图像匹配算法

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图像匹配算法基于像素比较求和实现。

差分矩阵求和

通过计算两个图像矩阵数据之间的差异分析图像的相似性，然后设置阀值进行比较，公式如下：

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差分矩阵 = 图像A矩阵数据 - 图像B矩阵数据

Python实现如下：

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def show_pic_location(img, findimg):
    """
    show_pic_location
    :param img:
    :param findimg:
    :return:
    """
    w = img.shape[1]
    h = img.shape[0]
    fw = findimg.shape[1]
    fh = findimg.shape[0]
    findpt = None
    for now_h in xrange(h - fh):
        for now_w in xrange(w - fw):
            comp_tz = img[now_h:now_h + fh, now_w: now_w + fw, :] - findimg
            if np.sum(comp_tz) < 1:
                findpt = now_w, now_h
    if findpt is not None:
        cv2.rectangle(img, findpt, (findpt[0] + fw, findpt[1] + fh), (255, 0, 0))
    return img

差分矩阵均值

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def show_pic_location(img, findimg):
    """
    show_pic_location
    :param img:
    :param findimg:
    :return:
    """
    w = img.shape[1]
    h = img.shape[0]
    fw = findimg.shape[1]
    fh = findimg.shape[0]
    findpt = None
    for now_h in xrange(h - fh):
        for now_w in xrange(w - fw):
            comp_tz = img[now_h:now_h + fh, now_w: now_w + fw, :] - findimg
            if abs(np.mean(comp_tz)) < 20:
                findpt = now_w, now_h
    if findpt is not None:
        cv2.rectangle(img, findpt, (findpt[0] + fw, findpt[1] + fh), (255, 0, 0))
    return img

欧氏距离匹配

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def show_pic_location(img, findimg):
    """
    show_pic_location
    :param img:
    :param findimg:
    :return:
    """
    w = img.shape[1]
    h = img.shape[0]
    fw = findimg.shape[1]
    fh = findimg.shape[0]

    minds = 1e8
    mincb_h = 0
    mincb_w = 0

    for now_h in xrange(h - fh):
        for now_w in xrange(w - fw):
            my_img = img[now_h:now_h + fh, now_w: now_w + fw, :]
            my_findimg = findimg
            myx = np.array(my_img)
            myy = np.array(my_findimg)
            dis = np.sqrt(np.sum((myx - myy) * (myx - myy)))
            if dis < minds:
                mincb_h = now_h
                mincb_w = now_w
                minds = dis
                print mincb_h, mincb_w, minds

    findpt = mincb_w, mincb_h
    cv2.rectangle(img, findpt, (findpt[0] + fw, findpt[1] + fh), (0, 0, 255))
    return img

添加噪音

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def add_noise(img):
    """
    add_noise
    :param img:
    :return:
    """
    count = 50000
    for k in xrange(count):
        xi = int(np.random.uniform(0, img.shape[1]))
        xj = int(np.random.uniform(0, img.shape[0]))
        img[xj, xi, 0] = 255 * np.random.rand()
        img[xj, xi, 1] = 255 * np.random.rand()
        img[xj, xi, 2] = 255 * np.random.rand()

原始图像
原始图像

待匹配图像

待匹配图像

加噪点匹配图像

加入噪点匹配图像

旋转加噪点匹配图像

旋转加噪点匹配图像

完整代码：

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#! /usr/bin/python
# -*- coding:utf-8 -*-
"""
@author: abc
@file: euclidean_distance.py
@date: 2016-12-09
@desc: 欧式距离匹配
"""
__author__ = "abc"

import cv2
import numpy as np


def show_pic_location(img, findimg):
    """
    show_pic_location
    :param img:
    :param findimg:
    :return:
    """
    w = img.shape[1]
    h = img.shape[0]
    fw = findimg.shape[1]
    fh = findimg.shape[0]

    minds = 1e8
    mincb_h = 0
    mincb_w = 0

    for now_h in xrange(h - fh):
        for now_w in xrange(w - fw):
            my_img = img[now_h:now_h + fh, now_w: now_w + fw, :]
            my_findimg = findimg
            dis = get_euclidean_distance(my_img, my_findimg)
            if dis < minds:
                mincb_h = now_h
                mincb_w = now_w
                minds = dis
                print mincb_h, mincb_w, minds

    findpt = mincb_w, mincb_h
    cv2.rectangle(img, findpt, (findpt[0] + fw, findpt[1] + fh), (0, 0, 255))
    return img


def get_euclidean_distance(x, y):
    """
    计算欧氏距离
    :param x:
    :param y:
    :return:
    """
    myx = np.array(x)
    myy = np.array(y)
    return np.sqrt(np.sum((myx - myy) * (myx - myy)))


def add_noise(img):
    """
    add_noise
    :param img:
    :return:
    """
    count = 50000
    for k in xrange(count):
        xi = int(np.random.uniform(0, img.shape[1]))
        xj = int(np.random.uniform(0, img.shape[0]))
        img[xj, xi, 0] = 255 * np.random.rand()
        img[xj, xi, 1] = 255 * np.random.rand()
        img[xj, xi, 2] = 255 * np.random.rand()


def handle_img(imgpath, imgpath1, imgpath2):
    """
    handle_img
    :param imgpath:
    :param imgpath1:
    :param imgpath2:
    :return:
    """
    myimg = cv2.imread(imgpath)
    myimg1 = cv2.imread(imgpath1)
    myimg2 = cv2.imread(imgpath2)

    add_noise(myimg)

    myimg = show_pic_location(myimg, myimg1)
    myimg = show_pic_location(myimg, myimg2)

    cv2.namedWindow('img')
    cv2.imshow('img', myimg)
    cv2.waitKey()
    cv2.destroyAllWindows()


if __name__ == "__main__":
    imgpath = "/home/abc/Projects/machine_learning/img/test_r45.png"
    imgpath1 = "/home/abc/Projects/machine_learning/img/test_1.png"
    imgpath2 = "/home/abc/Projects/machine_learning/img/test_2.png"
    handle_img(imgpath, imgpath1, imgpath2)