我想删除这张图片的背景,只得到一个人。我有上千张这样的图片,基本上是一个人和一个有点白的背景。
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)。然后我认为可以做的是,将边缘内的像素变白,而将边缘外的像素变黑。之后,可以对原始图像进行掩模,只得到人的照片。
然而,使用canny边缘检测器很难得到封闭的边界。使用Sobel过滤器的结果并不是那么差,但是我不知道如何继续。
编辑:
还可以去掉右手和裙子之间以及头发之间的背景吗?
发布于 2015-03-28 14:12:59
下面的代码应该可以让您开始使用。您可能希望使用程序顶部的参数来微调提取:
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:
发布于 2016-12-09 05:06:11
如果您不想用红色填充背景,但希望使其透明,您可以在解决方案中添加以下行:
# split image into channels
c_red, c_green, c_blue = cv2.split(img)
# merge with mask got on one of a previous steps
img_a = cv2.merge((c_red, c_green, c_blue, mask.astype('float32') / 255.0))
# show on screen (optional in jupiter)
%matplotlib inline
plt.imshow(img_a)
plt.show()
# save to disk
cv2.imwrite('girl_1.png', img_a*255)
# or the same using plt
plt.imsave('girl_2.png', img_a)
如果你愿意,你可以调整一些png压缩参数来使文件更小。
图片在下面的白色背景上。或者是黑色的- http://imgur.com/a/4NwmH
发布于 2016-12-11 05:23:08
https://stackoverflow.com/questions/29313667
复制相似问题