from __future__ import annotations from concurrent import futures import datetime import pytesseract import cv2 from .models import PredictedFrame, PredictedSubtitle class Video: path: str lang: str use_fullframe: bool num_frames: int fps: float pred_frames: List[PredictedFrame] pred_subs: List[PredictedSubtitle] def __init__(self, path: str): self.path = path v = cv2.VideoCapture(path) self.num_frames = int(v.get(cv2.CAP_PROP_FRAME_COUNT)) self.fps = v.get(cv2.CAP_PROP_FPS) v.release() def run_ocr(self, lang: str, time_start: str, time_end: str, use_fullframe: bool) -> None: self.lang = lang self.use_fullframe = use_fullframe ocr_start = self._frame_index(time_start) if time_start else 0 ocr_end = self._frame_index(time_end) if time_end else self.num_frames if ocr_end < ocr_start: raise ValueError('time_start is later than time_end') num_ocr_frames = ocr_end - ocr_start # get frames from ocr_start to ocr_end v = cv2.VideoCapture(self.path) v.set(cv2.CAP_PROP_POS_FRAMES, ocr_start) frames = (v.read()[1] for _ in range(num_ocr_frames)) # perform ocr to frames in parallel with futures.ProcessPoolExecutor() as pool: ocr_map = pool.map(self._single_frame_ocr, frames, chunksize=10) self.pred_frames = [PredictedFrame(i + ocr_start, data) for i, data in enumerate(ocr_map)] v.release() # convert time str to frame index def _frame_index(self, time: str) -> int: t = time.split(':') t = list(map(float, t)) if len(t) == 3: td = datetime.timedelta(hours=t[0], minutes=t[1], seconds=t[2]) elif len(t) == 2: td = datetime.timedelta(minutes=t[0], seconds=t[1]) else: raise ValueError( 'time data "{}" does not match format "%H:%M:%S"'.format(time)) index = int(td.total_seconds() * self.fps) if index > self.num_frames or index < 0: raise ValueError( 'time data "{}" exceeds video duration'.format(time)) return index def _single_frame_ocr(self, img) -> str: if not self.use_fullframe: # only use bottom half of the frame by default img = img[img.shape[0] // 2:, :] config = r'--tessdata-dir "tessdata"' return pytesseract.image_to_data(img, lang=self.lang, config=config) def get_subtitles(self) -> str: self._generate_subtitles() return ''.join( '{}\n{} --> {}\n{}\n\n'.format( i, self._srt_timestamp(sub.index_start), self._srt_timestamp(sub.index_end), sub.text) for i, sub in enumerate(self.pred_subs)) def _generate_subtitles(self) -> None: self.pred_subs = [] if self.pred_frames is None: raise AttributeError( 'Please call self.run_ocr() first to perform ocr on frames') # divide ocr of frames into subtitle paragraphs using sliding window WIN_BOUND = int(self.fps // 2) # 1/2 sec sliding window boundary bound = WIN_BOUND i = 0 j = 1 while j < len(self.pred_frames): fi, fj = self.pred_frames[i], self.pred_frames[j] if fi.is_similar_to(fj): bound = WIN_BOUND elif bound > 0: bound -= 1 else: # divide subtitle paragraphs para_new = j - WIN_BOUND self._append_sub( PredictedSubtitle(self.pred_frames[i:para_new])) i = para_new j = i bound = WIN_BOUND j += 1 # also handle the last remaining frames if i < len(self.pred_frames) - 1: self._append_sub(PredictedSubtitle(self.pred_frames[i:])) def _append_sub(self, sub: PredictedSubtitle) -> None: if len(sub.text) == 0: return # merge new sub to the last subs if they are similar while self.pred_subs and sub.is_similar_to(self.pred_subs[-1]): ls = self.pred_subs[-1] del self.pred_subs[-1] sub = PredictedSubtitle(ls.frames + sub.frames) self.pred_subs.append(sub) def _srt_timestamp(self, frame_index: int) -> str: td = datetime.timedelta(seconds=frame_index / self.fps) ms = td.microseconds // 1000 m, s = divmod(td.seconds, 60) h, m = divmod(m, 60) return '{:02d}:{:02d}:{:02d},{:03d}'.format(h, m, s, ms)