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Minor cleanup

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* Rename some variables.
* Delete some old comments.
* Move some variable initializations to the top of the function.
This commit is contained in:
Tony S Yu
2012-05-08 21:32:07 -04:00
parent 883dd24cdb
commit 753e999a7a
1 changed files with 18 additions and 21 deletions
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skimage/feature/_template.pyx
+18 -21
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@@ -56,24 +56,22 @@ cdef float sum_integral(np.ndarray[float, ndim=2, mode="c"] sat,
def match_template(np.ndarray[float, ndim=2, mode="c"] image,
np.ndarray[float, ndim=2, mode="c"] template,
str method):
# convolve the image with template by frequency domain multiplication
cdef np.ndarray[float, ndim=2] result
cdef np.ndarray[float, ndim=2, mode="c"] integral_sum
cdef np.ndarray[float, ndim=2, mode="c"] integral_sqr
cdef float template_mean = np.mean(template)
cdef float template_norm
cdef float inv_area
# when `dtype=float` is used, ascontiguousarray returns ``double``.
result = np.ascontiguousarray(fftconvolve(image, np.fliplr(template),
mode="valid"), dtype=np.float32)
# calculate squared integral images used for normalization
cdef np.ndarray[float, ndim=2, mode="c"] integral_sum
cdef np.ndarray[float, ndim=2, mode="c"] integral_sqr
if method == 'norm-coeff':
integral_sum = integral.integral_image(image)
integral_sqr = integral.integral_image(image**2)
# use inversed area for accuracy
cdef float inv_area = 1.0 / (template.shape[0] * template.shape[1])
cdef float template_norm
cdef float template_mean = np.mean(template)
inv_area = 1.0 / (template.shape[0] * template.shape[1])
if method == 'norm-corr':
# calculate template norm according to the following:
@@ -87,9 +85,8 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
else:
template_norm = sqrt((template_mean ** 2)) / sqrt(inv_area)
# define window of template size in squared integral image
cdef int i, j
cdef float num, window_sum2, window_mean2, normed, t,
cdef float num, 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]):
@@ -99,19 +96,19 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
i_end = i + template.shape[0] - 1
j_end = j + template.shape[1] - 1
window_mean2 = 0
window_mean_sqr = 0
if method == 'norm-coeff':
t = sum_integral(integral_sum, i, j, i_end, j_end)
window_mean2 = t * t * inv_area
num -= t*template_mean
# calculate squared template window sum in the image
window_sum2 = sum_integral(integral_sqr, i, j, i_end, j_end)
window_sum = sum_integral(integral_sum, i, j, i_end, j_end)
window_mean_sqr = window_sum * window_sum * inv_area
num -= window_sum * template_mean
normed = sqrt(window_sum2 - window_mean2) * template_norm
window_sqr_sum = sum_integral(integral_sqr, i, j, i_end, j_end)
den = sqrt(window_sqr_sum - window_mean_sqr) * template_norm
# enforce some limits
if fabs(num) < normed:
num /= normed
elif fabs(num) < normed*1.125:
if fabs(num) < den:
num /= den
elif fabs(num) < den * 1.125:
if num > 0:
num = 1
else: