Python cv2.floodFill() Examples

def fillhole(input_image):
    '''
    input gray binary image  get the filled image by floodfill method
    Note: only holes surrounded in the connected regions will be filled.
    :param input_image:
    :return:
    '''
    im_flood_fill = input_image.copy()
    h, w = input_image.shape[:2]
    mask = np.zeros((h + 2, w + 2), np.uint8)
    im_flood_fill = im_flood_fill.astype("uint8")
    cv.floodFill(im_flood_fill, mask, (0, 0), 255)
    im_flood_fill_inv = cv.bitwise_not(im_flood_fill)
    img_out = input_image | im_flood_fill_inv
    return img_out 

def fill_hole(input_mask):
    h, w = input_mask.shape
    canvas = np.zeros((h + 2, w + 2), np.uint8)
    canvas[1:h + 1, 1:w + 1] = input_mask.copy()

    mask = np.zeros((h + 4, w + 4), np.uint8)

    cv2.floodFill(canvas, mask, (0, 0), 1)
    canvas = canvas[1:h + 1, 1:w + 1].astype(np.bool)

    return (~canvas | input_mask.astype(np.uint8)) 

def flood_fill_single(im, seed_point):
    """Perform a single flood fill operation.

    # Arguments
        image: an image. the image should consist of white background, black lines and black fills.
               the white area is unfilled area, and the black area is filled area.
        seed_point: seed point for trapped-ball fill, a tuple (integer, integer).
    # Returns
        an image after filling.
    """
    pass1 = np.full(im.shape, 255, np.uint8)

    im_inv = cv2.bitwise_not(im)

    mask1 = cv2.copyMakeBorder(im_inv, 1, 1, 1, 1, cv2.BORDER_CONSTANT, 0)
    _, pass1, _, _ = cv2.floodFill(pass1, mask1, seed_point, 0, 0, 0, 4)

    return pass1 

def update(dummy=None):
        if seed_pt is None:
            cv2.imshow('floodfill', img)
            return
        flooded = img.copy()
        mask[:] = 0
        lo = cv2.getTrackbarPos('lo', 'floodfill')
        hi = cv2.getTrackbarPos('hi', 'floodfill')
        flags = connectivity
        if fixed_range:
            flags |= cv2.FLOODFILL_FIXED_RANGE
        cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
        cv2.imshow('floodfill', flooded) 

def trapped_ball_fill_single(image, seed_point, radius):
    """Perform a single trapped ball fill operation.

    # Arguments
        image: an image. the image should consist of white background, black lines and black fills.
               the white area is unfilled area, and the black area is filled area.
        seed_point: seed point for trapped-ball fill, a tuple (integer, integer).
        radius: radius of ball shape.
    # Returns
        an image after filling.
    """
    ball = get_ball_structuring_element(radius)

    pass1 = np.full(image.shape, 255, np.uint8)
    pass2 = np.full(image.shape, 255, np.uint8)

    im_inv = cv2.bitwise_not(image)

    # Floodfill the image
    mask1 = cv2.copyMakeBorder(im_inv, 1, 1, 1, 1, cv2.BORDER_CONSTANT, 0)
    _, pass1, _, _ = cv2.floodFill(pass1, mask1, seed_point, 0, 0, 0, 4)

    # Perform dilation on image. The fill areas between gaps became disconnected.
    pass1 = cv2.morphologyEx(pass1, cv2.MORPH_DILATE, ball, anchor=(-1, -1), iterations=1)
    mask2 = cv2.copyMakeBorder(pass1, 1, 1, 1, 1, cv2.BORDER_CONSTANT, 0)

    # Floodfill with seed point again to select one fill area.
    _, pass2, _, rect = cv2.floodFill(pass2, mask2, seed_point, 0, 0, 0, 4)
    # Perform erosion on the fill result leaking-proof fill.
    pass2 = cv2.morphologyEx(pass2, cv2.MORPH_ERODE, ball, anchor=(-1, -1), iterations=1)

    return pass2 

def compute_dismap(dismap, bbox):
    x_min, y_min, x_max, y_max = bbox[:]

    # draw bounding box
    cv2.line(dismap, (x_min, y_min), (x_max, y_min), color=1, thickness=1)
    cv2.line(dismap, (x_min, y_min), (x_min, y_max), color=1, thickness=1)
    cv2.line(dismap, (x_max, y_max), (x_max, y_min), color=1, thickness=1)
    cv2.line(dismap, (x_max, y_max), (x_min, y_max), color=1, thickness=1)

    tmp = (dismap > 0).astype(np.uint8)  # mark boundary
    tmp_ = deepcopy(tmp)

    fill_mask = np.ones((tmp.shape[0] + 2, tmp.shape[1] + 2)).astype(np.uint8)
    fill_mask[1:-1, 1:-1] = tmp_
    cv2.floodFill(tmp_, fill_mask, (int((x_min + x_max) / 2), int((y_min + y_max) / 2)), 5) # fill pixel inside bounding box

    tmp_ = tmp_.astype(np.int8)
    tmp_[tmp_ == 5] = -1  # pixel inside bounding box
    tmp_[tmp_ == 0] = 1  # pixel on and outside bounding box

    tmp = (tmp == 0).astype(np.uint8)

    dismap = cv2.distanceTransform(tmp, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)  # compute distance inside and outside bounding box
    dismap = tmp_ * dismap + 128

    dismap[dismap > 255] = 255
    dismap[dismap < 0] = 0

    dismap = dismap.astype(np.uint8)

    return dismap 

def get_mask(ucm,viz=False):
    ucm = ucm.copy()
    h,w = ucm.shape[:2]
    mask = np.zeros((h-2,w-2),'float32')

    i = 0
    sx,sy = np.where(mask==0)
    seed = get_seed(sx,sy,ucm)
    areas = []
    labels=[]
    while seed is not None and i<1000:
        cv2.floodFill(mask,ucm,seed,i+1)
        # calculate the area (no. of pixels):
        areas.append(np.sum(mask==i+1))
        labels.append(i+1)

        # get the location of the next seed:
        sx,sy = np.where(mask==0)
        seed = get_seed(sx,sy,ucm)
        i += 1
    print "  > terminated in %d steps"%i

    if viz:
        plt.imshow(mask)
        plt.show()

    return mask,np.array(areas),np.array(labels) 

def _get_traffic_light_channel_from_top_down_rgb(self,
                                                     img,
                                                     tl_bbox_colors=[[
                                                         200, 0, 0
                                                     ], [13, 0,
                                                         196], [5, 200, 0]]):
        """
        Returns a mask of the traffic light extent bounding boxes seen from a
        top-down view. The bounding boxes in the mask are colored differently
        depending on the state of each traffic light.

        *Note: Not sure why the colors do not match up with the original colors
        the boxes are drawn with. The default colors are estimates learned by
        examining the output of the top down camera.

        Args:
            img: Top-down RGB frame with traffic light extent bboxes drawn.
            tl_bbox_colors: The colors of the traffic light extent bboxes.
        """
        if tl_bbox_colors is None:
            tl_bbox_colors = TL_STATE_TO_PIXEL_COLOR.values()
        h, w = img.shape[:2]
        tl_mask = np.zeros((h + 2, w + 2), np.uint8)
        # Grayscale values for different traffic light states
        vals = [33, 66, 99]
        for i, bbox_color in enumerate(tl_bbox_colors):
            tl_mask_for_bbox_color = np.zeros((h + 2, w + 2), np.uint8)
            # Using a tolerance of 20 to locate correct boxes.
            mask = np.all(abs(img - bbox_color) < 20, axis=2).astype(np.uint8)
            # Flood fill from (0, 0) corner.
            cv2.floodFill(mask, tl_mask_for_bbox_color, (0, 0), 1)
            # Invert image so mask highlights lights.
            tl_mask_for_bbox_color = 1 - tl_mask_for_bbox_color
            tl_mask += tl_mask_for_bbox_color * vals[i]
        # Remove extra rows and cols added for floodFill.
        return tl_mask[1:-1, 1:-1] 

def _mouse_callback(self, event, x, y, flags, *userdata):

        if event != cv.EVENT_LBUTTONDOWN:
            return

        modifier = flags & (ALT_KEY + SHIFT_KEY)

        self._flood_mask[:] = 0
        cv.floodFill(
            self.img,
            self._flood_mask,
            (x, y),
            0,
            self.tolerance,
            self.tolerance,
            self._flood_fill_flags,
        )
        flood_mask = self._flood_mask[1:-1, 1:-1].copy()

        if modifier == (ALT_KEY + SHIFT_KEY):
            self.mask = cv.bitwise_and(self.mask, flood_mask)
        elif modifier == SHIFT_KEY:
            self.mask = cv.bitwise_or(self.mask, flood_mask)
        elif modifier == ALT_KEY:
            self.mask = cv.bitwise_and(self.mask, cv.bitwise_not(flood_mask))
        else:
            self.mask = flood_mask

        self._update() 

def remove_remains(img, interest_point):
    """
    Remove remains which are not adjacent with interest_point
    :param img: Input image
    :param interest_point: Center point where we want to remain
    :return: Image which adjacent with interest_point
    """
    img = img.astype(np.uint8)
    h, w = img.shape[:2]
    mask = np.zeros((h + 2, w + 2), np.uint8)

    img_inv = img.copy()
    cv2.floodFill(img_inv, mask, tuple(interest_point), 0)
    img -= img_inv

    return img 

def update(dummy=None):
        if seed_pt is None:
            cv2.imshow('floodfill', img)
            return
        flooded = img.copy()
        mask[:] = 0
        lo = cv2.getTrackbarPos('lo', 'floodfill')
        hi = cv2.getTrackbarPos('hi', 'floodfill')
        flags = connectivity
        if fixed_range:
            flags |= cv2.FLOODFILL_FIXED_RANGE
        cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
        cv2.imshow('floodfill', flooded) 

def update(dummy=None):
        if seed_pt is None:
            cv2.imshow('floodfill', img)
            return
        flooded = img.copy()
        mask[:] = 0
        lo = cv2.getTrackbarPos('lo', 'floodfill')
        hi = cv2.getTrackbarPos('hi', 'floodfill')
        flags = connectivity
        if fixed_range:
            flags |= cv2.FLOODFILL_FIXED_RANGE
        cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
        cv2.imshow('floodfill', flooded) 

def SegmentArm(self, frame):
        """ segments the arm region based on depth """
        # find center (21x21 pixel) region of image frame
        centerHalf = 10 # half-width of 21 is 21/2-1
        center = frame[self.imgHeight/2-centerHalf:self.imgHeight/2+centerHalf,
            self.imgWidth/2-centerHalf:self.imgWidth/2+centerHalf]

        # find median depth value of center region
        center = np.reshape(center, np.prod(center.shape))
        medVal = np.median( np.reshape(center, np.prod(center.shape)) )

        # try this instead:
        absDepthDev = 14
        frame = np.where(abs(frame-medVal) <= absDepthDev, 128, 0).astype(np.uint8)

        # morphological
        kernel = np.ones((3,3), np.uint8)
        frame = cv2.morphologyEx(frame, cv2.MORPH_CLOSE, kernel)

        # connected component
        smallKernel = 3
        frame[self.imgHeight/2-smallKernel:self.imgHeight/2+smallKernel,
            self.imgWidth/2-smallKernel:self.imgWidth/2+smallKernel] = 128

        mask = np.zeros((self.imgHeight+2,self.imgWidth+2), np.uint8)
        flood = frame.copy()
        cv2.floodFill(flood, mask, (self.imgWidth/2,self.imgHeight/2), 255, flags=4|(255<<8))

        ret,flooded = cv2.threshold(flood, 129, 255, cv2.THRESH_BINARY)

        return flooded 

def find_largest_feature(inp_img, scan_tl=None, scan_br=None):
	"""
	Uses the fact the `floodFill` function returns a bounding box of the area it filled to find the biggest
	connected pixel structure in the image. Fills this structure in white, reducing the rest to black.
	"""
	img = inp_img.copy()  # Copy the image, leaving the original untouched
	height, width = img.shape[:2]

	max_area = 0
	seed_point = (None, None)

	if scan_tl is None:
		scan_tl = [0, 0]

	if scan_br is None:
		scan_br = [width, height]

	# Loop through the image
	for x in range(scan_tl[0], scan_br[0]):
		for y in range(scan_tl[1], scan_br[1]):
			# Only operate on light or white squares
			if img.item(y, x) == 255 and x < width and y < height:  # Note that .item() appears to take input as y, x
				area = cv2.floodFill(img, None, (x, y), 64)
				if area[0] > max_area:  # Gets the maximum bound area which should be the grid
					max_area = area[0]
					seed_point = (x, y)

	# Colour everything grey (compensates for features outside of our middle scanning range
	for x in range(width):
		for y in range(height):
			if img.item(y, x) == 255 and x < width and y < height:
				cv2.floodFill(img, None, (x, y), 64)

	mask = np.zeros((height + 2, width + 2), np.uint8)  # Mask that is 2 pixels bigger than the image

	# Highlight the main feature
	if all([p is not None for p in seed_point]):
		cv2.floodFill(img, mask, seed_point, 255)

	top, bottom, left, right = height, 0, width, 0

	for x in range(width):
		for y in range(height):
			if img.item(y, x) == 64:  # Hide anything that isn't the main feature
				cv2.floodFill(img, mask, (x, y), 0)

			# Find the bounding parameters
			if img.item(y, x) == 255:
				top = y if y < top else top
				bottom = y if y > bottom else bottom
				left = x if x < left else left
				right = x if x > right else right

	bbox = [[left, top], [right, bottom]]
	return img, np.array(bbox, dtype='float32'), seed_point 

def find_all_template(im_source, im_search, threshold=0.8, rgb=False, max_count=10):
    """根据输入图片和参数设置,返回所有的图像识别结果."""
    # 第一步：校验图像输入
    check_source_larger_than_search(im_source, im_search)

    # 第二步：计算模板匹配的结果矩阵res
    res = _get_template_result_matrix(im_source, im_search)

    # 第三步：依次获取匹配结果
    result = []
    h, w = im_search.shape[:2]

    while True:
        # 本次循环中,取出当前结果矩阵中的最优值
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        # 求取可信度:
        confidence = _get_confidence_from_matrix(im_source, im_search, max_loc, max_val, w, h, rgb)

        if confidence < threshold or len(result) > max_count:
            break

        # 求取识别位置: 目标中心 + 目标区域:
        middle_point, rectangle = _get_target_rectangle(max_loc, w, h)
        one_good_match = generate_result(middle_point, rectangle, confidence)

        result.append(one_good_match)

        # 屏蔽已经取出的最优结果,进入下轮循环继续寻找:
        # cv2.floodFill(res, None, max_loc, (-1000,), max(max_val, 0), flags=cv2.FLOODFILL_FIXED_RANGE)
        cv2.rectangle(res, (int(max_loc[0] - w / 2), int(max_loc[1] - h / 2)), (int(max_loc[0] + w / 2), int(max_loc[1] + h / 2)), (0, 0, 0), -1)

    return result if result else None 

def find_all_results(self):
        """基于模板匹配查找多个目标区域的方法."""
        # 第一步：校验图像输入
        check_source_larger_than_search(self.im_source, self.im_search)

        # 第二步：计算模板匹配的结果矩阵res
        res = self._get_template_result_matrix()

        # 第三步：依次获取匹配结果
        result = []
        h, w = self.im_search.shape[:2]

        while True:
            # 本次循环中,取出当前结果矩阵中的最优值
            min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
            # 求取可信度:
            confidence = self._get_confidence_from_matrix(max_loc, max_val, w, h)

            if confidence < self.threshold or len(result) > self.MAX_RESULT_COUNT:
                break

            # 求取识别位置: 目标中心 + 目标区域:
            middle_point, rectangle = self._get_target_rectangle(max_loc, w, h)
            one_good_match = generate_result(middle_point, rectangle, confidence)

            result.append(one_good_match)

            # 屏蔽已经取出的最优结果,进入下轮循环继续寻找:
            # cv2.floodFill(res, None, max_loc, (-1000,), max(max_val, 0), flags=cv2.FLOODFILL_FIXED_RANGE)
            cv2.rectangle(res, (int(max_loc[0] - w / 2), int(max_loc[1] - h / 2)), (int(max_loc[0] + w / 2), int(max_loc[1] + h / 2)), (0, 0, 0), -1)

        return result if result else None