资源简介
AnomalyDetectionCVPR2018-master工程，用于异常事件检测，异常行为识别等
代码片段和文件信息
from keras.models import Sequential
from keras.layers import Dense Dropout Activation
from keras.regularizers import l2
from keras.optimizers import SGD Adagrad
from scipy.io import loadmat savemat
from keras.models import model_from_json
import theano.tensor as T
import theano
import csv
import configparser
import collections
import time
import csv
from math import factorial
import os
from os import listdir
import skimage.transform
from skimage import color
from os.path import isfile join
import numpy as np
import numpy
from datetime import datetime
from scipy.spatial.distance import cdistpdistsquareform
import theano.sandbox
#import c3D_model
#import Initialization_function
#from moviepy.editor import VideoFileClip
#from IPython.display import Image display
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import os sys
import pickle
from PyQt4 import QtGui # If PyQt4 is not working in your case you can try PyQt5
seed = 7
numpy.random.seed（seed）
def load_model（json_path）:
model = model_from_json（open（json_path）.read（））
return model
def load_weights（model weight_path）:
dict2 = loadmat（weight_path）
dict = conv_dict（dict2）
i = 0
for layer in model.layers:
weights = dict[str（i）]
layer.set_weights（weights）
i += 1
return model
def conv_dict（dict2）: # Helper function to save the model
i = 0
dict = {}
for i in range（len（dict2））:
if str（i） in dict2:
if dict2[str（i）].shape == （0 0）:
dict[str（i）] = dict2[str（i）]
else:
weights = dict2[str（i）][0]
weights2 = []
for weight in weights:
if weight.shape in [（1 x） for x in range（0 5000）]:
weights2.append（weight[0]）
else:
weights2.append（weight）
dict[str（i）] = weights2
return dict
def savitzky_golay（y window_size order deriv=0 rate=1）:
#try:
window_size = np.abs（np.int（window_size））
order = np.abs（np.int（order））
#except ValueError msg:
# raise ValueError（“window_size and order have to be of type int“）
if window_size % 2 != 1 or window_size < 1:
raise TypeError（“window_size size must be a positive odd number“）
if window_size < order + 2:
raise TypeError（“window_size is too small for the polynomials order“）
order_range = range（order + 1）
half_window = （window_size - 1） // 2
b = np.mat（[[k ** i for i in order_range] for k in range（-half_window half_window + 1）]）
m = np.linalg.pinv（b）.A[deriv] * rate ** deriv * factorial（deriv）
firstvals = y[0] - np.abs（y[1:half_window + 1][::-1] - y[0]）
lastvals = y[-1] + np.abs（y[-half_window - 1:-1][::-1] - y[-1]）
y = np.concatenate（（firstvals y lastvals））
return np.convolve（m[::-1] ymode=‘valid‘）
# Load Video
def load_dataset_One_Video_Features（Test_Video_Path）:
Vid 属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
目录 0 2018-11-30 18:51 AnomalyDetectionCVPR2018-master\
目录 0 2018-12-03 09:50 AnomalyDetectionCVPR2018-master\Dataset\
目录 0 2018-11-30 18:45 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\
目录 0 2018-11-30 18:45 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000001.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000017.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000033.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000049.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000065.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000081.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000097.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000113.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000129.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000145.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000161.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000177.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000193.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000209.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000225.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000241.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000257.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000273.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000289.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000305.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000321.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000337.fc6-1
文件 16404 2018-11-29 17:57 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features\Avg\000353.fc6-1
目录 0 2018-12-03 10:03 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features_txt\
文件 1179680 2018-12-03 10:03 AnomalyDetectionCVPR2018-master\Dataset\C3D_Features_txt\Avg_C.txt
文件 8150 2018-11-30 18:51 AnomalyDetectionCVPR2018-master\Demo_GUI.py
文件 3468 2018-10-18 18:34 AnomalyDetectionCVPR2018-master\Evaluate_Anomaly_Detector.m
............此处省略38个文件信息
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