Python简单的批量校色工具
程序化美术
很久很久以前，老大给了个任务，因为里面照片扫描（Photogrammetry）用的越来越多， 我们需要一个能保证扫描出来的模型拥有比较精准的固有色，要写一个批量校色的小工具集成到照片扫描工具里。
结果最近其他工作室也要类似的功能，就有把之前的脚本翻出来写了个小工具， 发现这个脚本里面还是有一些比较有用的东西，就干脆发上来提供给有需要的人（ 其实我也是抄的别人的~~~ ）。
首先，校色（color-correcting）不是调色（color-grading），并不是为了调出好看的效果而执行的步骤，是为了尽可能的保证在不同的光照环境， 不同的拍摄设置下，依旧能够得到相对准的颜色的步骤。
既然目的是得到准确的颜色，那么必然要有一个颜色的标准值，我们项目中使用的是 X-Rite ColorChecker Passport
就是这个玩意：
玩摄影的朋友们一定很熟悉这个家伙，拍摄之前先用这个进行一下白平衡校准啥的，这里就不细讲了，我们的流程是对拍摄后的照片进行校色。
拍摄流程：
每拍摄一组照片之前， 在与被拍摄物体较为接近的光照条件下拍摄一张校色卡 ，然后保持相机参数不变，拍摄物件。
项目里的图片不能放上来，就大概想象一下吧：
主要拍摄红框里面那部分，我们的规则是这 部分至少要占图片的30%以上的面积 ，ruo实际使用中发现拍小点也行。
那么照片拍好了，我们的思路是：
1，把拍有色卡的那张照片的色卡颜色值提取出来，也即是 拍摄颜色值 。
2，把这一组 拍摄颜色值 与 标准色卡值 进行比较并校色，检测校色之后的值偏差是否足够小。
3，利用 拍摄颜色值 对接下来同一组照片进行校色。
而 标准色卡颜色值 （RGB-space， 实际校色是使用的是XYZ-space）:
思路理清楚了， 我么看是看代码，显目里面的用的python：
首先是提取色卡颜色值，我们这里用了一个叫做colour-checker-detection 的库来检测色卡的位置与颜色：
如果是手动校色的话，这一步通常使用一个叫做： ColorChecker Camera Calibration v2.2.0 的工具来生成一个profile, 然后通过其他软件进行校色，实际使用中感觉实在太麻烦，不能自动化，还经常出来颜色不对，我们就没有采用。
def getColorCorrectionSwatches(colorChecker,cacheFolder):
# we use no brighten by default
bNoAutoBrighten = True
if not os.path.exists(colorChecker):
return False, False, []
#利用rawpy将raw文件转换成tiff文件格式方便校色
colorCheckerTiff = convert2sRGBTiff(colorChecker, cacheFolder)
#First blur the image to get rid of noise in colorchecker
#对有色卡的照片进行模糊操作， 这样能去点部分噪点达到更好的检测
print("blurring " + colorCheckerTiff)
blurtiff = os.path.join(cacheFolder, os.path.splitext(os.path.basename(colorChecker))[0]+ "_blur" + '.tiff')
img = PythonMagick.Image(colorCheckerTiff)
img.blur(0,10)
img.write(blurtiff)
#Retrieve the color correction swatch values from the given image
print(f"Detecting color checker in {blurtiff}")
#将图片中的非线性R'G'B'值转换成线性的RGB值
image = colour.cctf_decoding(colour.io.read_image(blurtiff))
#检测图片中的色卡值
swatches = detect_colour_checkers_segmentation(image)
if len(swatches) < 1:
return False, False, []
#对每个检测到的色卡值进行校色，并返回结果最准确的那个。
Vresult, swatch = VerifyColorSwatches(swatches)
return Vresult, bNoAutoBrighten, swatch
def VerifyColorSwatches(swatches):
deviation = []
#标准色卡颜色值
rgb_RCCL = colour.XYZ_to_RGB(colour.xyY_to_XYZ(list(REFERENCE_COLOUR_CHECKER.data.values())),
D65, D65, colour.RGB_COLOURSPACES['sRGB'].matrix_XYZ_to_RGB)
for swatch in swatches:
swatch_cc = colour.colour_correction(swatch, swatch, REFERENCE_SWATCHES)
CCL = swatch_cc
totalsum = 0.0
for i in range(len(rgb_RCCL)):
totalsum += abs(rgb_RCCL[i][0]-CCL[i][0]) + abs(rgb_RCCL[i][1]-CCL[i][1]) + abs(rgb_RCCL[i][2]-CCL[i][2])
deviation.append(totalsum)
print("swatches deviations: "+str(deviation))
min_d = 100.0
min_i = -1
for i in range(len(deviation)):
if deviation[i] < min_d:
min_d = deviation[i]
min_i = i
print("The lowest devi is: " + str(min_d))
result = False
#通常插值和在8.0一下都可以接受
if min_d < 3.0:
print("devi is good ! ")
result = True
elif min_d < 5.0:
print("devi is near average ! ")
result = True
elif min_d < 8.0:
print("devi is near BAD ! the COLORS will be off!!!!")
result = True
else:
print("devi is BAD ! the Swatches are completely off!!!!")
result = False
return result, swatches[min_i]
def convert2sRGBTiff(file, outfolder, bNo_Auto_Bright = True):
Convert ALL to tiff in sRGB space
tiffFile = ""
extention = file.rsplit(".",1)[-1]
tiffFile = os.path.join(outfolder, os.path.splitext(os.path.basename(file))[0] + '.tiff')
#对于非raw格式，只做一个简单的转换
if (extention == "jpg" or extention == "JPG" or
extention == "png" or extention == "PNG" or
extention == "tga" or extention == "TGA"):
im = imageio.imread(file)
imageio.imsave(tiffFile, im)
return tiffFile
#对raw格式，设置为不裁剪高光，不自动加亮，使用默认白平衡（校色会自带白平衡）， 使用sRGB gamma
else:
try:
raw = rawpy.imread(file)
rgb = raw.postprocess(highlight_mode=0, no_auto_bright=bNo_Auto_Bright, use_camera_wb=True, gamma=(2.4, 12.92))
tiffFile = os.path.join(outfolder, os.path.splitext(os.path.basename(file))[0] + '.tiff')
imageio.imsave(tiffFile, rgb)
return tiffFile
except:
print("Unsupported Format!!")
return False现在我们得到了一个较为准确的 拍摄颜色值（ swatch ）
我们可以利用它对每张图片进行校色，一个loop就行， 因为校色过程比较慢，也可以使用多线程，或者直接丢到渲染及上面去（推荐，项目里面是用的一个8台机器的小农场，同时也是用来搞照片扫描的）
核心部分大概是这样的：
for file in files:
#利用rawpy将raw文件转换成tiff文件格式方便校色
tifffile = convert2sRGBTiff(file, CacheDir)
print("ColorCorrecting: "+tifffile)
#将图片中的非线性R'G'B'值转换成线性的RGB值
image = colour.cctf_decoding(colour.io.read_image(os.path.join(CacheDir, tifffile)))
#使用拍摄颜色值（swatch）进行校色
cc_image = colour.colour_correction(image, swatch, REFERENCE_SWATCHES, 'Finlayson 2015')
#这里将校色完成的图片存为32位图像
tiff_CC_32 = os.path.join(CacheDir, os.path.splitext(os.path.basename(file))[0] + "_CC" +'.tiff')
#将图片中的线性的RGB值值转换成非线性R'G'B'
colour.io.write_image(colour.cctf_encoding(cc_image), tiff_CC_32)# write out 32bit image
print("Save CC image to jpg: "+file)
cctifffile = os.path.join(CacheDir, os.path.splitext(os.path.basename(file))[0] + '.tiff')
#ccpngfile = os.path.join(folderpath,'ColorCorrected', os.path.splitext(os.path.basename(file))[0] + '.tiff')
#转换为8位图片以节约空间
img = PythonMagick.Image(tiff_CC_32)
img.depth(8)
img.write(cctifffile)
#项目要求转换为JPG并缩放尺寸
Final_ccJpgfile = os.path.join(colorCorrectFolder, os.path.splitext(os.path.basename(file))[0] + '.jpg')
img = Image.open(cctifffile)
"""disable resize
if max(img.size[0], img.size[1]) > DestFrameSize:
ResizePercent = DestFrameSize/float(max(img.size[0], img.size[1]))
img = img.resize((int(img.size[0]*ResizePercent), int(img.size[1]*ResizePercent)), 5)
img.save(Final_ccJpgfile, quality=100, subsampling=0)到这一步，照片已经校色完毕了，但是有一部分软件对照片要求metadata，就是拍摄相机类型，焦距，ISO, 快门速度那一类的信息。这些信息通常是内置在照片里的，但是我们这么转换来转换去的，这部分信息就丢了，现在我们利用 exiftool 这个工具把信息都传递过来：
exiftoolPath = "exiftool.exe -m -overwrite_original_in_place -tagsFromFile"
def TransferMetaData(SourceFile, DistFile):
command = exiftoolPath
command += " "+ SourceFile
command += " "+ DistFile
os.system(command)
大功告成！！
现在输出的照片便是较好色的图片，用来做扫面还是贴图随你便，如果不放心的见可以进PS吸一下颜色和标准色对比~~~~
完整的小工具代码：
import os, array
import shutil, glob
import multiprocessing
import sys, time
import tkinter as tk
import tkinter.filedialog
import threading
import colour
from PIL import Image
from colour_checker_detection import detect_colour_checkers_segmentation
import imageio, rawpy
import PythonMagick
from collections import OrderedDict
from OpenImageIO import ImageOutput, ImageInput
D65 = colour.CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
REFERENCE_COLOUR_CHECKER = colour.CCS_COLOURCHECKERS['ColorChecker24 - After November 2014']
REFERENCE_SWATCHES = colour.XYZ_to_RGB(
colour.xyY_to_XYZ(list(REFERENCE_COLOUR_CHECKER.data.values())),
REFERENCE_COLOUR_CHECKER.illuminant, D65,
colour.RGB_COLOURSPACES['sRGB'].matrix_XYZ_to_RGB)
exiftoolPath = "exiftool.exe -m -overwrite_original_in_place -tagsFromFile"
#Global variables
ColorChecker = ""
PhotosDir = ""
OutPutDir = ""
#ProcessNum = 4
status = ""
bBusy = False
PhotoNum = 0
def getColorCorrectionSwatches(colorChecker,cacheFolder):
# we use no brighten by default
bNoAutoBrighten = True
if not os.path.exists(colorChecker):
return False, False, []
#利用rawpy将raw文件转换成tiff文件格式方便校色
colorCheckerTiff = convert2sRGBTiff(colorChecker, cacheFolder)
#First blur the image to get rid of noise in colorchecker
#对有色卡的照片进行模糊操作， 这样能去点部分噪点达到更好的检测
print("blurring " + colorCheckerTiff)
blurtiff = os.path.join(cacheFolder, os.path.splitext(os.path.basename(colorChecker))[0]+ "_blur" + '.tiff')
img = PythonMagick.Image(colorCheckerTiff)
img.blur(0,10)
img.write(blurtiff)
#Retrieve the color correction swatch values from the given image
print(f"Detecting color checker in {blurtiff}")
#将图片中的非线性R'G'B'值转换成线性的RGB值
image = colour.cctf_decoding(colour.io.read_image(blurtiff))
#检测图片中的色卡值
swatches = detect_colour_checkers_segmentation(image)
if len(swatches) < 1:
return False, False, []
#对每个检测到的色卡值进行校色，并返回结果最准确的那个。
Vresult, swatch = VerifyColorSwatches(swatches)
return Vresult, bNoAutoBrighten, swatch
def VerifyColorSwatches(swatches):
deviation = []
#标准色卡颜色值
rgb_RCCL = colour.XYZ_to_RGB(colour.xyY_to_XYZ(list(REFERENCE_COLOUR_CHECKER.data.values())),
D65, D65, colour.RGB_COLOURSPACES['sRGB'].matrix_XYZ_to_RGB)
for swatch in swatches:
swatch_cc = colour.colour_correction(swatch, swatch, REFERENCE_SWATCHES)
CCL = swatch_cc
totalsum = 0.0
for i in range(len(rgb_RCCL)):
totalsum += abs(rgb_RCCL[i][0]-CCL[i][0]) + abs(rgb_RCCL[i][1]-CCL[i][1]) + abs(rgb_RCCL[i][2]-CCL[i][2])
deviation.append(totalsum)
print("swatches deviations: "+str(deviation))
min_d = 100.0
min_i = -1
for i in range(len(deviation)):
if deviation[i] < min_d:
min_d = deviation[i]
min_i = i
print("The lowest devi is: " + str(min_d))
result = False
#通常插值和在8.0一下都可以接受
if min_d < 3.0:
print("devi is good ! ")
result = True
elif min_d < 5.0:
print("devi is near average ! ")
result = True
elif min_d < 8.0:
print("devi is near BAD ! the COLORS will be off!!!!")
result = True
else:
print("devi is BAD ! the Swatches are completely off!!!!")
result = False
return result, swatches[min_i]
def CCProcess(files, Vresult, swatch, colorCorrectFolder, CacheDir):
#folder = folderpath.split("\\")[-1]
#LocalCcCache = os.path.join(LocalCachPath, folder, "Cache")
for file in files:
#利用rawpy将raw文件转换成tiff文件格式方便校色
tifffile = convert2sRGBTiff(file, CacheDir)
print("ColorCorrecting: "+tifffile)
#将图片中的非线性R'G'B'值转换成线性的RGB值
image = colour.cctf_decoding(colour.io.read_image(os.path.join(CacheDir, tifffile)))
#使用拍摄颜色值（swatch）进行校色
cc_image = colour.colour_correction(image, swatch, REFERENCE_SWATCHES, 'Finlayson 2015')
#这里将校色完成的图片存为32位图像
tiff_CC_32 = os.path.join(CacheDir, os.path.splitext(os.path.basename(file))[0] + "_CC" +'.tiff')
#将图片中的线性的RGB值值转换成非线性R'G'B'
colour.io.write_image(colour.cctf_encoding(cc_image), tiff_CC_32)# write out 32bit image
print("Save CC image to jpg: "+file)
cctifffile = os.path.join(CacheDir, os.path.splitext(os.path.basename(file))[0] + '.tiff')
#ccpngfile = os.path.join(folderpath,'ColorCorrected', os.path.splitext(os.path.basename(file))[0] + '.tiff')
#转换为8位图片以节约空间
img = PythonMagick.Image(tiff_CC_32)
img.depth(8)
img.write(cctifffile)
#项目要求转换为JPG并缩放尺寸
Final_ccJpgfile = os.path.join(colorCorrectFolder, os.path.splitext(os.path.basename(file))[0] + '.jpg')
img = Image.open(cctifffile)
"""disable resize
if max(img.size[0], img.size[1]) > DestFrameSize:
ResizePercent = DestFrameSize/float(max(img.size[0], img.size[1]))
img = img.resize((int(img.size[0]*ResizePercent), int(img.size[1]*ResizePercent)), 5)
img.save(Final_ccJpgfile, quality=100, subsampling=0)
TransferMetaData(file, Final_ccJpgfile)
def convert2sRGBTiff(file, outfolder, bNo_Auto_Bright = True):
Convert ALL to tiff in sRGB space
tiffFile = ""
extention = file.rsplit(".",1)[-1]
tiffFile = os.path.join(outfolder, os.path.splitext(os.path.basename(file))[0] + '.tiff')
#对于非raw格式，只做一个简单的转换
if (extention == "jpg" or extention == "JPG" or
extention == "png" or extention == "PNG" or
extention == "tga" or extention == "TGA"):
im = imageio.imread(file)
imageio.imsave(tiffFile, im)
return tiffFile
#对raw格式，设置为不裁剪高光，不自动加亮，使用默认白平衡（校色会自带白平衡）， 使用sRGB gamma
else:
try:
raw = rawpy.imread(file)
rgb = raw.postprocess(highlight_mode=0, no_auto_bright=bNo_Auto_Bright, use_camera_wb=True, gamma=(2.4, 12.92))
tiffFile = os.path.join(outfolder, os.path.splitext(os.path.basename(file))[0] + '.tiff')
imageio.imsave(tiffFile, rgb)
return tiffFile
except:
print("Unsupported Format!!")
return False
def TransferMetaData(SourceFile, DistFile):
command = exiftoolPath
command += " "+ SourceFile
command += " "+ DistFile
os.system(command)
def CleanUP(CacheDir):
if os.path.exists(CacheDir):
shutil.rmtree(CacheDir)
def MainCCProcess(ColorChecker, folderpath, OutPutDir, Threads):
DisableButtons()
Format = ColorChecker.rsplit(".",1)[-1]
FileList = glob.glob(os.path.join(folderpath, '*.'+Format))
if len(FileList) < 2:
print("No file to CC!")
return False
#CCDir = os.path.join(folderpath, "ColorCorrected")
#os.makedirs(CCDir, exist_ok=True)
CCDir = OutPutDir
CleanUP(CCDir)
os.makedirs(CCDir, exist_ok=True)
CacheDir = os.path.join(CCDir,"Cache")
os.makedirs(CacheDir, exist_ok=True)
global PhotoNum
PhotoNum = len(FileList)
print("Prepare ColorChecker")
Vresult, bNoAutoBrighten, swatch = getColorCorrectionSwatches(ColorChecker, CacheDir)
if Vresult:
print("ColorCheckerDetected")
else:
print("ColorCheckerDetectionFailed, Abort")
CleanUp(CCDir)
return False
print("***Start Mass ColorCorrcting***")
Process = []
FramePerProcess = int(len(FileList)/int(Threads))
for i in range(Threads+1):
LastFrame = min((i+1)*FramePerProcess, len(FileList))
files = FileList[i*FramePerProcess:LastFrame]
x = multiprocessing.Process(target=CCProcess, args=(files, Vresult, swatch, CCDir, CacheDir,))
Process.append(x)
x.start()
time.sleep(5)
for ps in Process:
ps.join()
EnableButtons()
def ColorCorrect():
if ColorChecker == "" or PhotosDir == "" or OutPutDir == "":
print("Invalid Settings!")
return False
BakeT = threading.Thread(target=MainCCProcess, args=(ColorChecker,PhotosDir,OutPutDir,int(ProcessNumBlock.get()),))
BakeT.start()
#MainCCProcess("F:\PhotoGrammetry\ColorCorrector\\123\ColorChecker.dng","F:\PhotoGrammetry\ColorCorrector\\123",4)
def Choose_ColourChecker():
global ColorChecker
filename = tk.filedialog.askopenfilename()
ColorCheckerBlock["text"] = filename
ColorChecker = filename
print(filename)
def Choose_PhotoDir():
global PhotosDir
filedir = tk.filedialog.askdirectory()
PhotosDirBlock["text"] = filedir
PhotosDir = filedir
print(filedir)
def Choose_OutPutDir():
global OutPutDir
filedir = tk.filedialog.askdirectory()
OutPutDirBlock["text"] = filedir
OutPutDir = filedir
print(filedir)
def DisableButtons():
ChooseColorChecker["state"] = tk.DISABLED
ChoosePhotosDir["state"] = tk.DISABLED
ChooseOutputDir["state"] = tk.DISABLED
StartButton["state"] = tk.DISABLED
global bBusy
bBusy = True
def EnableButtons():
ChooseColorChecker["state"] = tk.NORMAL
ChoosePhotosDir["state"] = tk.NORMAL
ChooseOutputDir["state"] = tk.NORMAL
StartButton["state"] = tk.NORMAL
global bBusy, status
bBusy = False
status = "WaitingForCommand"
def GetProgress():
global PhotoNum
if PhotoNum == 0:
return 0
else:
FileNum = len(glob.glob(os.path.join(OutPutDir, '*.jpg')))
progress = int(float(FileNum)/float(PhotoNum)*100.0)
return progress
if __name__ == "__main__":
multiprocessing.freeze_support()
Window = tk.Tk()
Window.title("ColourCorrector")
Window.geometry("800x150")
#DecimateBakeButton = tk.Button(Window, text = "CC", width = 20, height = 1, command=ColorCorrect)
#DecimateBakeButton.grid(row=0,column=0,sticky="W")
bDiffuse = tk.BooleanVar()
bNormal = tk.BooleanVar()
bAO = tk.BooleanVar()
bVColortoTex = tk.BooleanVar()
TriCount = tk.IntVar()
#UI Elements
#l.grid(row=3)
ColorCheckerL = tk.Label(Window, text = "ColourChecker(色卡路径)： ", width = 25, height = 1 )
ColorCheckerL.grid(row=0, column=0)
ColorCheckerBlock = tk.Label(Window, text = "None(未指定色卡)", width = 50, height = 1)
ColorCheckerBlock.grid(row=0, column=1)
ChooseColorChecker = tk.Button(Window, text = "...", width = 4, height = 1, command=Choose_ColourChecker)
ChooseColorChecker.grid(row=0,column=2,sticky="E")
PhotosDirL = tk.Label(Window, text = "Photos(待较色路径)： ", width = 25, height = 1 )
PhotosDirL.grid(row=1, column=0)
PhotosDirBlock = tk.Label(Window, text = "None(未指定路径)", width = 50, height = 1 )
PhotosDirBlock.grid(row=1, column=1)
ChoosePhotosDir = tk.Button(Window, text = "...", width = 4, height = 1, command=Choose_PhotoDir)#
ChoosePhotosDir.grid(row=1,column=2,sticky="E")
OutPutDirL = tk.Label(Window, text = "OutPut(输出路径)： ", width = 25, height = 1)
OutPutDirL.grid(row=2, column=0)
OutPutDirBlock = tk.Label(Window, text = "None(未指定输出)", width = 50, height = 1)
OutPutDirBlock.grid(row=2, column=1)
ChooseOutputDir = tk.Button(Window, text = "...", width = 4, height = 1, command=Choose_OutPutDir)
ChooseOutputDir.grid(row=2,column=2,sticky="E")
ProcessNumL = tk.Label(Window, text = "ProcessNum(进程数)", width = 30, height = 1)
ProcessNumL.grid(row=3,column=0,sticky="E")
ProcessNumBlock = tk.Entry(Window, width = 15)
ProcessNumBlock.insert(0, "3")
ProcessNumBlock.grid(row=3,column=1,sticky="W")
StartButton = tk.Button(Window, text = "Start ColorCorrection(开始较色)", width = 30, height = 1, command=ColorCorrect)
StartButton.grid(row=3,column=2,sticky="W")
l = tk.Label(Window, text = "ABC", width = 80, height = 1, font=("Arial"))
l.place(x=5,y=125)
def UpdateStatus():
global status, bBusy
percent = GetProgress()
status = str(percent)+"%"
if bBusy:
if status[-3:] == "...":
status = status[:-3]
else:
status += "."