Self Introduction
I am Masataka @plinecom, a pipeline engineer at digitalbigmo, Inc.
What is this?
I was given tens of thousands of large uncompressed OpenEXR files at work. Since OpenEXR has a lossless compression option, I want to convert this large number of uncompressed OpenEXR files to lossless compressed ZIP OpenEXR.
I will record how to batch-process them using Python.
For now, the code
import OpenEXR
import Imath
import glob
import os
if __name__ == '__main__':
for path in glob.glob('/foo/bar/*.exr'):
print(path)
dir_name = os.path.dirname(path)
base_name = os.path.basename(path)
InputFile(path) # Read EXR file
header = ret.header() # Get OpenEXR header info
print(header)
# Change Compression Type
header['compression'] = Imath.Compression(Imath.Compression.ZIP_COMPRESSION)
print(header)
pix = {}
for ch in header['channels']: # Get channel name from header
pix[ch] = ret.channel(ch) # Get channel image data
ret.close() # Close Read File handler
# Open the write file and Set header info
OutputFile(dir_name + '/' + base_name, header)
exr.writePixels(pix) # Write Pixel Data
exr.close() # Close Write File handler
Required Python external modules
- OpenEXR
pip install OpenEXR
You will install the download from PyPI using pip, and the Imath module with it.
However, you will need to install Python's header file for the build process. If you cannot prepare it, it is easier to build it from Python.
I also prepared a Dockerfile. Please forgive me if some files are downloaded from rpmfusion, which is a bit weird. If you have a better way, please let me know.
FROM rockylinux:8
RUN dnf install -y which python3 python3-devel gcc gcc-c++ epel-release zlib-devel
RUN dnf config-manager --set-enabled powertools
RUN dnf localinstall -y --nogpgcheck https://download1.rpmfusion.org/free/el/rpmfusion-free-release-8.noarch.rpm
RUN dnf install -y OpenEXR-devel
RUN pip3 install OpenEXR
RUN dnf install -y --nogpgcheck RUN dnf install -y
docker pull plinecom/py_exr_zip
Explanation of code
Load EXR file
InputFile(path) # Read EXR file
Specify the compression method
header = ret.header()
# Change Compression Type
header['compression'] = Imath.Compression(Imath.Compression.ZIP_COMPRESSION)
If you write the compression method directly in the text and assign it, it will fail; you must pass an Imath.Compression object initialized with the Imath.Compression.* enumerator. In this case, we want to compress ZIP, so we pass Imath.Compression.ZIP_COMPRESSION. Since we do not change anything other than the compression method, we use the header information of the original file for the rest of the header information.
Writing files
## Open the write file and Set header info
OutputFile(dir_name + '/' + base_name, header)
exr.writePixels(pix) # Write Pixel Data
exr.close() # Close Write File handler
Create a file based on the diverted header, specifying the destination to write to, write pixel data, and close the file.
Explanation of OpenEXR compression options
Lossless compression options.
RLE
Run-length compression. It is a simple compression process but very fast because it takes only one pass and requires very little computation. It is very fast because it is a simple compression process that takes only one pass and requires very little computation. Since this alone reduces the size of the file by about 90%, there is no reason to choose uncompressed. ~~~ so don't send me uncompressed OpenEXR files. ~~~
ZIP
You all know ZIP compression. It compresses an image by dividing it into 16 lines, and it can reduce the size to about half of the uncompressed size.
ZIPS
ZIP compresses images by 16 lines, but ZIPS compresses by 1 line. S is for scan lines, not plural S. I heard a rumor that ZIPS compresses images faster with compositing software such as Nuke, but I have yet to feel any difference. Compression efficiency is lower than ZIP.
PIZ
PIZ compresses image data in the frequency domain using a wavelet transform, which is upward compatible with the Fourier transform. While it offers the best compression ratio, it is also very computationally intensive, and the sacrifice in computation time is too great for a compression that is a few percent smaller than ZIP compression.
Compression that can be lossless or lossy.
PXR24
PXR24 compresses data with quantization reduced to 24 bits. 16-bit half-precision floating-point data and 16-bit integer data below 24 bits are lossless compressions, but 32-bit single-precision floating-point data are lossy compression. You should handle this point cautiously, so do not select this option if you are unsure.
Lossy compression option
Other lossy compression options are as follows.
- B44
- B44A
- DWAA
- DWAB
However, most of us use OpenEXR for high quality. We will not use lossy compression, which may cause an accident due to different decompression results at the delivery destination. If you are a large studio that produces your master's, it may be an option in terms of storage efficiency.
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