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Added curved fingerprint model generation, refactored most of the code.

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master
Rostislav Lán 2 years ago
parent c5d412e462
commit 390232c4f0
2 changed files with 368 additions and 202 deletions
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85
src/filters.py
Unescape View File

@ -8,6 +8,7 @@ import numpy as np
import cv2 as cv
from skimage import filters as skiflt
from skimage import restoration as skirest
from scipy import signal as sig
# Parent class for all the filters
@ -33,8 +34,8 @@ class convolve(filter):
kernel = np.array(params["kernel"]) if params["kernel"] else np.ones(
(ksize, ksize), np.float32) / np.sqrt(ksize)
print("with params: ksize: " +
str(ksize) + " kernel: \n" + str(kernel))
#print("with params: ksize: " +
# str(ksize) + " kernel: \n" + str(kernel))
self.img = cv.filter2D(self.img, -1, kernel)
@ -56,8 +57,8 @@ class blur(filter):
anchor = (-1, -1)
ksize = int(params["ksize"]) if params["ksize"] else 3
print("with params: ksize: " +
str(ksize) + " anchor: " + str(anchor))
#print("with params: ksize: " +
# str(ksize) + " anchor: " + str(anchor))
self.img = cv.blur(self.img, ksize=(ksize, ksize), anchor=anchor)
@ -72,13 +73,15 @@ class gaussian(filter):
sigmaX = float(params["sigmaX"]) if params["sigmaX"] else 0
sigmaY = float(params["sigmaY"]) if params["sigmaY"] else 0
print("with params: ksize: " + str(ksize) +
" sigmaX: " + str(sigmaX) + " sigmaY: " + str(sigmaY))
#print("with params: ksize: " + str(ksize) +
# " sigmaX: " + str(sigmaX) + " sigmaY: " + str(sigmaY))
self.img = cv.GaussianBlur(self.img, (ksize, ksize), sigmaX, sigmaY)
class median(filter):
''' Median blur filter from OpenCV.
''' Median blur filter from scikit-image.
Using this over opencv version as that one is limited to 5x5 kernel.
'''
def __init__(self, img):
super().__init__(img)
@ -86,8 +89,8 @@ class median(filter):
def apply(self, params):
ksize = int(params["ksize"]) if params["ksize"] else 3
print("with params: ksize: " + str(ksize))
self.img = cv.medianBlur(np.float32(self.img), ksize)
#print("with params: ksize: " + str(ksize))
self.img = skiflt.median(self.img, footprint=np.ones((ksize, ksize)))
class bilateral(filter):
@ -102,8 +105,9 @@ class bilateral(filter):
sigmaColor = int(params["sigmaColor"]) if params["sigmaColor"] else 75
sigmaSpace = int(params["sigmaSpace"]) if params["sigmaSpace"] else 75
print("with params: d: " + str(d) + " sigmaColor: " +
str(sigmaColor) + " sigmaSpace: " + str(sigmaSpace))
#print("with params: d: " + str(d) + " sigmaColor: " +
# str(sigmaColor) + " sigmaSpace: " + str(sigmaSpace))
self.img = np.uint8(self.img)
self.img = cv.bilateralFilter(self.img, d, sigmaColor, sigmaSpace)
@ -119,8 +123,8 @@ class denoise(filter):
sWS = int(params["searchWindowSize"]
) if params["searchWindowSize"] else 21
print("with params: h: " + str(h) +
" tWS: " + str(tWS) + " sWS: " + str(sWS))
#print("with params: h: " + str(h) +
# " tWS: " + str(tWS) + " sWS: " + str(sWS))
self.img = np.uint8(self.img)
self.img = cv.fastNlMeansDenoising(
self.img, h, tWS, sWS)
@ -146,9 +150,9 @@ class denoise_bilateral(filter):
) if params["channelAxis"] else None
iterations = int(params["iterations"]) if params["iterations"] else 1
print("with params: sigma_color: " + str(sigmaColor) +
" sigma_spatial: " + str(sigmaSpace) + " channel_axis: " +
str(channelAxis) + " iterations: " + str(iterations))
#print("with params: sigma_color: " + str(sigmaColor) +
# " sigma_spatial: " + str(sigmaSpace) + " channel_axis: " +
# str(channelAxis) + " iterations: " + str(iterations))
for i in range(iterations):
self.img = skirest.denoise_bilateral(
@ -172,8 +176,8 @@ class denoise_tv_chambolle(filter):
) if params["channelAxis"] else None
iterations = int(params["iterations"]) if params["iterations"] else 1
print("with params: weight: " + str(weight) +
" channel_axis: " + str(channelAxis) + " iterations: " + str(iterations))
#print("with params: weight: " + str(weight) +
# " channel_axis: " + str(channelAxis) + " iterations: " + str(iterations))
for i in range(iterations):
self.img = skirest.denoise_tv_chambolle(
self.img, weight=weight, channel_axis=channelAxis)
@ -190,7 +194,7 @@ class sharpen(filter):
kernel = np.matrix(params["kernel"]) if params["kernel"] else np.array(
[[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
print("with params: kernel: \n" + str(kernel))
#print("with params: kernel: \n" + str(kernel))
self.img = cv.filter2D(self.img, ddepth=-1, kernel=kernel)
@ -211,8 +215,8 @@ class unsharp_mask(filter):
blurred = cv.medianBlur(self.img, ksize)
lap = cv.Laplacian(blurred, cv.CV_32F)
print("with params: strength: " +
str(strength) + " ksize: " + str(ksize))
#print("with params: strength: " +
# str(strength) + " ksize: " + str(ksize))
self.img = blurred - strength*lap
@ -235,8 +239,8 @@ class unsharp_mask_scikit(filter):
) if params["channelAxis"] else None
#self.img = cv.cvtColor(self.img, cv.COLOR_GRAY2RGB)
print("with params: radius: " +
str(radius) + " amount: " + str(amount))
#print("with params: radius: " +
# str(radius) + " amount: " + str(amount))
self.img = skiflt.unsharp_mask(
self.img, radius=radius, amount=amount, channel_axis=channelAxis)
#self.img = cv.cvtColor(self.img, cv.COLOR_RGB2GRAY)
@ -265,8 +269,39 @@ class morph(filter):
else:
anchor = (-1, -1)
print("with params: kernel: \n" + str(kernel) + " anchor: " +
str(anchor) + " iterations: " + str(iterations) + " op: " + str(op))
#print("with params: kernel: \n" + str(kernel) + " anchor: " +
# str(anchor) + " iterations: " + str(iterations) + " op: " + str(op))
self.img = cv.morphologyEx(
np.uint8(self.img), op=op, kernel=kernel,
anchor=anchor, iterations=iterations)
class gabor(filter):
''' Gabor filter from OpenCV.
Performs Gabor filtering on the image.
'''
def __init__(self, img):
super().__init__(img)
# TODO: not working properly
def apply(self, params):
ksize = int(params["ksize"]) if params["ksize"] else 31
sigma = float(params["sigma"]) if params["sigma"] else 10.0
theta = params["theta"] if params["theta"] else [0,np.pi/16,np.pi-np.pi/16]
lambd = float(params["lambd"]) if params["lambd"] else 10.0
gamma = float(params["gamma"]) if params["gamma"] else 0.02
psi = float(params["psi"]) if params["psi"] else 0.0
filters = []
for i in range(len(theta)):
g_kernel = cv.getGaborKernel(ksize=(ksize, ksize), sigma=sigma, theta=theta[i], lambd=lambd, gamma=gamma, psi=psi)
g_kernel = g_kernel / 1.5 * g_kernel.sum()
filters.append(g_kernel)
tmp = np.zeros_like(self.img)
for i in range(len(filters)):
tmp = cv.filter2D(self.img, -1, kernel=filters[i])
self.img += np.maximum(self.img, tmp)

485
src/main.py
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@ -14,8 +14,8 @@ 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
@ -25,96 +25,141 @@ class app:
def __init__(self):
# Parse arguments from command line
self.parse_arguments()
self.params = {}
# List and dict for filters and corresponding parameters
self.filters = []
# Parse configuration from json file
self.params = {}
# Parse configuration from json config file
if self.args.config:
self.config_file = self.args.config[0]
self.preset_name = self.args.config[1]
self.config_file, self.preset_name = self.args.config
self.config = json.load(open(self.config_file))
self.parse_conf()
# If no config file given, expect filters in command line
else:
if not self.args.filters:
print("No filters given, saving original image")
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 '=' in filter, it is a new filter
# 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
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])
if self.args.stl_file and len(self.args.stl_file) == 3:
self.stl_file = self.args.stl_file[0]
self.height_line = float(self.args.stl_file[1])
self.height_base = float(self.args.stl_file[2])
self.mode = "2d"
elif self.args.stl_file and len(self.args.stl_file) == 2:
self.stl_file = self.args.stl_file[0]
self.height_line = float(self.args.stl_file[1])
self.mode = "3d"
else:
print("No STL file given, saving image only")
exit(1)
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()
self.run_filtering()
else:
print("Input file does not exist", file=sys.stderr)
exit(1)
def run(self):
# read as numpy.array
self.img = cv.imread(
self.input_file, cv.IMREAD_GRAYSCALE).astype(np.uint8)
self.error_exit("Input file " + self.input_file +
" does not exist")
self.width = self.img.shape[1]
self.height = self.img.shape[0]
self.print_size(self.img.shape)
fig = plt.figure(figsize=(self.width/self.dpi, self.height/self.dpi),
frameon=False, dpi=self.dpi)
if self.args.stl_file:
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
# Get stl filename
self.stl_file = self.args.stl_file[0]
if self.mirror is True:
self.mirror_image()
# Get mode and model parameters
if self.args.planar:
self.mode = "2d"
# Apply all filters and save image
self.apply_filter()
self.save_image(fig, ax)
plt.close()
if self.args.stl_file:
self.make_model()
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 in the filter method.
'''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"}
"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
@ -122,168 +167,157 @@ class app:
params[key] = params[key]
def parse_conf(self):
''' Parse configuration file if one was given and store filters with their parameters
'''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:
print("Preset not found", file=sys.stderr)
self.error_exit("Preset not found")
def parse_arguments(self):
''' Parse arguments from command line
def error_exit(self, message):
'''Print error message and exit.
'''
parser = ap.ArgumentParser(prog='main.py',
description='Program for processing a 2D image into 3D fingerprint.',
usage='%(prog)s [-h] [-m | --mirror | --no-mirror] input_file output_file dpi \
([-c config_file preset | --config config_file preset] | [filters ...]) \
[-s stl_file | --stl_file stl_file depth_total depth_line]')
print("ERROR:", message, file=sys.stderr)
exit(1)
# positional arguments
parser.add_argument("input_file", type=str, help="location with input file")
parser.add_argument("output_file", type=str, help="output file location")
parser.add_argument("dpi", type=int, help="scanner dpi")
# boolean switch argument
parser.add_argument('-m', "--mirror", help="mirror input image", type=bool, action=ap.BooleanOptionalAction)
#------------------------- FILTERING -------------------------#
# 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="make planar model from processed image", required=False)
def run_filtering(self):
'''Load from input file, store as numpy.array,
process image using filters and save to output file.
'''
# file with configuration containing presets, new 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')
self.img = cv.imread(
self.input_file, cv.IMREAD_GRAYSCALE).astype(np.uint8)
# 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=value1 param2=value2 filter_name2 param1=value1...]")
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)
self.args = parser.parse_args()
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
def filter_factory(self, filter_name):
''' Selects filter method of filters library.
'''
if self.mirror is True:
self.mirror_image()
print("Applying " + filter_name + " filter ", end='')
return getattr(flt, filter_name)
# Apply all filters and save image
self.apply_filters()
self.save_image(fig, ax)
plt.close()
def mirror_image(self):
''' Mirror image when mirroring is needed,
should be used only if we want a positive model
'''
'''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_filter(self):
''' Apply filters to image.
Apply the filters one by one, if none were given, just save original image output.
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:
# No filter given, just save the image
pass
else:
# Apply all filters
for i, filter_name in enumerate(self.filters):
filter = self.filter_factory(filter_name)
# 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.
Colormap set to grayscale to avoid color mismatch.
'''Save processed image to the output file.
'''
print("Saving image to ", self.output_file, file=sys.stderr)
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='figure')
fig.savefig(fname=self.output_file, dpi=self.dpi)
def print_size(self, size):
print("Image of height: " + str(size[0]) +
" px and width: " + str(size[1]) + " px", file=sys.stderr)
#------------------------- STL GENERATION -------------------------#
def make_model(self):
'''After processing image, make a lithophane from it.
def run_stl(self):
'''Make heightmap, create mesh and save as stl file.
'''
print("Making heighthmap", file=sys.stderr)
self.prepare_heightmap()
if self.mode == "2d":
print("Converting to stl format", file=sys.stderr)
self.make_stl_planar()
plt.show()
print(f"Saving lithophane to ", self.stl_file, file=sys.stderr)
self.save_stl_2d()
elif self.mode == "3d":
self.map_image_to_3d()
plt.show()
self.save_stl_3d()
self.make_stl_curved()
else:
print("Mode not supported", file=sys.stderr)
exit(1)
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):
''' Create numpy meshgrid.
Modify image values to get usable depth values.
'''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:
print("Depth of plate height must be positive", file=sys.stderr)
exit(1)
self.error_exit("Depth of plate height must be positive")
if self.height_line + self.height_base <= 0:
print("Line depth must be less than plate thickness", file=sys.stderr)
exit(1)
print("Base height:", self.height_base,
"mm, lines depth/height:", self.height_line, "mm")
self.error_exit("Line depth must be less than plate thickness")
# Transform image values to get a heightmap
if self.height_line < 0:
self.img = (self.height_base + (1 - self.img/255)
* self.height_line)
else:
self.img = (self.height_base + (1 - self.img/255)
* self.height_line)
self.img = (self.height_base + (1 - self.img/255)
* self.height_line)
if self.mode == "3d":
#TODO add some checks and print info
pass
# 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 add_faces(self, faces, c):
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 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
@ -299,7 +333,7 @@ class app:
self.meshgrid_2d = np.meshgrid(x, y)
# Add the image matrix to the 2D meshgrid and create 1D array of 3D points
# 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)
@ -320,42 +354,42 @@ class app:
vertices.append([vertex_arr[i+1][j]])
vertices.append([vertex_arr[i+1][j+1]])
count = self.add_faces(faces, count)
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):
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.add_faces(faces, count)
count = self.append_faces(faces, count)
# Horizontal side faces
for j in range(self.height - 1):
vertices.append([vertex_arr[j][0]])
vertices.append([vertex_arr[j+1][0]])
vertices.append([null_arr[j][0]])
vertices.append([null_arr[j+1][0]])
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.add_faces(faces, count)
count = self.append_faces(faces, count)
max = self.width - 1
vertices.append([vertex_arr[j+1][max]])
vertices.append([vertex_arr[j][max]])
vertices.append([null_arr[j+1][max]])
vertices.append([null_arr[j][max]])
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.add_faces(faces, count)
count = self.append_faces(faces, count)
# Vertical side faces
for j in range(self.width - 1):
@ -364,7 +398,7 @@ class app:
vertices.append([null_arr[0][j+1]])
vertices.append([null_arr[0][j]])
count = self.add_faces(faces, count)
count = self.append_faces(faces, count)
max = self.height - 1
@ -373,41 +407,60 @@ class app:
vertices.append([null_arr[max][j]])
vertices.append([null_arr[max][j+1]])
count = self.add_faces(faces, count)
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_mesh_2d = mesh.Mesh(np.zeros(faces.shape[0], dtype=mesh.Mesh.dtype))
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_mesh_2d.vectors[i][j] = vertices[face[j], :]
def save_stl_2d(self):
''' Save final mesh to stl file.
'''
self.stl_mesh_2d.save(self.stl_file)
self.stl_lithophane.vectors[i][j] = vertices[face[j], :]
def map_image_to_3d(self):
''' Map fingerprint to finger model.
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)
z1 = np.logspace(0, 10, int(np.ceil(self.width / 2)), base=0.7)
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 * pow(np.log(i+2), -1)))
z = np.concatenate((z, ztemp + 25*(((i+50)/20)**(-1/2))))
z = z.reshape(-1, 1)
self.meshgrid_3d = np.meshgrid(x, y)
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)
@ -416,41 +469,119 @@ class app:
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.add_faces(faces, count)
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)
#self.finger_base = mesh.Mesh(np.zeros(, dtype=mesh.Mesh.dtype))
# 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
# linear projection
# extrude lines in 1 direction
# cylinder / circular projection
# extrude lines in direction of a suitable cylinder
# normal projection
# extrude lines in the direction of normals of given finger model
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))
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], :]
def save_stl_3d(self):
''' Save final mesh to stl file.
# print(self.mesh_finger.normals)
def save_stl(self):
'''Save final mesh to stl file.
'''
self.mesh_finger.save(self.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()

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