“白平衡”是一个覆盖面很广的主题，但是我看到的大多数答案都涉及到整个图像的自动白平衡技术，而对于白，灰和黑的已知点却一无所知。从已知的角度来看，我似乎找不到许多涵盖白平衡的东西。我有一个脚本（如下），该脚本拍摄一张色卡（Spyder Checkr 48），并返回白色，20％灰色和黑色色卡块：
Color L A B sR sG sB aR aG aB
Card White 96.04 2.16 2.6 249 242 238 247 242 237
20% Gray 80.44 1.17 2.05 202 198 195 199 196 193
Card Black 16.91 1.43 -0.81 43 41 43 46 46 47
问题：由于我知道图像特定部分的地面真实度LAB，sRGB和AdobeRGB值，因此什么是对图像进行白平衡的最佳方法？
Here is a link to the images I am working with.这是用于提取色卡块的代码（我目前在Windows python 3.7上运行该代码）：
from __future__ import print_function
import cv2
import imutils
import numpy as np
from matplotlib import pyplot as plt
import os
import sys
image = cv2.imread("PATH_TO_IMAGE")
template = cv2.imread("PATH_TO_TEMPLATE")
rtemplate = cv2.imread("PATH_TO_RIGHT_TEMPLATE")
def sift(image):
sift = cv2.xfeatures2d.SIFT_create()
kp, des = sift.detectAndCompute(image, None)
return kp, des
def sift_match(im1, im2, vis=False, save=False):
MIN_MATCH_COUNT = 10
FLANN_INDEX_KDTREE = 0
kp1, des1 = sift(im1)
kp2, des2 = sift(im2)
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=7)
search_params = dict(checks=100)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# Need to draw only good matches, so create a mask
matchesMask = [[0, 0] for i in range(len(matches))]
if vis is True:
draw_params = dict(matchColor=(0, 255, 0),
singlePointColor=(255, 0, 0),
matchesMask=matchesMask,
flags=0)
im3 = cv2.drawMatchesKnn(im1, kp1, im2, kp2, matches, None, **draw_params)
if save:
cv2.imwrite("tempSIFT_Match.png", im3)
plt.imshow(im3), plt.show()
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append(m)
return kp1, des1, kp2, des2, good
def smartextractor(im1, im2, vis=False):
# Detect features and compute descriptors.
kp1, d1, kp2, d2, matches = sift_match(im1, im2, vis)
kp1 = np.asarray(kp1)
kp2 = np.asarray(kp2)
# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = kp1[match.queryIdx].pt
points2[i, :] = kp2[match.trainIdx].pt
# Find homography
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
if h is None:
print("could not find homography")
return None, None
# Use homography
height, width, channels = im2.shape
im1Reg = cv2.warpPerspective(im1, h, (width, height))
return im1Reg, h
def show_images(images, cols=1, titles=None):
"""
Display a list of images in a single figure with matplotlib.
"""
assert ((titles is None) or (len(images) == len(titles)))
n_images = len(images)
if titles is None: titles = ['Image (%d)' % i for i in range(1, n_images + 1)]
fig = plt.figure()
for n, (image, title) in enumerate(zip(images, titles)):
a = fig.add_subplot(cols, np.ceil(n_images / float(cols)), n + 1)
if image.ndim == 2:
plt.gray()
plt.imshow(image)
a.set_title(title)
fig.set_size_inches(np.array(fig.get_size_inches()) * n_images)
plt.show()
def Sobel(img, bilateralFilter=True):
# timestart = time.clock()
try:
img = cv2.imread(img, 0)
except TypeError:
None
try:
rheight, rwidth, rdepth = img.shape
img1 = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
except ValueError:
raise TypeError
# cv2.imwrite('temp.png',img)
_, s, v = cv2.split(img1)
b, g, r = cv2.split(img)
if bilateralFilter is True:
s = cv2.bilateralFilter(s, 11, 17, 17)
v = cv2.bilateralFilter(v, 11, 17, 17)
b = cv2.bilateralFilter(b, 11, 17, 17)
g = cv2.bilateralFilter(g, 11, 17, 17)
r = cv2.bilateralFilter(r, 11, 17, 17)
# calculate sobel in x,y,diagonal directions with the following kernels
sobelx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=np.float32)
sobely = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=np.float32)
sobeldl = np.array([[0, 1, 2], [-1, 0, 1], [-2, -1, 0]], dtype=np.float32)
sobeldr = np.array([[2, 1, 0], [1, 0, -1], [0, -1, -2]], dtype=np.float32)
# calculate the sobel on value of hsv
gx = cv2.filter2D(v, -1, sobelx)
gy = cv2.filter2D(v, -1, sobely)
gdl = cv2.filter2D(v, -1, sobeldl)
gdr = cv2.filter2D(v, -1, sobeldr)
# combine sobel on value of hsv
xylrv = 0.25 * gx + 0.25 * gy + 0.25 * gdl + 0.25 * gdr
# calculate the sobel on saturation of hsv
sx = cv2.filter2D(s, -1, sobelx)
sy = cv2.filter2D(s, -1, sobely)
sdl = cv2.filter2D(s, -1, sobeldl)
sdr = cv2.filter2D(s, -1, sobeldr)
# combine sobel on value of hsv
xylrs = 0.25 * sx + 0.25 * sy + 0.25 * sdl + 0.25 * sdr
# combine value sobel and saturation sobel
xylrc = 0.5 * xylrv + 0.5 * xylrs
xylrc[xylrc < 6] = 0
# calculate the sobel on value on green
grx = cv2.filter2D(g, -1, sobelx)
gry = cv2.filter2D(g, -1, sobely)
grdl = cv2.filter2D(g, -1, sobeldl)
grdr = cv2.filter2D(g, -1, sobeldr)
# combine sobel on value on green
xylrgr = 0.25 * grx + 0.25 * gry + 0.25 * grdl + 0.25 * grdr
# calculate the sobel on blue
bx = cv2.filter2D(b, -1, sobelx)
by = cv2.filter2D(b, -1, sobely)
bdl = cv2.filter2D(b, -1, sobeldl)
bdr = cv2.filter2D(b, -1, sobeldr)
# combine sobel on value on blue
xylrb = 0.25 * bx + 0.25 * by + 0.25 * bdl + 0.25 * bdr
# calculate the sobel on red
rx = cv2.filter2D(r, -1, sobelx)
ry = cv2.filter2D(r, -1, sobely)
rdl = cv2.filter2D(r, -1, sobeldl)
rdr = cv2.filter2D(r, -1, sobeldr)
# combine sobel on value on red
xylrr = 0.25 * rx + 0.25 * ry + 0.25 * rdl + 0.25 * rdr
# combine value sobel and saturation sobel
xylrrgb = 0.33 * xylrgr + 0.33 * xylrb + 0.33 * xylrr
xylrrgb[xylrrgb < 6] = 0
# combine HSV and RGB sobel outputs
xylrc = 0.5 * xylrc + 0.5 * xylrrgb
xylrc[xylrc < 6] = 0
xylrc[xylrc > 25] = 255
return xylrc
print("extracting image")
extractedImage, _ = smartextractor(image, template)
print("extracting right image")
rextractedImage, _ = smartextractor(extractedImage, rtemplate, vis=False)
grextractedImage = cv2.cvtColor(rextractedImage, cv2.COLOR_BGR2GRAY)
bfsobelImg = Sobel(rextractedImage)
sobelImg = Sobel(rextractedImage, bilateralFilter=False)
csobelImg = cv2.add(bfsobelImg, sobelImg)
csobelImg[csobelImg < 6] = 0
csobelImg[csobelImg > 18] = 255
csobelImg = csobelImg.astype(np.uint8)
img2 = csobelImg.copy()
ret, thresh = cv2.threshold(img2, 18, 255, 0)
contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
count = 0
trigger = False
for c in contours:
# approximate the contour
peri = cv2.arcLength(c, True)
contours[count] = cv2.approxPolyDP(c, 0.05 * peri, True)
if len(contours[count]) == 4:
if trigger is False:
screenCnt = contours[count]
trigger = True
count += 1
tl = screenCnt[0]
tr = screenCnt[1]
bl = screenCnt[3]
br = screenCnt[2]
tLy, tLx = tl[0]
tRy, tRx = tr[0]
bLy, bLx = bl[0]
bRy, bRx = br[0]
ratio = .15
realSpace = (3/16)
boxwidth = int(((tRx - tLx) + (bRx - bLx))*.5 - (tLx + bLx)*.5)
boxheight = int(((bRy - tRy) + (bLy - tLy))*.5 - (tRy + tLy)*.5)
spaceWidth = int((boxwidth + boxheight)*.5*realSpace)
boxcenter = [int(((bRy - tRy)*.5 + (bLy - tLy)*.5)*.5), int(((tRx - tLx)*.5 + (bRx - bLx)*.5)*.5)]
roitl = [boxcenter[0] - int(ratio*boxheight), boxcenter[1] - int(ratio*boxwidth)]
roitr = [boxcenter[0] - int(ratio*boxheight), boxcenter[1] + int(ratio*boxwidth)]
roibl = [boxcenter[0] + int(ratio*boxheight), boxcenter[1] - int(ratio*boxwidth)]
roibr = [boxcenter[0] + int(ratio*boxheight), boxcenter[1] + int(ratio*boxwidth)]
spacing = int((boxwidth + boxheight)*.5)+spaceWidth
roiWhite = np.array((roitl, roitr, roibr, roibl))
roiGray = np.array(([roitl[1], roitl[0]+spacing*1], [roitr[1], roitr[0]+spacing*1],
[roibr[1], roibr[0]+spacing*1], [roibl[1], roibl[0]+spacing*1]))
roiBlack = np.array(([roitl[1], roitl[0]+spacing*6], [roitr[1], roitr[0]+spacing*6],
[roibr[1], roibr[0]+spacing*6], [roibl[1], roibl[0]+spacing*6]))
whiteAvgb, whiteAvgg, whiteAvgr, _ = cv2.mean(rextractedImage[(roitl[0]+spacing*0):(roibr[0]+spacing*0),
roitl[1]:roibr[1]])
grayAvgb, grayAvgg, grayAvgr, _ = cv2.mean(rextractedImage[(roitl[0]+spacing*1):(roibr[0]+spacing*1),
roitl[1]:roibr[1]])
blackAvgb, blackAvgg, blackAvgr, _ = cv2.mean(rextractedImage[(roitl[0]+spacing*6):(roibr[0]+spacing*6),
roitl[1]:roibr[1]])
whiteROI = rextractedImage[(roitl[0]+spacing*0):(roibr[0]+spacing*0), roitl[1]:roibr[1]]
grayROI = rextractedImage[(roitl[0]+spacing*1):(roibr[0]+spacing*1), roitl[1]:roibr[1]]
blackROI = rextractedImage[(roitl[0]+spacing*6):(roibr[0]+spacing*6), roitl[1]:roibr[1]]
imageList = [whiteROI, grayROI, blackROI]
show_images(imageList, cols=1)
correctedImage = rextractedImage.copy()
whiteROI[:, :, 0] = whiteAvgb
whiteROI[:, :, 1] = whiteAvgg
whiteROI[:, :, 2] = whiteAvgr
grayROI[:, :, 0] = grayAvgb
grayROI[:, :, 1] = grayAvgg
grayROI[:, :, 2] = grayAvgr
blackROI[:, :, 0] = blackAvgb
blackROI[:, :, 1] = blackAvgg
blackROI[:, :, 2] = blackAvgr
imageList = [whiteROI, grayROI, blackROI]
show_images(imageList, cols=1)
# SPYDER COLOR CHECKR Values: http://www.bartneck.de/2017/10/24/patch-color-definitions-for-datacolor-spydercheckr-48/
blank = np.zeros_like(csobelImg)
maskedImg = blank.copy()
maskedImg = cv2.fillConvexPoly(maskedImg, roiWhite, 255)
maskedImg = cv2.fillConvexPoly(maskedImg, roiGray, 255)
maskedImg = cv2.fillConvexPoly(maskedImg, roiBlack, 255)
res = cv2.bitwise_and(rextractedImage, rextractedImage, mask=maskedImg)
# maskedImg = cv2.fillConvexPoly(maskedImg, roi2Black, 255)
cv2.drawContours(blank, contours, -1, 255, 3)
outputSquare = np.zeros_like(csobelImg)
cv2.drawContours(outputSquare, [screenCnt], -1, 255, 3)
imageList = [rextractedImage, grextractedImage, bfsobelImg, sobelImg, csobelImg, blank, outputSquare, maskedImg, res]
show_images(imageList, cols=3)
sys.exit()
答案 0 :(得分：1)
鉴于白色补丁的RGB值，可以通过除以该值来校正图像的白平衡。也就是说，应用线性变换使白色色块在三个通道中具有相同的电平：
lum = (whiteR + whiteG + whiteB)/3
imgR = imgR * lum / whiteR
imgG = imgG * lum / whiteG
imgB = imgB * lum / whiteB
乘以lum可以使平均强度保持不变。
（lum的计算如果使用适当的权重会更好一些：0.2126、0.7152、0.0722，但我想保持简单。这只会在输入白色偏离标准的情况下有很大的不同。 ，在这种情况下，您还会遇到其他问题。）
请注意，该转换最适用于线性RGB空间。如果图像以sRGB或类似格式存储（来自相机的原始图像将是线性RGB，JPEG将是sRGB），则图像和白色的RGB值均应首先转换为线性RGB。有关方程式，请参见here。
为了获得更高的精度，您还可以使用灰色色块的RGB值来应用以上内容。对于每个通道，取自白色和灰色色块的平均乘法因子（whiteR/lum），然后将其应用于图像。
在确定白色RGB值并校正白平衡之前，可以从图像中减去黑电平。这样可以改善对比度和色彩感知能力，但不能改善白平衡。
全色校正要复杂得多，我不再赘述。