレクトリの作成
make_dirs.makeDir(savePath)
zahyou = Make_wavedata.todo(path) #オプティカルフローで各特徴点の移動を推定
print(zahyou)
if os.path.isdir('/media/koshiba/Data/opticalflow/point_data/' + dirName +
'/' + videoName + '/category.txt'):
f = open(
'/media/koshiba/Data/opticalflow/point_data/' + dirName + '/' +
videoName + '/category.txt', 'rb')
noise = pickle.load(f)
else:
noise = clusteringPoint.todo(path, zahyou) #手動で分類する
make_figure.todo(zahyou, savePath) #取得した特徴点の動きをグラフにする
make_fft.doGet(zahyou, savePath) #取得した特徴点の動きをFFT変換する
predList = [[], [], []]
accuracy = ['-1', '-1', '-1']
precision = ['-1', '-1', '-1']
recall = ['-1', '-1', '-1']
specificity = ['-1', '-1', '-1']
tmp = 0
for index1, pred in enumerate(zahyou):
for index2, answer in enumerate(noise):
#print(index1, index2)
if (pred[index2] == 0 or pred[index2] == -1):
if answer == 0:
predList[index1].append(0)
else:
predList[index1].append(3)
else:
if answer == 1:
predList[index1].append(1)
else:
predList[index1].append(2)
predAll = len(predList[index1])
tp = predList[index1].count(1)
tn = predList[index1].count(0)
fp = predList[index1].count(2)
fn = predList[index1].count(3)
print(predAll, tp, tn)
accuracy[index1] = str((tp + tn) / predAll)
if tp + fp != 0:
precision[index1] = str(tp / (tp + fp))
if tp + fn != 0:
recall[index1] = str(tp / (tp + fn))
if tn + fp != 0:
specificity[index1] = str(tn / (fp + tn))
print(zahyou)
#elapsed_time = time.time() - start
#print ("elapsed_time:{0}".format(elapsed_time) + "[sec]")
print('accuracy:' + accuracy[0] + ' ' + accuracy[1] + ' ' + accuracy[2])
print('precision' + precision[0] + ' ' + precision[1] + ' ' + precision[2])
print('recall' + recall[0] + ' ' + recall[1] + ' ' + recall[2])
print('specificity' + specificity[0] + ' ' + specificity[1] + ' ' +
specificity[2])
f = open(savePath + '/pointData_' + videoName + '.txt', 'wb')
pickle.dump(zahyou, f)
import pandas as pd
from pythonFile import click_pct, k_means, timestump
import math
from tkinter import filedialog
import scipy.stats
import os
import time
# ファイルダイアログからファイル選択
typ = [('', '*')]
dir = 'C:\\pg'
path = filedialog.askopenfilename(filetypes=typ, initialdir=dir)
time_data = timestump.get_time()
start = time.time()
noise = clusteringPoint.todo(path)
classList = Make_wavedata.todo(path, time_data)
savePict.todo(path, classList)
print(noise)
predList = [[], [], []]
accuracy = ['-1', '-1', '-1']
precision = ['-1', '-1', '-1']
recall = ['-1', '-1', '-1']
specificity = ['-1', '-1', '-1']
tmp = 0
for index1, pred in enumerate(classList):
for index2, answer in enumerate(noise):
if answer == pred[index2]:
predList[index1].append(answer)
else:
