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from __future__ import annotations
from typing import Dict, List
from moviepy.editor import VideoFileClip
from ...data import (
MediaMap,
MetaInfo,
MetaInfoList,
Clip,
ClipSeq,
H5pyMediaMapEmb,
MediaMapEmb,
MediaMapSeq,
)
from ...utils import load_dct_from_file
from .video_clip import VideoClipSeq
from .video_meta_info import VideoMetaInfo
from .vision_object import Roles
from .vision_frame import FrameSeq
class VideoMap(MediaMap):
def __init__(
self,
meta_info: VideoMetaInfo = None,
clipseq: VideoClipSeq = None,
frameseq: FrameSeq = None,
stageseq: VideoClipSeq = None,
leading_roles: Roles = None,
emb: MediaMapEmb = None,
**kwargs,
):
super().__init__(meta_info, clipseq, stageseq, frameseq, emb, **kwargs)
self.leading_roles = leading_roles
def preprocess(self):
super().preprocess()
self.spread_metainfo_2_clip(
target_keys=[
"media_path",
"media_map_path",
"emb_path",
"media_duration",
"mediaid",
"media_name",
"target_width",
"target_height",
"content_box",
"target_width_height_ratio",
"emb",
]
)
@property
def media_path(self):
return self.meta_info.media_path
@property
def media_map_path(self):
return self.meta_info.media_map_path
@property
def fps(self):
return self.meta_info.fps
def to_dct(
self, target_keys: List[str] = None, ignored_keys: List[str] = None
) -> Dict:
dct = {}
dct["meta_info"] = self.meta_info.to_dct(
target_keys=target_keys, ignored_keys=ignored_keys
)
dct["clipseq"] = self.clipseq.to_dct(
target_keys=target_keys, ignored_keys=ignored_keys
)
if self.frameseq is not None:
dct["frameseq"] = self.frameseq.to_dct(
target_keys=target_keys, ignored_keys=ignored_keys
)
if self.stageseq is not None:
dct["stageseq"] = self.stageseq.to_dct(
target_keys=target_keys, ignored_keys=ignored_keys
)
if self.stageseq is not None:
dct["leading_roles"] = self.leading_roles.to_dct()
return dct
@classmethod
def from_path(
cls, shot_transition: str, scene_transition: str, face: str
) -> VideoMap:
raise NotImplementedError
@classmethod
def from_dir(cls, path: str) -> VideoMap:
shot_transition = None
scene_transition = None
semantic_emb = None
face = None
return cls.from_path(shot_transition, scene_transition)
@classmethod
def from_data(
cls, data: Dict, emb: H5pyMediaMapEmb, media_path: str = None, **kwargs
) -> VideoMap:
meta_info = VideoMetaInfo.from_data(data.get("meta_info", {}))
meta_info.media_path = media_path
clipseq = VideoClipSeq.from_data(data.get("clipseq", []))
frameseq = FrameSeq.from_data(data.get("frameseq", []))
stageseq = VideoClipSeq.from_data(data.get("stageseq", []))
leading_roles = Roles.from_data(data.get("leading_roles", []))
target_keys = ["meta_info", "clipseq", "frameseq", "stageseq", "leading_roles"]
dct = {k: data[k] for k in data.keys() if k not in target_keys}
dct.update(**kwargs)
video_map = VideoMap(
meta_info=meta_info,
clipseq=clipseq,
frameseq=frameseq,
stageseq=stageseq,
leading_roles=leading_roles,
emb=emb,
**dct,
)
return video_map
class VideoMapSeq(MediaMapSeq):
def __init__(self, maps: List[VideoMap]) -> None:
super().__init__(maps)
@property
def fps(self):
return max(m.fps for m in self.maps)
# def merge_face_into_video_info(video_info: VideoInfo, face: dict) -> VideoInfo:
# """融合读取的多个人脸检测信息到 视频谱面中
# Args:
# video_info (VideoInfo): 待融合的视频谱面
# face (dict): 待融合的人脸检测信息，key是视频文件名
# Returns:
# VideoInfo: 融合后的人脸谱面信息
# """
# for c_idx, clip in enumerate(video_info.clipseq):
# frame_start = clip.frame_start
# frame_end = clip.frame_end
# frames_in_clip = []
# videoinfo_name, ext = get_file_name_ext(os.path.basename(clip.videoinfo_path))
# video_face = face[videoinfo_name]
# for face_frame in video_face["clips"]:
# if (
# face_frame["frame_idx"] >= frame_start
# and face_frame["frame_idx"] <= frame_end
# ):
# frame = Frame(
# **face_frame,
# width=video_info.meta_info.width,
# height=video_info.meta_info.height,
# )
# frames_in_clip.append(frame)
# video_info.clipseq[c_idx].frames = frames_in_clip
# return video_info
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