def smooth(grayscale, gaussian_std=5.0):
"""
Smooths an image with a Gaussian filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param gaussian_std: Std-deviation of the Gaussian filter.
:type gaussian_std: float,>0
:return: smoothed: The smoothed image.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
grayscale = grayscale.astype('float32')
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
smoothed = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Smooth(im, smoothed, cv.CV_GAUSSIAN, 0, 0, gaussian_std)
result_a = cv_to_numpy(smoothed).squeeze()
return result_a
def gradient(grayscale, aperture_size=3):
"""
Computes the gradient of an image using a Sobel filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param aperture_size: Aperture of the Sobel filter (odd).
:type aperture_size: int,odd,>=1
:return: gx: Gradient in the *x* direction.
:rtype: array(HxW,float)
:return: gy: Gradient in the *y* direction.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 1, 0, aperture_size)
gx = cv_to_numpy(sobel).squeeze()
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 0, 1, aperture_size)
gy = cv_to_numpy(sobel).squeeze()
return gx.astype('float32'), gy.astype('float32')
def smooth(grayscale, gaussian_std=5.0):
"""
Smooths an image with a Gaussian filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param gaussian_std: Std-deviation of the Gaussian filter.
:type gaussian_std: float,>0
:return: smoothed: The smoothed image.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
grayscale = grayscale.astype('float32')
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
smoothed = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Smooth(im, smoothed, cv.CV_GAUSSIAN, 0, 0, gaussian_std)
result_a = cv_to_numpy(smoothed).squeeze()
return result_a
def gradient(grayscale, aperture_size=3):
"""
Computes the gradient of an image using a Sobel filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param aperture_size: Aperture of the Sobel filter (odd).
:type aperture_size: int,odd,>=1
:return: gx: Gradient in the *x* direction.
:rtype: array(HxW,float)
:return: gy: Gradient in the *y* direction.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 1, 0, aperture_size)
gx = cv_to_numpy(sobel).squeeze()
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 0, 1, aperture_size)
gy = cv_to_numpy(sobel).squeeze()
return gx.astype('float32'), gy.astype('float32')
def posneg(value, max_value=None, skim=0, nan_color=[0.5, 0.5, 0.5], zero_color=[1.0, 1.0, 1.0]):
"""
Converts a 2D float value to a RGB representation, where
red is positive, blue is negative, white is zero.
:param value: The field to represent.
:type value: array[HxW]
:param max_value: Maximum of absolute value (if None, detect).
:type max_value: float,>0
:param skim: Fraction to skim (in percent).
:type skim: float,>0,<100
:param nan_color: Color to give for regions of NaN and Inf.
:type nan_color: color
:return: posneg: A RGB image.
:rtype: array[HxWx3](uint8)
"""
check_2d_array(value, 'input to posneg')
# TODO: put this in reprep
value = value.copy()
if value.ndim > 2:
value = value.squeeze()
if value.dtype == np.dtype('uint8'):
value = value.astype('float32')
if len(value.shape) != 2:
raise Exception('I expected a H x W image, got shape %s.' %
str(value.shape))
isfinite = np.isfinite(value)
isnan = np.logical_not(isfinite)
# set nan to 0
value[isnan] = 0
if max_value is None:
abs_value = abs(value)
if skim != 0:
abs_value = skim_top(abs_value, skim)
max_value = np.max(abs_value)
if max_value == 0:
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
for i in range(3):
result[:, :, i] = zero_color[i] * 255
return result
assert np.isfinite(max_value)
positive = np.minimum(np.maximum(value, 0), max_value) / max_value
negative = np.maximum(np.minimum(value, 0), -max_value) / -max_value
positive_part = (positive * 255).astype('uint8')
negative_part = (negative * 255).astype('uint8')
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
anysign = np.maximum(positive_part, negative_part)
R = 255 - negative_part[:, :]
G = 255 - anysign
B = 255 - positive_part[:, :]
# remember the nans
R[isnan] = nan_color[0] * 255
G[isnan] = nan_color[1] * 255
B[isnan] = nan_color[2] * 255
result[:, :, 0] = R
result[:, :, 1] = G
result[:, :, 2] = B
return result
def scale(value, min_value=None, max_value=None,
min_color=[1, 1, 1], max_color=[0, 0, 0],
nan_color=[1, 0, 0]):
"""
Provides a RGB representation of the values by interpolating the range
[min(value),max(value)] into the colorspace [min_color, max_color].
:param value: The field to represent.
:type value: array[HxW],H>0,W>0
:param max_value: If specified, everything *above* is clipped.
:type max_value: float
:param min_value: If specified, everything *below* is clipped.
:type min_value: float
:param min_color: Color to give to the minimum values.
:type min_color: color
:param max_color: Color to give to the maximum values.
:type max_color: color
:param nan_color: Color to give for regions of NaN and Inf.
:type nan_color: color
:return: scale: A RGB image.
:rtype: array[HxWx3](uint8)
"""
# Raises :py:class:`.ValueError` if min_value == max_value
check_2d_array(value, 'input to scale()')
# assert_finite(value)
value = value.copy()
if value.ndim > 2:
value = value.squeeze()
if value.dtype == np.dtype('uint8') or value.dtype == np.dtype('int'):
value = value.astype('float32')
# require_shape((gt(0), gt(0)), value)
min_color = np.array(min_color)
max_color = np.array(max_color)
nan_color = np.array(nan_color)
# require_shape((3,), min_color)
# require_shape((3,), max_color)
isnan = np.logical_not(np.isfinite(value))
if max_value is None:
value[isnan] = -np.inf
max_value = np.max(value)
if min_value is None:
value[isnan] = np.inf
min_value = np.min(value)
if (max_value == min_value
or np.isnan(min_value)
or np.isnan(max_value)):
# XXX: maybe allow set this case as an exception?
# raise ValueError('I end up with max_value = %s = %s = min_value.' % \
# (max_value, min_value))
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
result[:, :, :] = 255
return result
value01 = (value - min_value) / (max_value - min_value)
# Cut at the thresholds
value01 = np.maximum(value01, 0)
value01 = np.minimum(value01, 1)
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
for u in [0, 1, 2]:
col = 255 * ((1 - value01) * min_color[u] \
+ (value01) * max_color[u])
col[isnan] = nan_color[u] * 255
result[:, :, u] = col
return result
def posneg(value,
max_value=None,
skim=0,
nan_color=[0.5, 0.5, 0.5],
zero_color=[1.0, 1.0, 1.0]):
"""
Converts a 2D float value to a RGB representation, where
red is positive, blue is negative, white is zero.
:param value: The field to represent.
:type value: array[HxW]
:param max_value: Maximum of absolute value (if None, detect).
:type max_value: float,>0
:param skim: Fraction to skim (in percent).
:type skim: float,>0,<100
:param nan_color: Color to give for regions of NaN and Inf.
:type nan_color: color
:return: posneg: A RGB image.
:rtype: array[HxWx3](uint8)
"""
check_2d_array(value, 'input to posneg')
# TODO: put this in reprep
value = value.copy()
if value.ndim > 2:
value = value.squeeze()
if value.dtype == np.dtype('uint8'):
value = value.astype('float32')
if len(value.shape) != 2:
raise Exception('I expected a H x W image, got shape %s.' %
str(value.shape))
isfinite = np.isfinite(value)
isnan = np.logical_not(isfinite)
# set nan to 0
value[isnan] = 0
if max_value is None:
abs_value = abs(value)
if skim != 0:
abs_value = skim_top(abs_value, skim)
max_value = np.max(abs_value)
if max_value == 0:
result = np.zeros((value.shape[0], value.shape[1], 3),
dtype='uint8')
for i in range(3):
result[:, :, i] = zero_color[i] * 255
return result
assert np.isfinite(max_value)
positive = np.minimum(np.maximum(value, 0), max_value) / max_value
negative = np.maximum(np.minimum(value, 0), -max_value) / -max_value
positive_part = (positive * 255).astype('uint8')
negative_part = (negative * 255).astype('uint8')
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
anysign = np.maximum(positive_part, negative_part)
R = 255 - negative_part[:, :]
G = 255 - anysign
B = 255 - positive_part[:, :]
# remember the nans
R[isnan] = nan_color[0] * 255
G[isnan] = nan_color[1] * 255
B[isnan] = nan_color[2] * 255
result[:, :, 0] = R
result[:, :, 1] = G
result[:, :, 2] = B
return result
def scale(value,
min_value=None,
max_value=None,
min_color=[1, 1, 1],
max_color=[0, 0, 0],
nan_color=[1, 0, 0]):
"""
Provides a RGB representation of the values by interpolating the range
[min(value),max(value)] into the colorspace [min_color, max_color].
:param value: The field to represent.
:type value: array[HxW],H>0,W>0
:param max_value: If specified, everything *above* is clipped.
:type max_value: float
:param min_value: If specified, everything *below* is clipped.
:type min_value: float
:param min_color: Color to give to the minimum values.
:type min_color: color
:param max_color: Color to give to the maximum values.
:type max_color: color
:param nan_color: Color to give for regions of NaN and Inf.
:type nan_color: color
:return: scale: A RGB image.
:rtype: array[HxWx3](uint8)
"""
# Raises :py:class:`.ValueError` if min_value == max_value
check_2d_array(value, 'input to scale()')
# assert_finite(value)
value = value.copy()
if value.ndim > 2:
value = value.squeeze()
if value.dtype == np.dtype('uint8') or value.dtype == np.dtype('int'):
value = value.astype('float32')
# require_shape((gt(0), gt(0)), value)
min_color = np.array(min_color)
max_color = np.array(max_color)
nan_color = np.array(nan_color)
# require_shape((3,), min_color)
# require_shape((3,), max_color)
isnan = np.logical_not(np.isfinite(value))
if max_value is None:
value[isnan] = -np.inf
max_value = np.max(value)
if min_value is None:
value[isnan] = np.inf
min_value = np.min(value)
if (max_value == min_value or np.isnan(min_value) or np.isnan(max_value)):
# XXX: maybe allow set this case as an exception?
# raise ValueError('I end up with max_value = %s = %s = min_value.' % \
# (max_value, min_value))
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
result[:, :, :] = 255
return result
value01 = (value - min_value) / (max_value - min_value)
# Cut at the thresholds
value01 = np.maximum(value01, 0)
value01 = np.minimum(value01, 1)
result = np.zeros((value.shape[0], value.shape[1], 3), dtype='uint8')
for u in [0, 1, 2]:
col = 255 * ((1 - value01) * min_color[u] \
+ (value01) * max_color[u])
col[isnan] = nan_color[u] * 255
result[:, :, u] = col
return result
