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import os
import streamlit as st
from pathlib import Path
from landingai.predict import Predictor
from landingai.pipeline.image_source import NetworkedCamera, FrameSet
from landingai.pipeline.postprocessing import get_class_counts
from landingai.st_utils import render_svg
VIDEO_CACHE_PATH = Path("cached_data")
VIDEO_CACHE_PATH.mkdir(exist_ok=True, parents=True)
VIDEO_CACHE_PATH = VIDEO_CACHE_PATH / "latest.mp4"
VIDEO_LEN_SEC = 10
FPS = 2
PLAYLIST_URL = (
"https://live.hdontap.com/hls/hosb1/topanga_swellmagnet.stream/playlist.m3u8"
)
API_KEY = os.environ["API_KEY"]
ENDPOINT_ID = os.environ["ENDPOINT_ID"]
render_svg(Path("./assets/landing-logo.svg").read_text())
st.title("Topanga Beach Surfer Counter")
st.write(
"This application will grab the latest 10s clip of surfers from the Topanga Beach surf cam "
"and count the number of surfers there. It uses a model built with LandingLens to detect "
"the surfers. You can build your own model at [landing.ai](https://landing.ai/) or run the "
"code yourself by getting it from our [github page](https://github.com/landing-ai/landingai-python/tree/main/examples/apps/surfer-count)."
)
def get_latest_surfer_count():
vid_src = NetworkedCamera(PLAYLIST_URL, fps=FPS)
surfer_model = Predictor(ENDPOINT_ID, api_key=API_KEY)
frs = FrameSet()
for i, frame in enumerate(vid_src):
if i >= VIDEO_LEN_SEC * FPS:
break
frs.extend(frame)
frs.run_predict(predictor=surfer_model).overlay_predictions()
frs.save_video(str(VIDEO_CACHE_PATH), video_fps=FPS, image_src="overlay")
counts = get_class_counts(frs)
if "surfer" in counts:
surfers = counts["surfer"] / (VIDEO_LEN_SEC * FPS)
else:
surfers = 0
st.video(open(VIDEO_CACHE_PATH, "rb").read())
st.write(f"Surfer count: **{surfers}**")
st.title("Surfer Counter")
button = st.button("Get Topanga Beach Surfer Count", on_click=get_latest_surfer_count)
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