查找黑色轮廓之间的区域

Question

我有一个白色的轮廓，里面和周围都是黑色的。我需要找到最大的圆，里面只有白色区域，没有洞，并且不包含轮廓外部的黑色区域。现在，我找不到好的方法来做到这一点。我在Python语言中使用openCv和Numpy。当然，如果需要任何其他库，我很乐意听到。

##Update##

一开始，我试着使用这段代码，但它给了我一个最大的圆圈，没有考虑其中的漏洞：

def get_biggest_area_inside_contour(self,image,index):

image_bin=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY) 
retval,image_bin=cv2.threshold(image_bin,biggest_circle_thresh,255,cv2.THRESH_BINARY) 
contours, hierarchy = cv2.findContours(image_bin.copy(),cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE) 
image_with_contour=np.zeros(image.shape, np.uint8) 
dist=np.zeros((image.shape[0],image.shape[1])) 
for ind_y in range(image.shape[0]): 
    for ind_x in range(image.shape[1]): 
        dist[ind_y,ind_x] = cv2.pointPolygonTest(max_contour,(ind_x,ind_y),True)

正如我回答的那样，这个问题有一个解决方案。问题是，我没有设法将代码从MATLAB传递到python。代码是：

def get_biggest_area_inside_contour(self,image,index):

    image_bin=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
    _,image_bin=cv2.threshold(image_bin,biggest_circle_thresh,255,cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(image_bin.copy(),cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
    max_contour=contours[0]
    max_area=cv2.contourArea(contours[0])
    #Find the biggest white contour, some times can be little white surrond by black area
    for _cnt in contours:
        _area = cv2.contourArea(_cnt)
        if _area>max_area:
            max_area=_area
            max_contour=_cnt 
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    _blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    _threshold = cv2.threshold(_blurred, biggest_circle_thresh, 255, cv2.THRESH_BINARY)[1]
    result_img=cv2.drawContours(_threshold,max_contour,-1,(255,255,255),1)
    contours, _ = cv2.findContours(result_img.copy(),cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
    #cv2.namedWindow('img {}'.format(index), cv2.WINDOW_NORMAL)
    #cv2.imshow('img {}'.format(index), result_img)

    apdp = cv2.approxPolyDP(max_contour,1,True)
    ap = [(a[0][0],a[0][1]) for a in apdp]
    vor_ap = Voronoi(ap,incremental=True)
    spined = []
    for ridge in contours:
        x, y, w, h = cv2.boundingRect(ridge)
        if cv2.pointPolygonTest(max_contour,(x,y),True) < 0.0 or cv2.pointPolygonTest(max_contour,(x+w,y+h),True) < 0.0:
            continue
        else:
            if tuple((x,y)) not in spined:
                spined.append([tuple((x,y))])
            if tuple((x+w,y)) not in spined:
                spined.append([tuple((x+w,y))])
            if tuple((x,y+h)) not in spined:
                spined.append([tuple((x,y+h))])  
            if tuple((x+w,y+h)) not in spined:
                spined.append([tuple((x+w,y+h))])                                 
    if len(spined)>0:
        for index in range(0,len(spined)):
            vor_ap.add_points(spined[index])
    fig=plt.figure(index)
    ax = fig.add_subplot(111)        
    voronoi_plot_2d(vor_ap, point_size=5, ax=ax)
    plt.scatter([s[0][0] for s in spined],[s[0][1] for s in spined])
    plt.plot([a[1] for a in ap],[a[0] for a in ap])
    if len(spined)>0:
        borders_and_holes=ap
        for i in range (0,len(spined)):
            borders_and_holes.append(spined[i])
        points=np.vstack(borders_and_holes)
        dt = Delaunay(points)
        ##cc = compute_triangle_circumcenters(dt.points, dt.simplices) Not sure how to do this
        #plt.plot(cc[:, 0], cc[:, 1], ".k")
    plt.show()

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Answer 1

您可以执行距离变换(在OpenCV中)。

反转图片，使条纹为白色(真)，中心为黑色(假)。你可能需要对你的图像进行阈值处理，也许可以去掉中间的污点。

使用L2距离(欧几里得)应用距离变换。

找到最高峰(minMaxLoc)。

最高峰是你的圆心，它的值是半径。