圖像分割理論簡介

圖像分割是機器學習的重要組成部分，無論是前期feed給深度學習模型，還是作為後期在行人檢測、醫學領域的圖像精確分割等都具有重要意義。圖像分割的理論前提在於圖像所構成矩陣在圖像內不同物體間邊緣的梯度驟變，如同物體的像素如同水流，再與不同物體像素匯合時顏色的差別就會形成邊界。基於OpenCV的watershed、p-algrorithm可以很好的將邊界提取，當然，在特定領域，需要基於CNN對不同圖片做大量學習也可以識別物體或人物。

1.approximate segmentation

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('coins.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
 

2.前景、背景分離(為進一步分離不同硬幣之間的細小邊界)

kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)

# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)

# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)

# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
 

3.前景、背景標簽標記，將背景標為0

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)

# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

# Now, mark the region of unknown with zero
markers[unknown==255] = 0

*說明不同顏色表示硬幣的中心

4.應用whtershed算法，將標記圖像改良，並將邊界區域標位-1

markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
 

最終結果

*其他請參閱

圖像分割