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About this Project

This project is being developed as a practical part of bachelor's thesis at Brno Universtiy of Technology - Faculty of Information Studies. It is as of now a work in progress, no results guaranteed.

The topic of this thesis is Generating a 3D Fingerprint Model. This application can be used to apply series of image processing filters to a fingerprint image to make it more suitable for conversion to 3D stl model and printing. It also includes the functionality to use generated image as a height map for generating an stl model. This model can either be planar or curved.

Prerequisites

For now this is only viable for ubuntu gnu/linux machines Before cloning repository, you need these to succesfully use the application.

python version 3.10 is a requirement might work on earlier python 3 versions
```
sudo apt install python3.10
```
virtualenv for virtual enviroment creation
```
pip install virtualenv
```

Installation

Go to a suitable installation folder, for example Documents.
```
cd /home/username/Documents
```

Clone the repository to a suitable directory, for example

git clone ssh://git@strade.fit.vutbr.cz:3022/xlanro00/BP_DP-xlanro00.git

Go inside cloned directory
```
cd BP_DP-xlanro00
```

Create and enter the virtual enviroment.

virtualenv .venv && source .venv/bin/activate

Install required python modules from requirements.txt.
```
pip install -r requirements.txt
```
Run the application, as an example there is a file in res/examples called Palec_P4.tif. This is shown in the section below.

Filtering images

You will need to enter the virtual enviroment every time you want to use the application.
```
source .venv/bin/activate
```

Once all the requirements are installed, the program is ready to use. There are two ways to enter the filters:

manually list filter names and parameters from command line

python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_shell.png 600 total_variation weight=0.15 median ksize=5

from preset saved in a json config file, that can be used to tune and modify existing presetrs, or create new ones

python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_preset.png 600 --config config/config.json git_example

Configuration and presets

There is an option to input the filter series as a preset from json configuration file.

General format Woking example

General format	Woking example
`{ "preset": [ { "name": "filter_name", "parameter": value, "parameter": value }, { "name": "filter_name", "parameter": value } ], "preset": [ ... ] ... }`	`{ "git_example": [ { "name": "denoise_tv_chambolle", "weight": 0.01, "iterations": 1 }, { "name": "median", "ksize": 3 } ] }`


{
    "preset": [
        {
            "name": "filter_name",
            "parameter": value,
            "parameter": value
        },
        {
            "name": "filter_name",
            "parameter": value
        }
    ],
    "preset": [
        ...
    ]
    ...
}


{
    "git_example": [
        {
            "name": "denoise_tv_chambolle",
            "weight": 0.01,
            "iterations": 1
        },
        {
            "name": "median",
            "ksize": 3
        }
    ]
}

All the filters used and their parameters are described below.

Available filters with parameters

median blur
- ksize - Kernel size (int)
gaussian blur
- sigma - Gaussian kernel standart deviation (int)
bilateral blur
- diameter - Diameter of pixel neighborhood used for filtering (int)
- sigmaColor - Standard deviation for grayvalue/color distance (int)
- sigmaSpace - Standard deviation for range distance in pixels (int)
bilateral_scikit
- sigmaColor - Standard deviation for grayvalue/color distance (float)
- sigmaSpace - Standard deviation for range distance in pixels (float)
nlmeans (non-local means)
- patch_size - Size of patches used for denoising (int)
- patch_distance - Distance in pixels where to search for patches (int)
- h - Cut-off distance, higher means more smoothed image (float)
total_variation
- weight - Denoising weight. (float)
block_match
- sigma - ? (?)
unsharp mask scikit
- radius - Radius of the gaussian filter (int)
- amount - Strength of the unsharp mask (float)
farid
meijering
sato
hessian
- sigmas - ? (float)
invert
scale_values
binarize
- threshold - value to cut differentiate pixels (int)
- maxval - maximal value (int) ??
- type - ? (str)
binarize_otsu
add_margin
- margin - number of pixels to add to the sides of the image (int)
- color - color value of newly added pixels (int)
erode
- kernel - kernel shape (numpy.matrix)
dilate
- kernel - kernel shape (numpy.matrix)

Comparison

Image before processing the fingerprint and after applying a presets.

Before	After

Generating curved finger model

It is possible to generate 3D printable stl model using --stl switch, which requires aditional parameter containing stl filename. In base mode the output model will be a curved finger model, with optional parameters following the filename controlling its shape. First optional parameter is papilar line height, second rate of curvature along x axis and the third is the rate of curvature along y axis.

General form for curved stl generation

python3 src/main.py input_file output_file dpi --config config_file preset --stl c height_line height_base curvature_x curvature_y

Working example curved stl generation

python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_preset.png 600 --config config/config.json git_example --stl c 2 10 2 2

Generating planar finger model

Using -p switch makes the generated model planar. Optional parameters are model base thickness and papilar lines height, they are set after stl file name.

General form for planar stl generation

python3 src/main.py input_file output_file dpi --config config_file preset --stl p height_line height_base

Working example planar stl generation

python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_preset.png 600 --config config/config.json git_example --stl p 2 10

Mapping to existing finger model

This section will be added later, (if implemented) mapping of fingerprint to a given finger model.

General form for planar stl generation

python3 src/main.py input_file output_file dpi --config config_file preset --stl m height_line height_base finger_file

Usage

Program for processing a 2D image into 3D fingerprint.

positional arguments: input_file input file path output_file output file path dpi dpi of used scanner filters list of filter names and their parameters in form [filter_name1 param1=value param2=value filter_name2 param1=value...]

options: -h, --help show this help message and exit -m, --mirror, --no-mirror switch to mirror input image -s [STL_FILE ...], --stl_file [STL_FILE ...] create stl model from processed image -c CONFIG CONFIG, --config CONFIG CONFIG pair: name of the config file with presets, name of the preset

Roadmap

Load and store image
Apply basic image processing filters
- Scale the image using given dpi
Create filter library with more filters
- Add more suitable filters to the library
Use presets from config files
- Add the option to save current filter preset to config file
Add the option to modify filter parameters
Convert the processed image to stl lithophane
Add the option to curve the lithophane into the shape of a finger
Add the option to map the fingerprint onto a given finger model
Export final model ready for 3D print

Author

Rostislav Lán - xlanro00@stud.fit.vutbr.cz

Supervisor

Ing. Petr Malaník

Links

Project Link: https://strade.fit.vutbr.cz/git/xlanro00/BP_DP-xlanro00

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