如何从这种图像中删除背景? [英] How do I remove the background from this kind of image?

问题描述

我想删除此图像的背景以仅获取此人.我有成千上万张这样的图像，基本上是一个人和一个有点发白的背景.

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 过滤器(来自 skimage 库).然后我认为可以做的是，将边缘内的像素变白，而将边缘内的像素变黑.之后，可以对原始图像进行遮罩，以仅获取人的照片.

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canny边缘检测器很难得到一个封闭的边界.使用 Sobel 过滤器的结果还不错，但是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.

是否也可以去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

以下代码应该可以帮助您入门.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)
# Previously, for a previous version of cv2, this line was: 
#  contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
# Thanks to notes from commenters, I've updated the code but left this note
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:

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