python图像预处理库对比

Lycon

A minimal and fast image library for Python and C++.

Lycon is a small subset of optimized image operations derived from OpenCV.

Current set of features include:

• Reading and writing JPEG and PNG images
• Fast SIMD optimized image resizing
• Zero-copy interop with NumPy whenever possible

Tested on:

• Linux (Ubuntu 14.04) with Python2.7.6 and 3.5.2.
• macOS (Sierra, 10.12) with Python 2.7.11 and 3.5.1.

Install

pip install lycon

Native extension dependencies:

• CMake 2.8 or newer
• C++ toolchain
• LibJPEG
• LibPNG

Ubuntu

Single-line command for installing all dependencies:

sudo apt-get install cmake build-essential libjpeg-dev libpng-dev

Anaconda

When working within an Anaconda Python distribution, it is recommended to use the latest cmake version (3.6 or newer). Older versions can lead to a mismatch between the libpng and libjpeg headers used to build Lycon (usually the system headers), and the linked library (which may be preempted by the Anaconda-scoped version). To install the latest cmake version:

conda install cmake

Example

import lycon

# Load an image as a numpy array
img = lycon.load('mittens.jpg')
# Resize the image using bicubic interpolation
resized = lycon.resize(img, width=256, height=512, interpolation=lycon.Interpolation.CUBIC)
# Crop the image (like any regular numpy array)
cropped = resized[:100, :200]
# Save the image
lycon.save('cropped-mittens.png', cropped)

Limitations

Compared to other image processing libraries (OpenCV, pillow, scikit-image), Lycon offers a very limited set of operations. Intended usages include data loaders for deep learning, mass image resizing, etc.

Advantages over OpenCV

• Drastically smaller (at the cost of drastically fewer features)
• Python module installable via pip
• Images use the more common RGB ordering (vs OpenCV’s BGR)

However, if you already have OpenCV installed, Lycon’s advantages are minimal.

Advantages over PIL(low)

• Faster
• First-class NumPy support
• Full support for floating point images

Advantages over Scikit-Image

• Drastically faster

Benchmarks

• The table below lists execution time (in seconds), averaged across 10 runs
• The multiplier next to the time is the relative slowdown compared to Lycon

Operation	Lycon	OpenCV	PIL	Scikit-Image
Upsample: Nearest	0.1944	0.1948 (1x)	2.1342 (11x)	30.8982 (158.9x)
Upsample: Bilinear	0.4852	0.4940 (1x)	7.2940 (15x)	45.9095 (94.6x)
Upsample: Bicubic	1.8162	1.8182 (1x)	8.9589 (4.9x)	120.1645 (66.1x)
Upsample: Lanczos	4.5641	4.5714 (1x)	10.7517 (2.3x)
Upsample: Area	0.4801	0.4931 (1x)
Downsample: Nearest	0.0183	0.0181 (1x)	0.4379 (24.2x)	3.6101 (199.9x)
Downsample: Bilinear	0.0258	0.0257 (1x)	1.3122 (51x)	4.8487 (188.4x)
Downsample: Bicubic	0.1324	0.1329 (1x)	1.8153 (13.7x)	9.4905 (71.6x)
Downsample: Lanczos	0.3317	0.3328 (1x)	2.4058 (7.2x)
Downsample: Area	0.0258	0.0259 (1x)
Read: JPG	0.3409	0.5085 (1.5x)	1.4081 (4.1x)	1.4628 (4.3x)
Read: PNG	1.2114	1.3245 (1.1x)	1.8274 (1.5x)	1.8674 (1.5x)
Write: JPG	0.4760	0.6046 (1.3x)	2.3823 (5x)	5.0159 (10.5x)
Write: PNG	2.1421	2.2370 (1x)	9.0580 (4.2x)	11.6060 (5.4x)

• Blank cells indicate that the operation is not supported by the library
• All operations performed on a 16k (15360 x 8640) RGB image
• Tests performed on Ubuntu 14.04 running on an Intel Core i7 (Skylake)
• OpenCV 3.2+ (master: a85b4b5), Pillow 4.0.0, skimage 0.12.3, Python 2.7.3
• OpenCV can potentially achieve better performance with GPU implementations and proprietary libraries like Intel IPP

License

• All code derived from the OpenCV project is licensed under the 3-clause BSD License.
• All Lycon-specific modifications are licensed under the MIT license.

See LICENSE for further details.