(2021)matlab代码重改python代码，对应函数——全！

蔚落 2021-06-24 13:58 301阅读 0赞

## @前言 ##

因工程需要将matlab代码重构成pyhton代码，以下是个人历程，希望对后来者有所帮助。有错误麻烦积极指正，我将不胜感激。

1.  matlab 函数 对应python基本函数文档下载（**全**）：[猛点免费下载][Link 1]
2.  matlab 对应 numpy：[https://numpy.org/doc/stable/user/numpy-for-matlab-users.html\#table-of-rough-matlab-numpy-equivalents][https_numpy.org_doc_stable_user_numpy-for-matlab-users.html_table-of-rough-matlab-numpy-equivalents]

*  matla和pyhton `图像读取差别`
    
        cv2读入的形式为B G R
        matlab读入的形式为R G B
 *  matlab 加载mat文件`load('file.mat');`
    
        import scipy.io as scio
        
        load_mat = scio.loadmat("file.mat")
        变量a = load_mat["变量a"]
        变量b = load_mat["变量b"]
 *  matlab 变换矩阵形状`reshape(A,[row col])`
    
        A.reshape((row, col), order='F')
 *  matlab 复制矩阵行列`repmat(A ,[row col])`
    
        np.tile(A ,[row col])
 *  matlab 拼接矩阵`[a(:) b(:) c(:)....n(:)`\]
    
        np.c_[a.flatten(), b.flatten(), c.flatten(), d..flatten()]
 *  `conv2(gray_temp1, Gx, 'same');`
    
        scipy.ndimage.filters.convolve1d(gray_temp1, Gx, axis=1, mode='constant')
 *  `circshift(MSCN,[1 0]);`
    
        np.roll(MSCN, 1, axis=0)
 *  `mrdivide(A,B);`
    
        # A * B逆
        np.matmul(A, np.linalg.inv(B))
 *  `mtimes(A, B);`
    
        # A * B
        np.matmul(A, B)
 *  `length(A);`
    
        max(A.shape)
 *  `rgb2hsv`
    
        def rgb2hsv(img):
            """ :param img: 彩色图片 :return: H、S、V 三个通道 """
            HSV = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
            H, S, V = cv2.split(HSV)
            return H, S, V
 *  `// matlab 对图像进行高斯滤波`  
    `MSCN_window = fspecial('gaussian',7,7/6);`  
    `mu = imfilter(Ig,MSCN_window,'replicate');`

def gaussian_filter(img, Hsize=(3, 3), sigma=0.5, boedertype=cv2.BORDER_REPLICATE):
         """ 对图像进行高斯滤波 :param img: 待滤波图像 :param Hsize: 核的尺寸 :param sigma: 标准差 :param boedertype: 边界填充模式 :return: 滤波后的图像 """
         r, c = Hsize
         gaussian_kernel = np.multiply(cv2.getGaussianKernel(r, sigma), (cv2.getGaussianKernel(c, sigma)).T)
         
         filter_img = cv2.filter2D(img, -1, gaussian_kernel, borderType=boedertype)
         return filter_img

*  `im2col(MSCN, [ps ps], 'distinct');`

def im2col(mtx, block_size):
        mtx_shape = mtx.shape
        if mtx_shape[0] % block_size[0] != 0:
            sx = mtx_shape[0] // block_size[0] + 1
        else:
            sx = mtx_shape[0] // block_size[0]
    
        if mtx_shape[1] % block_size[1] != 0:
            sy = mtx_shape[1] // block_size[1] + 1
        else:
            sy = mtx_shape[1] // block_size[1]
    
        result = np.empty((block_size[0] * block_size[1], sx * sy))
        
        for i in range(sy):
            for j in range(sx):
    
                Zeros = np.zeros((block_size[0], block_size[1]))
                block = mtx[j * block_size[0]: j * block_size[0] + block_size[0], i * block_size[1]: i * block_size[1] + block_size[1]]
    
                if block.shape[0] < Zeros.shape[0] and block.shape[1] == Zeros.shape[1]:
                    concat = np.zeros((Zeros.shape[0] - block.shape[0], Zeros.shape[1]))
                    block = np.vstack((block, concat))
    
                if block.shape[1] < Zeros.shape[1] and block.shape[0] == Zeros.shape[0]:
                    concat = np.zeros((Zeros.shape[0], Zeros.shape[1] - block.shape[1]))
                    block = np.hstack((block, concat))
    
                if block.shape[0] < Zeros.shape[0] and block.shape[1] < Zeros.shape[1]:
                    concat = np.zeros((block_size[0], block_size[1]))
                    concat[:block.shape[0], :block.shape[1]] = block
                    block = concat
    
                for line in range(block.shape[0]):
                    Zeros[line, :] = block[line, :]
                result[:, i * sx + j] = Zeros.ravel(order='F')
        return result

*  `entropy`
    
        def entropy(img):
            rows, cols = img.shape
            entropy_list = []
            for col in range(cols):
        
                hist, _ = np.histogram(img[:, col], 256, [0, 256])
                noZero_list = []
                index = np.where(hist != 0)[0]
                for idx in index:
                    noZero_list.append(hist[idx])
                noZero_array = np.array(noZero_list, dtype=np.float32)
                noZero_array = noZero_array / rows
                E = -np.sum(noZero_array * np.log2(noZero_array), axis=0)
                entropy_list.append(E)
        
            return np.array(entropy_list, dtype=np.float32)

[Link 1]: http://mathesaurus.sourceforge.net/matlab-python-xref.pdf
[https_numpy.org_doc_stable_user_numpy-for-matlab-users.html_table-of-rough-matlab-numpy-equivalents]: https://numpy.org/doc/stable/user/numpy-for-matlab-users.html#table-of-rough-matlab-numpy-equivalents