From 823d84a77a6565ceb98ba7c1f52cc0264ff88206 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Tue, 26 Apr 2022 17:24:23 +0200
Subject: [PATCH] Removed bob.sp test requirement
---
bob/bio/vein/tests/test_tools.py | 155 -------------------------------
1 file changed, 155 deletions(-)
diff --git a/bob/bio/vein/tests/test_tools.py b/bob/bio/vein/tests/test_tools.py
index 86d876b..dda3ac2 100644
--- a/bob/bio/vein/tests/test_tools.py
+++ b/bob/bio/vein/tests/test_tools.py
@@ -646,161 +646,6 @@ def test_intersection_ratio():
)
-def test_correlation():
-
- # A test for convolution performance. Correlations are used on the Miura
- # Match algorithm, therefore we want to make sure we can perform them as fast
- # as possible.
- import numpy
- import scipy.signal
- import bob.sp
-
- # Rough example from Vera fingervein database
- Y = 250
- X = 600
- CH = 80
- CW = 90
-
- def gen_ab():
- a = numpy.random.randint(256, size=(Y, X)).astype(float)
- b = numpy.random.randint(256, size=(Y - CH, X - CW)).astype(float)
- return a, b
-
- def bob_function(a, b):
-
- # rotate input image by 180 degrees
- b = numpy.rot90(b, k=2)
-
- # Determine padding size in x and y dimension
- size_a = numpy.array(a.shape)
- size_b = numpy.array(b.shape)
- outsize = size_a + size_b - 1
-
- # Determine 2D cross correlation in Fourier domain
- a2 = numpy.zeros(outsize)
- a2[0 : size_a[0], 0 : size_a[1]] = a
- Fa = bob.sp.fft(a2.astype(numpy.complex128))
-
- b2 = numpy.zeros(outsize)
- b2[0 : size_b[0], 0 : size_b[1]] = b
- Fb = bob.sp.fft(b2.astype(numpy.complex128))
-
- conv_ab = numpy.real(bob.sp.ifft(Fa * Fb))
-
- h, w = size_a - size_b + 1
-
- Nm = conv_ab[
- size_b[0] - 1 : size_b[0] - 1 + h, size_b[1] - 1 : size_b[1] - 1 + w
- ]
-
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-
- # this is our output
- Nmm = Nm[t0, s0]
-
- # normalizes the output by the number of pixels lit on the input
- # matrices, taking into consideration the surface that produced the
- # result (i.e., the eroded model and part of the probe)
- h, w = b.shape
- return Nmm / (
- sum(sum(b)) + sum(sum(a[t0 : t0 + h - 2 * CH, s0 : s0 + w - 2 * CW]))
- )
-
- def scipy_function(a, b):
- b = numpy.rot90(b, k=2)
-
- Nm = scipy.signal.convolve2d(a, b, "valid")
-
- # figures out where the maximum is on the resulting matrix
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-
- # this is our output
- Nmm = Nm[t0, s0]
-
- # normalizes the output by the number of pixels lit on the input
- # matrices, taking into consideration the surface that produced the
- # result (i.e., the eroded model and part of the probe)
- h, w = b.shape
- return Nmm / (
- sum(sum(b)) + sum(sum(a[t0 : t0 + h - 2 * CH, s0 : s0 + w - 2 * CW]))
- )
-
- def scipy2_function(a, b):
- b = numpy.rot90(b, k=2)
- Nm = scipy.signal.fftconvolve(a, b, "valid")
-
- # figures out where the maximum is on the resulting matrix
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-
- # this is our output
- Nmm = Nm[t0, s0]
-
- # normalizes the output by the number of pixels lit on the input
- # matrices, taking into consideration the surface that produced the
- # result (i.e., the eroded model and part of the probe)
- h, w = b.shape
- return Nmm / (
- sum(sum(b)) + sum(sum(a[t0 : t0 + h - 2 * CH, s0 : s0 + w - 2 * CW]))
- )
-
- def scipy3_function(a, b):
- Nm = scipy.signal.correlate2d(a, b, "valid")
-
- # figures out where the maximum is on the resulting matrix
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-
- # this is our output
- Nmm = Nm[t0, s0]
-
- # normalizes the output by the number of pixels lit on the input
- # matrices, taking into consideration the surface that produced the
- # result (i.e., the eroded model and part of the probe)
- h, w = b.shape
- return Nmm / (
- sum(sum(b)) + sum(sum(a[t0 : t0 + h - 2 * CH, s0 : s0 + w - 2 * CW]))
- )
-
- a, b = gen_ab()
-
- assert numpy.allclose(bob_function(a, b), scipy_function(a, b))
- assert numpy.allclose(scipy_function(a, b), scipy2_function(a, b))
- assert numpy.allclose(scipy2_function(a, b), scipy3_function(a, b))
-
- # if you want to test timings, uncomment the following section
- """
- import time
-
- start = time.clock()
- N = 10
- for i in range(N):
- a, b = gen_ab()
- bob_function(a, b)
- total = time.clock() - start
- print('bob implementation, %d iterations - %.2e per iteration' % (N, total/N))
-
- start = time.clock()
- for i in range(N):
- a, b = gen_ab()
- scipy_function(a, b)
- total = time.clock() - start
- print('scipy+convolve, %d iterations - %.2e per iteration' % (N, total/N))
-
- start = time.clock()
- for i in range(N):
- a, b = gen_ab()
- scipy2_function(a, b)
- total = time.clock() - start
- print('scipy+fftconvolve, %d iterations - %.2e per iteration' % (N, total/N))
-
- start = time.clock()
- for i in range(N):
- a, b = gen_ab()
- scipy3_function(a, b)
- total = time.clock() - start
- print('scipy+correlate2d, %d iterations - %.2e per iteration' % (N, total/N))
- """
-
-
def test_hamming_distance():
from ..algorithm.HammingDistance import HammingDistance
--
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