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https://github.com/lilohuang/pyturbojpeg

PyTurboJPEG is a highly optimized Python wrapper of libjpeg-turbo (TurboJPEG API) which supports x86 and ARM architecture.
https://github.com/lilohuang/pyturbojpeg

arm decoding embedded-systems image-processing jpeg-turbo libjpeg libjpeg-turbo macos opencv-python pypy python python-wrapper python2 python3 real-time simd-optimizations simd-parallelism turbo-jpeg turbojpeg x86

Last synced: about 1 month ago
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PyTurboJPEG is a highly optimized Python wrapper of libjpeg-turbo (TurboJPEG API) which supports x86 and ARM architecture.

Host: GitHub
URL: https://github.com/lilohuang/pyturbojpeg
Owner: lilohuang
License: mit
Created: 2018-01-11T15:57:52.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2024-09-17T15:02:41.000Z (about 2 months ago)
Last Synced: 2024-10-12T09:02:18.962Z (about 1 month ago)
Topics: arm, decoding, embedded-systems, image-processing, jpeg-turbo, libjpeg, libjpeg-turbo, macos, opencv-python, pypy, python, python-wrapper, python2, python3, real-time, simd-optimizations, simd-parallelism, turbo-jpeg, turbojpeg, x86
Language: Python
Homepage:
Size: 107 KB
Stars: 252
Watchers: 8
Forks: 44
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # PyTurboJPEG

A Python wrapper of libjpeg-turbo for decoding and encoding JPEG image.

## Prerequisites

- [libjpeg-turbo](https://github.com/libjpeg-turbo/libjpeg-turbo/releases)

- [numpy](https://github.com/numpy/numpy)

## Example

```python

import cv2

from turbojpeg import TurboJPEG, TJPF_GRAY, TJSAMP_GRAY, TJFLAG_PROGRESSIVE, TJFLAG_FASTUPSAMPLE, TJFLAG_FASTDCT

# specifying library path explicitly

# jpeg = TurboJPEG(r'D:\turbojpeg.dll')

# jpeg = TurboJPEG('/usr/lib64/libturbojpeg.so')

# jpeg = TurboJPEG('/usr/local/lib/libturbojpeg.dylib')

# using default library installation

jpeg = TurboJPEG()

# decoding input.jpg to BGR array

in_file = open('input.jpg', 'rb')

bgr_array = jpeg.decode(in_file.read())

in_file.close()

cv2.imshow('bgr_array', bgr_array)

cv2.waitKey(0)

# decoding input.jpg to BGR array with fast upsample and fast DCT. (i.e. fastest speed but lower accuracy)

in_file = open('input.jpg', 'rb')

bgr_array = jpeg.decode(in_file.read(), flags=TJFLAG_FASTUPSAMPLE|TJFLAG_FASTDCT)

in_file.close()

cv2.imshow('bgr_array', bgr_array)

cv2.waitKey(0)

# direct rescaling 1/2 while decoding input.jpg to BGR array

in_file = open('input.jpg', 'rb')

bgr_array_half = jpeg.decode(in_file.read(), scaling_factor=(1, 2))

in_file.close()

cv2.imshow('bgr_array_half', bgr_array_half)

cv2.waitKey(0)

# getting possible scaling factors for direct rescaling

scaling_factors = jpeg.scaling_factors

# decoding JPEG image properties

in_file = open('input.jpg', 'rb')

width, height, jpeg_subsample, jpeg_colorspace = jpeg.decode_header(in_file.read())

in_file.close()

# decoding input.jpg to YUV array

in_file = open('input.jpg', 'rb')

buffer_array, plane_sizes = jpeg.decode_to_yuv(in_file.read())

in_file.close()

# decoding input.jpg to YUV planes

in_file = open('input.jpg', 'rb')

planes = jpeg.decode_to_yuv_planes(in_file.read())

in_file.close()

# encoding BGR array to output.jpg with default settings.

out_file = open('output.jpg', 'wb')

out_file.write(jpeg.encode(bgr_array))

out_file.close()

# encoding BGR array to output.jpg with TJSAMP_GRAY subsample.

out_file = open('output_gray.jpg', 'wb')

out_file.write(jpeg.encode(bgr_array, jpeg_subsample=TJSAMP_GRAY))

out_file.close()

# encoding BGR array to output.jpg with quality level 50. 

out_file = open('output_quality_50.jpg', 'wb')

out_file.write(jpeg.encode(bgr_array, quality=50))

out_file.close()

# encoding BGR array to output.jpg with quality level 100 and progressive entropy coding.

out_file = open('output_quality_100_progressive.jpg', 'wb')

out_file.write(jpeg.encode(bgr_array, quality=100, flags=TJFLAG_PROGRESSIVE))

out_file.close()

# decoding input.jpg to grayscale array

in_file = open('input.jpg', 'rb')

gray_array = jpeg.decode(in_file.read(), pixel_format=TJPF_GRAY)

in_file.close()

cv2.imshow('gray_array', gray_array)

cv2.waitKey(0)

# scale with quality but leaves out the color conversion step

in_file = open('input.jpg', 'rb')

out_file = open('scaled_output.jpg', 'wb')

out_file.write(jpeg.scale_with_quality(in_file.read(), scaling_factor=(1, 4), quality=70))

out_file.close()

in_file.close()

# lossless crop image

out_file = open('lossless_cropped_output.jpg', 'wb')

out_file.write(jpeg.crop(open('input.jpg', 'rb').read(), 8, 8, 320, 240))

out_file.close()

```

```python

# using PyTurboJPEG with ExifRead to transpose an image if the image has an EXIF Orientation tag.

#

# pip install PyTurboJPEG -U

# pip install exifread -U

import cv2

import numpy as np

import exifread

from turbojpeg import TurboJPEG

def transposeImage(image, orientation):

    """See Orientation in https://www.exif.org/Exif2-2.PDF for details."""

    if orientation == None: return image

    val = orientation.values[0]

    if val == 1: return image

    elif val == 2: return np.fliplr(image)

    elif val == 3: return np.rot90(image, 2)

    elif val == 4: return np.flipud(image)

    elif val == 5: return np.rot90(np.flipud(image), -1)

    elif val == 6: return np.rot90(image, -1)

    elif val == 7: return np.rot90(np.flipud(image))

    elif val == 8: return np.rot90(image)

# using default library installation

turbo_jpeg = TurboJPEG()

# open jpeg file

in_file = open('foobar.jpg', 'rb')

# parse orientation

orientation = exifread.process_file(in_file).get('Image Orientation', None)

# seek file position back to 0 before decoding JPEG image

in_file.seek(0)

# start to decode the JPEG file

image = turbo_jpeg.decode(in_file.read())

# transpose image based on EXIF Orientation tag

transposed_image = transposeImage(image, orientation)

# close the file since it's no longer needed.

in_file.close()

cv2.imshow('transposed_image', transposed_image)

cv2.waitKey(0)

```

## Installation

### macOS

- brew install jpeg-turbo

- pip install -U git+https://github.com/lilohuang/PyTurboJPEG.git

### Windows 

- Download [libjpeg-turbo official installer](https://sourceforge.net/projects/libjpeg-turbo/files) 

- pip install -U git+https://github.com/lilohuang/PyTurboJPEG.git

### Linux

- RHEL/CentOS/Fedora

  - Download [libjpeg-turbo.repo](https://libjpeg-turbo.org/pmwiki/uploads/Downloads/libjpeg-turbo.repo) to /etc/yum.repos.d/

  - sudo yum install libjpeg-turbo-official

  - pip install -U git+https://github.com/lilohuang/PyTurboJPEG.git

- Ubuntu

  - sudo apt-get update

  - sudo apt-get install libturbojpeg

  - pip install -U git+https://github.com/lilohuang/PyTurboJPEG.git

## Benchmark 

### macOS

- macOS Sierra 10.12.6

- Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz

- opencv-python 3.4.0.12 (pre-built)

- turbo-jpeg 1.5.3 (pre-built)

| Function              | Wall-clock time |

| ----------------------|-----------------|

| cv2.imdecode()        |   0.528 sec     |

| TurboJPEG.decode()    |   0.191 sec     |

| cv2.imencode()        |   0.875 sec     |

| TurboJPEG.encode()    |   0.176 sec     |

### Windows 

- Windows 7 Ultimate 64-bit

- Intel(R) Xeon(R) E3-1276 v3 CPU @ 3.60 GHz

- opencv-python 3.4.0.12 (pre-built)

- turbo-jpeg 1.5.3 (pre-built)

| Function              | Wall-clock time |

| ----------------------|-----------------|

| cv2.imdecode()        |   0.358 sec     |

| TurboJPEG.decode()    |   0.135 sec     |

| cv2.imencode()        |   0.581 sec     |

| TurboJPEG.encode()    |   0.140 sec     |