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

1. Go to a suitable installation folder, for example Documents.

   cd /home/username/Documents
   

2. Clone the repository to a suitable directory, for example

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

3. Go inside cloned directory

   cd BP_DP-xlanro00
   

4. Create and enter the virtual enviroment.

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

5. Install required python modules from requirements.txt.

   pip install -r requirements.txt
   

6. 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:

1. manually list filter names and parameters from command line

   python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_cline.png 600 total_variation weight=0.15 median ksize=5
   

2. 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. This preset is automatically stored inside a json file, which serves as a database for storing filters. This prevents losing filter preset information when modifying filter which was used to generate 3D models.

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 } ] }

There is also an option to save current command line setting as a preset using -d switch:

• General command for saving filter preset

  python3 src/main.py input_file output_file dpi -d new_preset_name filters
  

• Working example

  python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_cline.png 600 -d preset_gaussian gaussian sigma=1
  

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

usage: main.py [-h] [-m | --mirror | --no-mirror] input_file output_file dpi ([-c | --config config_file preset] | [filters ...]) [-s | --stl_file p height_line height_base | --stl_file c height_line height_base curv_rate_x curv_rate_y | --stl_file m height_line height_base finger_file]

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