def main():
p = opt.ArgumentParser(description="""
Computes textural tissue descriptors from an RGB image (of an H&E slide).
""")
p.add_argument('img_file', action='store', help='RGB image file of an H&E slide')
p.add_argument('out_file', action='store', default='descriptors.dat',
help='Name of the result file')
# p.add_argument('model_file', action='store', help='Models file')
p.add_argument('--scale', action='store', type=float, default=1.0,
help='Scale of the image at which the descriptors are computed (default: 1.0)')
p.add_argument('--ngl', type=int, default=16, action='store',
help='Number of grey levels in H- and E-images (default: 16)')
p.add_argument('--wsize', action='store', type=int, default=50,
help='Sliding window size (default: 50)')
p.add_argument('--mask', action='store_true',
help='')
args = p.parse_args()
img_file = args.img_file
# model_file = args.model_file
n_grey_levels = args.ngl
w_size = args.wsize
scale = args.scale
out_file = args.out_file
base_name = os.path.basename(img_file).split('.')
if len(base_name) > 1: # at least 1 suffix .ext
base_name.pop() # drop the extension
base_name = '.'.join(base_name) # reassemble the rest of the list into file name
img = skimage.io.imread(img_file)
# with ModelPersistence(model_file, 'r', format='pickle') as d:
# rgb_models = d['models']
img_h, img_e = rgb2he(img, normalize=True)
img_h = requantize(img_h, nlevels=n_grey_levels, method='linear')
img_e = requantize(img_e, nlevels=n_grey_levels, method='linear')
G = GaborDescriptor()
if args.mask:
mask, _ = tissue_region_from_rgb(img, _min_area=150)
g_h = get_gabor_desc(img_h, G, w_size, scale, mask)
g_e = get_gabor_desc(img_e, G, w_size, scale, mask)
else:
g_h = get_gabor_desc(img_h, G, w_size, scale)
g_e = get_gabor_desc(img_e, G, w_size, scale)
with open(out_file, 'w') as f:
for d in g_h:
f.write('\t'.join(str(x) for x in d))
f.write('\n')
for d in g_e:
f.write('\t'.join(str(x) for x in d))
f.write('\n')
return
def main():
p = opt.ArgumentParser(
description="""
Produces a mask covering the tissue region in the image.
"""
)
p.add_argument("img_file", action="store", help="RGB image file")
p.add_argument(
"--prefix", action="store", help="optional prefix for the result files: prefix_tissue_mask.pbm", default=None
)
p.add_argument("--minarea", action="store", help="object smaller than this will be removed", default=150)
p.add_argument(
"--gth", action="store", help="if provided, indicates the threshold in the green channel", default=None
)
p.add_argument("--meta", action="store_true", help="store meta information associated with the results")
args = p.parse_args()
base_name = os.path.basename(args.img_file).split(".")
if len(base_name) > 1: # at least 1 suffix .ext
base_name.pop() # drop the extension
base_name = ".".join(base_name) # reassemble the rest of the list into file name
if args.prefix is not None:
pfx = args.prefix
else:
pfx = base_name
img = skimage.io.imread(args.img_file)
mask, g_th = tissue_region_from_rgb(img, _min_area=args.minarea, _g_th=args.gth)
skimage.io.imsave(pfx + "_tissue_mask.pbm", 255 * mask.astype("uint8"))
if args.meta:
r = ET.Element("meta", attrib={"processor": "wsi_mask"})
t = ET.SubElement(r, "file")
t.text = args.img_file
t = ET.SubElement(r, "parameters")
t1 = ET.SubElement(t, "prefix")
t1.text = args.prefix
t1 = ET.SubElement(t, "minarea")
t1.text = str(args.minarea)
t1 = ET.SubElement(t, "gth")
t1.text = str(args.gth)
t = ET.SubElement(r, "outfile")
t.text = pfx + "_tissue_mask.pbm"
t = ET.SubElement(r, "gth_res")
t.text = str(g_th)
raw_txt = ET.tostring(r, "utf-8")
reparsed = minidom.parseString(raw_txt)
pp_txt = reparsed.toprettyxml(indent=" ")
meta_file = open(pfx + "_tissue_mask.meta.xml", "w")
meta_file.write(pp_txt)
return
def run():
global img_file, res_prefix, s_factors, n_splits, ovlap
# print(img_file)
# print(res_prefix)
# print(s_factors)
# print(n_splits)
# print(ovlap)
r = ET.Element('meta')
t = ET.SubElement(r, 'file')
t.text = img_file
t = ET.SubElement(r, 'parameters')
t1 = ET.SubElement(t, 'prefix')
t1.text = res_prefix
t1 = ET.SubElement(t, 'shrink')
for s in s_factors:
t2 = ET.SubElement(t1, 'factor')
t2.text = str(s)
t1 = ET.SubElement(t, 'split')
for s in n_splits:
t2 = ET.SubElement(t1, 'tile')
t2.text = str(s)
t1 = ET.SubElement(t, 'overlap')
t1.text = str(ovlap)
img = VImage.VImage(img_file)
t1 = ET.SubElement(r, 'original')
t2 = ET.SubElement(t1, 'width')
t2.text = str(img.Xsize())
t2 = ET.SubElement(t1, 'height')
t2.text = str(img.Ysize())
t2 = ET.SubElement(t1, 'channels')
t2.text = str(img.Bands())
t2 = ET.SubElement(t1, 'xres')
t2.text = str(img.Xres())
t2 = ET.SubElement(t1, 'yres')
t2.text = str(img.Yres())
t2 = ET.SubElement(t1, 'scale')
t2.text = '1.0'
t2 = ET.SubElement(t1, 'tile')
t2.text = '(1, 1)'
path = res_prefix + '/' + os.path.basename(img_file)
if os.path.exists(path):
print('Warning: Overwriting old files!')
else:
os.mkdir(path)
print("ROI detection: ")
# Find the ROI:
# img_scaled = img.shrink(100, 100)
os.spawnv(os.P_WAIT, 'div100', ['./div100', img_file, path + '/small.ppm'])
# save downscaled image - not the best way for going to Scikit Image,
# but for large images we go through disk I/O anyway:
# print(" -saving small version of the image")
# img_scaled.write(path+'/small.ppm')
print(" -read into scikit-learn")
img_scaled = imread(path + '/small.ppm')
# compute a minimal area based on the resolution of the image
# -the image is 100x smaller than the original -> resolution is
print(" -computing mask")
xres, yres = img.Xres() / 100, img.Yres() / 100
min_area = 4 * min(xres, yres) # ~4 mm**2
mask, _ = tissue_region_from_rgb(img_scaled, min_area)
# save the mask:
print(" -saving mask")
imsave(path + '/' + 'mask_div100.pbm', mask)
t2 = ET.SubElement(t1, 'mask')
t2.text = 'mask_div100.pbm'
# coordinates of the ROI encompassing the objects, at 0.01 of original image
print(" -detect ROI")
rmin, cmin, rmax, cmax = bounding_box(mask)
mask = mask[rmin:rmax + 1, cmin:cmax + 1]
# get back to original coordinates, with an approximation of 100 pixels...
rmin *= 100
cmin *= 100
rmax = min((rmax + 1) * 100, img.Ysize())
cmax = min((cmax + 1) * 100, img.Xsize())
t2 = ET.SubElement(t1, 'roi', {
'xmin': str(cmin),
'ymin': str(rmin),
'xmax': str(cmax),
'ymax': str(rmax)
})
print("...end ROI detection.")
# Save initial level 0:
print("Crop ROI and save...")
img_cropped = img.extract_area(cmin, rmin, cmax - cmin + 1,
rmax - rmin + 1)
img_cropped.write(path + '/pyramid-level_0.ppm')
new_width, new_height = img_cropped.Xsize(), img_cropped.Ysize()
img_cropped = None
print("...OK")
mask = None
img = None # done with it
gc.collect()
# Generate the pyramid
t1 = ET.SubElement(r, 'pyramid')
t2 = ET.SubElement(t1, 'level', {
'value': '0',
'file': 'pyramid-level_0.ppm'
})
t2 = ET.SubElement(t1, 'level', {
'value': '1',
'file': 'pyramid-level_1.ppm'
})
t2 = ET.SubElement(t1, 'level', {
'value': '2',
'file': 'pyramid-level_2.ppm'
})
t2 = ET.SubElement(t1, 'level', {
'value': '3',
'file': 'pyramid-level_3.ppm'
})
t2 = ET.SubElement(t1, 'level', {
'value': '4',
'file': 'pyramid-level_4.ppm'
})
t2 = ET.SubElement(t1, 'level', {
'value': '5',
'file': 'pyramid-level_5.ppm'
})
# call external tool:
print("Computing pyramid...")
os.spawnv(os.P_WAIT, 'pyr', [
'./pyr', path + '/pyramid-level_0.ppm', path + '/pyramid',
str(n_levels)
])
print("...done")
k = 0
for l in np.arange(n_levels + 1):
f = s_factors[l]
pt = path + '/' + str(s_factors[l])
if not os.path.exists(pt):
os.mkdir(pt)
t1 = ET.SubElement(r, 'version')
t2 = ET.SubElement(t1, 'scale')
t2.text = str(f)
n_horiz, n_vert = n_splits[k]
t2 = ET.SubElement(t1, 'split')
t2.text = str((n_horiz, n_vert))
# img_scaled = img_cropped.shrink(f, f)
# load the corresponding level in the pyramid:
img_scaled = VImage.VImage(path + '/pyramid-level_' + str(l) + '.ppm')
width = img_scaled.Xsize()
height = img_scaled.Ysize()
w = width / n_horiz
h = height / n_vert
ov = ovlap / 100.0 / 2.0 # ovlap is in % and we need half of it
sv = int(n_vert != 1)
sh = int(n_horiz != 1)
print('Processing scale %d and tile %d,%d' % (f, n_horiz, n_vert))
y0 = 0
for i in np.arange(n_vert):
x0 = 0
if i < n_vert - 1:
y1 = int(y0 + h * (1.0 + sv * ov)) - 1
else:
y1 = height - 1
for j in np.arange(n_horiz):
if j < n_horiz - 1:
x1 = int(x0 + w * (1.0 + sh * ov)) - 1
else:
x1 = width - 1
tile_name = 'tile_' + str(i) + '_' + str(j) + '.' + res_format
res_file = pt + '/' + tile_name
print('Save to' + res_file)
t2 = ET.SubElement(
t1, 'tile', {
'name': tile_name,
'x0': str(x0),
'y0': str(y0),
'x1': str(x1),
'y1': str(y1)
})
#print('x0 = %d, y0 = %d, x1 = %d, y1 = %d, sh = %d, ov = %f; image: %d x %d' % (x0, y0, x1, y1, sh, ov, width, height))
# do the actual work...
img_sub = img_scaled.extract_area(x0, y0, x1 - x0 + 1,
y1 - y0 + 1)
img_sub.write(res_file)
x0 = int(x1 + 1 - 2.0 * w * ov)
y0 = int(y1 + 1 - 2.0 * w * ov)
k += 1
raw_txt = ET.tostring(r, 'utf-8')
reparsed = minidom.parseString(raw_txt)
pp_txt = reparsed.toprettyxml(indent=' ')
#print(pp_txt)
meta_file = open(path + '/meta.xml', 'w')
meta_file.write(pp_txt)
return
def main():
p = opt.ArgumentParser(description="""
Produces a mask covering the tissue region in the image.
""")
p.add_argument('img_file', action='store', help='RGB image file')
p.add_argument(
'--prefix',
action='store',
help='optional prefix for the result files: prefix_tissue_mask.pbm',
default=None)
p.add_argument('--minarea',
action='store',
help='object smaller than this will be removed',
default=150)
p.add_argument(
'--gth',
action='store',
help='if provided, indicates the threshold in the green channel',
default=None)
p.add_argument('--meta',
action='store_true',
help='store meta information associated with the results')
args = p.parse_args()
base_name = os.path.basename(args.img_file).split('.')
if len(base_name) > 1: # at least 1 suffix .ext
base_name.pop() # drop the extension
base_name = '.'.join(
base_name) # reassemble the rest of the list into file name
if args.prefix is not None:
pfx = args.prefix
else:
pfx = base_name
img = skimage.io.imread(args.img_file)
mask, g_th = tissue_region_from_rgb(img,
_min_area=args.minarea,
_g_th=args.gth)
skimage.io.imsave(pfx + '_tissue_mask.pbm', 255 * mask.astype('uint8'))
if args.meta:
r = ET.Element('meta', attrib={'processor': 'wsi_mask'})
t = ET.SubElement(r, 'file')
t.text = args.img_file
t = ET.SubElement(r, 'parameters')
t1 = ET.SubElement(t, 'prefix')
t1.text = args.prefix
t1 = ET.SubElement(t, 'minarea')
t1.text = str(args.minarea)
t1 = ET.SubElement(t, 'gth')
t1.text = str(args.gth)
t = ET.SubElement(r, 'outfile')
t.text = pfx + '_tissue_mask.pbm'
t = ET.SubElement(r, 'gth_res')
t.text = str(g_th)
raw_txt = ET.tostring(r, 'utf-8')
reparsed = minidom.parseString(raw_txt)
pp_txt = reparsed.toprettyxml(indent=' ')
meta_file = open(pfx + '_tissue_mask.meta.xml', 'w')
meta_file.write(pp_txt)
return
def run():
global img_file, res_prefix, s_factors, n_splits, ovlap
# print(img_file)
# print(res_prefix)
# print(s_factors)
# print(n_splits)
# print(ovlap)
r = ET.Element('meta')
t = ET.SubElement(r, 'file')
t.text = img_file
t = ET.SubElement(r, 'parameters')
t1 = ET.SubElement(t, 'prefix')
t1.text = res_prefix
t1 = ET.SubElement(t, 'shrink')
for s in s_factors:
t2 = ET.SubElement(t1, 'factor')
t2.text = str(s)
t1 = ET.SubElement(t, 'split')
for s in n_splits:
t2 = ET.SubElement(t1, 'tile')
t2.text = str(s)
t1 = ET.SubElement(t, 'overlap')
t1.text = str(ovlap)
img = VImage.VImage(img_file)
t1 = ET.SubElement(r, 'original')
t2 = ET.SubElement(t1, 'width')
t2.text = str(img.Xsize())
t2 = ET.SubElement(t1, 'height')
t2.text = str(img.Ysize())
t2 = ET.SubElement(t1, 'channels')
t2.text = str(img.Bands())
t2 = ET.SubElement(t1, 'xres')
t2.text = str(img.Xres())
t2 = ET.SubElement(t1, 'yres')
t2.text = str(img.Yres())
t2 = ET.SubElement(t1, 'scale')
t2.text = '1.0'
t2 = ET.SubElement(t1, 'tile')
t2.text = '(1, 1)'
path = res_prefix + '/' + os.path.basename(img_file)
if os.path.exists(path):
print('Warning: Overwriting old files!')
else:
os.mkdir(path)
print("ROI detection: ")
# Find the ROI:
# img_scaled = img.shrink(100, 100)
os.spawnv(os.P_WAIT, 'div100', ['./div100', img_file, path+'/small.ppm'])
# save downscaled image - not the best way for going to Scikit Image,
# but for large images we go through disk I/O anyway:
# print(" -saving small version of the image")
# img_scaled.write(path+'/small.ppm')
print(" -read into scikit-learn")
img_scaled = imread(path+'/small.ppm')
# compute a minimal area based on the resolution of the image
# -the image is 100x smaller than the original -> resolution is
print(" -computing mask")
xres, yres = img.Xres() / 100, img.Yres() / 100
min_area = 4 * min(xres, yres) # ~4 mm**2
mask, _ = tissue_region_from_rgb(img_scaled, min_area)
# save the mask:
print(" -saving mask")
imsave(path + '/' + 'mask_div100.pbm', mask)
t2 = ET.SubElement(t1, 'mask')
t2.text = 'mask_div100.pbm'
# coordinates of the ROI encompassing the objects, at 0.01 of original image
print(" -detect ROI")
rmin, cmin, rmax, cmax = bounding_box(mask)
mask = mask[rmin:rmax+1, cmin:cmax+1]
# get back to original coordinates, with an approximation of 100 pixels...
rmin *= 100
cmin *= 100
rmax = min((rmax + 1) * 100, img.Ysize())
cmax = min((cmax + 1) * 100, img.Xsize())
t2 = ET.SubElement(t1, 'roi',
{'xmin': str(cmin), 'ymin': str(rmin), 'xmax': str(cmax), 'ymax': str(rmax)})
print("...end ROI detection.")
# Save initial level 0:
print("Crop ROI and save...")
img_cropped = img.extract_area(cmin, rmin, cmax-cmin+1, rmax-rmin+1)
img_cropped.write(path + '/pyramid-level_0.ppm')
new_width, new_height = img_cropped.Xsize(), img_cropped.Ysize()
img_cropped = None
print("...OK")
mask = None
img = None # done with it
gc.collect()
# Generate the pyramid
t1 = ET.SubElement(r, 'pyramid')
t2 = ET.SubElement(t1, 'level', {'value': '0', 'file': 'pyramid-level_0.ppm'})
t2 = ET.SubElement(t1, 'level', {'value': '1', 'file': 'pyramid-level_1.ppm'})
t2 = ET.SubElement(t1, 'level', {'value': '2', 'file': 'pyramid-level_2.ppm'})
t2 = ET.SubElement(t1, 'level', {'value': '3', 'file': 'pyramid-level_3.ppm'})
t2 = ET.SubElement(t1, 'level', {'value': '4', 'file': 'pyramid-level_4.ppm'})
t2 = ET.SubElement(t1, 'level', {'value': '5', 'file': 'pyramid-level_5.ppm'})
# call external tool:
print("Computing pyramid...")
os.spawnv(os.P_WAIT, 'pyr', ['./pyr', path+'/pyramid-level_0.ppm', path+'/pyramid', str(n_levels)])
print("...done")
k = 0
for l in np.arange(n_levels+1):
f = s_factors[l]
pt = path + '/' + str(s_factors[l])
if not os.path.exists(pt):
os.mkdir(pt)
t1 = ET.SubElement(r, 'version')
t2 = ET.SubElement(t1, 'scale')
t2.text = str(f)
n_horiz, n_vert = n_splits[k]
t2 = ET.SubElement(t1, 'split')
t2.text = str((n_horiz, n_vert))
# img_scaled = img_cropped.shrink(f, f)
# load the corresponding level in the pyramid:
img_scaled = VImage.VImage(path + '/pyramid-level_' + str(l) + '.ppm')
width = img_scaled.Xsize()
height = img_scaled.Ysize()
w = width / n_horiz
h = height / n_vert
ov = ovlap/100.0/2.0 # ovlap is in % and we need half of it
sv = int(n_vert != 1)
sh = int(n_horiz != 1)
print('Processing scale %d and tile %d,%d' % (f, n_horiz, n_vert))
y0 = 0
for i in np.arange(n_vert):
x0 = 0
if i < n_vert - 1:
y1 = int(y0 + h * (1.0 + sv*ov)) - 1
else:
y1 = height - 1
for j in np.arange(n_horiz):
if j < n_horiz - 1:
x1 = int(x0 + w * (1.0 + sh*ov)) - 1
else:
x1 = width - 1
tile_name = 'tile_' + str(i) + '_' + str(j) + '.' + res_format
res_file = pt + '/' + tile_name
print('Save to' + res_file)
t2 = ET.SubElement(t1, 'tile',
{'name': tile_name, 'x0':str(x0), 'y0':str(y0), 'x1':str(x1), 'y1':str(y1)})
#print('x0 = %d, y0 = %d, x1 = %d, y1 = %d, sh = %d, ov = %f; image: %d x %d' % (x0, y0, x1, y1, sh, ov, width, height))
# do the actual work...
img_sub = img_scaled.extract_area(x0, y0, x1-x0+1, y1-y0+1)
img_sub.write(res_file)
x0 = int(x1 + 1 - 2.0 * w * ov)
y0 = int(y1 + 1 - 2.0 * w * ov)
k += 1
raw_txt = ET.tostring(r, 'utf-8')
reparsed = minidom.parseString(raw_txt)
pp_txt = reparsed.toprettyxml(indent=' ')
#print(pp_txt)
meta_file = open(path+'/meta.xml', 'w')
meta_file.write(pp_txt)
return
def main():
p = opt.ArgumentParser(description="""
Computes textural tissue descriptors from an RGB image (of an H&E slide).
""")
p.add_argument('img_file',
action='store',
help='RGB image file of an H&E slide')
p.add_argument('out_file',
action='store',
default='descriptors.dat',
help='Name of the result file')
# p.add_argument('model_file', action='store', help='Models file')
p.add_argument(
'--scale',
action='store',
type=float,
default=1.0,
help=
'Scale of the image at which the descriptors are computed (default: 1.0)'
)
p.add_argument(
'--ngl',
type=int,
default=16,
action='store',
help='Number of grey levels in H- and E-images (default: 16)')
p.add_argument('--wsize',
action='store',
type=int,
default=50,
help='Sliding window size (default: 50)')
p.add_argument('--mask', action='store_true', help='')
args = p.parse_args()
img_file = args.img_file
# model_file = args.model_file
n_grey_levels = args.ngl
w_size = args.wsize
scale = args.scale
out_file = args.out_file
base_name = os.path.basename(img_file).split('.')
if len(base_name) > 1: # at least 1 suffix .ext
base_name.pop() # drop the extension
base_name = '.'.join(
base_name) # reassemble the rest of the list into file name
img = skimage.io.imread(img_file)
# with ModelPersistence(model_file, 'r', format='pickle') as d:
# rgb_models = d['models']
img_h, img_e = rgb2he(img, normalize=True)
img_h = requantize(img_h, nlevels=n_grey_levels, method='linear')
img_e = requantize(img_e, nlevels=n_grey_levels, method='linear')
G = GaborDescriptor()
if args.mask:
mask, _ = tissue_region_from_rgb(img, _min_area=150)
g_h = get_gabor_desc(img_h, G, w_size, scale, mask)
g_e = get_gabor_desc(img_e, G, w_size, scale, mask)
else:
g_h = get_gabor_desc(img_h, G, w_size, scale)
g_e = get_gabor_desc(img_e, G, w_size, scale)
with open(out_file, 'w') as f:
for d in g_h:
f.write('\t'.join(str(x) for x in d))
f.write('\n')
for d in g_e:
f.write('\t'.join(str(x) for x in d))
f.write('\n')
return
def main():
p = opt.ArgumentParser(description="""
Constructs a dictionary for image representation based on a set of specified local
descriptors. The dictionary is built from a set of images given as a list in an
input file.
""")
p.add_argument('config', action='store', help='a configuration file')
args = p.parse_args()
cfg_file = args.config
parser = SafeConfigParser()
parser.read(cfg_file)
#---------
# sampler:
if not parser.has_section('sampler'):
raise ValueError('"sampler" section is mandatory')
if not parser.has_option('sampler', 'type'):
raise ValueError('"sampler.type" is mandatory')
tmp = parser.get('sampler', 'type').lower()
if tmp not in ['random', 'sliding']:
raise ValueError('Unkown sampling type')
sampler_type = tmp
if not parser.has_option('sampler', 'window_size'):
raise ValueError('"sampler.window_size" is mandatory')
wnd_size = ast.literal_eval(parser.get('sampler', 'window_size'))
if type(wnd_size) != tuple:
raise ValueError('"sampler.window_size" specification error')
it_start = (0,0)
it_step = (1,1)
if sampler_type == 'sliding':
if parser.has_option('sampler', 'start'):
it_start = ast.literal_eval(parser.get('sampler','start'))
if parser.has_option('sampler', 'step'):
it_step = ast.literal_eval(parser.get('sampler','step'))
nwindows = parser.getint('sampler', 'nwindows')
local_descriptors = []
#---------
# haar:
if parser.has_section('haar'):
tmp = True
if parser.has_option('haar', 'norm'):
tmp = parser.getboolean('haar', 'norm')
if len(parser.items('haar')) == 0:
# empty section, use defaults
h = HaarLikeDescriptor(HaarLikeDescriptor.haars1())
else:
h = HaarLikeDescriptor([ast.literal_eval(v) for n, v in parser.items('haar')
if n.lower() != 'norm'],
_norm=tmp)
local_descriptors.append(h)
#---------
# identity:
if parser.has_section('identity'):
local_descriptors.append(IdentityDescriptor())
#---------
# stats:
if parser.has_section('stats'):
tmp = []
if parser.has_option('stats', 'mean') and parser.getboolean('stats', 'mean'):
tmp.append('mean')
if parser.has_option('stats', 'std') and parser.getboolean('stats', 'std'):
tmp.append('std')
if parser.has_option('stats', 'kurtosis') and parser.getboolean('stats', 'kurtosis'):
tmp.append('kurtosis')
if parser.has_option('stats', 'skewness') and parser.getboolean('stats', 'skewness'):
tmp.append('skewness')
if len(tmp) == 0:
tmp = None
local_descriptors.append(StatsDescriptor(tmp))
#---------
# hist:
if parser.has_section('hist'):
tmp = (0.0, 1.0)
tmp2 = 10
if parser.has_option('hist', 'min_max'):
tmp = ast.literal_eval(parser.get('hist', 'min_max'))
if type(tmp) != tuple:
raise ValueError('"hist.min_max" specification error')
if parser.has_option('hist', 'nbins'):
tmp2 = parser.getint('hist', 'nbins')
local_descriptors.append(HistDescriptor(_interval=tmp, _nbins=tmp2))
#---------
# HoG
if parser.has_section('hog'):
tmp = 9
tmp2 = (128, 128)
tmp3 = (4, 4)
if parser.has_option('hog', 'norient'):
tmp = parser.getint('hog', 'norient')
if parser.has_option('hog', 'ppc'):
tmp2 = ast.literal_eval(parser.get('hog', 'ppc'))
if type(tmp2) != tuple:
raise ValueError('"hog.ppc" specification error')
if parser.has_option('hog', 'cpb'):
tmp3 = ast.literal_eval(parser.get('hog', 'cpb'))
if type(tmp3) != tuple:
raise ValueError('"hog.cpb" specification error')
local_descriptors.append(HOGDescriptor(_norient=tmp, _ppc=tmp2, _cpb=tmp3))
#---------
# LBP
if parser.has_section('lbp'):
tmp = 3
tmp2 = 8*tmp
tmp3 = 'uniform'
if parser.has_option('lbp', 'radius'):
tmp = parser.getint('lbp', 'radius')
if parser.has_option('lbp', 'npoints'):
tmp2 = parser.getint('lbp', 'npoints')
if tmp2 == 0:
tmp2 = 8* tmp
if parser.has_option('lbp', 'method'):
tmp3 = parser.get('lbp', 'method')
local_descriptors.append(LBPDescriptor(radius=tmp, npoints=tmp2, method=tmp3))
#---------
# Gabor
if parser.has_section('gabor'):
tmp = np.array([0.0, np.pi / 4.0, np.pi / 2.0, 3.0 * np.pi / 4.0], dtype=np.double)
tmp2 = np.array([3.0 / 4.0, 3.0 / 8.0, 3.0 / 16.0], dtype=np.double)
tmp3 = np.array([1.0, 2 * np.sqrt(2.0)], dtype=np.double)
if parser.has_option('gabor', 'theta'):
tmp = ast.literal_eval(parser.get('gabor', 'theta'))
if parser.has_option('gabor', 'freq'):
tmp2 = ast.literal_eval(parser.get('gabor', 'freq'))
if parser.has_option('gabor', 'sigma'):
tmp3 = ast.literal_eval(parser.get('gabor', 'sigma'))
local_descriptors.append(GaborDescriptor(theta=tmp, freq=tmp2, sigma=tmp3))
print('No. of descriptors: ', len(local_descriptors))
#---------
# data
if not parser.has_section('data'):
raise ValueError('Section "data" is mandatory.')
data_path = parser.get('data', 'input_path')
img_ext = parser.get('data', 'image_type')
res_path = parser.get('data', 'output_path')
img_files = glob.glob(data_path + '/*.' + img_ext)
if len(img_files) == 0:
return
## Process:
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0) # unbuferred output
for img_name in img_files:
print("Image: ", img_name, " ...reading... ", end='')
im = imread(img_name)
print("preprocessing... ", end='')
# -preprocessing
if im.ndim == 3:
im_h, _, _ = rgb2he2(im)
else:
raise ValueError('Input image must be RGB.')
# detect object region:
# -try to load a precomputed mask:
mask_file_name = data_path+'/mask/'+ \
os.path.splitext(os.path.split(img_name)[1])[0]+ \
'_tissue_mask.pbm'
if os.path.exists(mask_file_name):
print('(loading mask)...', end='')
mask = imread(mask_file_name)
mask = img_as_bool(mask)
mask = remove_small_objects(mask, min_size=500, connectivity=1, in_place=True)
else:
print('(computing mask)...', end='')
mask, _ = tissue_region_from_rgb(im, _min_area=500)
row_min, col_min, row_max, col_max = bounding_box(mask)
im_h[np.logical_not(mask)] = 0 # make sure background is 0
mask = None
im = None
im_h = im_h[row_min:row_max+1, col_min:col_max+1]
print("growing the bag...", end='')
# -image bag growing
bag = None # bag for current image
for d in local_descriptors:
if bag is None:
bag = grow_bag_from_new_image(im_h, d, wnd_size, nwindows, discard_empty=True)
else:
bag[d.name] = grow_bag_with_new_features(im_h, bag['regs'], d)[d.name]
# save the results for each image, one file per descriptor
desc_names = bag.keys()
desc_names.remove('regs') # keep all keys but the regions
# -save the ROI from the original image:
res_file = res_path + '/' + 'roi-' + \
os.path.splitext(os.path.split(img_name)[1])[0] + '.dat'
with open(res_file, 'w') as f:
f.write('\t'.join([str(x_) for x_ in [row_min, row_max, col_min, col_max]]))
for dn in desc_names:
res_file = res_path + '/' + dn + '_bag-' + \
os.path.splitext(os.path.split(img_name)[1])[0] + '.dat'
with open(res_file, 'w') as f:
n = len(bag[dn]) # total number of descriptors of this type
for i in range(n):
s = '\t'.join([str(x_) for x_ in bag['regs'][i]]) + '\t' + \
'\t'.join([str(x_) for x_ in bag[dn][i]]) + '\n'
f.write(s)
print('OK')
bag = None
gc.collect()
gc.collect()
def main():
p = opt.ArgumentParser(description="""
Constructs a dictionary for image representation based on a set of specified local
descriptors. The dictionary is built from a set of images given as a list in an
input file.
""")
p.add_argument('config', action='store', help='a configuration file')
args = p.parse_args()
cfg_file = args.config
parser = SafeConfigParser()
parser.read(cfg_file)
#---------
# sampler:
if not parser.has_section('sampler'):
raise ValueError('"sampler" section is mandatory')
if not parser.has_option('sampler', 'type'):
raise ValueError('"sampler.type" is mandatory')
tmp = parser.get('sampler', 'type').lower()
if tmp not in ['random', 'sliding']:
raise ValueError('Unkown sampling type')
sampler_type = tmp
if not parser.has_option('sampler', 'window_size'):
raise ValueError('"sampler.window_size" is mandatory')
wnd_size = ast.literal_eval(parser.get('sampler', 'window_size'))
if type(wnd_size) != tuple:
raise ValueError('"sampler.window_size" specification error')
it_start = (0, 0)
it_step = (1, 1)
if sampler_type == 'sliding':
if parser.has_option('sampler', 'start'):
it_start = ast.literal_eval(parser.get('sampler', 'start'))
if parser.has_option('sampler', 'step'):
it_step = ast.literal_eval(parser.get('sampler', 'step'))
nwindows = parser.getint('sampler', 'nwindows')
local_descriptors = []
#---------
# haar:
if parser.has_section('haar'):
tmp = True
if parser.has_option('haar', 'norm'):
tmp = parser.getboolean('haar', 'norm')
if len(parser.items('haar')) == 0:
# empty section, use defaults
h = HaarLikeDescriptor(HaarLikeDescriptor.haars1())
else:
h = HaarLikeDescriptor([
ast.literal_eval(v)
for n, v in parser.items('haar') if n.lower() != 'norm'
],
_norm=tmp)
local_descriptors.append(h)
#---------
# identity:
if parser.has_section('identity'):
local_descriptors.append(IdentityDescriptor())
#---------
# stats:
if parser.has_section('stats'):
tmp = []
if parser.has_option('stats', 'mean') and parser.getboolean(
'stats', 'mean'):
tmp.append('mean')
if parser.has_option('stats', 'std') and parser.getboolean(
'stats', 'std'):
tmp.append('std')
if parser.has_option('stats', 'kurtosis') and parser.getboolean(
'stats', 'kurtosis'):
tmp.append('kurtosis')
if parser.has_option('stats', 'skewness') and parser.getboolean(
'stats', 'skewness'):
tmp.append('skewness')
if len(tmp) == 0:
tmp = None
local_descriptors.append(StatsDescriptor(tmp))
#---------
# hist:
if parser.has_section('hist'):
tmp = (0.0, 1.0)
tmp2 = 10
if parser.has_option('hist', 'min_max'):
tmp = ast.literal_eval(parser.get('hist', 'min_max'))
if type(tmp) != tuple:
raise ValueError('"hist.min_max" specification error')
if parser.has_option('hist', 'nbins'):
tmp2 = parser.getint('hist', 'nbins')
local_descriptors.append(HistDescriptor(_interval=tmp, _nbins=tmp2))
#---------
# HoG
if parser.has_section('hog'):
tmp = 9
tmp2 = (128, 128)
tmp3 = (4, 4)
if parser.has_option('hog', 'norient'):
tmp = parser.getint('hog', 'norient')
if parser.has_option('hog', 'ppc'):
tmp2 = ast.literal_eval(parser.get('hog', 'ppc'))
if type(tmp2) != tuple:
raise ValueError('"hog.ppc" specification error')
if parser.has_option('hog', 'cpb'):
tmp3 = ast.literal_eval(parser.get('hog', 'cpb'))
if type(tmp3) != tuple:
raise ValueError('"hog.cpb" specification error')
local_descriptors.append(
HOGDescriptor(_norient=tmp, _ppc=tmp2, _cpb=tmp3))
#---------
# LBP
if parser.has_section('lbp'):
tmp = 3
tmp2 = 8 * tmp
tmp3 = 'uniform'
if parser.has_option('lbp', 'radius'):
tmp = parser.getint('lbp', 'radius')
if parser.has_option('lbp', 'npoints'):
tmp2 = parser.getint('lbp', 'npoints')
if tmp2 == 0:
tmp2 = 8 * tmp
if parser.has_option('lbp', 'method'):
tmp3 = parser.get('lbp', 'method')
local_descriptors.append(
LBPDescriptor(radius=tmp, npoints=tmp2, method=tmp3))
#---------
# Gabor
if parser.has_section('gabor'):
tmp = np.array([0.0, np.pi / 4.0, np.pi / 2.0, 3.0 * np.pi / 4.0],
dtype=np.double)
tmp2 = np.array([3.0 / 4.0, 3.0 / 8.0, 3.0 / 16.0], dtype=np.double)
tmp3 = np.array([1.0, 2 * np.sqrt(2.0)], dtype=np.double)
if parser.has_option('gabor', 'theta'):
tmp = ast.literal_eval(parser.get('gabor', 'theta'))
if parser.has_option('gabor', 'freq'):
tmp2 = ast.literal_eval(parser.get('gabor', 'freq'))
if parser.has_option('gabor', 'sigma'):
tmp3 = ast.literal_eval(parser.get('gabor', 'sigma'))
local_descriptors.append(
GaborDescriptor(theta=tmp, freq=tmp2, sigma=tmp3))
print('No. of descriptors: ', len(local_descriptors))
#---------
# data
if not parser.has_section('data'):
raise ValueError('Section "data" is mandatory.')
data_path = parser.get('data', 'input_path')
img_ext = parser.get('data', 'image_type')
res_path = parser.get('data', 'output_path')
img_files = glob.glob(data_path + '/*.' + img_ext)
if len(img_files) == 0:
return
## Process:
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0) # unbuferred output
for img_name in img_files:
print("Image: ", img_name, " ...reading... ", end='')
im = imread(img_name)
print("preprocessing... ", end='')
# -preprocessing
if im.ndim == 3:
im_h, _, _ = rgb2he2(im)
else:
raise ValueError('Input image must be RGB.')
# detect object region:
# -try to load a precomputed mask:
mask_file_name = data_path+'/mask/'+ \
os.path.splitext(os.path.split(img_name)[1])[0]+ \
'_tissue_mask.pbm'
if os.path.exists(mask_file_name):
print('(loading mask)...', end='')
mask = imread(mask_file_name)
mask = img_as_bool(mask)
mask = remove_small_objects(mask,
min_size=500,
connectivity=1,
in_place=True)
else:
print('(computing mask)...', end='')
mask, _ = tissue_region_from_rgb(im, _min_area=500)
row_min, col_min, row_max, col_max = bounding_box(mask)
im_h[np.logical_not(mask)] = 0 # make sure background is 0
mask = None
im = None
im_h = im_h[row_min:row_max + 1, col_min:col_max + 1]
print("growing the bag...", end='')
# -image bag growing
bag = None # bag for current image
for d in local_descriptors:
if bag is None:
bag = grow_bag_from_new_image(im_h,
d,
wnd_size,
nwindows,
discard_empty=True)
else:
bag[d.name] = grow_bag_with_new_features(im_h, bag['regs'],
d)[d.name]
# save the results for each image, one file per descriptor
desc_names = bag.keys()
desc_names.remove('regs') # keep all keys but the regions
# -save the ROI from the original image:
res_file = res_path + '/' + 'roi-' + \
os.path.splitext(os.path.split(img_name)[1])[0] + '.dat'
with open(res_file, 'w') as f:
f.write('\t'.join(
[str(x_) for x_ in [row_min, row_max, col_min, col_max]]))
for dn in desc_names:
res_file = res_path + '/' + dn + '_bag-' + \
os.path.splitext(os.path.split(img_name)[1])[0] + '.dat'
with open(res_file, 'w') as f:
n = len(bag[dn]) # total number of descriptors of this type
for i in range(n):
s = '\t'.join([str(x_) for x_ in bag['regs'][i]]) + '\t' + \
'\t'.join([str(x_) for x_ in bag[dn][i]]) + '\n'
f.write(s)
print('OK')
bag = None
gc.collect()
gc.collect()
