#?-*-?coding:?utf-8?-*-
import?matplotlib
matplotlib.use（‘TkAgg‘）
from?numpy?import?*
import?numpy
import?maxflow
from?PIL?import?Image
from?matplotlib?import?pyplot?as?plt
from?pylab?import?*
import?cv2

#The?function?implements?graph?cut?by?partitioning?a?directed?graph?into?two?disjoint?sets?foreground?and?background...
def?graph（file?#?input?image
k?#?kappa?value?-->?similar?pixels?have?weight?close?to?kappa
s?#?Sigma?value?-->?determines?how?fast?the?values?decay?towards?zero?with?increasing?dissimilarity.
fore?#?foreground?area?--->?should?be?input?by?the?user?manually.
back）:?#?background?area?--->?should?be?input?by?the?user?manually.
????I?=?（Image.open（file）.convert（‘L‘））?#?read?image?L?=?R?*?299/1000?+?G?*?587/1000?+?B?*?114/1000
????If?=?I.crop（fore）?#?take?a?part?of?the?foreground
????Ib?=?I.crop（back）?#?take?a?part?of?the?background
????IIfIb?=?array（I）array（If）array（Ib）?#?convert?all?the?images?to?arrays?to?calculation
????IfmeanIbmean?=?mean（cv2.calcHist（[If][0]None[256][0256]））mean（cv2.calcHist（[Ib][0]None[256][0256]））?#Taking?the?mean?of?the?histogram
????FB?=??ones（shape?=?I.shape）ones（shape?=?I.shape）?#initalizing?the?foreground/background?probability?vector
????Im?=?I.reshape（-11）?#Coverting?the?image?array?to?a?vector?for?ease.
????mn?=?I.shape[0]I.shape[1]#?copy?the?size
????gpic?=?maxflow.Graph[int]（mn）maxflow.Graph[int]（）?#?define?the?graph
????structure?=?np.array（[[inf?0?0]
??????????????????????????[inf?0?0]
??????????????????????????[inf?0?0]
?????????????????????????]）?#?initializing?the?structure....
????sourcesinkJ?=?m*nm*n+1I?#?Defining?the?Source?and?Sink?（terminal）nodes.
????nodesnodeids?=?g.add_nodes（m*n）pic.add_grid_nodes（J.shape）?#?Adding?non-nodes
????pic.add_grid_edges（nodeids0）pic.add_grid_tedges（nodeids?J?255-J）
????gr?=?pic.maxflow（）
????IOut?=?pic.get_grid_segments（nodeids）
????for?i?in?range（I.shape[0]）:?#?Defining?the?Probability?function....
????????for?j?in?range（I.shape[1]）:
????????????F[ij]?=?-log（abs（I[ij]?-?Ifmean）/（abs（I[ij]?-?Ifmean）+abs（I[ij]?-?Ibmean）））?#?Probability?of?a?pixel?being?foreground
????????????B[ij]?=?-log（abs（I[ij]?-?Ibmean）/（abs（I[ij]?-?Ibmean）+abs（I[ij]?-?Ifmean）））?#?Probability?of?a?pixel?being?background
????FB?=?F.reshape（-11）B.reshape（-11）?#?convertingb??to?column?vector?for?ease
????for?i?in?range（Im.shape[0]）:
????????Im[i]?=?Im[i]?/?linalg.norm（Im[i]）?#?normalizing?the?input?image?vector?
????w?=?structure?#?defining?the?weight???????
????for?i?in?range（m*n）:#checking?the?4-neighborhood?pixels
????????ws=（F[i]/（F[i]+B[i]））?#?source?weight
????????wt=（B[i]/（F[i]+B[i]））?#?sink?weight
????????g.add_tedge（iws[0]wt）?#?edges?between?pixels?and?terminal
????????if?i%n?!=?0:?#?for?left?pixels
????????????w?=?k*exp（-（abs（Im[i]-Im[i-1]）**2）/s）?#?the?cost?function?for?two?p