JPG Process

get_orientation_code(stream: Union[str, Path, bytes]) -> Union[ROTATE, None]

jpgencode(img: np.ndarray, quality: int = 90) -> Union[bytes, None]

jpgdecode(byte_: bytes) -> Union[np.ndarray, None]

jpgread(img_file: Union[str, Path]) -> Union[np.ndarray, None]

These functions provide support for encoding, decoding, and reading JPG images, as well as automatically adjusting image orientation from EXIF data.

Description

• get_orientation_code: Extracts orientation information from the image's EXIF data and converts it into a code suitable for image rotation. This step is automatically performed in the jpgdecode and jpgread functions to ensure that the image is correctly oriented when read.
• jpgencode: Encodes a NumPy image array into a JPG format byte string. The quality parameter allows for balancing image quality and file size.
• jpgdecode: Decodes a JPG format byte string into a NumPy image array, adjusting the image orientation based on EXIF data.
• jpgread: Reads a JPG image file, decodes it into a NumPy image array, and adjusts the image orientation based on EXIF data.

Parameters

jpgencode

• img (np.ndarray): The image array to encode.
• quality (int): The encoding quality, ranging from 1 to 100. Default is 90.

jpgdecode

• byte_ (bytes): The JPG format byte string to decode.

jpgread

• img_file (Union[str, Path]): The path to the JPG image file to read.

Example

jpgencode

import numpy as np
import capybara as cb

img_array = np.random.rand(100, 100, 3) * 255
encoded_bytes = cb.jpgencode(img_array, quality=95)

jpgdecode

decoded_img = cb.jpgdecode(encoded_bytes)

jpgread

img_array = cb.jpgread('path/to/image.jpg')

Additional Notes: TurboJPEG

TurboJPEG is an efficient JPEG image processing library that offers fast encoding, decoding, compression, and decompression functionalities. The jpgencode and jpgdecode functions utilize TurboJPEG for encoding and decoding JPG images. TurboJPEG is a Python wrapper for libjpeg-turbo, providing faster image processing speeds and supporting multiple image formats.

• Features:

  • High efficiency: Leverages the high-performance features of the libjpeg-turbo library, significantly improving image processing speed compared to traditional JPEG methods.
  • Ease of use: Provides a clear and simple API, making it easy for developers to implement efficient JPEG image processing in applications.
  • Flexibility: Supports various image quality and compression settings to meet different requirements for image quality and file size.
  • Cross-platform: Supports multiple operating systems, including Windows, macOS, and Linux, making it convenient for use in different development environments.

After installation, you can use TurboJPEG for encoding and decoding in Python as follows:

from turbojpeg import TurboJPEG

# Initialize TurboJPEG instance
jpeg = TurboJPEG()

# Decode
bgr_array = jpeg.decode(byte_)

# Encode
byte_ = jpeg.encode(img, quality=quality)