–– coding: utf-8 ––

”’
作者 : 丁毅
开发工夫 : 2021/5/9 16:30
”’
import cv2
import numpy as np

图像细化

def VThin(image, array):

rows, cols = image.shape
NEXT = 1
for i in range(rows):
    for j in range(cols):
        if NEXT == 0:
            NEXT = 1
        else:
            M = int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) if 0 < j < cols - 1 else 1
            if image[i, j] == 0 and M != 0:
                a = [0]*9
                for k in range(3):
                    for l in range(3):
                        if -1 < (i - 1 + k) < rows and -1 < (j - 1 + l) < cols and image[i - 1 + k, j - 1 + l] == 255:
                            a[k * 3 + l] = 1
                sum = a[0] * 1 + a[1] * 2 + a[2] * 4 + a[3] * 8 + a[5] * 16 + a[6] * 32 + a[7] * 64 +  a[8] * 128
                image[i, j] = array[sum]*255
                if array[sum] == 1:
                    NEXT = 0
return image

def HThin(image, array):

rows, cols = image.shape
NEXT = 1
for j in range(cols):
    for i in range(rows):
        if NEXT == 0:
            NEXT = 1
        else:
            M = int(image[i-1, j]) + int(image[i, j]) + int(image[i+1, j]) if 0 < i < rows-1 else 1
            if image[i, j] == 0 and M != 0:
                a = [0]*9
                for k in range(3):
                    for l in range(3):
                        if -1 < (i-1+k) < rows and -1 < (j-1+l) < cols and image[i-1+k, j-1+l] == 255:
                            a[k*3+l] = 1
                sum = a[0]*1+a[1]*2+a[2]*4+a[3]*8+a[5]*16+a[6]*32+a[7]*64+a[8]*128
                image[i, j] = array[sum]*255
                if array[sum] == 1:
                    NEXT = 0
return image

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]

显示灰度图

img = cv2.imread(r”C:\Users\pc\Desktop\vas0.png”,0)
cv2.imshow(“img1”,img)

自适应阈值宰割

img2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17, 4)
cv2.imshow(‘img2’, img2)

图像反色

img3 = cv2.bitwise_not(img2)
cv2.imshow(“img3”, img3)

图像扩大

img4 = cv2.copyMakeBorder(img3, 1, 1, 1, 1, cv2.BORDER_REFLECT)
cv2.PayPal 下载 imshow(“img4”, img4)
contours, hierarchy = cv2.findContours(img4, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

打消小面积

img5 = img4
for i in range(len(contours)):

area = cv2.contourArea(contours[i])
if (area < 80) | (area > 10000):
    cv2.drawContours(img5, [contours[i]], 0, 0, -1)

cv2.imshow(“img5”, img5)
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img5, connectivity=8, ltype=None)

print(stats)

s = sum(stats)
img6 = np.ones(img5.shape, np.uint8) * 0
for (i, label) in enumerate(np.unique(labels)):

# 如果是背景，疏忽
if label == 0:
    # print("[INFO] label: 0 (background)")
    continue
numPixels = stats[i][-1]
div = (stats[i][4]) / s[4]
# print(div)
# 判断区域是否满足面积要求
if round(div, 3) > 0.002:
    color = 255
    img6[labels == label] = color

cv2.imshow(“img6”, img6)

图像反色

img7 = cv2.bitwise_not(img6)

图像细化

for i in range(10):

VThin(img7, array)
HThin(img7, array)

cv2.imshow(“img7”,img7)

边缘检测

img8 = cv2.Canny(img6, 80, 255)
cv2.imshow(“img8”, img8)

使灰度图黑白颠倒

img9 = cv2.bitwise_not(img8)
cv2.imshow(“img9”, img9)
cv2.waitKey(0)