def LBP(img, save=False, parms=None, subtract=False, method='uniform'):
"""Get the LBP image
(reference: http://goo.gl/aeADZd)
@param img: image array
Keyword arguments:
save -- True to save the image
parms -- [points, radius] (default: None)
subtract -- True to subtract values to pts (default: False)
"""
from skimage.feature import local_binary_pattern
if is_rgb(img):
img = conv_gray(img)
if parms is None:
pts = int(img.shape[0] * img.shape[1] * 0.0003)
radius = min(img.shape[0], img.shape[1]) * 0.015
else:
pts = parms[0]
radius = parms[1]
lbp = local_binary_pattern(img, pts, radius, method=method)
if subtract:
lbp = np.abs(lbp - pts)
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(lbp,
fname='lbp_cm.png',
show_text=False,
show_axis=False,
norm=False)
return lbp
def overlay_text(img, save=False, T_H=1):
"""Get D, more details see http://goo.gl/d3GQ3T
@param img: image array
Keyword arguments:
T_H -- threshold used for the transition map
save -- True to save the image
"""
tilde_s = tilde_s(img, save=save)
intensity = intensity(img, save=save)
diff_tilde_s = np.diff(tilde_s)
diff_int = np.absolute(np.diff(intensity))
D_L = cal_d(diff_tilde_s, diff_int) + 1
D_R = cal_d(diff_tilde_s, diff_int, left=False)
T = np.where(D_R > D_L, 1, 0)
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(T,
fname='T_cm.png',
show_text=False,
show_axis=False,
norm=False)
return T
def max_s(img, save=False):
"""Get maxS, more details see http://goo.gl/d3GQ3T
@param img: image array
Keyword arguments:
save -- True to save the image (default: False)
"""
intensity = intensity(img, save=save)
max_s = np.where(intensity > 0.5, 2 * (0.5 - intensity), 2 * intensity)
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(max_s,
fname='max_s_cm.png',
show_text=False,
show_axis=False,
norm=False)
return max_s
def morph_dilation(img, rs=0.01, save=False):
"""Apply Morphological dilation transform
@param img: image array
Keyword arguments:
shape -- width of the kernel
save -- True to save the image
"""
shape = int(min(img.shape[0], img.shape[1]) * rs)
kernel = np.ones((shape, shape), np.uint8)
dil = cv2.dilate(img, kernel, iterations=1)
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(dil,
fname='dil_cm.png',
show_text=False,
show_axis=False,
norm=False)
return dil
def morph_closing(img, hr=0.1, wr=0.2, save=False):
"""Apply Morphological closing transform
@param img: image array
Keyword arguments:
hr -- ratio to the height, for closing window (default: 0.1)
wr -- ratio to the width, for closing window (default: 0.2)
save -- True to save the image
"""
h = int(img.shape[0] * hr)
w = int(img.shape[1] * wr)
kernel = np.ones((h, w), np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(closing,
fname='closing_cm.png',
show_text=False,
show_axis=False,
norm=False)
return closing
def satuation(img, save=False):
"""Get the image satuation
@param img: image array
Keyword arguments:
save -- True to save the image (default: False)
"""
if not is_rgb(img):
print('ERROR: Cannot support grayscale images')
sys.exit(0)
np.seterr(divide='ignore')
sat = 1 - np.divide(3, (img.sum(axis=2) * img.min(axis=2)))
sat[np.isneginf(sat)] = 0
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(sat,
fname='sat_cm.png',
show_text=False,
show_axis=False,
norm=False)
return sat
def intensity(img, save=False):
"""Get the pixel intensity
@param img: image array
Keyword arguments:
save -- True to save the image
"""
if is_rgb(img):
intensity = conv_gray(img)
else:
intensity = img
intensity = intensity.astype(float)
intensity *= (1.0 / intensity.max())
if save:
from dyda_utils import plot
plot.Plot().plot_matrix(intensity,
fname='intensity_cm.png',
show_text=False,
show_axis=False,
norm=False)
return intensity