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1/3 of the doc-strings complete. Doc decorator created and added to a _utilies.py module.

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sccolbert
2009-10-30 00:33:53 +01:00
parent 43cc6b95a1
commit 592c5793dc
2 changed files with 3878 additions and 2549 deletions
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scikits/image/opencv/opencv_cv.c
+3584 -2485
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scikits/image/opencv/opencv_cv.pyx
+294 -64
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@@ -13,6 +13,7 @@ from opencv_constants import *
from opencv_cv import *
from _libimport import cv
from _utilities import cvdoc
# setup numpy tables for this module
np.import_array()
@@ -276,18 +277,37 @@ cdef cvDrawChessboardCornersPtr c_cvDrawChessboardCorners
c_cvDrawChessboardCorners = (<cvDrawChessboardCornersPtr*><size_t>
ctypes.addressof(cv.cvDrawChessboardCorners))[0]
####################################
#-------------------------------------------------------------------------------
# Function Implementations
####################################
#-------------------------------------------------------------------------------
#--------
# cvSobel
#--------
@cvdoc(
description = \
'''Apply the Sobel operator to the input image.''',
signature = \
'''cvSobel(src, xorder=1, yorder=0, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, int8, float32]
The source image.
xorder : integer
The x order of the Sobel operator.
yorder : integer
The y order of the Sobel operator.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new which is the result of applying the Sobel
operator to src.''',
package = 'cv',
group = 'image')
def cvSobel(np.ndarray src, int xorder=1, int yorder=0,
int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_dtype(src, [UINT8, INT8, FLOAT32])
assert_nchannels(src, [1])
@@ -312,13 +332,28 @@ def cvSobel(np.ndarray src, int xorder=1, int yorder=0,
return out
def cvLaplace(np.ndarray src, int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
#----------
# cvLaplace
#----------
@cvdoc(
description = \
'''Apply the Laplace operator to the input image.''',
signature = \
'''cvLaplace(src, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, int8, float32]
The source image.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new which is the result of applying the Laplace
operator to src.''',
package = 'cv',
group = 'image')
def cvLaplace(np.ndarray src, int aperture_size=3):
validate_array(src)
assert_dtype(src, [UINT8, INT8, FLOAT32])
@@ -344,15 +379,34 @@ def cvLaplace(np.ndarray src, int aperture_size=3):
return out
#--------
# cvCanny
#--------
@cvdoc(
description = \
'''Apply Canny edge detection to the input image.''',
signature = \
'''cvCanny(src, threshold1=10, threshold2=50, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8]
The source image.
threshold1 : float
The lower threshold used for edge linking.
threshold2 : float
The upper threshold used to find strong edges.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new which is the result of applying Canny
edge detection to src.''',
package = 'cv',
group = 'image')
def cvCanny(np.ndarray src, double threshold1=10, double threshold2=50,
int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_dtype(src, [UINT8])
assert_nchannels(src, [1])
@@ -372,12 +426,27 @@ def cvCanny(np.ndarray src, double threshold1=10, double threshold2=50,
return out
#------------------
# cvPreCornerDetect
#------------------
@cvdoc(
description = \
'''Calculate the feature map for corner detection.''',
signature = \
'''cvPreCornerDetect(src, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, float32]
The source image.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new array of the corner candidates.''',
package = 'cv',
group = 'image')
def cvPreCornerDetect(np.ndarray src, int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_dtype(src, [UINT8, FLOAT32])
@@ -398,15 +467,36 @@ def cvPreCornerDetect(np.ndarray src, int aperture_size=3):
return out
#-------------------------
# cvCornerEigenValsAndVecs
#-------------------------
@cvdoc(
description = \
'''Calculates the eigenvalues and eigenvectors of image
blocks for corner detection.''',
signature = \
'''cvCornerEigenValsAndVecs(src, block_size=3, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, float32]
The source image.
block_size : integer
The size of the neighborhood in which to calculate
the eigenvalues and eigenvectors.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new array of the eigenvalues and eigenvectors.
The shape of this array is (height, width, 6),
Where height and width are the same as that
of src.''',
package = 'cv',
group = 'image')
def cvCornerEigenValsAndVecs(np.ndarray src, int block_size=3,
int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_nchannels(src, [1])
assert_dtype(src, [UINT8, FLOAT32])
@@ -430,13 +520,32 @@ def cvCornerEigenValsAndVecs(np.ndarray src, int block_size=3,
return out.reshape(out.shape[0], -1, 6)
#--------------------
# cvCornerMinEigenVal
#--------------------
@cvdoc(
description = \
'''Calculates the minimum eigenvalues of gradient matrices
for corner detection.''',
signature = \
'''cvCornerMinEigenVal(src, block_size=3, aperture_size=3)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, float32]
The source image.
block_size : integer
The size of the neighborhood in which to calculate
the eigenvalues.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.''',
returns = \
'''out : ndarray
A new array of the eigenvalues.''',
package = 'cv',
group = 'image')
def cvCornerMinEigenVal(np.ndarray src, int block_size=3,
int aperture_size=3):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_nchannels(src, [1])
@@ -457,13 +566,40 @@ def cvCornerMinEigenVal(np.ndarray src, int block_size=3,
return out
#---------------
# cvCornerHarris
#---------------
@cvdoc(
description = \
'''Applies the Harris edge detector to the input image.''',
signature = \
'''cvCornerHarris(src, block_size=3, aperture_size=3, k=0.04)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, float32]
The source image.
block_size : integer
The size of the neighborhood in which to apply the detector.
aperture_size : integer=[3, 5, 7]
The size of the Sobel kernel.
k : float
Harris detector free parameter. See Notes.''',
returns = \
'''out : ndarray
A new array of the Harris corners.''',
notes = \
'''The function cvCornerHarris() runs the Harris edge
detector on the image. Similarly to cvCornerMinEigenVal()
and cvCornerEigenValsAndVecs(), for each pixel it calculates
a gradient covariation matrix M over a block_size X block_size
neighborhood. Then, it stores det(M) - k * trace(M)**2
to the output image. Corners in the image can be found as the
local maxima of the output image.''',
package = 'cv',
group = 'image')
def cvCornerHarris(np.ndarray src, int block_size=3, int aperture_size=3,
double k=0.04):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_nchannels(src, [1])
@@ -484,16 +620,46 @@ def cvCornerHarris(np.ndarray src, int block_size=3, int aperture_size=3,
return out
#-------------------
# cvFindCornerSubPix
#-------------------
@cvdoc(
description = \
'''Refines corner locations to sub-pixel accuracy.''',
signature = \
'''cvFindCornerSubPix(src, corners, win, zero_zone=(-1, -1),
iterations=0, epsilon=1e-5)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8]
The source image.
corners : ndarray, shape=(N x 2)
An initial approximation of the corners in the image.
The corners will be refined in-place in this array.
win : tuple, (height, width)
The window within which the function iterates until it
converges on the real corner. The actual window is twice
the size of what is declared here. (an OpenCV peculiarity).
zero_zone : Half of the size of the dead region in the middle
of the search zone over which the calculations are not
performed. It is used sometimes to avoid possible
singularities of the autocorrelation matrix.
The value of (-1,-1) indicates that there is no such size.
iterations : integer
The maximum number of iterations to perform. If 0,
the function iterates until the error is less than epsilon.
epsilon : float
The epsilon error, below which the function terminates.
Can be used in combination with iterations.''',
returns = \
'''None. The array corners is modified in place.''',
package = 'cv',
group = 'image')
def cvFindCornerSubPix(np.ndarray src, np.ndarray corners, win,
zero_zone=(-1, -1), int iterations=0,
double epsilon=1e-5):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
validate_array(src)
assert_nchannels(src, [1])
assert_dtype(src, [UINT8])
@@ -528,17 +694,55 @@ def cvFindCornerSubPix(np.ndarray src, np.ndarray corners, win,
c_cvFindCornerSubPix(&srcimg, cvcorners, count, cvwin, cvzerozone, crit)
return corners
return None
#----------------------
# cvGoodFeaturesToTrack
#----------------------
@cvdoc(
description = \
'''Determines strong corners in an image.''',
signature = \
'''cvGoodFeaturesToTrack(src, corner_count, quality_level,
min_distance, block_size=3,
use_harris=0, k=0.04)''',
parameters = \
'''src : ndarray, 2D, dtype=[uint8, float32]
The source image.
corner_count : int
The maximum number of corners to find.
Only found corners are returned.
quality_level : float
Multiplier for the max/min eigenvalue;
specifies the minimal accepted quality of
image corners.
min_distance : float
Limit, specifying the minimum possible
distance between the returned corners;
Euclidian distance is used.
block_size : integer
The size of the neighborhood in which to apply the detector.
use_harris : integer
If nonzero, Harris operator (cvCornerHarris())
is used instead of default cvCornerMinEigenVal()
k : float
Harris detector free parameter.
Used only if use_harris != 0.''',
returns = \
'''out : ndarray
The locations of the found corners in the image.''',
notes = \
'''This function finds distinct and strong corners
in an image which can be used as features in a tracking
algorithm. It also insures that features are distanced
from one another by at least min_distance.''',
package = 'cv',
group = 'image')
def cvGoodFeaturesToTrack(np.ndarray src, int corner_count,
double quality_level, double min_distance,
np.ndarray mask=None, int block_size=3,
int use_harris=0, double k=0.04):
"""
better doc string needed.
for now:
http://opencv.willowgarage.com/documentation/cvreference.html
"""
int block_size=3, int use_harris=0, double k=0.04):
validate_array(src)
assert_dtype(src, [UINT8, FLOAT32])
@@ -565,12 +769,9 @@ def cvGoodFeaturesToTrack(np.ndarray src, int corner_count,
populate_iplimage(src, &srcimg)
populate_iplimage(eig, &eigimg)
populate_iplimage(temp, &tempimg)
if mask is None:
maskimg = NULL
else:
validate_array(mask)
assert_nchannels(mask, [1])
populate_iplimage(mask, maskimg)
# don't need to support ROI. The user can just pass a slice.
maskimg = NULL
c_cvGoodFeaturesToTrack(&srcimg, &eigimg, &tempimg, cvcorners,
&ncorners_found, quality_level, min_distance,
@@ -579,10 +780,8 @@ def cvGoodFeaturesToTrack(np.ndarray src, int corner_count,
return out[:ncorners_found]
def cvGetRectSubPix(np.ndarray src, size, center):
''' Retrieves the pixel rectangle from an image with
sub-pixel accuracy.
'''
Paramters:
src - source image.
size - two tuple (height, width) of rectangle (ints)
@@ -596,6 +795,37 @@ def cvGetRectSubPix(np.ndarray src, size, center):
A new image of the extracted rectangle. The same dtype as the src image.
'''
#----------------
# cvGetRectSubPix
#----------------
@cvdoc(
description = \
'''Retrieves the pixel rectangle from an image with
sub-pixel accuracy.''',
signature = \
'''cvGetRectSubPix(src, size, center)''',
parameters = \
'''src : ndarray
The source image.
size : two tuple, integers, (height, width)
The size of the rectangle to extract.
center : two tuple, floats, (x, y)
The center location of the rectangle.
The center must lie within the image, but the
rectangle may extend beyond the bounds of the image.''',
returns = \
'''out : ndarray
The extracted rectangle of the image.''',
notes = \
'''The center of the specified rectangle must
lie within the image, but the bounds of the rectangle
may extend beyond the image. Border replication is used
to fill in missing pixels.''',
package = 'cv',
group = 'image')
def cvGetRectSubPix(np.ndarray src, size, center):
validate_array(src)
cdef np.npy_intp* shape = clone_array_shape(src)