Question

我想在keras中使用 LSTM进行视频处理。但视频数据中LSTM的输入形状有问题。请告诉我，我的代码是否正确？？？而且我知道LSTM的视频时长没有限制，但是当我使用**

为True时：

在我的代码中

**才能获取所有我出错的帧，并且每个视频必须获取 40帧。请指导我解决我的问题。我有 297个视频，我的数据形状为（297,40,100,100），其中297个为样本总数，40个为每个视频的帧数，100 * 100为像素。如何获得每个视频的所有帧没有长度限制的LSTM 模型？

    listdata_frame = []
    num_channel=1
    w=100
    h=100
    opt=[]
    cap = cv2.VideoCapture(vid)
    batch_size = 2
    num_classes = 3
    epochs = 10

    ret, frame1 = cap.read()
    frame1 = cv2.resize(frame1, (w,h))
    prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)

    #while(1):
    for k in range(40):
        ret, frame2 = cap.read()
        if not ret:
            break
        else:

            frame2 = cv2.resize(frame2, (w,h))
            next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
            #next = img_to_array(next)
            #next = next.shape[0]
            #next= next.transpose(Image.FLIP_LEFT_RIGHT)
            opt.append(next)
            prvs = next


    cap.release()        
    cv2.destroyAllWindows()
    input = np.array(opt)
    input = input.astype('float32')
    listdata_frame.append(input)
    #opt=[]
    img_data1= np.array(listdata_frame)  
    num_samples1 = len(img_data1)

    #Assign Label to each class
    label1=np.ones((num_samples1),dtype = int)
    label1[0:2]= 0
    label1[2:4] = 1
    label1[4:] = 2

    Y=np_utils.to_categorical(label1,num_classes)

    x,y=shuffle(img_data1,Y,random_state=2)
    x_train,x_test,y_train,y_test=train_test_split
               (x,y,test_size=0.2,random_state=4)

    timesteps = 40


    x_train_reshape = [x.reshape((timesteps, 100,-1)) for x in x_train]

    x_train = x_train.reshape(x_train.shape[0],
                x_train.shape[1]*x_train.shape[2], x_train.shape[3])
    x_train = np.array(x_train)

    n_classes= 3
    n_units=128
    n_epochs=5
    batch_size = 2

    model = Sequential()
    model.add(LSTM(n_units, input_shape=(x_train.shape[1],
                 x_train.shape[2])))
    model.add(Dense(n_classes, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                optimizer='rmsprop',metrics=['accuracy'])

    model.fit(x_train, y_train, 
                 batch_size= batch_size, epochs=n_epochs, shuffle=False)

keras中LSTM的视频输入形状

0 个答案: