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Merge pull request #6 from ClinicalGraphics/fasthog

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Fasthog
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Tim Sheerman-Chase
2015-06-18 21:49:14 +01:00
parent 83739644ec 167c8eeeb6
commit f426800439
2 changed files with 39 additions and 31 deletions
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skimage/feature/_hog.py
+4 -2
View File
@@ -124,15 +124,17 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
from .. import draw
radius = min(cx, cy) // 2 - 1
orientations_arr = np.array(orientations)
orientations_arr = np.arange(orientations)
dx_arr = radius * np.cos(orientations_arr / orientations_arr * np.pi)
dy_arr = radius * np.sin(orientations_arr / orientations_arr * np.pi)
cc = cy + cy
cr = cx + cx
hog_image = np.zeros((sy, sx), dtype=float)
for x in range(n_cellsx):
for y in range(n_cellsy):
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 * cc // 2, x * cr // 2])
rr, cc = draw.line(int(centre[0] - dx),
int(centre[1] + dy),
skimage/feature/_hoghistogram.pyx
+35 -29
View File
@@ -5,8 +5,8 @@
import numpy as np
cimport numpy as cnp
cdef float cell_hog(cnp.float64_t[:, :] magnitude,
cnp.float64_t[:, :] orientation,
cdef float cell_hog(double[:, ::1] magnitude,
double[:, ::1] orientation,
float orientation_start, float orientation_end,
int cell_columns, int cell_rows,
int column_index, int row_index,
@@ -24,9 +24,9 @@ cdef float cell_hog(cnp.float64_t[:, :] magnitude,
orientation_end : float
Orientation range end.
cell_columns : int
Pixels per cell (x).
Pixels per cell (rows).
cell_rows : int
Pixels per cell (y).
Pixels per cell (columns).
column_index : int
Block column index.
row_index : int
@@ -41,28 +41,36 @@ cdef float cell_hog(cnp.float64_t[:, :] magnitude,
total : float
The total HOG value.
"""
cdef int cell_column, cell_row
cdef int cell_column, cell_row, cell_row_index, cell_column_index, \
range_columns_start, range_columns_stop, range_rows_start, \
range_rows_stop
range_rows_stop = cell_rows/2
range_rows_start = -range_rows_stop
range_columns_stop = cell_columns/2
range_columns_start = -range_columns_stop
cdef float total = 0.
for cell_row in range(-cell_rows/2, cell_rows/2):
for cell_row in range(range_rows_start, range_rows_stop):
cell_row_index = row_index + cell_row
if (cell_row_index < 0 or cell_row_index >= size_rows):
continue
for cell_column in range(-cell_columns/2, cell_columns/2):
for cell_column in range(range_columns_start, range_columns_stop):
cell_column_index = column_index + cell_column
if (cell_column_index < 0 or cell_column_index >= size_columns
or orientation[cell_row_index, cell_column_index]
or orientation[cell_row_index, cell_column_index]
>= orientation_start
or orientation[cell_row_index, cell_column_index]
< orientation_end): continue
or orientation[cell_row_index, cell_column_index]
< orientation_end):
continue
total += magnitude[cell_row_index, cell_column_index]
return total
def hog_histograms(cnp.float64_t[:, :] gradient_columns,
cnp.float64_t[:, :] gradient_rows,
def hog_histograms(double[:, ::1] gradient_columns,
double[:, ::1] gradient_rows,
int cell_columns, int cell_rows,
int size_columns, int size_rows,
int number_of_cells_columns, int number_of_cells_rows,
@@ -73,39 +81,39 @@ def hog_histograms(cnp.float64_t[:, :] gradient_columns,
Parameters
----------
gradient_columns : ndarray
First order image gradients (x).
First order image gradients (rows).
gradient_rows : ndarray
First order image gradients (y).
First order image gradients (columns).
cell_columns : int
Pixels per cell (x).
Pixels per cell (rows).
cell_rows : int
Pixels per cell (y).
Pixels per cell (columns).
size_columns : int
Number of columns.
size_rows : int
Number of rows.
number_of_cells_columns : int
Number of cells (x).
Number of cells (rows).
number_of_cells_rows : int
Number of cells (y).
Number of cells (columns).
number_of_orientations : int
Number of orientation bins.
orientation_histogram : ndarray
The histogram array which is modified in place.
"""
cdef cnp.float64_t[:, :] magnitude = np.hypot(gradient_columns,
cdef double[:, ::1] magnitude = np.hypot(gradient_columns,
gradient_rows)
cdef cnp.float64_t[:, :] orientation = \
cdef double[:, ::1] orientation = \
np.arctan2(gradient_rows, gradient_columns) * (180 / np.pi) % 180
cdef int i, x, y, o, yi, xi, cy1, cy2, cx1, cx2
cdef float orientation_start, orientation_end
cdef int i, x, y, o, yi, xi, cc, cr, x0, y0
cdef float orientation_start, orientation_end, \
number_of_orientations_per_180
x0 = cell_columns / 2
y0 = cell_rows / 2
cy2 = cell_rows * number_of_cells_rows
cx2 = cell_columns * number_of_cells_columns
cc = cell_rows * number_of_cells_rows
cr = cell_columns * number_of_cells_columns
number_of_orientations_per_180 = 180. / number_of_orientations
# compute orientations integral images
@@ -113,17 +121,16 @@ def hog_histograms(cnp.float64_t[:, :] gradient_columns,
# isolate orientations in this range
orientation_start = number_of_orientations_per_180 * (i + 1)
orientation_end = number_of_orientations_per_180 * i
x = x0
y = y0
yi = 0
xi = 0
while y < cy2:
while y < cc:
xi = 0
x = x0
while x < cx2:
while x < cr:
orientation_histogram[yi, xi, i] = cell_hog(magnitude,
orientation, orientation_start, orientation_end,
cell_columns, cell_rows, x, y, size_columns, size_rows)
@@ -132,4 +139,3 @@ def hog_histograms(cnp.float64_t[:, :] gradient_columns,
yi += 1
y += cell_rows