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Korijn van Golen
2015-06-15 11:49:21 +02:00
parent 5aad171d13
commit c756d2d492
2 changed files with 73 additions and 16 deletions
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skimage/feature/_hog.py
+2 -2
View File
@@ -117,8 +117,8 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
# compute orientations integral images
orientation_histogram = np.zeros((n_cellsy, n_cellsx, orientations))
_hoghistogram.HogHistograms(gx, gy, cx, cy, sx, sy, n_cellsx, n_cellsy, visualise, orientations,
orientation_histogram)
_hoghistogram.HogHistograms(gx, gy, cx, cy, sx, sy, n_cellsx, n_cellsy,
visualise, orientations, orientation_histogram)
# now for each cell, compute the histogram
hog_image = None
skimage/feature/_hoghistogram.pyx
+71 -14
View File
@@ -1,43 +1,99 @@
# cython: profile=True
# cython: cdivision=True
# cython: boundscheck=False
# cython: wraparound=False
import cmath, math
cimport numpy as np
import numpy as np
from scipy import pi, arctan2, cos, sin
cimport numpy as np
# cnp.float64_t[:, :] magnitude
cdef float CellHog(np.ndarray[np.float64_t, ndim=2] magnitude,
np.ndarray[np.float64_t, ndim=2] orientation,
float ori1, float ori2,
int cx, int cy, int xi, int yi, int sx, int sy):
"""CellHog
Parameters
----------
magnitude : ndarray
Coordinate to be clipped.
orientation : ndarray
The lower bound.
ori1 : float
The higher bound.
ori2 : float
The higher bound.
cx : int
The higher bound.
cy : int
The higher bound.
xi : int
The higher bound.
yi : int
The higher bound.
sx : int
The higher bound.
sy : int
The higher bound.
Returns
-------
total : float
The total HOG value.
"""
cdef int cx1, cy1
cdef float total = 0.
for cy1 in range(-cy/2, cy/2):
for cx1 in range(-cx/2, cx/2):
if yi + cy1 < 0: continue
if yi + cy1 >= sy: continue
if xi + cx1 < 0: continue
if xi + cx1 >= sx: continue
if orientation[yi + cy1, xi + cx1] >= ori1: continue
if orientation[yi + cy1, xi + cx1] < ori2: continue
if (yi + cy1 < 0
or yi + cy1 >= sy
or xi + cx1 < 0
or xi + cx1 >= sx
or orientation[yi + cy1, xi + cx1] >= ori1
or orientation[yi + cy1, xi + cx1] < ori2): continue
total += magnitude[yi + cy1, xi + cx1]
return total
def HogHistograms(np.ndarray[np.float64_t, ndim=2] gx, \
def HogHistograms(np.ndarray[np.float64_t, ndim=2] gx,
np.ndarray[np.float64_t, ndim=2] gy,
int cx, int cy, #Pixels per cell
int sx, int sy, #Image size
int n_cellsx, int n_cellsy,
int visualise, int orientations,
np.ndarray[np.float64_t, ndim=3] orientation_histogram):
"""HogHistograms
cdef np.ndarray[np.float64_t, ndim=2] magnitude = np.sqrt(gx**2 + gy**2)
cdef np.ndarray[np.float64_t, ndim=2] orientation = arctan2(gy, gx) * (180 / pi) % 180
Parameters
----------
gx : ndarray
Coordinate to be clipped.
gy : ndarray
The lower bound.
cx : int
The higher bound.
cy : int
The higher bound.
sx : int
The higher bound.
sy : int
The higher bound.
n_cellsx : int
The higher bound.
n_cellsy : int
The higher bound.
visualise : int
The higher bound.
orientations : int
The higher bound.
orientation_histogram : ndarray
The histogram to fill.
"""
cdef np.ndarray[np.float64_t, ndim=2] magnitude = np.hypot(gx, gy)
cdef np.ndarray[np.float64_t, ndim=2] orientation = (
np.arctan2(gy, gx) * (180 / np.pi) % 180)
cdef int i, x, y, o, yi, xi, cy1, cy2, cx1, cx2
cdef float ori1, ori2
@@ -61,7 +117,8 @@ def HogHistograms(np.ndarray[np.float64_t, ndim=2] gx, \
x = cx / 2
while x < cx2:
orientation_histogram[yi, xi, i] = CellHog(magnitude, orientation, ori1, ori2, cx, cy, x, y, sx, sy)
orientation_histogram[yi, xi, i] = CellHog(magnitude,
orientation, ori1, ori2, cx, cy, x, y, sx, sy)
xi += 1
x += cx