def mkhdr(outputPath,
inputPaths,
responseLUTPaths,
baseExposureIndex,
writeIntermediate=False,
outputGamut=1):
'''
Create an HDR image from a series of individual exposures
If the images are non-linear, a series of response LUTs can be used to
linearize the data
'''
global temp_dirs
# Create buffers for inputs
inputBuffers = []
inputAttributes = []
# Read images
for inputPath in inputPaths:
print("Reading input image : %s" % inputPath)
# Read
inputBufferRaw = loadImageBuffer(inputPath, outputGamut=outputGamut)
# Reset the orientation
ImageBufReorient(inputBufferRaw, inputBufferRaw.orientation)
# Get attributes
(channelType, width, height, channels, orientation, metadata,
inputSpec) = ImageAttributes(inputBufferRaw)
# Cast to half by adding with a const half buffer.
inputBufferHalf = ImageBufMakeConstant(width, height, channels,
oiio.HALF)
ImageBufAlgo.add(inputBufferHalf, inputBufferHalf, inputBufferRaw)
# Get exposure-specific information
exposure = getExposureInformation(metadata)
print("\tChannel Type : %s" % (channelType))
print("\tWidth : %s" % (width))
print("\tHeight : %s" % (height))
print("\tChannels : %s" % (channels))
print("\tOrientation : %s" % (orientation))
print("\tMetadata # : %s" % (len(metadata)))
print("\tExposure : %s" % (exposure))
# Store pixels and image attributes
inputBuffers.append(inputBufferHalf)
inputAttributes.append((channelType, width, height, channels,
orientation, metadata, exposure, inputSpec))
# Get the base exposure information
# All other images will be scaled to match this exposure
if baseExposureIndex >= 0:
baseExposureIndex = max(0, min(len(inputPaths) - 1, baseExposureIndex))
else:
multithreaded = True
if multithreaded:
threads = cpu_count()
baseExposureIndex = findBaseExposureIndexMultithreaded(
inputPaths, width, height, channels, threads)
else:
baseExposureIndex = findBaseExposureIndexSerial(
inputBuffers, width, height, channels)
baseExposureInfo = inputAttributes[baseExposureIndex][6]
baseInputspec = inputAttributes[baseExposureIndex][7]
print("")
print("Base exposure index : %d" % baseExposureIndex)
print("Base exposure info : %s" % baseExposureInfo)
# Find the lowest and highest exposures
exposureAdjustments = [
getExposureAdjustment(x[6], baseExposureInfo) for x in inputAttributes
]
minExposureOffsetIndex = exposureAdjustments.index(
min(exposureAdjustments))
maxExposureOffsetIndex = exposureAdjustments.index(
max(exposureAdjustments))
print("Max exposure index : %d" % minExposureOffsetIndex)
print("Min exposure index : %d" % maxExposureOffsetIndex)
print("\nBegin processing\n")
# Two buffers needed for algorithm
imageSum = ImageBufMakeConstant(width,
height,
channels,
oiio.HALF,
inputSpec=baseInputspec)
weightSum = ImageBufMakeConstant(width, height, channels, oiio.HALF)
# Re-used intermediate buffers
color = ImageBufMakeConstant(width, height, channels, oiio.HALF)
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
weightedColor = ImageBufMakeConstant(width, height, channels, oiio.HALF)
# Process images
for inputIndex in range(len(inputPaths)):
inputPathComponents = (os.path.splitext(inputPaths[inputIndex])[0],
".exr")
intermediate = 0
ImageBufAlgo.zero(color)
ImageBufAlgo.zero(weight)
ImageBufAlgo.zero(weightedColor)
print("Processing input image : %s" % inputPaths[inputIndex])
inputBuffer = inputBuffers[inputIndex]
# Copy the input buffer data
ImageBufAlgo.add(color, color, inputBuffer)
if writeIntermediate:
intermediatePath = "%s_int%d.float_buffer%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
# Linearize using LUTs
if responseLUTPaths:
for responseLUTPath in responseLUTPaths:
print("\tApplying LUT %s" % responseLUTPath)
ImageBufAlgo.ociofiletransform(
color, color, os.path.abspath(responseLUTPath))
if writeIntermediate:
intermediatePath = "%s_int%d.linearized%s" % (
inputPathComponents[0], intermediate,
inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
# Get exposure offset
inputExposureInfo = inputAttributes[inputIndex][6]
exposureAdjustment = getExposureAdjustment(inputExposureInfo,
baseExposureInfo)
exposureScale = pow(2, exposureAdjustment)
print("\tScaling by %s stops (%s mul)" %
(exposureAdjustment, exposureScale))
# Re-expose input
ImageBufAlgo.mul(color, color, exposureScale)
if writeIntermediate:
intermediatePath = "%s_int%d.exposure_adjust%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
print("\tComputing image weight")
# Weight
lut = []
if inputIndex == minExposureOffsetIndex:
lut.append(1)
if inputIndex == maxExposureOffsetIndex:
lut.append(2)
if lut:
print("\tUsing LUT %s in weighting calculation" % lut)
weightIntermediate = intermediate if writeIntermediate else 0
ImageBufWeight(weight, inputBuffer, lut=lut)
if writeIntermediate:
intermediatePath = "%s_int%d.weight%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weight, intermediatePath)
print("\tMultiply by weight")
# Multiply color by weight
ImageBufAlgo.mul(weightedColor, weight, color)
if writeIntermediate:
intermediatePath = "%s_int%d.color_x_weight%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weightedColor, intermediatePath)
print("\tAdd values into sum")
# Sum weighted color and weight
ImageBufAlgo.add(imageSum, imageSum, weightedColor)
ImageBufAlgo.add(weightSum, weightSum, weight)
if writeIntermediate:
intermediatePath = "%s_int%d.color_x_weight_sum%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(imageSum, intermediatePath)
intermediatePath = "%s_int%d.weight_sum%s" % (
inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weightSum, intermediatePath)
# Divid out weights
print("Dividing out weights")
ImageBufAlgo.div(imageSum, imageSum, weightSum)
# Write to disk
print("Writing result : %s" % outputPath)
ImageBufWrite(imageSum, outputPath)
# Clean up temp folders
for temp_dir in temp_dirs:
#print( "Removing : %s" % temp_dir )
shutil.rmtree(temp_dir)
def mkhdr(outputPath,
inputPaths,
responseLUTPaths,
baseExposureIndex,
writeIntermediate = False,
outputGamut = 1,
compression = None,
compressionQuality = 0,
rawSaturationPoint = -1.0,
alignImages = False,
dcrawVariant = None):
'''
Create an HDR image from a series of individual exposures
If the images are non-linear, a series of response LUTs can be used to
linearize the data
'''
global temp_dirs
# Set up capture of
old_stdout, old_stderr = sys.stdout, sys.stderr
redirected_stdout = sys.stdout = Logger()
redirected_stderr = sys.stderr = Logger()
# Create buffers for inputs
inputBuffers = []
inputAttributes = []
# Read images
for inputPath in inputPaths:
print( "Reading input image : %s" % inputPath )
# Read
inputBufferRaw = loadImageBuffer( inputPath, outputGamut=outputGamut,
rawSaturationPoint=rawSaturationPoint,
dcrawVariant=dcrawVariant )
# Reset the orientation
print( "\tRaw Orientation : %d" % inputBufferRaw.orientation)
ImageBufReorient(inputBufferRaw, inputBufferRaw.orientation)
# Get attributes
(channelType, width, height, channels, orientation, metadata, inputSpec) = ImageAttributes(inputBufferRaw)
# Cast to half by adding with a const half buffer.
inputBufferHalf = ImageBufMakeConstant(width, height, channels, oiio.HALF)
ImageBufAlgo.add(inputBufferHalf, inputBufferHalf, inputBufferRaw)
# Get exposure-specific information
exposure = getExposureInformation(metadata)
print( "\tChannel Type : %s" % (channelType) )
print( "\tWidth : %s" % (width) )
print( "\tHeight : %s" % (height) )
print( "\tChannels : %s" % (channels) )
print( "\tOrientation : %s" % (orientation) )
print( "\tExposure : %s" % (exposure) )
print( "\tMetadata # : %s" % (len(metadata)) )
# Store pixels and image attributes
inputBuffers.append( inputBufferHalf )
inputAttributes.append( (channelType, width, height, channels, orientation, metadata, exposure, inputSpec) )
# Get the base exposure information
# All other images will be scaled to match this exposure
if baseExposureIndex >= 0:
baseExposureIndex = max(0, min(len(inputPaths)-1, baseExposureIndex))
else:
multithreaded = True
if multithreaded:
threads = cpu_count()
baseExposureIndex = findBaseExposureIndexMultithreaded(inputPaths, width, height, channels, threads)
else:
baseExposureIndex = findBaseExposureIndexSerial(inputBuffers, width, height, channels)
baseExposureMetadata = inputAttributes[baseExposureIndex][5]
baseExposureInfo = inputAttributes[baseExposureIndex][6]
baseInputspec = inputAttributes[baseExposureIndex][7]
print( "" )
print( "Base exposure index : %d" % baseExposureIndex )
print( "Base exposure info : %s" % baseExposureInfo )
# Find the lowest and highest exposures
exposureAdjustments = [getExposureAdjustment(x[6], baseExposureInfo) for x in inputAttributes]
minExposureOffsetIndex = exposureAdjustments.index(min(exposureAdjustments))
maxExposureOffsetIndex = exposureAdjustments.index(max(exposureAdjustments))
print( "Max exposure index : %d" % minExposureOffsetIndex )
print( "Min exposure index : %d" % maxExposureOffsetIndex )
print( "\nBegin processing\n" )
# Two buffers needed for algorithm
imageSum = ImageBufMakeConstant(width, height, channels, oiio.HALF,
inputSpec=baseInputspec)
weightSum = ImageBufMakeConstant(width, height, channels, oiio.HALF)
# Re-used intermediate buffers
color = ImageBufMakeConstant(width, height, channels, oiio.HALF)
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
weightedColor = ImageBufMakeConstant(width, height, channels, oiio.HALF)
# Process images
for inputIndex in range(len(inputPaths)):
inputPathComponents = (os.path.splitext( inputPaths[inputIndex] )[0], ".exr")
intermediate = 0
ImageBufAlgo.zero( color )
ImageBufAlgo.zero( weight )
ImageBufAlgo.zero( weightedColor )
print( "Processing input image : %s" % inputPaths[inputIndex] )
inputBuffer = inputBuffers[inputIndex]
# Copy the input buffer data
ImageBufAlgo.add(color, color, inputBuffer)
if writeIntermediate:
intermediatePath = "%s_int%d.float_buffer%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
# Find the image alignment matrix to align this exposure with the base exposure
if alignImages:
try:
if inputIndex != baseExposureIndex:
if cv2:
print( "\tAligning image %d to base exposure %d " % (inputIndex, baseExposureIndex) )
warpMatrix = find2dAlignmentMatrix(inputBuffer, inputBuffers[baseExposureIndex])
# reformat for OIIO's warp
w = map(float, list(warpMatrix.reshape(1,-1)[0]))
warpTuple = (w[0], w[1], 0.0, w[3], w[4], 0.0, w[2], w[5], 1.0)
print( warpTuple )
warped = ImageBuf()
result = ImageBufAlgo.warp(warped, color, warpTuple)
if result:
print( "\tImage alignment warp succeeded." )
if writeIntermediate:
intermediatePath = "%s_int%d.warped%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(warped, intermediatePath)
color = warped
else:
print( "\tImage alignment warp failed." )
if writeIntermediate:
intermediate += 1
else:
print( "\tSkipping image alignment. OpenCV not defined" )
if writeIntermediate:
intermediate += 1
else:
print( "\tSkipping alignment of base exposure to itself")
if writeIntermediate:
intermediate += 1
except:
print( "Exception in image alignment" )
print( '-'*60 )
traceback.print_exc()
print( '-'*60 )
# Weight
print( "\tComputing image weight" )
lut = []
if inputIndex == minExposureOffsetIndex:
lut.append(1)
if inputIndex == maxExposureOffsetIndex:
lut.append(2)
if lut:
print( "\tUsing LUT %s in weighting calculation" % lut )
ImageBufWeight(weight, color, lut=lut)
if writeIntermediate:
intermediatePath = "%s_int%d.weight%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weight, intermediatePath)
# Linearize using LUTs
if responseLUTPaths:
for responseLUTPath in responseLUTPaths:
print( "\tApplying LUT %s" % responseLUTPath )
ImageBufAlgo.ociofiletransform(color, color, os.path.abspath(responseLUTPath) )
if writeIntermediate:
intermediatePath = "%s_int%d.linearized%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
# Get exposure offset
inputExposureInfo = inputAttributes[inputIndex][6]
exposureAdjustment = getExposureAdjustment(inputExposureInfo, baseExposureInfo)
exposureScale = pow(2, exposureAdjustment)
# Re-expose input
print( "\tScaling by %s stops (%s mul)" % (exposureAdjustment, exposureScale) )
ImageBufAlgo.mul(color, color, exposureScale)
if writeIntermediate:
intermediatePath = "%s_int%d.exposure_adjust%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(color, intermediatePath)
# Multiply color by weight
print( "\tMultiply by weight" )
ImageBufAlgo.mul(weightedColor, weight, color)
if writeIntermediate:
intermediatePath = "%s_int%d.color_x_weight%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weightedColor, intermediatePath)
print( "\tAdd values into sum" )
# Sum weighted color and weight
ImageBufAlgo.add(imageSum, imageSum, weightedColor)
ImageBufAlgo.add(weightSum, weightSum, weight)
if writeIntermediate:
intermediatePath = "%s_int%d.color_x_weight_sum%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(imageSum, intermediatePath)
intermediatePath = "%s_int%d.weight_sum%s" % (inputPathComponents[0], intermediate, inputPathComponents[1])
intermediate += 1
ImageBufWrite(weightSum, intermediatePath)
# Divid out weights
print( "Dividing out weights" )
ImageBufAlgo.div(imageSum, imageSum, weightSum)
# Write to disk
print( "Writing result : %s" % outputPath )
# Restore regular streams
sys.stdout, sys.stderr = old_stdout, old_stderr
additionalAttributes = {}
additionalAttributes['inputPaths'] = " ".join(inputPaths)
additionalAttributes['stdout'] = "".join(redirected_stdout.log)
additionalAttributes['stderr'] = "".join(redirected_stderr.log)
ImageBufWrite(imageSum, outputPath,
compression=compression,
compressionQuality=compressionQuality,
metadata=baseExposureMetadata,
additionalAttributes=additionalAttributes)
# Clean up temp folders
for temp_dir in temp_dirs:
#print( "Removing : %s" % temp_dir )
shutil.rmtree(temp_dir)
for temp_dir in temp_dirs:
temp_dirs.remove(temp_dir)
