def make_patches(data_root, patches_root, patch_size, outline_filled=None, remove_filled=False, min_widths=('def',),
mirror=True, rotations=(0,), translations=((0, 0),), distinguishability_threshold=.5, num_workers=0,
random_samples=None, leave_width_percentile=None):
if num_workers != 0:
from pathos.multiprocessing import cpu_count, ProcessingPool
from pathos.threading import ThreadPool
if num_workers == -1:
optimal_workers = cpu_count() - 1
workers_pool = ProcessingPool(optimal_workers)
else:
workers_pool = ProcessingPool(num_workers)
print(f'Workers pool: {workers_pool}')
savers_pool = ThreadPool(1)
saving_patches_in_bg = savers_pool.amap(lambda a: None, [])
else:
workers_pool = 0
path = lambda basename, origin, width='def', ori='def', rot=0, t=(0, 0): os.path.join(patches_root, basename,
'{}x{}'.format(*patch_size),
'width_{}'.format(width),
'orientation_{}'.format(ori),
'rotated_deg_{}'.format(rot),
'translated_{}_{}'.format(*t),
'{}_{}.svg'.format(*origin))
orientations = ['def']
if mirror:
orientations.append('mir')
if random_samples is not None:
min_widths_all = deepcopy(min_widths)
orientations_all = deepcopy(orientations)
rotations_all = deepcopy(rotations)
translations_all = deepcopy(translations)
source_images = glob(os.path.join(data_root, '**', '*.svg'), recursive=True)
for file in source_images:
print('Processing file {}'.format(file))
basename = file[len(data_root) + 1:-4] # split data_root and extension
vector_image = VectorImage.from_svg(file)
if remove_filled:
vector_image.remove_filled()
if outline_filled is not None:
vector_image.leave_only_contours(outline_filled)
if leave_width_percentile is not None:
vector_image.leave_width_percentile(leave_width_percentile)
if random_samples is not None:
min_widths = np.random.choice(min_widths_all, size=min(random_samples, len(min_widths_all)), replace=False)
orientations = np.random.choice(orientations_all, size=min(random_samples, len(orientations_all)),
replace=False)
rotations = np.random.choice(rotations_all, size=min(random_samples, len(rotations_all)), replace=False)
translations = translations_all[
np.random.choice(len(translations_all), size=min(random_samples, len(translations_all)), replace=False)]
for width in min_widths:
print('\twidth {}'.format(width))
if width == 'def':
vector_image_scaled = vector_image
else:
vector_image_scaled = vector_image.copy()
vector_image_scaled.scale_to_width('min', width)
for orientation in orientations:
print('\t\torientation {}'.format(orientation))
if orientation == 'def':
vector_image_reoriented = vector_image_scaled
else:
vector_image_reoriented = vector_image_scaled.mirrored()
for rotation in rotations:
print('\t\t\trotation {}'.format(rotation))
vector_image_rotated = vector_image_reoriented.rotated(rotation, adjust_view=True)
for translation in translations:
print('\t\t\t\ttranslation {}'.format(translation))
vector_image_translated = vector_image_rotated.translated(translation, adjust_view=True)
vector_patches = vector_image_translated.split_to_patches(patch_size, workers=workers_pool)
if num_workers != 0:
print('\t\t\t\t\twaiting for previous batch to be saved')
saving_patches_in_bg.get()
def simplify_and_save(vector_patch, basename=basename, width=width, orientation=orientation,
rotation=rotation, translation=translation):
vector_patch.simplify_segments(distinguishability_threshold=distinguishability_threshold)
if len(vector_patch.paths) == 0:
return
save_path = path(basename,
(int(vector_patch.x.as_pixels()), int(vector_patch.y.as_pixels())), width,
orientation, rotation, translation)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
vector_patch.save(save_path)
if num_workers == 0:
print('\t\t\t\t\tsaving patches')
for vector_path in vector_patches.reshape(-1):
simplify_and_save(vector_path)
else:
print('\t\t\t\t\tsaving patches')
saving_patches_in_bg = savers_pool.amap(simplify_and_save, vector_patches.reshape(-1))
if num_workers != 0:
workers_pool.close()
workers_pool.join()
workers_pool.clear()
savers_pool.close()
savers_pool.join()
savers_pool.clear()
