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 grow_bag_from_new_image(image, desc, w_size, n_obj, **kwargs):
"""
Extracts local descriptors from a new image.
:param image: numpy.array
Image data (single channel).
:param desc: LocalDescriptor
Local descriptor for feature extraction.
:param w_size: tuple
(width, height) of the sub-windows from the image.
:param n_obj: int
Maximum number of objects to be added to the bag.
:param kwargs: dict
Other parameters:
'roi': region of interest (default: None)
'sampling_strategy': how the image should be sampled:
'random' for random sampling
'sliding' for systematic, sliding window scanning
of the image
'it_start': where the scanning of the image starts (for
sliding window sampling strategy) (default (0,0))
'it_step': step from one window to the next (for
sliding window sampling strategy) (default (1,1))
'discard_empty': (boolean) whether an empy patch should still
be processed or simply discarded. Default: False
:return: dict
A dictionary with two elements:
<name of the descriptor>: list
'regions': list
The first list contains the feature descriptors.
The second list contains the corresponding window positions.
See also: grow_bag_with_new_features
"""
if 'roi' not in kwargs:
roi = None
else:
roi = kwargs['roi']
if 'it_start' not in kwargs:
it_start = (0,0)
else:
it_start = kwargs['it_start']
if 'it_step' not in kwargs:
it_step = (1,1)
else:
it_step = kwargs['it_step']
if 'sampling_strategy' not in kwargs:
sampling_strategy = 'random'
else:
sampling_strategy = kwargs['sampling_strategy']
if 'discard_empty' in kwargs:
discard_empty = kwargs['discard_empty']
else:
discard_empty = False
w_offset = (0, 0)
if isinstance(desc, HaarLikeDescriptor):
# this one works on integral images
image = intg_image(image)
# the sliding window should also be increased by 1:
w_offset = (1, 1)
w_size = (w_size[0] + w_offset[0], w_size[1] + w_offset[1])
# create iterator:
sampling_strategy = sampling_strategy.lower()
if sampling_strategy == 'random':
if roi is None:
itw = random_window(image.shape, w_size, n_obj)
else:
itw = random_window_on_regions(image.shape, roi, w_size, n_obj)
elif sampling_strategy == 'sliding':
if roi is None:
itw = sliding_window(image.shape, w_size, start=it_start, step=it_step)
else:
itw = sliding_window_on_regions(image.shape, roi, w_size, step=it_step)
else:
raise ValueError('Unknown strategy.')
bag = []
wnd = []
n = 0
for r in itw:
if discard_empty and image[r[0]:r[1], r[2]:r[3]].sum() < 1e-16:
continue
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
bag.append(desc.compute(image[r[0]:r[1], r[2]:r[3]]))
n += 1
if n > n_obj:
break
return {desc.name: bag, 'regs': wnd}
def main():
p = opt.ArgumentParser(description="""
Assigns the regions of an image to the clusters of a codebook.
""")
p.add_argument('image', action='store', help='image file name')
p.add_argument('model', action='store', help='model file name')
p.add_argument('out_file', action='store', help='results file name')
p.add_argument('-r', '--roi', action='store', nargs=4, type=int,
help='region of interest from the image as: row_min row_max col_min col_max',
default=None)
args = p.parse_args()
wsize = 32
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)
desc = GaborDescriptor(theta=tmp, freq=tmp2, sigma=tmp3)
image = skimage.io.imread(args.image)
if image.ndim == 3:
im_h, _ = rgb2he(image, normalize=True)
im_h = equalize_adapthist(im_h)
im_h = rescale_intensity(im_h, out_range=(0,255))
im_h = im_h.astype(np.uint8)
image = im_h
im_h = None
if args.roi is None:
roi = (0, image.shape[0]-1, 0, image.shape[1]-1)
else:
roi = args.roi
with ModelPersistence(args.model, 'r', format='pickle') as mp:
codebook = mp['codebook']
avg_dist = None
sd_dist = None
if 'avg_dist_to_centroid' in mp:
avg_dist = mp['avg_dist_to_centroid']
if 'stddev_dist_to_centroid' in mp:
sd_dist = mp['stddev_dist_to_centroid']
itw = sliding_window_on_regions(image.shape, [tuple(roi)], (wsize,wsize), step=(wsize,wsize))
wnd = []
labels = []
dists = []
buff_size = 100 # every <buff_size> patches we do a classification
X = np.zeros((buff_size, codebook.cluster_centers_[0].shape[0]))
k = 0
for r in itw:
# adjust if needed:
r2 = (r[0], r[1], r[2], r[3])
wnd.append(r2)
X[k,:] = desc.compute(image[r[0]:r[1], r[2]:r[3]])
k += 1
if k == buff_size:
y = codebook.predict(X)
Z = codebook.transform(X)
labels.extend(y.tolist())
dists.extend(Z[np.arange(buff_size), y].tolist()) # get the distances to the centroids of the assigned clusters
k = 0 # reset the block
if k != 0:
# it means some data is accumulated in X but not yet classified
y = codebook.predict(X[0:k,])
Z = codebook.transform(X[0:k,])
labels.extend(y.tolist())
dists.extend(Z[np.arange(k), y].tolist()) # get the distances to the centroids of the assigned clusters
# save data
with open(args.out_file, 'w') as f:
n = len(wnd) # total number of descriptors of this type
for k in range(n):
s = '\t'.join([str(x_) for x_ in wnd[k]]) + '\t' + str(labels[k]) + \
'\t' + str(dists[k]) + '\n'
f.write(s)
def main():
p = opt.ArgumentParser(description="""
Assigns the regions of an image to the clusters of a codebook.
""")
p.add_argument('image', action='store', help='image file name')
p.add_argument('config', action='store', help='a configuration file')
p.add_argument(
'-r',
'--roi',
action='store',
nargs=4,
type=int,
help=
'region of interest from the image as: row_min row_max col_min col_max',
default=None)
args = p.parse_args()
img_file = args.image
cfg_file = args.config
image_orig = skimage.io.imread(img_file)
if image_orig.ndim == 3:
im_h, _, _ = rgb2he2(image_orig)
if args.roi is None:
roi = (0, im_h.shape[0] - 1, 0, im_h.shape[1] - 1)
else:
roi = args.roi
# Process configuration file:
parser = SafeConfigParser()
parser.read(cfg_file)
if not parser.has_section('data'):
raise RuntimeError('Section [data] is mandatory')
wsize = (32, 32)
if parser.has_option('data', 'window_size'):
wsize = ast.literal_eval(parser.get('data', 'window_size'))
if not parser.has_option('data', 'model'):
raise RuntimeError('model file name is missing in [data] section')
model_file = parser.get('data', 'model')
with ModelPersistence(model_file, 'r', format='pickle') as mp:
codebook = mp['codebook']
Xm = mp['shift']
Xs = mp['scale']
standardize = mp['standardize']
if parser.has_option('data', 'output'):
out_file = parser.get('data', 'output')
else:
out_file = 'output.dat'
descriptors = read_local_descriptors_cfg(parser)
# For the moment, it is assumed tha only one type of local descriptors is
# used - no composite feature vectors. This will change in the future but,
# for the moment only the first type of descriptor in "descriptors" list
# is used, and the codebook is assumed to be constructed using the same.
desc = descriptors[0]
print(img_file)
print(wsize)
print(roi[0], roi[1], roi[2], roi[3])
w_offset = (0, 0)
if isinstance(desc, HaarLikeDescriptor):
# this one works on integral images
image = intg_image(im_h)
# the sliding window should also be increased by 1:
w_offset = (1, 1)
wsize = (wsize[0] + w_offset[0], wsize[1] + w_offset[1])
else:
image = im_h
itw = sliding_window_on_regions(image.shape, [tuple(roi)],
wsize,
step=wsize)
wnd = []
labels = []
buff_size = 10000 # every <buff_size> patches we do a classification
X = np.zeros((buff_size, codebook.cluster_centers_[0].shape[0]))
k = 0
if standardize: # placed here, to avoid testing inside the loop
for r in itw:
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
X[k, :] = desc.compute(image[r[0]:r[1], r[2]:r[3]])
k += 1
if k == buff_size:
X = (X - Xm) / Xs
labels.extend(codebook.predict(X).tolist())
k = 0 # reset the block
else:
for r in itw:
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
X[k, :] = desc.compute(image[r[0]:r[1], r[2]:r[3]])
k += 1
if k == buff_size:
labels.extend(codebook.predict(X).tolist())
k = 0 # reset the block
if k != 0:
# it means some data is accumulated in X but not yet classified
if standardize:
X[0:k + 1, ] = (X[0:k + 1, ] - Xm) / Xs
labels.extend(codebook.predict(X[0:k + 1, ]).tolist())
with open(out_file, 'w') as f:
n = len(wnd) # total number of descriptors of this type
for k in range(n):
s = '\t'.join([str(x_)
for x_ in wnd[k]]) + '\t' + str(labels[k]) + '\n'
f.write(s)
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="""
Assigns the regions of an image to the clusters of a codebook.
""")
p.add_argument('image', action='store', help='image file name')
p.add_argument('config', action='store', help='a configuration file')
p.add_argument('-r', '--roi', action='store', nargs=4, type=int,
help='region of interest from the image as: row_min row_max col_min col_max',
default=None)
args = p.parse_args()
img_file = args.image
cfg_file = args.config
image_orig = skimage.io.imread(img_file)
if image_orig.ndim == 3:
im_h, _, _ = rgb2he2(image_orig)
if args.roi is None:
roi = (0, img.shape[0]-1, 0, img.shape[1]-1)
else:
roi = args.roi
# Process configuration file:
parser = SafeConfigParser()
parser.read(cfg_file)
if not parser.has_section('data'):
raise RuntimeError('Section [data] is mandatory')
wsize = (32, 32)
if parser.has_option('data', 'window_size'):
wsize = ast.literal_eval(parser.get('data', 'window_size'))
if not parser.has_option('data', 'model'):
raise RuntimeError('model file name is missing in [data] section')
model_file = parser.get('data', 'model')
with ModelPersistence(model_file, 'r', format='pickle') as mp:
codebook = mp['codebook']
Xm = mp['shift']
Xs = mp['scale']
standardize = mp['standardize']
if parser.has_option('data', 'output'):
out_file = parser.get('data', 'output')
else:
out_file = 'output.dat'
descriptors = read_local_descriptors_cfg(parser)
# For the moment, it is assumed tha only one type of local descriptors is
# used - no composite feature vectors. This will change in the future but,
# for the moment only the first type of descriptor in "descriptors" list
# is used, and the codebook is assumed to be constructed using the same.
desc = descriptors[0]
print(img_file)
print(wsize)
print(roi[0], roi[1], roi[2], roi[3])
w_offset = (0, 0)
if isinstance(desc, HaarLikeDescriptor):
# this one works on integral images
image = intg_image(im_h)
# the sliding window should also be increased by 1:
w_offset = (1, 1)
wsize = (wsize[0] + w_offset[0], wsize[1] + w_offset[1])
else:
image = im_h
itw = sliding_window_on_regions(image.shape, [tuple(roi)], wsize, step=wsize)
wnd = []
labels = []
buff_size = 10000 # every <buff_size> patches we do a classification
X = np.zeros((buff_size, codebook.cluster_centers_[0].shape[0]))
k = 0
if standardize: # placed here, to avoid testing inside the loop
for r in itw:
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
X[k,:] = desc.compute(image[r[0]:r[1], r[2]:r[3]])
k += 1
if k == buff_size:
X = (X - Xm) / Xs
labels.extend(codebook.predict(X).tolist())
k = 0 # reset the block
else:
for r in itw:
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
X[k,:] = desc.compute(image[r[0]:r[1], r[2]:r[3]])
k += 1
if k == buff_size:
labels.extend(codebook.predict(X).tolist())
k = 0 # reset the block
if k != 0:
# it means some data is accumulated in X but not yet classified
if standardize:
X[0:k+1,] = (X[0:k+1,] - Xm) / Xs
labels.extend(codebook.predict(X[0:k+1,]).tolist())
with open(out_file, 'w') as f:
n = len(wnd) # total number of descriptors of this type
for k in range(n):
s = '\t'.join([str(x_) for x_ in wnd[k]]) + '\t' + str(labels[k]) + '\n'
f.write(s)
def grow_bag_from_new_image(image, desc, w_size, n_obj, **kwargs):
"""
Extracts local descriptors from a new image.
:param image: numpy.array
Image data (single channel).
:param desc: LocalDescriptor
Local descriptor for feature extraction.
:param w_size: tuple
(width, height) of the sub-windows from the image.
:param n_obj: int
Maximum number of objects to be added to the bag.
:param kwargs: dict
Other parameters:
'roi': region of interest (default: None)
'sampling_strategy': how the image should be sampled:
'random' for random sampling
'sliding' for systematic, sliding window scanning
of the image
'it_start': where the scanning of the image starts (for
sliding window sampling strategy) (default (0,0))
'it_step': step from one window to the next (for
sliding window sampling strategy) (default (1,1))
'discard_empty': (boolean) whether an empy patch should still
be processed or simply discarded. Default: False
:return: dict
A dictionary with two elements:
<name of the descriptor>: list
'regions': list
The first list contains the feature descriptors.
The second list contains the corresponding window positions.
See also: grow_bag_with_new_features
"""
if 'roi' not in kwargs:
roi = None
else:
roi = kwargs['roi']
if 'it_start' not in kwargs:
it_start = (0, 0)
else:
it_start = kwargs['it_start']
if 'it_step' not in kwargs:
it_step = (1, 1)
else:
it_step = kwargs['it_step']
if 'sampling_strategy' not in kwargs:
sampling_strategy = 'random'
else:
sampling_strategy = kwargs['sampling_strategy']
if 'discard_empty' in kwargs:
discard_empty = kwargs['discard_empty']
else:
discard_empty = False
w_offset = (0, 0)
if isinstance(desc, HaarLikeDescriptor):
# this one works on integral images
image = intg_image(image)
# the sliding window should also be increased by 1:
w_offset = (1, 1)
w_size = (w_size[0] + w_offset[0], w_size[1] + w_offset[1])
# create iterator:
sampling_strategy = sampling_strategy.lower()
if sampling_strategy == 'random':
if roi is None:
itw = random_window(image.shape, w_size, n_obj)
else:
itw = random_window_on_regions(image.shape, roi, w_size, n_obj)
elif sampling_strategy == 'sliding':
if roi is None:
itw = sliding_window(image.shape,
w_size,
start=it_start,
step=it_step)
else:
itw = sliding_window_on_regions(image.shape,
roi,
w_size,
step=it_step)
else:
raise ValueError('Unknown strategy.')
bag = []
wnd = []
n = 0
for r in itw:
if discard_empty and image[r[0]:r[1], r[2]:r[3]].sum() < 1e-16:
continue
# adjust if needed:
r2 = (r[0], r[1] - w_offset[1], r[2], r[3] - w_offset[0])
wnd.append(r2)
bag.append(desc.compute(image[r[0]:r[1], r[2]:r[3]]))
n += 1
if n > n_obj:
break
return {desc.name: bag, 'regs': wnd}