def djvu_binarise(image_filepath, **kwargs):
"""
*smoothness*
The amount of effect that parent blocks have on their children
blocks. Higher values will result in more smoothness between
blocks. Expressed as a percentage between 0.0 and 1.0.
*max_block_size*
The size of the largest block to determine a threshold.
*min_block_size*
The size of the smallest block to determine a threshold.
*block_factor*
The number of child blocks (in each direction) per parent block.
For instance, a *block_factor* of 2 results in 4 children per
parent.
"""
input_image = load_image_for_job(image_filepath,
gamera.plugins.threshold.djvu_threshold)
output_image = input_image.djvu_threshold( \
kwargs['smoothness'],
kwargs['max_block_size'],
kwargs['min_block_size'],
kwargs['block_factor'])
return {'tiff': output_image}
def pitch_find(xml_filepath, segmented, page_sequence, **kwargs):
# Run a rank filter on the image to make the staves bigger
# for pitch detection
print "loading image... performing rank filter"
input_image = load_image_for_job(segmented, gamera.plugins.misc_filters.rank)
# XXX: Parameters to change?
rank_image = input_image.rank(9, 9, 0)
#gamera.core.save_image(rank_image, "aomr2-rank.tiff")
print "... done. Finding pitches"
try:
aomr_obj = AomrObject(rank_image, \
discard_size=kwargs['discard_size'],
lines_per_staff=4,
staff_finder=0,
staff_removal=0,
binarization=0)
glyphs = gamera.gamera_xml.glyphs_from_xml(xml_filepath)
recognized_glyphs = aomr_obj.run(glyphs)
data = json.loads(recognized_glyphs)
mei_file = AomrMeiOutput(data, str(segmented), str(page_sequence))
except AomrUnableToFindStavesError as e:
#if something goes wrong, this will create an empty mei file (instead of crashing)
print e
mei_file = AomrMeiOutput({}, str(segmented), str(page_sequence))
print "... done. Writing MEI"
return {
'mei': mei_file
}
def border_remover(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, gamera.toolkits.border_removal.plugins.border_removal.border_removal)
mask = input_image.border_removal() # use defaults
output_image = input_image.mask(mask)
return {
"tiff": output_image
}
def find_staves(image_filepath, **kwargs):
"""
*num_lines*:
Number of lines within one staff. When zero, the number is automatically
detected.
*scan_lines*:
Number of vertical scan lines for extracting candidate points.
*blackness*:
Required blackness on the connection line between two candidate points
in order to consider them matching.
*tolerance*:
How much the vertical black runlength of a candidate point may deviate
from staffline_height. When negative, this value is set to
*max([2, staffline_height / 4])*.
"""
# Perform a Rank filter to make the stafflines thicker
input_image = load_image_for_job(image_filepath,
gamera.plugins.misc_filters.rank)
rank_image = input_image.rank(9, 9, 0)
#both 0's can be parameterized, first one is staffline_height and second is staffspace_height, both default 0
#the constructor converts to onebit if its not ONEBIT. Note that it will simply convert, no binarisation process
staff_finder = gamera.toolkits.musicstaves.StaffFinder_miyao(
rank_image, 0, 0)
staff_finder.find_staves(kwargs['num_lines'], kwargs['scanlines'],
kwargs['blackness'], kwargs['tolerance'])
poly_list = staff_finder.get_polygon()
poly_list = fix_poly_point_list(poly_list, staff_finder.staffspace_height)
poly_json_list = create_polygon_outer_points_json_dict(poly_list)
stafffind_json_list = create_json_from_poly_list(poly_list)
encoded1 = json.dumps(poly_json_list)
encoded2 = json.dumps(stafffind_json_list)
return {
'json': encoded1,
'json2': encoded2,
}
def rank_filter(image_filepath, **kwargs):
"""
*rank_val* (1, 2, ..., *k* * *k*)
The rank of the windows pixels to select for the center. (k*k+1)/2 is
equivalent to the median.
*k* (3, 5 ,7, ...)
The window size (must be odd).
*border_treatment* (0, 1)
When 0 ('padwhite'), window pixels outside the image are set to white.
When 1 ('reflect'), reflecting boundary conditions are used.
"""
input_image = load_image_for_job(image_filepath,
gamera.plugins.misc_filters.rank)
output_image = input_image.rank( \
kwargs['rank_val'],
kwargs['k'],
kwargs['border_treatment'])
return {'tiff': output_image}
def rank_filter(image_filepath, **kwargs):
"""
*rank_val* (1, 2, ..., *k* * *k*)
The rank of the windows pixels to select for the center. (k*k+1)/2 is
equivalent to the median.
*k* (3, 5 ,7, ...)
The window size (must be odd).
*border_treatment* (0, 1)
When 0 ('padwhite'), window pixels outside the image are set to white.
When 1 ('reflect'), reflecting boundary conditions are used.
"""
input_image = load_image_for_job(image_filepath, gamera.plugins.misc_filters.rank)
output_image = input_image.rank( \
kwargs['rank_val'],
kwargs['k'],
kwargs['border_treatment'])
return {
'tiff': output_image
}
def find_staves(image_filepath, **kwargs):
"""
*num_lines*:
Number of lines within one staff. When zero, the number is automatically
detected.
*scan_lines*:
Number of vertical scan lines for extracting candidate points.
*blackness*:
Required blackness on the connection line between two candidate points
in order to consider them matching.
*tolerance*:
How much the vertical black runlength of a candidate point may deviate
from staffline_height. When negative, this value is set to
*max([2, staffline_height / 4])*.
"""
# Perform a Rank filter to make the stafflines thicker
input_image = load_image_for_job(image_filepath, gamera.plugins.misc_filters.rank)
rank_image = input_image.rank(9, 9, 0)
# both 0's can be parameterized, first one is staffline_height and second is staffspace_height, both default 0
# the constructor converts to onebit if its not ONEBIT. Note that it will simply convert, no binarisation process
staff_finder = gamera.toolkits.musicstaves.StaffFinder_miyao(rank_image, 0, 0)
staff_finder.find_staves(kwargs["num_lines"], kwargs["scanlines"], kwargs["blackness"], kwargs["tolerance"])
poly_list = staff_finder.get_polygon()
poly_list = fix_poly_point_list(poly_list, staff_finder.staffspace_height)
poly_json_list = create_polygon_outer_points_json_dict(poly_list)
stafffind_json_list = create_json_from_poly_list(poly_list)
encoded1 = json.dumps(poly_json_list)
encoded2 = json.dumps(stafffind_json_list)
return {"json": encoded1, "json2": encoded2}
def djvu_binarise(image_filepath, **kwargs):
"""
*smoothness*
The amount of effect that parent blocks have on their children
blocks. Higher values will result in more smoothness between
blocks. Expressed as a percentage between 0.0 and 1.0.
*max_block_size*
The size of the largest block to determine a threshold.
*min_block_size*
The size of the smallest block to determine a threshold.
*block_factor*
The number of child blocks (in each direction) per parent block.
For instance, a *block_factor* of 2 results in 4 children per
parent.
"""
input_image = load_image_for_job(image_filepath, gamera.plugins.threshold.djvu_threshold)
output_image = input_image.djvu_threshold(
kwargs["smoothness"], kwargs["max_block_size"], kwargs["min_block_size"], kwargs["block_factor"]
)
return {"tiff": output_image}
def auto_rotate(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, correct_rotation)
output_image = input_image.correct_rotation(0)
return {
'tiff': output_image
}
def auto_rotate(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, correct_rotation)
output_image = input_image.correct_rotation(0)
return {'tiff': output_image}
def brink_binarise(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, brink_threshold)
output_image = input_image.brink_threshold()
return {'tiff': output_image}
def simple_binarise(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath,
gamera.plugins.threshold.threshold)
output_image = input_image.threshold(kwargs['threshold'])
return {'tiff': output_image}
def brink_binarise(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, brink_threshold)
output_image = input_image.brink_threshold()
return {"tiff": output_image}
def simple_binarise(image_filepath, **kwargs):
input_image = load_image_for_job(image_filepath, gamera.plugins.threshold.threshold)
output_image = input_image.threshold(kwargs["threshold"])
return {"tiff": output_image}
