第三次opencv作业（边缘检测，二值图，骨架提取）

最新推荐文章于 2024-06-07 06:16:32 发布

monbry

最新推荐文章于 2024-06-07 06:16:32 发布

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分类专栏： numpy 文章标签： python opencv 计算机视觉

本文链接：https://blog.youkuaiyun.com/monbry/article/details/114021566

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import scipy.signal as signal
import cv2 as cv
import random
import easygui as g
import imutils
import time
import math

array = [0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\
         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\
         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\
         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\
         1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\
         1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\
         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\
         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\
         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\
         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\
         1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\
         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\
         1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,\
         1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0]
#映射表

def Morphology_Dilate(img, Dil_time=1):
    H, W = img.shape

    # kernel
    MF = np.array(((0, 1, 0),
                   (1, 0, 1),
                   (0, 1, 0)), dtype=np.int)

    # each dilate time
    out = img.copy()
    for i in range(Dil_time):
        tmp = np.pad(out, (1, 1), 'edge')
        for y in range(1, H):
            for x in range(1, W):
                if np.sum(MF * tmp[y - 1:y + 2, x - 1:x + 2]) >= 255:
                    out[y, x] = 255

    return out

def Morphology_Erode(img, Erode_time=1):
    H, W = img.shape
    out = img.copy()

    # kernel
    MF = np.array(((0, 1, 0),
                   (1, 0, 1),
                   (0, 1, 0)), dtype=np.int)

    # each erode
    for i in range(Erode_time):
        tmp = np.pad(out, (1, 1), 'edge')
        # erode
        for y in range(1, H):
            for x in range(1, W):
                if np.sum(MF * tmp[y - 1:y + 2, x - 1:x + 2]) < 255 * 4:
                    out[y, x] = 0

    return out

def Thin(image, array):
    h, w = image.shape
    iThin = image.copy()

    for i in range(h):
        for j in range(w):
            if image[i, j] == 0:
                a = [1] *