使用keras将彩色数据集更改为灰度

Question

我从 AstroNN 库加载了一个数据集。因为我相信图像的颜色不是对星系形成进行分类的一个因素，所以我想将所有数据集转换为灰度以减小图像的大小。我应该如何对整个数据集执行此操作？

这是我加载数据集并将其拆分的部分代码：

import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import tensorflow.keras.layers as tfl
from astroNN.datasets import load_galaxy10
from tensorflow.keras import utils
import numpy as np
import tensorflow_datasets as tfds
from tensorflow.keras.utils import to_categorical
import h5py
import matplotlib.pyplot as plt
from matplotlib.pyplot import imread
import scipy
import pandas as pd
import math
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras import layers , models
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.model_selection import train_test_split
import lodgepole.image_tools as lit



# To load images and labels (will download automatically at the first time)
# First time downloading location will be ~/.astroNN/datasets/
images, labels = load_galaxy10()

# To convert the labels to categorical 10 classes
labels = utils.to_categorical(labels, 10)

# To convert to desirable type
labels = labels.astype(np.float32)
images = images.astype(np.float32)


#Split into train and test set

train_idx, test_idx = train_test_split(np.arange(labels.shape[0]), test_size=0.1)
train_ds, train_labels, test_ds, test_labels = images[train_idx], labels[train_idx], images[test_idx], labels[test_idx]

Answer 1

您可以通过使用 np.mean(array,axis=2).

获取每个图像在通道轴上的平均值来实现

我已经下载了您的数据并再次查看了它。由于您的第一个维度是 batch_size，因此您不想在第三个轴上进行平均。您在第 4 轴上为通道着色，以便我们对其进行平均并获得所需的形式。

你不必为了训练你的网络而将它们变成灰度，但如果你特别想要它，你可以去检查以下内容 link： How can I convert an RGB image into grayscale in Python?

使用keras将彩色数据集更改为灰度

changing colorful dataset to Grayscale using keras

computer-vision

deep-learning

conv-neural-network

keras

tensorflow