python VS matlab: reshape/max/matrix index等方法比较

matlab是进行学术研究必备的工具软件，python是进行工程实践的必备软件。因为matlab中矩阵运算的方便高效，有些代码前期会在matlab中编写，后面再转到python。在转换的过程中，发现matlab和python中一些功能函数的不同，现总结如下，欢迎大家批评指正！

1.reshape

matlab

reshape(1:12,3,4)

python

    a = np.array(range(1,13)).reshape(-1,4)print(a)

对比发现，matlab和python对数组的存储方式不同，==前者按列，后者按行。==
python中要得到同matlab的数组，可通过矩阵转置实现：

对于高维数组，通过矩阵转置就不方便了，在matlab中可以通过permute函数实现矩阵维度的交换：

%% 2D
data2D = reshape(1:12,3,4)
data2D = permute(data2D,[2,1])%% 3D
data3D = reshape(1:12,3,2,2)  % 维度顺序为：1，2，3
data3D = permute(data3D,[3,2,1]) % 即实现1，3维度置换，为 2*2*3. ps:可以再测试[3,1,2]

对于在图像频率滤波和光度立体的基于Fourier基函数深度估计中应用广泛的FFT，同样存在matlab、python的主轴不同问题。

对于三维数组的FFT变换，转换方式如下：

data = reshape(1:12,3,2,2)
% newdata = permute(data,[3,2,1])
%% 除了用permute函数，也可以自定义实现
newdata = zeros(2,2,3);
for i = 1:2for j = 1:2newdata(j,i,:) = data(:,i,j); % 转置end
end
newdata
fft(newdata)

    data = np.array(range(1,13)).reshape(-1,2,3)print(data)print(np.fft.fft(data))print(np.fft.fft(data,axis=0))

2.max

matlab

clc
close all
clear allTestMaxfunction  TestMax()
%% scalar
res = max(6,[1 4 6 7 9 0 3])   % 逐个比较,输出数组
res = max(3,[1+i,2-2i,3+4i,4-6i,-i,5-2i]) % 只与复数的实部比较
res = max(1+3i,[2+3i,2-2i,3+4i,4-6i,3-i,5-2i])  % 按照模长比较data = [1+i,2-2i,3+4i,4-6i,-i,5-2i;2+3i,2-2i,3+4i,4-6i,3-i,5-2i]
data(1) = 0 % 数组在matlab中按列存储,每个元素对应一个索引,将第一个元素设为0，与data(1,:)相区别end

def TestMax():# res = max(3,np.array([1,4,6,7,9,0,3]))  #  error# res = np.max(3,np.array([1,4,6,7,9,0,3]))  # error# res = np.maximum(3,np.array([1,4,6,7,9,0,3]))  # 同matlab中 max# res = np.maximum(3,np.array([1-1j,4+1j,6-2j,7,9+3j,0-2j,3-1j]))  # 同matlab中 maxres = np.maximum(3+1j,np.array([1-1j,4+1j,6-2j,7,9+3j,0-2j,3-1j]))  # 同matlab中 maxprint(res)data = np.array([[1+1j,2-2j,3+4j,4-6j,-1j,5-2j],[2+3j,2-2j,3+4j,4-6j,3-1j,5-2j]])data[0] = 0  # 结果同data[0,:]  将第一行全设为0print(data)

3.index

matlab是矩阵实验室(Matrix laboratory)的简称，原就是为了方便高效进行矩阵运算而开发。特点是矩阵index从1开始，支持end操作，矩阵的切片操作十分简便。
以矩阵的中心差分说明numpy包和matlab在矩阵操作上的异同。

matlab

function TestIndex()img = imread('demoshape.png');if(numel(size(img)) > 2)img = rgb2gray(img);end%% 与原图不同大小px = img(2:end,:) - img(1:end-1,:); % 数据类型均为uint8, 负数强制为0. python中负数强制为其补数qy = img(:,2:end) - img(:,1:end-1);%% 与原图相同大小ppx = img([2:end end],:) - img([1 1:end-1],:);qqy = img(:,[2:end end]) - img(:,[1 1:end-1]);figuresubplot(231)imshow(img)subplot(232)imshow(px)subplot(233)imshow(qy)subplot(234)imshow(ppx)subplot(235)imshow(qqy)canny = edge(img,'canny',0.1);subplot(236)imshow(canny)
end

matlab的imshow函数显示的原因，有些细节需要放大才会显示清楚，建议自己运行下代码查看结果。

python
关键在于如何在python中方便高效地表达矩阵的索引操作：

end - k 的表达
k:end，m:end - k的表达

def CalcImgDiff(imglight,imgdark,opt: int = 0):assert imglight.shape == imgdark.shapeH,W = imglight.shapeimgdiff = np.zeros_like(imglight,dtype=np.uint8)if opt == 0:return imglight - imgdarkelif opt == 1:for i in range(H):for j in range(W):if imglight[i,j] > imgdark[i,j]:imgdiff[i,j] = imglight[i,j] - imgdark[i,j]elif opt == 2:for i in range(H):for j in range(W):imgdiff[i, j] = abs(imglight[i, j] - imgdark[i, j])return imgdiffdef TestIndex():img = cv2.imread('./imgs/others/img/demoshape.png',0)# img_ref = scio.loadmat('./imgs/others/img/demoshape_img.mat')['img']# px_ref = scio.loadmat('./imgs/others/img/demoshape_px.mat')['px']# qy_ref = scio.loadmat('./imgs/others/img/demoshape_qy.mat')['qy']# ppx_ref = scio.loadmat('./imgs/others/img/demoshape_ppx.mat')['ppx']# qqy_ref = scio.loadmat('./imgs/others/img/demoshape_qqy.mat')['qqy']img = cv2.resize(img,(400,300))H,W = img.shapeimglight = img[1:H, :]imgdark = img[0:H - 1, :]px = CalcImgDiff(imglight,imgdark,0)imglight = img[:,1:W]imgdark = img[:,0:W-1]qy = CalcImgDiff(imglight, imgdark,0)# 关键如何表达 [1:end end]row_former_index = list(range(0, H - 1))row_former_index.insert(0, 0)row_latter_index = list(range(1, H))row_latter_index.append(-1)col_former_index = list(range(0, W - 1))col_former_index.insert(0, 0)col_latter_index = list(range(1, W))col_latter_index.append(-1)imglight = img[row_latter_index, :]imgdark = img[row_former_index, :]ppx =  CalcImgDiff(imglight,imgdark,0)imglight = img[:, col_latter_index]imgdark = img[:, col_former_index]qqy =  CalcImgDiff(imglight,imgdark,0)plt.figure()plt.subplot(231)plt.imshow(img,cmap=plt.cm.gray,vmin=0,vmax=255)plt.subplot(232)plt.imshow(px,cmap=plt.cm.gray,vmin=0,vmax=255)plt.subplot(233)plt.imshow(qy,cmap=plt.cm.gray,vmin=0,vmax=255)plt.subplot(234)plt.imshow(ppx,cmap=plt.cm.gray,vmin=0,vmax=255)plt.subplot(235)plt.imshow(qqy,cmap=plt.cm.gray,vmin=0,vmax=255)canny = cv2.Canny(img,1,255)plt.subplot(236)plt.imshow(canny,cmap=plt.cm.gray,vmin=0,vmax=255)plt.show()

总结：
matlab: index从1开始，python： index从0开始
matlab：m:n，列表结果包含n，python：m:n，列表结果不含n
具体表达如下，其中 i∈N+;j,k∈Ni\in \mathbb{N^+}; j, k \in \mathbb{N} i∈N+;j,k∈N

matlab：A(end-k,:) python : A[H - k - 1,:]
matlab: A(i:end-j,:) python : A[i - 1:H - j,:]