Image Optimizer

Summary

• Pre-requisites;
• Installation;
• Usage;
  • Optional parameters;
• Supported formats;
• Running tests;
  • Steps to run tests;
  • Example output;
• Author.

Pre-requisites

In order to use, you will need Python 3.6 or greater installed. In Linux systems, it usually comes installed as default.
To check if you have it installed, just open the terminal and run the command below:

python3 --version

If you don't have it installed yet, go to Python official website and check the installation process.

Installation

Open the project folder in the terminal, and run the command below to install the project:

pip install -e .

Usage

After installation, you can optimize an image by running:

image-optimizer input_image.[jpg,jpeg,png] output_image.[jpg,jpeg,png]

Important

The first argument is the path to the input image. For example, if you have an image called cat.jpg inside a folder called animals and you want to optimize it, you can run:

image-optimizer animals/cat.jpg animals/cat_optimized.jpg

Important

The file extensions of the input and output files must be the same. For example, if you're optimizing a .jpg file, the output file must also have a .jpg extension.

Optional parameters

In addition to the mandatory input and output file paths, the image-optimizer offers optional parameters to customize the behavior of the image optimization process. These parameters give you control over the compression quality, resizing scale, and the verbosity of the process. Below is a detailed explanation of each optional parameter:

• --quality

Description: This parameter controls the quality of the image after compression. The value must be an integer between 0 (maximum compression, lowest quality) and 100 (no compression, maximum quality). Default value: 85

Usage:

image-optimizer input_image.jpg output_image.jpg --quality 75

This example will compress the image and save it with a quality level of 75.

• --scale

Description: This parameter allows you to resize the image by a given scale factor. The value must be a float between 0.1 (10% of the original size) and 1.0 (100%, i.e., no resizing). Default value: 0.8

Usage:

image-optimizer input_image.jpg output_image.jpg --scale 0.5

This example resizes the image to 50% of its original dimensions.

• --verbose / --no-verbose

Description: This boolean flag controls whether to print messages about the optimization process. By default, --verbose is enabled, which prints a message when the image is successfully optimized. You can disable this by using --no-verbose. Default value: --verbose (enabled by default)

Usage:
Verbose output (enabled):

image-optimizer input_image.jpg output_image.jpg --verbose

This will display a success message after the image is optimized.

No verbose output (disabled):

image-optimizer input_image.jpg output_image.jpg --no-verbose

This will suppress any messages during the optimization process.

Supported formats

Currently, the supported formats are .jpg, .jpeg, and .png.

Note

If you try to optimize a GIF file using image-optimizer, the animation will be lost. I may add support for GIF optimization (keeping the animation) in the future.

Running tests

To ensure that everything is working correctly, you can run the provided unit tests. These tests validate that the image-optimizer behaves as expected in various scenarios (e.g., handling valid and invalid input files).

Steps to run tests

Open your terminal in the root project folder. Then run the following command to execute all the tests:

python -m unittest discover

This command will automatically discover and run all test files in the project.

Example output

If the tests pass, you should see output like this:

....
----------------------------------------------------------------------
Ran 4 tests in 0.015s

OK

Author

This project was made with ❤️ by mayraamaral.