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import logging
import os
import sys
import zipfile
from enum import Enum
from typing import Any, Dict, List, Optional
os.system("python3 -m pip uninstall -y typing-extensions")
os.system("python3 -m pip install -U typing-extensions")
os.system(
"python3 -m pip install -q --progress-bar off streamlink gradio tiktoken ultralytics pillow innertube opencv-python"
)
import cv2
import gradio as gr
import innertube
import numpy as np
import streamlink
from PIL import Image, ImageDraw, ImageFont
from ultralytics import YOLO
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
model = YOLO("yolov8x.pt")
class SearchFilter(Enum):
LIVE = ("EgJAAQ%3D%3D", "Live")
VIDEO = ("EgIQAQ%3D%3D", "Video")
def __init__(self, code, human_readable):
self.code = code
self.human_readable = human_readable
def __str__(self):
return self.human_readable
class SearchService:
@staticmethod
def search(
query: Optional[str], filter: SearchFilter = SearchFilter.VIDEO
) -> (List[Dict[str, Any]], Optional[str]):
client = innertube.InnerTube("WEB", "2.20230920.00.00")
response = SearchService._search(query, filter)
results = SearchService.parse(response)
return results
@staticmethod
def parse(data: Dict[str, Any]) -> List[Dict[str, Any]]:
results = []
items = []
contents = (
data.get("contents", {})
.get("twoColumnSearchResultsRenderer", {})
.get("primaryContents", {})
.get("sectionListRenderer", {})
.get("contents", [])
)
if contents:
items = contents[0].get("itemSectionRenderer", {}).get("contents", [])
for item in items:
if "videoRenderer" in item:
renderer = item["videoRenderer"]
video_id = renderer.get("videoId", "")
thumbnail_urls = [
thumb.get("url", "")
for thumb in renderer.get("thumbnail", {}).get("thumbnails", [])
]
title_text = "".join(
[
run.get("text", "")
for run in renderer.get("title", {}).get("runs", [])
]
)
result = {
"video_id": video_id,
"thumbnail_urls": thumbnail_urls,
"title": title_text,
}
results.append(result)
return results
@staticmethod
def _search(
query: Optional[str] = None, filter: SearchFilter = SearchFilter.VIDEO
) -> Dict[str, Any]:
client = innertube.InnerTube("WEB", "2.20230920.00.00")
response = client.search(query=query, params=filter.code if filter else None)
return response
@staticmethod
def get_youtube_url(video_id: str) -> str:
return f"https://www.youtube.com/watch?v={video_id}"
@staticmethod
def get_stream_url(youtube_url):
try:
session = streamlink.Streamlink()
streams = session.streams(youtube_url)
if streams:
best_stream = streams.get("best")
return best_stream.url if best_stream else None
else:
logging.warning("No streams found for this URL")
return None
except Exception as e:
logging.warning(f"An error occurred: {e}")
return None
class LiveStreamAnnotator:
def __init__(self):
logging.getLogger().setLevel(logging.DEBUG)
self.model = YOLO("yolov8x.pt")
self.font_path = self.download_font(
"https://www.fontsquirrel.com/fonts/download/open-sans",
"open-sans.zip",
)
self.current_page_token = None
self.streams = self.fetch_live_streams("world live cams")
# Gradio UI Elements
initial_gallery_items = [
(stream["thumbnail_url"], stream["title"]) for stream in self.streams
]
self.gallery = gr.Gallery(
label="Live YouTube Videos",
value=initial_gallery_items,
show_label=False,
columns=[3],
rows=[10],
object_fit="contain",
height="auto",
)
self.search_input = gr.Textbox(label="Search Live YouTube Videos")
self.stream_input = gr.Textbox(label="URL of Live YouTube Video")
self.output_image = gr.AnnotatedImage(show_label=False)
self.search_button = gr.Button("Search")
self.submit_button = gr.Button("Detect Objects", variant="primary", size="lg")
self.prev_page_button = gr.Button("Previous Page", interactive=False)
self.next_page_button = gr.Button("Next Page", interactive=False)
@staticmethod
def download_font(url, save_path):
os.system(f"wget {url} -O {save_path}")
with zipfile.ZipFile(save_path, "r") as zip_ref:
zip_ref.extractall(".")
return os.path.join(".", "OpenSans-Regular.ttf")
def capture_frame(self, url):
stream_url = SearchService.get_stream_url(url)
if not stream_url:
return self.create_error_image("No stream found"), []
frame = self.get_frame(stream_url)
if frame is None:
return self.create_error_image("Failed to capture frame"), []
return self.process_frame(frame)
def get_frame(self, stream_url):
if not stream_url:
return None
try:
cap = cv2.VideoCapture(stream_url)
ret, frame = cap.read()
cap.release()
if ret:
return cv2.resize(frame, (1920, 1080))
else:
logging.warning(
"Unable to process the HLS stream with cv2.VideoCapture."
)
return None
except Exception as e:
logging.warning(f"An error occurred while capturing the frame: {e}")
return None
def process_frame(self, frame):
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = self.model(frame_rgb)
annotations = self.get_annotations(results)
return Image.fromarray(frame_rgb), annotations
def get_annotations(self, results):
annotations = []
for result in results:
for box in result.boxes:
class_id = int(box.cls[0])
class_name = result.names[class_id]
bbox = tuple(map(int, box.xyxy[0]))
annotations.append((bbox, class_name))
return annotations
@staticmethod
def create_error_image(message):
error_image = np.zeros((1920, 1080, 3), dtype=np.uint8)
pil_image = Image.fromarray(error_image)
draw = ImageDraw.Draw(pil_image)
font = ImageFont.truetype("/usr/share/fonts/open-sans/OpenSans-Regular.ttf", 24)
text_size = draw.textsize(message, font=font)
position = ((1920 - text_size[0]) // 2, (1080 - text_size[1]) // 2)
draw.text(position, message, (0, 0, 255), font=font)
return cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
def fetch_live_streams(self, query=""):
streams = []
results = SearchService.search(
query if query else "world live cams", SearchFilter.LIVE
)
for result in results:
if "video_id" in result and "thumbnail_urls" in result:
streams.append(
{
"thumbnail_url": result["thumbnail_urls"][0]
if result["thumbnail_urls"]
else "",
"title": result["title"],
"video_id": result["video_id"],
"label": result["video_id"],
}
)
return streams
def render(self):
with gr.Blocks(
title="Object Detection in Live YouTube Streams",
css="footer {visibility: hidden}",
) as app:
gr.HTML(
"<center><h1><b>Object Detection in Live YouTube Streams</b></h1></center>"
)
with gr.Column():
self.stream_input.render()
with gr.Group():
self.output_image.render()
self.submit_button.render()
with gr.Group():
with gr.Row():
self.search_input.render()
self.search_button.render()
with gr.Row():
self.gallery.render()
@self.gallery.select(
inputs=None, outputs=[self.output_image, self.stream_input]
)
def on_gallery_select(evt: gr.SelectData):
selected_index = evt.index
if selected_index is not None and selected_index < len(self.streams):
selected_stream = self.streams[selected_index]
stream_url = SearchService.get_youtube_url(
selected_stream["video_id"]
)
frame_output = self.capture_frame(stream_url)
return frame_output, stream_url
return None, ""
@self.search_button.click(
inputs=[self.search_input], outputs=[self.gallery]
)
def on_search_click(query):
self.streams = self.fetch_live_streams(query)
gallery_items = [
(stream["thumbnail_url"], stream["title"])
for stream in self.streams
]
return gallery_items
@self.submit_button.click(
inputs=[self.stream_input], outputs=[self.output_image]
)
def annotate_stream(url):
return self.capture_frame(url)
app.queue().launch(
share=True, debug=True, quiet=True, show_api=False, height=800
)
if __name__ == "__main__":
LiveStreamAnnotator().render()
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