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Fix some performance regressions in Cython implementation of FAST

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Johannes Schönberger
2013-08-23 22:39:11 +02:00
parent 3a37505d22
commit 97f0a3886c
1 changed files with 17 additions and 19 deletions
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skimage/feature/corner_cy.pyx
+17 -19
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@@ -82,22 +82,18 @@ def corner_moravec(image, Py_ssize_t window_size=1):
return np.asarray(out)
cdef int[:] RP = (np.round(3 * np.sin(2 * np.pi * np.arange(16, dtype=np.double) / 16))).astype(np.int32)
cdef int[:] CP = (np.round(3 * np.cos(2 * np.pi * np.arange(16, dtype=np.double) / 16))).astype(np.int32)
cdef inline _get_fast_corner_response(double[:, ::1] image, Py_ssize_t i,
Py_ssize_t j, char[:] bins,
char check_state, int n,
double threshold,
double[:] circle_intensities):
cdef inline double _get_fast_corner_response(double[:, ::1] image,
Py_ssize_t i, Py_ssize_t j,
char[:] bins, char state, int n,
double threshold,
double[:] circle_intensities):
cdef int consecutive_count = 0
cdef double sum_b = 0
cdef double sum_d = 0
cdef double curr_pixel = image[i, j]
cdef Py_ssize_t l, m
for l in range(15 + n):
if bins[l % 16] == check_state:
if bins[l % 16] == state:
consecutive_count += 1
if consecutive_count == n:
for m in range(16):
@@ -132,6 +128,10 @@ def _corner_fast(double[:, ::1] image, int n, double threshold):
cdef double[:, ::1] corner_response = np.zeros((rows, cols),
dtype=np.double)
cdef char[:] rp = np.array([-3, -3, -2, -1, 0, 1, 2, 3, 3, 3, 2, 1, 0,
-1, -2, -1, -3], dtype=np.int8)
cdef char[:] cp = np.array([0, 1, 2, 3, 3, 3, 2, 1, 0, -1, -2, -3,
-3, -3, -2, -3], dtype=np.int8)
cdef double[:] circle_intensities = np.zeros(16, dtype=np.double)
for i in range(3, rows - 3):
@@ -144,7 +144,7 @@ def _corner_fast(double[:, ::1] image, int n, double threshold):
upper_threshold = current_pixel + threshold
for k in range(16):
circle_intensities[k] = image[i + RP[k], j + CP[k]]
circle_intensities[k] = image[i + rp[k], j + cp[k]]
if circle_intensities[k] > upper_threshold:
# Brighter pixel
bins[k] = 'b'
@@ -165,15 +165,13 @@ def _corner_fast(double[:, ::1] image, int n, double threshold):
if speed_sum_d < 3 and speed_sum_b < 3:
continue
corner_response[i, j] = _get_fast_corner_response(image, i, j,
bins, 'b', n,
threshold,
circle_intensities)
corner_response[i, j] = \
_get_fast_corner_response(image, i, j, bins, 'b', n,
threshold, circle_intensities)
if corner_response[i, j] == 0:
corner_response[i, j] = _get_fast_corner_response(image, i, j,
bins, 'd',
n, threshold,
circle_intensities)
corner_response[i, j] = \
_get_fast_corner_response(image, i, j, bins, 'd', n,
threshold, circle_intensities)
return np.asarray(corner_response)