关于OpenCV的图像矩阵拼接（Python版本）及numpy.concatenate函数介绍

功能：给定任意大小的两个图片（矩阵），水平连接成一个图片（矩阵）。高度不同时，使用黑色作为高度较小者的边缘填充，图片垂直居中。

import cv2
import numpy as np

def image_join(image1, image2):
    """
    水平合并两个opencv图像矩阵为一个图像矩阵
    :param image1:
    :param image2:
    :return:
    """
    h1, w1 = image1.shape[0:2]
    h2, w2 = image2.shape[0:2]

    if h1 > h2:
        margin_height = h1 - h2
        if margin_height % 2 == 1:
            margin_top = int(margin_height / 2)
            margin_bottom = margin_top + 1
        else:
            margin_top = margin_bottom = int((h1 - h2)/2)
        image2 = cv2.copyMakeBorder(image2, margin_top, margin_bottom, 0, 0, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    elif h2 > h1:
        margin_height = h2 - h1
        if margin_height % 2 == 1:
            margin_top = int(margin_height / 2)
            margin_bottom = margin_top + 1
        else:
            margin_top = margin_bottom = int(margin_height / 2)
        image1 = cv2.copyMakeBorder(image1, margin_top, margin_bottom, 0, 0, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    return np.concatenate((image1, image2), axis=1)

顺便介绍一下numpy的concatenate()函数:

def concatenate(arrays, axis=None, out=None):
    """
    concatenate((a1, a2, ...), axis=0, out=None)

    Join a sequence of arrays along an existing axis.

    Parameters
    ----------
    a1, a2, ... : sequence of array_like
        The arrays must have the same shape, except in the dimension
        corresponding to `axis` (the first, by default).
    axis : int, optional
        The axis along which the arrays will be joined.  If axis is None,
        arrays are flattened before use.  Default is 0.
    out : ndarray, optional
        If provided, the destination to place the result. The shape must be
        correct, matching that of what concatenate would have returned if no
        out argument were specified.

    Returns
    -------
    res : ndarray
        The concatenated array.
    """

参数arrays：可接收一个矩阵列表，函数会把说有矩阵依次连接。

参数axis：连接方向（轴向），axis=0时，垂直连接；axis=1时，水平连接；axis=None时，扁平输出，即把二维矩阵中的每个元素横向顺序依次放到一个一维矩阵（列表）中。

参数out：指定时，会把结果放到这个参数中；out=None时，直接把结果返回出去。

 

最好的说明是官方举例：

"""
Examples
    --------
    >>> a = np.array([[1, 2], [3, 4]])
    >>> b = np.array([[5, 6]])
    >>> np.concatenate((a, b), axis=0)
    array([[1, 2],
           [3, 4],
           [5, 6]])
    >>> np.concatenate((a, b.T), axis=1)
    array([[1, 2, 5],
           [3, 4, 6]])
    >>> np.concatenate((a, b), axis=None)
    array([1, 2, 3, 4, 5, 6]
"""