- html - 出于某种原因,IE8 对我的 Sass 文件中继承的 html5 CSS 不友好?
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景?
白平衡是一个相当广泛的主题,但我看到的大多数答案都涵盖了整个图像的自动白平衡技术,该图像没有已知的白色、灰色和黑色点。我似乎找不到许多从已知点涵盖白平衡的内容。我有一个脚本(下面),它拍摄色卡(Spyder Checkr 48)的图像并返回白色、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
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()
最佳答案
给定白块的 RGB 值,可以通过除以该值来校正图像的白平衡。也就是说,应用线性变换使白块在三个 channel 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
lum
使平均强度不会改变。
lum
的计算会更好:0.2126、0.7152、0.0722,但我想保持简单。如果输入的白色偏离标记,这只会产生很大的不同,在这种情况下你也会有其他问题。)
whiteR/lum
),并将其应用于图像。
关于python-3.x - 白平衡来自已知点的照片,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/54470148/
我正在处理一组标记为 160 个组的 173k 点。我想通过合并最接近的(到 9 或 10 个组)来减少组/集群的数量。我搜索过 sklearn 或类似的库,但没有成功。 我猜它只是通过 knn 聚类
我有一个扁平数字列表,这些数字逻辑上以 3 为一组,其中每个三元组是 (number, __ignored, flag[0 or 1]),例如: [7,56,1, 8,0,0, 2,0,0, 6,1,
我正在使用 pipenv 来管理我的包。我想编写一个 python 脚本来调用另一个使用不同虚拟环境(VE)的 python 脚本。 如何运行使用 VE1 的 python 脚本 1 并调用另一个 p
假设我有一个文件 script.py 位于 path = "foo/bar/script.py"。我正在寻找一种在 Python 中通过函数 execute_script() 从我的主要 Python
这听起来像是谜语或笑话,但实际上我还没有找到这个问题的答案。 问题到底是什么? 我想运行 2 个脚本。在第一个脚本中,我调用另一个脚本,但我希望它们继续并行,而不是在两个单独的线程中。主要是我不希望第
我有一个带有 python 2.5.5 的软件。我想发送一个命令,该命令将在 python 2.7.5 中启动一个脚本,然后继续执行该脚本。 我试过用 #!python2.7.5 和http://re
我在 python 命令行(使用 python 2.7)中,并尝试运行 Python 脚本。我的操作系统是 Windows 7。我已将我的目录设置为包含我所有脚本的文件夹,使用: os.chdir("
剧透:部分解决(见最后)。 以下是使用 Python 嵌入的代码示例: #include int main(int argc, char** argv) { Py_SetPythonHome
假设我有以下列表,对应于及时的股票价格: prices = [1, 3, 7, 10, 9, 8, 5, 3, 6, 8, 12, 9, 6, 10, 13, 8, 4, 11] 我想确定以下总体上最
所以我试图在选择某个单选按钮时更改此框架的背景。 我的框架位于一个类中,并且单选按钮的功能位于该类之外。 (这样我就可以在所有其他框架上调用它们。) 问题是每当我选择单选按钮时都会出现以下错误: co
我正在尝试将字符串与 python 中的正则表达式进行比较,如下所示, #!/usr/bin/env python3 import re str1 = "Expecting property name
考虑以下原型(prototype) Boost.Python 模块,该模块从单独的 C++ 头文件中引入类“D”。 /* file: a/b.cpp */ BOOST_PYTHON_MODULE(c)
如何编写一个程序来“识别函数调用的行号?” python 检查模块提供了定位行号的选项,但是, def di(): return inspect.currentframe().f_back.f_l
我已经使用 macports 安装了 Python 2.7,并且由于我的 $PATH 变量,这就是我输入 $ python 时得到的变量。然而,virtualenv 默认使用 Python 2.6,除
我只想问如何加快 python 上的 re.search 速度。 我有一个很长的字符串行,长度为 176861(即带有一些符号的字母数字字符),我使用此函数测试了该行以进行研究: def getExe
list1= [u'%app%%General%%Council%', u'%people%', u'%people%%Regional%%Council%%Mandate%', u'%ppp%%Ge
这个问题在这里已经有了答案: Is it Pythonic to use list comprehensions for just side effects? (7 个答案) 关闭 4 个月前。 告
我想用 Python 将两个列表组合成一个列表,方法如下: a = [1,1,1,2,2,2,3,3,3,3] b= ["Sun", "is", "bright", "June","and" ,"Ju
我正在运行带有最新 Boost 发行版 (1.55.0) 的 Mac OS X 10.8.4 (Darwin 12.4.0)。我正在按照说明 here构建包含在我的发行版中的教程 Boost-Pyth
学习 Python,我正在尝试制作一个没有任何第 3 方库的网络抓取工具,这样过程对我来说并没有简化,而且我知道我在做什么。我浏览了一些在线资源,但所有这些都让我对某些事情感到困惑。 html 看起来
我是一名优秀的程序员,十分优秀!