def test_trainData(self):
"""
Tests data loading process.
"""
fm = FingerMatch('bf')
fm.loadData(PATH, limit=3)
fm.trainData()
for i, img in enumerate(fm.images):
self.assertIsNotNone(
img.descriptors,
msg=
f'ERROR: Profile not built for the {i}-th image in the class.')
self.assertNotEqual(
len(img.descriptors),
0,
msg=
f'ERROR: Profile not built for the {i}-th image in the class.')
# Test
path_test = '../data/Fingerprints - Set B/101_1.tif'
# Image loading
img_test = load_image(path_test, True)
scores = fm.matchFingerprint(img_test, verbose=False)
def test_process_minutiae(self):
# Tests minutiae creation output.
path = '../../data/Fingerprints - Set A/101_1.tif'
img = load_image(path, True)
img = enhance_image(img, padding=5)
minutiae = process_minutiae(img)
self.assertIsNotNone(minutiae, msg='ERROR: Wrong output.')
def test_enhance_image(self):
# Tests image enhancing output and data loss.
# Revert gray colour levels. Match scale with the raw image for comparison.
path = '../../data/Fingerprints - Set A/101_1.tif'
# Image loading
img = load_image(path, True)
img = enhance_image(img, padding=5)
def test_matchFingerprintTree(self):
"""
"""
path_base = '/Users/Orchestrator/Desktop/Toptal/Fingerprints/dragos-iliuta/data/Others/base'
path_test = '/Users/Orchestrator/Desktop/Toptal/Fingerprints/dragos-iliuta/data/Others/test'
fmt = FingerMatch('tree')
fmt.loadData(path_base, limit=3)
fmt.trainData()
# Image loading
path_test = f'{path_test}/102_2.tif'
img_test = load_image(path_test, True)
fmt.matchFingerprint(img_test, verbose=False)
def test_matchFingerprintBF(self):
"""
"""
path_base = '/Users/Orchestrator/Desktop/Toptal/Fingerprints/dragos-iliuta/data/Others/base'
path_test = '/Users/Orchestrator/Desktop/Toptal/Fingerprints/dragos-iliuta/data/Others/test'
fm = FingerMatch('bf')
fm.loadData(path_base, limit=3)
fm.trainData()
# Image loading
img_test = load_image(f'{path_test}/101_2.tif', True)
fm.matchFingerprint(img_test, verbose=False)
def loadData(self,
path: str,
image_format: str = 'tif',
limit: int = None) -> None:
"""
Load data that matches the image_format, from the given path. Each image is processed and stored.
"""
img_paths = [glob.glob(f'{path}/*.tif', recursive=True)][0]
try:
assert len(img_paths) > 0
except:
raise FileNotFoundError(
f'ERROR: No image files available to extract from the path {path}'
)
if limit is not None:
# Restrict sample size.
img_paths = img_paths[:limit]
start = time.time()
for p in tqdm(img_paths, desc='Loading data.'):
# Image loading
image = load_image(p, True)
image = (image_enhance(image) * 255).astype('uint8')
try:
# Image properties definition.
img_id = re.search(f'(.+?).{image_format}',
os.path.basename(p)).group(1)
except AttributeError:
raise Exception(f'ERROR: Unknown image id for {p}')
# Create new profile for the given image and store it.
self.images.append(Image(img_id, p, image, None))
print(
f'\nINFO: Dataset loaded successfully. Duration: {round(time.time() - start, 2)} sec'
)
from libs.basics import load_image, display_image
from libs.minutiae import process_minutiae, generate_tuple_profile
from libs.enhancing import enhance_image
from libs.matching import match_tuples, build_edges, edge_matching
import unittest
path_base = '../../data/Fingerprints - Set A/101_2.tif'
path_test = '../../data/Fingerprints - Set A/101_2.tif'
img_base = load_image(path_base, True)
img_base = enhance_image(img_base, padding=5)
img_test = load_image(path_test, True)
img_test = enhance_image(img_test, padding=5)
# Confirmed point matching.
TUPLE_BASE = generate_tuple_profile(process_minutiae(img_base))
TUPLE_TEST = generate_tuple_profile(process_minutiae(img_test))
class TestMatching(unittest.TestCase):
def test_match_tuples(self):
# Tests minutiae creation output.
ccpb_base, ccpb_test = match_tuples(TUPLE_BASE, TUPLE_TEST)
print('---')
def test_edge_matching(self):