train.py

from keras.preprocessing.image import img_to_array,load_img
import numpy as np
import glob
import os 

from keras.layers import Conv3D,ConvLSTM2D,Conv3DTranspose
from keras.stae_models import Sequential
from keras.callbacks import stae_modelCheckpoint, EarlyStopping
import imutils

store_image=[]
train_path='./train'
fps=5
trian_videos=os.listdir('train_path')
train_images_path=train_path+'/frames'
os.makedir(train_images_path)

def store_inarray(image_path):
   	image=load_img(image_path)
    image=img_to_array(image)
    image=cv2.resize(image, (227,227), interpolation = cv2.INTER_AREA)
    gray=0.2989*image[:,:,0]+0.5870*image[:,:,1]+0.1140*image[:,:,2]
    store_image.append(gray)

for video in train_videos:
        os.system( 'ffmpeg -i {}/{} -r 1/{}  {}/frames/%03d.jpg'.format(train_path,video,fps,train_path))
        images=os.listdir(train_images_path)
        for image in images:
            image_path=framepath+ '/'+ image
	        store_inarray(image_path)

store_image=np.array(store_image)
a,b,c=store_image.shape
store_image.resize(b,c,a)
store_image=(store_image-store_image.mean())/(store_image.std())
store_image=np.clip(store_image,0,1)
np.save('training.npy',store_image)


#define stae_model

stae_model=Sequential()
stae_model.add(Conv3D(filters=128,kernel_size=(11,11,1),strides=(4,4,1),padding='valid',input_shape=(227,227,10,1),activation='tanh'))
stae_model.add(Conv3D(filters=64,kernel_size=(5,5,1),strides=(2,2,1),padding='valid',activation='tanh'))
stae_model.add(ConvLSTM2D(filters=64,kernel_size=(3,3),strides=1,padding='same',dropout=0.4,recurrent_dropout=0.3,return_sequences=True))
stae_model.add(ConvLSTM2D(filters=32,kernel_size=(3,3),strides=1,padding='same',dropout=0.3,return_sequences=True))
stae_model.add(ConvLSTM2D(filters=64,kernel_size=(3,3),strides=1,return_sequences=True, padding='same',dropout=0.5))
stae_model.add(Conv3DTranspose(filters=128,kernel_size=(5,5,1),strides=(2,2,1),padding='valid',activation='tanh'))
stae_model.add(Conv3DTranspose(filters=1,kernel_size=(11,11,1),strides=(4,4,1),padding='valid',activation='tanh'))

stae_model.compile(optimizer='adam',loss='mean_squared_error',metrics=['accuracy'])


training_data=np.load('training.npy')
frames=training_data.shape[2]
frames=frames-frames%10

training_data=training_data[:,:,:frames]
training_data=training_data.reshape(-1,227,227,10)
training_data=np.expand_dims(training_data,axis=4)
target_data=training_data.copy()

epochs=5
batch_size=1

callback_save = ModelCheckpoint("saved_model.h5",
								monitor="mean_squared_error", save_best_only=True)

callback_early_stopping = EarlyStopping(monitor='val_loss', patience=3)


stae_model.fit(training_data,target_data,
          batch_size=batch_size,
          epochs=epochs,
          callbacks = [callback_save,callback_early_stopping]
          )
stae_model.save("saved_model.h5")