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from __future__ import annotations
from typing import List
import sys
import multiprocessing
import pytesseract
import cv2
from . import constants
from . import utils
from .models import PredictedFrame, PredictedSubtitle
from .opencv_adapter import Capture
class Video:
path: str
lang: str
use_fullframe: bool
num_frames: int
fps: float
height: int
pred_frames: List[PredictedFrame]
pred_subs: List[PredictedSubtitle]
def __init__(self, path: str):
self.path = path
with Capture(path) as v:
self.num_frames = int(v.get(cv2.CAP_PROP_FRAME_COUNT))
self.fps = v.get(cv2.CAP_PROP_FPS)
self.height = int(v.get(cv2.CAP_PROP_FRAME_HEIGHT))
def run_ocr(self, lang: str, time_start: str, time_end: str,
conf_threshold: int, use_fullframe: bool) -> None:
self.lang = lang
self.use_fullframe = use_fullframe
ocr_start = utils.get_frame_index(time_start, self.fps) if time_start else 0
ocr_end = utils.get_frame_index(time_end, self.fps) 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
with Capture(self.path) as v, multiprocessing.Pool() as pool:
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
it_ocr = pool.imap(self._image_to_data, frames, chunksize=10)
self.pred_frames = [
PredictedFrame(i + ocr_start, data, conf_threshold)
for i, data in enumerate(it_ocr)
]
def _image_to_data(self, img) -> str:
if not self.use_fullframe:
# only use bottom half of the frame by default
img = img[self.height // 2:, :]
img = cv2.bitwise_not(cv2.bitwise_and(img, img, mask=cv2.inRange(img, (190, 190, 190), (255, 255, 255))))
config = '--tessdata-dir "{}"'.format(constants.TESSDATA_DIR)
try:
return pytesseract.image_to_data(img, lang=self.lang, config=config)
except Exception as e:
sys.exit('{}: {}'.format(e.__class__.__name__, e))
def get_subtitles(self, sim_threshold: int) -> str:
self._generate_subtitles(sim_threshold)
return ''.join(
'{}\n{} --> {}\n{}\n\n'.format(
i,
utils.get_srt_timestamp(sub.index_start, self.fps),
utils.get_srt_timestamp(sub.index_end, self.fps),
sub.text)
for i, sub in enumerate(self.pred_subs))
def _generate_subtitles(self, sim_threshold: int) -> 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], sim_threshold))
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:], sim_threshold))
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, sub.sim_threshold)
self.pred_subs.append(sub)