def findAverageWeightFromPath(inputPath, width, height, channels):
'''
Find the average weight of an image, specified by it's file path
'''
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
temp = ImageBufMakeConstant(1, 1, channels, oiio.HALF)
try:
print("\tReading image : %s" % inputPath)
inputBufferRaw = ImageBuf(inputPath)
# Cast to half by adding with a const half buffer.
inputBufferHalf = ImageBufMakeConstant(width, height, channels,
oiio.HALF)
ImageBufAlgo.add(inputBufferHalf, inputBufferHalf, inputBufferRaw)
print("\tComputing Weight")
ImageBufWeight(weight, inputBufferHalf)
# Compute the average weight by resizing to 1x1
print("\tResizing")
# Not using multithreading here, as this function will be called within
# Python's multhreading framework
ImageBufAlgo.resize(temp, weight, filtername='box')
# Get the average weight value
weight = temp.getpixel(0, 0)
#print( "\tWeight : %s" % str(weight) )
averageWeight = sum(map(float, weight)) / channels
except Exception, e:
print("Exception in findAverageWeightFromPath")
print(repr(e))
def writeTexture(scrBuffer, outFile):
global errorFlag
try:
config = ImageSpec()
# config.attribute("maketx:highlightcomp", 1)
config.attribute("maketx:filtername", "lanczos3")
# config.attribute("maketx:opaquedetect", 1)
config.attribute("maketx:oiio options", 1)
scrBuffer.set_write_tiles(tileSize, tileSize)
ImageBufAlgo.make_texture(oiio.MakeTxTexture, scrBuffer, outFile,
config)
errorFlag = 0
except Exception as e:
errorFlag = 1
tqdm.write(
prefix + Fore.RED +
"Error on conversion. Maybe wrong/corrupt texture file or resolution too high."
)
def findAverageWeightFromPath(inputPath, width, height, channels):
'''
Find the average weight of an image, specified by it's file path
'''
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
temp = ImageBufMakeConstant(1, 1, channels, oiio.HALF)
try:
print( "\tReading image : %s" % inputPath )
inputBufferRaw = ImageBuf( inputPath )
# Cast to half by adding with a const half buffer.
inputBufferHalf = ImageBufMakeConstant(width, height, channels, oiio.HALF)
ImageBufAlgo.add(inputBufferHalf, inputBufferHalf, inputBufferRaw)
print( "\tComputing Weight" )
ImageBufWeight(weight, inputBufferHalf)
# Compute the average weight by resizing to 1x1
print( "\tResizing" )
# Not using multithreading here, as this function will be called within
# Python's multhreading framework
ImageBufAlgo.resize(temp, weight, filtername='box')
# Get the average weight value
weight = temp.getpixel(0,0)
#print( "\tWeight : %s" % str(weight) )
averageWeight = sum(map(float, weight))/channels
except Exception, e:
print( "Exception in findAverageWeightFromPath" )
print( repr(e) )
def __init__(self, image_a, image_b):
self.debug = False
self.fail_threshold = 0.1
self.warn_threshold = 0.01
self.image_a_buffer = ImageBuf()
self.image_b_buffer = ImageBuf()
# remove alpha channel from input images
ImageBufAlgo.channels(
self.image_a_buffer,
ImageBuf(image_a),
('R', 'G', 'B')
)
ImageBufAlgo.channels(
self.image_b_buffer,
ImageBuf(image_b),
('R', 'G', 'B'),
)
# protected
self._image_a_location = image_a
self._image_b_location = image_b
self._file_ext = os.path.splitext(image_a)[-1]
self._compare_results = CompareResults()
def _open(self, source, size=3):
erode = ImageBuf(source.spec())
ImageBufAlgo.erode(erode, source, size, size)
dilate = ImageBuf(source.spec())
ImageBufAlgo.dilate(dilate, erode, size, size)
return dilate
def resizeHDR(scrBuffer, width, height):
srcSpec = scrBuffer.spec()
resizedBuffer = ImageBuf(
ImageSpec(width, height, srcSpec.nchannels, srcSpec.format))
ImageBufAlgo.resize(resizedBuffer, scrBuffer, filtername=resizeFilter)
threadResult.put(resizedBuffer)
return resizedBuffer
def blurImage(srcBuffer):
K = ImageBuf()
ImageBufAlgo.make_kernel(K, blurFilter, blurAmountX, blurAmountY)
Blurred = ImageBuf()
ImageBufAlgo.convolve(Blurred, srcBuffer, K)
threadResult.put(Blurred)
return Blurred
def _dilate(self, source):
dilate = ImageBuf(source.spec())
ImageBufAlgo.dilate(
dilate,
source,
4,
4,
)
return dilate
def _median(self, source, size=5):
size = int(size)
median = ImageBuf(source.spec())
ImageBufAlgo.median_filter(
median,
source,
size,
size
)
return median
def compare_image_pixels(self, result_image, correct_result_image, threshold):
self.fail_test_if_no_oiio()
"""
Given a file to find it results, compare it against the correct result.
Returns None if the input is not an image.
Returns oiio.CompareResults if results can be compared.
"""
compresults = oiio.CompareResults()
ImageBufAlgo.compare(result_image, correct_result_image, threshold, threshold, compresults)
return compresults
def create_diff_buffer(self):
"""Create a difference image buffer from image_a and image_b
Returns:
ImageBuf: new difference image buffer
"""
diff_buffer = ImageBuf(self.image_a_buffer.spec())
ImageBufAlgo.sub(diff_buffer, self.image_a_buffer, self.image_b_buffer)
ImageBufAlgo.abs(diff_buffer, diff_buffer)
return diff_buffer
def findAverageWeight(imageBuffer, width, height, channels):
'''
Get the average value of the weighted image
'''
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
temp = ImageBufMakeConstant(1, 1, channels, oiio.HALF)
print("\tComputing Weight")
ImageBufWeight(weight, imageBuffer)
# Compute the average weight by resizing to 1x1
print("\tResizing")
# The nthreads argument doesn't seem to have much effect
ImageBufAlgo.resize(temp, weight, nthreads=cpu_count(), filtername='box')
# Get the average weight value
averageWeight = sum(map(float, temp.getpixel(0, 0))) / channels
return averageWeight
def findAverageWeight(imageBuffer, width, height, channels):
'''
Get the average value of the weighted image
'''
weight = ImageBufMakeConstant(width, height, channels, oiio.HALF)
temp = ImageBufMakeConstant(1, 1, channels, oiio.HALF)
print( "\tComputing Weight" )
ImageBufWeight(weight, imageBuffer)
# Compute the average weight by resizing to 1x1
print( "\tResizing" )
# The nthreads argument doesn't seem to have much effect
ImageBufAlgo.resize(temp, weight, nthreads=cpu_count(), filtername='box')
# Get the average weight value
averageWeight = sum(map(float, temp.getpixel(0,0)))/channels
return averageWeight
def ImageBufReorient(imageBuf, orientation):
'''
Resets the orientation of the image
'''
'''
Orientation 6 and 8 seem to be reversed in OIIO, at least for Canon
cameras... This needs to be investigated further.
'''
if orientation == 6:
imageBuf.specmod().attribute("Orientation", 1)
ImageBufAlgo.rotate270(imageBuf, imageBuf)
ImageBufAlgo.reorient(imageBuf, imageBuf)
elif orientation == 8:
imageBuf.specmod().attribute("Orientation", 1)
ImageBufAlgo.rotate90(imageBuf, imageBuf)
ImageBufAlgo.reorient(imageBuf, imageBuf)
else:
ImageBufAlgo.reorient(imageBuf, imageBuf)
def ImageBufReorient(imageBuf, orientation):
'''
Resets the orientation of the image
'''
'''
Orientation 6 and 8 seem to be reversed in OIIO, at least for Canon
cameras... This needs to be investigated further.
'''
if orientation == 6:
imageBuf.specmod().attribute ("Orientation", 1)
ImageBufAlgo.rotate90(imageBuf, imageBuf)
ImageBufAlgo.reorient (imageBuf, imageBuf)
elif orientation == 8:
imageBuf.specmod().attribute ("Orientation", 1)
ImageBufAlgo.rotate270(imageBuf, imageBuf)
ImageBufAlgo.reorient (imageBuf, imageBuf)
else:
ImageBufAlgo.reorient (imageBuf, imageBuf)
def _blur(self, source, size=1.0):
"""Apply gaussian blur to given image
Args:
source (ImageBuf): Image buffer which to blur
size (float): Blur size
Return:
ImageBuf: Blurred image
"""
source = self._open(source)
kernel = ImageBuf(source.spec())
ImageBufAlgo.make_kernel(
kernel,
"gaussian",
size, size
)
blurred = ImageBuf(source.spec())
ImageBufAlgo.convolve(blurred, source, kernel)
return blurred
def main():
options, args = parseOptions()
tile_x = options.tile_x
tile_y = options.tile_y
frame = options.frame
output = options.output
filemask = options.filemask
tile_files = []
tiles_lost = []
for i in xrange(0, (tile_x * tile_y)):
filepath = filemask % (i, frame)
if not os.path.exists(filepath):
tiles_lost += [filepath]
continue
tile_files += [filepath]
if len(tile_files) != (tile_x * tile_y):
raise Exception("Tile not found: %s" % tiles_lost)
#TODO: merge metadata from tiles
spec = ImageBuf(str(tile_files[0])).spec()
spec_e = ImageSpec(spec.full_width, spec.full_height, spec.nchannels,
spec.format)
extended = ImageBuf(spec_e)
for filename in tile_files:
img = ImageBuf(filename)
ImageBufAlgo.paste(extended,
img.xbegin,
img.ybegin,
img.zbegin,
0,
img,
nthreads=4)
extended.write(output)
if not image.has_error:
image.set_write_format(format)
image.write(filename)
if image.has_error:
print "Error writing", filename, ":", image.geterror()
######################################################################
# main test starts here
try:
# Some handy images to work with
gridname = "../../../../../oiio-images/grid.tif"
grid = ImageBuf(gridname)
checker = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.checker(checker, 8, 8, 8, (0, 0, 0), (1, 1, 1))
gray128 = make_constimage(128, 128, 3, oiio.HALF, (0.5, 0.5, 0.5))
gray64 = make_constimage(64, 64, 3, oiio.HALF, (0.5, 0.5, 0.5))
# black
b = ImageBuf(ImageSpec(320, 240, 3, oiio.UINT8))
ImageBufAlgo.zero(b)
write(b, "black.tif")
# fill (including use of ROI)
b = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.fill(b, (1, 0.5, 0.5))
ImageBufAlgo.fill(b, (0, 1, 0), oiio.ROI(100, 180, 100, 180))
write(b, "filled.tif")
# checker
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)
if not image.has_error :
image.write (filename, format)
if image.has_error :
print ("Error writing", filename, ":", image.geterror())
######################################################################
# main test starts here
try:
# Some handy images to work with
gridname = os.path.join(OIIO_TESTSUITE_IMAGEDIR, "grid.tif")
grid = ImageBuf (gridname)
checker = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.checker (checker, 8, 8, 8, (0,0,0), (1,1,1))
gray128 = make_constimage (128, 128, 3, oiio.HALF, (0.5,0.5,0.5))
gray64 = make_constimage (64, 64, 3, oiio.HALF, (0.5,0.5,0.5))
tahoetiny = ImageBuf("../oiiotool/src/tahoe-tiny.tif")
# black
# b = ImageBuf (ImageSpec(320,240,3,oiio.UINT8))
b = ImageBufAlgo.zero (roi=oiio.ROI(0,320,0,240,0,1,0,3))
write (b, "black.tif", oiio.UINT8)
# fill (including use of ROI)
b = ImageBuf (ImageSpec(256,256,3,oiio.UINT8));
ImageBufAlgo.fill (b, (1,0.5,0.5))
ImageBufAlgo.fill (b, (0,1,0), oiio.ROI(100,180,100,180))
write (b, "filled.tif", oiio.UINT8)
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 convertColor(srcBuffer, fromColor="linear", toColor="sRGB"):
Dst = ImageBuf()
ImageBufAlgo.colorconvert(Dst, srcBuffer, fromColor, toColor)
threadResult.put(Dst)
return Dst
print("[", end="")
for c in range(spec.nchannels):
print(fmt.format(p[c]), end=" ")
print("] ", end="")
print("")
######################################################################
# main test starts here
try:
# Some handy images to work with
gridname = os.path.join(OIIO_TESTSUITE_IMAGEDIR, "grid.tif")
grid = ImageBuf(gridname)
checker = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.checker(checker, 8, 8, 8, (0, 0, 0), (1, 1, 1))
gray128 = make_constimage(128, 128, 3, oiio.HALF, (0.5, 0.5, 0.5))
gray64 = make_constimage(64, 64, 3, oiio.HALF, (0.5, 0.5, 0.5))
tahoetiny = ImageBuf(OIIO_TESTSUITE_ROOT + "/oiiotool/src/tahoe-tiny.tif")
# black
# b = ImageBuf (ImageSpec(320,240,3,oiio.UINT8))
b = ImageBufAlgo.zero(roi=oiio.ROI(0, 320, 0, 240, 0, 1, 0, 3))
write(b, "black.tif", oiio.UINT8)
# fill (including use of ROI)
b = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.fill(b, (1, 0.5, 0.5))
ImageBufAlgo.fill(b, (0, 1, 0), oiio.ROI(100, 180, 100, 180))
write(b, "filled.tif", oiio.UINT8)
def premultiply_image(cls, img_pixels: np.array) -> np.array:
""" Premultiply a numpy image with itself """
a = cls.np_to_imagebuf(img_pixels)
ImageBufAlgo.premult(a, a)
return a.get_pixels(a.spec().format, a.spec().roi_full)
image.set_write_format (format)
image.write (filename)
if image.has_error :
print "Error writing", filename, ":", image.geterror()
######################################################################
# main test starts here
try:
# Some handy images to work with
gridname = "../../../../../oiio-images/grid.tif"
grid = ImageBuf (gridname)
checker = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.checker (checker, 8, 8, 8, (0,0,0), (1,1,1))
gray128 = make_constimage (128, 128, 3, oiio.HALF, (0.5,0.5,0.5))
gray64 = make_constimage (64, 64, 3, oiio.HALF, (0.5,0.5,0.5))
# black
b = ImageBuf (ImageSpec(320,240,3,oiio.UINT8))
ImageBufAlgo.zero (b)
write (b, "black.tif")
# fill (including use of ROI)
b = ImageBuf (ImageSpec(256,256,3,oiio.UINT8));
ImageBufAlgo.fill (b, (1,0.5,0.5))
ImageBufAlgo.fill (b, (0,1,0), oiio.ROI(100,180,100,180))
write (b, "filled.tif")
# checker
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)
if not image.has_error:
image.set_write_format(format)
image.write(filename)
if image.has_error:
print "Error writing", filename, ":", image.geterror()
######################################################################
# main test starts here
try:
# Some handy images to work with
gridname = "../../../../../oiio-images/grid.tif"
grid = ImageBuf(gridname)
checker = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.checker(checker, 8, 8, 8, (0, 0, 0), (1, 1, 1))
gray128 = make_constimage(128, 128, 3, oiio.HALF, (0.5, 0.5, 0.5))
# black
b = ImageBuf(ImageSpec(320, 240, 3, oiio.UINT8))
ImageBufAlgo.zero(b)
write(b, "black.tif")
# fill (including use of ROI)
b = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
ImageBufAlgo.fill(b, (1, 0.5, 0.5))
ImageBufAlgo.fill(b, (0, 1, 0), oiio.ROI(100, 180, 100, 180))
write(b, "filled.tif")
# checker
b = ImageBuf(ImageSpec(256, 256, 3, oiio.UINT8))
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)
