forked from pradana.aumars/videocr
112 lines
3.7 KiB
Markdown
112 lines
3.7 KiB
Markdown
# videocr
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Extract hardcoded (burned-in) subtitles from videos using the [Tesseract](https://github.com/tesseract-ocr/tesseract) OCR engine with Python.
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Input a video with hardcoded subtitles:
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<p float="left">
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<img width="430" alt="screenshot" src="https://user-images.githubusercontent.com/10210967/56873658-3b76dd00-6a34-11e9-95c6-cd6edc721f58.png">
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<img width="430" alt="screenshot" src="https://user-images.githubusercontent.com/10210967/56873659-3b76dd00-6a34-11e9-97aa-2c3e96fe3a97.png">
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</p>
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```python
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# example.py
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from videocr import get_subtitles
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if __name__ == '__main__': # This check is mandatory for Windows.
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print(get_subtitles('video.mp4', lang='chi_sim+eng', sim_threshold=70, conf_threshold=65))
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```
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`$ python3 example.py`
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Output:
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```
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0
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00:00:01,042 --> 00:00:02,877
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喝 点 什么 ?
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What can I get you?
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1
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00:00:03,044 --> 00:00:05,463
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我 不 知道
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Um, I'm not sure.
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2
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00:00:08,091 --> 00:00:10,635
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休闲 时 光 …
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For relaxing times, make it...
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3
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00:00:10,677 --> 00:00:12,595
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三 得 利 时 光
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Bartender, Bob Suntory time.
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4
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00:00:14,472 --> 00:00:17,142
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我 要 一 杯 伏特 加
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Un, I'll have a vodka tonic.
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5
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00:00:18,059 --> 00:00:19,019
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谢谢
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Laughs Thanks.
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```
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## Performance
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The OCR process is CPU intensive. It takes 3 minutes on my dual-core laptop to extract a 20 seconds video. More CPU cores will make it faster.
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## Installation
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1. Install [Tesseract](https://github.com/tesseract-ocr/tesseract/wiki) and make sure it is in your `$PATH`
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2. `$ pip install videocr`
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## API
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1. Return subtitle string in SRT format
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```python
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get_subtitles(
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video_path: str, lang='eng', time_start='0:00', time_end='',
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conf_threshold=65, sim_threshold=90, use_fullframe=False)
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```
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2. Write subtitles to `file_path`
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```python
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save_subtitles_to_file(
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video_path: str, file_path='subtitle.srt', lang='eng', time_start='0:00', time_end='',
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conf_threshold=65, sim_threshold=90, use_fullframe=False)
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```
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### Parameters
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- `lang`
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The language of the subtitles. You can extract subtitles in almost any language. All language codes on [this page](https://github.com/tesseract-ocr/tesseract/wiki/Data-Files#data-files-for-version-400-november-29-2016) (e.g. `'eng'` for English) and all script names in [this repository](https://github.com/tesseract-ocr/tessdata_fast/tree/master/script) (e.g. `'HanS'` for simplified Chinese) are supported.
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Note that you can use more than one language, e.g. `lang='hin+eng'` for Hindi and English together.
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Language files will be automatically downloaded to your `~/tessdata`. You can read more about Tesseract language data files on their [wiki page](https://github.com/tesseract-ocr/tesseract/wiki/Data-Files).
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- `conf_threshold`
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Confidence threshold for word predictions. Words with lower confidence than this value will be discarded. The default value `65` is fine for most cases.
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Make it closer to 0 if you get too few words in each line, or make it closer to 100 if there are too many excess words in each line.
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- `sim_threshold`
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Similarity threshold for subtitle lines. Subtitle lines with larger [Levenshtein](https://en.wikipedia.org/wiki/Levenshtein_distance) ratios than this threshold will be merged together. The default value `90` is fine for most cases.
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Make it closer to 0 if you get too many duplicated subtitle lines, or make it closer to 100 if you get too few subtitle lines.
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- `time_start` and `time_end`
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Extract subtitles from only a clip of the video. The subtitle timestamps are still calculated according to the full video length.
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- `use_fullframe`
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By default, only the bottom half of each frame is used for OCR. You can explicitly use the full frame if your subtitles are not within the bottom half of each frame.
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