OpenCV删除背景

Question

我正在尝试删除一些图像的背景，调整一些值，并使用一些方法，如morphologyEx给我一个可接受的结果，但仍有一些洞，在最后一种情况下，这些洞甚至不能填充迭代每个轮廓并用-1绘制它。我可以看到阈值图像真的很好，使整个形状与线条，但我不知道如何继续。。。

更新 我改变了我的代码，所以我得到了更好的结果，但我仍然有一些洞。。。如果我能填补这些漏洞，剧本将是完美的。

def get_contrasted(image, type="dark", level=3):
    maxIntensity = 255.0 # depends on dtype of image data
    phi = 1
    theta = 1

    if type == "light":
        newImage0 = (maxIntensity/phi)*(image/(maxIntensity/theta))**0.5
        newImage0 = array(newImage0,dtype=uint8)
        return newImage0
    elif type == "dark":
        newImage1 = (maxIntensity/phi)*(image/(maxIntensity/theta))**level
        newImage1 = array(newImage1,dtype=uint8)

        return newImage1

def sharp(image, level=3):
    f = cv2.GaussianBlur(image, (level,level), level)
    f = cv2.addWeighted(image, 1.5, f, -0.5, 0)
    return f

original_image = imread('imagen.jpg')
# 1 Convert to gray & Normalize
gray_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
gray_img = sharp(get_contrasted(gray_img))
gray_img = normalize(gray_img, None, 0, 255, NORM_MINMAX, CV_8UC1)
imshow("Gray", gray_img)

# 2 Find Threshold
gray_blur = cv2.GaussianBlur(gray_img, (7, 7), 0)
adapt_thresh_im = cv2.adaptiveThreshold(gray_blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 1)
max_thresh, thresh_im = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
thresh = cv2.bitwise_or(adapt_thresh_im, thresh_im)

# 3 Dilate
gray = cv2.Canny(thresh, 88, 400, apertureSize=3)
gray = cv2.dilate(gray, None, iterations=8)
gray = cv2.erode(gray, None, iterations=8)
imshow("Trheshold", gray)

# 4 Flood
contours, _ = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contour_info = []
for c in contours:
    contour_info.append((
        c,
        cv2.isContourConvex(c),
        cv2.contourArea(c),
    ))
contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
max_contour = contour_info[0]
holes = np.zeros(gray_img.shape, np.uint8)
drawContours(holes, max_contour, 0, 255, -1)
imshow("Holes", holes)

mask = cv2.GaussianBlur(holes, (15, 15), 0)
mask = np.dstack([mask] * 3)  # Create 3-channel alpha mask

mask = mask.astype('float32') / 255.0  # Use float matrices,
img = original_image.astype('float32') / 255.0  # for easy blending
masked = (mask * img) + ((1 - mask) * (0,0,1))  # Blend
masked = (masked * 255).astype('uint8')

imshow("Maked", masked)
waitKey()

0原件

1个阈值

2个孔

3最终图像

Answer 1

当我在处理同一个问题时，在Python中找到了一个解决方案（使用opencv2），我想在这里也分享一下。希望有帮助。

import numpy as np
import cv2

cv2.namedWindow('image', cv2.WINDOW_NORMAL)

#Load the Image
imgo = cv2.imread('koAl2.jpg')
height, width = imgo.shape[:2]

#Create a mask holder
mask = np.zeros(imgo.shape[:2],np.uint8)

#Grab Cut the object
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)

#Hard Coding the Rect The object must lie within this rect.
rect = (10,10,width-30,height-30)
cv2.grabCut(imgo,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img1 = imgo*mask[:,:,np.newaxis]

#Get the background
background = imgo - img1

#Change all pixels in the background that are not black to white
background[np.where((background > [0,0,0]).all(axis = 2))] = [255,255,255]

#Add the background and the image
final = background + img1

#To be done - Smoothening the edges

cv2.imshow('image', final )

k = cv2.waitKey(0)

if k==27:
    cv2.destroyAllWindows()

Answer 2

@dhanushka的方法很好。这是我的Python版本：

def get_holes(image, thresh):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

    im_bw = cv.threshold(gray, thresh, 255, cv.THRESH_BINARY)[1]
    im_bw_inv = cv.bitwise_not(im_bw)

    contour, _ = cv.findContours(im_bw_inv, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE)
    for cnt in contour:
        cv.drawContours(im_bw_inv, [cnt], 0, 255, -1)

    nt = cv.bitwise_not(im_bw)
    im_bw_inv = cv.bitwise_or(im_bw_inv, nt)
    return im_bw_inv


def remove_background(image, thresh, scale_factor=.25, kernel_range=range(1, 15), border=None):
    border = border or kernel_range[-1]

    holes = get_holes(image, thresh)
    small = cv.resize(holes, None, fx=scale_factor, fy=scale_factor)
    bordered = cv.copyMakeBorder(small, border, border, border, border, cv.BORDER_CONSTANT)

    for i in kernel_range:
        kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (2*i+1, 2*i+1))
        bordered = cv.morphologyEx(bordered, cv.MORPH_CLOSE, kernel)

    unbordered = bordered[border: -border, border: -border]
    mask = cv.resize(unbordered, (image.shape[1], image.shape[0]))
    fg = cv.bitwise_and(image, image, mask=mask)
    return fg


img = cv.imread('koAl2.jpg')
nb_img = remove_background(img, 230)

Answer 3

使用一个不断增大的内核迭代地执行洞图像的形态闭合。但是，在此之前，我建议您调整孔图像的大小（使用最近邻插值），这样就不必使用大的内核。在下面的代码（C++）中，我将空穴图像调整为其原始尺寸的25%。

要减少对边框的影响，请在应用迭代关闭之前，使用copyMakeBorder添加一个由零组成的恒定边框。因为我们在这里使用15次迭代，所以使图像周围的边框大于15。

所以步骤是

• 调整孔图像的大小
• 添加零边框
• 用一个不断增大的内核迭代地关闭图像
• 删除边框
• 现在我们有一个小面具。将此蒙版调整为原始图像大小

<代码> C++。我对Python不是很熟悉。

    // read the image and the holes
    Mat im = imread("koAl2.jpg");
    Mat holes = imread("GuICX.jpg", 0);
    // resize
    Mat small, bordered;
    resize(holes, small, Size(), .25, .25);
    // add a zero border
    int b = 20;
    copyMakeBorder(small, bordered, b, b, b, b, BORDER_CONSTANT, Scalar(0));
    // close
    for (int i = 1; i < 15; i++)
    {
        Mat kernel = getStructuringElement(MORPH_ELLIPSE, cv::Size(2*i+1, 2*i+1));
        morphologyEx(bordered, bordered, MORPH_CLOSE, kernel, Point(-1, -1), 1);
    }
    // remove border
    Mat mask = bordered(Rect(b, b, small.cols, small.rows));
    // resize the mask
    Mat largeMask;
    resize(mask, largeMask, Size(im.cols, im.rows));
    // the foreground
    Mat fg;
    im.copyTo(fg, largeMask);

输出（不是按原始比例）看起来很好，只是它将底部的背景区域作为前景。