基于模块差的统计情况呈现

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素材

min 与 max 的分布结果

代码示例

# python3.6
# utf-8
# LF

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D

import show_3d

def get_1D_2_2D(x):
    d = {0:[-1, -1], 1:[-1, 0], 2:[-1, 1], 3:[0, 1], 4:[1, 1], 5:[1, 0], 6:[1, -1], 7:[0, -1]}
    return d[x]

def get_dif(img):
    raw, col = img.shape
    dif = np.zeros((raw - 1, col - 1, 8))

    for i in range(raw - 1):
        for j in range(col - 1):
            for m in range(8):
                d = get_1D_2_2D(m)
                if(i + d[0] >= 0 and i + d[0] + 1 <= raw - 1 and j + d[1] >= 0 and j + d[1] + 1 <= col -1):
                    dif[i][j][m] = abs(int(img[i][j]) - int(img[i + d[0]][j + d[1]])) + \
                                    abs(int(img[i][j + 1]) - int(img[i + d[0]][j + d[1] + 1])) + \
                                    abs(int(img[i + 1][j + 1]) - int(img[i + d[0] + 1][j + d[1] + 1])) + \
                                    abs(int(img[i + 1][j]) - int(img[i + d[0] + 1][j + d[1]]))
    return dif

def drew_graph(array, flag):
    cnt = np.zeros(100)
    raw, col, _ = array.shape
    for i in range(raw):
        for j in range(col):
            d = sorted(array[i][j])
            # print(d)
            if int(d[flag]) < 100:
                cnt[int(d[flag])] += 1

    plt.plot(cnt, color='red')
    # plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    plt.title('max', fontsize=24, color='black')
    plt.savefig('fenbu.png')
    plt.show()

def get_show(ary):
    raw, col, _ = ary.shape
    imin = np.zeros((raw, col))
    for i in range(raw):
        for j in range(col):
            d = sorted(ary[i][j])
            if d[0] <= 20:
                imin[i][j] = 255
    cv.imwrite('min.png', imin)

    imax = np.zeros((raw, col))
    for i in range(raw):
        for j in range(col):
            d = sorted(ary[i][j])
            if d[7] > 60:
                imax[i][j] = 255
    cv.imwrite('max.png', imax)

def get_3d(ary):
    raw, col, _ = ary.shape
    minmax = 0
    for i in range(raw):
        for j in range(col):
            d = sorted(ary[i][j])
            if d[0] > minmax:
                minmax = d[0]

    maxmax = 0
    for i in range(raw):
        for j in range(col):
            d = sorted(ary[i][j])
            if d[7] > maxmax:
                maxmax = d[7]

    data = np.zeros((int(minmax) + 1, int(maxmax) + 1))
    for i in range(raw):
        for j in range(col):
            d = sorted(ary[i][j])
            data[int(d[0])][int(d[7])] += 1

    data = pd.DataFrame(data)
    data.to_excel('data.xlsx')
    '''
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    for i in range(int(minmax) + 1):
        for j in range(int(maxmax) + 1):
            ax.scatter(i, j, data[i][j], marker='o')
    plt.savefig('3d.png')
    plt.show()
    '''

def main(imgdir):
    img = cv.imread(imgdir, 0)
    differ = get_dif(img)
    # drew_graph(differ, 7)
    # get_show(differ)
    get_3d(differ)

if __name__ == '__main__':
    img_dir = '03.png'
    main(img_dir)