cxx-image-io 1.1.7

Image IO APIs for wide range image formats (Camera manufacturer RAW images, jpg, png, tif, dng, bmp, yuv, nv12 ), support Exif, interact nicely with numpy array.

CXX Image IO

Introduction

CXX Image IO is a Python project which provides the image IO interfaces, binding with the C++ library: https://github.com/emmcb/cxx-image. These IO interfaces are designed to read and write images in many file formats in generic way and to interact nicely with numpy array.

Image format	Read	Write	EXIF	Pixel precision	Pixel type	File extension	Sidecar needed
BMP	x	x		8 bits	Grayscale, RGB, RGBA	.bmp
CFA	x	x		16 bits	Bayer	.cfa
DNG	x	x	x	16 bits, float	Bayer, RGB	.dng
JPEG	x	x	x	8 bits	Grayscale, RGB	.jpg, .jpeg
MIPI RAW	x	x		10 bits, 12 bits	Bayer	.RAWMIPI, .RAWMIPI10, .RAWMIPI12	x
PLAIN RAW	x	x		*	*	.raw .plain16, .nv12, .yuv, *	x
PNG	x	x		8 bits, 16 bits	Grayscale, RGB, RGBA	.png
TIFF	x	x	x	8 bits, 16 bits, float	Bayer, RGB	.tif, .tiff

Since version v1.1.2, cxx-image-io supports reading Bayer RAW images from reflex cameras into numpy.array, while also parsing some EXIF information. It is integrated with the C++ library LibRaw https://www.libraw.org/.

The RAW image formats of the following camera manufacturers are supported, user can open these camera raw files by read_image, get image as numpy.array format:

Camera manufacturer	Image format
Canon	CR2
Nikon	NEF
Sony	ARW
Panasonic	RW2
Kodak	DCR
Samsung	SRW
Olympus	ORF
Leica	RAW
Pentax	PEF

Getting Started

Prerequisites

This projet currently supports Python from 3.10 to 3.14 on

• Windows: x86_64
• Linux: x86_64 and aarch64, glibc v2.28+, musl libc v1.2+
• MacOS: x86_64 and arm64, v11.0+

Installation

The python package cxx-image-io is to be installed by pip

pip install cxx-image-io

Usage

Image reading basic example

read_image is able to read a image file and return a numpy array and ImageMetadata object.

from cxx_image_io import read_image
from cxx_image_io import ImageMetadata
import numpy as np
from pathlib import Path

image, metadata = read_image(Path('/path/to/image.jpg'))
assert isinstance(image, np.ndarray)

print('Type:', image.dtype)
print('Shape:', image.shape)

image is a numpy array which is suitable for the image processing afterwards.

The print result could be like this:

Type: uint8
Shape: (551, 603, 3)

ImageMetadata is the information about the image, the component fileInfo including the pixel type, pixel precision and image layout, which define fundamentally how the pixels arranged in buffer.

print(metadata.fileInfo)

The print result could be like this:

{'pixelPrecision': 8, 'imageLayout': 'interleaved', 'pixelType': 'rgb'}

metadata.fileInfo shows: image is a 3-dimensional numpy array, where rgb 3 channels interleaved, in each channel, pixel depth is 8 bits.

ImageMetadata has more components than fileInfo, it also includes ExifMetadata, help(ImageMetadata) will show the details.

Read Camera manufacturer RAW image example:

reading camera manufacturer raw image is the same method as we read from .jpg or .tif, just call read_image.

from cxx_image_io import read_image
from cxx_image_io import ImageMetadata
import numpy as np
from pathlib import Path

image, metadata = read_image(Path('test/images/RAW_CANON_EOS_1DX.CR2'))
assert isinstance(image, np.ndarray)

print('Type:', image.dtype)
print('Shape:', image.shape)

print(metadata)

The print result will be like:

Type: uint16
Shape: (3584, 5344)
{
   "fileInfo":{
      "width":5344,
      "height":3584,
      "pixelPrecision":14,
      "imageLayout":"planar",
      "pixelType":"bayer_rggb",
      "pixelRepresentation":"uint16"
   },
   "exifMetadata":{
      "imageWidth":5218,
      "imageHeight":3482,
      "make":"Canon",
      "model":"EOS-1D X",
      "orientation":1,
      "exposureTime":[
         1,
         80
      ],
      "fNumber":[
         56,
         10
      ],
      "isoSpeedRatings":1600,
      "dateTimeOriginal":"2012:09:06 07:48:25",
      "focalLength":[
         700,
         10
      ]
   },
   "shootingParams":{

   },
   "cameraControls":{
      "whiteBalance":[
         1.75390625,
         1.4775390625
      ]
   },
   "calibrationData":{
      "blackLevel":2048,
      "whiteLevel":15438,
      "colorMatrix":[
         [
            1.9404515027999878,
            -1.1166307926177979,
            0.17617927491664886
         ],
         [
            -0.21374493837356567,
            1.6440128087997437,
            -0.430267870426178
         ],
         [
            0.021280933171510696,
            -0.5217925906181335,
            1.500511646270752
         ]
      ]
   },
   "semanticMasks":[

   ],
   "LibRawParams":{
      "rawWidth":5344,
      "rawHeight":3584,
      "rawWidthVisible":5218,
      "rawHeightVisible":3482,
      "topMargin":100,
      "leftMargin":126
   }
}

Note that camera manufacturer RAW image metadata has an additional Parameters: LibRawParams, it contains the coordinates of the original RAW sizes, including the top and left margins, as well as the actual visible area's width and height in the original RAW.

According to these inforamtions, user can corp the visable zone on the RAW image numpy array.

Image reading with sidecar JSON

Some file formats need to know in advance some informations about the image. For example, the PLAIN RAW format is just a simple dump of a buffer into a file, thus it needs to know how to interpret the data.

Bayer Plain Raw 16 bits

image, metadata = read_image(Path('/path/to/image.plain16'))
print('Type:', image.dtype)
print('Shape:', image.shape)
print(metadata.fileInfo)

In this case, user need to have an image sidecar JSON located next to the image file as the same name and path '/path/to/image.json'

{
    "fileInfo": {
        "format": "plain",
        "height": 3072,
        "width": 4080
        "pixelPrecision": 16,
        "pixelType": "bayer_gbrg",
    }
}

After image reading, the information in JSON sidecar is parsed in ImageMetadata object.

The print result of will be like this:

Type: uint16
Shape: (3072, 4080)

{'width': 4080, 'height': 3072, 'pixelPrecision': 16, 'imageLayout': 'planar', 'pixelType': 'bayer_gbrg'}

metadata.fileInfo shows that image is a 2-dimensional numpy array, where pixel is Bayer type, planar layout (not interleaved), pixel depth is 16 bits.

metadata.fileInfo has more attributes to describe the file info, like widthAlignment, heightAlignment, please use help(metadata.fileInfo) to see the details.

Image sidecar is not mandatory, for the other formats which have already image information in their header, like jpg, png, tif, cfa. we don't need to provide image metadata.

Other image reading with sidecar examples

Click to unfold other image format sidecar examples

Packed RAW MIPI 12 bits:

python code

image, metadata = read_image(Path('/path/to/image.RAWMIPI12'))

sidecar json

{
    "fileInfo": {
        "fileFormat": "raw12",
        "height": 3000,
        "width": 4000,
        "pixelPrecision": 12,
        "pixelType": "bayer_gbrg"
    }
}

Packed RAW MIPI 10 bits:

python code

image, metadata = read_image(Path('/path/to/image.RAWMIPI'))

sidecar json

{
    "fileInfo": {
        "height": 3000,
        "width": 4000,
        "format": "raw10",
        "pixelPrecision": 10,
        "pixelType": "bayer_grbg"
    }
}

YUV 420 buffer 8 bits:

python code

image, metadata = read_image(Path('/path/to/image.yuv'))

sidecar json

{
    "fileInfo": {
        "format": "plain",
        "height": 300,
        "width": 400,
        "imageLayout": "yuv_420"
    }
}

NV12 buffer 8 bits:

python code

image, metadata = read_image(Path('/path/to/image.nv12'))

sidecar json

{
    "fileInfo": {
        "format": "plain",
        "height": 300,
        "width": 400,
        "imageLayout": "nv12"
    }
}

Split and merge image channels

After calling read_image, cxx-image-io provides a public API split_image_channels which helps to split to different colors channels, so that user can do the different processes on them. The function return type is a dictionary which contains the different color channel name as keys, and the value in numpy array of one single channel.

before calling write_image, cxx-image-io provides a public API merge_image_channels which helps to merge different colors channels to a numpy array buffer.

from cxx_image_io import read_image, split_image_channels, merge_image_channels, ImageLayout, ImageMetadata, PixelRepresentation, PixelType
import numpy as np
from pathlib import Path

rgb, metadata = read_image(Path('rgb_8bit.jpg'))

channels = split_image_channels(rgb, metadata)

# print(channels['r'])  # Red channel in numpy array
# print(channels['g'])  # Green channel in numpy array
# print(channels['b'])  # Blue channel in numpy array

rgb_post = merge_image_channels(channels, metadata)

np.array_equal(rgb, rgb_post)

cfa, metadata = read_image(Path('bayer_16bit.plain16'))

channels = split_image_channels(cfa, metadata)

# print(channels['gr'])  # Bayer Gr pixels in numpy array
# print(channels['r'])  # Bayer R pixels in numpy array
# print(channels['b'])  # Bayer B pixels in numpy array
# print(channels['gb'])  # Bayer Gb pixels in numpy array

cfa_post = merge_image_channels(channels, metadata)

np.array_equal(cfa, cfa_post)

yuv, metadata = read_image(Path('raw.nv12'))

channels = split_image_channels(yuv, metadata)

# print(channels['y'])  # Y plane in numpy array
# print(channels['u'])  # U plane in numpy array
# print(channels['v'])  # V plane in numpy array

yuv_post = merge_image_channels(channels, metadata)

np.array_equal(yuv, yuv_post)

Image writing

write_image is able to write a numpy array to image file.

To write the pure numpy array to different image file extensions. User need to define the following fundamental parameters in ImageMetadata which is part of ImageWriter.Options. In order to call the specific C++ image libraries with them.

from cxx_image_io import ImageMetadata, ImageWriter, FileFormat, PixelType, ImageLayout
from cxx_image_io import write_image
import numpy as np
from pathlib import Path

metadata = ImageMetadata()
metadata.fileInfo.pixelType = PixelType.RGB
metadata.fileInfo.imageLayout = ImageLayout.INTERLEAVED

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.jpg'), image, write_options)

write_image can determine the image format by file extensions, but some formats don't not rely on a specific extension, for example the PLAIN format that allows to directly dump the image buffer to a file. In this case, the format can be specified through ImageWriter.Options.

metadata = ImageMetadata()
metadata.fileInfo.pixelType = PixelType.BAYER_GBRG
metadata.fileInfo.imageLayout = ImageLayout.PLANAR

write_options = ImageWriter.Options(metadata)
write_options.fileFormat = FileFormat.PLAIN

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.plain16'), image, write_options)

Other image writing examples

Click to unfold other image writing examples

Packed RAW MIPI 12 bits:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.BAYER_GBRG  # adapt with User's RAW Bayer pattern.
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 12

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.RAWMIPI12'), image, write_options)

Packed RAW MIPI 10 bits:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.BAYER_GBRG # to adapt with User's RAW Bayer pattern.
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 10

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.RAWMIPI10'), image, write_options)

YUV 420 buffer 8 bits:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.YUV
metadata.fileInfo.imageLayout = ImageLayout.YUV_420

write_options = ImageWriter.Options(metadata)
write_options.fileFormat = FileFormat.PLAIN

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.yuv'), image, write_options)

NV12 buffer 8 bits:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.YUV
metadata.fileInfo.imageLayout = ImageLayout.NV12

write_options = ImageWriter.Options(metadata)
write_options.fileFormat = FileFormat.PLAIN

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.nv12'), image, write_options)

Bayer dng 12 bits:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.BAYER_RGGB  # adapt with User's RAW Bayer pattern.
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 12

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.dng'), image, write_options)

RGB 8 bits images (jpg, png, tif, bmp):

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.RGB
metadata.fileInfo.imageLayout = ImageLayout.INTERLEAVED

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.jpg'), image, write_options)

CFA 16 bits image:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.BAYER_RGGB  # adapt with User's RAW Bayer pattern.
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 16

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.cfa'), image, write_options)

Grayscale 16 bits png image:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.GRAYSCALE
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 16

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.png'), image, write_options)

Bayer 16 bits tif image:

metadata = ImageMetadata()

metadata.fileInfo.pixelType = PixelType.BAYER_RGGB  # adapt with User's RAW Bayer pattern.
metadata.fileInfo.imageLayout = ImageLayout.PLANAR
metadata.fileInfo.pixelPrecision = 16

write_options = ImageWriter.Options(metadata)

assert isinstance(image, np.ndarray)
write_image(Path('/path/to/image.tif'), image, write_options)

EXIF

Some image formats, like JPEG and TIFF, support EXIF reading and writing.

If supported, EXIF can be read by calling read_exif and be written by calling write_exif.

from cxx_image_io import read_exif, write_exif
from pathlib import Path

exif = read_exif(Path('/path/to/image.jpg'))

print(exif)

write_exif(Path('path/to/new_image.jpg'), exif)

print(exif) will give the following output like:

{
  'make': 'Canon',
  'model': 'Canon EOS 40D',
  'orientation': 1,
  'software': 'GIMP 2.4.5',
  'exposureTime': [1, 160],
  'fNumber': [71, 10],
  'isoSpeedRatings': 100,
  'dateTimeOriginal': '2008:05:30 15:56:01',
  'exposureBiasValue': [0, 1],
  'focalLength': [135, 1]
}

user can use help(exif) to see the definition of ExifMetdata.

EXIF metadata can be read and written along with an image by specifying them in the ImageMetadata. In this case, the EXIF wil be read and written when calling read_image and write_image.

image, metadata = read_image(Path('/path/to/image.jpg'))
metadata.exifMetadata.make = 'Custom'
write_options = ImageWriter.Options(metadata)
write_image(Path('/path/to/image.jpg'), image, write_options)

Dependencies

This project has the dependencies of the following libraries by cmake FetchContent:

Statically linked

• libjpeg: https://libjpeg.sourceforge.net/
• libpng: http://www.libpng.org/pub/png/libpng.html
• libtiff: https://libtiff.gitlab.io/libtiff/
• adobe dng sdk: https://helpx.adobe.com/camera-raw/digital-negative.html
• pybind11 (BSD-2): https://github.com/pybind/pybind11
• cxx-image (Apache 2.0): https://github.com/emmcb/cxx-image

Dynamically linked

• libexif (LGPL v2.1): https://libexif.github.io/
• libraw (LGPL v2.1 and CDDL): https://www.libraw.org/

License

This project is licensed under the MIT License - see the LICENSE.md file for details.