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Segmentation.py
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# -*- coding = utf-8 -*-
# @Time : 2021-12-10 10:46
# @Author: gla1ve
# @File: Segmentation.py
# @Software: PyCharm
# 人生苦短， 我用python(划掉) Java
import math
import cv2
import numpy as np
import warnings
class Segmentation:
# cv2.imread第二个参数是0的时候表示读取灰度图。
def __init__(self, pic_path):
self.pic_path = pic_path
self.pic = cv2.imread(self.pic_path, 0).astype(np.uint8)
self.HH = self.pic.shape[0] - 1
self.WW = self.pic.shape[1] - 1
def ShowPic(self):
cv2.imshow('PKQ', self.pic)
cv2.waitKey(0) # 0一直显示，直到有键盘输入。也可以是其他数字，毫秒级
# 基于灰度直方图的阈值分割
def Gray_Histogram_Segmentation(self):
output = np.zeros(self.pic.shape, np.uint8)
for i in range(1, self.pic.shape[0] - 1):
for j in range(1, self.pic.shape[1] - 1):
if self.pic[i][j] <= 100:
output[i][j] = 0
else:
output[i][j] = 255
cv2.imwrite('img/Segmentation.bmp', output)
cv2.imshow('Gray_Histogram_Segmentation', output)
cv2.waitKey(0)
# 基于类内方差最小值的分割(最小类内方差法，也叫作均匀性度量法)
def Minimum_Variance(self):
pic_data = self.pic
# 遍历0到255 的所有阈值分割的条件【这里可以二分嘛？】
res, Variance_min = 0, 1e9
for th in range(0, 250, 30):
left = [pic_data[i][j] for i in range(self.HH) for j in range(self.WW) if pic_data[i][j] >= th]
right = [pic_data[i][j] for i in range(self.HH) for j in range(self.WW) if pic_data[i][j] < th]
tmp = (len(left)) / (len(left) + len(right)) * np.var(left) + \
(len(right)) / (len(left) + len(right)) * np.var(right)
if tmp < Variance_min:
Variance_min, res = tmp, th
for i in range(self.HH):
for j in range(self.WW):
if pic_data[i][j] <= res:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
cv2.imwrite('img/Segmentation.bmp', pic_data)
cv2.imshow('Minimum_Variance', pic_data)
cv2.waitKey(0)
# p 参数生长
def P_Parameter(self):
pic_data = np.zeros((self.HH, self.WW), np.uint8)
img = self.pic
th, p = 127, 0.2
left = [img[x][y] for x in range(self.HH) for y in range(self.WW) if img[x][y] < th]
ps = len(left) / self.HH / self.WW
while math.fabs(ps - p) > 0.04:
if ps < p:
th += 1
else:
th -= 1
if ps == 255:
break
left = [img[x][y] for x in range(self.HH) for y in range(self.WW) if img[x][y] < th]
ps = len(left) / self.HH / self.WW
for i in range(self.HH):
for j in range(self.WW):
if img[i][j] <= th:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
# print(th)
cv2.imwrite('img/Segmentation.bmp', pic_data)
cv2.imshow('P_Parameter', pic_data)
cv2.waitKey(0)
# 腐蚀
def Erosion(self):
pic_data = np.zeros((self.HH, self.WW), np.uint8)
img = self.pic
for i in range(self.HH):
for j in range(self.WW):
if math.fabs(img[i][j] - 60) <= 60:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 0 and pic_data[i + 1][j] == 0 and pic_data[i][j + 1] == 0 \
and pic_data[i + 1][j + 1] == 0:
img[i][j] = 0
else:
img[i][j] = 255
cv2.imwrite('img/Segmentation.bmp', img)
cv2.imshow('Erosion', img)
cv2.waitKey(0)
# 膨胀
def Inflation(self):
pic_data = np.zeros((self.HH, self.WW), np.uint8)
img = self.pic
for i in range(self.HH):
for j in range(self.WW):
if math.fabs(img[i][j] - 60) <= 60:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 255 and pic_data[i + 1][j] == 255 and pic_data[i][j + 1] == 255 and \
pic_data[i + 1][j + 1] == 255:
img[i][j] = 255
else:
img[i][j] = 0
cv2.imwrite('img/Segmentation.bmp', img)
cv2.imshow('Inflation', img)
cv2.waitKey(0)
# 闭运算
def Close_Operation(self):
pic_data = np.zeros((self.HH, self.WW), np.uint8)
img = self.pic
for i in range(self.HH):
for j in range(self.WW):
if math.fabs(img[i][j] - 60) <= 60:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 0 and pic_data[i + 1][j] == 0 and pic_data[i][j + 1] == 0 \
and pic_data[i + 1][j + 1] == 0:
img[i][j] = 0
else:
img[i][j] = 255
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 255 and pic_data[i + 1][j] == 255 and pic_data[i][j + 1] == 255 and \
pic_data[i + 1][j + 1] == 255:
img[i][j] = 255
else:
img[i][j] = 0
cv2.imwrite('img/Segmentation.bmp', img)
cv2.imshow('Close_Operation', img)
cv2.waitKey(0)
# 开运算
def Open_Operation(self):
pic_data = np.zeros((self.HH, self.WW), np.uint8)
img = self.pic
for i in range(self.HH):
for j in range(self.WW):
if math.fabs(img[i][j] - 60) <= 60:
pic_data[i][j] = 0
else:
pic_data[i][j] = 255
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 255 and pic_data[i + 1][j] == 255 and pic_data[i][j + 1] == 255 and \
pic_data[i + 1][j + 1] == 255:
img[i][j] = 255
else:
img[i][j] = 0
for i in range(self.HH - 1):
for j in range(self.WW - 1):
if pic_data[i][j] == 0 and pic_data[i + 1][j] == 0 and pic_data[i][j + 1] == 0 \
and pic_data[i + 1][j + 1] == 0:
img[i][j] = 0
else:
img[i][j] = 255
cv2.imwrite('img/Segmentation.bmp', img)
cv2.imshow('Open_Operation', img)
cv2.waitKey(0)
if __name__ == '__main__':
warnings.filterwarnings('ignore')
path = 'img/build.bmp'
b = Segmentation(path)
b.Open_Operation()