This Chapter is all about image segmentation.
I still not finished whole chapter, but here try to publish some for reference.

這里寫目錄標題

• • 基礎知識

import sys import numpy as np import cv2 import matplotlib import matplotlib.pyplot as plt import PIL from PIL import Imageprint(f"Python version: {sys.version}") print(f"Numpy version: {np.__version__}") print(f"Opencv version: {cv2.__version__}") print(f"Matplotlib version: {matplotlib.__version__}") print(f"Pillow version: {PIL.__version__}") def normalize(x, ymin=0, ymax=1):"""Description:- Map x into desired range[ymin, ymax], according to the math function $$y = \frac{(y_{\text{max}} - y_{\text{min}}) (x - x_{\text{min}})}{(x_{\text{max}} - x_{\text{min}})} + y_{\text{min}}$$Parameters:- x: input array- ymin: desired min value, such as -1, or whatever you want- ymax: desired max value, such as 1, or other you need"""result = (ymax - ymin) * (x - x.min()) / (x.max()-x.min()) + ymin#################### old one ####################### result = (x - x.min()) / (x.max() - x.min())return result

基礎知識

令$R$表示一幅圖像占據的整個空間區域。我們可以將圖像分割視為把$R$分為$n$個子區域$R_1，R_2，?，R_n$的過程，滿足：

• (a) ${?}_{i=1}^n{R}_i=R$。
• (b) $R_i,i=0,1,2,?,n$ 是一個連通集。
• (c) 對所有$i$和$j$， $i=j,R_i?R_j=?$。
• (d) $Q(R_i)=TRUE,i=0,1,2,?,n$。
• (e) 對于任何鄰接區域$R_i$和$R_j$， $Q(R_i?R_j)=FALSE$。

其中$Q(R_i)$是定義在集合$R_k$中的點上的一個謂詞邏輯。

def std_seg(image, thred=0, stride=4):"""Description:Segment image caculating standard deviationParemeters:image: input grayscale imagethred: thredshold of the standard diviationstride: control the neighborhoodReturn:Binary segment image"""h, w = image.shape[:2]result = image.copy()# here we use stride the create non-overlap region, if we not need stride here, we still can get very good result# or we set stride smaller than 8, then we can get better resultfor i in range(0, h, stride):for j in range(0, w, stride):temp = image[i:i+stride, j:j+stride]if np.std(temp) <= thred:result[i:i+stride, j:j+stride] = 0else:result[i:i+stride, j:j+stride] = 255return result # standard deviation segment, according the result below, it seems the img_f is not 8x8 region, is about 4-5 img_d = cv2.imread("DIP_Figures/DIP3E_Original_Images_CH10/Fig1001(d)(noisy_region).tif", -1) img_seg = std_seg(img_d, thred=0, stride=5)fig = plt.figure(figsize=(10, 5)) plt.subplot(1, 2, 1), plt.imshow(img_d, 'gray'), plt.xticks([]), plt.yticks([]) plt.subplot(1, 2, 2), plt.imshow(img_seg, 'gray'), plt.xticks([]), plt.yticks([]) plt.tight_layout() plt.show()

總結

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