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Change _template.match_template variables to float.

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Template and image in test are converted to float32 before passing to `match_template`. This change is temporary: `match_template` should convert these variables internally.
This commit is contained in:
Tony S Yu
2011-12-26 11:18:41 -08:00
parent e8461e22dd
commit 2a00e24b12
2 changed files with 18 additions and 16 deletions
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skimage/feature/_template.pyx
+16 -15
View File
@@ -8,12 +8,12 @@ from skimage.transform import integral
cdef extern from "math.h":
double sqrt(double x)
double fabs(double x)
float sqrt(float x)
float fabs(float x)
@cython.boundscheck(False)
cdef double sum_integral(np.ndarray[np.double_t, ndim=2, mode="c"] sat,
cdef float sum_integral(np.ndarray[float, ndim=2, mode="c"] sat,
int r0, int c0, int r1, int c1):
"""
Using a summed area table / integral image, calculate the sum
@@ -25,7 +25,7 @@ cdef double sum_integral(np.ndarray[np.double_t, ndim=2, mode="c"] sat,
Parameters
----------
sat : ndarray of double_t
sat : ndarray of float
Summed area table / integral image.
r0, c0 : int
Top-left corner of block to be summed.
@@ -37,7 +37,7 @@ cdef double sum_integral(np.ndarray[np.double_t, ndim=2, mode="c"] sat,
S : int
Sum over the given window.
"""
cdef double S = 0
cdef float S = 0
S += sat[r1, c1]
@@ -53,27 +53,28 @@ cdef double sum_integral(np.ndarray[np.double_t, ndim=2, mode="c"] sat,
@cython.boundscheck(False)
def match_template(np.ndarray[np.double_t, ndim=2, mode="c"] image,
np.ndarray[np.double_t, ndim=2, mode="c"] template,
def match_template(np.ndarray[float, ndim=2, mode="c"] image,
np.ndarray[float, ndim=2, mode="c"] template,
int num_type):
# convolve the image with template by frequency domain multiplication
cdef np.ndarray[np.double_t, ndim=2] result
cdef np.ndarray[float, ndim=2] result
# when `dtype=float` is used, ascontiguousarray returns ``double``.
result = np.ascontiguousarray(fftconvolve(image, np.fliplr(template),
mode="valid"), dtype=np.double)
mode="valid"), dtype=np.float32)
# calculate squared integral images used for normalization
cdef np.ndarray[np.double_t, ndim=2, mode="c"] integral_sum
cdef np.ndarray[np.double_t, ndim=2, mode="c"] integral_sqr
cdef np.ndarray[float, ndim=2, mode="c"] integral_sum
cdef np.ndarray[float, ndim=2, mode="c"] integral_sqr
if num_type == 1:
integral_sum = integral.integral_image(image)
integral_sqr = integral.integral_image(image**2)
# use inversed area for accuracy
cdef double inv_area = 1.0 / (template.shape[0] * template.shape[1])
cdef float inv_area = 1.0 / (template.shape[0] * template.shape[1])
# calculate template norm according to the following:
# variance ** 2 = 1/K Sigma[(x_k - mean) ** 2]
# = 1/K Sigma[x_k ** 2] - mean ** 2
cdef double template_norm
cdef double template_mean = np.mean(template)
cdef float template_norm
cdef float template_mean = np.mean(template)
if num_type == 0:
template_norm = sqrt((np.std(template) ** 2 +
@@ -83,7 +84,7 @@ def match_template(np.ndarray[np.double_t, ndim=2, mode="c"] image,
# define window of template size in squared integral image
cdef int i, j
cdef double num, window_sum2, window_mean2, normed, t,
cdef float num, window_sum2, window_mean2, normed, t,
# move window through convolution results, normalizing in the process
for i in range(result.shape[0] - 1):
for j in range(result.shape[1] - 1):
skimage/feature/tests/test_template.py
+2 -1
View File
@@ -5,8 +5,9 @@ from numpy.random import randn
def test_template():
size = 100
image = np.zeros((400, 400))
image = np.zeros((400, 400), dtype=np.float32)
target = np.tri(size) + np.tri(size)[::-1]
target = target.astype(np.float32)
target_positions = [(50, 50), (200, 200)]
for x, y in target_positions:
image[x:x + size, y:y + size] = target