def process_rgb_histogram(hist, omit=[]):
"""
omit unwanted bins and normalize histogram by dividing out total # pixels
:param hist: color channel histogram with 256 bins
:param omit: pixel value bins to omit, default []
:return: normalized histogram (np.array)
"""
import numpy as np
from accessory import get_iterable
if len(hist) != 256:
msg = 'ERROR [process_rgb_histogram] Input histogram must contain ' \
'256 bins (1 per pixel). Actual bin number = %d. ' % len(hist)
print(msg)
logging.error(msg)
sys.exit()
hist = np.array(hist).astype('float')
for ii in get_iterable(omit):
hist[ii] = 0
tot_pix = sum(hist)
try:
norm_hist = np.divide(hist, tot_pix)
except RuntimeWarning:
msg = 'Trying to normalize histogram by dividing by 0 ' \
'Setting norm_hist to 0 in result.'
logging.debug(msg)
norm_hist = np.zeros(len(hist))
return norm_hist
def rgbstring2index(rgbstring):
"""
converts RGB string into array containing corresponding channel indices
:param rgbstring: string of letters specifying desired color channels
:return: array of rgb channel index (np.array)
"""
import numpy as np
import re
from accessory import get_iterable
if len(re.findall('[rgb]', rgbstring.lower())) != len(rgbstring):
msg = 'ERROR [rgbstring2index] Input string ' + \
rgbstring + 'must contain only r, g, or b characters.'
print(msg)
logging.error(msg)
sys.exit()
idx = np.array([])
if 'r' in rgbstring.lower():
idx = np.append(idx, 0)
if 'g' in rgbstring.lower():
idx = np.append(idx, 1)
if 'b' in rgbstring.lower():
idx = np.append(idx, 2)
idx = idx.astype('int')
return get_iterable(idx)
def calc_median(img, omit=[]):
"""
calculate median pixel value of image
:param img: 2D array of pixel values
:param omit: pixel values to omit from calculation
:return: median
"""
import numpy as np
from accessory import get_iterable
if np.ndim(img) > 2:
msg = 'ERROR [calc_median] Input image must be 2D grayscale (not ' \
'RGB).'
logging.error(msg)
print(msg)
sys.exit()
# collapse image into array
pixels = np.ravel(np.array(img))
pixels = pixels.astype('float')
# omit specified pixel values from median calculation
for omit_idx in get_iterable(omit):
pixels[pixels == omit_idx] = np.nan
pixels = pixels[np.isfinite(pixels)]
median = np.median(pixels)
return median
def calc_mode(hist, omit=[]):
"""
calculate mode from histogram
:param hist: histogram values for 0-255
:param omit: pixel values to omit from calculation
:return: mode
"""
import numpy as np
from accessory import get_iterable
if np.ndim(hist) > 1:
msg = 'ERROR [calc_mode] Input must be 1D array of histogram ' \
'frequency values.'
logging.error(msg)
print(msg)
sys.exit()
if np.size(hist) > 256:
msg = 'ERROR [calc_mode] Input histogram data has > 256 bins.'
logging.error(msg)
print(msg)
sys.exit()
for omit_idx in get_iterable(omit):
hist[omit_idx] = 0
# find the index of the histogram maximum
mode = np.argmax(hist)
return mode
def test_get_iterable():
from accessory import get_iterable
import numpy as np
vec = []
actual = [ii for ii in get_iterable(vec)]
expected = []
assert np.array_equal(actual, expected)
vec = 4
actual = [ii for ii in get_iterable(vec)]
expected = [vec]
assert np.array_equal(actual, expected)
vec = (0, 5)
actual = [ii for ii in get_iterable(vec)]
expected = vec
assert np.array_equal(actual, expected)
def otsu_threshold(img, omit=[], verb=False, outfile='./otsu_img.png'):
"""
calculate the global otsu's threshold for image
:param img: 2D array of pixel values
:param omit: pixel values to omit from calculation
:param verb: verbose mode to display threshold image
:param outfile: file to output otsu image if verbose
:return: threshold (float)
"""
from skimage.filters import threshold_otsu
import numpy as np
from accessory import get_iterable
if np.ndim(img) > 2:
msg = 'ERROR [otsu_threshold] Input image must be 2D grayscale (not ' \
'RGB).'
logging.error(msg)
print(msg)
sys.exit()
otsu_img = np.array(img)
# set omitted pixel values as 0
for omit_idx in get_iterable(omit):
otsu_img[img == omit_idx] = np.nan
otsu_img[np.isnan(otsu_img)] = 0
threshold_global_otsu = threshold_otsu(otsu_img)
if verb:
import matplotlib.pyplot as plt
from accessory import create_dir
global_otsu = otsu_img >= threshold_global_otsu
plt.imshow(global_otsu, cmap=plt.cm.gray)
msg = '[otsu_threshold] Saving Otsu threshold image: %s' % outfile
logging.info(msg)
print(msg)
create_dir(outfile)
plt.savefig(outfile)
plt.clf()
return threshold_global_otsu