def compute_boxmap(binary, scale, threshold=(.5, 4), dtype='i'):
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
boxmap = np.zeros(binary.shape, dtype)
for o in bysize:
if sl.area(o)**.5 < threshold[0] * scale:
continue
if sl.area(o)**.5 > threshold[1] * scale:
continue
boxmap[o] = 1
return boxmap
def compute_boxmap(binary, scale, threshold=(.5, 4), dtype='i'):
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
boxmap = np.zeros(binary.shape, dtype)
for o in bysize:
if sl.area(o)**.5 < threshold[0]*scale:
continue
if sl.area(o)**.5 > threshold[1]*scale:
continue
boxmap[o] = 1
return boxmap
def compute_boxmap(binary: np.array, scale: float, threshold: Tuple[float, int] = (.5, 4), dtype: str = 'i') -> np.array:
"""
Returns grapheme cluster-like boxes based on connected components.
"""
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
boxmap = np.zeros(binary.shape, dtype)
for o in bysize:
if sl.area(o)**.5 < threshold[0]*scale:
continue
if sl.area(o)**.5 > threshold[1]*scale:
continue
boxmap[o] = 1
return boxmap
def compute_boxmap(binary: np.array,
scale: float,
threshold: Tuple[float, int] = (.5, 4),
dtype: str = 'i') -> np.array:
"""
Returns grapheme cluster-like boxes based on connected components.
"""
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
boxmap = np.zeros(binary.shape, dtype)
for o in bysize:
if sl.area(o)**.5 < threshold[0] * scale:
continue
if sl.area(o)**.5 > threshold[1] * scale:
continue
boxmap[o] = 1
return boxmap
def estimate_scale(binary):
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
scalemap = np.zeros(binary.shape)
for o in bysize:
if np.amax(scalemap[o]) > 0:
continue
scalemap[o] = sl.area(o)**0.5
scale = np.median(scalemap[(scalemap > 3) & (scalemap < 100)])
return scale
def estimate_scale(binary):
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
scalemap = np.zeros(binary.shape)
for o in bysize:
if np.amax(scalemap[o]) > 0:
continue
scalemap[o] = sl.area(o)**0.5
scale = np.median(scalemap[(scalemap > 3) & (scalemap < 100)])
return scale
def estimate_scale(binary: np.array) -> float:
"""
Estimates image scale based on number of connected components.
"""
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
scalemap = np.zeros(binary.shape)
for o in bysize:
if np.amax(scalemap[o]) > 0:
continue
scalemap[o] = sl.area(o)**0.5
scale = np.median(scalemap[(scalemap > 3) & (scalemap < 100)])
return scale
def estimate_scale(binary: np.array) -> float:
"""
Estimates image scale based on number of connected components.
"""
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
scalemap = np.zeros(binary.shape)
for o in bysize:
if np.amax(scalemap[o]) > 0:
continue
scalemap[o] = sl.area(o)**0.5
scale = np.median(scalemap[(scalemap > 3) & (scalemap < 100)])
return scale
