"""Main file of the project, contains filtering and stl generation functions .. moduleauthor:: xlanro00 """ # Import basic libraries import argparse as ap from os.path import exists import hashlib # Libraries for image processing import numpy as np import matplotlib.pyplot as plt import cv2 as cv from stl import mesh import trimesh import trimesh.transformations as tmtra import trimesh.remesh as tmrem # Import custom image filter library import filters as flt import config_parser as cp import log import math class fingerprint_app: '''Main class for the application. ''' 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 cp.parse_conf(self.preset_name, self.filters, self.params, self.config_file) elif self.args.filters: filter_index = 0 log.print_message( "No config file given, using command line arguments") # Otherwise expect filters from command line for filter_part in self.args.filters: # If no '=' char in filter, it is a new filter name if filter_part.find('=') == -1: self.filters.append(filter_part) filter_index += 1 # create empty dict for params self.params[filter_index] = {} # Otherwise it's a parameter for current filter else: key, value = filter_part.split('=') self.params[filter_index][key] = value cp.parse_params(self.params[filter_index]) # If database flag is set, save filters to database as a new preset if self.args.database: cp.save_preset(self.filters, self.params, self.args.database[0]) else: log.print_message("No filters given, saving original image") # Set input and output file paths, dpi and mirror flag for easier readability 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: log.error_exit("Input file " + self.input_file + " does not exist") if self.args.stl: self.parse_stl() def parse_arguments(self): '''Parse arguments from command line using argparse library. ''' parser = ap.ArgumentParser(prog='main.py', description='Program for transforming a 2D image into 3D fingerprint.', usage='%(prog)s [-h] [-m | --mirror | --no-mirror] input_file output_file dpi ([-c | --config config_file preset] | [filters ...]) [-s | --stl p height_line height_base | --stl c height_line curv_rate_x curv_rate_y | --stl m height_line iter finger_x finger_y finger_z] [-d | --database database_filename]') # 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", type=str, nargs='*', help="create stl model from processed image") # 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...]") parser.add_argument('-d', '--database', nargs=1, help='switch to store presets in config database') self.args = parser.parse_args() def parse_stl(self): '''Parse arguments for stl generation. ''' # Get stl filename self.stl_path = self.output_file.rsplit('/', 1)[0] + '/' self.mode = self.args.stl[0] log.print_message("Stl generation in", self.mode, "mode") # Default values for stl generation parameters def_val = {"hl": 2, "hb": 10, "crx": 2, "cry": 2, "it": 2, "fx": 0, "fy": 0, "fz": 0, "f": "res/finger_backup/finger-mod.stl"} # Get mode and model parameters if self.mode == 'p': self.height_line = float(self.args.stl[1]) if len( self.args.stl) > 1 else def_val.get("hl") self.height_base = float(self.args.stl[2]) if len( self.args.stl) > 2 else def_val.get("hb") if len(self.args.stl) < 3: log.print_message( "Warning: Too few arguments, using some default values") log.print_message("Base height:", self.height_base, "mm, lines depth/height:", self.height_line, "mm") elif self.mode == 'c': self.height_line = float(self.args.stl[1]) if len( self.args.stl) > 1 else def_val.get("hl") self.height_base = float(self.args.stl[2]) if len( self.args.stl) > 2 else def_val.get("hb") self.curv_rate_x = float(self.args.stl[3]) if len( self.args.stl) > 3 else def_val.get("crx") self.curv_rate_y = float(self.args.stl[4]) if len( self.args.stl) > 4 else def_val.get("cry") if len(self.args.stl) < 5: log.print_message( "Warning: Too few arguments, using some default values") log.print_message("Line height:", self.height_line, "mm, base height:", self.height_base, "mm, x axis curvature:", self.curv_rate_x, ", y axis curvature:", self.curv_rate_y) elif self.mode == 'm': self.height_line = float(self.args.stl[1]) if len( self.args.stl) > 1 else def_val.get("hl")/10 self.iter = int(self.args.stl[2]) if len( self.args.stl) > 2 else def_val.get("it") self.finger_x = float(self.args.stl[3]) if len( self.args.stl) > 3 else def_val.get("fx") self.finger_y = float(self.args.stl[4]) if len( self.args.stl) > 4 else def_val.get("fy") self.finger_z = float(self.args.stl[5]) if len( self.args.stl) > 5 else def_val.get("fz") self.finger_name = str(self.args.stl[6]) if len( self.args.stl) > 6 else def_val.get("f") if len(self.args.stl) < 6: log.print_message( "Warning: Too few arguments, using some default values") log.print_message("Line height:", self.height_line, "mm, iterations:", self.iter, ", finger position:", self.finger_x, self.finger_y, self.finger_z, "mm, finger model:", self.finger_name) else: log.error_exit("Unrecognized generation mode") self.run_stl() # ------------------------- FILTERING -------------------------# def run_filtering(self): '''Read input file, store as numpy.array, uint8, grayscale. Call function to apply the filters and a function to save it to output file. ''' self.img = cv.imread( self.input_file, cv.IMREAD_GRAYSCALE).astype(np.uint8) # Gets empty figure and ax with dimensions of input image self.height, self.width = self.img.shape self.fig, ax = self.get_empty_figure() if self.mirror is True: self.mirror_image() # Apply all filters and save image self.apply_filters() self.save_image(self.fig, ax) plt.close() def get_empty_figure(self): '''Return empty figure with one ax, which has dimensions of the input image. ''' 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) return fig, ax def mirror_image(self): '''Mirror image using opencv. Should be used to cancel implicit mirroring. ''' log.print_message("Mirroring image") 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: for i, filter_name in enumerate(self.filters): # Get filter class from filter.py, use the apply method try: filter = getattr(flt, filter_name) except AttributeError: log.error_exit("Filter " + filter_name + " not found") log.print_message("Applying filter:", filter_name) for param in self.params[i+1]: if self.params[i+1][param] is not None: log.print_message("\twith parameter", param, "=", str(self.params[i+1][param])) filter.apply(self, self.params[i+1]) else: pass def save_image(self, fig, ax): '''Save processed image to the output file. :param fig: figure used to render image. :param ax: Ax used to render image. ''' log.print_message("Saving image to", self.output_file) # Colormap must be set to grayscale to avoid color mismatch. ax.imshow(self.img, cmap="gray") fig.savefig(fname=self.output_file) # ------------------------- STL GENERATION -------------------------# def run_stl(self): '''Choose correct generation code based on mode. ''' self.prepare_heightmap() # create ID for the model from all its parameters self.get_ID() # Create a mesh using one of two modes if self.mode == "p": self.make_stl_planar() elif self.mode == "c": self.make_stl_curved() elif self.mode == "m": # Load the finger model self.finger = trimesh.load(self.finger_name) self.make_stl_map() plt.show() self.save_stl() def prepare_heightmap(self): '''Scale image values to get values from 0 to 255. Check validity of dimension parameters. Then compute base and papilar lines height. Prepare meshgrid, array which later serves to store point coordinates. ''' if self.img.dtype != np.uint8: log.print_message("Converting heightmap to uint8") self.img = self.img / np.max(self.img) * 255 self.img = self.img.astype(np.uint8) if self.mode == "p": if self.height_base <= 0: log.error_exit("Depth of plate height must be positive") if self.height_line + self.height_base <= 0: log.error_exit("Line depth must be less than plate thickness") if self.mode == "c": # Don't need to check curvature, check only heights if self.height_base <= 0 or self.height_line <= 0: log.error_exit("Base and line height must both be positive") if self.mode == "m": if self.height_line <= 0: log.error_exit("Line height must be positive") if self.iter < 0: log.error_exit( "Number of iterations must be positive orr zero") self.height_base = 0 # Transform image values to get a heightmap self.img = (self.height_base + (1 - self.img/255) * self.height_line) # 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 = np.meshgrid(x, y) def write_stl_header(self): '''Write parameter string to stl header. This header is 80 bytes long, so the data needs to be shortened to fit. If the parameter string is too long, a warning is printed and the data is truncated. ''' # Truncate if necessary if (len(self.param_string) >= 80): self.param_string = self.param_string[:80] log.print_message("Warning: Parameter string too long, truncating") # Overwrite stl header (which is only 80 bytes) log.print_message("Writing info to stl header") with open(self.stl_filename, "r+") as f: f.write(self.param_string) def get_ID(self): '''Get a unique ID for the model, which is used in filename and on the model backside. Also create parameter string for stl header, which is used to create ID using hash function MD5. ''' # these are the same for all types of models param_list = [self.input_file, str(self.dpi)] # add parameters specific to the model creation process if self.args.config: param_list.extend([self.config_file, self.preset_name]) else: # add filters with their params filter_list = [] for i in range(len(self.filters)): tmp_params = [] for j in self.params[i+1]: if self.params[i+1][j] != None: tmp_params.append( str(j[:1] + ":" + str(self.params[i+1][j]))) tmp_params = ",".join(tmp_params) tmp = str(self.filters[i][0:1] + self.filters[i][-1:]) if tmp_params != "": tmp = tmp + ";" + str(tmp_params) filter_list.append(tmp) filter_string = ">".join(filter_list) param_list.append(filter_string) # these are the same for all types of models param_list.extend([str(self.height_line), str(self.height_base)]) # add parameters specific to the model type if self.mode == "c": param_list.extend([str(self.curv_rate_x), str(self.curv_rate_y)]) elif self.mode == "m": param_list.extend( [str(self.height_line), str(self.iter), str(self.finger_x), str(self.finger_y), str(self.finger_z), str(self.finger_name)]) param_list.append(self.mode) if self.args.mirror: param_list.append("F") # string that will later be put inside the header of an stl file # fill the rest with the ending char to rewrite any leftover header # this is done for easier parsing of the header self.param_string = "\\".join(param_list) self.param_string = self.param_string + \ "\n" * (80 - len(self.param_string)) # hash the param string to get unique ID, this will be put in filename and on the back of the model # not using built-in hash function because it's seed cannot be set to constant number # don't need to worry about collisions and security, just need a relatively unique ID self.id = str(hashlib.md5( self.param_string.encode('utf-8')).hexdigest())[:10] def append_faces(self, faces, c): '''Add faces to the list of faces. :param faces: Array with faces. :param c: Indices of currently added faces. ''' faces.append([c, c + 1, c + 2]) faces.append([c + 1, c + 3, c + 2]) return c + 4 def engrave_text(self, bottom_vert_arr, top_vert_arr): '''Engrave text on the back of the model. Create an empty image, fill it with color and draw text on it. :param bottom_vert_arr: Bottom vertex array. :param top_vert_arr: Top vertex array ''' fig, ax = self.get_empty_figure() # paint the background black ax.plot([0, 1], [0, 1], c="black", lw=self.width) # extract filename text = self.stl_path.split("/")[-1].split(".")[0] + self.id fontsize = 28 # create text object, paint it white t = ax.text(0.5, 0.5, text, ha="center", va="center", fontsize=fontsize, c="white", rotation=90, wrap=True, clip_on=True) # adjust fontsize to fit text in the image # matplotlib does not support multiline text, wrapping is broken rend = fig.canvas.get_renderer() while (t.get_window_extent(rend).width > self.width or t.get_window_extent(rend).height > self.height): fontsize -= 0.3 t.set_fontsize(fontsize) # update figure, read pixels and reshape to 3d array fig.canvas.draw() data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) plt.close() # scale inscription layer to suitable height data = (data/255)/10 for i in range(self.height): if self.mode == "p": for j in range(self.width): bottom_vert_arr[i][j][2] = data[i][j][0] elif self.mode == "c": for j in range(self.width): bottom_vert_arr[i][j][2] += data[i][j][0] if (bottom_vert_arr[i][j][2] < (top_vert_arr[i][0][2])): bottom_vert_arr[i][j][2] = top_vert_arr[i][0][2] if (bottom_vert_arr[i][j][2] < (top_vert_arr[0][j][2])): bottom_vert_arr[i][j][2] = top_vert_arr[0][j][2] return bottom_vert_arr def create_stl_mesh(self, faces, vertices): '''Create mesh from faces and vertices arrays. :param faces: Vector of face indices :param vertices: Vector of vertices ''' # Convert lists to numpy arrays faces = np.array(faces) vertices = np.array(vertices) c = 0 # Create the mesh - vertices.shape (no_faces, 3, 3) self.stl_model = mesh.Mesh( np.zeros(faces.shape[0], dtype=mesh.Mesh.dtype)) for i, face in enumerate(faces): for j in range(3): self.stl_model.vectors[i][j] = vertices[face[j], :] # Prints out generation progress if i % 100 == 0: percentage = round(i/len(faces) * 100, 2) if percentage > c: log.print_message("Creating model " + str(c) + "%") c += 10 log.print_message("Model creation finished") def create_faces(self, top_vert_arr, bottom_vert_arr): '''Create faces for the model. Iterate over all vertices, append to vector and create faces from indices. :param bottom_vert_arr: Bottom vertex array. :param top_vert_arr: Top vertex array ''' count = 0 vertices = [] faces = [] max_width = self.width - 1 max_height = self.height - 1 # Front side faces and vertices for i in range(self.height - 1): for j in range(self.width - 1): vertices.extend([[top_vert_arr[i][j]], [top_vert_arr[i][j+1]], [top_vert_arr[i+1][j]], [top_vert_arr[i+1][j+1]]]) count = self.append_faces(faces, count) # Back side faces and vertices for i in range(self.height - 1): for j in range(self.width - 1): vertices.extend([[bottom_vert_arr[i][j]], [bottom_vert_arr[i+1][j]], [bottom_vert_arr[i][j+1]], [bottom_vert_arr[i+1][j+1]]]) count = self.append_faces(faces, count) # Horizontal side faces and vertices for i in range(self.height - 1): vertices.extend([[top_vert_arr[i][0]], [top_vert_arr[i+1][0]], [bottom_vert_arr[i][0]], [bottom_vert_arr[i+1][0]]]) count = self.append_faces(faces, count) vertices.extend([[top_vert_arr[i+1][max_width]], [top_vert_arr[i][max_width]], [bottom_vert_arr[i+1][max_width]], [bottom_vert_arr[i][max_width]]]) count = self.append_faces(faces, count) # Vertical side faces and vertices for j in range(self.width - 1): vertices.extend([[top_vert_arr[0][j+1]], [top_vert_arr[0][j]], [bottom_vert_arr[0][j+1]], [bottom_vert_arr[0][j]]]) count = self.append_faces(faces, count) vertices.extend([[top_vert_arr[max_height][j]], [top_vert_arr[max_height][j+1]], [bottom_vert_arr[max_height][j]], [bottom_vert_arr[max_height][j+1]]]) count = self.append_faces(faces, count) return faces, vertices def make_stl_planar(self): '''Create vertices from meshgrid, add z coordinates from processed image heightmap. ''' # Add the image matrix to the 2D meshgrid and create 1D array of 3D points tmp_vert_arr = np.vstack(list(map(np.ravel, self.meshgrid))).T top_vert_arr = np.concatenate( (tmp_vert_arr, (self.img / 10).reshape(-1, 1)), axis=1) # Convert 1D array back to matrix of 3D points top_vert_arr = top_vert_arr.reshape(self.height, self.width, 3) # Prepare image with plotted text for the backside of the lithophane bottom_vert_arr = np.copy(top_vert_arr) # Engrave text on the back of the model self.engrave_text(bottom_vert_arr, top_vert_arr) # Create all vertices, faces faces, vertices = self.create_faces(top_vert_arr, bottom_vert_arr) # Add the created vertices and faces to a mesh self.create_stl_mesh(faces, vertices) def make_stl_curved(self): '''Compute curved surface offset. Create vertices from meshgrid, add z coordinates from processed image heightmap. ''' # Calculate the curved surface values x = np.arange(self.width) y = np.arange(self.height)[:, np.newaxis] x = (2*x / self.width) - 1 z = np.sqrt(1 - x**2) * self.curv_rate_x**2 z = np.tile(z, (self.height, 1)) z *= np.sqrt((1 - ((self.height - y) / self.height)**2) * self.curv_rate_y**2) z = z.reshape(-1, 1) # Make a copy of z for the bottom side z_cpy = z.copy() # Reshape img and add it to height values self.img = (self.img / 10).reshape(-1, 1) z += self.img vert_arr_tmp = np.vstack(list(map(np.ravel, self.meshgrid))).T # for top side top_vert_arr = np.concatenate((vert_arr_tmp, z), axis=1) top_vert_arr = top_vert_arr.reshape(self.height, self.width, 3) # for bottom side bottom_vert_arr = np.concatenate((vert_arr_tmp, z_cpy), axis=1) bottom_vert_arr = bottom_vert_arr.reshape(self.height, self.width, 3) # Engrave text on the back of the model self.engrave_text(bottom_vert_arr, top_vert_arr) # Create all vertices, faces faces, vertices = self.create_faces(top_vert_arr, bottom_vert_arr) # Add the created vertices and faces to a mesh self.create_stl_mesh(faces, vertices) def make_stl_map(self): '''Map fingerprint to a given finger model. ''' # Conversion constants for mm and pixels mm2px = self.dpi/25.4 px2mm = 25.4/self.dpi # Finds the image pixel closest to finger vertice in 2D plane def find_nearest(ver1, ver2, img): searched_point = np.array([ver1, ver2]) min1 = math.floor(ver1*mm2px) max1 = math.ceil(ver1*mm2px) min2 = math.floor(ver2*mm2px) max2 = math.ceil(ver2*mm2px) min_dist_point = img[min2][min1] for i in range(min2, max2 - 1): for j in range(min1, max1 - 1): if np.linalg.norm(img[i][j] - searched_point) < min_dist_point: min_dist_point = img[i][j] return min_dist_point # Implicitly translate finger model to match middle of the fingerprint # This can be modified using finger_x, y and z parameters x = (self.width * px2mm / 2) + self.finger_x y = (self.height * px2mm / 2) + self.finger_y z = self.finger_z matrix = tmtra.translation_matrix([x, y, z]) self.finger.apply_transform(matrix) # Subdivide the finger mesh to allow for more precision # This can be skipped if the model is already dense enough vertices, faces = tmrem.subdivide_loop( self.finger.vertices, self.finger.faces, iterations=self.iter) # For logging progress c = 0 for k, vertice in enumerate(vertices): # Skip vertices under plane xy # also skip vertices under the fingerprint image, # they are all unused if vertice[2] < 0 or vertice[1] > self.height * px2mm: continue # This is the easiest way to avoid indexing errors # Those errors are caused by vertices outside of the image # When this occurs, input parameters need to be adjusted try: # Find the closest point in the image # To the 2D image projection of vertice, add its value point = find_nearest(vertice[0], vertice[1], self.img) except IndexError: log.error_exit( "Fingerprint image is outside of the finger model") vertices[k][2] += point # Prints out generation progress if k % 1000 == 0: percentage = round(k/len(vertices) * 100, 2) if percentage > c: log.print_message("Carving finger: " + str(c) + "%") c += 10 self.stl_model = trimesh.Trimesh(vertices, faces) log.print_message("Carving finger finished") def save_stl(self): '''Save final mesh to stl file. ''' # Create output file name, save it and write header with file info self.stl_filename = self.output_file.split( ".")[0] + "_" + self.id + ".stl" if (self.mode == "m"): self.stl_model.export(file_obj=self.stl_filename) else: self.stl_model.save(self.stl_filename) log.print_message("Saving model to", self.stl_filename) self.write_stl_header() fingerprint_app()