Automated T-Shirt Mockup Generator

This project is a collection of Python scripts that automate the process of generating mockups by overlaying design images on predefined areas in mockup templates. It works with simple or complex mockup images including those where the placeholder box is rotated.

The script first identifies a target box within reference mockup images by looking for a specific color (e.g., black). It calculates the position, size, and rotation of this box relative to the overall image, and saves these details for multiple mockups in a JSON file.

Then, using these parameters, the script places design images onto real mockups (i.e., without target boxes). It scales and rotates the design according to each mockup's specific parameters, ensuring a real-life appearance. The top of the design aligns with the top of the box, and it is horizontally centered for a seamlessly fitting design in the mockup.

The tool takes two directories as input: one with the design images and the other with the mockup templates. It generates a new image for every combination of design and mockup, storing mockups in an output directory.

This tool is extremely helpful for designers, marketers, or anyone who needs to quickly generate a large number of mockups for design presentations, marketing collateral or e-commerce listings.

Directories Tree

Here is the structure of the project directory:

/ShirtMockupGenerator
│   calculate_box_pos.py
│   create_mockups.py
│   README.md
│   requirements.txt
|   LICENSE
|   parameters.json
│
└───images
│   │   mockups
│   │   designs
│   │   output
|   |   box_mockups

Getting Started

Follow these instructions to get the Shirt Mockup Generator running on your local machine.

Prerequisites

You should have the following installed:

Python 3.8 or later
Pillow
cv2 (OpenCV)
numpy
scikit-image

Installing

Install the required Python dependencies using pip:

pip install -r requirements.txt

Usage

Step 1: Prepare Your Files and Directories

Prepare your directories for your design images, mockup images, and outputs (if they don't yet exist). Also, prepare your design images and place color boxes where you want your designs to be positioned on your mockup images. You can use any available software (e.g. Photoshop, GIMP). The color of the box should be unique and not utilized elsewhere in your images. Then export the mockups to a different directory but with the same name.

Your directory structure should look like this:

my_directory
├── designs
│   ├── design1.png
│   ├── design2.jpg
│   └── ...
├── mockups
│   ├── mockup1.png
│   ├── mockup2.jpg
│   └── ...
├── box_mockups
│   ├── mockup1.png
│   ├── mockup2.jpg
│   └── ...
└── output

Step 2: Calculate Parameters

The second step is to calculate the bounding box parameters for your mockup images. It identifies a specific color in your image (for example, a black box), and calculates the position, size, and rotation of this box.

Run the calculate_box_pos.py script by passing the RGB or HEX color, and directory for the mockup images as command-line arguments:

python calculate_box_pos.py --color R G B --dir image_directory

or

python calculate_box_pos.py --hex_color HEX --dir image_directory

Replace R G B with the RGB values of the color in your image and HEX with the Hexadecimal value of the color. Replace image_directory with the directory that holds your mockup images. This will generate a parameters.json file with the calculated parameters.

Step 3: Create Mockups

In the third step, use the calculated parameters to position your designs onto the product mockups.

Run the create_mockups.py script by passing the paths to the parameters file, design images directory, mockup images directory, and the output directory as command-line arguments:

python create_mockups.py --param_file parameters.json --design_dir design_directory --mockup_dir mockup_directory --output_dir output_directory

Replace design_directory, mockup_directory, and output_directory with the corresponding directory paths.

Example:

python calculate_box_pos.py --color 0 0 0 --dir box_mockups
python create_mockups.py --param_file parameters.json --design_dir designs --mockup_dir mockups --output_dir output

or

python calculate_box_pos.py --hex_color #000000 --dir box_mockups
python create_mockups.py --param_file parameters.json --design_dir designs --mockup_dir mockups --output_dir output

In the above examples, the scripts will look for black color (0, 0, 0 in RGB or #000000 in HEX format) in images located in the box_mockups directory. The positioned images will then be saved in the output directory.

File Descriptions

calculate_box_pos.py: This script analyzes the base mockup images and calculates the parameters necessary for placing the user's shirt design properly on each mockup.
create_mockup.py: This script takes user design images and places them onto the base mockups using the parameters computed with calculate_box_pos.py.
images/mockups: Contains the base mockup images.
images/designs: Contains the user's shirt design images.
images/output: Stores the generated mockups.
images/parameters.json: Holds the computed parameters for each mockup.

License

This project is licensed under the MIT License.

Donate

If you find this project useful, you can show your appreciation and support further development by making a donation:

PayPal - You can donate through PayPal here.
Bitcoin - Send your donations to the following Bitcoin address: 0x6746eD80f13a4dE4920B88CEF0E1597A9c6775CC.

Any amount is appreciated and will help continue the ongoing development of this project!

License

lmsutools/automated_mockups

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