imgtools-m8 1.0.0

Simple image tools package. Used to convert, downscale or upscale images.

imgtools_m8

Simple image tools package. Used to convert, downscale and/or upscale images.

Use deep learning and cv2 to upscale image using Xavier Weber models, (more info here).

Installation

Install from GitHub repository :

To install directly from GitHub:

$ python3 -m pip install "git+https://github.com/mano8/imgtools_m8 --upgrade"

To install from PypI :

python3 -m pip install imgtools_m8 --upgrade

How to use

This package automatically covert, downscale and/or upscale an image file or a list of images from directory defined in source_path property, to output_path directory.
See examples for more use case. (See accepted extensions from cv2 documentation)

It is possible to resize images with different options:

• fixed_width: resize image to exact width (in pixel)
• fixed_height: resize image to exact height (in pixel)
• fixed_size: resize image depending on first limitation reached (height or width)
• fixed_width and fixed_height: resize image depending on first limitation reached.
  same as fixed_size but can set different height/width values.

Example :
The source file is 340px width and 216px height.

Recien llegado by @Cezar yañez

We want resized output file to exact width of 1900px and 1200px. And we need output formats as JPEG (with 80% quality) and WEBP (with 70% quality)

    >>> output_formats = [
                {  # Get resized output file to exact width of 1600px
                    'fixed_width': 1900,
                    'formats': [
                        # JPEg defauts are 'quality': 95, 'progressive': 0, 'optimize': 0
                        {'ext': '.jpg', 'quality': 80, 'progressive': 1, 'optimize': 1},
                    ]
                },
                {  # Get resized output file to exact width of 800px
                    'fixed_width': 1200,
                    'formats': [
                        {'ext': '.jpg', 'quality': 80},
                        {'ext': '.webp', 'quality': 70}
                        {'ext': '.png', 'compression': 2}
                    ]
                }
            ]
        >>> imgtools = ImageTools(
            source_path="./tests/dummy_dir/recien_llegado.jpg",
            output_path="/my/output/path/directory",
            output_formats=output_formats
        )
        >>> imgtools.run()

This will create 4 files in the output directory :

• Two JPEG files resized as defined width (1900px and 1200px), with 80% quality, JPEG progressive and optimize features enabled
• Two WEBP files resized as defined width (1900px and 1200px), with 70% quality
• One PNG file resized as defined width (1900px), with PNG compression level = 2

The output file names are set as:

    >>> 'originalName'_'imageWidth'x'imageHeight'.'outputExtension'
    >>> # egg :
    >>> originalFileName_1400x1360.jpeg

One of above results is :

recien_llegado_1200x762.jpg by @Cezar yañez

In this case source file is precessed as:

• upscale 2x (source file is now 680px/432px)
• upscale 2x (source file is now 1360px/864px)
• for 1200px width output downscale and save as recien_llegado_1200x762.jpg recien_llegado_1200x762.webp recien_llegado_1200x762.png
• upscale 2x (source file is now 2720px/1728px)
• for 1900px width output downscale and save as recien_llegado_1900x1207.jpeg an .webp

By default, the image tool use EDSR_x2.pb deep learning model, to improve quality.

To load any compatible model of your choice to upscale the images, you can define model_conf property.

The imgtools_m8 only contain EDSR (x2, x3, x4) models.

If you want uses one of them set model_conf property as:

    >>> # Set EDSR_x4 model to upscale images
    >>> model_conf = {
        'model_name': 'edsr',
        'scale': 4,
    }
    >>> # Or set FSRCNN_x2 model to upscale images
    >>> model_conf = {
        'model_name': 'edsr',
        'scale': 2,
    }

In case you want uses another compatible model, you may download the desired .pb file, and set model_conf property as:

    >>> # Set TF-ESPCN_x2 model to upscale images
    >>> model_conf = {
        'path': "/path/to/your/downloaded/model/directory",
        'model_name': 'espcn',
        'scale': 2,
    }

Here a complete example using TF-ESPCN_x2 model to upscale images :

    >>> # Set TF-ESPCN_x2 model to upscale images
    >>> model_conf = {
        'path': "/path/to/your/downloaded/model/directory",
        'model_name': 'espcn',
        'scale': 2,
    }
    >>> output_formats = [
                {  # Get resized output file to exact width of 1600px
                    'fixed_width': 1900,
                    'formats': [
                        # JPEg defauts are 'quality': 95, 'progressive': 0, 'optimize': 0
                        {'ext': '.jpg', 'quality': 80, 'progressive': 1, 'optimize': 1},
                    ]
                },
                {  # Get resized output file to exact width of 800px
                    'fixed_width': 1200,
                    'formats': [
                        {'ext': '.jpg', 'quality': 80},
                        {'ext': '.webp', 'quality': 70}
                        {'ext': '.png', 'compression': 2}
                    ]
                }
            ]
        >>> imgtools = ImageTools(
            source_path="./tests/dummy_dir/recien_llegado.jpg",
            output_path="/my/output/path/directory",
            output_formats=output_formats,
            model_conf=model_conf
        )
        >>> imgtools.run()