Spaces:
Running
Running
File size: 22,180 Bytes
1835398 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 | import numpy as np
import cv2
from scipy import signal
from scipy import ndimage
import math
import scipy
class FingerprintImageEnhancer(object):
def __init__(self):
self.ridge_segment_blksze = 32
self.ridge_segment_thresh = 0.1
self.gradient_sigma = 1
self.block_sigma = 7
self.orient_smooth_sigma = 7
self.ridge_freq_blksze = 38
self.ridge_freq_windsze = 15 #width change (thick -> 15) (default -> 5)
self.min_wave_length = 7
self.max_wave_length = 15
self.kx = 0.65
self.ky = 0.65
self.angleInc = 3
self.ridge_filter_thresh = -3
self._mask = []
self._normim = []
self._orientim = []
self._mean_freq = []
self._median_freq = []
self._freq = []
self._freqim = []
self._binim = []
def __normalise(self, img, mean, std):
normed = (img - np.mean(img)) / (np.std(img))
return (normed)
def __ridge_segment(self, img):
# RIDGESEGMENT - Normalises fingerprint image and segments ridge region
#
# Function identifies ridge regions of a fingerprint image and returns a
# mask identifying this region. It also normalises the intesity values of
# the image so that the ridge regions have zero mean, unit standard
# deviation.
#
# This function breaks the image up into blocks of size blksze x blksze and
# evaluates the standard deviation in each region. If the standard
# deviation is above the threshold it is deemed part of the fingerprint.
# Note that the image is normalised to have zero mean, unit standard
# deviation prior to performing this process so that the threshold you
# specify is relative to a unit standard deviation.
#
# Usage: [normim, mask, maskind] = ridgesegment(im, blksze, thresh)
#
# Arguments: im - Fingerprint image to be segmented.
# blksze - Block size over which the the standard
# deviation is determined (try a value of 16).
# thresh - Threshold of standard deviation to decide if a
# block is a ridge region (Try a value 0.1 - 0.2)
#
# Ouput: normim - Image where the ridge regions are renormalised to
# have zero mean, unit standard deviation.
# mask - Mask indicating ridge-like regions of the image,
# 0 for non ridge regions, 1 for ridge regions.
# maskind - Vector of indices of locations within the mask.
#
# Suggested values for a 500dpi fingerprint image:
#
# [normim, mask, maskind] = ridgesegment(im, 16, 0.1)
#
# See also: RIDGEORIENT, RIDGEFREQ, RIDGEFILTER
### REFERENCES
# Peter Kovesi
# School of Computer Science & Software Engineering
# The University of Western Australia
# pk at csse uwa edu au
# http://www.csse.uwa.edu.au/~pk
rows, cols = img.shape
im = self.__normalise(img, 0, 1) # normalise to get zero mean and unit standard deviation
new_rows = int(self.ridge_segment_blksze * np.ceil((float(rows)) / (float(self.ridge_segment_blksze))))
new_cols = int(self.ridge_segment_blksze * np.ceil((float(cols)) / (float(self.ridge_segment_blksze))))
padded_img = np.zeros((new_rows, new_cols))
stddevim = np.zeros((new_rows, new_cols))
padded_img[0:rows][:, 0:cols] = im
for i in range(0, new_rows, self.ridge_segment_blksze):
for j in range(0, new_cols, self.ridge_segment_blksze):
block = padded_img[i:i + self.ridge_segment_blksze][:, j:j + self.ridge_segment_blksze]
stddevim[i:i + self.ridge_segment_blksze][:, j:j + self.ridge_segment_blksze] = np.std(block) * np.ones(block.shape)
stddevim = stddevim[0:rows][:, 0:cols]
self._mask = stddevim > self.ridge_segment_thresh
mean_val = np.mean(im[self._mask])
std_val = np.std(im[self._mask])
self._normim = (im - mean_val) / (std_val)
def __ridge_orient(self):
# RIDGEORIENT - Estimates the local orientation of ridges in a fingerprint
#
# Usage: [orientim, reliability, coherence] = ridgeorientation(im, gradientsigma,...
# blocksigma, ...
# orientsmoothsigma)
#
# Arguments: im - A normalised input image.
# gradientsigma - Sigma of the derivative of Gaussian
# used to compute image gradients.
# blocksigma - Sigma of the Gaussian weighting used to
# sum the gradient moments.
# orientsmoothsigma - Sigma of the Gaussian used to smooth
# the final orientation vector field.
# Optional: if ommitted it defaults to 0
#
# Output: orientim - The orientation image in radians.
# Orientation values are +ve clockwise
# and give the direction *along* the
# ridges.
# reliability - Measure of the reliability of the
# orientation measure. This is a value
# between 0 and 1. I think a value above
# about 0.5 can be considered 'reliable'.
# reliability = 1 - Imin./(Imax+.001);
# coherence - A measure of the degree to which the local
# area is oriented.
# coherence = ((Imax-Imin)./(Imax+Imin)).^2;
#
# With a fingerprint image at a 'standard' resolution of 500dpi suggested
# parameter values might be:
#
# [orientim, reliability] = ridgeorient(im, 1, 3, 3);
#
# See also: RIDGESEGMENT, RIDGEFREQ, RIDGEFILTER
### REFERENCES
# May 2003 Original version by Raymond Thai,
# January 2005 Reworked by Peter Kovesi
# October 2011 Added coherence computation and orientsmoothsigma made optional
#
# School of Computer Science & Software Engineering
# The University of Western Australia
# pk at csse uwa edu au
# http://www.csse.uwa.edu.au/~pk
rows,cols = self._normim.shape
#Calculate image gradients.
sze = np.fix(6*self.gradient_sigma)
if np.remainder(sze,2) == 0:
sze = sze+1
gauss = cv2.getGaussianKernel(int(sze),self.gradient_sigma)
f = gauss * gauss.T
fy,fx = np.gradient(f) #Gradient of Gaussian
Gx = signal.convolve2d(self._normim, fx, mode='same')
Gy = signal.convolve2d(self._normim, fy, mode='same')
Gxx = np.power(Gx,2)
Gyy = np.power(Gy,2)
Gxy = Gx*Gy
#Now smooth the covariance data to perform a weighted summation of the data.
sze = np.fix(6*self.block_sigma)
gauss = cv2.getGaussianKernel(int(sze), self.block_sigma)
f = gauss * gauss.T
Gxx = ndimage.convolve(Gxx,f)
Gyy = ndimage.convolve(Gyy,f)
Gxy = 2*ndimage.convolve(Gxy,f)
# Analytic solution of principal direction
denom = np.sqrt(np.power(Gxy,2) + np.power((Gxx - Gyy),2)) + np.finfo(float).eps
sin2theta = Gxy/denom # Sine and cosine of doubled angles
cos2theta = (Gxx-Gyy)/denom
if self.orient_smooth_sigma:
sze = np.fix(6*self.orient_smooth_sigma)
if np.remainder(sze,2) == 0:
sze = sze+1
gauss = cv2.getGaussianKernel(int(sze), self.orient_smooth_sigma)
f = gauss * gauss.T
cos2theta = ndimage.convolve(cos2theta,f) # Smoothed sine and cosine of
sin2theta = ndimage.convolve(sin2theta,f) # doubled angles
self._orientim = np.pi/2 + np.arctan2(sin2theta,cos2theta)/2
def __ridge_freq(self):
# RIDGEFREQ - Calculates a ridge frequency image
#
# Function to estimate the fingerprint ridge frequency across a
# fingerprint image. This is done by considering blocks of the image and
# determining a ridgecount within each block by a call to FREQEST.
#
# Usage:
# [freqim, medianfreq] = ridgefreq(im, mask, orientim, blksze, windsze, ...
# minWaveLength, maxWaveLength)
#
# Arguments:
# im - Image to be processed.
# mask - Mask defining ridge regions (obtained from RIDGESEGMENT)
# orientim - Ridge orientation image (obtained from RIDGORIENT)
# blksze - Size of image block to use (say 32)
# windsze - Window length used to identify peaks. This should be
# an odd integer, say 3 or 5.
# minWaveLength, maxWaveLength - Minimum and maximum ridge
# wavelengths, in pixels, considered acceptable.
#
# Output:
# freqim - An image the same size as im with values set to
# the estimated ridge spatial frequency within each
# image block. If a ridge frequency cannot be
# found within a block, or cannot be found within the
# limits set by min and max Wavlength freqim is set
# to zeros within that block.
# medianfreq - Median frequency value evaluated over all the
# valid regions of the image.
#
# Suggested parameters for a 500dpi fingerprint image
# [freqim, medianfreq] = ridgefreq(im,orientim, 32, 5, 5, 15);
#
# See also: RIDGEORIENT, FREQEST, RIDGESEGMENT
# Reference:
# Hong, L., Wan, Y., and Jain, A. K. Fingerprint image enhancement:
# Algorithm and performance evaluation. IEEE Transactions on Pattern
# Analysis and Machine Intelligence 20, 8 (1998), 777 789.
### REFERENCES
# Peter Kovesi
# School of Computer Science & Software Engineering
# The University of Western Australia
# pk at csse uwa edu au
# http://www.csse.uwa.edu.au/~pk
rows, cols = self._normim.shape
freq = np.zeros((rows, cols))
for r in range(0, rows - self.ridge_freq_blksze, self.ridge_freq_blksze):
for c in range(0, cols - self.ridge_freq_blksze, self.ridge_freq_blksze):
blkim = self._normim[r:r + self.ridge_freq_blksze][:, c:c + self.ridge_freq_blksze]
blkor = self._orientim[r:r + self.ridge_freq_blksze][:, c:c + self.ridge_freq_blksze]
freq[r:r + self.ridge_freq_blksze][:, c:c + self.ridge_freq_blksze] = self.__frequest(blkim, blkor)
self._freq = freq * self._mask
freq_1d = np.reshape(self._freq, (1, rows * cols))
ind = np.where(freq_1d > 0)
ind = np.array(ind)
ind = ind[1, :]
non_zero_elems_in_freq = freq_1d[0][ind]
self._mean_freq = np.mean(non_zero_elems_in_freq)
self._median_freq = np.median(non_zero_elems_in_freq) # does not work properly
self._freq = self._mean_freq * self._mask
def __frequest(self, blkim, blkor):
# FREQEST - Estimate fingerprint ridge frequency within image block
#
# Function to estimate the fingerprint ridge frequency within a small block
# of a fingerprint image. This function is used by RIDGEFREQ
#
# Usage:
# freqim = freqest(im, orientim, windsze, minWaveLength, maxWaveLength)
#
# Arguments:
# im - Image block to be processed.
# orientim - Ridge orientation image of image block.
# windsze - Window length used to identify peaks. This should be
# an odd integer, say 3 or 5.
# minWaveLength, maxWaveLength - Minimum and maximum ridge
# wavelengths, in pixels, considered acceptable.
#
# Output:
# freqim - An image block the same size as im with all values
# set to the estimated ridge spatial frequency. If a
# ridge frequency cannot be found, or cannot be found
# within the limits set by min and max Wavlength
# freqim is set to zeros.
#
# Suggested parameters for a 500dpi fingerprint image
# freqim = freqest(im,orientim, 5, 5, 15);
#
# See also: RIDGEFREQ, RIDGEORIENT, RIDGESEGMENT
### REFERENCES
# Peter Kovesi
# School of Computer Science & Software Engineering
# The University of Western Australia
# pk at csse uwa edu au
# http://www.csse.uwa.edu.au/~pk
rows, cols = np.shape(blkim)
# Find mean orientation within the block. This is done by averaging the
# sines and cosines of the doubled angles before reconstructing the
# angle again. This avoids wraparound problems at the origin.
cosorient = np.mean(np.cos(2 * blkor))
sinorient = np.mean(np.sin(2 * blkor))
orient = math.atan2(sinorient, cosorient) / 2
# Rotate the image block so that the ridges are vertical
# ROT_mat = cv2.getRotationMatrix2D((cols/2,rows/2),orient/np.pi*180 + 90,1)
# rotim = cv2.warpAffine(im,ROT_mat,(cols,rows))
rotim = scipy.ndimage.rotate(blkim, orient / np.pi * 180 + 90, axes=(1, 0), reshape=False, order=3,
mode='nearest')
# Now crop the image so that the rotated image does not contain any
# invalid regions. This prevents the projection down the columns
# from being mucked up.
cropsze = int(np.fix(rows / np.sqrt(2)))
offset = int(np.fix((rows - cropsze) / 2))
rotim = rotim[offset:offset + cropsze][:, offset:offset + cropsze]
# Sum down the columns to get a projection of the grey values down
# the ridges.
proj = np.sum(rotim, axis=0)
dilation = scipy.ndimage.grey_dilation(proj, self.ridge_freq_windsze, structure=np.ones(self.ridge_freq_windsze))
temp = np.abs(dilation - proj)
peak_thresh = 2
maxpts = (temp < peak_thresh) & (proj > np.mean(proj))
maxind = np.where(maxpts)
rows_maxind, cols_maxind = np.shape(maxind)
# Determine the spatial frequency of the ridges by divinding the
# distance between the 1st and last peaks by the (No of peaks-1). If no
# peaks are detected, or the wavelength is outside the allowed bounds,
# the frequency image is set to 0
if (cols_maxind < 2):
return(np.zeros(blkim.shape))
else:
NoOfPeaks = cols_maxind
waveLength = (maxind[0][cols_maxind - 1] - maxind[0][0]) / (NoOfPeaks - 1)
if waveLength >= self.min_wave_length and waveLength <= self.max_wave_length:
return(1 / np.double(waveLength) * np.ones(blkim.shape))
else:
return(np.zeros(blkim.shape))
def __ridge_filter(self):
# RIDGEFILTER - enhances fingerprint image via oriented filters
#
# Function to enhance fingerprint image via oriented filters
#
# Usage:
# newim = ridgefilter(im, orientim, freqim, kx, ky, showfilter)
#
# Arguments:
# im - Image to be processed.
# orientim - Ridge orientation image, obtained from RIDGEORIENT.
# freqim - Ridge frequency image, obtained from RIDGEFREQ.
# kx, ky - Scale factors specifying the filter sigma relative
# to the wavelength of the filter. This is done so
# that the shapes of the filters are invariant to the
# scale. kx controls the sigma in the x direction
# which is along the filter, and hence controls the
# bandwidth of the filter. ky controls the sigma
# across the filter and hence controls the
# orientational selectivity of the filter. A value of
# 0.5 for both kx and ky is a good starting point.
# showfilter - An optional flag 0/1. When set an image of the
# largest scale filter is displayed for inspection.
#
# Output:
# newim - The enhanced image
#
# See also: RIDGEORIENT, RIDGEFREQ, RIDGESEGMENT
# Reference:
# Hong, L., Wan, Y., and Jain, A. K. Fingerprint image enhancement:
# Algorithm and performance evaluation. IEEE Transactions on Pattern
# Analysis and Machine Intelligence 20, 8 (1998), 777 789.
### REFERENCES
# Peter Kovesi
# School of Computer Science & Software Engineering
# The University of Western Australia
# pk at csse uwa edu au
# http://www.csse.uwa.edu.au/~pk
im = np.double(self._normim)
rows, cols = im.shape
newim = np.zeros((rows, cols))
freq_1d = np.reshape(self._freq, (1, rows * cols))
ind = np.where(freq_1d > 0)
ind = np.array(ind)
ind = ind[1, :]
# Round the array of frequencies to the nearest 0.01 to reduce the
# number of distinct frequencies we have to deal with.
non_zero_elems_in_freq = freq_1d[0][ind]
non_zero_elems_in_freq = np.double(np.round((non_zero_elems_in_freq * 100))) / 100
unfreq = np.unique(non_zero_elems_in_freq)
# Generate filters corresponding to these distinct frequencies and
# orientations in 'angleInc' increments.
sigmax = 1 / unfreq[0] * self.kx
sigmay = 1 / unfreq[0] * self.ky
sze = int(np.round(3 * np.max([sigmax, sigmay])))
x, y = np.meshgrid(np.linspace(-sze, sze, (2 * sze + 1)), np.linspace(-sze, sze, (2 * sze + 1)))
reffilter = np.exp(-(((np.power(x, 2)) / (sigmax * sigmax) + (np.power(y, 2)) / (sigmay * sigmay)))) * np.cos(
2 * np.pi * unfreq[0] * x) # this is the original gabor filter
filt_rows, filt_cols = reffilter.shape
angleRange = int(180 / self.angleInc)
gabor_filter = np.array(np.zeros((angleRange, filt_rows, filt_cols)))
for o in range(0, angleRange):
# Generate rotated versions of the filter. Note orientation
# image provides orientation *along* the ridges, hence +90
# degrees, and imrotate requires angles +ve anticlockwise, hence
# the minus sign.
rot_filt = scipy.ndimage.rotate(reffilter, -(o * self.angleInc + 90), reshape=False)
gabor_filter[o] = rot_filt
# Find indices of matrix points greater than maxsze from the image
# boundary
maxsze = int(sze)
temp = self._freq > 0
validr, validc = np.where(temp)
temp1 = validr > maxsze
temp2 = validr < rows - maxsze
temp3 = validc > maxsze
temp4 = validc < cols - maxsze
final_temp = temp1 & temp2 & temp3 & temp4
finalind = np.where(final_temp)
# Convert orientation matrix values from radians to an index value
# that corresponds to round(degrees/angleInc)
maxorientindex = np.round(180 / self.angleInc)
orientindex = np.round(self._orientim / np.pi * 180 / self.angleInc)
# do the filtering
for i in range(0, rows):
for j in range(0, cols):
if (orientindex[i][j] < 1):
orientindex[i][j] = orientindex[i][j] + maxorientindex
if (orientindex[i][j] > maxorientindex):
orientindex[i][j] = orientindex[i][j] - maxorientindex
finalind_rows, finalind_cols = np.shape(finalind)
sze = int(sze)
for k in range(0, finalind_cols):
r = validr[finalind[0][k]]
c = validc[finalind[0][k]]
img_block = im[r - sze:r + sze + 1][:, c - sze:c + sze + 1]
newim[r][c] = np.sum(img_block * gabor_filter[int(orientindex[r][c]) - 1])
self._binim = newim < self.ridge_filter_thresh
def save_enhanced_image(self, path):
# saves the enhanced image at the specified path
cv2.imwrite(path, 255 - (255 * self._binim))
# image = (255 * self._binim)
# print(image)
# print(255 - image)
def enhance(self, img, resize=False):
# main function to enhance the image.
# calls all other subroutines
if(resize):
rows, cols = np.shape(img)
aspect_ratio = np.double(rows) / np.double(cols)
new_rows = 450 # randomly selected number
new_cols = new_rows / aspect_ratio
img = cv2.resize(img, (int(new_cols), int(new_rows)))
self.__ridge_segment(img) # normalise the image and find a ROI
self.__ridge_orient() # compute orientation image
self.__ridge_freq() # compute major frequency of ridges
self.__ridge_filter() # filter the image using oriented gabor filter
return(self._binim)
|