# Tencent is pleased to support the open source community by making GNES available.
#
# Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import math
from typing import List
from gnes.preprocessor.base import BaseVideoPreprocessor
from gnes.proto import gnes_pb2, array2blob, blob2array
from gnes.preprocessor.io_utils import video
from gnes.preprocessor.helper import compute_descriptor, compare_descriptor, detect_peak_boundary, compare_ecr
[docs]class ShotDetectPreprocessor(BaseVideoPreprocessor):
store_args_kwargs = True
def __init__(self,
descriptor: str = 'block_hsv_histogram',
distance_metric: str = 'bhattacharya',
detect_method: str = 'threshold',
frame_size: str = None,
frame_rate: int = 10,
vframes: int = -1,
sframes: int = -1,
drop_raw_data: bool = False,
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.frame_size = frame_size
self.descriptor = descriptor
self.distance_metric = distance_metric
self.detect_method = detect_method
self.frame_rate = frame_rate
self.vframes = vframes
self.sframes = sframes
self.drop_raw_data = drop_raw_data
self._detector_kwargs = kwargs
[docs] def detect_shots(self, frames: 'np.ndarray') -> List[List['np.ndarray']]:
descriptors = []
for frame in frames:
descriptor = compute_descriptor(
frame, method=self.descriptor, **self._detector_kwargs)
descriptors.append(descriptor)
# compute distances between frames
if self.distance_metric == 'edge_change_ration':
dists = compare_ecr(descriptors, **self._detector_kwargs)
else:
dists = [
compare_descriptor(pair[0], pair[1], self.distance_metric)
for pair in zip(descriptors[:-1], descriptors[1:])
]
self._detector_kwargs['neigh_avg'] = 0
shot_bounds = detect_peak_boundary(dists, self.detect_method, **self._detector_kwargs)
shots = []
for ci in range(0, len(shot_bounds) - 1):
shots.append(frames[shot_bounds[ci]:shot_bounds[ci + 1]].copy())
return shots
[docs] def apply(self, doc: 'gnes_pb2.Document') -> None:
super().apply(doc)
video_frames = []
if doc.WhichOneof('raw_data'):
raw_type = type(getattr(doc, doc.WhichOneof('raw_data')))
if doc.raw_bytes:
video_frames = video.capture_frames(
input_data=doc.raw_bytes,
scale=self.frame_size,
fps=self.frame_rate,
vframes=self.vframes)
elif raw_type == gnes_pb2.NdArray:
video_frames = blob2array(doc.raw_video)
if self.vframes > 0:
video_frames = video_frames[0:self.vframes, :].copy()
num_frames = len(video_frames)
if num_frames > 0:
shots = self.detect_shots(video_frames)
for ci, frames in enumerate(shots):
c = doc.chunks.add()
c.doc_id = doc.doc_id
c.offset = ci
shot_len = len(frames)
c.weight = shot_len / num_frames
if self.sframes > 0 and shot_len > self.sframes:
if shot_len >= 2 * self.sframes:
step = math.ceil(shot_len / self.sframes)
frames = frames[::step]
else:
idx = np.sort(np.random.choice(shot_len, self.sframes, replace=False))
frames = [frames[idx_] for idx_ in idx]
chunk_data = np.array(frames)
c.blob.CopyFrom(array2blob(chunk_data))
else:
self.logger.error(
'bad document: "raw_bytes" or "raw_video" is empty!')
else:
self.logger.error('bad document: "raw_data" is empty!')
if self.drop_raw_data:
self.logger.info("document raw data will be cleaned!")
doc.ClearField('raw_data')