Python Numpy 展平 RGB 图像阵列
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Numpy flatten RGB image array
提问by apples-oranges
I have 1,000 RGB images (64X64) which I want to convert to an (m, n) array.
我有 1,000 张 RGB 图像 (64X64)，我想将其转换为 (m, n) 数组。
I use this:
我用这个：
import numpy as np
from skdata.mnist.views import OfficialImageClassification
from matplotlib import pyplot as plt
from PIL import Image
import glob
import cv2
x_data = np.array( [np.array(cv2.imread(imagePath[i])) for i in range(len(imagePath))] )
print x_data.shape
Which gives me: (1000, 64, 64, 3)
这给了我： (1000, 64, 64, 3)
Now if I do:
现在，如果我这样做：
pixels = x_data.flatten()
print pixels.shape
I get: (12288000,)
我得到： (12288000,)
However, I require an array with these dimensions: (1000, 12288)
但是，我需要一个具有以下维度的数组： (1000, 12288)
How can I achieve that?
我怎样才能做到这一点？
采纳答案by Ray
Apply the numpy method reshape()after applying flatten()to the flattened array:
应用到扁平化数组reshape()后应用numpy 方法flatten()：
x_data = np.array( [np.array(cv2.imread(imagePath[i])) for i in range(len(imagePath))] )
pixels = x_data.flatten().reshape(1000, 12288)
print pixels.shape
回答by lskrinjar
Try this:
尝试这个：
d1, d2, d3, d4 = x_data.shape
then using numpy.reshape()
然后使用 numpy.reshape()
x_data_reshaped = x_data.reshape((d1, d2*d3*d4))
or
或者
x_data_reshaped = x_data.reshape((d1, -1))
(Numpy infers the the value instead of -1from original length and defined dimension d1)
（Numpy 推断值而不是-1从原始长度和定义的尺寸d1）
回答by James Evans
You can iterate over your images array and flatten each row independently.
您可以遍历图像数组并独立展平每一行。
numImages = x_data.shape[0]
flattened = np.array([x_data[i].flatten() for i in range(0,numImages)])
回答by Limkin
You could also use this: X is your 2D picture with size 32x32 for example and the -1 it simply means that it is an unknown dimension and we want numpy to figure it out. And numpy will figure this by looking at the 'length of the array and remaining dimensions' and making sure it satisfies the above mentioned criteria (What does -1 mean in numpy reshape?). T means to invert the transposition of tensors when using the axes keyword argument (https://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html).
你也可以使用这个：X 是你的 2D 图片，例如大小为 32x32 和 -1 它只是意味着它是一个未知的维度，我们希望 numpy 弄清楚它。并且 numpy 将通过查看“数组的长度和剩余维度”并确保它满足上述标准（numpy reshape 中 -1 是什么意思？）来计算这一点。T 表示在使用轴关键字参数 ( https://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html)时反转张量的转置。
X_flatten = X.reshape(X.shape[0], -1).T
