Python cv2.getStructuringElement方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def find_components(im, max_components=16):
    """Dilate the image until there are just a few connected components.
    Returns contours for these components."""
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
    dilation = dilate(im, kernel, 6)

    count = 21
    n = 0
    sigma = 0.000

    while count > max_components:
        n += 1
        sigma += 0.005
        result = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        if len(result) == 3:
            _, contours, hierarchy = result
        elif len(result) == 2:
            contours, hierarchy = result
        possible = find_likely_rectangles(contours, sigma)
        count = len(possible)

    return (dilation, possible, n) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def spline_transform_multi(img, mask):
    bimask=mask>0
    M,N=np.where(bimask)
    w=np.ptp(N)+1
    h=np.ptp(M)+1
    kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
    bound=cv2.dilate(bimask.astype('uint8'),kernel)-bimask
    y,x=np.where(bound>0)

    if x.size>4:
        newxy=thin_plate_transform(x,y,w,h,mask.shape[:2],num_points=5)

        new_img=cv2.remap(img,newxy,None,cv2.INTER_LINEAR)
        new_msk=cv2.remap(mask,newxy,None,cv2.INTER_NEAREST)
    elif x.size>0:
        new_img=img
        new_msk=mask
    return new_img,new_msk 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def SeamlessClone_trimap(srcIm,dstIm,imMask,offX,offY):
    dstIm=dstIm.copy()
    bimsk=imMask>0

    new_msk=np.zeros(dstIm.shape[:2],dtype='uint8')
    new_msk[offY:offY+imMask.shape[0],offX:offX+imMask.shape[1]]=imMask

    dstIm[new_msk>0]=srcIm[imMask>0]

    kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
    bimsk=bimsk.astype('uint8')
    bdmsk=cv2.dilate(bimsk,kernel)-cv2.erode(bimsk,kernel)
    mask255=bdmsk>0
    mask255=(mask255*255).astype('uint8')

    offCenter=(int(offX+imMask.shape[1]/2),int(offY+imMask.shape[0]/2))

    if np.any(bdmsk>0):
        outputIm=cv2.seamlessClone(srcIm,dstIm,mask255,offCenter,cv2.MIXED_CLONE)
    else:
        outputIm=dstIm
        #when one object have very few pixels, bdmsk will be totally zero, which will cause segmentation fault.

    return outputIm,new_msk 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def _morphological_process(image, kernel_size=5):
    """
    morphological process to fill the hole in the binary segmentation result
    :param image:
    :param kernel_size:
    :return:
    """
    if len(image.shape) == 3:
        raise ValueError('Binary segmentation result image should be a single channel image')

    if image.dtype is not np.uint8:
        image = np.array(image, np.uint8)

    kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))

    # close operation fille hole
    closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)

    return closing 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def predict0():
    Vnet3d = Vnet3dModule(256, 256, 64, inference=True, model_path="model\\Vnet3dModule.pd")
    for filenumber in range(30):
        batch_xs = np.zeros(shape=(64, 256, 256))
        for index in range(64):
            imgs = cv2.imread(
                "D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + ".bmp", 0)
            batch_xs[index, :, :] = imgs[128:384, 128:384]

        predictvalue = Vnet3d.prediction(batch_xs)

        for index in range(64):
            result = np.zeros(shape=(512, 512), dtype=np.uint8)
            result[128:384, 128:384] = predictvalue[index]
            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
            result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel)
            cv2.imwrite(
                "D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + "mask.bmp",
                result) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def update(dummy=None):
        sz = cv2.getTrackbarPos('op/size', 'morphology')
        iters = cv2.getTrackbarPos('iters', 'morphology')
        opers = cur_mode.split('/')
        if len(opers) > 1:
            sz = sz - 10
            op = opers[sz > 0]
            sz = abs(sz)
        else:
            op = opers[0]
        sz = sz*2+1

        str_name = 'MORPH_' + cur_str_mode.upper()
        oper_name = 'MORPH_' + op.upper()
        st = cv2.getStructuringElement(getattr(cv2, str_name), (sz, sz))
        res = cv2.morphologyEx(img, getattr(cv2, oper_name), st, iterations=iters)

        draw_str(res, (10, 20), 'mode: ' + cur_mode)
        draw_str(res, (10, 40), 'operation: ' + oper_name)
        draw_str(res, (10, 60), 'structure: ' + str_name)
        draw_str(res, (10, 80), 'ksize: %d  iters: %d' % (sz, iters))
        cv2.imshow('morphology', res) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def skeletonize(image_in):
    '''Inputs and grayscale image and outputs a binary skeleton image'''
    size = np.size(image_in)
    skel = np.zeros(image_in.shape, np.uint8)

    ret, image_edit = cv2.threshold(image_in, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
    done = False

    while not done:
        eroded = cv2.erode(image_edit, element)
        temp = cv2.dilate(eroded, element)
        temp = cv2.subtract(image_edit, temp)
        skel = cv2.bitwise_or(skel, temp)
        image_edit = eroded.copy()

        zeros = size - cv2.countNonZero(image_edit)
        if zeros == size:
            done = True

    return skel 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def blend_non_transparent(sprite, background_img):
    gray_overlay = cv2.cvtColor(background_img, cv2.COLOR_BGR2GRAY)
    overlay_mask = cv2.threshold(gray_overlay, 1, 255, cv2.THRESH_BINARY)[1]

    overlay_mask = cv2.erode(overlay_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    overlay_mask = cv2.blur(overlay_mask, (3, 3))

    background_mask = 255 - overlay_mask

    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    sprite_part = (sprite * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (background_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    return np.uint8(cv2.addWeighted(sprite_part, 255.0, overlay_part, 255.0, 0.0)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def maximizeContrast(imgGrayscale):

    height, width = imgGrayscale.shape

    imgTopHat = np.zeros((height, width, 1), np.uint8)
    imgBlackHat = np.zeros((height, width, 1), np.uint8)

    structuringElement = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

    imgTopHat = cv2.morphologyEx(imgGrayscale, cv2.MORPH_TOPHAT, structuringElement)
    imgBlackHat = cv2.morphologyEx(imgGrayscale, cv2.MORPH_BLACKHAT, structuringElement)

    imgGrayscalePlusTopHat = cv2.add(imgGrayscale, imgTopHat)
    imgGrayscalePlusTopHatMinusBlackHat = cv2.subtract(imgGrayscalePlusTopHat, imgBlackHat)

    return imgGrayscalePlusTopHatMinusBlackHat
# end function 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def generate_edge(label, edge_width=10, area_thrs=200):
    label_list = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]
    edge = np.zeros_like(label, dtype=np.uint8)
    for i in np.unique(label):
        # have no instance
        if i < 1000 or (i // 1000) not in label_list:
            continue
        
        # filter out small objects
        mask = (label == i).astype(np.uint8)
        if mask.sum() < area_thrs:
            continue
        
        rmin, rmax, cmin, cmax = _get_bbox(mask)
        mask_edge = _generate_edge(mask[rmin:rmax+1, cmin:cmax+1])
        edge[rmin:rmax+1, cmin:cmax+1][mask_edge > 0] = 255
    
    # dilation on edge
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (edge_width, edge_width))
    edge = cv2.dilate(edge, kernel)
    return edge 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def sobelOperT(self, img, blursize, morphW, morphH):
        '''
            No different with sobelOper ? 
        '''
        blur = cv2.GaussianBlur(img, (blursize, blursize), 0, 0, cv2.BORDER_DEFAULT)

        if len(blur.shape) == 3:
            gray = cv2.cvtColor(blur, cv2.COLOR_RGB2GRAY)
        else:
            gray = blur

        x = cv2.Sobel(gray, cv2.CV_16S, 1, 0, 3)
        absX = cv2.convertScaleAbs(x)
        grad = cv2.addWeighted(absX, 1, 0, 0, 0)

        _, threshold = cv2.threshold(grad, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)

        element = cv2.getStructuringElement(cv2.MORPH_RECT, (morphW, morphH))
        threshold = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, element)

        return threshold 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def colorSearch(self, src, color, out_rect):
        """

        :param src:
        :param color:
        :param out_rect: minAreaRect
        :return: binary
        """
        color_morph_width = 10
        color_morph_height = 2

        match_gray = colorMatch(src, color, False)

        _, src_threshold = cv2.threshold(match_gray, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)

        element = cv2.getStructuringElement(cv2.MORPH_RECT, (color_morph_width, color_morph_height))
        src_threshold = cv2.morphologyEx(src_threshold, cv2.MORPH_CLOSE, element)

        out = src_threshold.copy()

        _, contours, _ = cv2.findContours(src_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

        for cnt in contours:
            mr = cv2.minAreaRect(cnt)
            if self.verifySizes(mr):
                out_rect.append(mr)

        return out 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def findPiccircle(frame, color):

	hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)	
	color_dict = color_list.getColorList()
	mask = cv2.inRange(hsv, color_dict[color][0], color_dict[color][1])
	dilated = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
	## 需要修改minRadius以及maxRadius，用来限制识别圆的大小，排除其他的干扰
	circles = cv2.HoughCircles(dilated, cv2.HOUGH_GRADIENT, 1, 1000, param1=15, param2=10, minRadius=15, maxRadius=50)
	
	center = None
	if circles is not None:
		x, y, radius = circles[0][0]
		center = (x, y)
		cv2.circle(frame, center, radius, (0, 255, 0), 2)
		cv2.circle(frame, center, 2, (0,255,0), -1, 8, 0 );
		print('圆心：{}, {}'.format(x, y))
		
	cv2.imshow('result', frame)	
	
	if center != None:
		return center 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def blend_non_transparent(face_img, overlay_img):
    # Let's find a mask covering all the non-black (foreground) pixels
    # NB: We need to do this on grayscale version of the image
    gray_overlay = cv2.cvtColor(overlay_img, cv2.COLOR_BGR2GRAY)
    overlay_mask = cv2.threshold(gray_overlay, 1, 255, cv2.THRESH_BINARY)[1]

    # Let's shrink and blur it a little to make the transitions smoother...
    overlay_mask = cv2.erode(overlay_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    overlay_mask = cv2.blur(overlay_mask, (3, 3))

    # And the inverse mask, that covers all the black (background) pixels
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0)) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getStructuringElement [as 别名]
def backprojection(target, roihist):
    '''图像预处理'''
    hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
    dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)
    # Now convolute with circular disc
    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7))
    cv2.filter2D(dst,-1,disc,dst)
    # threshold and binary AND
    ret,binary = cv2.threshold(dst,80,255,0)
    # 创建 核
    kernel = np.ones((5,5), np.uint8)
    iter_time = 1
    # 闭运算
    binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel,iterations=iter_time)

    thresh = cv2.merge((binary,binary,binary))
    target_filter = cv2.bitwise_and(target,thresh)
    
    return binary, target_filter