如何从此类图像中删除背景？ [英] How do I remove the background from this kind of image?

问题描述

我想删除这张图片的背景只是为了得到这个人。我有这样的数千张图像，基本上是一个人和一些有点白色的背景。

I want to remove the background of this image to get the person only. I have thousand of images like this, basically, a person and a somewhat whitish background.

我所做的是使用边缘检测器，如canny边缘检测器或sobel滤波器（来自 skimage 库）。那么我认为可以做的是，使边缘内的像素变白并使像素变黑。之后，原始图像可以被掩盖以仅获取人物的图像。

What I have done is to use edge detector like canny edge detector or sobel filter (from skimage library). Then what I think possible to do is, whiten the pixels within the edges and blacken the pixels without. Afterwards, the original image can be mask to get the picture of the person only.

然而，使用精确边缘检测器很难获得封闭边界。使用Sobel过滤器的结果并不差，但我不知道如何从那里开始。

However, it's hard to get a closed boundary using canny edge detector. Result using Sobel filter is not that bad, however I don't how to proceed from there.

编辑：

是否可以删除右手和裙子之间以及头发之间的背景？

Is it possible to also remove the background between the right hand and the skirt and between hairs?

推荐答案

以下代码可以帮助您入门。您可能想要使用程序顶部的参数来微调您的提取：

The following code should get you started. You may want to play around with the parameters at the top of the program to fine-tune your extraction:

import cv2
import numpy as np

#== Parameters =======================================================================
BLUR = 21
CANNY_THRESH_1 = 10
CANNY_THRESH_2 = 200
MASK_DILATE_ITER = 10
MASK_ERODE_ITER = 10
MASK_COLOR = (0.0,0.0,1.0) # In BGR format


#== Processing =======================================================================

#-- Read image -----------------------------------------------------------------------
img = cv2.imread('C:/Temp/person.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#-- Edge detection -------------------------------------------------------------------
edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)
edges = cv2.dilate(edges, None)
edges = cv2.erode(edges, None)

#-- Find contours in edges, sort by area ---------------------------------------------
contour_info = []
contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
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]

#-- Create empty mask, draw filled polygon on it corresponding to largest contour ----
# Mask is black, polygon is white
mask = np.zeros(edges.shape)
cv2.fillConvexPoly(mask, max_contour[0], (255))

#-- Smooth mask, then blur it --------------------------------------------------------
mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)
mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)
mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)
mask_stack = np.dstack([mask]*3)    # Create 3-channel alpha mask

#-- Blend masked img into MASK_COLOR background --------------------------------------
mask_stack  = mask_stack.astype('float32') / 255.0          # Use float matrices, 
img         = img.astype('float32') / 255.0                 #  for easy blending

masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR) # Blend
masked = (masked * 255).astype('uint8')                     # Convert back to 8-bit 

cv2.imshow('img', masked)                                   # Display
cv2.waitKey()

#cv2.imwrite('C:/Temp/person-masked.jpg', masked)           # Save

输出：

这篇关于如何从此类图像中删除背景？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！