def extract_core_point_position(img, block_size, tolerance):
im = Image.fromarray(np.array(img_as_ubyte(img)))
im = im.convert("L")
f = lambda x, y: 2 * x * y
g = lambda x, y: x**2 - y**2
angles = utils.calculate_angles(im, block_size, f, g)
angles = utils.smooth_angles(angles)
singularities_positions = poincare.calculate_singularities(
im, angles, tolerance, block_size)
core_point_position = compute_core_point_position(singularities_positions)
return core_point_position
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Gabor filter applied")
parser.add_argument("image", nargs=1, help="Path to image")
parser.add_argument("block_size", nargs=1, help="Block size")
parser.add_argument("--save",
action='store_true',
help="Save result image as src_image_enhanced.gif")
args = parser.parse_args()
im = Image.open(args.image[0])
im = im.convert("L") # covert to grayscale
im.show()
W = int(args.block_size[0])
f = lambda x, y: 2 * x * y
g = lambda x, y: x**2 - y**2
angles = utils.calculate_angles(im, W, f, g)
print "calculating orientation done"
angles = utils.smooth_angles(angles)
print "smoothing angles done"
result = gabor(im, W, angles)
result.show()
if args.save:
base_image_name = os.path.splitext(os.path.basename(args.image[0]))[0]
im.save(base_image_name + "_enhanced.gif", "GIF")
for i in range(1, x / W - 1):
for j in range(1, y / W - 1):
box = (i * W, j * W, min(i * W + W, x), min(j * W + W, y))
freq_img.paste(freqs[i][j] * 255.0 * 1.2, box)
return freq_img
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Image frequency")
parser.add_argument("image", nargs=1, help = "Path to image")
parser.add_argument("block_size", nargs=1, help = "Block size")
parser.add_argument('--smooth', "-s", action='store_true', help = "Use Gauss for smoothing")
args = parser.parse_args()
im = Image.open(args.image[0])
im = im.convert("L") # covert to grayscale
im.show()
W = int(args.block_size[0])
f = lambda x, y: 2 * x * y
g = lambda x, y: x ** 2 - y ** 2
angles = utils.calculate_angles(im, W, f, g)
if args.smooth:
angles = utils.smooth_angles(angles)
freq_img = freq_img(im, W, angles)
freq_img.show()
def main2():
# alignMinutias((2,8, 'loop'), [[40, 120, 'bip']])
parser = argparse.ArgumentParser(
description=
"Gabor, thinning, extract minutias, calculate poincare and align minutias."
)
parser.add_argument("image", nargs=1, help="Path to image")
# parser.add_argument("block_size", nargs=1, help = "Block size")
parser.add_argument("tolerance",
nargs=1,
help="Tolerance for Poincare index")
parser.add_argument("block_size", nargs=1, help="Blocksize")
parser.add_argument(
'--preprocess',
"-p",
action='store_true',
help="Preprocess the image with: Gabor filtering and thinning")
parser.add_argument('--smooth',
"-s",
action='store_true',
help="Use Gauss for smoothing")
parser.add_argument("--save",
action='store_true',
help="Save result image as src_image_enhanced.gif")
args = parser.parse_args()
imagepath = args.image[0]
im = None
#im = Image.open(args.image[0])
# im = Image.open("ppf-test_enhanced_thinned.gif")
#im = im.convert("L") # covert to grayscale
# im.show()
W = int(args.block_size[0])
#W = 16
print "Block-size: ", W
singularity_type = None
f = lambda x, y: 2 * x * y
g = lambda x, y: x**2 - y**2
#print "Gabor filter and thinning, pointcare, extract minutias, alignment, exporting minutias to file ..."
print "image: ", os.path.splitext(imagepath)
base_image_name = os.path.splitext(os.path.basename(imagepath))[0]
image_enhanced_loc = os.path.splitext(
imagepath)[0] + "_enhanced_thinned.gif"
image_enhanced_minutiae_loc = os.path.splitext(
imagepath)[0] + "_enhanced_thinned_minutiaes.png"
image_enhanced_minutiae_aligned_loc = os.path.splitext(
imagepath)[0] + "_enhanced_thinned_minutiaes_aligned.png"
dumpfilename = os.path.splitext(imagepath)[0] + ".yaml"
print "image enhanced: ", image_enhanced_loc
# f['image']
if args.preprocess or os.path.isfile(image_enhanced_loc) == False:
print "Enhanced image does not exists. Enhancing."
print "Gabor filtering and thinning ..."
im = Image.open(imagepath)
im = im.convert("L") # covert to grayscale
# gabor filter
angles = utils.calculate_angles(im, W, f, g)
print "calculating orientation done"
angles = utils.smooth_angles(angles)
print "smoothing angles done"
im = gabor(im, W, angles)
# im.show()
# thinning
im = make_thin(im)
# im.show()
if args.save:
im.save(image_enhanced_loc, "GIF")
if args.preprocess:
im.close()
return
else:
print "Processing enhanced image."
if im is not None:
im.close()
im = Image.open(image_enhanced_loc)
im = im.convert("L") # covert to grayscale
# continue
# get image bounds
(maxx, maxy) = im.size
bounds = (0, maxx, 0, maxy)
print "bounds: ", bounds
angles = utils.calculate_angles(im, W, f, g)
if args.smooth:
angles = utils.smooth_angles(angles)
singularity_type = None
result, cores = calculate_singularities(im,
angles,
int(args.tolerance[0]),
W,
singularity_type=singularity_type)
print "cores: ", cores
core = chooseCore(cores, W)
print "Selected core: ", core
# result.show()
# align bounds with respect to the core
# bounds2 = alignBounds(core, bounds)
# calculate minutias
im, minutiaes = calculate_minutiaes(im)
im.show()
print "minutias: ", minutiaes
# plotAndSaveMinutiae(minutiaes, image_enhanced_minutiae_loc)
plotAndSaveMinutiasPIL(minutiaes, image_enhanced_minutiae_loc, im.size)
minutiaes = alignMinutias(core, minutiaes) # aligning
print "aminutia: ", minutiaes
# plotAndSaveMinutiae(minutiaes, image_enhanced_minutiae_aligned_loc)
plotAndSaveMinutiasPIL(minutiaes, image_enhanced_minutiae_aligned_loc,
im.size)
des = {'minutiaes': minutiaes, 'core': core, 'bounds': bounds}
with open(dumpfilename, 'wb') as handle:
json.dump(des, handle)
print ""
def main1():
# alignMinutias((2,8, 'loop'), [[40, 120, 'bip']])
parser = argparse.ArgumentParser(
description=
"Gabor, thinning, extract minutias, calculate poincare and align minutias."
)
parser.add_argument("image", nargs=1, help="Path to image")
# parser.add_argument("block_size", nargs=1, help = "Block size")
parser.add_argument("tolerance",
nargs=1,
help="Tolerance for Poincare index")
parser.add_argument("block_size", nargs=1, help="Blocksize")
parser.add_argument(
'--preprocess',
"-p",
action='store_true',
help="Preprocess the image with: Gabor filtering and thinning")
parser.add_argument('--smooth',
"-s",
action='store_true',
help="Use Gauss for smoothing")
parser.add_argument("--save",
action='store_true',
help="Save result image as src_image_enhanced.gif")
args = parser.parse_args()
#im = Image.open(args.image[0])
# im = Image.open("ppf-test_enhanced_thinned.gif")
#im = im.convert("L") # covert to grayscale
# im.show()
W = int(args.block_size[0])
#W = 16
print "Block-size: ", W
singularity_type = None
f = lambda x, y: 2 * x * y
g = lambda x, y: x**2 - y**2
files = []
for i in range(1, 4):
for j in range(1, 4):
if i < 10:
finputminutia = '/Users/Admin/fvs/samples/DB3_B/10' + str(
i) + '_' + str(j) + '.tif'
else:
finputminutia = '/Users/Admin/fvs/samples/DB3_B/1' + str(
i) + '_' + str(j) + '.tif'
files.append({'i': i, 'j': j, 'image': finputminutia})
ffirst_singularity = {}
print "Gabor filter, thinning, pointcare, extract minutias, alignment, exporting minutias to file ..."
for file in files:
i = file['i']
j = file['j']
print "image: ", os.path.splitext(file['image'])
base_image_name = os.path.splitext(os.path.basename(file['image']))[0]
image_enhanced_loc = "/Users/Admin/fvs/samples/dumps2/" + base_image_name + "_enhanced.gif"
image_enhanced_minutiae_loc = "/Users/Admin/fvs/samples/dumps2/" + base_image_name + "_enhanced_minutiaes.png"
image_enhanced_minutiae_aligned_loc = "/Users/Admin/fvs/samples/dumps2/" + base_image_name + "_enhanced_minutiaes_aligned.png"
print "image enhanced: ", image_enhanced_loc
# f['image']
if args.preprocess or os.path.isfile(image_enhanced_loc) == False:
print "Enhanced image does not exists. Enhancing."
im = Image.open(file['image'])
im = im.convert("L") # covert to grayscale
# gabor filter
angles = utils.calculate_angles(im, W, f, g)
print "calculating orientation done"
angles = utils.smooth_angles(angles)
print "smoothing angles done"
im = gabor(im, W, angles)
# im.show()
# thinning
im = make_thin(im)
# im.show()
if args.save:
im.save(image_enhanced_loc, "GIF")
if args.preprocess:
continue
else:
print "Processing enhanced image."
im = Image.open(image_enhanced_loc)
im = im.convert("L") # covert to grayscale
# continue
# get image bounds
(maxx, maxy) = im.size
bounds = (0, maxx, 0, maxy)
print "bounds: ", bounds
angles = utils.calculate_angles(im, W, f, g)
if args.smooth:
angles = utils.smooth_angles(angles)
if i in ffirst_singularity:
singularity_type = ffirst_singularity[i]
else:
singularity_type = None
result, cores = calculate_singularities(
im,
angles,
int(args.tolerance[0]),
W,
singularity_type=singularity_type)
print "cores: ", cores
core = chooseCore(cores, W)
print "Selected core: ", core
if j == 1:
ffirst_singularity[i] = core[3]
# result.show()
# align bounds with respect to the core
# bounds2 = alignBounds(core, bounds)
# calculate minutias
im, minutiaes = calculate_minutiaes(im)
im.show()
print "minutias: ", minutiaes
# plotAndSaveMinutiae(minutiaes, image_enhanced_minutiae_loc)
plotAndSaveMinutiasPIL(minutiaes, image_enhanced_minutiae_loc, im.size)
minutiaes = alignMinutias(core, minutiaes) # aligning
print "aminutia: ", minutiaes
# plotAndSaveMinutiae(minutiaes, image_enhanced_minutiae_aligned_loc)
plotAndSaveMinutiasPIL(minutiaes, image_enhanced_minutiae_aligned_loc,
im.size)
des = {'minutiaes': minutiaes, 'core': core, 'bounds': bounds}
print os.path.splitext(file['image'])[0]
dumpfilename = "/Users/Admin/fvs/samples/dumps2/" + os.path.basename(
file['image']) + ".yaml"
with open(dumpfilename, 'wb') as handle:
json.dump(des, handle)
print ""
if im is not None:
im.close()
