def binary_objects(binary: np.array) -> np.array:
"""
Labels features in an array and segments them into objects.
"""
labels, _ = morph.label(binary)
objects = morph.find_objects(labels)
return objects
def compute_lines(segmentation, spread, scale):
"""Given a line segmentation map, computes a list
of tuples consisting of 2D slices and masked images."""
lobjects = morph.find_objects(segmentation)
lines = []
for i, o in enumerate(lobjects):
if o is None:
continue
if sl.dim1(o) < 2 * scale or sl.dim0(o) < scale:
continue
mask = (segmentation[o] == i + 1)
if np.amax(mask) == 0:
continue
result = record()
result.label = i + 1
result.bounds = o
polygon = []
if ((segmentation[o] != 0) == (segmentation[o] != i + 1)).any():
ppoints = draw_polygon(spread[o], i + 1)
ppoints = ppoints[1:] if ppoints else []
polygon = [(o[0].start + p[0], o[1].start + p[1]) for p in ppoints]
if not polygon:
polygon = [(o[0].start, o[1].start), (o[0].stop, o[1].start),
(o[0].stop, o[1].stop), (o[0].start, o[1].stop)]
result.polygon = polygon
result.mask = mask
lines.append(result)
return lines
def binary_objects(binary: np.array) -> np.array:
"""
Labels features in an array and segments them into objects.
"""
labels, _ = morph.label(binary)
objects = morph.find_objects(labels)
return objects
def compute_lines(segmentation, scale):
"""Given a line segmentation map, computes a list
of tuples consisting of 2D slices and masked images."""
lobjects = morph.find_objects(segmentation)
lines = []
for i, o in enumerate(lobjects):
if o is None:
continue
if sl.dim1(o) < 2 * scale or sl.dim0(o) < scale:
continue
mask = (segmentation[o] == i + 1)
if np.amax(mask) == 0:
continue
result = record()
result.label = i + 1
result.bounds = o
result.mask = mask
lines.append(result)
return lines
def remove_hlines(binary, scale, maxsize=10):
"""
Removes horizontal black lines that only interfere with page segmentation.
Args:
binary (numpy.array):
scale (float):
maxsize (int): maximum size of removed lines
Returns:
numpy.array containing the filtered image.
"""
labels, _ = morph.label(binary)
objects = morph.find_objects(labels)
for i, b in enumerate(objects):
if sl.width(b) > maxsize * scale:
labels[b][labels[b] == i + 1] = 0
return np.array(labels != 0, 'B')
def compute_lines(segmentation, scale):
"""Given a line segmentation map, computes a list
of tuples consisting of 2D slices and masked images."""
lobjects = morph.find_objects(segmentation)
lines = []
for i, o in enumerate(lobjects):
if o is None:
continue
if sl.dim1(o) < 2*scale or sl.dim0(o) < scale:
continue
mask = (segmentation[o] == i+1)
if np.amax(mask) == 0:
continue
result = record()
result.label = i+1
result.bounds = o
result.mask = mask
lines.append(result)
return lines
def remove_hlines(binary, scale, maxsize=10):
"""
Removes horizontal black lines that only interfere with page segmentation.
Args:
binary (numpy.array):
scale (float):
maxsize (int): maximum size of removed lines
Returns:
numpy.array containing the filtered image.
"""
labels, _ = morph.label(binary)
objects = morph.find_objects(labels)
for i, b in enumerate(objects):
if sl.width(b) > maxsize*scale:
labels[b][labels[b] == i+1] = 0
return np.array(labels != 0, 'B')
def remove_hlines(binary: np.ndarray,
scale: float,
maxsize: int = 10) -> np.ndarray:
"""
Removes horizontal black lines that only interfere with page segmentation.
Args:
binary:
scale:
maxsize: maximum size of removed lines
Returns:
numpy.ndarray containing the filtered image.
"""
logger.debug('Filtering horizontal lines')
labels, _ = morph.label(binary)
objects = morph.find_objects(labels)
for i, b in enumerate(objects):
if sl.width(b) > maxsize * scale:
labels[b][labels[b] == i + 1] = 0
return np.array(labels != 0, 'B')
def binary_objects(binary):
labels, n = morph.label(binary)
objects = morph.find_objects(labels)
return objects
def binary_objects(binary):
labels, n = morph.label(binary)
objects = morph.find_objects(labels)
return objects
