Python cv2 模块，GC_INIT_WITH_MASK 实例源码

def grabcutbb(im, bbv):
    mask = np.full(im.shape[:2],cv2.GC_PR_BGD,np.uint8)

    for bb in bbv:
        if bb[4]:
            cv2.rectangle(mask, (bb[0], bb[1]), (bb[2], bb[3]), int(cv2.GC_FGD), -1)
        else:
            cv2.rectangle(mask, (bb[0], bb[1]), (bb[2], bb[3]), int(cv2.GC_BGD), -1)

    bgdModel = np.zeros((1,65),np.float64)
    fgdModel = np.zeros((1,65),np.float64)

    rect = (0, im.shape[:2][0]/2, im.shape[:2][1], im.shape[:2][0])

    cv2.grabCut(im, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_MASK)

    mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')

    return mask2

def grab_cut_mask(img_col, mask, debug=False):
    assert isinstance(img_col, numpy.ndarray), 'image must be a numpy array'
    assert isinstance(mask, numpy.ndarray), 'mask must be a numpy array'
    assert img_col.ndim == 3, 'skin detection can only work on color images'
    assert mask.ndim == 2, 'mask must be 2D'

    kernel = numpy.ones((50, 50), numpy.float32) / (50 * 50)
    dst = cv2.filter2D(mask, -1, kernel)
    dst[dst != 0] = 255
    free = numpy.array(cv2.bitwise_not(dst), dtype=numpy.uint8)

    if debug:
        scripts.display('not skin', free)
        scripts.display('grabcut input', mask)

    grab_mask = numpy.zeros(mask.shape, dtype=numpy.uint8)
    grab_mask[:, :] = 2
    grab_mask[mask == 255] = 1
    grab_mask[free == 255] = 0

    if numpy.unique(grab_mask).tolist() == [0, 1]:
        logger.debug('conducting grabcut')
        bgdModel = numpy.zeros((1, 65), numpy.float64)
        fgdModel = numpy.zeros((1, 65), numpy.float64)

        if img_col.size != 0:
            mask, bgdModel, fgdModel = cv2.grabCut(img_col, grab_mask, None, bgdModel, fgdModel, 5,
                                                   cv2.GC_INIT_WITH_MASK)
            mask = numpy.where((mask == 2) | (mask == 0), 0, 1).astype(numpy.uint8)
        else:
            logger.warning('img_col is empty')

    return mask

def grabcuthm(im, hm):
    size = hm.shape

    bright = np.amax(hm)

    ret,fgd = cv2.threshold(hm, FGD_BOUND * bright, 1 * bright, cv2.THRESH_BINARY)
    fgd[1:size[0]/2] = 0
    fgd[1:size[0], 1:size[1]/4] = 0
    fgd[1:size[0], size[1]*3/4:size[1]] = 0

    ret,pr_fgd = cv2.threshold(hm, FGD_BGD_SEP * bright, 1 * bright, cv2.THRESH_BINARY)
    pr_fgd -= fgd

    ret, bgd = cv2.threshold(hm, BGD_BOUND * bright, 1 * bright, cv2.THRESH_BINARY_INV)
    bgd[size[0]/3:size[0]] = 0

    ret,pr_bgd = cv2.threshold(hm, FGD_BGD_SEP * bright, 1 * bright, cv2.THRESH_BINARY_INV)
    pr_bgd -= bgd

    mask = cv2.GC_BGD * bgd + cv2.GC_FGD * fgd + cv2.GC_PR_BGD * pr_bgd + cv2.GC_PR_FGD * pr_fgd
    mask = mask.astype(np.uint8, copy=False)

    bgdModel = np.zeros((1,65),np.float64)
    fgdModel = np.zeros((1,65),np.float64)

    rect = (0, im.shape[:2][0]/2, im.shape[:2][1], im.shape[:2][0])

    cv2.grabCut(im, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_MASK)
    mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')

    return mask2

def run(self, ips, snap, img, para = None):
        msk = ips.mark.buildmsk(img.shape)
        bgdModel = np.zeros((1,65),np.float64)
        fgdModel = np.zeros((1,65),np.float64)
        msk, bgdModel, fgdModel = cv2.grabCut(snap, msk,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
        img[msk%2 == 0] //= 3