def todo(path, zahyou):
import numpy as np
import cv2
from pythonFile import click_pct, getVideoData
import os
import pickle
padding = 10 # 特徴点検出領域の半径
p = 10
winsize = 25
c = [[255, 0, 0], [0, 0, 255], [0, 255, 0], [0, 255, 255]] # 特徴点の色
selectDir = ['cat1', 'cat2', 'cat3', 'cat4']
# 読み込む動画の設定
videoDir = path[:path.rfind('/')]
dirName = getVideoData.getDirName(path)
videoName = getVideoData.getVideoName(path)
savePath = '/media/koshiba/Data/opticalflow/point_data/' + dirName + '/' + videoName
cap = cv2.VideoCapture(path)
print(path[path.rfind('/') + 1:])
# 動画の設定
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
rot = 0
# 動画が横向きならば縦向きに回転させる
if width > height:
rot = 1
tmp = width
width = height
height = tmp
print(videoName)
# Shi-Tomashiのコーナー検出パラメータ
feature_params = dict(
maxCorners=255, # 保持するコーナー数,int
qualityLevel=0.2, # 最良値(最大個数値の割合),double
minDistance=7, # この距離内のコーナーを棄却,double
blockSize=7, # 使用する近傍領域のサイズ,int
useHarrisDetector=False, # FalseならShi-Tomashi法
# k=0.04, # Harris法の測度に使用
)
# 最初のフレームを読み込む
ret, first_frame = cap.read()
if rot == 1:
first_frame = np.rot90(first_frame, -1)
#グレースケール変換
first_gray = cv2.cvtColor(first_frame, cv2.COLOR_BGR2GRAY)
# 読み込んだフレームの特徴点を探す
prev_points = cv2.goodFeaturesToTrack(
image=first_gray[p:-p][p:-p], # 入力画像
mask=None, # mask=0のコーナーを無視
**feature_params)
flow_layer = np.zeros_like(first_frame)
# 一度すべての点をノイズとする
noise = [0] * len(prev_points)
for i in prev_points:
flow_layer = cv2.circle(
flow_layer, # 描く画像
(int(i[0][0]), int(i[0][1])), # 線を引く始点
2, # 線を引く終点
color=c[0], # 描く色
thickness=3 # 線の太さ
)
frame = cv2.add(first_frame, flow_layer)
save_layer_list = [np.zeros_like(first_frame)] * len(zahyou)
selectDirList = ['/cat1/'] * len(zahyou)
#######################################
# クリックした特徴点を正常な特徴点とする
#######################################
while True:
# クリックした座標を保存
ret, points = click_pct.give_coorList(frame)
points = np.array(points, dtype='int')
# クリックした座標の周囲の点を正常な特徴点とする
for p in points:
area = [
p[0] - padding, p[0] + padding, p[1] - padding, p[1] + padding
]
for index, prev in enumerate(prev_points):
if (area[0] <= int(prev[0][0])) and (area[1] >= int(
prev[0][0])) and (area[2] <= int(
prev[0][1])) and (area[3] >= int(prev[0][1])):
if (noise[index] == 0):
noise[index] = 1
#break
elif (noise[index] == 1):
noise[index] = 2
#break
elif (noise[index] == 2):
noise[index] = 3
#break
elif (noise[index] == 3):
noise[index] = 0
#break
# 特徴点を描画するlayer
cat1_layer = np.zeros_like(first_frame)
cat2_layer = np.zeros_like(first_frame)
cat3_layer = np.zeros_like(first_frame)
cat4_layer = np.zeros_like(first_frame)
for index, prev in enumerate(prev_points):
save_layer = np.zeros_like(first_frame)
save_layer_list[index] = cv2.circle(
save_layer, # 描く画像
(int(prev[0][0]), int(prev[0][1])), # 線を引く始点
5, # 線を引く終点
color=c[noise[index]], # 描く色
thickness=3 # 線の太さ
)
if noise[index] == 0:
selectDirList[index] = '/cat1/'
cat1_layer = cv2.circle(
cat1_layer, # 描く画像
(int(prev[0][0]), int(prev[0][1])), # 線を引く始点
5, # 線を引く終点
color=c[noise[index]], # 描く色
thickness=3 # 線の太さ
)
elif noise[index] == 1:
selectDirList[index] = '/cat2/'
cat2_layer = cv2.circle(
cat2_layer, # 描く画像
(int(prev[0][0]), int(prev[0][1])), # 線を引く始点
5, # 線を引く終点
color=c[noise[index]], # 描く色
thickness=3 # 線の太さ
)
elif noise[index] == 2:
selectDirList[index] = '/cat3/'
cat3_layer = cv2.circle(
cat3_layer, # 描く画像
(int(prev[0][0]), int(prev[0][1])), # 線を引く始点
5, # 線を引く終点
color=c[noise[index]], # 描く色
thickness=3 # 線の太さ
)
elif noise[index] == 3:
selectDirList[index] = '/cat4/'
cat4_layer = cv2.circle(
cat4_layer, # 描く画像
(int(prev[0][0]), int(prev[0][1])), # 線を引く始点
5, # 線を引く終点
color=c[noise[index]], # 描く色
thickness=3 # 線の太さ
)
# フレームに特徴点layerを追加する
frame = cv2.add(first_frame, cat1_layer)
frame = cv2.add(frame, cat2_layer)
frame = cv2.add(frame, cat3_layer)
frame = cv2.add(frame, cat4_layer)
layer_list = [cat1_layer, cat2_layer, cat3_layer, cat4_layer]
if ret == 1:
break
# 結果画像の表示
# cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
# cv2.imshow("frame", frame)
# cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite(savePath + '/' + videoName + '.jpg', frame)
# 分類結果の画像を保存する
for n, layer in enumerate(layer_list):
frame = cv2.add(first_frame, layer)
cv2.imwrite(
savePath + '/' + selectDir[n] + '/' + videoName + '_' +
selectDir[n] + '.jpg', frame)
for index in range(len(zahyou)):
save_frame = cv2.add(first_frame, save_layer_list[index])
cv2.imwrite(
savePath + '/' + selectDirList[index] + '/pict/' + videoName +
'_' + str(index + 1) + '.jpg', save_frame)
category = np.array(noise)
print(path[:path.find('/video')] + "/opticalflow/point_data/" + dirName +
'/' + videoName + '/category.txt')
f = open(
path[:path.find('/video')] + "/opticalflow/point_data/" + dirName +
'/' + videoName + '/category.txt', "wb")
pickle.dump(category, f)
return zahyou
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)
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']
recall = ['-1', '-1', '-1']
specificity = ['-1', '-1', '-1']
tmp = 0
