def main():
p = opt.ArgumentParser(description="""
Emphasizes the patches with a given code (from BoT) by reducing the contrast of the rest of the image.
"""
)
p.add_argument('image', action='store', help='image file name')
p.add_argument('res_image', action='store', help='name of the resulting image')
p.add_argument('bot_result', action='store', help='a file with BoT coding for regions')
p.add_argument('bot_code', action='store', help='the code of the regions to be emphasized', type=int)
p.add_argument('-g', '--gamma', action='store', nargs=1, type=float,
help='the gamma level of the background regions',
default=0.2)
args = p.parse_args()
img = skimage.io.imread(args.image)
regs = []
with ModelPersistence(args.bot_result, 'r', format='pickle') as d:
block_codes = d['l1_codes']
regs = d['regs']
#print(block_codes)
#print(args.bot_code)
# filter regions of interest:
roi = [ regs[k] for k in np.where(np.array(block_codes, dtype=np.int) == args.bot_code)[0] ]
#print(roi)
img = enhance_patches(img, roi, _gamma=args.gamma)
skimage.io.imsave(args.res_image, img)
return
def main():
p = opt.ArgumentParser(description="""
Emphasizes some regions of an image by reducing the contrast of the rest of the image.
""",
epilog="""
The regions are specified in an external file where each (rectangular) region is given in
terms of corner coordinates - row min, row max, col min, col max - and a (numeric) label.
The user must specify the label of the regions to be emphasized.
""")
p.add_argument('image', action='store', help='image file name')
p.add_argument('result', action='store', help='result image file')
p.add_argument('regions',
action='store',
help='file with the regions of interest')
p.add_argument(
'label',
action='store',
nargs='+',
type=int,
help=
'one or more labels (space-separated) of the regions to be emphasized')
p.add_argument('-g',
'--gamma',
action='store',
nargs=1,
type=float,
help='the gamma level of the background regions',
default=0.2)
args = p.parse_args()
img_file = args.image
res_file = args.result
reg_file = args.regions
labels = args.label
gam = args.gamma
img = skimage.io.imread(img_file)
regs = []
with open(reg_file, 'r') as f:
lines = f.readlines()
for l in lines:
r = [int(x_) for x_ in l.strip().split()[:-1]]
if r[4] in labels:
regs.append(r[0:4])
img = enhance_patches(img, regs, _gamma=gam)
skimage.io.imsave(res_file, img)
return
def main():
p = opt.ArgumentParser(description="""
Emphasizes some regions of an image by reducing the contrast of the rest of the image.
""",
epilog="""
The regions are specified in an external file where each (rectangular) region is given in
terms of corner coordinates - row min, row max, col min, col max - and a (numeric) label.
The user must specify the label of the regions to be emphasized.
""")
p.add_argument('image', action='store', help='image file name')
p.add_argument('result', action='store', help='result image file')
p.add_argument('regions', action='store', help='file with the regions of interest')
p.add_argument('label', action='store', nargs='+', type=int,
help='one or more labels (space-separated) of the regions to be emphasized')
p.add_argument('-g', '--gamma', action='store', nargs=1, type=float,
help='the gamma level of the background regions',
default=0.2)
args = p.parse_args()
img_file = args.image
res_file = args.result
reg_file = args.regions
labels = args.label
gam = args.gamma
img = skimage.io.imread(img_file)
regs = []
with open(reg_file, 'r') as f:
lines = f.readlines()
for l in lines:
r = [int(x_) for x_ in l.strip().split()[:-1]]
if r[4] in labels:
regs.append(r[0:4])
img = enhance_patches(img, regs, _gamma=gam)
skimage.io.imsave(res_file, img)
return
def main():
p = opt.ArgumentParser(description="""
Segments a number of rectangular contexts from a H&E slide. The contexts are clusters
of similar regions of the image. The similarity is based on various textural
descriptors.
""")
p.add_argument('img_file', action='store', help='RGB image file')
p.add_argument('ctxt',
action='store',
help='Number of contexts to extract',
type=int)
p.add_argument('wsize',
action='store',
help='Size of the (square) regions',
type=int)
p.add_argument(
'roi',
action='store',
help='a file with ROI coordinates (and context descriptors)')
p.add_argument('label',
action='store',
help='the cluster label of interest')
p.add_argument('--prefix',
action='store',
help='optional prefix for the resulting files',
default=None)
p.add_argument(
'--gabor',
action='store_true',
help='compute Gabor descriptors and generate the corresponding contexts'
)
p.add_argument(
'--lbp',
action='store_true',
help=
'compute LBP (local binary patterns) descriptors and generate the corresponding contexts'
)
p.add_argument(
'--mfs',
action='store_true',
help=
'compute fractal descriptors and generate the corresponding contexts')
p.add_argument('--eosine',
action='store_true',
help='should also Eosine component be processed?')
p.add_argument('--scale',
action='store',
type=float,
default=1.0,
help='scaling factor for ROI coordinates')
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
ROIs = []
for l in file(args.roi).readlines():
# extract the coordinates and the label from each ROI
# (one per row):
lb, row_min, row_max, col_min, col_max = map(lambda _x: int(float(_x)),
l.split('\t')[1:5])
row_min = int(mh.floor(row_min * args.scale))
row_max = int(mh.floor(row_max * args.scale))
col_min = int(mh.floor(col_min * args.scale))
col_max = int(mh.floor(col_max * args.scale))
if lb == args.label:
ROIs.append([row_min, row_max, col_min, col_max])
im = imread(args.img_file)
print("Original image size:", im.shape)
# get the H and E planes:
h, e, _ = rgb2he2(im)
if args.gabor:
print("---------> Gabor descriptors:")
g = GaborDescriptor()
desc_label = 'gabor'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize,
args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc,
desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.haralick:
print("---------> Haralick descriptors:")
g = GLCMDescriptor()
desc_label = 'haralick'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize,
args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc,
desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.lbp:
print("---------> LBP descriptors:")
g = LBPDescriptor()
desc_label = 'lbp'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize,
args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc,
desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.mfs:
print("---------> MFS descriptors:")
g = MFSDescriptor()
desc_label = 'mfs'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape,
ROIs,
(args.wsize, args.wsize),
step=(args.wsize,
args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc,
desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
return
def main():
p = opt.ArgumentParser(description="""
Segments a number of rectangular contexts from a H&E slide. The contexts are clusters
of similar regions of the image. The similarity is based on various textural
descriptors.
""")
p.add_argument('img_file', action='store', help='RGB image file')
p.add_argument('ctxt', action='store', help='Number of contexts to extract', type=int)
p.add_argument('wsize', action='store', help='Size of the (square) regions', type=int)
p.add_argument('roi', action='store', help='a file with ROI coordinates (and context descriptors)')
p.add_argument('label', action='store', help='the cluster label of interest')
p.add_argument('--prefix', action='store',
help='optional prefix for the resulting files',
default=None)
p.add_argument('--gabor', action='store_true',
help='compute Gabor descriptors and generate the corresponding contexts')
p.add_argument('--lbp', action='store_true',
help='compute LBP (local binary patterns) descriptors and generate the corresponding contexts')
p.add_argument('--mfs', action='store_true',
help='compute fractal descriptors and generate the corresponding contexts')
p.add_argument('--eosine', action='store_true', help='should also Eosine component be processed?')
p.add_argument('--scale', action='store', type=float, default=1.0,
help='scaling factor for ROI coordinates')
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
ROIs = []
for l in file(args.roi).readlines():
# extract the coordinates and the label from each ROI
# (one per row):
lb, row_min, row_max, col_min, col_max = map(lambda _x: int(float(_x)), l.split('\t')[1:5])
row_min = int(mh.floor(row_min * args.scale))
row_max = int(mh.floor(row_max * args.scale))
col_min = int(mh.floor(col_min * args.scale))
col_max = int(mh.floor(col_max * args.scale))
if lb == args.label:
ROIs.append([row_min, row_max, col_min, col_max])
im = imread(args.img_file)
print("Original image size:", im.shape)
# get the H and E planes:
h, e, _ = rgb2he2(im)
if args.gabor:
print("---------> Gabor descriptors:")
g = GaborDescriptor()
desc_label = 'gabor'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_h_'+str(k)+'.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_e_'+str(k)+'.ppm', im2)
print("OK")
if args.haralick:
print("---------> Haralick descriptors:")
g = GLCMDescriptor()
desc_label = 'haralick'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_h_'+str(k)+'.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_e_'+str(k)+'.ppm', im2)
print("OK")
if args.lbp:
print("---------> LBP descriptors:")
g = LBPDescriptor()
desc_label = 'lbp'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_h_'+str(k)+'.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_e_'+str(k)+'.ppm', im2)
print("OK")
if args.mfs:
print("---------> MFS descriptors:")
g = MFSDescriptor()
desc_label = 'mfs'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_h_'+str(k)+'.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window_on_regions(h.shape, ROIs, (args.wsize,args.wsize),
step=(args.wsize,args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt, criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack( (z2, z1) )
np.savetxt(pfx+'_'+desc_label+'_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1,1+args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx+'_'+desc_label+'_e_'+str(k)+'.ppm', im2)
print("OK")
return
def main():
p = opt.ArgumentParser(description="""
Segments a number of rectangular contexts from a H&E slide. The contexts are clusters
of similar regions of the image. The similarity is based on various textural
descriptors.
""")
p.add_argument('img_file', action='store', help='RGB image file')
p.add_argument('ctxt',
action='store',
help='Number of contexts to extract',
type=int)
p.add_argument('wsize',
action='store',
help='Size of the (square) regions',
type=int)
p.add_argument('--prefix',
action='store',
help='optional prefix for the resulting files',
default=None)
p.add_argument(
'--gabor',
action='store_true',
help='compute Gabor descriptors and generate the corresponding contexts'
)
p.add_argument(
'--lbp',
action='store_true',
help=
'compute LBP (local binary patterns) descriptors and generate the corresponding contexts'
)
p.add_argument(
'--mfs',
action='store_true',
help=
'compute fractal descriptors and generate the corresponding contexts')
p.add_argument(
'--haralick',
action='store_true',
help=
'compute Haralick descriptors and generate the corresponding contexts')
p.add_argument('--row_min',
action='store',
type=int,
help='start row (rows start at 0)',
default=0)
p.add_argument('--col_min',
action='store',
type=int,
help='start column (columns start at 0)',
default=0)
p.add_argument('--row_max',
action='store',
type=int,
help='end row (maximum: image height-1)',
default=0)
p.add_argument('--col_max',
action='store',
type=int,
help='end column (maximum: image width-1)',
default=0)
p.add_argument('--eosine',
action='store_true',
help='should also Eosine component be processed?')
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
im = imread(args.img_file)
print("Original image size:", im.shape)
row_min = min(max(args.row_min, 0), im.shape[0] - 2)
col_min = min(max(args.col_min, 0), im.shape[1] - 2)
row_max = max(min(args.row_max, im.shape[0] - 1), 0)
col_max = max(min(args.col_max, im.shape[1] - 1), 0)
if row_max == 0:
row_max = im.shape[0] - 1
if col_max == 0:
col_max = im.shape[1] - 1
if row_max - row_min < args.wsize or col_max - col_min < args.wsize:
raise ValueError('Window size too large for requested image size.')
im = im[row_min:row_max + 1, col_min:col_max + 1, :]
# crop the image to multiple of wsize:
nh, nw = mh.floor(im.shape[0] / args.wsize), mh.floor(im.shape[1] /
args.wsize)
dh, dw = mh.floor((im.shape[0] - nh * args.wsize) / 2), mh.floor(
(im.shape[1] - nw * args.wsize) / 2)
im = im[dh:dh + nh * args.wsize, dw:dw + nw * args.wsize, :]
print("Image cropped to:", im.shape)
imsave(pfx + '_cropped.ppm', im)
# get the H and E planes:
h, e, _ = rgb2he2(im)
if args.gabor:
print("---------> Gabor descriptors:")
g = GaborDescriptor()
desc_label = 'gabor'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(dsc, desc_label) # col 0: row_min, col 2: col_min
z1[:, 0] += row_min + dh
z1[:, 2] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc,
desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.haralick:
print("---------> Haralick descriptors:")
g = GLCMDescriptor()
desc_label = 'haralick'
print("------------> H plane")
# on H-plane:
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc, desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_gabor(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc,
desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.lbp:
print("---------> LBP descriptors:")
g = LBPDescriptor()
desc_label = 'lbp'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc, desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_lbp(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc,
desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
if args.mfs:
print("---------> MFS descriptors:")
g = MFSDescriptor()
desc_label = 'mfs'
# on H-plane:
print("------------> H plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(h, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc, desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_h.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_h_' + str(k) + '.ppm', im2)
if args.eosine:
# repeat on E plane:
print("------------> E plane")
img_iterator = sliding_window(h.shape, (args.wsize, args.wsize),
step=(args.wsize, args.wsize))
dsc = get_local_desc(e, g, img_iterator, desc_label)
dst = pdist_mfs(dsc)
cl = average(dst)
id = fcluster(cl, t=args.ctxt,
criterion='maxclust') # get the various contexts
# save clustering/contexts - remember, the coordinates are in the
# current image system which might have been cropped from the original ->
# should add back the shift
z1 = desc_to_matrix(
dsc,
desc_label) # col 0:4 [row_min, row_max, col_min, col_max]
z1[:, 0:2] += row_min + dh
z1[:, 2:4] += col_min + dw
z2 = np.matrix(id).transpose()
z2 = np.hstack((z2, z1))
np.savetxt(pfx + '_' + desc_label + '_e.dat', z2, delimiter="\t")
# save visualizations
for k in range(1, 1 + args.ctxt):
i = np.where(id == k)[0]
p = [dsc[j]['roi'] for j in i]
im2 = enhance_patches(im, p)
imsave(pfx + '_' + desc_label + '_e_' + str(k) + '.ppm', im2)
print("OK")
return
