Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
173
examples/video-processing/video-processing.py
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173
examples/video-processing/video-processing.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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import cv2
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import numpy as np
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, InputImageRawFrame, LLMRunFrame, OutputImageRawFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.daily.transport import DailyParams, DailyTransport
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load_dotenv(override=True)
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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audio_out_10ms_chunks=2,
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video_in_enabled=True,
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video_out_enabled=True,
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video_out_is_live=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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audio_out_10ms_chunks=2,
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video_in_enabled=True,
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video_out_enabled=True,
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video_out_is_live=True,
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),
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}
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class EdgeDetectionProcessor(FrameProcessor):
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def __init__(self, video_out_width, video_out_height: int):
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super().__init__()
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self._video_out_width = video_out_width
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self._video_out_height = video_out_height
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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# Send back the user's camera video with edge detection applied
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if isinstance(frame, InputImageRawFrame) and frame.transport_source == "camera":
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# Convert bytes to NumPy array
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img = np.frombuffer(frame.image, dtype=np.uint8).reshape(
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(frame.size[1], frame.size[0], 3)
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)
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# perform edge detection only on camera frames
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img = cv2.cvtColor(cv2.Canny(img, 100, 200), cv2.COLOR_GRAY2BGR)
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# convert the size if needed
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desired_size = (self._video_out_width, self._video_out_height)
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if frame.size != desired_size:
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resized_image = cv2.resize(img, desired_size)
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out_frame = OutputImageRawFrame(resized_image.tobytes(), desired_size, frame.format)
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await self.push_frame(out_frame)
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else:
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out_frame = OutputImageRawFrame(
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image=img.tobytes(), size=frame.size, format=frame.format
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)
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await self.push_frame(out_frame)
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else:
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await self.push_frame(frame, direction)
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SYSTEM_INSTRUCTION = f"""
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"You are Gemini Chatbot, a friendly, helpful robot.
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Your goal is to demonstrate your capabilities in a succinct way.
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Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
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Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
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"""
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async def run_bot(pipecat_transport):
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llm = GeminiLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
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transcribe_user_audio=True,
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system_instruction=SYSTEM_INSTRUCTION,
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)
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messages = [
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{
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"role": "developer",
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"content": "Start by greeting the user warmly and introducing yourself.",
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}
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]
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context = LLMContext(messages)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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pipecat_transport.input(),
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user_aggregator,
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llm, # LLM
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EdgeDetectionProcessor(
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pipecat_transport._params.video_out_width,
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pipecat_transport._params.video_out_height,
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), # Sending the video back to the user
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pipecat_transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@task.rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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logger.info("Pipecat client ready.")
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# Kick off the conversation.
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await task.queue_frames([LLMRunFrame()])
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@pipecat_transport.event_handler("on_client_connected")
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async def on_client_connected(transport, participant):
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logger.info("Pipecat Client connected")
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if isinstance(transport, DailyTransport):
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await pipecat_transport.capture_participant_video(participant["id"], framerate=30)
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else:
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await pipecat_transport.capture_participant_video("camera")
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@pipecat_transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Pipecat Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False, force_gc=True)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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