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
python类GC_INIT_WITH_MASK的实例源码
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
