# 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 ```sh sudo apt install python3.10 ``` * virtualenv for virtual enviroment creation ```sh pip install virtualenv ``` # Installation 1. Go to a suitable installation folder, for example Documents. ```sh cd /home/username/Documents ``` 2. Clone the repository to a suitable directory, for example ```sh git clone ssh://git@strade.fit.vutbr.cz:3022/xlanro00/BP_DP-xlanro00.git ``` 3. Go inside cloned directory ```sh cd BP_DP-xlanro00 ``` 4. Create and enter the virtual enviroment. ```sh virtualenv .venv && source .venv/bin/activate ``` 5. Install required python modules from requirements.txt. ```sh 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. ```sh 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 ```sh 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 ``` 2. from preset saved in a json config file, that can be used to tune and modify existing presetrs, or create new ones ```sh 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 

{
    "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 ```sh 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 ```sh 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 ```sh python3 src/main.py input_file output_file dpi --config config_file preset --stl p height_line height_base ``` * Working example planar stl generation ```sh 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 ```sh 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 - [x] Load and store image - [x] Apply basic image processing filters - [X] Scale the image using given dpi - [X] Create filter library with more filters - [X] Add more suitable filters to the library - [x] Use presets from config files - [ ] Add the option to save current filter preset to config file - [X] Add the option to modify filter parameters - [X] Convert the processed image to stl lithophane - [X] 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 - [X] 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](https://strade.fit.vutbr.cz/git/xlanro00/BP_DP-xlanro00)