def ImageBufWeight(weight, inputBuffer, gamma=0.75, clip=0.05, lut=None):
'''
Apply a bell / triangular weight function to an Image Buffer
'''
(channelType, width, height, channels, orientation, metadata,
inputSpec) = ImageAttributes(inputBuffer)
temp = ImageBufMakeConstant(width, height, channels, oiio.HALF)
grey05 = ImageBufMakeConstant(width, height, channels, oiio.HALF,
tuple([0.5] * channels))
if lut:
ImageBufAlgo.add(temp, temp, inputBuffer)
if 1 in lut:
ImageBufAlgo.clamp(temp, temp, tuple([0.5] * channels),
tuple([1.0] * channels))
if 2 in lut:
ImageBufAlgo.clamp(temp, temp, tuple([0.0] * channels),
tuple([0.5] * channels))
#print( "\tLUT application : %s" % result )
ImageBufAlgo.absdiff(temp, grey05, temp)
else:
ImageBufAlgo.absdiff(temp, grey05, inputBuffer)
ImageBufAlgo.sub(temp, grey05, temp)
ImageBufAlgo.div(temp, temp, 0.5)
ImageBufAlgo.sub(temp, temp, clip)
ImageBufAlgo.mul(temp, temp, 1.0 / (1.0 - clip))
ImageBufAlgo.clamp(temp, temp, tuple([0.0] * channels),
tuple([1.0] * channels))
ImageBufAlgo.pow(weight, temp, gamma)
def ImageBufWeight(weight, inputBuffer, gamma=0.75, clip=0.05, lut=None):
'''
Apply a bell / triangular weight function to an Image Buffer
'''
(channelType, width, height, channels, orientation, metadata, inputSpec) = ImageAttributes(inputBuffer)
temp = ImageBufMakeConstant(width, height, channels, oiio.HALF )
grey05 = ImageBufMakeConstant(width, height, channels, oiio.HALF, tuple([0.5]*channels) )
if lut:
ImageBufAlgo.add(temp, temp, inputBuffer)
if 1 in lut:
ImageBufAlgo.clamp(temp, temp, tuple([0.5]*channels), tuple([1.0]*channels))
if 2 in lut:
ImageBufAlgo.clamp(temp, temp, tuple([0.0]*channels), tuple([0.5]*channels))
#print( "\tLUT application : %s" % result )
ImageBufAlgo.absdiff(temp, grey05, temp)
else:
ImageBufAlgo.absdiff(temp, grey05, inputBuffer)
ImageBufAlgo.sub(temp, grey05, temp)
ImageBufAlgo.div(temp, temp, 0.5)
ImageBufAlgo.sub(temp, temp, clip)
ImageBufAlgo.mul(temp, temp, 1.0/(1.0-clip))
ImageBufAlgo.clamp(temp, temp, tuple([0.0]*channels), tuple([1.0]*channels))
ImageBufAlgo.pow(weight, temp, gamma)
make_constimage(64, 64, 3, oiio.HALF, (0.1, 0.1, 0.1), 20, 20),
)
write(b, "add.exr")
# sub
b = ImageBuf()
ImageBufAlgo.sub(
b,
make_constimage(64, 64, 3, oiio.HALF, (0.1, 0.2, 0.3)),
make_constimage(64, 64, 3, oiio.HALF, (0.1, 0.1, 0.1), 20, 20),
)
write(b, "sub.exr")
# mul
b = ImageBuf()
ImageBufAlgo.mul(b, gray128, 1.5)
write(b, "cmul1.exr")
b = ImageBuf()
ImageBufAlgo.mul(b, gray128, (1.5, 1, 0.5))
write(b, "cmul2.exr")
# FIXME -- image multiplication; it's not in testsuite/oiiotool either
# b = ImageBuf()
# ImageBufAlgo.mul (b, make_constimage(64,64,3,oiio.HALF,(.1,.2,.3)),
# make_constimage(64,64,3,oiio.HALF,(.1,.1,.1),20,20))
# write (b, "mul.exr")
# pow
b = ImageBuf()
ImageBufAlgo.pow(b, gray128, 2)
write(b, "cpow1.exr")
b = ImageBuf()
# Test --absdiff and --abs
# First, make a test image that's 0.5 on the left, -0.5 on the right
a = ImageBuf(ImageSpec(128, 128, 3, oiio.HALF))
ImageBufAlgo.fill(a, (0.5, 0.5, 0.5))
ImageBufAlgo.fill(a, (-0.25, -0.25, -0.25), oiio.ROI(0, 64, 0, 128))
b = ImageBuf()
ImageBufAlgo.abs(b, a)
write(b, "abs.exr", oiio.HALF)
b = ImageBuf()
ImageBufAlgo.absdiff(b, a, (0.2, 0.2, 0.2))
write(b, "absdiff.exr", oiio.HALF)
a = ImageBuf()
# mul
b = ImageBuf()
ImageBufAlgo.mul(b, gray128, 1.5)
write(b, "cmul1.exr")
b = ImageBuf()
ImageBufAlgo.mul(b, gray128, (1.5, 1, 0.5))
write(b, "cmul2.exr")
# FIXME -- image multiplication; it's not in testsuite/oiiotool either
# b = ImageBuf()
# ImageBufAlgo.mul (b, make_constimage(64,64,3,oiio.HALF,(.1,.2,.3)),
# make_constimage(64,64,3,oiio.HALF,(.1,.1,.1),20,20))
# write (b, "mul.exr")
# div
b = ImageBuf()
ImageBufAlgo.div(b, gray64,
make_constimage(64, 64, 3, oiio.HALF, (2.0, 1, 0.5)))
write(b, "div.exr", oiio.HALF)
# Test --absdiff and --abs
# First, make a test image that's 0.5 on the left, -0.5 on the right
a = ImageBuf (ImageSpec(128,128,3,oiio.HALF))
ImageBufAlgo.fill (a, (0.5,0.5,0.5))
ImageBufAlgo.fill (a, (-0.25,-0.25,-0.25), oiio.ROI(0,64,0,128))
b = ImageBuf()
ImageBufAlgo.abs (b, a)
write (b, "abs.exr", oiio.HALF)
b = ImageBuf()
ImageBufAlgo.absdiff (b, a, (0.2,0.2,0.2))
write (b, "absdiff.exr", oiio.HALF)
a = ImageBuf()
# mul
b = ImageBuf()
ImageBufAlgo.mul (b, gray128, 1.5)
write (b, "cmul1.exr")
b = ImageBuf()
ImageBufAlgo.mul (b, gray128, (1.5,1,0.5))
write (b, "cmul2.exr")
b = ImageBuf()
ImageBufAlgo.mul (b, make_constimage(64,64,3,oiio.HALF,(.5,.5,.5)),
make_constimage(64,64,3,oiio.HALF,(1.5,1,0.5)))
write (b, "mul.exr", oiio.HALF)
# mad
b = ImageBuf()
ImageBufAlgo.mad (b, make_constimage(64,64,3,oiio.HALF,(.5,.5,.5)),
make_constimage(64,64,3,oiio.HALF,(1.5,1,0.5)),
make_constimage(64,64,3,oiio.HALF,(0.1,0.1,0.1)))
write (b, "mad.exr", oiio.HALF)
make_constimage(64, 64, 3, oiio.HALF, (.1, .1, .1), 20, 20))
write(b, "sub.exr")
# Test --absdiff and --abs
# First, make a test image that's 0.5 on the left, -0.5 on the right
a = ImageBuf(ImageSpec(128, 128, 3, oiio.HALF))
ImageBufAlgo.fill(a, (0.5, 0.5, 0.5))
ImageBufAlgo.fill(a, (-0.25, -0.25, -0.25), oiio.ROI(0, 64, 0, 128))
b = ImageBufAlgo.abs(a)
write(b, "abs.exr", oiio.HALF)
b = ImageBufAlgo.absdiff(a, (0.2, 0.2, 0.2))
write(b, "absdiff.exr", oiio.HALF)
a = ImageBuf()
# mul
b = ImageBufAlgo.mul(gray128, 1.5)
write(b, "cmul1.exr")
b = ImageBufAlgo.mul(gray128, (1.5, 1, 0.5))
write(b, "cmul2.exr")
b = ImageBufAlgo.mul(make_constimage(64, 64, 3, oiio.HALF, (.5, .5, .5)),
make_constimage(64, 64, 3, oiio.HALF, (1.5, 1, 0.5)))
write(b, "mul.exr", oiio.HALF)
# mad
b = ImageBufAlgo.mad(
make_constimage(64, 64, 3, oiio.HALF, (.5, .5, .5)),
make_constimage(64, 64, 3, oiio.HALF, (1.5, 1, 0.5)),
make_constimage(64, 64, 3, oiio.HALF, (0.1, 0.1, 0.1)))
write(b, "mad.exr", oiio.HALF)
b = ImageBufAlgo.mad(make_constimage(64, 64, 3, oiio.HALF, (.5, .5, .5)),
(1.5, 1, 0.5), (0.1, 0.1, 0.1))
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)
def compare(self, diff_image_location=None, blur=10, raise_exception=True):
"""Compare the two given images
Args:
diff_image_location (str): file path for difference image.
Written only if there are failures
blur (float): image blur to apply before comparing
"""
if not diff_image_location:
diff_image_location = os.path.dirname(self._image_a_location)
self.blur_images(blur)
ImageBufAlgo.compare(
self.image_a_buffer,
self.image_b_buffer,
self.fail_threshold,
self.warn_threshold,
self._compare_results,
)
diff_buffer = self.create_diff_buffer()
if self.debug:
self.image_a_buffer.write(
'{}/{}_debug{}'.format(
diff_image_location,
os.path.basename(self._image_a_location),
self._file_ext,
)
)
self.image_b_buffer.write(
'{}/{}_debug{}'.format(
diff_image_location,
os.path.basename(self._image_b_location),
self._file_ext,
)
)
if self._compare_results.nfail > 0:
ImageBufAlgo.color_map(diff_buffer, diff_buffer, -1, 'inferno')
remap_buffer = ImageBuf()
multiplier = 5
ImageBufAlgo.mul(
remap_buffer,
diff_buffer,
(multiplier, multiplier, multiplier, 1.0),
)
ImageBufAlgo.add(remap_buffer, self.image_a_buffer, remap_buffer)
msg = report_msg.format(
failures=self._compare_results.nfail,
warn=self._compare_results.nwarn,
meanerror=self._compare_results.meanerror,
rmserror=self._compare_results.rms_error,
psnr=self._compare_results.PSNR
)
remap_buffer.write(
'{}/{}-{}_diff{}'.format(
diff_image_location,
os.path.basename(self._image_a_location),
os.path.basename(self._image_b_location),
self._file_ext,
)
)
self.image_a_buffer.write(
'{}/{}_debug{}'.format(
diff_image_location,
'1_a',
self._file_ext,
)
)
self.image_b_buffer.write(
'{}/{}_debug{}'.format(
diff_image_location,
'1_b',
self._file_ext,
)
)
if raise_exception:
raise ImageDifferenceError(msg)
else:
print(msg)
