def grow_bag_with_new_features(image, regions, desc):
"""
Returns the features corresponding to a list of regions. This
is usually used for adding new features to an existing bag,
where the list of regions has been obtained from an iterator.
:param image: numpy.array
Image data (single channel).
:param regions: list
A list of regions [(row_min, row_max, col_min, col_max),...]
:param desc: LocalDescriptor
Descriptor used for extracting the features.
: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
(identical with the input regions list)
See also: grow_bag_from_new_image
"""
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)
bag = []
for w in regions:
# adjust if needed:
r = (w[0], w[1] + w_offset[1], w[2], w[3] + w_offset[0])
bag.append(desc.compute(image[r[0]:r[1], r[2]:r[3]]))
return {desc.name: bag, 'regs': regions}
def grow_bag_with_new_features(image, regions, desc):
"""
Returns the features corresponding to a list of regions. This
is usually used for adding new features to an existing bag,
where the list of regions has been obtained from an iterator.
:param image: numpy.array
Image data (single channel).
:param regions: list
A list of regions [(row_min, row_max, col_min, col_max),...]
:param desc: LocalDescriptor
Descriptor used for extracting the features.
: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
(identical with the input regions list)
See also: grow_bag_from_new_image
"""
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)
bag = []
for w in regions:
# adjust if needed:
r = (w[0], w[1] + w_offset[1], w[2], w[3] + w_offset[0])
bag.append(desc.compute(image[r[0]:r[1], r[2]:r[3]]))
return {desc.name: bag, 'regs': regions}
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('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="""
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}
