├── .gitignore
├── LICENSE
├── README.md
├── dev-requirements.in
├── dev-requirements.txt
├── etc
├── ROBOTO_LICENSE
├── roboto.ttf
└── screenshot.png
├── hlsstream
├── __init__.py
├── __main__.py
├── api.py
├── input.py
├── stream.py
└── sync.py
├── requirements.in
├── requirements.txt
└── static
└── index.html
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
131 | video/*
132 | !video/__keep__
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2022 Christoph Heindl
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # python-hls-stream
2 |
3 | A minimal proof-of-concept to demonstrate real-time (seekable) HLS streaming from Python with dynamic timeline marker support.
4 |
5 |
6 |
7 |
8 | Front-end of HLS streaming demo. Click to view video.
9 |
10 |
11 | The video linked above shows a real-time HTTP Live Streaming (HLS) generated in Python using ffmpeg. Ocassionally, an event (mint squares) is emitted that leads to markers being added to the HTML5 client table and the timeline. Depending on the HLS configuration you can leave the live edge and seek backwards in time.
12 |
13 | ## Architecture
14 | The system combines multiple processes to generated the desired result
15 | - `hlsstream.sync` (Python) key/value cache for inter-process communication based on `multiprocessing.SyncManager`.
16 | - `hlsstream.stream` (Python) generates the checkerboard images and encodes them as HLS stream using `ffmpeg`. Additionally, events (mint squares) are randomly emitted and stored in the cache as time/text dict.
17 | - `hlsstream.api` (Python) a web-API that exposes the HLS stream plus a marker query API using `fastAPI`. Additionally it serves `index.html` that contains an embedded video-player along with business logic for handling markers.
18 | - `frontend` (HTML5) client frontend using `video-js`.
19 |
20 | ## Limitations
21 | Keep in mind, this is a proof-of-concept and thus expect glitches and other issues.
22 | - `hlsstream.sync` For production you should switch this for `redis` or `memcached`.
23 | - `hlsstream.stream` The event detections (mint-square) is not based on computer-vision but based on checkerboard generator knowledge. In reality, you will have separate detection services that read images and emit marker events.
24 | - `hlsstream.stream` HLS stream encoder expects `rawvideo` (images) input. Except for setting a target FPS, I did not find a way to provide a PTS per frame. Hence, `ffmpeg` assumes `1/FPS` between two frames, even if reality the FPS varies. To resolve, you should keep track of generator timestamps vs target timestamps and if an event needs to be generated, convert from generator timestamp to target timestamp. The demo currently employs a busy-waiting strategy to keep timestamps closest to target fps. This leads to high CPU usage.
25 | - `hlsstream.api` currently configures CORS very carelessly. In production you will need to restrict it accordingly.
26 | - `frontend` attempts to determine the endpoints of the timeline of the `video-js` player. The current method seems to work when live or when not tracking the live stream, but might fail when `hlsstream.stream` is terminated. A refresh should fix things.
27 |
28 |
29 | ## Clocks
30 | The system involves several clocks that need to be synchronized.
31 | - `GEN`: Generator clock in [sec]. This clock is usually built into capturing devices such as cameras.
32 | - `HLS`: HLS stream clock in [sec]. Frames from `GEN` are encoded for `HLS`. When `HLS` assumes a fixed encoding frame-rate, you need to keep track of `GEN` and `HLS` timestamps. While your system internal process will use `GEN` timestamps, the API should report `HLS` timestamps.
33 | - `CLIENT`: HMTL video-js clock in [sec]. When the client connects to the HLS stream, the client clock is reset. To seek the video correctly, we need to convert markers from `HLS <-> CLIENT`. If we assume this transformation takes only an offset and if we assume that the segment duration is constant, we can compute the offset as
34 |
35 | offset = HLS-sequence * HLS-duration
36 |
37 | ## Usage
38 | Python 3.9 is required. This should work on linux/windows.
39 | ```bash
40 | $ pip -m venv --upgrade-deps .venv
41 | $ source .venv/bin/activate
42 | (.venv) $ pip install pip-tools
43 | (.venv) $ pip-sync requirements.txt dev-requirements.txt
44 | (.venv) $ python -m hlsstream
45 | ```
46 |
47 | Point your browser to `http://127.0.0.1:5000`. Same commands, except for how to activate the venv, apply to Windows.
48 |
--------------------------------------------------------------------------------
/dev-requirements.in:
--------------------------------------------------------------------------------
1 | -c requirements.txt
2 | black
3 | flake8
4 | pytest
5 | matplotlib
--------------------------------------------------------------------------------
/dev-requirements.txt:
--------------------------------------------------------------------------------
1 | #
2 | # This file is autogenerated by pip-compile with python 3.9
3 | # To update, run:
4 | #
5 | # pip-compile dev-requirements.in
6 | #
7 | atomicwrites==1.4.0
8 | # via pytest
9 | attrs==21.4.0
10 | # via pytest
11 | black==22.3.0
12 | # via -r dev-requirements.in
13 | click==8.1.3
14 | # via
15 | # -c requirements.txt
16 | # black
17 | colorama==0.4.4
18 | # via
19 | # -c requirements.txt
20 | # click
21 | # pytest
22 | cycler==0.11.0
23 | # via matplotlib
24 | flake8==4.0.1
25 | # via -r dev-requirements.in
26 | fonttools==4.33.3
27 | # via matplotlib
28 | iniconfig==1.1.1
29 | # via pytest
30 | kiwisolver==1.4.2
31 | # via matplotlib
32 | matplotlib==3.5.2
33 | # via -r dev-requirements.in
34 | mccabe==0.6.1
35 | # via flake8
36 | mypy-extensions==0.4.3
37 | # via black
38 | numpy==1.22.3
39 | # via
40 | # -c requirements.txt
41 | # matplotlib
42 | packaging==21.3
43 | # via
44 | # matplotlib
45 | # pytest
46 | pathspec==0.9.0
47 | # via black
48 | pillow==9.1.0
49 | # via matplotlib
50 | platformdirs==2.5.2
51 | # via black
52 | pluggy==1.0.0
53 | # via pytest
54 | py==1.11.0
55 | # via pytest
56 | pycodestyle==2.8.0
57 | # via flake8
58 | pyflakes==2.4.0
59 | # via flake8
60 | pyparsing==3.0.8
61 | # via
62 | # matplotlib
63 | # packaging
64 | pytest==7.1.2
65 | # via -r dev-requirements.in
66 | python-dateutil==2.8.2
67 | # via matplotlib
68 | six==1.16.0
69 | # via python-dateutil
70 | tomli==2.0.1
71 | # via
72 | # black
73 | # pytest
74 | typing-extensions==4.2.0
75 | # via
76 | # -c requirements.txt
77 | # black
78 |
--------------------------------------------------------------------------------
/etc/ROBOTO_LICENSE:
--------------------------------------------------------------------------------
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/etc/roboto.ttf:
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https://raw.githubusercontent.com/cheind/python-hls-stream/92e8630cdfd877ea47b1c925873b31b8fc4fbe04/etc/roboto.ttf
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/etc/screenshot.png:
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https://raw.githubusercontent.com/cheind/python-hls-stream/92e8630cdfd877ea47b1c925873b31b8fc4fbe04/etc/screenshot.png
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/hlsstream/__init__.py:
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https://raw.githubusercontent.com/cheind/python-hls-stream/92e8630cdfd877ea47b1c925873b31b8fc4fbe04/hlsstream/__init__.py
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/hlsstream/__main__.py:
--------------------------------------------------------------------------------
1 | # https://www.programmersought.com/article/54568977634/
2 | # https://github.com/kkroening/ffmpeg-python/issues/154
3 | # https://github.com/kkroening/ffmpeg-python/tree/master/examples
4 |
5 | from subprocess import Popen
6 | import sys
7 | import time
8 |
9 | if __name__ == "__main__":
10 |
11 | processes: list[Popen] = []
12 | try:
13 | processes.append(
14 | Popen([sys.executable, "-m", "hlsstream.sync"], start_new_session=True)
15 | )
16 | processes.append(
17 | Popen([sys.executable, "-m", "hlsstream.api"], start_new_session=True)
18 | )
19 | processes.append(
20 | Popen([sys.executable, "-m", "hlsstream.stream"], start_new_session=True)
21 | )
22 | while True:
23 | time.sleep(1.0)
24 | except KeyboardInterrupt:
25 | print("Ctrl C")
26 | finally:
27 | for p in processes:
28 | p.terminate()
29 | p.wait()
30 |
--------------------------------------------------------------------------------
/hlsstream/api.py:
--------------------------------------------------------------------------------
1 | import uvicorn
2 | import json
3 | from fastapi import FastAPI, Response
4 | from fastapi.staticfiles import StaticFiles
5 | from starlette.responses import FileResponse
6 | from fastapi.middleware.cors import CORSMiddleware
7 | from fastapi.responses import JSONResponse
8 |
9 | from .sync import Cache
10 |
11 |
12 | cache = Cache()
13 |
14 |
15 | app = FastAPI()
16 | app.add_middleware(
17 | CORSMiddleware,
18 | allow_origins=["*"],
19 | allow_credentials=True,
20 | allow_methods=["*"],
21 | allow_headers=["*"],
22 | )
23 |
24 |
25 | @app.get("/video/{fileName}")
26 | async def video(response: Response, fileName: str):
27 | response.headers["Content-Type"] = "application/x-mpegURL"
28 | return FileResponse("video/" + fileName, filename=fileName)
29 |
30 |
31 | @app.get("/markers")
32 | async def markers(response: Response, ts_start: float = -1.0):
33 | markers = cache.get("markers", [])
34 | markers = [m for m in markers if m["time"] > ts_start]
35 | return JSONResponse(content=json.dumps({"markers": markers}))
36 |
37 |
38 | app.mount("/", StaticFiles(directory="static", html=True), name="static")
39 |
40 |
41 | def main():
42 | uvicorn.run(
43 | "hlsstream.api:app",
44 | host="0.0.0.0",
45 | port=5000,
46 | log_level="debug",
47 | reload=True,
48 | debug=True,
49 | )
50 |
51 |
52 | if __name__ == "__main__":
53 | main()
54 |
--------------------------------------------------------------------------------
/hlsstream/input.py:
--------------------------------------------------------------------------------
1 | import dataclasses
2 | from typing import Iterator, Optional
3 | import numpy as np
4 | import time
5 | import logging
6 | import datetime
7 |
8 | _logger = logging.getLogger("input")
9 |
10 |
11 | def rate_limited_loop(fps: float) -> Iterator[float]:
12 | """Rate limited loop."""
13 |
14 | def _busy_wait_until(tend: float):
15 | while tend - time.perf_counter() > 0.0:
16 | pass
17 |
18 | td = 1 / fps
19 | t_start = time.perf_counter()
20 | t_next = t_start + td
21 | rate_failed_emitted = False
22 | while True:
23 | t_cur = time.perf_counter() - t_start
24 | yield t_cur
25 | remain = max(t_next - time.perf_counter(), 0)
26 | if remain > 0.1:
27 | time.sleep(remain)
28 | elif remain > 0.0:
29 | _busy_wait_until(t_next)
30 | elif remain < 0.0 and not rate_failed_emitted:
31 | rate_failed_emitted = True
32 | _logger.warning(f"Too slow at {t_cur}")
33 | t_next += td
34 |
35 |
36 | @dataclasses.dataclass
37 | class Event:
38 | at: float
39 | until: float
40 | name: str
41 |
42 | @staticmethod
43 | def create_random(lambd: float = 20.0, dur: float = 2.0):
44 | event_at = time.time() + np.random.exponential(scale=20)
45 | event_name = datetime.datetime.fromtimestamp(event_at).strftime("%H:%M:%S")
46 | event_until = event_at + 2.0
47 | return Event(event_at, event_until, f"Event at {event_name}")
48 |
49 |
50 | def chessboard_generator(
51 | shape: tuple[int, int],
52 | roll: int,
53 | block_size: int = 100,
54 | fps: Optional[int] = 10000,
55 | noise_std: float = 0.0,
56 | ) -> Iterator[tuple[float, np.ndarray, bool]]:
57 | """Generates rolling chessboard images.
58 |
59 | Returns:
60 | timestamp: timestamp relative to start of generator [sec]
61 | image: rgb24 image
62 | ev: true if an event is currently active, false otherwise
63 | """
64 |
65 | num_blocks = (
66 | int(np.ceil(shape[0] / block_size)),
67 | int(np.ceil(shape[1] / block_size)),
68 | )
69 | check = np.zeros(num_blocks)
70 | check[1::2, ::2] = 1
71 | check[::2, 1::2] = 1
72 |
73 | img = np.expand_dims(np.kron(check, np.ones((block_size, block_size))), -1)
74 | img = np.tile(img, (1, 1, 3))
75 | img += np.random.randn(*img.shape) * noise_std
76 | img = np.clip(img, 0.0, 1.0).astype(np.uint8) * 255
77 | img = img[: shape[0], : shape[1]]
78 |
79 | ev = Event.create_random(lambd=10, dur=2)
80 | ev_active = False
81 |
82 | total_roll = 0
83 | for ts in rate_limited_loop(fps=fps):
84 | t_cur = time.time()
85 | if t_cur > ev.until:
86 | ev = Event.create_random(lambd=10, dur=2)
87 | img[:block_size, :block_size] = (0, 0, 0)
88 | ev_active = False
89 | elif t_cur > ev.at:
90 | img[:block_size, :block_size] = (0, 255, 255)
91 | ev_active = True
92 | rolled = np.roll(img, total_roll, 1)
93 | yield ts, rolled, ev_active
94 | total_roll += roll
95 |
96 |
97 | def test_chessboard_gen():
98 | import matplotlib.pyplot as plt
99 |
100 | shape = (200, 320)
101 | fps = 30
102 | roll_over = 5 # roll over in 5 secs
103 | roll = int(np.ceil(shape[1] / (roll_over * fps)))
104 | gen = chessboard_generator(shape, roll=roll, block_size=20, fps=fps)
105 |
106 | fig, ax = plt.subplots()
107 | img_ = ax.imshow(next(gen)[1])
108 | prev_ev = False
109 | while True:
110 | plt.pause(1e-5)
111 | ts, img, ev = next(gen)
112 | img_.set_data(img)
113 | if not prev_ev and ev:
114 | print("now")
115 | prev_ev = ev
116 |
117 |
118 | if __name__ == "__main__":
119 | test_chessboard_gen()
120 |
--------------------------------------------------------------------------------
/hlsstream/stream.py:
--------------------------------------------------------------------------------
1 | from typing import Callable, Any
2 | from pathlib import Path
3 | import ffmpeg
4 | import enum
5 | import numpy as np
6 | import time
7 | import shutil
8 |
9 |
10 | class HLSPresets(enum.Enum):
11 | DEFAULT_CPU = {
12 | "vcodec": "libx264",
13 | "preset": "veryfast",
14 | "video_bitrate": "6M",
15 | "maxrate": "6M",
16 | "bufsize": "6M",
17 | }
18 | DEFAULT_GPU = {
19 | "vcodec": "h264_nvenc",
20 | "preset": "p3", # https://gist.github.com/nico-lab/e1ba48c33bf2c7e1d9ffdd9c1b8d0493
21 | "tune": "ll",
22 | "video_bitrate": "6M",
23 | "maxrate": "6M",
24 | "bufsize": "6M",
25 | }
26 |
27 |
28 | # For preset settings
29 | # https://obsproject.com/blog/streaming-with-x264#:~:text=x264%20has%20several%20CPU%20presets,%2C%20slower%2C%20veryslow%2C%20placebo.
30 |
31 |
32 | class HLSEncoder:
33 | def __init__(
34 | self,
35 | out_path: Path,
36 | shape: tuple[int, int] = (1080, 1920),
37 | input_fps: int = 30,
38 | use_wallclock_pts: bool = False,
39 | preset: HLSPresets = HLSPresets.DEFAULT_CPU,
40 | **hls_kwargs,
41 | ) -> None:
42 | self.out_path = out_path
43 | self.shape = shape
44 |
45 | self.inp_settings = {
46 | "format": "rawvideo",
47 | "pix_fmt": "rgb24",
48 | "s": "{}x{}".format(shape[1], shape[0]),
49 | "r": input_fps,
50 | "use_wallclock_as_timestamps": use_wallclock_pts,
51 | }
52 | self.enc_settings = {
53 | "format": "hls",
54 | "pix_fmt": "yuv420p",
55 | "hls_time": 2,
56 | "hls_list_size": 2 * 60 / 2, # 10 minutes keep
57 | "hls_flags": "delete_segments", # remove outdated segments from disk
58 | "start_number": 0,
59 | **preset.value,
60 | **hls_kwargs,
61 | }
62 | # Compute keyframe interval for most precise segment duration
63 | # Note, -g (GOP) and keyint_min is necessary to get exact duration segments.
64 | # https://sites.google.com/site/linuxencoding/x264-ffmpeg-mapping#:~:text=%2Dg%20(FFmpeg,Recommended%20default%3A%20250
65 | nkey = self.enc_settings["hls_time"] * self.inp_settings["r"]
66 | self.enc_settings["g"] = nkey
67 | self.enc_settings["keyint_min"] = nkey
68 |
69 | self.proc: Callable[[np.ndarray[np.uint8, Any]]] = None
70 | self.time: float = 0.0
71 |
72 | def __enter__(self) -> "HLSEncoder":
73 | self.time = 0.0
74 | self.proc = (
75 | ffmpeg.input("pipe:", **self.inp_settings)
76 | .drawtext(
77 | start_number=0,
78 | fontsize="(h/10)",
79 | fontfile="etc/roboto.ttf",
80 | x="(w-text_w)/2",
81 | y="h*0.8",
82 | timecode="00:00:00:00",
83 | timecode_rate=self.inp_settings["r"],
84 | fontcolor="white",
85 | escape_text=True,
86 | box="1",
87 | boxcolor="black",
88 | )
89 | .output(str(self.out_path), **self.enc_settings)
90 | .overwrite_output()
91 | .run_async(pipe_stdin=True)
92 | )
93 | return self
94 |
95 | def __exit__(self, type, value, traceback):
96 | self.proc.stdin.close()
97 | self.proc = None
98 |
99 | def __call__(self, rgb24: np.ndarray[np.uint8, Any]) -> float:
100 | if self.inp_settings["use_wallclock_as_timestamps"]:
101 | start_time = time.time() # not very precise
102 | else:
103 | start_time = self.time
104 | self.time += 1 / self.inp_settings["r"]
105 | self.proc.stdin.write(rgb24.tobytes())
106 | return start_time
107 |
108 |
109 | def main():
110 | from .input import chessboard_generator
111 | from .sync import Cache
112 | from pathlib import Path
113 |
114 | outpath = Path("video").resolve()
115 | shutil.rmtree(str(outpath))
116 | outpath.mkdir(parents=True, exist_ok=True)
117 |
118 | shape = (180, 320)
119 | fps = 30
120 | roll = int(np.ceil(shape[1] / (30 * fps)))
121 | gen = chessboard_generator(shape, roll, 20, fps=fps)
122 | enc = HLSEncoder(
123 | "video/chessboard.m3u8",
124 | shape=shape,
125 | input_fps=fps,
126 | use_wallclock_pts=False,
127 | preset=HLSPresets.DEFAULT_CPU,
128 | )
129 | cache = Cache()
130 |
131 | markers = []
132 | cache.set("markers", markers)
133 | virtual_ts = 0.0
134 | with enc:
135 | ev_prev = False
136 | while True:
137 | ts, img, ev = next(gen)
138 | enc(img)
139 | virtual_ts += 1 / fps
140 | if not ev_prev and ev:
141 | markers.append(
142 | {
143 | "time": virtual_ts,
144 | "text": f"Marker {len(markers)+1}",
145 | }
146 | )
147 | cache.set("markers", markers)
148 | ev_prev = ev
149 |
150 |
151 | if __name__ == "__main__":
152 | main()
153 |
--------------------------------------------------------------------------------
/hlsstream/sync.py:
--------------------------------------------------------------------------------
1 | from multiprocessing.managers import SyncManager
2 | from typing import Any
3 |
4 | SYNC_ADDR = "127.0.0.1"
5 | SYNC_PORT = 5001
6 | SYNC_PWD = b"password"
7 |
8 |
9 | class CacheSyncManager(SyncManager):
10 | ...
11 |
12 |
13 | _syncdict = {}
14 |
15 |
16 | def _get_dict():
17 | return _syncdict
18 |
19 |
20 | class Cache:
21 | def __init__(self) -> None:
22 | self.manager = CacheSyncManager((SYNC_ADDR, SYNC_PORT), authkey=SYNC_PWD)
23 | self.manager.connect()
24 | CacheSyncManager.register("syncdict")
25 | self.syndict = self.manager.syncdict()
26 |
27 | def set(self, key: str, value: Any):
28 | self.syndict.update([(key, value)])
29 |
30 | def get(self, key: str, default: Any = None) -> Any:
31 | return self.syndict.get(key, default)
32 |
33 |
34 | if __name__ == "__main__":
35 |
36 | CacheSyncManager.register("syncdict", _get_dict)
37 | manager = CacheSyncManager((SYNC_ADDR, SYNC_PORT), authkey=SYNC_PWD)
38 | manager.get_server().serve_forever()
39 |
--------------------------------------------------------------------------------
/requirements.in:
--------------------------------------------------------------------------------
1 | ffmpeg-python
2 | numpy
3 | uvicorn
4 | fastapi
5 | schedule
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | #
2 | # This file is autogenerated by pip-compile with python 3.9
3 | # To update, run:
4 | #
5 | # pip-compile requirements.in
6 | #
7 | anyio==3.5.0
8 | # via starlette
9 | asgiref==3.5.1
10 | # via uvicorn
11 | click==8.1.3
12 | # via uvicorn
13 | colorama==0.4.4
14 | # via click
15 | fastapi==0.76.0
16 | # via -r requirements.in
17 | ffmpeg-python==0.2.0
18 | # via -r requirements.in
19 | future==0.18.2
20 | # via ffmpeg-python
21 | h11==0.13.0
22 | # via uvicorn
23 | idna==3.3
24 | # via anyio
25 | numpy==1.22.3
26 | # via -r requirements.in
27 | pydantic==1.9.0
28 | # via fastapi
29 | schedule==1.1.0
30 | # via -r requirements.in
31 | sniffio==1.2.0
32 | # via anyio
33 | starlette==0.18.0
34 | # via fastapi
35 | typing-extensions==4.2.0
36 | # via
37 | # pydantic
38 | # starlette
39 | uvicorn==0.17.6
40 | # via -r requirements.in
41 |
--------------------------------------------------------------------------------
/static/index.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 | HLS Streaming Demo
7 |
8 |
9 |
10 |
11 |
12 |
74 |
75 |
76 |
77 |
78 |
79 |
HLS Streaming with timeline-markers.
80 |
Shown below, is a real-time generated HLS stream of a
81 | rolling checkerboard
82 | pattern.
83 | Ocassionally an event (mint square) is emitted that leads to markers being added
84 | to the table and the timeline. Depending on the HLS configuration you can leave the live edge
85 | and seek backwards in time.
