fastai-rawpy connects fast.ai with RawPy, so now fast.ai supports RAW image files
Project description
Connecting fast.ai with RawPy
Here we can make fast.ai framework support RawPy library, hence use RAW image files on fastai.
The list of all the RAW image files that can be supported by rawpy (based on LibRaw):
https://www.libraw.org/supported-cameras
Partial list of the supported RAW formats:
- *.IIQ (Phase One)
- *.3FR (Hasselblad)
- *.DCR, *.K25, *.KDC (Kodak)
- *.CRW *.CR2 *.CR3 (Canon)
- *.ERF (Epson)
- *.MEF (Mamiya)
- *.MOS (Leaf)
- *.NEF *.NRW (Nikon)
- *.ORF (Olympus)
- *.PEF (Pentax)
- *.RW2 (Panasonic)
- *.ARW, *.SRF, *.SR2 (Sony)
How to install
Use:
pip install fastai-rawpy
to install this package.
How to import fastai-rawpy
Use:
from fastairawpy import *
in order to import RawImageBlock
as well as all of the necessary functions/classes/etc that adjust fast.ai so it supports RawPy.
How to use fastai-rawpy
Upon using fast.ai, wherever you could have used ImageBlock
, just type in RawImageBlock
.
examples
Example (1)
dblock = DataBlock(blocks=(ImageBlock(cls=PILImage),Categorize))
This line creates two blocks (input and target) of an ImageBlock
, that is a transform to be applied to create items of image type. The transform uses PILImage
as a data-holder of the image files (such as JPG, PNG, BMP, ...).
dblock = DataBlock(blocks=(RawImageBlock,Categorize))
But this line uses RawImageBlock
transform, that is applied automatically on built-in RAWImage
items. RAWImage
is a data-holder of RAW image files, alike PILImage
, but is based on rawpy
package.
Example (2)
This line code snippet creates two RawImageBlock
blocks of input and target, where the parameters being passed in define the postprocessing of each blocks' image:
dblock = DataBlock(blocks=(RawImageBlock( # Input
gamma=(2.222, 4.5), # Gamma values
output_color=rawpy.ColorSpace.sRGB, # sRGB Color Space
output_bps=16), # Bits_per_pixel
RawImageBlock( # Target
use_camera_wb=True, # White blanace
output_color=rawpy.ColorSpace.sRGB, # sRGB Color Space
output_bps=16) # Bits_per_pixel
)
)
The full list of parameters: https://letmaik.github.io/rawpy/api/rawpy.Params.html
Why use fastai-rawpy
RAW format has a bit-depth of 16bits for every channel (R,G,B[,G]), meaning: every pixel gets values in between 0 up to... 65536!
2^16=65536
Therefore, a RAW image file would normally have a larger range of values for every pixel. This can help training a model become more precise, when switching from JPG to RAW.
Note: in practice, every DSLR stores RAW files and postprocesses these files into other formats such as JPG. For instance, it crops and compresses the JPG image out of the RAW file.
Performance
Behind the stage
New objects that create the bond between fast.ai and rawpy
class TensorRawImage
+ class RAWImage
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