针对现实中捕捉到的图像，我们可能感兴趣的只是其中某些区域，因此，就希望能够把感兴趣区域分割出来。一方面，方便对目标区域整体进行处理；另一方面也大大减少了计算量。对于目标分类时，这种处理方法尤其有效。一般有两种实现方法：GrabCut和风水岭。
一. GrabCut方法
1. 算法实现步骤：
（1）利用矩形选取或定义图像中含有一个多个物体，矩形外的区域将被自动认为为背景。
（2）对于用户定义的矩形区域，可用背景中的数据来区分其前景和背景区域。
（3）高斯混合模式(GMM)对背景和前景建模，未定义的像素点标记为可能的前景或背景。
（4）图像中每一个像素都被看作通过虚拟边与周围像素相连接，而每条边都有一个属于前景或背景的概率（基于与周围像素颜色上的相似性）。
（5）每个像素(即：算法中的节点)会与一个前景或背景节点连接，如图1所示。
（6）在节点完成连接后，若节点之间边属于不同终端（即：一各节点为前景，另一个节点为背景），则会切断他们之间的边，这样便能将图像各部分分割出来，如图2所示。
图1：
图2：
2. 代码实现：
import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread('statue_small.jpg') mask = np.zeros(img.shape[:2],np.uint8) bgdModel = np.zeros((1,65),np.float64) fgdModel = np.zeros((1,65),np.float64) ''' cv2.grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) → mask, bgdModel, fgdModel Parameters: img – Input 8-bit 3-channel image. mask – Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of following values: GC_BGD defines an obvious background pixels. GC_FGD defines an obvious foreground (object) pixel. GC_PR_BGD defines a possible background pixel. GC_PR_FGD defines a possible foreground pixel. rect – ROI containing a segmented object. The pixels outside of the ROI are marked as “obvious background”. The parameter is only used when mode==GC_INIT_WITH_RECT . bgdModel – Temporary array for the background model. Do not modify it while you are processing the same image. fgdModel – Temporary arrays for the foreground model. Do not modify it while you are processing the same image. iterCount – Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL . mode – Operation mode that could be one of the following: GC_INIT_WITH_RECT The function initializes the state and the mask using the provided rectangle. After that it runs iterCount iterations of the algorithm. GC_INIT_WITH_MASK The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are automatically initialized with GC_BGD . GC_EVAL The value means that the algorithm should just resume. ''' rect = (100,1,421,378) cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT) mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8') img = img*mask2[:,:,np.newaxis] plt.subplot(121), plt.imshow(img) plt.title("grabcut"), plt.xticks([]), plt.yticks([]) plt.subplot(122), plt.imshow(cv2.cvtColor(cv2.imread('statue_small.jpg'), cv2.COLOR_BGR2RGB)) plt.title("original"), plt.xticks([]), plt.yticks([]) plt.show()
3. 运行效果图：
二. 分水岭算法
该算法可将图像中的高密度区域（变化很多）转化为“山脉”，将低密度区域（变化很少）转化为“山谷”，这样有助于分割目标。开始从用户指定点持续灌入“水”直到这些区域开始汇聚，为了阻止不同山谷的水汇聚，可以设置一些栅栏。通过这种方式，最终将图像分割成相应的标记区域。
1. 代码实现：
import numpy as np import cv2 img1 = cv2.imread('basil.jpg') img = np.copy(img1) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) kernel = np.ones((3,3),np.uint8) #官网：https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.html?highlight=morphologyex #参考：http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html #去除噪声数据，cv2.morphologyEx()一种对图像进行膨胀后再腐蚀的算法 opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) #获取大部分的背景图像 sure_bg = cv2.dilate(opening,kernel,iterations=3) #通过cv2.distanceTransform()获取前景区域 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) ''' cv2.subtract(src1, src2[, dst[, mask[, dtype]]]) → dst Parameters: src1 – first input array or a scalar. src2 – second input array or a scalar. dst – output array of the same size and the same number of channels as the input array.mask – optional operation mask; this is an 8-bit single channel array that specifies elements of the output array to be changed. dtype – optional depth of the output array (see the details below). ''' unknown = cv2.subtract(sure_bg,sure_fg) ''' 通过函数cv2.connectedComponents()设置“栅栏”来阻止水汇聚 用0标记图像的背景，然后其他对象用从1开始的整数标记；且输出的结果中使用 unknown 定义未知区域 ''' ret, markers = cv2.connectedComponents(sure_fg) #在背景区域加1，将不确定的区域设置为0 markers = markers+1 markers[unknown==255] = 0 ''' cv2.watershed(image, markers) → markers Parameters: image – Input 8-bit 3-channel image. markers – Input/output 32-bit single-channel image (map) of markers. It should have the same size as image . ''' #官网：https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_watershed/py_watershed.html?highlight=morphologyex #边界区域被标记为-1，因此设置边界为红色 markers = cv2.watershed(img,markers) img[markers == -1] = [0,0,255] #显示图像 cv2.imshow('original', img1) cv2.imshow('img', img) cv2.waitKey(0) cv2.destroyAllWindows()
2. 运行效果图：
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