wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-08-14 12:50:39 +08:00
Code Issues Packages Projects Releases Wiki Activity

Moved more computations out of loops and added division from future

Browse Source
This commit is contained in:
Ivo Flipse
2015-06-18 18:55:32 +02:00
parent f633262f03
commit ded652c2ab
2 changed files with 11 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/feature/_hog.py
+7 -3
View File
@@ -1,3 +1,4 @@
from __future__ import division
import numpy as np import numpy as np
from .._shared.utils import assert_nD from .._shared.utils import assert_nD
from . import _hoghistogram from . import _hoghistogram
@@ -123,13 +124,16 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
from .. import draw from .. import draw
radius = min(cx, cy) // 2 - 1 radius = min(cx, cy) // 2 - 1
orientations_arr = np.array(orientations)
dx_arr = radius * np.cos(orientations_arr / orientations_arr * np.pi)
dy_arr = radius * np.sin(orientations_arr / orientations_arr * np.pi)
hog_image = np.zeros((sy, sx), dtype=float) hog_image = np.zeros((sy, sx), dtype=float)
for x in range(n_cellsx): for x in range(n_cellsx):
for y in range(n_cellsy): for y in range(n_cellsy):
for o in range(orientations): for o, dx, dy in zip(orientations_arr, dx_arr, dy_arr):
centre = tuple([y * cy + cy // 2, x * cx + cx // 2]) centre = tuple([y * cy + cy // 2, x * cx + cx // 2])
dx = radius * np.cos(float(o) / orientations * np.pi)
dy = radius * np.sin(float(o) / orientations * np.pi)
rr, cc = draw.line(int(centre[0] - dx), rr, cc = draw.line(int(centre[0] - dx),
int(centre[1] + dy), int(centre[1] + dy),
int(centre[0] + dx), int(centre[0] + dx),
skimage/feature/_hoghistogram.pyx
+4 -2
View File
@@ -106,11 +106,13 @@ def hog_histograms(cnp.float64_t[:, :] gradient_columns,
cy2 = cell_rows * number_of_cells_rows cy2 = cell_rows * number_of_cells_rows
cx2 = cell_columns * number_of_cells_columns cx2 = cell_columns * number_of_cells_columns
number_of_orientations_per_180 = 180. / number_of_orientations
# compute orientations integral images # compute orientations integral images
for i in range(number_of_orientations): for i in range(number_of_orientations):
# isolate orientations in this range # isolate orientations in this range
orientation_start = 180. / number_of_orientations * (i + 1) orientation_start = number_of_orientations_per_180 * (i + 1)
orientation_end = 180. / number_of_orientations * i orientation_end = number_of_orientations_per_180 * i
x = x0 x = x0
y = y0 y = y0