import cv2
import matplotlib.pyplot as plt
#設(shè)定閾值
thresh=130
#載入原圖，并轉(zhuǎn)化為灰度圖像
img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.png',0)
img_original=cv2.resize(img_original,(0,0),fx=0.3,fy=0.3)
#采用5種閾值類型（thresholding type）分割圖像
retval1,img_binary=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY)
retval2,img_binary_invertion=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY_INV)
retval3,img_trunc=cv2.threshold(img_original,thresh,255,cv2.THRESH_TRUNC)
retval4,img_tozero=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO)
retval5,img_tozero_inversion=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO_INV)
#采用plt.imshow()顯示圖像
imgs=[img_original,img_binary,img_binary_invertion,img_trunc,img_tozero,img_tozero_inversion]
titles=['original','binary','binary_inv','trunc','tozero','tozero_inv']
for i in range(6):
    plt.subplot(2,3,i+1)
    plt.imshow(imgs[i],'gray')
    plt.title(titles[i])
    plt.xticks([])
    plt.yticks([])
plt.show()

import cv2
import numpy as np
import matplotlib.pyplot as plt
#載入原圖，轉(zhuǎn)化為灰度圖像,并通過cv2.resize()等比調(diào)整圖像大小
img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.png',0)
img_original=cv2.resize(img_original,(0,0),fx=0.3,fy=0.3)
#初始化閾值，定義全局變量imgs
thresh=130
imgs=0
#創(chuàng)建滑動(dòng)條回調(diào)函數(shù)，參數(shù)thresh為滑動(dòng)條對應(yīng)位置的數(shù)值
def threshold_segmentation(thresh):
    #采用5種閾值類型（thresholding type）分割圖像
    retval1,img_binary=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY)
    retval2,img_binary_invertion=cv2.threshold(img_original,thresh,255,cv2.THRESH_BINARY_INV)
    retval3,img_trunc=cv2.threshold(img_original,thresh,255,cv2.THRESH_TRUNC)
    retval4,img_tozero=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO)
    retval5,img_tozero_inversion=cv2.threshold(img_original,thresh,255,cv2.THRESH_TOZERO_INV)
    #由于cv2.imshow()顯示的是多維數(shù)組（ndarray）,因此我們通過np.hstack(數(shù)組水平拼接)
    #和np.vstack(豎直拼接)拼接數(shù)組，達(dá)到同時(shí)顯示多幅圖的目的
    img1=np.hstack([img_original,img_binary,img_binary_invertion])
    img2=np.hstack([img_trunc,img_tozero,img_tozero_inversion])
    global imgs
    imgs=np.vstack([img1,img2])
#新建窗口
cv2.namedWindow('Images')
#新建滑動(dòng)條，初始位置為130
cv2.createTrackbar('threshold value','Images',130,255,threshold_segmentation)
#第一次調(diào)用函數(shù)
threshold_segmentation(thresh)
#顯示圖像
while(1):
    cv2.imshow('Images',imgs)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()

import cv2
import matplotlib.pyplot as plt
#載入原圖
img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.png',0)
#全局閾值分割
retval,img_global=cv2.threshold(img_original,130,255,cv2.THRESH_BINARY)
#自適應(yīng)閾值分割
img_ada_mean=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,3)
img_ada_gaussian=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3)
imgs=[img_original,img_global,img_ada_mean,img_ada_gaussian]
titles=['Original Image','Global Thresholding(130)','Adaptive Mean','Adaptive Guassian',]
#顯示圖片
for i in range(4):
    plt.subplot(2,2,i+1)
    plt.imshow(imgs[i],'gray')
    plt.title(titles[i])
    plt.xticks([])
    plt.yticks([])
plt.show()

import cv2
import matplotlib.pyplot as plt
#載入原圖
img_original=cv2.imread(r'E:\py\python3.7\test2\test14yuzhi\cell.png',0)
#高斯濾波
img_blur=cv2.GaussianBlur(img_original,(13,13),13)  #根據(jù)情況修改參數(shù)
#自適應(yīng)閾值分割
img_thresh=cv2.adaptiveThreshold(img_original,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3)
img_thresh_blur=cv2.adaptiveThreshold(img_blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,15,3)
#顯示圖像
imgs=[img_thresh,img_thresh_blur]
titles=['img_thresh','img_thresh_blur']
for i in range(2):
    plt.subplot(1,2,i+1)
    plt.imshow(imgs[i],'gray')
    plt.title(titles[i])
    plt.xticks([])
    plt.yticks([])
plt.show()