"""! @file main.py @brief Main file for the application @author xlanro00 """ # Import basic libraries import argparse as ap import sys import json from os.path import exists # Libraries for image processing import numpy as np import matplotlib.pyplot as plt #from PIL import Image import cv2 as cv from stl import mesh import math # Import custom image filter library import filters as flt class app: def __init__(self): # Parse arguments from command line self.parse_arguments() # List and dict for filters and corresponding parameters self.filters = [] self.params = {} # Parse configuration from json config file if self.args.config: self.config_file, self.preset_name = self.args.config self.config = json.load(open(self.config_file)) self.parse_conf() elif self.args.filters: print("No config file given, using command line arguments") i = 0 # Otherwise expect filters from command line for filter in self.args.filters: if filter.find('=') == -1: # if no '=' char in filter, it is a new filter name self.filters.append(filter) i += 1 self.params[i] = {} # create empty dict for params else: # else it's a parameter for current filter key, value = filter.split('=') self.params[i][key] = value self.parse_params(self.params[i]) else: print("No filters given, saving original image") self.input_file = self.args.input_file self.output_file = self.args.output_file self.dpi = self.args.dpi self.mirror = True if self.args.mirror else False if exists(self.input_file): self.run_filtering() else: self.error_exit("Input file " + self.input_file + " does not exist") if self.args.stl_file: # Get stl filename self.stl_file = self.args.stl_file[0] # Get mode and model parameters if self.args.planar: self.mode = "2d" if len(self.args.stl_file) < 3: self.height_base = 10 self.height_line = 2 print( "Warning: Too few arguments, using default values (10mm base, 2mm lines)") else: self.height_line = float(self.args.stl_file[1]) self.height_base = float(self.args.stl_file[2]) print("Base height:", self.height_base, "mm, lines depth/height:", self.height_line, "mm") else: self.mode = "3d" if len(self.args.stl_file) < 4: self.height_line = 2 self.curv_rate_x = 0.5 self.curv_rate_y = 0.5 print( "Warning: Too few arguments, using default values (2mm lines, curvature 0.5 on x, 0.5 on y)") else: self.height_line = float(self.args.stl_file[1]) self.curv_rate_x = float( self.args.stl_file[2]) # finger depth self.curv_rate_y = float( self.args.stl_file[3]) # finger depth # self.curv_rate_x = float(self.args.stl_file[2]) # excentricity x # self.curv_rate_y = float(self.args.stl_file[3]) # excentricity y print("Line height:", self.height_line,"mm, x axis curvature:", self.curv_rate_x, ", y axis curvature:", self.curv_rate_y) print(self.mode, "mode selected") self.run_stl() def parse_arguments(self): '''Parse arguments from command line using argparse library. ''' parser = ap.ArgumentParser(prog='main.py', description='Program for processing a 2D image into 3D fingerprint.', usage='%(prog)s [-h] [-m | --mirror | --no-mirror] [-p] input_file output_file dpi ([-c config_file preset | --config config_file preset] | [filters ...]) [-s stl_file | --stl stl_file height_line height_base | --stl_file stl_file height_line curv_rate_x curv_rate_y]') # positional arguments parser.add_argument("input_file", type=str, help="input file path") parser.add_argument("output_file", type=str, help="output file path") parser.add_argument("dpi", type=int, help="dpi of used scanner") # boolean switch argument parser.add_argument('-m', '--mirror', type=bool, action=ap.BooleanOptionalAction, help="switch to mirror input image") # another boolean switch argument, this time with value, name of the new file and dimensions parser.add_argument('-s', '--stl_file', type=str, nargs='*', help="create stl model from processed image") # another boolean switch argument, this enables 2d mode parser.add_argument('-p', '--planar', type=bool, action=ap.BooleanOptionalAction, help="make stl shape planar instead of curved one") # configuration file containing presets, preset name # pair argument - give both or none parser.add_argument('-c', '--config', nargs=2, help='pair: name of the config file with presets, name of the preset') # array of unknown length, all filter names saved inside parser.add_argument('filters', type=str, nargs='*', help="list of filter names and their parameters in form [filter_name1 param1=value param2=value filter_name2 param1=value...]") self.args = parser.parse_args() def parse_params(self, params): '''Parse parameters of filters. Set to None if not given. They are later set to default values in the filter method apply. ''' # TODO: possibly too bloated, sending all possible params to each filter possible_params = {"h", "searchWindowSize", "templateWindowSize", "ksize", "kernel", "sigmaX", "sigmaY", "sigmaColor", "sigmaSpace", "d", "anchor", "iterations", "op", "strength", "amount", "radius", "weight", "channelAxis", "theta", "sigma", "lambda", "gamma", "psi"} for key in possible_params: if params.get(key) is None: params[key] = None else: params[key] = params[key] def parse_conf(self): '''Parse configuration file if one was given. Store filters and their parameters. ''' # Find preset in config file if self.preset_name in self.config: filter_array = self.config[self.preset_name] # Iterate over filters in preset, store them and their parameters for i, filter in enumerate(range(len(filter_array)), start=1): self.filters.append(filter_array[filter]["name"]) self.params[i] = {} for attribute, value in filter_array[filter].items(): # Filter name isn't needed in here if attribute != "name": self.params[i][attribute] = value self.parse_params(self.params[i]) print("Loaded preset: " + self.preset_name + " from file: " + self.config_file) else: self.error_exit("Preset not found") def error_exit(self, message): '''Print error message and exit. ''' print("ERROR:", message, file=sys.stderr) exit(1) #------------------------- FILTERING -------------------------# def run_filtering(self): '''Load from input file, store as numpy.array, process image using filters and save to output file. ''' self.img = cv.imread( self.input_file, cv.IMREAD_GRAYSCALE).astype(np.uint8) self.height, self.width = self.img.shape print("Height: " + str(self.height) + " px and width: " + str(self.width) + " px", file=sys.stderr) size = (self.width/self.dpi, self.height/self.dpi) fig = plt.figure(figsize=size, frameon=False, dpi=self.dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) if self.mirror is True: self.mirror_image() # Apply all filters and save image self.apply_filters() self.save_image(fig, ax) plt.close() def mirror_image(self): '''Mirror image using opencv, should be used if we want a positive model. ''' print("Mirroring image", file=sys.stderr) self.img = cv.flip(self.img, 1) # 1 for vertical mirror def apply_filters(self): '''Apply filters to image one by one. In case none were given, pass and save original image to the output file. ''' if len(self.filters) == 0: pass else: # Apply all filters for i, filter_name in enumerate(self.filters): # Get filter class from filter.py, use the apply method filter = getattr(flt, filter_name) filter.apply(self, self.params[i+1]) def save_image(self, fig, ax): '''Save processed image to the output file. ''' print("Saving image to", self.output_file, file=sys.stderr) # Colormap must be set to grayscale to avoid color mismatch. ax.imshow(self.img, cmap="gray") fig.savefig(fname=self.output_file, dpi=self.dpi) #------------------------- STL GENERATION -------------------------# def run_stl(self): '''Make heightmap, create mesh and save as stl file. ''' self.prepare_heightmap() if self.mode == "2d": self.make_stl_planar() elif self.mode == "3d": self.make_stl_curved() else: self.error_exit("Mode not supported") plt.show() print(f"Saving model to ", self.stl_file, file=sys.stderr) self.save_stl() def prepare_heightmap(self): '''Modify image values to get usable height/depth values. Check validity of dimension parameters. ''' # TODO: redo, too complicated, add extra params, redo checks if self.img.dtype == np.float32 or self.img.dtype == np.float64: print("Converting to uint8", file=sys.stderr) self.img = self.img * 255 self.img = self.img.astype(np.uint8) print("Creating mesh", file=sys.stderr) if self.mode == "2d": if self.height_base <= 0: self.error_exit("Depth of plate height must be positive") if self.height_line + self.height_base <= 0: self.error_exit("Line depth must be less than plate thickness") # Transform image values to get a heightmap self.img = (self.height_base + (1 - self.img/255) * self.height_line) if self.mode == "3d": # TODO check curvature values and print info # TODO: copy pasta code, remove # Transform image values to get a heightmap self.img = (1 - self.img/255) * self.height_line def append_faces(self, faces, c): # Function to add faces to the list faces.append([c, c + 1, c + 2]) faces.append([c + 1, c + 3, c + 2]) return c + 4 def make_stl_planar(self): '''Create mesh from meshgrid. Create vertices from meshgrid, add depth values from image. Create faces from vertices. Add vectors and faces to the model. From wikipedia.org/wiki/STL_(file_format): ascii stl format consists of repeating structures: facet normal ni nj nk # normal vector outer loop vertex v1x v1y v1z # vertex 1 vertex v2x v2y v2z # vertex 2 vertex v3x v3y v3z # vertex 3 endloop endfacet ''' # This sets the size of stl model and number of subdivisions / triangles x = np.linspace(0, self.width * 25.4 / self.dpi, self.width) y = np.linspace(0, self.height * 25.4 / self.dpi, self.height) self.meshgrid_2d = np.meshgrid(x, y) # Add the image matrix to the 2D meshgrid and create 1D array of 3D pointsd vertex_arr = np.vstack(list(map(np.ravel, self.meshgrid_2d))).T z = (self.img / 10).reshape(-1, 1) vertex_arr = np.concatenate((vertex_arr, z), axis=1) # Convert 1D array back to matrix of 3D points vertex_arr = vertex_arr.reshape(self.height, self.width, 3) count = 0 vertices = [] faces = [] # Iterate over all vertices, create faces for i in range(self.height - 1): for j in range(self.width - 1): vertices.append([vertex_arr[i][j]]) vertices.append([vertex_arr[i][j+1]]) vertices.append([vertex_arr[i+1][j]]) vertices.append([vertex_arr[i+1][j+1]]) count = self.append_faces(faces, count) # Add faces for the backside of the lithophane null_arr = np.copy(vertex_arr) for i in range(self.height): for j in range(self.width): null_arr[i][j][2] = 0 # Back side faces for i in range(self.height - 1): for j in range(self.width - 1): vertices.append([null_arr[i][j]]) vertices.append([null_arr[i+1][j]]) vertices.append([null_arr[i][j+1]]) vertices.append([null_arr[i+1][j+1]]) count = self.append_faces(faces, count) # Horizontal side faces for i in range(self.height - 1): vertices.append([vertex_arr[i][0]]) vertices.append([vertex_arr[i+1][0]]) vertices.append([null_arr[i][0]]) vertices.append([null_arr[i+1][0]]) count = self.append_faces(faces, count) max = self.width - 1 vertices.append([vertex_arr[i+1][max]]) vertices.append([vertex_arr[i][max]]) vertices.append([null_arr[i+1][max]]) vertices.append([null_arr[i][max]]) count = self.append_faces(faces, count) # Vertical side faces for j in range(self.width - 1): vertices.append([vertex_arr[0][j+1]]) vertices.append([vertex_arr[0][j]]) vertices.append([null_arr[0][j+1]]) vertices.append([null_arr[0][j]]) count = self.append_faces(faces, count) max = self.height - 1 vertices.append([vertex_arr[max][j]]) vertices.append([vertex_arr[max][j+1]]) vertices.append([null_arr[max][j]]) vertices.append([null_arr[max][j+1]]) count = self.append_faces(faces, count) # Convert to numpy arrays faces = np.array(faces) vertices = np.array(vertices) # Create the mesh - vertices.shape (no_faces, 3, 3) self.stl_lithophane = mesh.Mesh( np.zeros(faces.shape[0], dtype=mesh.Mesh.dtype)) for i, face in enumerate(faces): for j in range(3): self.stl_lithophane.vectors[i][j] = vertices[face[j], :] def make_stl_curved(self): '''Map fingerprint to finger model. ''' # TODO: if this is the same as 2D, move to heightmap to reduce duplicate code x = np.linspace(0, self.width * 25.4 / self.dpi, self.width) y = np.linspace(0, self.height * 25.4 / self.dpi, self.height) self.meshgrid_3d = np.meshgrid(x, y) # Method 1 - logspace and logarithmic curve '''z1 = np.logspace(0, 10, int(np.ceil(self.width / 2)), base=0.7) z2 = np.logspace(10, 0, int(np.floor(self.width / 2)), base=0.7) ztemp = 5*np.concatenate((z1, z2)) z = np.array([]) for i in range(self.height): z = np.concatenate((z, ztemp + 25*(((i+50)/20)**(-1/2)))) z = z.reshape(-1, 1) self.img = (self.img / 10).reshape(-1, 1) z += self.img''' # Method 2 - 2 ellipses z = np.array([]) for x in range(self.width): z = np.append(z, np.sqrt(1 - (2*x/self.width - 1)**2) * (self.curv_rate_x**2)) z = np.tile(z, (self.height, 1)) for y in range(self.height): new = np.sqrt((1 - ((self.height - y)/self.height)**2) * (self.curv_rate_y**2)) z[y] = z[y] + new # TODO: clip responsivelly bottom = z[0][math.floor(self.width/2)] #top = self.curv_rate_x**2 + self.curv_rate_y #np.clip(z, bottom, top, out=z) z = z.reshape(-1, 1) self.img = (self.img / 10).reshape(-1, 1) z += self.img vertex_arr = np.vstack(list(map(np.ravel, self.meshgrid_3d))).T vertex_arr = np.concatenate((vertex_arr, z), axis=1) vertex_arr = vertex_arr.reshape(self.height, self.width, 3) count = 0 vertices = [] faces = [] min_point = 0 for i in range(self.height - 1): if vertex_arr[i][0][2] <= bottom: min_point = i # Add faces for the backside of the lithophane vec_side = (vertex_arr[self.height-1][0][2] - vertex_arr[min_point][0][2]) / (self.height - min_point) null_arr = np.copy(vertex_arr) for i in range(self.height): for j in range(self.width): null_arr[i][j][2] = 0 #null_arr[i][j][2] = bottom + vec_side * i # for smaller mesh # Iterate over all vertices, create faces for i in range(self.height - 1): for j in range(self.width - 1): if (vertex_arr[i][j][2] <= null_arr[i][j][2] or vertex_arr[i+1][j][2] <= null_arr[i+1][j][2] or vertex_arr[i][j+1][2] <= null_arr[i][j+1][2] or vertex_arr[i+1][j+1][2] <= null_arr[i+1][j+1][2]): continue vertices.append([vertex_arr[i][j]]) vertices.append([vertex_arr[i][j+1]]) vertices.append([vertex_arr[i+1][j]]) vertices.append([vertex_arr[i+1][j+1]]) count = self.append_faces(faces, count) # Rotated back side faces for i in range(self.height - 1): for j in range(self.width - 1): if (vertex_arr[i][j][2] <= null_arr[i][j][2]): continue vertices.append([null_arr[i][j]]) vertices.append([null_arr[i+1][j]]) vertices.append([null_arr[i][j+1]]) vertices.append([null_arr[i+1][j+1]]) count = self.append_faces(faces, count) # Horizontal side faces for i in range(self.height - 1): # right #if (vertex_arr[i][0][2] < null_arr[i][0][2]): # continue vertices.append([vertex_arr[i][0]]) vertices.append([vertex_arr[i+1][0]]) vertices.append([null_arr[i][0]]) vertices.append([null_arr[i+1][0]]) count = self.append_faces(faces, count) for i in range(self.height - 1): # left max = self.width - 1 #if (vertex_arr[i][max][2] < null_arr[i][max][2]): # continue vertices.append([vertex_arr[i+1][max]]) vertices.append([vertex_arr[i][max]]) vertices.append([null_arr[i+1][max]]) vertices.append([null_arr[i][max]]) count = self.append_faces(faces, count) # Vertical side faces for j in range(self.width - 1): # top #if (vertex_arr[0][j][2] < null_arr[0][j][2]): # continue vertices.append([vertex_arr[0][j+1]]) vertices.append([vertex_arr[0][j]]) vertices.append([null_arr[0][j+1]]) vertices.append([null_arr[0][j]]) count = self.append_faces(faces, count) for j in range(self.width - 1): # bottom max = self.height - 1 #if (vertex_arr[max][j][2] < null_arr[max][j][2]): # continue vertices.append([vertex_arr[max][j]]) vertices.append([vertex_arr[max][j+1]]) vertices.append([null_arr[max][j]]) vertices.append([null_arr[max][j+1]]) count = self.append_faces(faces, count) # Convert to numpy arrays faces = np.array(faces) vertices = np.array(vertices) # Create the mesh - vertices.shape (no_faces, 3, 3) self.mesh_finger = mesh.Mesh( np.zeros(faces.shape[0], dtype=mesh.Mesh.dtype)) for i, face in enumerate(faces): for j in range(3): self.mesh_finger.vectors[i][j] = vertices[face[j], :] # print(self.mesh_finger.normals) def save_stl(self): '''Save final mesh to stl file. ''' if self.mode == "3d": self.mesh_finger.save(self.stl_file) else: self.stl_lithophane.save(self.stl_file) # run the application image = app()