基于相邻格间相似度的分布情况

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工作概述

相似度探究

graph LR
    A(取 2 * 3 的像素点) -->B(视为两个相邻格子)
    B --> C(求取相似度 S)
    C -->| 存在阈值 D > S| D[属性相同]
    C -->| 存在阈值 D < S| E[属性不同]

代码示例

# utf-8
# python3.8

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

def get_unit_similarity(a_4, b_4, k):
    a_4 = sorted(a_4)
    b_4 = sorted(b_4)
    result = 0
    for i in range(4):
        result = result + (abs(int(a_4[i]) - int(b_4[i]))) ** k
    result = result ** (1/k)
    return int(result)

def get_channels_similarity(channel, k):
    row, col = channel.shape
    result = []
    for i in range(row - 2):
        for j in range(col - 2):
            array = [channel[i][j], channel[i][j + 1], channel[i + 1][j + 1], channel[i + 1][j]]
            r_array = [channel[i][j + 1], channel[i + 1][j + 1], channel[i][j + 2], channel[i + 1][j + 2]]
            d_array = [channel[i + 1][j + 1], channel[i + 1][j], channel[i + 2][j + 1], channel[i + 2][j]]
            r_similarity = get_unit_similarity(array, r_array, k)
            d_similarity = get_unit_similarity(array, d_array, k)
            result.append(r_similarity)
            result.append(d_similarity)
    return result

def get_img_similarity(img, k):
    p = cv.imread(img, 1)
    b, g, r = cv.split(p)
    b_similarity = get_channels_similarity(b, k)
    g_similarity = get_channels_similarity(g, k)
    r_similarity = get_channels_similarity(r, k)
    cnt = np.zeros(400)
    for i in range(len(b_similarity)):
        cnt[b_similarity[i]] += 1
    for i in range(len(g_similarity)):
        cnt[g_similarity[i]] += 1
    for i in range(len(r_similarity)):
        cnt[r_similarity[i]] += 1

    plt.plot(cnt, color='red')
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    plt.title(' 相似度分布图 k = ' + str(k), fontsize=24, color='black')
    plt.show()

if __name__ == '__main__':
    img_addr = 'img.png'
    get_img_similarity(img_addr, 1)

结果

省略 100 以后的数据，得到的放大图

噪声图