创建图像数据集

发布于 2025-01-24 09:01:28 字数 793 浏览 0 评论 0原文

我有一个带有38000张图像的文件夹，我正在尝试从它们中创建一个数据集，以在卷积LSTM中使用，该数据集以5D数组的格式（B，T，W，H，H，C）支持输入，其中B是B。样品的数量，t是样品的大小，其余数组是图像属性。

到目前为止，我已经设法使用以下代码创建了一个Numpy数组：

path = input("folder: ")
training_data = []
for img in os.listdir(path):
    pic = cv2.imread(os.path.join(path,img))
    pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
    pic = cv2.resize(pic,(64,64))
    training_data.append([pic])
np.save(os.path.join(path,'dataset'),np.array(training_data))

使用此代码，我可以使用Shape （38000、1、64、64、3）创建一个数组。

将作为来自移动MNIST等其他数据集的示例数据，我可以看到它是一个具有形状（10000、20、64、64、1）的数组，这意味着它包含10000个图像序列长度20。

如何从数据中创建一个包含图像序列（例如移动MNIST）的数组？

为了澄清，我需要创建一个带有38000张图像的数组，分为10个序列，从而形成形状（3800，10，64，64，64，3） -3800序列长度10 -

原文

I have a folder with 38000 images and I'm trying to create a dataset from them to use in a Convolutional LSTM, which supports input in the format of a 5d array (B,T,W,H,C) where B is the number of samples, T is the size of the samples and the rest of the array are the image properties.

So far I've managed to create a numpy array using the following code:

path = input("folder: ")
training_data = []
for img in os.listdir(path):
    pic = cv2.imread(os.path.join(path,img))
    pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
    pic = cv2.resize(pic,(64,64))
    training_data.append([pic])
np.save(os.path.join(path,'dataset'),np.array(training_data))

With this code I can create an array with the shape (38000, 1, 64, 64, 3).

Using as an example data from other datasets such as Moving MNIST I could see that it is an array with the shape (10000, 20, 64, 64, 1) which means that it contains 10000 sequences of images each of length 20.

How can I create an array from my data that contains image sequences like Moving MNIST?

To clarify, I need to create an array with my 38000 images divided into sequences of 10 resulting in an array with the shape (3800, 10, 64, 64, 3) - 3800 sequences each of length 10 -

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