python opencv 图片前景与背景的分割 - 好文

<>python opencv 图片前景与背景的分割

##kmeans 算法的研究

函数的格式为：kmeans(data, K, bestLabels, criteria, attempts, flags)
（1）data:
分类数据，最好是np.float32的数据，每个特征放一列。之所以是np.float32原因是这种数据类型运算速度快，同样的数据下如果是uint型数据将会慢死你。
(2) K: 分类数，opencv2的kmeans分类是需要已知分类数的。
(3) bestLabels：预设的分类标签：没有的话 None
(4) criteria：迭代停止的模式选择，这是一个含有三个元素的元组型数。格式为（type,max_iter,epsilon）
其中，type又有两种选择：
—–cv2.TERM_CRITERIA_EPS :精确度（误差）满足epsilon停止。
—- cv2.TERM_CRITERIA_MAX_ITER：迭代次数超过max_iter停止。
—-cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER，两者合体，任意一个满足结束。
（5）attempts：重复试验kmeans算法次数，将会返回最好的一次结果
（6）flags：初始类中心选择，两种方法
cv2.KMEANS_PP_CENTERS ; cv2.KMEANS_RANDOM_CENTERS

###完整代码
python opencv代码
''' Extract panel :kmeans聚类 ''' import cv2 import numpy as np import math def
panelAbstract(srcImage): # read pic shape imgHeight,imgWidth = srcImage.shape[:2
] imgHeight = int(imgHeight);imgWidth = int(imgWidth) # 均值聚类提取前景:二维转一维 imgVec =
np.float32(srcImage.reshape((-1,3))) criteria = (cv2.TERM_CRITERIA_EPS + cv2.
TERM_CRITERIA_MAX_ITER,10,1.0) flags = cv2.KMEANS_RANDOM_CENTERS ret,label,
clusCenter= cv2.kmeans(imgVec,2,None,criteria,10,flags) clusCenter = np.uint8(
clusCenter) clusResult = clusCenter[label.flatten()] imgres = clusResult.reshape
((srcImage.shape)) imgres = cv2.cvtColor(imgres,cv2.COLOR_BGR2GRAY) bwThresh =
int((np.max(imgres)+np.min(imgres))/2) _,thresh = cv2.threshold(imgres,bwThresh,
255,cv2.THRESH_BINARY_INV) threshRotate = cv2.merge([thresh,thresh,thresh]) #
确定前景外接矩形 #find contours imgCnt,contours, hierarchy = cv2.findContours(thresh,cv2
.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) minvalx = np.max([imgHeight,imgWidth]);
maxvalx= 0 minvaly = np.max([imgHeight,imgWidth]);maxvaly = 0 maxconArea = 0;
maxAreaPos= -1 for i in range(len(contours)): if maxconArea < cv2.contourArea(
contours[i]): maxconArea = cv2.contourArea(contours[i]) maxAreaPos = i objCont =
contours[maxAreaPos] # 旋转校正前景 rect = cv2.minAreaRect(objCont) for j in range(
len(objCont)): minvaly = np.min([minvaly,objCont[j][0][0]]) maxvaly = np.max([
maxvaly,objCont[j][0][0]]) minvalx = np.min([minvalx,objCont[j][0][1]]) maxvalx
= np.max([maxvalx,objCont[j][0][1]]) if rect[2] <=-45: rotAgl = 90 +rect[2] else
: rotAgl = rect[2] if rotAgl == 0: panelImg = srcImage[minvalx:maxvalx,minvaly:
maxvaly,:] else: rotCtr = rect[0] rotCtr = (int(rotCtr[0]),int(rotCtr[1]))
rotMdl= cv2.getRotationMatrix2D(rotCtr,rotAgl,1) imgHeight,imgWidth = srcImage.
shape[:2] #图像的旋转 dstHeight = math.sqrt(imgWidth *imgWidth + imgHeight*imgHeight)
dstRotimg= cv2.warpAffine(threshRotate,rotMdl,(int(dstHeight),int(dstHeight)))
dstImage= cv2.warpAffine(srcImage,rotMdl,(int(dstHeight),int(dstHeight)))
dstRotimg= cv2.cvtColor(dstRotimg,cv2.COLOR_BGR2GRAY) _,dstRotBW = cv2.threshold
(dstRotimg,127,255,0) imgCnt,contours, hierarchy = cv2.findContours(dstRotBW,cv2
.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) maxcntArea = 0;maxAreaPos = -1 for i in
range(len(contours)): if maxcntArea < cv2.contourArea(contours[i]): maxcntArea =
cv2.contourArea(contours[i]) maxAreaPos = i x,y,w,h = cv2.boundingRect(contours
[maxAreaPos]) #提取前景：panel panelImg = dstImage[int(y):int(y+h),int(x):int(x+w),:]
return panelImg if __name__=="__main__": srcImage = cv2.imread('mouse.png') a=
panelAbstract(srcImage) cv2.imshow('figa',a) cv2.waitKey(0) cv2.
destroyAllWindows()
##结果图片
原图片

聚类之后的图片

提取最小外接矩形

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