122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
from __future__ import annotations
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from typing import List
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import sys
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import multiprocessing
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import pytesseract
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import cv2
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from . import constants
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from . import utils
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from .models import PredictedFrame, PredictedSubtitle
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from .opencv_adapter import Capture
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class Video:
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path: str
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lang: str
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use_fullframe: bool
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num_frames: int
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fps: float
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height: int
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pred_frames: List[PredictedFrame]
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pred_subs: List[PredictedSubtitle]
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def __init__(self, path: str):
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self.path = path
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with Capture(path) as v:
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self.num_frames = int(v.get(cv2.CAP_PROP_FRAME_COUNT))
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self.fps = v.get(cv2.CAP_PROP_FPS)
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self.height = int(v.get(cv2.CAP_PROP_FRAME_HEIGHT))
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def run_ocr(self, lang: str, time_start: str, time_end: str,
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conf_threshold: int, use_fullframe: bool) -> None:
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self.lang = lang
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self.use_fullframe = use_fullframe
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ocr_start = utils.get_frame_index(time_start, self.fps) if time_start else 0
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ocr_end = utils.get_frame_index(time_end, self.fps) if time_end else self.num_frames
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if ocr_end < ocr_start:
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raise ValueError('time_start is later than time_end')
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num_ocr_frames = ocr_end - ocr_start
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# get frames from ocr_start to ocr_end
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with Capture(self.path) as v, multiprocessing.Pool() as pool:
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v.set(cv2.CAP_PROP_POS_FRAMES, ocr_start)
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frames = (v.read()[1] for _ in range(num_ocr_frames))
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# perform ocr to frames in parallel
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it_ocr = pool.imap(self._image_to_data, frames, chunksize=10)
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self.pred_frames = [
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PredictedFrame(i + ocr_start, data, conf_threshold)
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for i, data in enumerate(it_ocr)
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]
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def _image_to_data(self, img) -> str:
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if not self.use_fullframe:
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# only use bottom half of the frame by default
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img = img[self.height // 2:, :]
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img = cv2.bitwise_not(cv2.bitwise_and(img, img, mask=cv2.inRange(img, (190, 190, 190), (255, 255, 255))))
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config = '--tessdata-dir "{}"'.format(constants.TESSDATA_DIR)
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try:
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return pytesseract.image_to_data(img, lang=self.lang, config=config)
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except Exception as e:
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sys.exit('{}: {}'.format(e.__class__.__name__, e))
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def get_subtitles(self, sim_threshold: int) -> str:
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self._generate_subtitles(sim_threshold)
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return ''.join(
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'{}\n{} --> {}\n{}\n\n'.format(
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i,
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utils.get_srt_timestamp(sub.index_start, self.fps),
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utils.get_srt_timestamp(sub.index_end, self.fps),
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sub.text)
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for i, sub in enumerate(self.pred_subs))
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def _generate_subtitles(self, sim_threshold: int) -> None:
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self.pred_subs = []
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if self.pred_frames is None:
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raise AttributeError(
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'Please call self.run_ocr() first to perform ocr on frames')
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# divide ocr of frames into subtitle paragraphs using sliding window
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WIN_BOUND = int(self.fps // 2) # 1/2 sec sliding window boundary
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bound = WIN_BOUND
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i = 0
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j = 1
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while j < len(self.pred_frames):
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fi, fj = self.pred_frames[i], self.pred_frames[j]
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if fi.is_similar_to(fj):
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bound = WIN_BOUND
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elif bound > 0:
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bound -= 1
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else:
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# divide subtitle paragraphs
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para_new = j - WIN_BOUND
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self._append_sub(PredictedSubtitle(
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self.pred_frames[i:para_new], sim_threshold))
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i = para_new
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j = i
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bound = WIN_BOUND
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j += 1
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# also handle the last remaining frames
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if i < len(self.pred_frames) - 1:
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self._append_sub(PredictedSubtitle(
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self.pred_frames[i:], sim_threshold))
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def _append_sub(self, sub: PredictedSubtitle) -> None:
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if len(sub.text) == 0:
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return
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# merge new sub to the last subs if they are similar
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while self.pred_subs and sub.is_similar_to(self.pred_subs[-1]):
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ls = self.pred_subs[-1]
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del self.pred_subs[-1]
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sub = PredictedSubtitle(ls.frames + sub.frames, sub.sim_threshold)
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self.pred_subs.append(sub)
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