cykooz.resizer 3.0.0

A fast image resizer

cykooz.resizer

cykooz.resizer is package with the optimized version of image resizing based on Rust's crate fast_image_resize.

CHANGELOG

Installation

python3 -m pip install cykooz.resizer

Or with automatically installing Pillow:

python3 -m pip install cykooz.resizer[pillow]

Information

Supported pixel types and available optimisations:

Format	Description	SSE4.1	AVX2	Neon
U8	One u8 component per pixel (e.g. L)	+	+	+
U8x2	Two u8 components per pixel (e.g. LA)	+	+	+
U8x3	Three u8 components per pixel (e.g. RGB)	+	+	+
U8x4	Four u8 components per pixel (e.g. RGBA, RGBx, CMYK)	+	+	+
U16	One u16 components per pixel (e.g. L16)	+	+	+
U16x2	Two u16 components per pixel (e.g. LA16)	+	+	+
U16x3	Three u16 components per pixel (e.g. RGB16)	+	+	+
U16x4	Four u16 components per pixel (e.g. RGBA16, RGBx16, CMYK16)	+	+	+
I32	One i32 component per pixel	-	-	-
F32	One f32 component per pixel	-	-	-

Implemented resize algorithms:

• Nearest - is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output; for upscaling this means multiple pixels of the same color will be present.
• Convolution with different filters:
  • box
  • bilinear
  • catmull_rom
  • mitchell
  • gaussian
  • lanczos3
• Super sampling - is resizing an image in two steps. The first step uses the "nearest" algorithm. The second step uses "convolution" with configurable filter.

Usage Examples

Resize Pillow's image

from PIL import Image

from cykooz.resizer import FilterType, ResizeAlg, Resizer, ResizeOptions


resizer = Resizer()
dst_size = (255, 170)
dst_image = Image.new('RGBA', dst_size)

for i in range(1, 10):
    image = Image.open('nasa_%d-4928x3279.png' % i)
    resizer.resize_pil(image, dst_image)
    dst_image.save('nasa_%d-255x170.png' % i)

# Resize using a bilinear filter and ignoring an alpha channel.
image = Image.open('nasa-4928x3279.png')
resizer.resize_pil(
    image,
    dst_image,
    ResizeOptions(
        resize_alg=ResizeAlg.convolution(FilterType.bilinear),
        use_alpha=False,
    )
)

Resize raw image with an alpha channel

from cykooz.resizer import ImageData, PixelType, Resizer


def resize_raw(width: int, height: int, pixels: bytes):
    src_image = ImageData(
        width,
        height,
        PixelType.U8x4,
        pixels,
    )
    resizer = Resizer()
    dst_image = ImageData(255, 170, PixelType.U8x4)
    # By default, Resizer multiplies and divides by alpha channel
    # images with `U8x2`, `U8x4`, `U16x2` and `U16x4` pixels.
    resizer.resize(src_image, dst_image)
    return dst_image

Change used CPU-extensions

from cykooz.resizer import Resizer, CpuExtensions


resizer = Resizer()
resizer.cpu_extensions = CpuExtensions.sse4_1
...

Benchmarks

Environment:

• CPU: AMD Ryzen 9 5950X
• RAM: DDR4 4000 MHz
• Ubuntu 22.04 (linux 6.5.0)
• Python 3.10
• Rust 1.78.0
• cykooz.resizer = "3.0"

Other Python libraries used to compare of resizing speed:

• Pillow = "10.3.0" (https://pypi.org/project/Pillow/)

Resize algorithms:

• Nearest
• Convolution with Bilinear filter
• Convolution with Lanczos3 filter

Resize RGBA image 4928x3279 => 852x567

• Source image nasa-4928x3279.png

Package (time in ms)	nearest	bilinear	lanczos3
Pillow	0.93	104.77	191.08
cykooz.resizer	0.20	28.50	56.33
cykooz.resizer - sse4_1	0.20	12.28	24.31
cykooz.resizer - avx2	0.20	8.58	21.62

Resize grayscale (U8) image 4928x3279 => 852x567

• Source image nasa-4928x3279.png has converted into grayscale image with one byte per pixel.

Package (time in ms)	nearest	bilinear	lanczos3
Pillow	0.25	20.62	51.62
cykooz.resizer	0.18	6.25	13.06
cykooz.resizer - sse4_1	0.18	2.12	5.75
cykooz.resizer - avx2	0.18	1.96	4.41