import os
import av
from PIL import ImageFilter
from data_juicer.utils.constant import Fields
from data_juicer.utils.file_utils import transfer_filename
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.mm_utils import (close_video, detect_faces,
load_data_with_context, load_video,
process_each_frame)
from data_juicer.utils.model_utils import get_model, prepare_model
from ..base_op import OPERATORS, UNFORKABLE, Mapper
from ..op_fusion import LOADED_VIDEOS
cv2 = LazyLoader('cv2', 'opencv-python')
OP_NAME = 'video_face_blur_mapper'
[docs]
@UNFORKABLE.register_module(OP_NAME)
@OPERATORS.register_module(OP_NAME)
@LOADED_VIDEOS.register_module(OP_NAME)
class VideoFaceBlurMapper(Mapper):
"""Mapper to blur faces detected in videos.
"""
_default_kwargs = {
'scaleFactor': 1.1,
'minNeighbors': 3,
'minSize': None,
'maxSize': None,
}
[docs]
def __init__(self,
cv_classifier: str = '',
blur_type: str = 'gaussian',
radius: float = 2,
*args,
**kwargs):
"""
Initialization method.
:param cv_classifier: OpenCV classifier path for face detection.
By default, we will use 'haarcascade_frontalface_alt.xml'.
: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 cv_classifier == '':
cv_classifier = os.path.join(cv2.data.haarcascades,
'haarcascade_frontalface_alt.xml')
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. ')
if blur_type == 'mean':
self.blur = ImageFilter.BLUR
elif blur_type == 'box':
self.blur = ImageFilter.BoxBlur(radius)
else:
self.blur = ImageFilter.GaussianBlur(radius)
self.blur_type = blur_type
self.radius = radius
self.extra_kwargs = self._default_kwargs
for key in kwargs:
if key in self.extra_kwargs:
self.extra_kwargs[key] = kwargs[key]
self.model_key = prepare_model(model_type='opencv_classifier',
model_path=cv_classifier)
[docs]
def process_single(self, sample, context=False):
# there is no video in this sample
if self.video_key not in sample or not sample[self.video_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.video_key]
loaded_video_keys = sample[self.video_key]
sample, videos = load_data_with_context(sample, context,
loaded_video_keys, load_video)
model = get_model(self.model_key)
def _blur_func(frame):
image = frame.to_image()
dets = detect_faces(image, model, **self.extra_kwargs)
if len(dets) > 0:
for (x, y, w, h) in dets:
box = (x, y, x + w, y + h)
blured_roi = image.crop(box).filter(self.blur)
image.paste(blured_roi, box)
frame = av.VideoFrame.from_image(image)
return frame
processed_video_keys = {}
for video_key in loaded_video_keys:
# skip duplicate
if video_key in processed_video_keys:
continue
video = videos[video_key]
blured_video_key = transfer_filename(video_key, OP_NAME,
**self._init_parameters)
output_video_key = process_each_frame(video, blured_video_key,
_blur_func)
processed_video_keys[video_key] = output_video_key
if not context:
close_video(video)
# when the file is modified, its source file needs to be updated.
for i, value in enumerate(loaded_video_keys):
if sample[Fields.source_file][i] != value:
if processed_video_keys[value] != value:
sample[Fields.source_file][i] = value
sample[self.video_key] = [
processed_video_keys[key] for key in loaded_video_keys
]
return sample