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Renaming for clarity.

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Tony S Yu
2012-03-01 23:32:48 -05:00
parent f21f032bfe
commit 7caf85a5c1
1 changed files with 16 additions and 18 deletions
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skimage/feature/_template.pyx
+16 -18
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@@ -43,7 +43,7 @@ cdef extern from "math.h":
@cython.boundscheck(False)
cdef float sum_integral(np.ndarray[float, ndim=2, mode="c"] sat,
cdef float integrate(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
@@ -85,15 +85,15 @@ cdef float sum_integral(np.ndarray[float, ndim=2, mode="c"] sat,
@cython.boundscheck(False)
def match_template(np.ndarray[float, ndim=2, mode="c"] image,
np.ndarray[float, ndim=2, mode="c"] template):
cdef np.ndarray[float, ndim=2, mode="c"] result
cdef np.ndarray[float, ndim=2, mode="c"] integral_sum
cdef np.ndarray[float, ndim=2, mode="c"] integral_sqr
cdef np.ndarray[float, ndim=2, mode="c"] corr
cdef np.ndarray[float, ndim=2, mode="c"] image_sat
cdef np.ndarray[float, ndim=2, mode="c"] image_sqr_sat
cdef float template_mean = np.mean(template)
cdef float template_ssd
cdef float inv_area
integral_sum = integral.integral_image(image)
integral_sqr = integral.integral_image(image**2)
image_sat = integral.integral_image(image)
image_sqr_sat = integral.integral_image(image**2)
template -= template_mean
template_ssd = np.sum(template**2)
@@ -101,29 +101,27 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
inv_area = 1.0 / (template.shape[0] * template.shape[1])
# when `dtype=float` is used, ascontiguousarray returns ``double``.
result = np.ascontiguousarray(fftconvolve(image, np.fliplr(template),
mode="valid"), dtype=np.float32)
corr = np.ascontiguousarray(fftconvolve(image, np.fliplr(template),
mode="valid"), dtype=np.float32)
cdef int i, j
cdef float num, den, window_sqr_sum, window_mean_sqr, window_sum,
cdef float den, window_sqr_sum, window_mean_sqr, window_sum,
# move window through convolution results, normalizing in the process
for i in range(result.shape[0]):
for j in range(result.shape[1]):
num = result[i, j]
for i in range(corr.shape[0]):
for j in range(corr.shape[1]):
# subtract 1 because `i_end` and `j_end` are used for indexing into
# summed-area table, instead of slicing windows of the image.
i_end = i + template.shape[0] - 1
j_end = j + template.shape[1] - 1
window_sum = sum_integral(integral_sum, i, j, i_end, j_end)
window_sum = integrate(image_sat, i, j, i_end, j_end)
window_mean_sqr = window_sum * window_sum * inv_area
window_sqr_sum = sum_integral(integral_sqr, i, j, i_end, j_end)
window_sqr_sum = integrate(image_sqr_sat, i, j, i_end, j_end)
den = sqrt((window_sqr_sum - window_mean_sqr) * template_ssd)
if den == 0:
num = 0
corr[i, j] = 0
else:
num /= den
result[i, j] = num
return result
corr[i, j] /= den
return corr