import os
import numpy as np
from data_juicer.utils.constant import Fields
from data_juicer.utils.file_utils import transfer_filename
from data_juicer.utils.mm_utils import load_data_with_context, load_image
from ..base_op import OPERATORS, Mapper
from ..op_fusion import LOADED_IMAGES
OP_NAME = 'image_blur_mapper'
[docs]@OPERATORS.register_module(OP_NAME)
@LOADED_IMAGES.register_module(OP_NAME)
class ImageBlurMapper(Mapper):
"""Mapper to blur images.
"""
[docs] def __init__(self,
p: float = 0.2,
blur_type: str = 'gaussian',
radius: float = 2,
*args,
**kwargs):
"""
Initialization method.
:param p: Probability of the image being blured.
:param blur_type: Type of blur kernel, including
['mean', 'box', 'gaussian'].
:param radius: Radius of blur kernel.
:param args: extra args
:param kwargs: extra args
"""
super().__init__(*args, **kwargs)
self._init_parameters = self.remove_extra_parameters(locals())
if blur_type not in ['mean', 'box', 'gaussian']:
raise ValueError(
f'Blur_type [{blur_type}] is not supported. '
f'Can only be one of ["mean", "box", "gaussian"]. ')
if radius < 0:
raise ValueError('Radius must be >= 0. ')
self.p = p
from PIL import ImageFilter
if blur_type == 'mean':
self.blur = ImageFilter.BLUR
elif blur_type == 'box':
self.blur = ImageFilter.BoxBlur(radius)
else:
self.blur = ImageFilter.GaussianBlur(radius)
[docs] def process_single(self, sample, context=False):
# there is no image in this sample
if self.image_key not in sample or not sample[self.image_key]:
sample[Fields.source_file] = []
return sample
if Fields.source_file not in sample or not sample[Fields.source_file]:
sample[Fields.source_file] = sample[self.image_key]
# load images
loaded_image_keys = sample[self.image_key]
sample, images = load_data_with_context(sample, context,
loaded_image_keys, load_image)
processed = {}
for image_key in loaded_image_keys:
if image_key in processed:
continue
if self.p < np.random.rand():
processed[image_key] = image_key
else:
blured_image_key = transfer_filename(image_key, OP_NAME,
**self._init_parameters)
if not os.path.exists(
blured_image_key) or blured_image_key not in images:
blured_image = images[image_key].convert('RGB').filter(
self.blur)
images[blured_image_key] = blured_image
blured_image.save(blured_image_key)
if context:
sample[Fields.context][blured_image_key] = blured_image
processed[image_key] = blured_image_key
# when the file is modified, its source file needs to be updated.
for i, value in enumerate(loaded_image_keys):
if sample[Fields.source_file][i] != value:
if processed[value] != value:
sample[Fields.source_file][i] = value
sample[self.image_key] = [processed[key] for key in loaded_image_keys]
return sample