diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 525c80442..d5302703d 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -89,6 +89,7 @@ "integrity": "sha512-2BCOP7TN8M+gVDj7/ht3hsaO/B/n5oDbiAyyvnRlNOs+u1o+JWNYTQrmpuNp1/Wq2gcFrI01JAW+paEKDMx/CA==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@babel/code-frame": "^7.27.1", "@babel/generator": "^7.28.3", @@ -2557,6 +2558,7 @@ "integrity": "sha512-hHkbU/eoO3EG5/MZkuFSKmYqPbSVk5byPFa3e7y/8TybHiLMACgI8seVYlicwk7H5K/rI2px9xrQp/C+AUDTiQ==", "devOptional": true, "license": "MIT", + "peer": true, "dependencies": { "csstype": "^3.0.2" } @@ -2567,6 +2569,7 @@ "integrity": "sha512-qXRuZaOsAdXKFyOhRBg6Lqqc0yay13vN7KrIg4L7N4aaHN68ma9OK3NE1BoDFgFOTfM7zg+3/8+2n8rLUH3OKQ==", "devOptional": true, "license": "MIT", + "peer": true, "peerDependencies": { "@types/react": "^19.0.0" } @@ -2617,6 +2620,7 @@ "integrity": "sha512-B7RIQiTsCBBmY+yW4+ILd6mF5h1FUwJsVvpqkrgpszYifetQ2Ke+Z4u6aZh0CblkUGIdR59iYVyXqqZGkZ3aBw==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@typescript-eslint/scope-manager": "8.43.0", "@typescript-eslint/types": "8.43.0", @@ -2869,6 +2873,7 @@ "integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==", "dev": true, "license": "MIT", + "peer": true, "bin": { "acorn": "bin/acorn" }, @@ -3037,6 +3042,7 @@ } ], "license": "MIT", + "peer": true, "dependencies": { "baseline-browser-mapping": "^2.8.2", "caniuse-lite": "^1.0.30001741", @@ -3319,6 +3325,7 @@ "integrity": "sha512-QePbBFMJFjgmlE+cXAlbHZbHpdFVS2E/6vzCy7aKlebddvl1vadiC4JFV5u/wqTkNUwEV8WrQi257jf5f06hrg==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@eslint-community/eslint-utils": "^4.8.0", "@eslint-community/regexpp": "^4.12.1", @@ -4455,6 +4462,7 @@ } ], "license": "MIT", + "peer": true, "dependencies": { "nanoid": "^3.3.11", "picocolors": "^1.1.1", @@ -4533,6 +4541,7 @@ "resolved": "https://registry.npmjs.org/react/-/react-19.1.1.tgz", "integrity": "sha512-w8nqGImo45dmMIfljjMwOGtbmC/mk4CMYhWIicdSflH91J9TyCyczcPFXJzrZ/ZXcgGRFeP6BU0BEJTw6tZdfQ==", "license": "MIT", + "peer": true, "engines": { "node": ">=0.10.0" } @@ -4542,6 +4551,7 @@ "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.1.1.tgz", "integrity": "sha512-Dlq/5LAZgF0Gaz6yiqZCf6VCcZs1ghAJyrsu84Q/GT0gV+mCxbfmKNoGRKBYMJ8IEdGPqu49YWXD02GCknEDkw==", "license": "MIT", + "peer": true, "dependencies": { "scheduler": "^0.26.0" }, @@ -4899,6 +4909,7 @@ "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", "dev": true, "license": "MIT", + "peer": true, "engines": { "node": ">=12" }, @@ -4957,6 +4968,7 @@ "integrity": "sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ==", "dev": true, "license": "Apache-2.0", + "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" @@ -5079,6 +5091,7 @@ "integrity": "sha512-4cKBO9wR75r0BeIWWWId9XK9Lj6La5X846Zw9dFfzMRw38IlTk2iCcUt6hsyiDRcPidc55ZParFYDXi0nXOeLQ==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.5.0", @@ -5172,6 +5185,7 @@ "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", "dev": true, "license": "MIT", + "peer": true, "engines": { "node": ">=12" }, diff --git a/frontend/src/components/SettingsPanel.tsx b/frontend/src/components/SettingsPanel.tsx index b21d592bc..4f5964297 100644 --- a/frontend/src/components/SettingsPanel.tsx +++ b/frontend/src/components/SettingsPanel.tsx @@ -19,6 +19,7 @@ import { Input } from "./ui/input"; import { Button } from "./ui/button"; import { Toggle } from "./ui/toggle"; import { SliderWithInput } from "./ui/slider-with-input"; +import { OrderedMultiSelect } from "./ui/ordered-multi-select"; import { Hammer, Info, Minus, Plus, RotateCcw } from "lucide-react"; import { PARAMETER_METADATA } from "../data/parameterMetadata"; import { DenoisingStepsSlider } from "./DenoisingStepsSlider"; @@ -87,6 +88,12 @@ interface SettingsPanelProps { onVaceEnabledChange?: (enabled: boolean) => void; vaceContextScale?: number; onVaceContextScaleChange?: (scale: number) => void; + // Preprocessor settings + preprocessorType?: string | null; + onPreprocessorTypeChange?: (type: string | null) => void; + preprocessorTypes?: string[]; + onPreprocessorTypesChange?: (types: string[]) => void; + availablePreprocessors?: string[]; } export function SettingsPanel({ @@ -126,6 +133,11 @@ export function SettingsPanel({ onVaceEnabledChange, vaceContextScale = 1.0, onVaceContextScaleChange, + preprocessorType = null, + onPreprocessorTypeChange, + preprocessorTypes = [], + onPreprocessorTypesChange, + availablePreprocessors = [], }: SettingsPanelProps) { // Local slider state management hooks const noiseScaleSlider = useLocalSliderValue(noiseScale, onNoiseScaleChange); @@ -389,6 +401,73 @@ export function SettingsPanel({ )} + {/* Preprocessor - available for all pipelines in video mode */} + {inputMode === "video" && ( +
+
+ + {onPreprocessorTypesChange ? ( + { + // Format preprocessor ID for display + const displayNameMap: Record = { + depthanything: "Depth Anything", + passthrough: "Passthrough", + }; + const displayName = displayNameMap[id] || id + .split("-") + .map(word => word.charAt(0).toUpperCase() + word.slice(1)) + .join(" "); + return { value: id, label: displayName }; + })} + value={preprocessorTypes || []} + onChange={onPreprocessorTypesChange} + placeholder="Add preprocessor..." + disabled={isStreaming || isLoading} + className="flex-1" + /> + ) : ( + // Backward compatibility: single select + + )} +
+
+ )} + {currentPipeline?.supportsLoRA && (
0 ? fps.toFixed(1) : "N/A"; const bitrateValue = formatBitrate(bitrate); + const latencyValue = + latency !== undefined && latency !== null && latency > 0 + ? latency.toFixed(1) + : "N/A"; + const latencyUnit = latencyValue === "N/A" ? "" : "ms"; return (
+ {showLatency && ( + + )}
); diff --git a/frontend/src/components/ui/multi-select.tsx b/frontend/src/components/ui/multi-select.tsx new file mode 100644 index 000000000..fb78e4ecc --- /dev/null +++ b/frontend/src/components/ui/multi-select.tsx @@ -0,0 +1,106 @@ +import * as React from "react"; +import { Check, ChevronDown } from "lucide-react"; +import { cn } from "../../lib/utils"; +import { Button } from "./button"; + +interface MultiSelectProps { + options: Array<{ value: string; label: string }>; + value: string[]; + onChange: (value: string[]) => void; + placeholder?: string; + disabled?: boolean; + className?: string; +} + +export function MultiSelect({ + options, + value, + onChange, + placeholder = "Select...", + disabled = false, + className, +}: MultiSelectProps) { + const [open, setOpen] = React.useState(false); + const containerRef = React.useRef(null); + + React.useEffect(() => { + const handleClickOutside = (event: MouseEvent) => { + if ( + containerRef.current && + !containerRef.current.contains(event.target as Node) + ) { + setOpen(false); + } + }; + + if (open) { + document.addEventListener("mousedown", handleClickOutside); + } + + return () => { + document.removeEventListener("mousedown", handleClickOutside); + }; + }, [open]); + + const toggleOption = (optionValue: string) => { + if (disabled) return; + const newValue = value.includes(optionValue) + ? value.filter((v) => v !== optionValue) + : [...value, optionValue]; + onChange(newValue); + }; + + const displayText = + value.length === 0 + ? placeholder + : value.length === 1 + ? options.find((opt) => opt.value === value[0])?.label || value[0] + : `${value.length} selected`; + + return ( +
+ + {open && ( +
+
+ {options.map((option) => { + const isSelected = value.includes(option.value); + return ( +
toggleOption(option.value)} + > +
+ {isSelected && } +
+ {option.label} +
+ ); + })} +
+
+ )} +
+ ); +} diff --git a/frontend/src/components/ui/ordered-multi-select.tsx b/frontend/src/components/ui/ordered-multi-select.tsx new file mode 100644 index 000000000..318aefc25 --- /dev/null +++ b/frontend/src/components/ui/ordered-multi-select.tsx @@ -0,0 +1,185 @@ +import * as React from "react"; +import { Check, ChevronDown, ChevronUp, X } from "lucide-react"; +import { cn } from "../../lib/utils"; +import { Button } from "./button"; + +interface OrderedMultiSelectProps { + options: Array<{ value: string; label: string }>; + value: string[]; + onChange: (value: string[]) => void; + placeholder?: string; + disabled?: boolean; + className?: string; +} + +export function OrderedMultiSelect({ + options, + value, + onChange, + placeholder = "Select...", + disabled = false, + className, +}: OrderedMultiSelectProps) { + const [open, setOpen] = React.useState(false); + const containerRef = React.useRef(null); + + React.useEffect(() => { + const handleClickOutside = (event: MouseEvent) => { + if ( + containerRef.current && + !containerRef.current.contains(event.target as Node) + ) { + setOpen(false); + } + }; + + if (open) { + document.addEventListener("mousedown", handleClickOutside); + } + + return () => { + document.removeEventListener("mousedown", handleClickOutside); + }; + }, [open]); + + const toggleOption = (optionValue: string) => { + if (disabled) return; + const newValue = value.includes(optionValue) + ? value.filter((v) => v !== optionValue) + : [...value, optionValue]; + onChange(newValue); + }; + + const removeItem = (index: number) => { + if (disabled) return; + const newValue = value.filter((_, i) => i !== index); + onChange(newValue); + }; + + const moveUp = (index: number) => { + if (disabled || index === 0) return; + const newValue = [...value]; + [newValue[index - 1], newValue[index]] = [newValue[index], newValue[index - 1]]; + onChange(newValue); + }; + + const moveDown = (index: number) => { + if (disabled || index === value.length - 1) return; + const newValue = [...value]; + [newValue[index], newValue[index + 1]] = [newValue[index + 1], newValue[index]]; + onChange(newValue); + }; + + const getLabel = (value: string) => { + return options.find((opt) => opt.value === value)?.label || value; + }; + + const availableOptions = options.filter((opt) => !value.includes(opt.value)); + + return ( +
+ {/* Selected items in order */} + {value.length > 0 && ( +
+ {value.map((itemValue, index) => ( +
+
+ + {index + 1}. + + {getLabel(itemValue)} +
+
+ + + +
+
+ ))} +
+ )} + + {/* Dropdown to add more */} +
+ + {open && availableOptions.length > 0 && ( +
+
+ {availableOptions.map((option) => ( +
{ + toggleOption(option.value); + setOpen(false); + }} + > +
+ +
+ {option.label} +
+ ))} +
+
+ )} +
+
+ ); +} diff --git a/frontend/src/data/parameterMetadata.ts b/frontend/src/data/parameterMetadata.ts index 8f0be5adc..38853cd9c 100644 --- a/frontend/src/data/parameterMetadata.ts +++ b/frontend/src/data/parameterMetadata.ts @@ -82,4 +82,9 @@ export const PARAMETER_METADATA: Record = { tooltip: "The configuration of the sender that will send video to Spout-compatible apps like TouchDesigner, Resolume, OBS.", }, + preprocessorType: { + label: "Preprocessor:", + tooltip: + "Select a preprocessor to apply to video input. Depth Anything: extracts depth maps for VACE structural conditioning. Passthrough: passes video through unchanged. None: no preprocessing.", + }, }; diff --git a/frontend/src/hooks/useStreamLatency.ts b/frontend/src/hooks/useStreamLatency.ts new file mode 100644 index 000000000..50ac75e06 --- /dev/null +++ b/frontend/src/hooks/useStreamLatency.ts @@ -0,0 +1,54 @@ +import { useState, useEffect, useRef } from "react"; +import { getStreamStats } from "../lib/api"; + +interface UseStreamLatencyProps { + isStreaming: boolean; + inputMode: "text" | "video"; +} + +export function useStreamLatency({ + isStreaming, + inputMode, +}: UseStreamLatencyProps) { + const [latency, setLatency] = useState(null); + const statsIntervalRef = useRef(null); + + useEffect(() => { + // Only fetch latency for V2V mode (video input) + if (isStreaming && inputMode === "video") { + const fetchLatency = async () => { + try { + const stats = await getStreamStats(); + if (stats.latency !== undefined && stats.latency !== null) { + setLatency(stats.latency); + } + } catch (error) { + // Silently fail - latency is optional + console.debug("Failed to fetch latency:", error); + } + }; + + // Fetch immediately + fetchLatency(); + + // Then fetch every 1s for updates + statsIntervalRef.current = setInterval(fetchLatency, 1000); + } else { + // Clear interval and reset latency when not streaming or not V2V + if (statsIntervalRef.current) { + clearInterval(statsIntervalRef.current); + statsIntervalRef.current = null; + } + setLatency(null); + } + + return () => { + if (statsIntervalRef.current) { + clearInterval(statsIntervalRef.current); + statsIntervalRef.current = null; + } + }; + }, [isStreaming, inputMode]); + + return latency; +} diff --git a/frontend/src/hooks/useStreamState.ts b/frontend/src/hooks/useStreamState.ts index 5fa290775..b426a9dc4 100644 --- a/frontend/src/hooks/useStreamState.ts +++ b/frontend/src/hooks/useStreamState.ts @@ -10,6 +10,7 @@ import type { import { getHardwareInfo, getPipelineSchemas, + getPreprocessors, type HardwareInfoResponse, type PipelineSchemasResponse, } from "../lib/api"; @@ -148,6 +149,10 @@ export function useStreamState() { paused: false, loraMergeStrategy: "permanent_merge", inputMode: initialDefaults.inputMode, + // VACE and Preprocessor defaults + vaceEnabled: true, + preprocessorType: null, // No preprocessor by default (deprecated) + preprocessorTypes: [], // List of preprocessor types }); const [promptData, setPromptData] = useState({ @@ -160,13 +165,17 @@ export function useStreamState() { null ); - // Fetch pipeline schemas and hardware info on mount + // Store available preprocessors + const [availablePreprocessors, setAvailablePreprocessors] = useState([]); + + // Fetch pipeline schemas, hardware info, and preprocessors on mount useEffect(() => { const fetchInitialData = async () => { try { - const [schemasResult, hardwareResult] = await Promise.allSettled([ + const [schemasResult, hardwareResult, preprocessorsResult] = await Promise.allSettled([ getPipelineSchemas(), getHardwareInfo(), + getPreprocessors(), ]); if (schemasResult.status === "fulfilled") { @@ -205,6 +214,15 @@ export function useStreamState() { hardwareResult.reason ); } + + if (preprocessorsResult.status === "fulfilled") { + setAvailablePreprocessors(preprocessorsResult.value.preprocessors); + } else { + console.error( + "useStreamState: Failed to fetch preprocessors:", + preprocessorsResult.reason + ); + } } catch (error) { console.error("useStreamState: Failed to fetch initial data:", error); } @@ -290,5 +308,6 @@ export function useStreamState() { getDefaults, supportsNoiseControls, spoutAvailable, + availablePreprocessors, }; } diff --git a/frontend/src/hooks/useWebRTC.ts b/frontend/src/hooks/useWebRTC.ts index 01f985706..25974f90a 100644 --- a/frontend/src/hooks/useWebRTC.ts +++ b/frontend/src/hooks/useWebRTC.ts @@ -19,6 +19,8 @@ interface InitialParameters { kv_cache_attention_bias?: number; vace_ref_images?: string[]; vace_context_scale?: number; + preprocessor_type?: string | null; // Deprecated, use preprocessor_types + preprocessor_types?: string[] | null; } interface UseWebRTCOptions { @@ -327,6 +329,8 @@ export function useWebRTC(options?: UseWebRTCOptions) { spout_receiver?: { enabled: boolean; name: string }; vace_ref_images?: string[]; vace_context_scale?: number; + preprocessor_type?: string | null; // Deprecated, use preprocessor_types + preprocessor_types?: string[] | null; }) => { if ( dataChannelRef.current && diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index 50fa5b811..3dc7117f2 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -25,6 +25,8 @@ export interface WebRTCOfferRequest { kv_cache_attention_bias?: number; vace_ref_images?: string[]; vace_context_scale?: number; + preprocessor_type?: string | null; // Deprecated, use preprocessor_types + preprocessor_types?: string[] | null; }; } @@ -159,6 +161,28 @@ export const getPipelineStatus = async (): Promise => { return result; }; +export interface StreamStatsResponse { + latency?: number | null; +} + +export const getStreamStats = async (): Promise => { + const response = await fetch("/api/v1/stream/stats", { + method: "GET", + headers: { "Content-Type": "application/json" }, + signal: AbortSignal.timeout(5000), // 5 second timeout + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error( + `Stream stats failed: ${response.status} ${response.statusText}: ${errorText}` + ); + } + + const result = await response.json(); + return result; +}; + export const checkModelStatus = async ( pipelineId: string ): Promise => { @@ -408,6 +432,26 @@ export interface PipelineSchemasResponse { pipelines: Record; } +export interface PreprocessorsResponse { + preprocessors: string[]; +} + +export const getPreprocessors = async (): Promise => { + const response = await fetch("/api/v1/preprocessors", { + method: "GET", + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error( + `Get preprocessors failed: ${response.status} ${response.statusText}: ${errorText}` + ); + } + + const result = await response.json(); + return result; +}; + export const getPipelineSchemas = async (): Promise => { const response = await fetch("/api/v1/pipelines/schemas", { diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx index 0de1ef2e6..fbd7c5694 100644 --- a/frontend/src/pages/StreamPage.tsx +++ b/frontend/src/pages/StreamPage.tsx @@ -10,6 +10,7 @@ import { StatusBar } from "../components/StatusBar"; import { useWebRTC } from "../hooks/useWebRTC"; import { useVideoSource } from "../hooks/useVideoSource"; import { useWebRTCStats } from "../hooks/useWebRTCStats"; +import { useStreamLatency } from "../hooks/useStreamLatency"; import { usePipeline } from "../hooks/usePipeline"; import { useStreamState } from "../hooks/useStreamState"; import { usePipelines } from "../hooks/usePipelines"; @@ -78,6 +79,7 @@ export function StreamPage() { getDefaults, supportsNoiseControls, spoutAvailable, + availablePreprocessors, } = useStreamState(); // Prompt state - use unified default prompts based on mode @@ -168,6 +170,12 @@ export function StreamPage() { isStreaming, }); + // Get stream latency (only for V2V mode) + const latency = useStreamLatency({ + isStreaming, + inputMode: settings.inputMode ?? "text", + }); + // Video source for preview (camera or video) // Enable based on input mode, not pipeline category const { @@ -508,6 +516,26 @@ export function StreamPage() { } }; + const handlePreprocessorTypeChange = (type: string | null) => { + updateSettings({ preprocessorType: type }); + // Send preprocessor type update to backend if streaming (backward compatibility) + if (isStreaming) { + sendParameterUpdate({ + preprocessor_type: type, + }); + } + }; + + const handlePreprocessorTypesChange = (types: string[]) => { + updateSettings({ preprocessorTypes: types }); + // Send preprocessor types update to backend if streaming + if (isStreaming) { + sendParameterUpdate({ + preprocessor_types: types.length > 0 ? types : null, + }); + } + }; + const handleResetCache = () => { // Send reset cache command to backend sendParameterUpdate({ @@ -738,7 +766,15 @@ export function StreamPage() { // Add VACE parameters if pipeline supports VACE if (currentPipeline?.supportsVACE) { - const vaceEnabled = settings.vaceEnabled ?? currentMode !== "video"; + // Check if depth preprocessor is enabled + const preprocessorTypes = settings.preprocessorTypes || (settings.preprocessorType ? [settings.preprocessorType] : []); + const depthPreprocessorActive = preprocessorTypes.includes("depthanything"); + + // When depth preprocessor is enabled, VACE should be enabled for best results + // (though depth-only mode can work without VACE) + const vaceEnabled = + depthPreprocessorActive || + (settings.vaceEnabled ?? currentMode !== "video"); loadParams.vace_enabled = vaceEnabled; // Add VACE reference images if provided @@ -791,6 +827,8 @@ export function StreamPage() { spout_receiver?: { enabled: boolean; name: string }; vace_ref_images?: string[]; vace_context_scale?: number; + preprocessor_type?: string | null; // Deprecated, use preprocessor_types + preprocessor_types?: string[] | null; } = { // Signal the intended input mode to the backend so it doesn't // briefly fall back to text mode before video frames arrive @@ -831,6 +869,14 @@ export function StreamPage() { if (currentMode === "video") { initialParameters.noise_scale = settings.noiseScale ?? 0.7; initialParameters.noise_controller = settings.noiseController ?? true; + + // Include preprocessor settings if enabled + if (settings.preprocessorTypes && settings.preprocessorTypes.length > 0) { + initialParameters.preprocessor_types = settings.preprocessorTypes; + } else if (settings.preprocessorType) { + // Backward compatibility: single preprocessor_type + initialParameters.preprocessor_type = settings.preprocessorType; + } } // Spout settings - send if enabled @@ -1117,12 +1163,22 @@ export function StreamPage() { onVaceEnabledChange={handleVaceEnabledChange} vaceContextScale={settings.vaceContextScale ?? 1.0} onVaceContextScaleChange={handleVaceContextScaleChange} + preprocessorType={settings.preprocessorType ?? null} + onPreprocessorTypeChange={handlePreprocessorTypeChange} + preprocessorTypes={settings.preprocessorTypes || []} + onPreprocessorTypesChange={handlePreprocessorTypesChange} + availablePreprocessors={availablePreprocessors} /> {/* Status Bar */} - + {/* Download Dialog */} {pipelineNeedsModels && ( diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index ba373389a..6c292f78d 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -73,6 +73,9 @@ export interface SettingsState { vaceEnabled?: boolean; refImages?: string[]; vaceContextScale?: number; + // Preprocessor settings + preprocessorType?: string | null; // Deprecated, use preprocessorTypes + preprocessorTypes?: string[]; // List of preprocessor types } export interface PipelineInfo { diff --git a/pyproject.toml b/pyproject.toml index d4af5755a..a4821fd61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "daydream-scope" version = "0.1.0b2" description = "A tool for running and customizing real-time, interactive generative AI pipelines and models" readme = "README.md" -requires-python = ">=3.10.12" +requires-python = ">=3.10.12,<3.11" authors = [ {name = "Yondon Fu", email = "yondon@livepeer.org"}, {name = "Rafal Leszko", email = "rafal@livepeer.org"} @@ -56,6 +56,7 @@ dependencies = [ "triton-windows==3.4.0.post21; sys_platform == 'win32'", "SpoutGL>=0.1.1; sys_platform == 'win32'", "PyOpenGL>=3.1.10; sys_platform == 'win32'", + "pyzmq>=26.0.0", ] [project.scripts] diff --git a/src/scope/core/pipelines/depthanything/__init__.py b/src/scope/core/pipelines/depthanything/__init__.py new file mode 100644 index 000000000..fdd2ad730 --- /dev/null +++ b/src/scope/core/pipelines/depthanything/__init__.py @@ -0,0 +1,10 @@ +"""DepthAnything pipeline for video depth estimation. + +This module provides a pipeline for temporally consistent depth estimation +on video frames using Video-Depth-Anything. +""" + +from .model import VideoDepthAnythingModel +from .pipeline import DepthAnythingPipeline + +__all__ = ["DepthAnythingPipeline", "VideoDepthAnythingModel"] diff --git a/src/scope/core/pipelines/depthanything/model.py b/src/scope/core/pipelines/depthanything/model.py new file mode 100644 index 000000000..f2a0c17c7 --- /dev/null +++ b/src/scope/core/pipelines/depthanything/model.py @@ -0,0 +1,339 @@ +"""Video-Depth-Anything model wrapper for depth estimation. + +This module provides a wrapper around Video-Depth-Anything for consistent +depth estimation on video frames. + +Reference: https://github.com/DepthAnything/Video-Depth-Anything +Paper: Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (CVPR 2025) + +Setup: + The Video-Depth-Anything model code is automatically cloned from GitHub + on first use. Model weights are downloaded from HuggingFace Hub. +""" + +import logging +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as TF +from huggingface_hub import hf_hub_download + +logger = logging.getLogger(__name__) + +# Performance tuning: use streaming mode for real-time V2V +DEPTH_STREAMING_MODE = os.environ.get("DEPTH_STREAMING_MODE", "true").lower() in ( + "true", + "1", + "yes", +) +# Input size for depth model (lower = faster, 518 is default, try 308 or 392 for speed) +DEPTH_INPUT_SIZE = int(os.environ.get("DEPTH_INPUT_SIZE", "392")) + +# Where to clone the Video-Depth-Anything repo +VDA_REPO_URL = "https://github.com/DepthAnything/Video-Depth-Anything.git" +VDA_REPO_DIR = Path(__file__).parent / "vendor" / "Video-Depth-Anything" + +# Model configuration +MODEL_CONFIGS = { + "vits": { + "encoder": "vits", + "features": 64, + "out_channels": [48, 96, 192, 384], + "repo_id": "depth-anything/Video-Depth-Anything-Small", + "filename": "video_depth_anything_vits.pth", + }, + "vitb": { + "encoder": "vitb", + "features": 128, + "out_channels": [96, 192, 384, 768], + "repo_id": "depth-anything/Video-Depth-Anything-Base", + "filename": "video_depth_anything_vitb.pth", + }, + "vitl": { + "encoder": "vitl", + "features": 256, + "out_channels": [256, 512, 1024, 1024], + "repo_id": "depth-anything/Video-Depth-Anything-Large", + "filename": "video_depth_anything_vitl.pth", + }, +} + + +def _ensure_repo_cloned(): + """Ensure Video-Depth-Anything repo is cloned.""" + if VDA_REPO_DIR.exists() and (VDA_REPO_DIR / "video_depth_anything").exists(): + return + + logger.info(f"Cloning Video-Depth-Anything repo to {VDA_REPO_DIR}...") + VDA_REPO_DIR.parent.mkdir(parents=True, exist_ok=True) + + subprocess.run( + ["git", "clone", "--depth", "1", VDA_REPO_URL, str(VDA_REPO_DIR)], + check=True, + ) + logger.info("Video-Depth-Anything repo cloned successfully") + + +def _add_repo_to_path(): + """Add the Video-Depth-Anything repo to Python path.""" + repo_path = str(VDA_REPO_DIR) + if repo_path not in sys.path: + sys.path.insert(0, repo_path) + + +class VideoDepthAnythingModel: + """Video-Depth-Anything depth estimation model wrapper. + + This class provides a simple interface to load and run Video-Depth-Anything + models for temporally consistent depth estimation on video frames. + + Args: + encoder: Model encoder size ("vits", "vitb", or "vitl") + device: Torch device to run inference on + dtype: Data type for inference (default: torch.float16) + input_size: Input size for the model (default: 518, or DEPTH_INPUT_SIZE env var) + max_res: Maximum resolution for input (default: 1280) + streaming: Use streaming mode for real-time processing (default: DEPTH_STREAMING_MODE) + + Example: + >>> depth_model = VideoDepthAnythingModel(encoder="vitl", device="cuda") + >>> depth_frames = depth_model.infer(video_frames) # [F, H, W] in [0, 1] + """ + + def __init__( + self, + encoder: str = "vits", + device: torch.device | str = "cuda", + dtype: torch.dtype = torch.float16, + input_size: int | None = None, + max_res: int = 1280, + streaming: bool | None = None, + ): + if encoder not in MODEL_CONFIGS: + raise ValueError( + f"Unknown encoder: {encoder}. Choose from {list(MODEL_CONFIGS.keys())}" + ) + + self.encoder = encoder + self.device = torch.device(device) if isinstance(device, str) else device + self.dtype = dtype + self.input_size = input_size if input_size is not None else DEPTH_INPUT_SIZE + self.max_res = max_res + self.streaming = streaming if streaming is not None else DEPTH_STREAMING_MODE + + self.model = None + self.streaming_model = None # Separate model instance for streaming + self._model_loaded = False + + logger.info( + f"VideoDepthAnythingModel config: encoder={encoder}, input_size={self.input_size}, " + f"streaming={self.streaming}" + ) + + def _download_weights(self) -> Path: + """Download model weights from HuggingFace Hub.""" + config = MODEL_CONFIGS[self.encoder] + + logger.info(f"Downloading Video-Depth-Anything weights ({self.encoder})...") + weights_path = hf_hub_download( + repo_id=config["repo_id"], + filename=config["filename"], + ) + logger.info(f"Downloaded weights to: {weights_path}") + return Path(weights_path) + + def load_model(self): + """Load the Video-Depth-Anything model.""" + if self._model_loaded: + return + + # Ensure repo is cloned and in path + _ensure_repo_cloned() + _add_repo_to_path() + + config = MODEL_CONFIGS[self.encoder] + weights_path = self._download_weights() + + if self.streaming: + # Use streaming model for real-time processing (frame-by-frame with caching) + from video_depth_anything.video_depth_stream import ( + VideoDepthAnything as VDAStreamModel, + ) + + logger.info( + f"Loading Video-Depth-Anything STREAMING model ({self.encoder})..." + ) + + self.streaming_model = VDAStreamModel( + encoder=config["encoder"], + features=config["features"], + out_channels=config["out_channels"], + ) + + state_dict = torch.load(weights_path, map_location="cpu", weights_only=False) + self.streaming_model.load_state_dict(state_dict) + self.streaming_model = self.streaming_model.to( + device=self.device, dtype=torch.float32 + ) + self.streaming_model.eval() + + logger.info("Video-Depth-Anything STREAMING model loaded successfully") + else: + # Use batch model for offline processing + from video_depth_anything.video_depth import ( + VideoDepthAnything as VDAModel, + ) + + logger.info(f"Loading Video-Depth-Anything model ({self.encoder})...") + + self.model = VDAModel( + encoder=config["encoder"], + features=config["features"], + out_channels=config["out_channels"], + ) + + state_dict = torch.load(weights_path, map_location="cpu", weights_only=False) + self.model.load_state_dict(state_dict) + + # Note: We use float32 for the model because the VDA code has mixed precision + # operations that require float32 compatibility. The fp16/fp32 selection + # is handled internally by autocast during inference. + self.model = self.model.to(device=self.device, dtype=torch.float32) + self.model.eval() + + logger.info("Video-Depth-Anything model loaded successfully") + + self._model_loaded = True + + @torch.no_grad() + def infer( + self, + frames: torch.Tensor | np.ndarray, + num_frames_per_batch: int = 32, + ) -> torch.Tensor: + """Run depth estimation on video frames. + + Args: + frames: Video frames [F, H, W, C] in [0, 255] or [0, 1] + num_frames_per_batch: Number of frames to process at once (default: 32) + + Returns: + Depth maps [F, H, W] in [0, 1] range (higher = closer) + """ + if not self._model_loaded: + self.load_model() + + # Convert numpy to torch if needed + if isinstance(frames, np.ndarray): + frames = torch.from_numpy(frames) + + # Ensure float dtype and correct range [0, 255] + if frames.dtype == torch.uint8: + frames = frames.float() + elif frames.max() <= 1.0: + frames = frames.float() * 255.0 + else: + frames = frames.float() + + # Handle channel dimension - expected [F, H, W, C] + if frames.dim() == 4: + if frames.shape[1] in [1, 3]: + # [F, C, H, W] -> [F, H, W, C] + frames = frames.permute(0, 2, 3, 1) + + num_frames, H, W, C = frames.shape + + # Convert to numpy array for the VDA API + # The VDA model expects numpy array of shape [F, H, W, C] + frames_np = frames.cpu().numpy().astype(np.uint8) + + if self.streaming and self.streaming_model is not None: + # Use streaming mode - process frame by frame with caching + depth_list = [] + for i in range(num_frames): + frame_depth = self.streaming_model.infer_video_depth_one( + frames_np[i], + input_size=self.input_size, + device=str(self.device), + fp32=(self.dtype == torch.float32), + ) + depth_list.append(frame_depth) + + depths = np.stack(depth_list, axis=0) + else: + # Use batch mode + depths, _ = self.model.infer_video_depth( + frames_np, + target_fps=-1, # Not used for output + input_size=self.input_size, + device=str(self.device), + fp32=(self.dtype == torch.float32), + ) + + # depths is numpy array [F, H, W] of depth values + depth = torch.from_numpy(depths).float() + depth = depth.to(device=self.device) + + # Resize to original size if different + if depth.shape[1] != H or depth.shape[2] != W: + depth = depth.unsqueeze(1) # [F, 1, H, W] + depth = TF.interpolate( + depth, size=(H, W), mode="bilinear", align_corners=False + ) + depth = depth.squeeze(1) # [F, H, W] + + # Normalize to [0, 1] + depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8) + + return depth + + def infer_from_list( + self, + frames: list[torch.Tensor | np.ndarray], + num_frames_per_batch: int = 32, + ) -> torch.Tensor: + """Run depth estimation on a list of video frames. + + Args: + frames: List of video frames, each [H, W, C] in [0, 255] + num_frames_per_batch: Number of frames to process at once + + Returns: + Depth maps [F, H, W] in [0, 1] range + """ + # Stack frames into single tensor + if isinstance(frames[0], np.ndarray): + stacked = np.stack(frames, axis=0) + else: + stacked = torch.stack(frames, dim=0) + + return self.infer(stacked, num_frames_per_batch) + + def offload(self): + """Offload model from GPU to free memory. + + Call this after depth estimation is complete if you need to + free up GPU memory for other models. + """ + if self.model is not None: + self.model = self.model.cpu() + if self.streaming_model is not None: + self.streaming_model = self.streaming_model.cpu() + torch.cuda.empty_cache() + logger.info("Video-Depth-Anything model offloaded to CPU") + + def reset_streaming_state(self): + """Reset the streaming model's internal cache state. + + Call this when starting a new video stream to clear cached hidden states. + """ + if self.streaming_model is not None: + self.streaming_model.transform = None + self.streaming_model.frame_id_list = [] + self.streaming_model.frame_cache_list = [] + self.streaming_model.id = -1 + logger.debug("Streaming model state reset") diff --git a/src/scope/core/pipelines/depthanything/pipeline.py b/src/scope/core/pipelines/depthanything/pipeline.py new file mode 100644 index 000000000..74e53e0c8 --- /dev/null +++ b/src/scope/core/pipelines/depthanything/pipeline.py @@ -0,0 +1,227 @@ +"""DepthAnything pipeline for video depth estimation. + +This module provides a pipeline wrapper around Video-Depth-Anything for consistent +depth estimation on video frames. The depth maps can be used for visualization +or as conditioning signals for other pipelines. + +Reference: https://github.com/DepthAnything/Video-Depth-Anything +Paper: Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (CVPR 2025) + +Performance notes: + - Set streaming=True for real-time V2V (processes frame-by-frame with caching) + - Set input_size to lower values (e.g., 308, 392) for faster inference + - The "vits" encoder is fastest, "vitl" is most accurate +""" + +import logging +from typing import TYPE_CHECKING, Literal + +import numpy as np +import torch +import torch.nn.functional as TF + +from ..interface import Pipeline, Requirements +from ..process import postprocess_chunk, preprocess_chunk +from .model import VideoDepthAnythingModel +from .schema import DepthAnythingConfig + +if TYPE_CHECKING: + from ..schema import BasePipelineConfig + +logger = logging.getLogger(__name__) + + +class DepthAnythingPipeline(Pipeline): + """Pipeline for video depth estimation using Video-Depth-Anything. + + This pipeline provides temporally consistent depth estimation on video frames. + The output can be used for visualization or as conditioning signals for + other pipelines. + + Args: + height: Output height in pixels + width: Output width in pixels + encoder: Model encoder size ("vits", "vitb", or "vitl") + device: Torch device to run inference on + dtype: Data type for inference (default: torch.float16) + input_size: Input size for the model (lower = faster) + streaming: Use streaming mode for real-time processing + output_format: Output format ("grayscale" or "rgb") + + Example: + >>> pipeline = DepthAnythingPipeline(encoder="vitl", device="cuda") + >>> depth_output = pipeline(video=frames) # returns depth visualization + """ + + @classmethod + def get_config_class(cls) -> type["BasePipelineConfig"]: + return DepthAnythingConfig + + def __init__( + self, + height: int = 480, + width: int = 848, + encoder: Literal["vits", "vitb", "vitl"] = "vits", + device: torch.device | str | None = None, + dtype: torch.dtype = torch.float16, + input_size: int = 392, + streaming: bool = True, + output_format: Literal["grayscale", "rgb"] = "grayscale", + **kwargs, # Accept and ignore additional parameters like 'loras', 'config', etc. + ): + self.height = height + self.width = width + self.encoder = encoder + self.device = ( + torch.device(device) + if device is not None + else torch.device("cuda" if torch.cuda.is_available() else "cpu") + ) + self.dtype = dtype + self.input_size = input_size + self.streaming = streaming + self.output_format = output_format + + # Model will be loaded lazily + self._model: VideoDepthAnythingModel | None = None + self._model_loaded = False + + logger.info( + f"DepthAnythingPipeline initialized: encoder={encoder}, " + f"input_size={input_size}, streaming={streaming}, " + f"output_format={output_format}" + ) + + def _ensure_model_loaded(self): + """Ensure the depth model is loaded.""" + if self._model_loaded: + return + + self._model = VideoDepthAnythingModel( + encoder=self.encoder, + device=self.device, + dtype=self.dtype, + input_size=self.input_size, + streaming=self.streaming, + ) + self._model.load_model() + self._model_loaded = True + + def prepare(self, **kwargs) -> Requirements: + """Prepare the pipeline for inference. + + Returns: + Requirements object with input size requirements + """ + # Load model on prepare + self._ensure_model_loaded() + return Requirements(input_size=4) + + def __call__( + self, + **kwargs, + ) -> torch.Tensor: + """Process video frames and return depth maps. + + Args: + video: Input video frames as tensor [B, C, T, H, W] or list of frames + **kwargs: Additional parameters (ignored) + + Returns: + Depth maps in [T, H, W, C] format, values in [0, 1] range + """ + input_frames = kwargs.get("video") + + if input_frames is None: + raise ValueError("Input 'video' cannot be None for DepthAnythingPipeline") + + self._ensure_model_loaded() + + # Handle list input - convert to tensor [F, H, W, C] + frames = None + if isinstance(input_frames, list): + if len(input_frames) == 0: + raise ValueError("Input frames list cannot be empty") + + # Check if frames are 3D [H, W, C] or 4D [T, H, W, C] + first_frame = input_frames[0] + if isinstance(first_frame, torch.Tensor): + first_frame = first_frame.to(device=self.device, dtype=self.dtype) + else: + first_frame = torch.from_numpy(first_frame).to(device=self.device, dtype=self.dtype) + + if first_frame.dim() == 3: + # List of [H, W, C] frames - stack them directly + frames = torch.stack([f.to(device=self.device, dtype=self.dtype) if isinstance(f, torch.Tensor) else torch.from_numpy(f).to(device=self.device, dtype=self.dtype) for f in input_frames], dim=0) # [F, H, W, C] + input_height, input_width = frames.shape[1], frames.shape[2] + elif first_frame.dim() == 4: + # List of [T, H, W, C] frames - use preprocess_chunk + input_frames = preprocess_chunk( + input_frames, self.device, self.dtype + ) + # Continue to tensor handling below + else: + raise ValueError(f"Unexpected frame shape in list: {first_frame.shape}") + + # Handle tensor input format - expect [B, C, T, H, W] or [T, H, W, C] or [T, C, H, W] + if frames is None: + if not isinstance(input_frames, torch.Tensor): + input_frames = torch.from_numpy(input_frames).to(device=self.device, dtype=self.dtype) + else: + input_frames = input_frames.to(device=self.device, dtype=self.dtype) + + if input_frames.dim() == 5: + B, C, T, H, W = input_frames.shape + # Store original input dimensions to preserve them in output + input_height, input_width = H, W + # Rearrange to [T, H, W, C] for depth model + frames = input_frames[0].permute(1, 2, 3, 0) # [T, H, W, C] + elif input_frames.dim() == 4: + # Assume [T, C, H, W] or [T, H, W, C] + if input_frames.shape[1] == 3: + T, C, H, W = input_frames.shape + input_height, input_width = H, W + frames = input_frames.permute(0, 2, 3, 1) # [T, H, W, C] + else: + T, H, W, C = input_frames.shape + input_height, input_width = H, W + frames = input_frames + else: + raise ValueError(f"Unexpected input shape: {input_frames.shape}") + + # Convert to [0, 255] range if needed + if frames.max() <= 1.0: + frames = frames * 255.0 + + # Run depth estimation at original input resolution + depth = self._model.infer(frames) # [T, H, W] in [0, 1] + + # Format output based on output_format + # Note: VideoFrame requires RGB (3 channels), so we always output 3 channels + if self.output_format == "grayscale": + # [T, H, W] -> [T, H, W, 3] (replicate to 3 channels for RGB) + output = depth.unsqueeze(-1).repeat(1, 1, 1, 3) + elif self.output_format == "rgb": + # [T, H, W] -> [T, H, W, 3] (replicate to 3 channels) + output = depth.unsqueeze(-1).repeat(1, 1, 1, 3) + else: + raise ValueError(f"Unknown output_format: {self.output_format}") + + # Output should already be at input_height x input_width (no resizing needed) + # The depth model preserves input dimensions, so output matches input + + return postprocess_chunk(output.unsqueeze(0).permute(0, 1, 4, 2, 3)) + + def reset_streaming_state(self): + """Reset the streaming model's internal cache state. + + Call this when starting a new video stream to clear cached hidden states. + """ + if self._model is not None: + self._model.reset_streaming_state() + + def offload(self): + """Offload model from GPU to free memory.""" + if self._model is not None: + self._model.offload() + self._model_loaded = False diff --git a/src/scope/core/pipelines/depthanything/schema.py b/src/scope/core/pipelines/depthanything/schema.py new file mode 100644 index 000000000..405658184 --- /dev/null +++ b/src/scope/core/pipelines/depthanything/schema.py @@ -0,0 +1,64 @@ +"""Pydantic schema for DepthAnything pipeline configuration.""" + +from typing import Literal + +from pydantic import Field + +from ..base_schema import BasePipelineConfig, ModeDefaults, height_field, width_field + + +class DepthAnythingConfig(BasePipelineConfig): + """Configuration for the DepthAnything depth estimation pipeline. + + This pipeline uses Video-Depth-Anything for temporally consistent depth + estimation on video frames. The depth maps can be used as conditioning + signals for V2V pipelines or for visualization. + + Reference: https://github.com/DepthAnything/Video-Depth-Anything + Paper: Video Depth Anything: Consistent Depth Estimation for Super-Long Videos (CVPR 2025) + """ + + pipeline_id = "depthanything" + pipeline_name = "Depth Anything" + pipeline_description = ( + "Video depth estimation pipeline using Video-Depth-Anything for " + "temporally consistent depth maps. Useful for visualization or as " + "conditioning signals for other pipelines." + ) + pipeline_version = "1.0.0" + + # This pipeline doesn't need prompt input + supports_prompts = False + + # Estimated VRAM usage (vits model - small/fast) + estimated_vram_gb = 4.0 + + # Only video mode is supported (requires input frames) + modes = {"video": ModeDefaults(default=True, input_size=4)} + + # Resolution settings + height: int = height_field(default=480) + width: int = width_field(default=848) + + # Depth model settings + encoder: Literal["vits", "vitb", "vitl"] = Field( + default="vits", + description="Model encoder size: vits (fastest), vitb (balanced), vitl (most accurate)", + ) + + input_size: int = Field( + default=392, + ge=224, + le=518, + description="Input size for depth model (lower = faster, 518 is default, try 308 or 392 for speed)", + ) + + streaming: bool = Field( + default=True, + description="Use streaming mode for real-time processing (processes frame-by-frame with caching)", + ) + + output_format: Literal["grayscale", "rgb"] = Field( + default="grayscale", + description="Output format: grayscale (1 channel) or rgb (3 channels)", + ) diff --git a/src/scope/core/pipelines/depthanything/vendor/Video-Depth-Anything b/src/scope/core/pipelines/depthanything/vendor/Video-Depth-Anything new file mode 160000 index 000000000..4f5ae2317 --- /dev/null +++ b/src/scope/core/pipelines/depthanything/vendor/Video-Depth-Anything @@ -0,0 +1 @@ +Subproject commit 4f5ae23172ba60fd7bc11ef671cca678842c7072 diff --git a/src/scope/core/pipelines/longlive/test_video_depth.py b/src/scope/core/pipelines/longlive/test_video_depth.py new file mode 100644 index 000000000..b0db93069 --- /dev/null +++ b/src/scope/core/pipelines/longlive/test_video_depth.py @@ -0,0 +1,418 @@ +"""Test script for Video-Depth-Anything preprocessing with LongLive V2V pipeline. + +This script demonstrates using Video-Depth-Anything to generate depth maps +from input video, which are then used as VACE depth conditioning for the +LongLive pipeline (V2V workflow). + +The flow is: Input Video -> Video-Depth-Anything -> Depth Maps -> LongLive V2V + +Usage: + python -m scope.core.pipelines.longlive.test_video_depth + +Configuration: + Edit the CONFIG dictionary below to set your input video and output paths. +""" + +import time +from pathlib import Path + +import numpy as np +import torch +from diffusers.utils import export_to_video +from omegaconf import OmegaConf + +from scope.core.config import get_model_file_path, get_models_dir +from scope.core.preprocessors import VideoDepthAnything + +from ..video import load_video +from .pipeline import LongLivePipeline + +# ============================= CONFIGURATION ============================= + +CONFIG = { + # ===== MODE SELECTION ===== + # "depth_only": Generate NEW video guided by depth structure (T2V + depth) + # "v2v_depth": Transform input video with depth guidance (V2V + depth) + "mode": "v2v_depth", # Options: "depth_only" or "v2v_depth" + # ===== INPUT ===== + # Path to input video for V2V processing + "input_video": "frontend/public/assets/test.mp4", + # ===== VIDEO-DEPTH-ANYTHING SETTINGS ===== + # Encoder size: "vits" (small/fast), "vitb" (base), "vitl" (large/best quality) + "depth_encoder": "vitl", + # Number of frames to process per batch (reduce if OOM) + "depth_batch_size": 32, + # ===== GENERATION PARAMETERS ===== + # Prompt for video generation (describes the output style/content) + "prompt": "batman fighting crime", + "num_chunks": 3, # Number of generation chunks + "frames_per_chunk": 12, # Frames per chunk (12 = 3 latent * 4 temporal upsample) + "height": 512, + "width": 512, + "vace_context_scale": 0.7, # VACE conditioning strength (0.0-1.0) + # ===== V2V MODE SETTINGS (only used when mode="v2v_depth") ===== + # Noise scale for V2V (0.0-1.0, higher = more transformation) + "noise_scale": 0.7, + "noise_controller": True, + # ===== OUTPUT ===== + "output_dir": "video_depth_tests", # path/to/output_dir + "save_depth_video": True, # Save the depth map visualization +} + +# ========================= END CONFIGURATION ========================= + + +def resolve_path(path_str: str, relative_to: Path) -> Path: + """Resolve path relative to a base directory or as absolute.""" + path = Path(path_str) + if path.is_absolute(): + return path + return (relative_to / path).resolve() + + +def load_video_frames( + video_path: Path, + target_height: int, + target_width: int, + max_frames: int = None, +) -> np.ndarray: + """Load video frames from file. + + Args: + video_path: Path to video file + target_height: Target height for resizing + target_width: Target width for resizing + max_frames: Maximum number of frames to load (None = all frames) + + Returns: + Numpy array of shape [F, H, W, C] with values in [0, 255] + """ + print(f"Loading video: {video_path}") + + # Use load_video which returns [C, T, H, W] tensor in [0, 255] when normalize=False + video_tensor = load_video( + str(video_path), + num_frames=max_frames, + resize_hw=(target_height, target_width), + normalize=False, + ) + + # Convert from [C, T, H, W] to [F, H, W, C] + video_tensor = video_tensor.permute(1, 2, 3, 0) + + # Convert to numpy uint8 + frames_array = video_tensor.numpy().astype(np.uint8) + + print(f"Loaded {frames_array.shape[0]} frames at {target_height}x{target_width}") + return frames_array + + +def extract_depth_chunk( + depth_video: torch.Tensor, + chunk_index: int, + frames_per_chunk: int, +) -> torch.Tensor: + """Extract a chunk from depth video tensor. + + Args: + depth_video: Depth video tensor [1, 3, F, H, W] + chunk_index: Chunk index + frames_per_chunk: Number of frames per chunk + + Returns: + Chunk tensor [1, 3, frames_per_chunk, H, W] + """ + start_idx = chunk_index * frames_per_chunk + end_idx = start_idx + frames_per_chunk + + # Clamp to video length + total_frames = depth_video.shape[2] + end_idx = min(end_idx, total_frames) + + chunk = depth_video[:, :, start_idx:end_idx, :, :] + + # Pad if needed + if chunk.shape[2] < frames_per_chunk: + padding = frames_per_chunk - chunk.shape[2] + pad_frames = chunk[:, :, -1:, :, :].repeat(1, 1, padding, 1, 1) + chunk = torch.cat([chunk, pad_frames], dim=2) + + return chunk + + +def extract_video_chunk( + video_frames: list[torch.Tensor], + chunk_index: int, + frames_per_chunk: int, +) -> list[torch.Tensor]: + """Extract a chunk of frames from video. + + Args: + video_frames: List of video frames [H, W, C] + chunk_index: Chunk index + frames_per_chunk: Number of frames per chunk + + Returns: + List of frames for this chunk, each with shape [1, H, W, C] + """ + start_idx = chunk_index * frames_per_chunk + end_idx = start_idx + frames_per_chunk + + # Clamp to video length + end_idx = min(end_idx, len(video_frames)) + + chunk = video_frames[start_idx:end_idx] + + # Pad if needed + while len(chunk) < frames_per_chunk: + chunk.append(chunk[-1]) + + # Add temporal dimension to each frame: [H, W, C] -> [1, H, W, C] + chunk = [frame.unsqueeze(0) for frame in chunk] + + return chunk + + +def main(): + print("=" * 80) + print(" Video-Depth-Anything + LongLive Test") + print("=" * 80) + + config = CONFIG + mode = config.get("mode", "depth_only") + use_v2v = mode == "v2v_depth" + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Setup paths + script_dir = Path(__file__).parent + project_root = script_dir.parent.parent.parent.parent.parent + output_dir = resolve_path(config["output_dir"], script_dir) + output_dir.mkdir(exist_ok=True, parents=True) + + print(f"\nConfiguration:") + print(f" Mode: {mode}") + if use_v2v: + print(f" → V2V + Depth: Transform input video with depth guidance") + else: + print(f" → Depth-only: Generate new video guided by depth structure") + print(f" Input video: {config['input_video']}") + print(f" Depth encoder: {config['depth_encoder']}") + print(f" Resolution: {config['height']}x{config['width']}") + print(f" Chunks: {config['num_chunks']} x {config['frames_per_chunk']} frames") + print(f" VACE scale: {config['vace_context_scale']}") + if use_v2v: + print(f" Noise scale: {config['noise_scale']}") + print(f" Output: {output_dir}") + print(f" Device: {device}\n") + + # ===== Step 1: Load input video ===== + print("=" * 40) + print("Step 1: Loading input video") + print("=" * 40) + + input_video_path = resolve_path(config["input_video"], project_root) + if not input_video_path.exists(): + raise FileNotFoundError(f"Input video not found: {input_video_path}") + + total_frames = config["num_chunks"] * config["frames_per_chunk"] + video_frames_np = load_video_frames( + input_video_path, + config["height"], + config["width"], + max_frames=total_frames, + ) + + # Convert to list of tensors for pipeline + video_frames_list = [ + torch.from_numpy(video_frames_np[i]) for i in range(video_frames_np.shape[0]) + ] + print(f"Prepared {len(video_frames_list)} frames for processing\n") + + # ===== Step 2: Run Video-Depth-Anything ===== + print("=" * 40) + print("Step 2: Running Video-Depth-Anything") + print("=" * 40) + + depth_start = time.time() + + # Initialize and load model + depth_model = VideoDepthAnything( + encoder=config["depth_encoder"], + device=device, + dtype=torch.float16, + ) + depth_model.load_model() + + # Run depth estimation + depth_video = depth_model.process_video_for_vace( + video_frames_np, + config["height"], + config["width"], + ) + + depth_time = time.time() - depth_start + print(f"Depth estimation completed in {depth_time:.2f}s") + print(f"Depth tensor shape: {depth_video.shape}\n") + + # Save depth visualization if requested + if config["save_depth_video"]: + depth_vis = depth_video[0, 0].cpu().numpy() # [F, H, W] + depth_vis = ((depth_vis + 1.0) / 2.0 * 255).astype(np.uint8) + depth_vis_rgb = np.stack([depth_vis, depth_vis, depth_vis], axis=-1) + depth_vis_path = output_dir / "depth_visualization.mp4" + export_to_video(depth_vis_rgb / 255.0, depth_vis_path, fps=16) + print(f"Saved depth visualization: {depth_vis_path}\n") + + # Offload depth model to free GPU memory for the pipeline + depth_model.offload() + del depth_model + torch.cuda.empty_cache() + + # ===== Step 3: Initialize LongLive pipeline ===== + print("=" * 40) + print("Step 3: Initializing LongLive pipeline") + print("=" * 40) + + vace_path = str( + get_model_file_path("Wan2.1-VACE-1.3B/diffusion_pytorch_model.safetensors") + ) + + pipeline_config = OmegaConf.create( + { + "model_dir": str(get_models_dir()), + "generator_path": str( + get_model_file_path("LongLive-1.3B/models/longlive_base.pt") + ), + "lora_path": str(get_model_file_path("LongLive-1.3B/models/lora.pt")), + "vace_path": vace_path, + "text_encoder_path": str( + get_model_file_path( + "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ) + ), + "tokenizer_path": str( + get_model_file_path("Wan2.1-T2V-1.3B/google/umt5-xxl") + ), + "model_config": OmegaConf.load(script_dir / "model.yaml"), + "height": config["height"], + "width": config["width"], + } + ) + + # Set vace_in_dim for depth mode + pipeline_config.model_config.base_model_kwargs = ( + pipeline_config.model_config.base_model_kwargs or {} + ) + pipeline_config.model_config.base_model_kwargs["vace_in_dim"] = 96 + + pipeline = LongLivePipeline(pipeline_config, device=device, dtype=torch.bfloat16) + print("Pipeline initialized\n") + + # ===== Step 4: Generate video ===== + print("=" * 40) + mode_desc = "V2V + Depth" if use_v2v else "Depth-only (T2V)" + print(f"Step 4: Generating video ({mode_desc})") + print("=" * 40) + + outputs = [] + latency_measures = [] + fps_measures = [] + + frames_per_chunk = config["frames_per_chunk"] + + for chunk_index in range(config["num_chunks"]): + start_time = time.time() + + # Get depth chunk for this iteration + depth_chunk = extract_depth_chunk( + depth_video, + chunk_index, + frames_per_chunk, + ) + + # Prepare pipeline kwargs + kwargs = { + "prompts": [{"text": config["prompt"], "weight": 100}], + "vace_input_frames": depth_chunk, # Depth conditioning from Video-Depth-Anything + "vace_context_scale": config["vace_context_scale"], + "init_cache": chunk_index == 0, # Reset cache on first chunk + } + + # Add V2V parameters if in v2v_depth mode + if use_v2v: + video_chunk = extract_video_chunk( + video_frames_list, + chunk_index, + frames_per_chunk, + ) + kwargs["video"] = video_chunk + kwargs["noise_scale"] = config["noise_scale"] + kwargs["noise_controller"] = config["noise_controller"] + + print( + f"Chunk {chunk_index}: {'V2V + ' if use_v2v else ''}Depth mode, " + f"depth shape={depth_chunk.shape}" + ) + + # Generate + output = pipeline(**kwargs) + + # Metrics + num_output_frames, _, _, _ = output.shape + latency = time.time() - start_time + fps = num_output_frames / latency + + print( + f"Chunk {chunk_index}: Generated {num_output_frames} frames, " + f"latency={latency:.2f}s, fps={fps:.2f}" + ) + + latency_measures.append(latency) + fps_measures.append(fps) + outputs.append(output.detach().cpu()) + + # Concatenate outputs + output_video = torch.concat(outputs) + print(f"\nFinal output shape: {output_video.shape}") + + # Save output video + output_video_np = output_video.contiguous().numpy() + output_video_np = np.clip(output_video_np, 0.0, 1.0) + + output_filename = f"output_{mode}.mp4" + output_path = output_dir / output_filename + export_to_video(output_video_np, output_path, fps=16) + print(f"\nSaved output: {output_path}") + + # Also save the original input for comparison + input_save_path = output_dir / "input_video.mp4" + input_vis = video_frames_np[:output_video.shape[0]].astype(np.float32) / 255.0 + export_to_video(input_vis, input_save_path, fps=16) + print(f"Saved input: {input_save_path}") + + # ===== Statistics ===== + print("\n" + "=" * 40) + print("Performance Statistics") + print("=" * 40) + print( + f"Depth estimation: {depth_time:.2f}s " + f"({total_frames / depth_time:.2f} fps)" + ) + print( + f"Generation - Avg latency: {sum(latency_measures) / len(latency_measures):.2f}s, " + f"Avg FPS: {sum(fps_measures) / len(fps_measures):.2f}" + ) + print(f"Total depth + generation time: {depth_time + sum(latency_measures):.2f}s") + + print("\n" + "=" * 80) + print(" Test Complete") + print("=" * 80) + print(f"\nResults saved to: {output_dir}") + print(f" - Input video: input_video.mp4") + print(f" - Depth maps: depth_visualization.mp4") + print(f" - Output: {output_filename}") + + +if __name__ == "__main__": + main() diff --git a/src/scope/core/pipelines/registry.py b/src/scope/core/pipelines/registry.py index 34b28cbb2..c9a33e249 100644 --- a/src/scope/core/pipelines/registry.py +++ b/src/scope/core/pipelines/registry.py @@ -132,6 +132,7 @@ def _register_pipelines(): ), ("memflow", ".memflow.pipeline", "MemFlowPipeline"), ("passthrough", ".passthrough.pipeline", "PassthroughPipeline"), + ("depthanything", ".depthanything.pipeline", "DepthAnythingPipeline"), ] # Try to import and register each pipeline diff --git a/src/scope/core/pipelines/wan2_1/vace/utils/encoding.py b/src/scope/core/pipelines/wan2_1/vace/utils/encoding.py index 66d187b1f..35fef9831 100644 --- a/src/scope/core/pipelines/wan2_1/vace/utils/encoding.py +++ b/src/scope/core/pipelines/wan2_1/vace/utils/encoding.py @@ -60,6 +60,20 @@ def vace_encode_frames( reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks, strict=False)] inactive_stacked = torch.stack(inactive, dim=0).to(dtype=vae_dtype) reactive_stacked = torch.stack(reactive, dim=0).to(dtype=vae_dtype) + + # Validate shapes before encoding + # VAE stream_encode processes frames in chunks of 4, so we need at least 4 frames + if inactive_stacked.shape[2] < 4: + raise ValueError( + f"inactive_stacked has {inactive_stacked.shape[2]} frames, but VAE stream_encode requires at least 4 frames. " + f"Shape: {inactive_stacked.shape}" + ) + if reactive_stacked.shape[2] < 4: + raise ValueError( + f"reactive_stacked has {reactive_stacked.shape[2]} frames, but VAE stream_encode requires at least 4 frames. " + f"Shape: {reactive_stacked.shape}" + ) + # Use cache=True to ensure cache consistency for both inactive and reactive portions inactive_out = vae.encode_to_latent(inactive_stacked, use_cache=True) reactive_out = vae.encode_to_latent(reactive_stacked, use_cache=True) diff --git a/src/scope/core/plugins/hookspecs.py b/src/scope/core/plugins/hookspecs.py index 62ce4f672..364cf7c85 100644 --- a/src/scope/core/plugins/hookspecs.py +++ b/src/scope/core/plugins/hookspecs.py @@ -15,10 +15,17 @@ def register_pipelines(self, register): Args: register: Callback to register pipeline classes. - Usage: register(PipelineClass) + Usage: register(PipelineClass) or register(PipelineClass, also_preprocessor=True) + + Note: + Preprocessors are pipelines that implement the Pipeline interface + and can be used to preprocess video input. They are registered + through the same hook as regular pipelines. To make a pipeline + also available as a preprocessor, pass also_preprocessor=True. Example: @scope.core.hookimpl def register_pipelines(register): - register(MyPipeline) + register(MyPipeline) # Regular pipeline + register(MyPreprocessor, also_preprocessor=True) # Pipeline that's also a preprocessor """ diff --git a/src/scope/core/plugins/manager.py b/src/scope/core/plugins/manager.py index 9a724a42f..b34c41d6e 100644 --- a/src/scope/core/plugins/manager.py +++ b/src/scope/core/plugins/manager.py @@ -25,12 +25,22 @@ def register_plugin_pipelines(registry): Args: registry: PipelineRegistry to register pipelines with """ + from scope.core.preprocessors.registry import PreprocessorRegistry - def register_callback(pipeline_class): - """Callback function passed to plugins.""" + def register_callback(pipeline_class, also_preprocessor=False): + """Callback function passed to plugins. + + Args: + pipeline_class: Pipeline class to register + also_preprocessor: If True, also register as a preprocessor + """ config_class = pipeline_class.get_config_class() pipeline_id = config_class.pipeline_id registry.register(pipeline_id, pipeline_class) logger.info(f"Registered plugin pipeline: {pipeline_id}") + if also_preprocessor: + PreprocessorRegistry.register(pipeline_id, pipeline_class) + logger.info(f"Registered plugin preprocessor: {pipeline_id}") + pm.hook.register_pipelines(register=register_callback) diff --git a/src/scope/core/preprocessors/__init__.py b/src/scope/core/preprocessors/__init__.py new file mode 100644 index 000000000..44c70d018 --- /dev/null +++ b/src/scope/core/preprocessors/__init__.py @@ -0,0 +1,25 @@ +"""Preprocessors for video processing pipelines. + +This module contains external model wrappers that can be used to preprocess +video input before it enters the main diffusion pipeline. +""" + +from .async_preprocessor import AsyncPreprocessorClient, PreprocessorResult +from .registry import PreprocessorRegistry + +# Backward compatibility aliases +from .async_preprocessor import AsyncPreprocessorClient as DepthPreprocessorClient +from .async_preprocessor import PreprocessorResult as DepthResult + +# Re-export VideoDepthAnything from its new location in the depthanything pipeline +# for backwards compatibility +from scope.core.pipelines.depthanything import VideoDepthAnythingModel as VideoDepthAnything + +__all__ = [ + "VideoDepthAnything", + "AsyncPreprocessorClient", + "PreprocessorResult", + "PreprocessorRegistry", + "DepthPreprocessorClient", + "DepthResult", +] diff --git a/src/scope/core/preprocessors/async_depth_preprocessor.py b/src/scope/core/preprocessors/async_depth_preprocessor.py new file mode 100644 index 000000000..751e11db9 --- /dev/null +++ b/src/scope/core/preprocessors/async_depth_preprocessor.py @@ -0,0 +1,598 @@ +"""Asynchronous preprocessor using ZeroMQ for parallel processing. + +This module provides a preprocessing solution that runs in a completely +separate process (not subprocess), allowing the main pipeline to process frames +in parallel with preprocessing while maintaining complete CUDA context isolation. + +Architecture: + - preprocessor_worker_process.py: Runs as an independent process via subprocess.Popen, + loads the preprocessor pipeline, and processes frames received via ZeroMQ + - AsyncPreprocessorClient: Runs in the main process, sends frames to the worker + and receives preprocessed results asynchronously + +Usage: + # Start the worker process + client = AsyncPreprocessorClient(preprocessor_type="depthanything", encoder="vits") + client.start() + + # Send frames for processing (non-blocking) + client.submit_frames(frames, target_height=512, target_width=512) + + # Get processed results (returns latest available) + result = client.get_latest_result() + + # Stop the worker + client.stop() +""" + +import logging +import pickle +import signal +import subprocess +import sys +import tempfile +import threading +import time +from collections import deque +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np + +logger = logging.getLogger(__name__) + +# ZeroMQ socket configuration +ZMQ_TIMEOUT_MS = 1000 # 1 second timeout for recv +ZMQ_HWM = 100 # High water mark for socket buffers (number of messages) + + +def _find_free_port() -> int: + """Find an available port for ZeroMQ socket.""" + import socket + + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("", 0)) + s.listen(1) + port = s.getsockname()[1] + return port + + +@dataclass +class PreprocessorResult: + """Container for preprocessor result.""" + + chunk_id: int + data: Any # torch.Tensor [1, C, F, H, W] in [-1, 1] + timestamp: float + + + + +class AsyncPreprocessorClient: + """Client for asynchronous preprocessing. + + This class manages communication with the preprocessor worker process and provides + a non-blocking interface for submitting frames and retrieving preprocessed results. + + The worker runs as a completely separate process (via subprocess.Popen) for + complete CUDA context isolation from the main pipeline. + + The client maintains a result buffer that stores the most recent preprocessor results, + allowing the main pipeline to retrieve preprocessed frames without blocking on computation. + + Args: + preprocessor_type: Type of preprocessor ("depthanything", "passthrough", etc.) + encoder: For depthanything, encoder size ("vits", "vitb", or "vitl"). Ignored for other types. + input_port: ZeroMQ port for sending frames to worker + output_port: ZeroMQ port for receiving results from worker + result_buffer_size: Maximum number of results to buffer + + Example: + client = AsyncPreprocessorClient(preprocessor_type="depthanything", encoder="vits") + client.start() + + # Submit frames for processing (returns immediately) + client.submit_frames(frames, target_height=512, target_width=512) + + # Later, retrieve the result (non-blocking) + result = client.get_latest_result() + if result is not None: + preprocessed_tensor = result.data # Use in pipeline + + client.stop() + """ + + def __init__( + self, + preprocessor_type: str, + encoder: str | None = None, + input_port: int | None = None, + output_port: int | None = None, + result_buffer_size: int = 16, # Increased from 4 for better buffering + ): + self.preprocessor_type = preprocessor_type + self.encoder = encoder or "vits" # Default encoder for depthanything + # Ports will be dynamically allocated in start() if not provided + self.input_port = input_port + self.output_port = output_port + self.result_buffer_size = result_buffer_size + + # Process management (using subprocess.Popen for complete process isolation) + self._worker_process: subprocess.Popen | None = None + self._ready_file: Path | None = None # File-based ready signal + self._temp_dir: tempfile.TemporaryDirectory | None = None + + # ZeroMQ sockets (initialized in start()) + self._context = None + self._input_socket = None + self._output_socket = None + + # Result buffer - stores PreprocessorResult with torch tensors (converted in receiver thread) + self._result_buffer: deque[PreprocessorResult] = deque(maxlen=result_buffer_size) + self._result_lock = threading.Lock() + + # Receiver thread + self._receiver_thread: threading.Thread | None = None + self._receiver_running = False + + # Tracking + self._pending_chunks: set[int] = set() + self._pending_lock = threading.Lock() + self._next_chunk_id = 0 + + self._started = False + + # Throttling: track consumption rate to slow down worker when buffer is full + self._last_result_consumed_time = 0.0 + self._target_fps: float | None = None # Target FPS from main pipeline + self._throttle_lock = threading.Lock() + + def start(self, timeout: float = 60.0) -> bool: + """Start the preprocessor worker process and connect sockets. + + The worker is started as a completely separate process using subprocess.Popen, + ensuring complete CUDA context isolation from the main pipeline. + + Args: + timeout: Maximum time to wait for worker to be ready (seconds) + + Returns: + True if started successfully, False otherwise + """ + if self._started: + logger.warning("AsyncPreprocessorClient already started") + return True + + logger.info(f"Starting AsyncPreprocessorClient for {self.preprocessor_type}...") + + # Dynamically allocate ports if not provided + if self.input_port is None: + self.input_port = _find_free_port() + if self.output_port is None: + self.output_port = _find_free_port() + + logger.info( + f"Using ports: input={self.input_port}, output={self.output_port}" + ) + + # Create temporary directory for ready file + self._temp_dir = tempfile.TemporaryDirectory(prefix="preprocessor_worker_") + self._ready_file = Path(self._temp_dir.name) / "ready" + + # Start the worker as a completely separate process using subprocess.Popen + # This ensures complete CUDA context isolation + worker_module = "scope.core.preprocessors.preprocessor_worker_process" + cmd = [ + sys.executable, "-m", worker_module, + "--preprocessor-type", self.preprocessor_type, + "--input-port", str(self.input_port), + "--output-port", str(self.output_port), + "--ready-file", str(self._ready_file), + ] + # Add encoder argument only for depthanything + if self.preprocessor_type == "depthanything": + cmd.extend(["--encoder", self.encoder]) + + logger.info(f"Starting separate worker process: {' '.join(cmd)}") + + # Start the worker process + self._worker_process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, # Line buffered + ) + + # Start a thread to forward worker stdout to our logger + self._stdout_thread = threading.Thread( + target=self._forward_stdout, daemon=True + ) + self._stdout_thread.start() + + logger.info(f"Worker process started (PID={self._worker_process.pid})") + + # Wait for worker to be ready (file-based signaling) + logger.info(f"Waiting for preprocessor worker to initialize (timeout={timeout}s)...") + start_wait = time.time() + + while time.time() - start_wait < timeout: + # Check if process died + if self._worker_process.poll() is not None: + wait_time = time.time() - start_wait + logger.error( + f"Worker process died during startup (exit code: {self._worker_process.returncode}) " + f"after {wait_time:.1f}s" + ) + self.stop() + return False + + # Check if ready file exists + if self._ready_file.exists(): + break + + time.sleep(0.1) + else: + wait_time = time.time() - start_wait + logger.error(f"Preprocessor worker failed to start within timeout ({wait_time:.1f}s)") + self.stop() + return False + + wait_time = time.time() - start_wait + logger.info(f"Preprocessor worker ready after {wait_time:.1f}s, connecting sockets...") + + # Setup ZeroMQ client sockets + import zmq + + self._context = zmq.Context() + + # Push socket for sending frames + self._input_socket = self._context.socket(zmq.PUSH) + self._input_socket.setsockopt(zmq.SNDHWM, ZMQ_HWM) # Set high water mark + self._input_socket.connect(f"tcp://localhost:{self.input_port}") + + # Pull socket for receiving results + self._output_socket = self._context.socket(zmq.PULL) + self._output_socket.setsockopt(zmq.RCVHWM, ZMQ_HWM) # Set high water mark + self._output_socket.connect(f"tcp://localhost:{self.output_port}") + self._output_socket.setsockopt(zmq.RCVTIMEO, ZMQ_TIMEOUT_MS) + + # Start receiver thread + self._receiver_running = True + self._receiver_thread = threading.Thread( + target=self._receiver_loop, daemon=True + ) + self._receiver_thread.start() + + self._started = True + logger.info("AsyncPreprocessorClient started successfully") + return True + + def _forward_stdout(self): + """Forward worker stdout to our logger.""" + try: + if self._worker_process and self._worker_process.stdout: + for line in self._worker_process.stdout: + line = line.rstrip() + if line: + logger.info(f"[DepthWorker] {line}") + except Exception: + pass # Process closed + + def stop(self): + """Stop the preprocessor worker and clean up resources.""" + if not self._started and self._worker_process is None: + return + + logger.info("Stopping AsyncPreprocessorClient...") + + # Stop receiver thread + self._receiver_running = False + if self._receiver_thread is not None: + self._receiver_thread.join(timeout=2.0) + self._receiver_thread = None + + # Clean up ZeroMQ first (so worker recv() fails and can notice SIGTERM) + if self._input_socket is not None: + self._input_socket.close() + self._input_socket = None + if self._output_socket is not None: + self._output_socket.close() + self._output_socket = None + if self._context is not None: + self._context.term() + self._context = None + + # Signal worker to stop using SIGTERM + if self._worker_process is not None: + if self._worker_process.poll() is None: # Still running + logger.info(f"Sending SIGTERM to worker process (PID={self._worker_process.pid})...") + try: + self._worker_process.send_signal(signal.SIGTERM) + except OSError: + pass # Process already dead + + # Wait for graceful shutdown + try: + self._worker_process.wait(timeout=5.0) + logger.info(f"Worker process exited with code {self._worker_process.returncode}") + except subprocess.TimeoutExpired: + logger.warning("Worker process did not exit gracefully, killing...") + self._worker_process.kill() + try: + self._worker_process.wait(timeout=2.0) + except subprocess.TimeoutExpired: + pass + + self._worker_process = None + + # Clean up stdout forwarding thread + if hasattr(self, '_stdout_thread') and self._stdout_thread is not None: + self._stdout_thread.join(timeout=1.0) + self._stdout_thread = None + + # Clean up temp directory + if self._temp_dir is not None: + try: + self._temp_dir.cleanup() + except Exception: + pass + self._temp_dir = None + self._ready_file = None + + # Clear buffers + with self._result_lock: + self._result_buffer.clear() + with self._pending_lock: + self._pending_chunks.clear() + + self._started = False + logger.info("AsyncPreprocessorClient stopped") + + def submit_frames( + self, + frames: "np.ndarray | list", + target_height: int, + target_width: int, + ) -> int | None: + """Submit frames for preprocessing. + + This method is non-blocking and returns immediately after queuing the frames. + The chunk_id can be used to track which result corresponds to which input. + + Implements throttling: if buffer is nearly full, skips submission to create + backpressure and reduce GPU contention with main pipeline. + + Args: + frames: Video frames as numpy array [F, H, W, C] in [0, 255] range, + or list of tensors that will be converted + target_height: Target output height for preprocessed frames + target_width: Target output width for preprocessed frames + + Returns: + chunk_id: Unique identifier for this submission, or None if skipped due to throttling + + Raises: + RuntimeError: If client is not started + """ + if not self._started: + raise RuntimeError("AsyncPreprocessorClient not started") + + import torch + + # Throttling: skip submission if buffer is getting full + # This reduces GPU contention with main pipeline + with self._result_lock: + buffer_size = len(self._result_buffer) + + # Skip if buffer is more than half full (preprocessor is ahead of consumer) + if buffer_size > self.result_buffer_size // 2: + logger.debug( + f"Throttling preprocessor submission, buffer {buffer_size}/{self.result_buffer_size}" + ) + return None + + # Convert list of tensors to numpy array if needed + if isinstance(frames, list): + # Assume list of tensors, each (1, H, W, C) + stacked = torch.cat(frames, dim=0) # [F, H, W, C] + frames_np = stacked.cpu().numpy() + elif isinstance(frames, torch.Tensor): + frames_np = frames.cpu().numpy() + else: + frames_np = frames + + # Ensure correct dtype + if frames_np.dtype != np.uint8: + if frames_np.max() <= 1.0: + frames_np = (frames_np * 255).astype(np.uint8) + else: + frames_np = frames_np.astype(np.uint8) + + # Assign chunk ID + chunk_id = self._next_chunk_id + self._next_chunk_id += 1 + + # Track pending chunk + with self._pending_lock: + self._pending_chunks.add(chunk_id) + + # Send to worker + data = { + "chunk_id": chunk_id, + "frames": frames_np, + "target_height": target_height, + "target_width": target_width, + } + self._input_socket.send(pickle.dumps(data)) + + logger.debug( + f"Submitted chunk {chunk_id}, frames shape: {frames_np.shape}, " + f"target: {target_height}x{target_width}" + ) + return chunk_id + + def get_result(self, wait: bool = False, timeout: float = 5.0) -> PreprocessorResult | None: + """Get the latest preprocessor result. + + Args: + wait: If True, wait for a result if none available + timeout: Maximum time to wait (seconds) if wait=True + + Returns: + PreprocessorResult with preprocessed tensor, or None if no result available + """ + if wait: + start_time = time.time() + while time.time() - start_time < timeout: + with self._result_lock: + if self._result_buffer: + # Buffer now stores PreprocessorResult directly (tensor already converted) + result = self._result_buffer.popleft() + self._last_result_consumed_time = time.time() + return result + time.sleep(0.01) + return None + + with self._result_lock: + if self._result_buffer: + # Buffer now stores PreprocessorResult directly (tensor already converted) + result = self._result_buffer.popleft() + self._last_result_consumed_time = time.time() + return result + return None + + def get_latest_result(self) -> PreprocessorResult | None: + """Get the most recent preprocessor result, discarding older ones. + + Returns: + Most recent PreprocessorResult, or None if no results available + """ + with self._result_lock: + if not self._result_buffer: + return None + + # Get the latest result (tensor already converted in receiver thread) + result = self._result_buffer[-1] + + # Clear all older results + self._result_buffer.clear() + + self._last_result_consumed_time = time.time() + return result + + # Backward compatibility aliases + def get_depth_result(self, wait: bool = False, timeout: float = 5.0) -> PreprocessorResult | None: + """Backward compatibility alias for get_result().""" + return self.get_result(wait, timeout) + + def get_latest_depth_result(self) -> PreprocessorResult | None: + """Backward compatibility alias for get_latest_result().""" + return self.get_latest_result() + + def has_pending_results(self) -> bool: + """Check if there are pending chunks being processed.""" + with self._pending_lock: + return len(self._pending_chunks) > 0 + + def get_buffer_size(self) -> int: + """Get the number of results currently in buffer.""" + with self._result_lock: + return len(self._result_buffer) + + def is_running(self) -> bool: + """Check if the client is running.""" + return ( + self._started + and self._worker_process is not None + and self._worker_process.poll() is None # poll() returns None if still running + ) + + def set_target_fps(self, fps: float | None): + """Set target FPS to throttle depth processing to match main pipeline. + + When the depth worker is running much faster than the main pipeline, + this creates backpressure to avoid wasting GPU resources. + + Args: + fps: Target FPS (e.g., from main pipeline), or None to disable throttling + """ + with self._throttle_lock: + self._target_fps = fps + if fps is not None: + logger.debug(f"Depth throttle target set to {fps:.1f} FPS") + + def _receiver_loop(self): + """Background thread that receives results from the worker. + + Performs numpy→torch conversion here (async) to avoid blocking main thread. + Also implements throttling by sleeping when buffer is full. + """ + import torch + + logger.info("Depth result receiver thread started") + + while self._receiver_running: + try: + # Throttle: if buffer is getting full, slow down to match consumption rate + with self._result_lock: + buffer_size = len(self._result_buffer) + + if buffer_size >= self.result_buffer_size - 1: + # Buffer nearly full, sleep to allow consumer to catch up + # This creates backpressure to the worker + time.sleep(0.05) + continue + + # Receive result (with timeout to allow checking running flag) + import zmq + + message = self._output_socket.recv() + result = pickle.loads(message) + + chunk_id = result["chunk_id"] + + # Remove from pending + with self._pending_lock: + self._pending_chunks.discard(chunk_id) + + # Convert numpy to torch tensor HERE (async, not in main thread) + # This is the expensive operation we want to do in background + preprocessed_tensor = torch.from_numpy(result["data"]) + + # Pin memory for faster GPU transfer when .to(device) is called later + # This makes the subsequent cuda() call non-blocking and faster + if torch.cuda.is_available(): + preprocessed_tensor = preprocessed_tensor.pin_memory() + + # Create PreprocessorResult with tensor already converted + preprocessor_result = PreprocessorResult( + chunk_id=chunk_id, + data=preprocessed_tensor, + timestamp=result["timestamp"], + ) + + # Add to result buffer + with self._result_lock: + self._result_buffer.append(preprocessor_result) + + logger.debug( + f"Received preprocessor result for chunk {chunk_id}, " + f"buffer size: {len(self._result_buffer)}" + ) + + except Exception as e: + # zmq.error.Again is expected on timeout + if "Again" not in str(type(e)): + logger.error(f"Error in receiver loop: {e}") + continue + + logger.info("Preprocessor result receiver thread stopped") + + def __enter__(self): + self.start() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop() diff --git a/src/scope/core/preprocessors/async_preprocessor.py b/src/scope/core/preprocessors/async_preprocessor.py new file mode 100644 index 000000000..b57e94493 --- /dev/null +++ b/src/scope/core/preprocessors/async_preprocessor.py @@ -0,0 +1,598 @@ +"""Asynchronous preprocessor using ZeroMQ for parallel processing. + +This module provides a preprocessing solution that runs in a completely +separate process (not subprocess), allowing the main pipeline to process frames +in parallel with preprocessing while maintaining complete CUDA context isolation. + +Architecture: + - preprocessor_worker_process.py: Runs as an independent process via subprocess.Popen, + loads the preprocessor pipeline, and processes frames received via ZeroMQ + - AsyncPreprocessorClient: Runs in the main process, sends frames to the worker + and receives preprocessed results asynchronously + +Usage: + # Start the worker process + client = AsyncPreprocessorClient(preprocessor_type="depthanything", encoder="vits") + client.start() + + # Send frames for processing (non-blocking) + client.submit_frames(frames, target_height=512, target_width=512) + + # Get processed results (returns latest available) + result = client.get_latest_result() + + # Stop the worker + client.stop() +""" + +import logging +import pickle +import signal +import subprocess +import sys +import tempfile +import threading +import time +from collections import deque +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np + +logger = logging.getLogger(__name__) + +# ZeroMQ socket configuration +ZMQ_TIMEOUT_MS = 1000 # 1 second timeout for recv +ZMQ_HWM = 100 # High water mark for socket buffers (number of messages) + + +def _find_free_port() -> int: + """Find an available port for ZeroMQ socket.""" + import socket + + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("", 0)) + s.listen(1) + port = s.getsockname()[1] + return port + + +@dataclass +class PreprocessorResult: + """Container for preprocessor result.""" + + chunk_id: int + data: Any # torch.Tensor [1, C, F, H, W] in [-1, 1] + timestamp: float + + + + +class AsyncPreprocessorClient: + """Client for asynchronous preprocessing. + + This class manages communication with the preprocessor worker process and provides + a non-blocking interface for submitting frames and retrieving preprocessed results. + + The worker runs as a completely separate process (via subprocess.Popen) for + complete CUDA context isolation from the main pipeline. + + The client maintains a result buffer that stores the most recent preprocessor results, + allowing the main pipeline to retrieve preprocessed frames without blocking on computation. + + Args: + preprocessor_type: Type of preprocessor ("depthanything", "passthrough", etc.) + encoder: For depthanything, encoder size ("vits", "vitb", or "vitl"). Ignored for other types. + input_port: ZeroMQ port for sending frames to worker + output_port: ZeroMQ port for receiving results from worker + result_buffer_size: Maximum number of results to buffer + + Example: + client = AsyncPreprocessorClient(preprocessor_type="depthanything", encoder="vits") + client.start() + + # Submit frames for processing (returns immediately) + client.submit_frames(frames, target_height=512, target_width=512) + + # Later, retrieve the result (non-blocking) + result = client.get_latest_result() + if result is not None: + preprocessed_tensor = result.data # Use in pipeline + + client.stop() + """ + + def __init__( + self, + preprocessor_type: str, + encoder: str | None = None, + input_port: int | None = None, + output_port: int | None = None, + result_buffer_size: int = 16, # Increased from 4 for better buffering + ): + self.preprocessor_type = preprocessor_type + self.encoder = encoder or "vits" # Default encoder for depthanything + # Ports will be dynamically allocated in start() if not provided + self.input_port = input_port + self.output_port = output_port + self.result_buffer_size = result_buffer_size + + # Process management (using subprocess.Popen for complete process isolation) + self._worker_process: subprocess.Popen | None = None + self._ready_file: Path | None = None # File-based ready signal + self._temp_dir: tempfile.TemporaryDirectory | None = None + + # ZeroMQ sockets (initialized in start()) + self._context = None + self._input_socket = None + self._output_socket = None + + # Result buffer - stores PreprocessorResult with torch tensors (converted in receiver thread) + self._result_buffer: deque[PreprocessorResult] = deque(maxlen=result_buffer_size) + self._result_lock = threading.Lock() + + # Receiver thread + self._receiver_thread: threading.Thread | None = None + self._receiver_running = False + + # Tracking + self._pending_chunks: set[int] = set() + self._pending_lock = threading.Lock() + self._next_chunk_id = 0 + + self._started = False + + # Throttling: track consumption rate to slow down worker when buffer is full + self._last_result_consumed_time = 0.0 + self._target_fps: float | None = None # Target FPS from main pipeline + self._throttle_lock = threading.Lock() + + def start(self, timeout: float = 60.0) -> bool: + """Start the preprocessor worker process and connect sockets. + + The worker is started as a completely separate process using subprocess.Popen, + ensuring complete CUDA context isolation from the main pipeline. + + Args: + timeout: Maximum time to wait for worker to be ready (seconds) + + Returns: + True if started successfully, False otherwise + """ + if self._started: + logger.warning("AsyncPreprocessorClient already started") + return True + + logger.info(f"Starting AsyncPreprocessorClient for {self.preprocessor_type}...") + + # Dynamically allocate ports if not provided + if self.input_port is None: + self.input_port = _find_free_port() + if self.output_port is None: + self.output_port = _find_free_port() + + logger.info( + f"Using ports: input={self.input_port}, output={self.output_port}" + ) + + # Create temporary directory for ready file + self._temp_dir = tempfile.TemporaryDirectory(prefix="preprocessor_worker_") + self._ready_file = Path(self._temp_dir.name) / "ready" + + # Start the worker as a completely separate process using subprocess.Popen + # This ensures complete CUDA context isolation + worker_module = "scope.core.preprocessors.preprocessor_worker_process" + cmd = [ + sys.executable, "-m", worker_module, + "--preprocessor-type", self.preprocessor_type, + "--input-port", str(self.input_port), + "--output-port", str(self.output_port), + "--ready-file", str(self._ready_file), + ] + # Add encoder argument only for depthanything + if self.preprocessor_type == "depthanything": + cmd.extend(["--encoder", self.encoder]) + + logger.info(f"Starting separate worker process: {' '.join(cmd)}") + + # Start the worker process + self._worker_process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, # Line buffered + ) + + # Start a thread to forward worker stdout to our logger + self._stdout_thread = threading.Thread( + target=self._forward_stdout, daemon=True + ) + self._stdout_thread.start() + + logger.info(f"Worker process started (PID={self._worker_process.pid})") + + # Wait for worker to be ready (file-based signaling) + logger.info(f"Waiting for preprocessor worker to initialize (timeout={timeout}s)...") + start_wait = time.time() + + while time.time() - start_wait < timeout: + # Check if process died + if self._worker_process.poll() is not None: + wait_time = time.time() - start_wait + logger.error( + f"Worker process died during startup (exit code: {self._worker_process.returncode}) " + f"after {wait_time:.1f}s" + ) + self.stop() + return False + + # Check if ready file exists + if self._ready_file.exists(): + break + + time.sleep(0.1) + else: + wait_time = time.time() - start_wait + logger.error(f"Preprocessor worker failed to start within timeout ({wait_time:.1f}s)") + self.stop() + return False + + wait_time = time.time() - start_wait + logger.info(f"Preprocessor worker ready after {wait_time:.1f}s, connecting sockets...") + + # Setup ZeroMQ client sockets + import zmq + + self._context = zmq.Context() + + # Push socket for sending frames + self._input_socket = self._context.socket(zmq.PUSH) + self._input_socket.setsockopt(zmq.SNDHWM, ZMQ_HWM) # Set high water mark + self._input_socket.connect(f"tcp://localhost:{self.input_port}") + + # Pull socket for receiving results + self._output_socket = self._context.socket(zmq.PULL) + self._output_socket.setsockopt(zmq.RCVHWM, ZMQ_HWM) # Set high water mark + self._output_socket.connect(f"tcp://localhost:{self.output_port}") + self._output_socket.setsockopt(zmq.RCVTIMEO, ZMQ_TIMEOUT_MS) + + # Start receiver thread + self._receiver_running = True + self._receiver_thread = threading.Thread( + target=self._receiver_loop, daemon=True + ) + self._receiver_thread.start() + + self._started = True + logger.info("AsyncPreprocessorClient started successfully") + return True + + def _forward_stdout(self): + """Forward worker stdout to our logger.""" + try: + if self._worker_process and self._worker_process.stdout: + for line in self._worker_process.stdout: + line = line.rstrip() + if line: + logger.info(f"[PreprocessorWorker] {line}") + except Exception: + pass # Process closed + + def stop(self): + """Stop the preprocessor worker and clean up resources.""" + if not self._started and self._worker_process is None: + return + + logger.info("Stopping AsyncPreprocessorClient...") + + # Stop receiver thread + self._receiver_running = False + if self._receiver_thread is not None: + self._receiver_thread.join(timeout=2.0) + self._receiver_thread = None + + # Clean up ZeroMQ first (so worker recv() fails and can notice SIGTERM) + if self._input_socket is not None: + self._input_socket.close() + self._input_socket = None + if self._output_socket is not None: + self._output_socket.close() + self._output_socket = None + if self._context is not None: + self._context.term() + self._context = None + + # Signal worker to stop using SIGTERM + if self._worker_process is not None: + if self._worker_process.poll() is None: # Still running + logger.info(f"Sending SIGTERM to worker process (PID={self._worker_process.pid})...") + try: + self._worker_process.send_signal(signal.SIGTERM) + except OSError: + pass # Process already dead + + # Wait for graceful shutdown + try: + self._worker_process.wait(timeout=5.0) + logger.info(f"Worker process exited with code {self._worker_process.returncode}") + except subprocess.TimeoutExpired: + logger.warning("Worker process did not exit gracefully, killing...") + self._worker_process.kill() + try: + self._worker_process.wait(timeout=2.0) + except subprocess.TimeoutExpired: + pass + + self._worker_process = None + + # Clean up stdout forwarding thread + if hasattr(self, '_stdout_thread') and self._stdout_thread is not None: + self._stdout_thread.join(timeout=1.0) + self._stdout_thread = None + + # Clean up temp directory + if self._temp_dir is not None: + try: + self._temp_dir.cleanup() + except Exception: + pass + self._temp_dir = None + self._ready_file = None + + # Clear buffers + with self._result_lock: + self._result_buffer.clear() + with self._pending_lock: + self._pending_chunks.clear() + + self._started = False + logger.info("AsyncPreprocessorClient stopped") + + def submit_frames( + self, + frames: "np.ndarray | list", + target_height: int, + target_width: int, + ) -> int | None: + """Submit frames for preprocessing. + + This method is non-blocking and returns immediately after queuing the frames. + The chunk_id can be used to track which result corresponds to which input. + + Implements throttling: if buffer is nearly full, skips submission to create + backpressure and reduce GPU contention with main pipeline. + + Args: + frames: Video frames as numpy array [F, H, W, C] in [0, 255] range, + or list of tensors that will be converted + target_height: Target output height for preprocessed frames + target_width: Target output width for preprocessed frames + + Returns: + chunk_id: Unique identifier for this submission, or None if skipped due to throttling + + Raises: + RuntimeError: If client is not started + """ + if not self._started: + raise RuntimeError("AsyncPreprocessorClient not started") + + import torch + + # Throttling: skip submission if buffer is getting full + # This reduces GPU contention with main pipeline + with self._result_lock: + buffer_size = len(self._result_buffer) + + # Skip if buffer is more than half full (preprocessor is ahead of consumer) + if buffer_size > self.result_buffer_size // 2: + logger.debug( + f"Throttling preprocessor submission, buffer {buffer_size}/{self.result_buffer_size}" + ) + return None + + # Convert list of tensors to numpy array if needed + if isinstance(frames, list): + # Assume list of tensors, each (1, H, W, C) + stacked = torch.cat(frames, dim=0) # [F, H, W, C] + frames_np = stacked.cpu().numpy() + elif isinstance(frames, torch.Tensor): + frames_np = frames.cpu().numpy() + else: + frames_np = frames + + # Ensure correct dtype + if frames_np.dtype != np.uint8: + if frames_np.max() <= 1.0: + frames_np = (frames_np * 255).astype(np.uint8) + else: + frames_np = frames_np.astype(np.uint8) + + # Assign chunk ID + chunk_id = self._next_chunk_id + self._next_chunk_id += 1 + + # Track pending chunk + with self._pending_lock: + self._pending_chunks.add(chunk_id) + + # Send to worker + data = { + "chunk_id": chunk_id, + "frames": frames_np, + "target_height": target_height, + "target_width": target_width, + } + self._input_socket.send(pickle.dumps(data)) + + logger.debug( + f"Submitted chunk {chunk_id}, frames shape: {frames_np.shape}, " + f"target: {target_height}x{target_width}" + ) + return chunk_id + + def get_result(self, wait: bool = False, timeout: float = 5.0) -> PreprocessorResult | None: + """Get the latest preprocessor result. + + Args: + wait: If True, wait for a result if none available + timeout: Maximum time to wait (seconds) if wait=True + + Returns: + PreprocessorResult with preprocessed tensor, or None if no result available + """ + if wait: + start_time = time.time() + while time.time() - start_time < timeout: + with self._result_lock: + if self._result_buffer: + # Buffer now stores PreprocessorResult directly (tensor already converted) + result = self._result_buffer.popleft() + self._last_result_consumed_time = time.time() + return result + time.sleep(0.01) + return None + + with self._result_lock: + if self._result_buffer: + # Buffer now stores PreprocessorResult directly (tensor already converted) + result = self._result_buffer.popleft() + self._last_result_consumed_time = time.time() + return result + return None + + def get_latest_result(self) -> PreprocessorResult | None: + """Get the most recent preprocessor result, discarding older ones. + + Returns: + Most recent PreprocessorResult, or None if no results available + """ + with self._result_lock: + if not self._result_buffer: + return None + + # Get the latest result (tensor already converted in receiver thread) + result = self._result_buffer[-1] + + # Clear all older results + self._result_buffer.clear() + + self._last_result_consumed_time = time.time() + return result + + # Backward compatibility aliases + def get_depth_result(self, wait: bool = False, timeout: float = 5.0) -> PreprocessorResult | None: + """Backward compatibility alias for get_result().""" + return self.get_result(wait, timeout) + + def get_latest_depth_result(self) -> PreprocessorResult | None: + """Backward compatibility alias for get_latest_result().""" + return self.get_latest_result() + + def has_pending_results(self) -> bool: + """Check if there are pending chunks being processed.""" + with self._pending_lock: + return len(self._pending_chunks) > 0 + + def get_buffer_size(self) -> int: + """Get the number of results currently in buffer.""" + with self._result_lock: + return len(self._result_buffer) + + def is_running(self) -> bool: + """Check if the client is running.""" + return ( + self._started + and self._worker_process is not None + and self._worker_process.poll() is None # poll() returns None if still running + ) + + def set_target_fps(self, fps: float | None): + """Set target FPS to throttle preprocessor processing to match main pipeline. + + When the preprocessor worker is running much faster than the main pipeline, + this creates backpressure to avoid wasting GPU resources. + + Args: + fps: Target FPS (e.g., from main pipeline), or None to disable throttling + """ + with self._throttle_lock: + self._target_fps = fps + if fps is not None: + logger.debug(f"Depth throttle target set to {fps:.1f} FPS") + + def _receiver_loop(self): + """Background thread that receives results from the worker. + + Performs numpy→torch conversion here (async) to avoid blocking main thread. + Also implements throttling by sleeping when buffer is full. + """ + import torch + + logger.info("Depth result receiver thread started") + + while self._receiver_running: + try: + # Throttle: if buffer is getting full, slow down to match consumption rate + with self._result_lock: + buffer_size = len(self._result_buffer) + + if buffer_size >= self.result_buffer_size - 1: + # Buffer nearly full, sleep to allow consumer to catch up + # This creates backpressure to the worker + time.sleep(0.05) + continue + + # Receive result (with timeout to allow checking running flag) + import zmq + + message = self._output_socket.recv() + result = pickle.loads(message) + + chunk_id = result["chunk_id"] + + # Remove from pending + with self._pending_lock: + self._pending_chunks.discard(chunk_id) + + # Convert numpy to torch tensor HERE (async, not in main thread) + # This is the expensive operation we want to do in background + preprocessed_tensor = torch.from_numpy(result["data"]) + + # Pin memory for faster GPU transfer when .to(device) is called later + # This makes the subsequent cuda() call non-blocking and faster + if torch.cuda.is_available(): + preprocessed_tensor = preprocessed_tensor.pin_memory() + + # Create PreprocessorResult with tensor already converted + preprocessor_result = PreprocessorResult( + chunk_id=chunk_id, + data=preprocessed_tensor, + timestamp=result["timestamp"], + ) + + # Add to result buffer + with self._result_lock: + self._result_buffer.append(preprocessor_result) + + logger.debug( + f"Received preprocessor result for chunk {chunk_id}, " + f"buffer size: {len(self._result_buffer)}" + ) + + except Exception as e: + # zmq.error.Again is expected on timeout + if "Again" not in str(type(e)): + logger.error(f"Error in receiver loop: {e}") + continue + + logger.info("Preprocessor result receiver thread stopped") + + def __enter__(self): + self.start() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop() diff --git a/src/scope/core/preprocessors/depth_worker_process.py b/src/scope/core/preprocessors/depth_worker_process.py new file mode 100644 index 000000000..e6af7b632 --- /dev/null +++ b/src/scope/core/preprocessors/depth_worker_process.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +"""Standalone depth worker process. + +This script runs as a completely separate process (not subprocess) for depth estimation. +It is spawned via subprocess.Popen to ensure complete CUDA context isolation. + +Usage: + python -m scope.core.preprocessors.depth_worker_process \ + --encoder vitl \ + --input-port 5555 \ + --output-port 5556 \ + --ready-file /tmp/depth_worker_ready + +The ready-file is created when the model is loaded and ready to accept frames. +""" + +import argparse +import logging +import os +import pickle +import signal +import sys +import time +from pathlib import Path + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("DepthWorkerProcess") + + +def main(): + parser = argparse.ArgumentParser(description="Depth worker process") + parser.add_argument("--encoder", type=str, default="vits", choices=["vits", "vitb", "vitl"]) + parser.add_argument("--input-port", type=int, required=True) + parser.add_argument("--output-port", type=int, required=True) + parser.add_argument("--ready-file", type=str, required=True, + help="File to create when ready (signals parent process)") + args = parser.parse_args() + + logger.info(f"=" * 60) + logger.info(f"Depth Worker Process Starting") + logger.info(f"PID: {os.getpid()}") + logger.info(f"Encoder: {args.encoder}") + logger.info(f"Input port: {args.input_port}") + logger.info(f"Output port: {args.output_port}") + logger.info(f"=" * 60) + + # Import torch and check CUDA + import torch + if torch.cuda.is_available(): + logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}") + logger.info(f"CUDA device count: {torch.cuda.device_count()}") + else: + logger.warning("CUDA not available, falling back to CPU") + + # Import zmq + import zmq + + # ZeroMQ configuration + ZMQ_TIMEOUT_MS = 1000 # 1 second timeout for recv + ZMQ_HWM = 100 # High water mark for socket buffers + + # Load depth pipeline + logger.info("Loading DepthAnythingPipeline...") + from scope.core.pipelines.depthanything import DepthAnythingPipeline + + try: + depth_pipeline = DepthAnythingPipeline( + encoder=args.encoder, + device=torch.device("cuda"), + dtype=torch.float16, + input_size=392, # Default input size + streaming=True, # Use streaming mode for real-time processing + output_format="rgb", # Output format for depth preprocessing + ) + depth_pipeline.prepare() # Load the model + logger.info("DepthAnythingPipeline loaded successfully!") + except Exception as e: + logger.error(f"Failed to load DepthAnythingPipeline: {e}", exc_info=True) + sys.exit(1) + + # Setup ZeroMQ sockets + context = zmq.Context() + + # Pull socket for receiving frames + input_socket = context.socket(zmq.PULL) + input_socket.setsockopt(zmq.RCVHWM, ZMQ_HWM) + input_socket.bind(f"tcp://*:{args.input_port}") + input_socket.setsockopt(zmq.RCVTIMEO, ZMQ_TIMEOUT_MS) + + # Push socket for sending results + output_socket = context.socket(zmq.PUSH) + output_socket.setsockopt(zmq.SNDHWM, ZMQ_HWM) + output_socket.bind(f"tcp://*:{args.output_port}") + + logger.info(f"ZeroMQ sockets bound (input={args.input_port}, output={args.output_port})") + + # Signal readiness by creating the ready file + ready_path = Path(args.ready_file) + ready_path.parent.mkdir(parents=True, exist_ok=True) + ready_path.write_text(f"ready:{os.getpid()}") + logger.info(f"Ready signal written to {args.ready_file}") + + # Handle graceful shutdown + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + logger.info(f"Received signal {signum}, shutting down...") + shutdown_requested = True + + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + # Main processing loop + logger.info("Starting main processing loop...") + try: + while not shutdown_requested: + try: + # Receive frame data (with timeout to check shutdown flag) + message = input_socket.recv() + data = pickle.loads(message) + + chunk_id = data["chunk_id"] + frames = data["frames"] # numpy array [F, H, W, C] + target_height = data["target_height"] + target_width = data["target_width"] + + logger.debug(f"Received chunk {chunk_id}, frames shape: {frames.shape}") + + # Convert numpy to torch tensor and prepare as list of frames for pipeline + frames_tensor = torch.from_numpy(frames).float() # [F, H, W, C] + + # Convert to list of frames, each with shape [H, W, C] + frame_list = [frames_tensor[i] for i in range(frames_tensor.shape[0])] + + # Use the pipeline's __call__ method + start_time = time.time() + depth_output = depth_pipeline(video=frame_list) # Returns [T, H, W, 3] in [0, 1] + inference_time = time.time() - start_time + num_frames = frames.shape[0] + + logger.info( + f"Chunk {chunk_id}: {num_frames} frames in " + f"{inference_time:.3f}s ({num_frames / inference_time:.1f} FPS)" + ) + + # Convert pipeline output [T, H, W, 3] in [0, 1] to [1, 3, T, H, W] in [-1, 1] + T, H, W, C = depth_output.shape + + # Resize to target dimensions if needed + if H != target_height or W != target_width: + depth_output = depth_output.permute(0, 3, 1, 2) # [T, 3, H, W] + import torch.nn.functional as F + depth_output = F.interpolate( + depth_output, + size=(target_height, target_width), + mode="bilinear", + align_corners=False, + ) + depth_output = depth_output.permute(0, 2, 3, 1) # [T, H, W, 3] + + # Convert [T, H, W, 3] -> [T, 3, H, W] + depth_tensor = depth_output.permute(0, 3, 1, 2) # [T, 3, H, W] + + # Convert from [0, 1] to [-1, 1] + depth_tensor = depth_tensor * 2.0 - 1.0 + + # Add batch dimension and rearrange to [1, 3, T, H, W] + depth = depth_tensor.unsqueeze(0).permute(0, 2, 1, 3, 4) # [1, 3, T, H, W] + + # Keep as float32 for numpy serialization + depth_cpu = depth.float().cpu() + + # Send result + result = { + "chunk_id": chunk_id, + "depth": depth_cpu.numpy(), + "timestamp": time.time(), + } + output_socket.send(pickle.dumps(result)) + + logger.debug(f"Sent result for chunk {chunk_id}, depth shape: {depth_cpu.shape}") + + except zmq.error.Again: + # Timeout, continue to check shutdown flag + continue + except Exception as e: + logger.error(f"Error processing frame: {e}", exc_info=True) + continue + + except Exception as e: + logger.error(f"Fatal error: {e}", exc_info=True) + finally: + logger.info("Shutting down...") + input_socket.close() + output_socket.close() + context.term() + depth_pipeline.offload() + + # Remove ready file + if ready_path.exists(): + ready_path.unlink() + + logger.info("Shutdown complete") + + +if __name__ == "__main__": + main() diff --git a/src/scope/core/preprocessors/preprocessor_worker_process.py b/src/scope/core/preprocessors/preprocessor_worker_process.py new file mode 100644 index 000000000..00a65db80 --- /dev/null +++ b/src/scope/core/preprocessors/preprocessor_worker_process.py @@ -0,0 +1,476 @@ +#!/usr/bin/env python3 +"""Standalone preprocessor worker process. + +This script runs as a completely separate process (not subprocess) for preprocessing. +It is spawned via subprocess.Popen to ensure complete CUDA context isolation. + +Usage: + python -m scope.core.preprocessors.preprocessor_worker_process \ + --preprocessor-type depthanything \ + --encoder vits \ + --input-port 5555 \ + --output-port 5556 \ + --ready-file /tmp/preprocessor_worker_ready + +The ready-file is created when the model is loaded and ready to accept frames. +""" + +import argparse +import logging +import os +import pickle +import signal +import sys +import time +from pathlib import Path + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("PreprocessorWorkerProcess") + + +def load_preprocessor_pipeline(preprocessor_type: str, encoder: str | None = None): + """Load the appropriate preprocessor pipeline based on type.""" + import torch + + # Built-in preprocessors with custom initialization + if preprocessor_type == "depthanything": + from scope.core.pipelines.depthanything import DepthAnythingPipeline + + encoder = encoder or "vits" # Default to vits + logger.info(f"Loading DepthAnythingPipeline with encoder: {encoder}") + pipeline = DepthAnythingPipeline( + encoder=encoder, + device=torch.device("cuda"), + dtype=torch.float16, + input_size=392, # Default input size + streaming=True, # Use streaming mode for real-time processing + output_format="rgb", # Output format for depth preprocessing + ) + pipeline.prepare() # Load the model + logger.info("DepthAnythingPipeline loaded successfully!") + return pipeline + + elif preprocessor_type == "passthrough": + from scope.core.pipelines.passthrough import PassthroughPipeline + + logger.info("Loading PassthroughPipeline") + pipeline = PassthroughPipeline( + height=512, # Will be resized to match input + width=512, + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + logger.info("PassthroughPipeline loaded successfully!") + return pipeline + + # Try to load from registry (for plugin preprocessors) + else: + try: + from scope.core.preprocessors.registry import PreprocessorRegistry + from scope.core.pipelines.registry import PipelineRegistry + + # Check PreprocessorRegistry first + preprocessor_class = PreprocessorRegistry.get(preprocessor_type) + + # If not found, check PipelineRegistry (plugin preprocessors are pipelines) + if preprocessor_class is None: + preprocessor_class = PipelineRegistry.get(preprocessor_type) + + if preprocessor_class is None: + raise ValueError(f"Preprocessor {preprocessor_type} not found in registry") + + logger.info(f"Loading plugin preprocessor: {preprocessor_type}") + # Plugin pipelines are instantiated with load_params (which may be empty) + # Try instantiating with no parameters first (plugin pipelines should handle defaults) + try: + pipeline = preprocessor_class() + except TypeError: + # If that fails, try with device and dtype (common pattern) + try: + pipeline = preprocessor_class( + device=torch.device("cuda"), + dtype=torch.bfloat16, + ) + except TypeError: + # If that also fails, try with just device + pipeline = preprocessor_class( + device=torch.device("cuda"), + ) + + logger.info(f"Plugin preprocessor {preprocessor_type} loaded successfully!") + return pipeline + except Exception as e: + logger.error(f"Failed to load preprocessor {preprocessor_type} from registry: {e}") + raise ValueError(f"Unknown preprocessor type: {preprocessor_type}") from e + + +def process_frames_depthanything(pipeline, frames, target_height, target_width): + """Process frames using depthanything pipeline.""" + import torch + + # Convert numpy to torch tensor and prepare as list of frames for pipeline + frames_tensor = torch.from_numpy(frames).float() # [F, H, W, C] + + # Convert to list of frames, each with shape [H, W, C] + frame_list = [frames_tensor[i] for i in range(frames_tensor.shape[0])] + + # Use the pipeline's __call__ method + depth_output = pipeline(video=frame_list) # Returns [T, H, W, 3] in [0, 1] + + # Convert pipeline output [T, H, W, 3] in [0, 1] to [1, 3, T, H, W] in [-1, 1] + T, H, W, C = depth_output.shape + + # Resize to target dimensions if needed + if H != target_height or W != target_width: + depth_output = depth_output.permute(0, 3, 1, 2) # [T, 3, H, W] + import torch.nn.functional as F + depth_output = F.interpolate( + depth_output, + size=(target_height, target_width), + mode="bilinear", + align_corners=False, + ) + depth_output = depth_output.permute(0, 2, 3, 1) # [T, H, W, 3] + + # Convert [T, H, W, 3] -> [T, 3, H, W] + depth_tensor = depth_output.permute(0, 3, 1, 2) # [T, 3, H, W] + + # Convert from [0, 1] to [-1, 1] + depth_tensor = depth_tensor * 2.0 - 1.0 + + # Add batch dimension and rearrange to [1, 3, T, H, W] + result = depth_tensor.unsqueeze(0).permute(0, 2, 1, 3, 4) # [1, 3, T, H, W] + + return result.float().cpu() + + +def process_frames_passthrough(pipeline, frames, target_height, target_width): + """Process frames using passthrough pipeline (just returns input).""" + import torch + from einops import rearrange + + # Convert numpy to torch tensor + frames_tensor = torch.from_numpy(frames).float() # [F, H, W, C] + + # Convert to list of frames, each with shape [1, H, W, C] for pipeline + frame_list = [frames_tensor[i].unsqueeze(0) for i in range(frames_tensor.shape[0])] + + # Use the pipeline's __call__ method + output = pipeline(video=frame_list) # Returns [T, H, W, C] in [0, 1] + + # Convert [T, H, W, C] -> [T, C, H, W] + output_tensor = output.permute(0, 3, 1, 2) # [T, C, H, W] + + # Resize to target dimensions if needed + if output_tensor.shape[2] != target_height or output_tensor.shape[3] != target_width: + import torch.nn.functional as F + output_tensor = F.interpolate( + output_tensor, + size=(target_height, target_width), + mode="bilinear", + align_corners=False, + ) + + # Passthrough should keep values in [0, 1] range (not convert to [-1, 1]) + # VACE expects conditioning inputs in [0, 1] range + + # Add batch dimension and rearrange to [1, C, T, H, W] + # output_tensor is [T, C, H, W], need [1, C, T, H, W] + result = output_tensor.unsqueeze(0).permute(0, 2, 1, 3, 4) # [1, C, T, H, W] + + return result.float().cpu() + + +def process_frames_generic(pipeline, frames, target_height, target_width): + """Generic processing function for plugin preprocessors. + + This function handles plugin pipelines that may have different interfaces. + It tries multiple calling conventions to accommodate different pipeline types. + """ + import torch + import torch.nn.functional as F + + # Convert numpy to torch tensor + frames_tensor = torch.from_numpy(frames).float() # [F, H, W, C] + num_frames = frames_tensor.shape[0] + + # Try different calling conventions for plugin pipelines + output = None + + # Try 1: List of frames with video= keyword (standard interface) + try: + frame_list = [frames_tensor[i].unsqueeze(0) for i in range(num_frames)] + output = pipeline(video=frame_list) + except (TypeError, AttributeError, KeyError): + # Try 2: List of frames as positional argument + try: + frame_list = [frames_tensor[i] for i in range(num_frames)] + output = pipeline(frame_list) + except (TypeError, AttributeError): + # Try 3: Tensor input [B, C, T, H, W] format + try: + # Convert [F, H, W, C] -> [1, C, F, H, W] + input_tensor = frames_tensor.permute(3, 0, 1, 2).unsqueeze(0) # [1, C, F, H, W] + output = pipeline(input=input_tensor) + except (TypeError, AttributeError): + # Try 4: Direct tensor input + try: + input_tensor = frames_tensor.permute(3, 0, 1, 2).unsqueeze(0) # [1, C, F, H, W] + output = pipeline(input_tensor) + except Exception as e: + logger.error(f"Failed to call plugin pipeline with any known interface: {e}") + raise ValueError(f"Plugin pipeline does not support any known calling convention") from e + + if output is None: + raise ValueError("Plugin pipeline returned None") + + # Log the raw output for debugging + logger.debug(f"Plugin pipeline raw output type: {type(output)}, shape: {output.shape if isinstance(output, torch.Tensor) else 'N/A'}") + + # Normalize output to [T, H, W, C] format + if isinstance(output, torch.Tensor): + # Handle different output shapes + if output.dim() == 5: # [B, C, T, H, W] or [B, T, C, H, W] + if output.shape[1] == 3 or output.shape[2] == 3: # Channel dimension + # Assume [B, C, T, H, W] -> [T, H, W, C] + output = output.squeeze(0).permute(1, 2, 3, 0) # [T, H, W, C] + else: + # Assume [B, T, C, H, W] -> [T, H, W, C] + output = output.squeeze(0).permute(0, 2, 3, 1) # [T, H, W, C] + elif output.dim() == 4: # [T, C, H, W] or [T, H, W, C] + if output.shape[1] == 3: # [T, C, H, W] + output = output.permute(0, 2, 3, 1) # [T, H, W, C] + # else already [T, H, W, C] + elif output.dim() == 3: # [H, W, C] - single frame, repeat for all frames + output = output.unsqueeze(0).repeat(num_frames, 1, 1, 1) # [T, H, W, C] + else: + raise ValueError(f"Unexpected output shape from plugin pipeline: {output.shape}") + else: + raise ValueError(f"Plugin pipeline returned non-tensor type: {type(output)}") + + # Ensure output is in [0, 1] range (normalize if needed) + if output.max() > 1.0: + output = output / 255.0 + elif output.min() < 0.0: + # If in [-1, 1] range, convert to [0, 1] + output = (output + 1.0) / 2.0 + + # Convert [T, H, W, C] -> [T, C, H, W] + output_tensor = output.permute(0, 3, 1, 2) # [T, C, H, W] + + # Resize to target dimensions if needed + if output_tensor.shape[2] != target_height or output_tensor.shape[3] != target_width: + output_tensor = F.interpolate( + output_tensor.unsqueeze(0), # Add batch dim for interpolation + size=(target_height, target_width), + mode="bilinear", + align_corners=False, + ).squeeze(0) # Remove batch dim + + # Validate output has frames + if output_tensor.shape[0] == 0: + raise ValueError(f"Plugin pipeline returned empty output (0 frames)") + + # Ensure we have exactly 3 channels (RGB) for VACE compatibility + # VACE expects 3-channel RGB conditioning maps + if output_tensor.shape[1] == 1: + # Convert grayscale to RGB by repeating the channel + output_tensor = output_tensor.repeat(1, 3, 1, 1) + elif output_tensor.shape[1] != 3: + logger.warning(f"Plugin pipeline returned {output_tensor.shape[1]} channels, expected 1 or 3. Converting to 3 channels.") + # If more than 3 channels, take first 3; if less, pad with zeros + if output_tensor.shape[1] > 3: + output_tensor = output_tensor[:, :3, :, :] + else: + padding = torch.zeros( + (output_tensor.shape[0], 3 - output_tensor.shape[1], output_tensor.shape[2], output_tensor.shape[3]), + dtype=output_tensor.dtype, + device=output_tensor.device + ) + output_tensor = torch.cat([output_tensor, padding], dim=1) + + # Ensure we have at least 4 frames (VAE stream_encode requires chunks of 4) + # If we have fewer frames, pad by repeating the last frame + num_frames_output = output_tensor.shape[0] + if num_frames_output < 4: + logger.warning( + f"Plugin pipeline returned {num_frames_output} frames, but VAE requires at least 4. " + f"Padding by repeating the last frame." + ) + last_frame = output_tensor[-1:].repeat(4 - num_frames_output, 1, 1, 1) + output_tensor = torch.cat([output_tensor, last_frame], dim=0) + + # Add batch dimension and rearrange to [1, C, T, H, W] + # output_tensor is [T, C, H, W], need [1, C, T, H, W] + result = output_tensor.unsqueeze(0).permute(0, 2, 1, 3, 4) # [1, C, T, H, W] + + # Final validation - ensure result has correct shape [1, C, T, H, W] + if result.shape[0] != 1 or result.shape[1] != 3 or result.shape[2] == 0: + raise ValueError(f"Plugin pipeline returned invalid output shape: {result.shape}, expected [1, 3, T, H, W]") + + # Ensure we have at least 4 frames in the final result + if result.shape[2] < 4: + raise ValueError(f"Plugin pipeline returned {result.shape[2]} frames, but VAE requires at least 4 frames") + + logger.debug(f"Plugin preprocessor output shape: {result.shape}") + return result.float().cpu() + + +def main(): + parser = argparse.ArgumentParser(description="Preprocessor worker process") + parser.add_argument("--preprocessor-type", type=str, required=True, + help="Type of preprocessor to use (e.g., depthanything, passthrough, or plugin preprocessor ID)") + parser.add_argument("--encoder", type=str, default="vits", + choices=["vits", "vitb", "vitl"], + help="Encoder size for depthanything (ignored for other types)") + parser.add_argument("--input-port", type=int, required=True) + parser.add_argument("--output-port", type=int, required=True) + parser.add_argument("--ready-file", type=str, required=True, + help="File to create when ready (signals parent process)") + args = parser.parse_args() + + logger.info(f"=" * 60) + logger.info(f"Preprocessor Worker Process Starting") + logger.info(f"PID: {os.getpid()}") + logger.info(f"Preprocessor type: {args.preprocessor_type}") + if args.preprocessor_type == "depthanything": + logger.info(f"Encoder: {args.encoder}") + logger.info(f"Input port: {args.input_port}") + logger.info(f"Output port: {args.output_port}") + logger.info(f"=" * 60) + + # Import torch and check CUDA + import torch + if torch.cuda.is_available(): + logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}") + logger.info(f"CUDA device count: {torch.cuda.device_count()}") + else: + logger.warning("CUDA not available, falling back to CPU") + + # Import zmq + import zmq + + # ZeroMQ configuration + ZMQ_TIMEOUT_MS = 1000 # 1 second timeout for recv + ZMQ_HWM = 100 # High water mark for socket buffers + + # Load preprocessor pipeline + try: + pipeline = load_preprocessor_pipeline(args.preprocessor_type, args.encoder) + except Exception as e: + logger.error(f"Failed to load preprocessor pipeline: {e}", exc_info=True) + sys.exit(1) + + # Setup ZeroMQ sockets + context = zmq.Context() + + # Pull socket for receiving frames + input_socket = context.socket(zmq.PULL) + input_socket.setsockopt(zmq.RCVHWM, ZMQ_HWM) + input_socket.bind(f"tcp://*:{args.input_port}") + input_socket.setsockopt(zmq.RCVTIMEO, ZMQ_TIMEOUT_MS) + + # Push socket for sending results + output_socket = context.socket(zmq.PUSH) + output_socket.setsockopt(zmq.SNDHWM, ZMQ_HWM) + output_socket.bind(f"tcp://*:{args.output_port}") + + logger.info(f"ZeroMQ sockets bound (input={args.input_port}, output={args.output_port})") + + # Signal readiness by creating the ready file + ready_path = Path(args.ready_file) + ready_path.parent.mkdir(parents=True, exist_ok=True) + ready_path.write_text(f"ready:{os.getpid()}") + logger.info(f"Ready signal written to {args.ready_file}") + + # Handle graceful shutdown + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + logger.info(f"Received signal {signum}, shutting down...") + shutdown_requested = True + + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + # Select processing function based on preprocessor type + # Built-in preprocessors have specific processing functions + if args.preprocessor_type == "depthanything": + process_frames_fn = process_frames_depthanything + elif args.preprocessor_type == "passthrough": + process_frames_fn = process_frames_passthrough + else: + # Plugin preprocessors use generic processing that handles different interfaces + logger.info(f"Using generic processing for plugin preprocessor: {args.preprocessor_type}") + process_frames_fn = process_frames_generic + + # Main processing loop + logger.info("Starting main processing loop...") + try: + while not shutdown_requested: + try: + # Receive frame data (with timeout to check shutdown flag) + message = input_socket.recv() + data = pickle.loads(message) + + chunk_id = data["chunk_id"] + frames = data["frames"] # numpy array [F, H, W, C] + target_height = data["target_height"] + target_width = data["target_width"] + + logger.debug(f"Received chunk {chunk_id}, frames shape: {frames.shape}") + + # Process frames using the appropriate function + start_time = time.time() + result_tensor = process_frames_fn(pipeline, frames, target_height, target_width) + inference_time = time.time() - start_time + num_frames = frames.shape[0] + + logger.info( + f"Chunk {chunk_id}: {num_frames} frames in " + f"{inference_time:.3f}s ({num_frames / inference_time:.1f} FPS)" + ) + + # Send result + result = { + "chunk_id": chunk_id, + "data": result_tensor.numpy(), + "timestamp": time.time(), + } + output_socket.send(pickle.dumps(result)) + + logger.debug(f"Sent result for chunk {chunk_id}, shape: {result_tensor.shape}") + + except zmq.error.Again: + # Timeout, continue to check shutdown flag + continue + except Exception as e: + logger.error(f"Error processing frame: {e}", exc_info=True) + continue + + except Exception as e: + logger.error(f"Fatal error: {e}", exc_info=True) + finally: + logger.info("Shutting down...") + input_socket.close() + output_socket.close() + context.term() + + # Cleanup pipeline if it has an offload method + if hasattr(pipeline, "offload"): + pipeline.offload() + + # Remove ready file + if ready_path.exists(): + ready_path.unlink() + + logger.info("Shutdown complete") + + +if __name__ == "__main__": + main() diff --git a/src/scope/core/preprocessors/registry.py b/src/scope/core/preprocessors/registry.py new file mode 100644 index 000000000..a6da2a9b8 --- /dev/null +++ b/src/scope/core/preprocessors/registry.py @@ -0,0 +1,183 @@ +"""Preprocessor registry for centralized preprocessor management. + +This module provides a registry pattern similar to pipelines for managing +available preprocessors. Preprocessors are pipelines that can be used to +preprocess video input before it enters the main diffusion pipeline. +""" + +import importlib +import logging +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ..pipelines.interface import Pipeline + +logger = logging.getLogger(__name__) + + +class PreprocessorRegistry: + """Registry for managing available preprocessors. + + Preprocessors are pipelines that implement the Pipeline interface + and can be used to preprocess video input. + """ + + _preprocessors: dict[str, type["Pipeline"]] = {} + + @classmethod + def register(cls, preprocessor_id: str, preprocessor_class: type["Pipeline"]) -> None: + """Register a preprocessor class with its ID. + + Args: + preprocessor_id: Unique identifier for the preprocessor + preprocessor_class: Preprocessor class (must implement Pipeline interface) + """ + cls._preprocessors[preprocessor_id] = preprocessor_class + + @classmethod + def get(cls, preprocessor_id: str) -> type["Pipeline"] | None: + """Get a preprocessor class by its ID. + + Args: + preprocessor_id: Preprocessor identifier + + Returns: + Preprocessor class if found, None otherwise + """ + return cls._preprocessors.get(preprocessor_id) + + @classmethod + def list_preprocessors(cls) -> list[str]: + """Get list of all registered preprocessor IDs. + + Returns: + List of preprocessor IDs (built-in + plugin preprocessors from PipelineRegistry) + """ + # Start with explicitly registered preprocessors + preprocessor_ids = set(cls._preprocessors.keys()) + + # Also check PipelineRegistry for plugin preprocessors + # (plugins might be loaded after PreprocessorRegistry initialization) + try: + from scope.core.pipelines.registry import PipelineRegistry + + all_pipelines = PipelineRegistry.list_pipelines() + + # Built-in preprocessor IDs + builtin_preprocessor_ids = {"depthanything", "passthrough"} + + # Built-in pipeline IDs (that are NOT preprocessors) + builtin_pipeline_ids = { + "streamdiffusionv2", + "longlive", + "krea-realtime-video", + "reward-forcing", + "memflow", + } + + # Add plugin pipelines as preprocessors + for pipeline_id in all_pipelines: + if pipeline_id in builtin_preprocessor_ids: + # Already in our registry + continue + if pipeline_id in builtin_pipeline_ids: + # Built-in pipeline that's not a preprocessor, skip + continue + + # This is a plugin pipeline - add it as a preprocessor + preprocessor_ids.add(pipeline_id) + + # Also register it in our registry if not already there + if pipeline_id not in cls._preprocessors: + pipeline_class = PipelineRegistry.get(pipeline_id) + if pipeline_class is not None: + cls.register(pipeline_id, pipeline_class) + logger.debug(f"Auto-registered plugin pipeline as preprocessor: {pipeline_id}") + except ImportError: + # PipelineRegistry might not be available in all contexts + pass + + return sorted(list(preprocessor_ids)) + + +# Register all available preprocessors +def _register_preprocessors(): + """Register built-in preprocessors.""" + # Define preprocessor imports with their module paths and class names + # Note: Preprocessors are actually pipelines that can be used as preprocessors + # They need to implement the Pipeline interface + preprocessor_configs = [ + ("depthanything", "scope.core.pipelines.depthanything.pipeline", "DepthAnythingPipeline"), + ("passthrough", "scope.core.pipelines.passthrough.pipeline", "PassthroughPipeline"), + ] + + # Try to import and register each preprocessor + for preprocessor_id, module_path, class_name in preprocessor_configs: + try: + module = importlib.import_module(module_path) + preprocessor_class = getattr(module, class_name) + PreprocessorRegistry.register(preprocessor_id, preprocessor_class) + logger.debug(f"Registered preprocessor: {preprocessor_id}") + except ImportError as e: + logger.warning( + f"Failed to import preprocessor {preprocessor_id} from {module_path}: {e}. " + f"This preprocessor will not be available." + ) + except AttributeError as e: + logger.warning( + f"Preprocessor {preprocessor_id} does not have class {class_name} in {module_path}: {e}. " + f"This preprocessor will not be available." + ) + + +def _initialize_registry(): + """Initialize registry with built-in preprocessors and sync with PipelineRegistry.""" + # Register built-in preprocessors first + _register_preprocessors() + + # Sync plugin preprocessors from PipelineRegistry + # Plugin pipelines registered via register_pipelines hook are available as preprocessors + try: + from scope.core.pipelines.registry import PipelineRegistry + + # Get all registered pipelines + all_pipelines = PipelineRegistry.list_pipelines() + + # Built-in preprocessor IDs + builtin_preprocessor_ids = {"depthanything", "passthrough"} + + # Built-in pipeline IDs (that are NOT preprocessors) + builtin_pipeline_ids = { + "streamdiffusionv2", + "longlive", + "krea-realtime-video", + "reward-forcing", + "memflow", + } + + # Register plugin pipelines as preprocessors + # (exclude built-in pipelines that aren't preprocessors) + for pipeline_id in all_pipelines: + if pipeline_id in builtin_preprocessor_ids: + # Already registered as built-in preprocessor + continue + if pipeline_id in builtin_pipeline_ids: + # Built-in pipeline that's not a preprocessor, skip + continue + + # This is a plugin pipeline - register it as a preprocessor + pipeline_class = PipelineRegistry.get(pipeline_id) + if pipeline_class is not None: + PreprocessorRegistry.register(pipeline_id, pipeline_class) + logger.debug(f"Registered plugin pipeline as preprocessor: {pipeline_id}") + + except ImportError: + # PipelineRegistry might not be available in all contexts + logger.debug("PipelineRegistry not available, skipping sync") + + preprocessor_count = len(PreprocessorRegistry.list_preprocessors()) + logger.info(f"Preprocessor registry initialized with {preprocessor_count} preprocessor(s)") + + +# Auto-register preprocessors on module import +_initialize_registry() diff --git a/src/scope/server/app.py b/src/scope/server/app.py index f995f114d..ccfce9833 100644 --- a/src/scope/server/app.py +++ b/src/scope/server/app.py @@ -51,7 +51,9 @@ IceServersResponse, PipelineLoadRequest, PipelineSchemasResponse, + PreprocessorsResponse, PipelineStatusResponse, + StreamStatsResponse, WebRTCOfferRequest, WebRTCOfferResponse, ) @@ -358,6 +360,19 @@ async def get_pipeline_status( raise HTTPException(status_code=500, detail=str(e)) from e +@app.get("/api/v1/stream/stats", response_model=StreamStatsResponse) +async def get_stream_stats( + webrtc_manager: WebRTCManager = Depends(get_webrtc_manager), +): + """Get stream statistics (latency, etc.) from active WebRTC session.""" + try: + latency = webrtc_manager.get_latency() + return StreamStatsResponse(latency=latency) + except Exception as e: + logger.error(f"Error getting stream stats: {e}") + raise HTTPException(status_code=500, detail=str(e)) from e + + @app.get("/api/v1/pipelines/schemas", response_model=PipelineSchemasResponse) async def get_pipeline_schemas(): """Get configuration schemas and defaults for all available pipelines. @@ -387,6 +402,15 @@ async def get_pipeline_schemas(): return PipelineSchemasResponse(pipelines=pipelines) +@app.get("/api/v1/preprocessors", response_model=PreprocessorsResponse) +async def get_preprocessors(): + """Get list of all available preprocessors.""" + from scope.core.preprocessors.registry import PreprocessorRegistry + + preprocessors = PreprocessorRegistry.list_preprocessors() + return PreprocessorsResponse(preprocessors=preprocessors) + + @app.get("/api/v1/webrtc/ice-servers", response_model=IceServersResponse) async def get_ice_servers( webrtc_manager: WebRTCManager = Depends(get_webrtc_manager), @@ -980,8 +1004,17 @@ def _load_plugins(): click.echo(f" • {plugin_name}") -@main.command() -def pipelines(): +@main.group(invoke_without_command=True) +@click.pass_context +def pipelines(ctx): + """Manage pipelines.""" + # If no subcommand, list pipelines (backward compatibility) + if ctx.invoked_subcommand is None: + ctx.invoke(pipelines_list) + + +@pipelines.command("list") +def pipelines_list(): """List all available pipelines.""" @suppress_init_output @@ -1003,7 +1036,7 @@ def _load_pipelines(): click.echo(f" • {pipeline_id}") -@main.command(hidden=not _is_preview_enabled()) +@pipelines.command(hidden=not _is_preview_enabled()) @click.argument("packages", nargs=-1, required=False) @click.option("--upgrade", is_flag=True, help="Upgrade packages to the latest version") @click.option( @@ -1015,7 +1048,7 @@ def _load_pipelines(): "--pre", is_flag=True, help="Include pre-release and development versions" ) def install(packages, upgrade, editable, force_reinstall, no_cache_dir, pre): - """Install a plugin.""" + """Install a pipeline plugin.""" args = ["uv", "pip", "install"] if upgrade: args.append("--upgrade") @@ -1027,6 +1060,11 @@ def install(packages, upgrade, editable, force_reinstall, no_cache_dir, pre): args.append("--no-cache-dir") if pre: args.append("--pre") + + # Include current project as editable dependency so plugins can depend on daydream-scope + project_root = Path(__file__).parent.parent.parent.parent + args += ["--editable", str(project_root)] + args += list(packages) result = subprocess.run(args, capture_output=False) @@ -1035,6 +1073,203 @@ def install(packages, upgrade, editable, force_reinstall, no_cache_dir, pre): sys.exit(result.returncode) +@pipelines.command("uninstall", hidden=not _is_preview_enabled()) +@click.argument("packages", nargs=-1, required=True) +@click.option("-y", "--yes", is_flag=True, help="Don't ask for confirmation (not supported by uv, kept for compatibility)") +def pipelines_uninstall(packages, yes): + """Uninstall a pipeline plugin. + + Can uninstall by pipeline ID (e.g., 'test-generator') or package name (e.g., 'scope-test-generator'). + """ + @suppress_init_output + def _get_pipeline_package_mapping(): + """Try to map pipeline IDs to package names.""" + from scope.core.pipelines.registry import PipelineRegistry + + mapping = {} + # Common pattern: pipeline ID -> scope-{id} package name + builtin_pipelines = { + "streamdiffusionv2", "longlive", "krea-realtime-video", + "reward-forcing", "memflow", "passthrough", "depthanything" + } + for pipeline_id in PipelineRegistry.list_pipelines(): + # Check if it's a plugin (not built-in) + if pipeline_id not in builtin_pipelines: + # Try common package name patterns + if PipelineRegistry.get(pipeline_id) is not None: + # Try scope-{id} pattern + potential_package = f"scope-{pipeline_id}" + mapping[pipeline_id] = potential_package + return mapping + + # Map pipeline IDs to package names if needed + package_mapping = _get_pipeline_package_mapping() + mapped_packages = [] + for pkg in packages: + if pkg in package_mapping: + mapped_packages.append(package_mapping[pkg]) + click.echo(f"Mapping pipeline ID '{pkg}' to package '{package_mapping[pkg]}'") + else: + mapped_packages.append(pkg) + + args = ["uv", "pip", "uninstall"] + # uv pip uninstall doesn't support --yes flag, it always prompts + # The yes option is kept for compatibility but not used + args += mapped_packages + + result = subprocess.run(args, capture_output=False) + + if result.returncode != 0: + sys.exit(result.returncode) + + +@main.group(invoke_without_command=True) +@click.pass_context +def preprocessors(ctx): + """Manage preprocessors.""" + # If no subcommand, list preprocessors + if ctx.invoked_subcommand is None: + ctx.invoke(preprocessors_list) + + +@preprocessors.command("list") +def preprocessors_list(): + """List all available preprocessors.""" + + @suppress_init_output + def _load_preprocessors(): + from scope.core.preprocessors.registry import PreprocessorRegistry + + return PreprocessorRegistry.list_preprocessors() + + all_preprocessors = _load_preprocessors() + + if not all_preprocessors: + click.echo("No preprocessors available.") + return + + click.echo(f"{len(all_preprocessors)} preprocessor(s) available:\n") + + # List all preprocessors + for preprocessor_id in all_preprocessors: + click.echo(f" • {preprocessor_id}") + + +@preprocessors.command(hidden=not _is_preview_enabled()) +@click.argument("packages", nargs=-1, required=False) +@click.option("--upgrade", is_flag=True, help="Upgrade packages to the latest version") +@click.option( + "-e", "--editable", help="Install a project in editable mode from this path" +) +@click.option("--force-reinstall", is_flag=True, help="Force reinstall packages") +@click.option("--no-cache-dir", is_flag=True, help="Disable the cache") +@click.option( + "--pre", is_flag=True, help="Include pre-release and development versions" +) +def install(packages, upgrade, editable, force_reinstall, no_cache_dir, pre): + """Install a preprocessor plugin.""" + args = ["uv", "pip", "install"] + if upgrade: + args.append("--upgrade") + if editable: + args += ["--editable", editable] + if force_reinstall: + args.append("--force-reinstall") + if no_cache_dir: + args.append("--no-cache-dir") + if pre: + args.append("--pre") + + # Include current project as editable dependency so plugins can depend on daydream-scope + # This must come before the packages to install + project_root = Path(__file__).parent.parent.parent.parent + args += ["--editable", str(project_root)] + + args += list(packages) + + result = subprocess.run(args, capture_output=False) + + if result.returncode != 0: + sys.exit(result.returncode) + + +@preprocessors.command("uninstall", hidden=not _is_preview_enabled()) +@click.argument("packages", nargs=-1, required=True) +@click.option("-y", "--yes", is_flag=True, help="Don't ask for confirmation (not supported by uv, kept for compatibility)") +def preprocessors_uninstall(packages, yes): + """Uninstall a preprocessor plugin. + + Can uninstall by preprocessor ID (e.g., 'test-generator') or package name (e.g., 'scope-test-generator'). + """ + @suppress_init_output + def _get_preprocessor_package_mapping(): + """Try to map preprocessor IDs to package names.""" + from scope.core.preprocessors.registry import PreprocessorRegistry + from scope.core.pipelines.registry import PipelineRegistry + + mapping = {} + # Common pattern: preprocessor ID -> scope-{id} package name + for preprocessor_id in PreprocessorRegistry.list_preprocessors(): + # Check if it's a plugin (not built-in) + if preprocessor_id not in {"depthanything", "passthrough"}: + # Try common package name patterns + if PipelineRegistry.get(preprocessor_id) is not None: + # Try scope-{id} pattern + potential_package = f"scope-{preprocessor_id}" + mapping[preprocessor_id] = potential_package + return mapping + + # Map preprocessor IDs to package names if needed + package_mapping = _get_preprocessor_package_mapping() + mapped_packages = [] + for pkg in packages: + if pkg in package_mapping: + mapped_packages.append(package_mapping[pkg]) + click.echo(f"Mapping preprocessor ID '{pkg}' to package '{package_mapping[pkg]}'") + else: + mapped_packages.append(pkg) + + args = ["uv", "pip", "uninstall"] + # uv pip uninstall doesn't support --yes flag, it always prompts + # The yes option is kept for compatibility but not used + args += mapped_packages + + result = subprocess.run(args, capture_output=False) + + if result.returncode != 0: + sys.exit(result.returncode) + + +# Backward compatibility: keep old commands working +@main.command() +def pipelines_legacy(): + """List all available pipelines (legacy command, use 'pipelines list').""" + # Redirect to new command + from click import get_current_context + ctx = get_current_context() + ctx.invoke(pipelines.list) + + +@main.command(hidden=not _is_preview_enabled()) +@click.argument("packages", nargs=-1, required=False) +@click.option("--upgrade", is_flag=True, help="Upgrade packages to the latest version") +@click.option( + "-e", "--editable", help="Install a project in editable mode from this path" +) +@click.option("--force-reinstall", is_flag=True, help="Force reinstall packages") +@click.option("--no-cache-dir", is_flag=True, help="Disable the cache") +@click.option( + "--pre", is_flag=True, help="Include pre-release and development versions" +) +def install_legacy(packages, upgrade, editable, force_reinstall, no_cache_dir, pre): + """Install a plugin (legacy command, use 'pipelines install' or 'preprocessors install').""" + # Redirect to pipelines install for backward compatibility + from click import get_current_context + ctx = get_current_context() + ctx.invoke(pipelines.install, packages=packages, upgrade=upgrade, editable=editable, + force_reinstall=force_reinstall, no_cache_dir=no_cache_dir, pre=pre) + + @main.command(hidden=not _is_preview_enabled()) @click.argument("packages", nargs=-1, required=True) @click.option("-y", "--yes", is_flag=True, help="Don't ask for confirmation") diff --git a/src/scope/server/frame_processor.py b/src/scope/server/frame_processor.py index b6e18bbb6..372d8f498 100644 --- a/src/scope/server/frame_processor.py +++ b/src/scope/server/frame_processor.py @@ -1,10 +1,12 @@ import logging +import os import queue import threading import time from collections import deque from typing import Any +import numpy as np import torch from aiortc.mediastreams import VideoFrame @@ -12,9 +14,8 @@ logger = logging.getLogger(__name__) - # Multiply the # of output frames from pipeline by this to get the max size of the output queue -OUTPUT_QUEUE_MAX_SIZE_FACTOR = 3 +OUTPUT_QUEUE_MAX_SIZE_FACTOR = 5 # Increased from 3 to handle faster production rates # FPS calculation constants MIN_FPS = 1.0 # Minimum FPS to prevent division by zero @@ -39,11 +40,25 @@ def to_ndarray(self, format="rgb24"): return self._data +class _FrameWithTimestamp: + """Wrapper for frames with input timestamp for latency tracking.""" + + __slots__ = ["frame", "input_timestamp"] + + def __init__(self, frame, input_timestamp: float): + self.frame = frame + self.input_timestamp = input_timestamp + + def to_ndarray(self, format="rgb24"): + """Delegate to underlying frame's to_ndarray method.""" + return self.frame.to_ndarray(format=format) + + class FrameProcessor: def __init__( self, pipeline_manager: PipelineManager, - max_output_queue_size: int = 8, + max_output_queue_size: int = 60, # Increased from 8 to handle burst production max_parameter_queue_size: int = 8, max_buffer_size: int = 30, initial_parameters: dict = None, @@ -108,6 +123,18 @@ def __init__( # This determines whether we wait for video frames or generate immediately. self._video_mode = (initial_parameters or {}).get("input_mode") == "video" + # Async preprocessing state + self._preprocessor_types: list[str] = [] # Current preprocessor types (list) + self._latest_preprocessor_results: dict[str, Any] = {} # Dict of preprocessor_type -> cached result + self._preprocessor_submit_chunk_id = 0 # Counter for submitted chunks + self._last_preprocessor_submit_time = 0.0 + self._preprocessor_result_lock = threading.Lock() + + # End-to-end latency tracking + self._latency_samples = deque(maxlen=30) # Keep last 30 latency measurements + self._latency_lock = threading.Lock() + self._current_average_latency = 0.0 # Current average latency in ms + def start(self): if self.running: return @@ -124,6 +151,26 @@ def start(self): spout_config = self.parameters.pop("spout_receiver") self._update_spout_receiver(spout_config) + # Load preprocessors from initial parameters if specified + if "preprocessor_types" in self.parameters: + preprocessor_types = self.parameters.get("preprocessor_types") + if preprocessor_types: + self.pipeline_manager._load_preprocessors( + preprocessor_types, + encoder="vits" if "depthanything" in preprocessor_types else None + ) + self._preprocessor_types = preprocessor_types + elif "preprocessor_type" in self.parameters: + # Backward compatibility: handle single preprocessor_type + preprocessor_type = self.parameters.get("preprocessor_type") + if preprocessor_type is not None: + preprocessor_types = [preprocessor_type] + self.pipeline_manager._load_preprocessors( + preprocessor_types, + encoder="vits" if preprocessor_type == "depthanything" else None + ) + self._preprocessor_types = preprocessor_types + self.worker_thread = threading.Thread(target=self.worker_loop, daemon=True) self.worker_thread.start() @@ -198,8 +245,12 @@ def put(self, frame: VideoFrame) -> bool: # Track input frame timestamp for FPS measurement self.track_input_frame() + # Wrap frame with timestamp for end-to-end latency tracking + input_timestamp = time.time() + frame_with_timestamp = _FrameWithTimestamp(frame, input_timestamp) + with self.frame_buffer_lock: - self.frame_buffer.append(frame) + self.frame_buffer.append(frame_with_timestamp) return True def get(self) -> torch.Tensor | None: @@ -207,7 +258,39 @@ def get(self) -> torch.Tensor | None: return None try: - frame = self.output_queue.get_nowait() + queue_item = self.output_queue.get_nowait() + + # Unwrap frame and timestamp if wrapped for latency tracking + if isinstance(queue_item, tuple) and len(queue_item) == 2: + frame, input_timestamp = queue_item + # Calculate end-to-end latency from camera input to playback output + # This includes: buffer wait + preprocessing + pipeline processing + queue wait + output_timestamp = time.time() + latency_ms = (output_timestamp - input_timestamp) * 1000.0 + + # Track latency samples + with self._latency_lock: + self._latency_samples.append(latency_ms) + if len(self._latency_samples) >= 10: + # Log average latency every 10 frames + avg_latency = sum(self._latency_samples) / len(self._latency_samples) + min_latency = min(self._latency_samples) + max_latency = max(self._latency_samples) + logger.info( + f"[Latency] End-to-end: avg={avg_latency:.1f}ms, " + f"min={min_latency:.1f}ms, max={max_latency:.1f}ms " + f"(sample_size={len(self._latency_samples)})" + ) + # Update current average latency for API access + self._current_average_latency = avg_latency + self._latency_samples.clear() + elif len(self._latency_samples) > 0: + # Update average even if we don't have 10 samples yet + self._current_average_latency = sum(self._latency_samples) / len(self._latency_samples) + else: + # Backward compatibility: handle old format without timestamps + frame = queue_item + # Enqueue frame for async Spout sending (non-blocking) if self.spout_sender_enabled and self.spout_sender is not None: try: @@ -229,6 +312,14 @@ def get_current_pipeline_fps(self) -> float: with self.fps_lock: return self.current_pipeline_fps + def get_average_latency(self) -> float: + """Get the current average end-to-end latency in milliseconds. + + Returns 0.0 if no latency measurements are available yet. + """ + with self._latency_lock: + return self._current_average_latency + def get_output_fps(self) -> float: """Get the output FPS that frames should be sent at. @@ -243,7 +334,20 @@ def get_output_fps(self) -> float: return pipeline_fps # Use minimum to respect both input rate and pipeline capacity - return min(input_fps, pipeline_fps) + output_fps = min(input_fps, pipeline_fps) + + # Log FPS breakdown occasionally for debugging + if hasattr(self, '_last_fps_log_time'): + if time.time() - self._last_fps_log_time > 5.0: + logger.info( + f"[FPS] Input: {input_fps:.1f}, Pipeline: {pipeline_fps:.1f}, " + f"Output: {output_fps:.1f}" + ) + self._last_fps_log_time = time.time() + else: + self._last_fps_log_time = time.time() + + return output_fps def _get_input_fps(self) -> float | None: """Get the current measured input FPS. @@ -589,8 +693,12 @@ def _spout_receiver_loop(self): last_frame_time = time.time() spout_frame = _SpoutFrame(rgb_frame) + # Wrap with timestamp for latency tracking + input_timestamp = time.time() + frame_with_timestamp = _FrameWithTimestamp(spout_frame, input_timestamp) + with self.frame_buffer_lock: - self.frame_buffer.append(spout_frame) + self.frame_buffer.append(frame_with_timestamp) frame_count += 1 if frame_count % 100 == 0: @@ -651,6 +759,31 @@ def process_chunk(self): if "input_mode" in new_parameters: self._video_mode = new_parameters.get("input_mode") == "video" + # Handle preprocessor type changes + if "preprocessor_types" in new_parameters: + new_preprocessor_types = new_parameters.get("preprocessor_types") or [] + if set(new_preprocessor_types) != set(self._preprocessor_types): + # Update preprocessors + self.pipeline_manager._load_preprocessors( + new_preprocessor_types, + encoder="vits" if "depthanything" in new_preprocessor_types else None + ) + self._preprocessor_types = new_preprocessor_types + with self._preprocessor_result_lock: + self._latest_preprocessor_results.clear() + elif "preprocessor_type" in new_parameters: + # Backward compatibility: handle single preprocessor_type + new_preprocessor_type = new_parameters.get("preprocessor_type") + new_preprocessor_types = [new_preprocessor_type] if new_preprocessor_type else [] + if set(new_preprocessor_types) != set(self._preprocessor_types): + self.pipeline_manager._load_preprocessors( + new_preprocessor_types, + encoder="vits" if new_preprocessor_type == "depthanything" else None + ) + self._preprocessor_types = new_preprocessor_types + with self._preprocessor_result_lock: + self._latest_preprocessor_results.clear() + # Merge new parameters with existing ones to preserve any missing keys self.parameters = {**self.parameters, **new_parameters} except queue.Empty: @@ -695,6 +828,7 @@ def process_chunk(self): ) video_input = None + chunk_input_timestamp = None if requirements is not None: current_chunk_size = requirements.input_size with self.frame_buffer_lock: @@ -702,7 +836,11 @@ def process_chunk(self): # Sleep briefly to avoid busy waiting self.shutdown_event.wait(SLEEP_TIME) return - video_input = self.prepare_chunk(current_chunk_size) + prepare_start = time.time() + video_input, chunk_input_timestamp = self.prepare_chunk(current_chunk_size) + prepare_time = time.time() - prepare_start + if prepare_time > 0.01: + logger.debug(f"[Overhead] prepare_chunk: {prepare_time*1000:.1f}ms") try: # Pass parameters (excluding prepare-only parameters) call_params = dict(self.parameters.items()) @@ -716,18 +854,271 @@ def process_chunk(self): if lora_scales is not None: call_params["lora_scales"] = lora_scales - # Route video input based on VACE status - # We do not support combining normal V2V (denoising from noisy video latents) and VACE V2V editing + # Route video input based on VACE status and preprocessing if video_input is not None: vace_enabled = getattr(pipeline, "vace_enabled", False) - if vace_enabled: - # VACE V2V editing mode: route to vace_input_frames + # Get preprocessor types (support both new list format and old single value) + preprocessor_types = self.parameters.get("preprocessor_types") + if preprocessor_types is None: + # Backward compatibility: check for single preprocessor_type + preprocessor_type = self.parameters.get("preprocessor_type") + preprocessor_types = [preprocessor_type] if preprocessor_type else [] + + # Handle preprocessing (all preprocessors use the same async interface) + if preprocessor_types: + async_preprocessors = self.pipeline_manager.async_preprocessors + + # Verify all preprocessors are running + for preprocessor_type in preprocessor_types: + if preprocessor_type not in async_preprocessors: + raise RuntimeError( + f"Preprocessor {preprocessor_type} is enabled but not loaded" + ) + client = async_preprocessors[preprocessor_type] + if not client.is_running(): + raise RuntimeError( + f"Preprocessor {preprocessor_type} is enabled but not running" + ) + + # === ASYNC PREPROCESSING === + width, height = self._get_pipeline_dimensions() + + # Fast path for single preprocessor (optimized, matches old behavior) + if len(preprocessor_types) == 1: + preprocessor_type = preprocessor_types[0] + async_preprocessor_client = async_preprocessors[preprocessor_type] + + # === ASYNC PREPROCESSING === + # Track preprocessor latency + preprocessor_start_time = time.time() + + # Always submit current frames to ensure we process the most recent input + # This guarantees the pipeline always gets the latest frame, minimizing latency + preprocessor_submit_time = time.time() + async_preprocessor_client.submit_frames( + video_input, + target_height=height, + target_width=width, + ) + submit_time = time.time() - preprocessor_submit_time + if submit_time > 0.05: + logger.debug(f"[Latency] Preprocessor {preprocessor_type} submit: {submit_time*1000:.1f}ms") + + # Get latest available result (non-blocking, discards older results) + # This ensures we always use the most recent preprocessed frame + get_result_start = time.time() + preprocessor_result = async_preprocessor_client.get_latest_result() + get_result_time = time.time() - get_result_start + + # Use the fresh result directly (not cached) to ensure minimum latency + preprocessed_input = None + if preprocessor_result is not None: + # Track processing latency from result metadata if available + if hasattr(preprocessor_result, 'processing_time'): + processing_time = preprocessor_result.processing_time + logger.info( + f"[Latency] Preprocessor {preprocessor_type}: " + f"submit={submit_time*1000:.1f}ms, " + f"process={processing_time*1000:.1f}ms, " + f"get_result={get_result_time*1000:.1f}ms" + ) + + # Use the fresh result directly (not cached) for minimum latency + preprocessed_input = preprocessor_result.data + + gpu_transfer_time = 0.0 + if preprocessed_input is not None: + # Move to correct device and dtype + # non_blocking=True since memory is pinned in receiver thread + gpu_start = time.time() + preprocessed_input = preprocessed_input.to( + device=torch.device("cuda"), + dtype=torch.bfloat16, + non_blocking=True, + ) + gpu_transfer_time = time.time() - gpu_start + + # Log total preprocessor latency + total_preprocessor_time = time.time() - preprocessor_start_time + logger.info( + f"[Latency] Preprocessor {preprocessor_type} total: {total_preprocessor_time*1000:.1f}ms " + f"(submit={submit_time*1000:.1f}ms, gpu_transfer={gpu_transfer_time*1000:.1f}ms)" + ) + + logger.debug( + f"Using async {preprocessor_type} preprocessor, shape: {preprocessed_input.shape}" + ) + + # Use preprocessed input + if vace_enabled: + call_params["vace_input_frames"] = preprocessed_input + else: + call_params["video"] = preprocessed_input + else: + # No preprocessor result available yet, fall back to video input + logger.debug( + "No async preprocessor result available yet, using video input" + ) + if vace_enabled: + call_params["vace_input_frames"] = video_input + else: + call_params["video"] = video_input + + else: + # === SEQUENTIAL ASYNC PREPROCESSING (multiple preprocessors) === + # Preprocessors run sequentially: output of first becomes input to second, etc. + # Start with original video input + current_input = video_input + preprocessed_input = None + + # Track total preprocessing time for sequential preprocessors + sequential_preprocessor_start = time.time() + + # Process each preprocessor in order + for idx, preprocessor_type in enumerate(preprocessor_types): + client = async_preprocessors[preprocessor_type] + preprocessor_iter_start = time.time() + + # Always submit current frames to ensure we process the most recent input + # This guarantees the pipeline always gets the latest frame, minimizing latency + # Convert current_input to numpy format for submission + # current_input is list of tensors [(1, H, W, C), ...] or numpy array + if isinstance(current_input, list): + # Convert list of tensors to numpy array [F, H, W, C] + stacked = torch.cat(current_input, dim=0) # [F, H, W, C] + input_np = stacked.cpu().numpy() + elif isinstance(current_input, torch.Tensor): + # Tensor might be [1, C, T, H, W] or [T, H, W, C] + if current_input.dim() == 5: + # [1, C, T, H, W] -> [T, H, W, C] + input_np = current_input.squeeze(0).permute(1, 2, 3, 0).cpu().numpy() + else: + input_np = current_input.cpu().numpy() + else: + input_np = current_input + + # Ensure uint8 format [F, H, W, C] + if input_np.dtype != np.uint8: + if input_np.max() <= 1.0: + input_np = (input_np * 255).astype(np.uint8) + else: + input_np = input_np.astype(np.uint8) + + # Submit to preprocessor (always submit for minimum latency) + preprocessor_submit_time = time.time() + client.submit_frames( + input_np, + target_height=height, + target_width=width, + ) + submit_time = time.time() - preprocessor_submit_time + if submit_time > 0.05: + logger.debug(f"[Latency] Preprocessor {preprocessor_type} submit: {submit_time*1000:.1f}ms") + + # Get latest result (non-blocking, discards older results) + # This ensures we always use the most recent preprocessed frame + get_result_start = time.time() + result = client.get_latest_result() + get_result_time = time.time() - get_result_start + + # Use the fresh result directly (not cached) to ensure minimum latency + gpu_transfer_time = 0.0 + if result is not None: + # Track processing latency from result metadata if available + if hasattr(result, 'processing_time'): + processing_time = result.processing_time + logger.info( + f"[Latency] Preprocessor {preprocessor_type} ({idx+1}/{len(preprocessor_types)}): " + f"submit={submit_time*1000:.1f}ms, " + f"process={processing_time*1000:.1f}ms, " + f"get_result={get_result_time*1000:.1f}ms" + ) + + # Use the fresh result directly (not cached) for minimum latency + result_data = result.data + # Move to GPU + gpu_start = time.time() + result_tensor = result_data.to( + device=torch.device("cuda"), + dtype=torch.bfloat16, + non_blocking=True, + ) + gpu_transfer_time = time.time() - gpu_start + + # Log per-preprocessor latency + preprocessor_iter_time = time.time() - preprocessor_iter_start + logger.info( + f"[Latency] Preprocessor {preprocessor_type} ({idx+1}/{len(preprocessor_types)}) " + f"total: {preprocessor_iter_time*1000:.1f}ms " + f"(submit={submit_time*1000:.1f}ms, gpu_transfer={gpu_transfer_time*1000:.1f}ms)" + ) + + # Store final result for pipeline (keep tensor format) + preprocessed_input = result_tensor + + # Only convert to numpy if there's a next preprocessor + if idx < len(preprocessor_types) - 1: + # Convert result tensor [1, C, T, H, W] to numpy format for next preprocessor + # Extract frames: [1, C, T, H, W] -> [T, H, W, C] -> numpy [T, H, W, C] + T = result_tensor.shape[2] + result_tensor_permuted = result_tensor.squeeze(0).permute(1, 2, 3, 0) # [T, H, W, C] + + # Convert from [-1, 1] or [0, 1] range to [0, 255] uint8 + # Preprocessor outputs are typically in [-1, 1] for depthanything or [0, 1] for passthrough + if result_tensor_permuted.min() < 0: + # [-1, 1] range -> [0, 1] + result_tensor_permuted = (result_tensor_permuted + 1.0) / 2.0 + # [0, 1] -> [0, 255] uint8 + result_tensor_permuted = (result_tensor_permuted * 255.0).clamp(0, 255).to(torch.uint8) + + # Convert to numpy array [T, H, W, C] for next preprocessor + current_input = result_tensor_permuted.cpu().numpy() + + logger.debug( + f"Preprocessor {preprocessor_type} ({idx+1}/{len(preprocessor_types)}) output shape: {result_tensor.shape}" + ) + else: + # No result available yet, can't continue pipeline + logger.debug( + f"No result available yet for preprocessor {preprocessor_type} ({idx+1}/{len(preprocessor_types)}), using video input" + ) + preprocessed_input = None + break + + # Use final preprocessed input if available (inside sequential preprocessor block) + if preprocessed_input is not None: + # Log total sequential preprocessing time + total_sequential_time = time.time() - sequential_preprocessor_start + logger.info( + f"[Latency] Sequential preprocessors {preprocessor_types} total: {total_sequential_time*1000:.1f}ms" + ) + logger.debug( + f"Using sequential preprocessors {preprocessor_types}, final shape: {preprocessed_input.shape}" + ) + if vace_enabled: + call_params["vace_input_frames"] = preprocessed_input + else: + call_params["video"] = preprocessed_input + else: + # Not all preprocessor results available yet, fall back to video input + logger.debug( + f"Preprocessor pipeline incomplete, using video input" + ) + if vace_enabled: + call_params["vace_input_frames"] = video_input + else: + call_params["video"] = video_input + elif vace_enabled: + # Regular VACE V2V editing mode: route to vace_input_frames call_params["vace_input_frames"] = video_input else: # Normal V2V mode: route to video call_params["video"] = video_input + # Measure pipeline execution latency + pipeline_start_time = time.time() output = pipeline(**call_params) + pipeline_latency = time.time() - pipeline_start_time # Clear vace_ref_images from parameters after use to prevent sending them on subsequent chunks # vace_ref_images should only be sent when explicitly provided in parameter updates @@ -748,10 +1139,23 @@ def process_chunk(self): if not transition_active or transition is None: self.parameters.pop("transition", None) - processing_time = time.time() - start_time + pipeline_end_time = time.time() + processing_time = pipeline_end_time - start_time num_frames = output.shape[0] - logger.debug( - f"Processed pipeline in {processing_time:.4f}s, {num_frames} frames" + + # Calculate overhead (time before pipeline call) + # start_time is set at beginning of process_chunk + # pipeline call happens after parameter handling, prepare, and depth processing + overhead_time = processing_time - pipeline_latency + + logger.info( + f"[Latency] Pipeline: {pipeline_latency*1000:.1f}ms for {num_frames} frames " + f"({num_frames / pipeline_latency:.1f} FPS)" + ) + logger.info( + f"[Latency] Total chunk: {processing_time*1000:.1f}ms " + f"(pipeline={pipeline_latency*1000:.1f}ms, overhead={overhead_time*1000:.1f}ms, " + f"total_fps={num_frames / processing_time:.1f})" ) # Normalize to [0, 255] and convert to uint8 @@ -781,9 +1185,25 @@ def process_chunk(self): except queue.Empty: break + # Wrap output frames with input timestamp for end-to-end latency tracking + # This timestamp represents when the input frame arrived from the camera, + # and will be used to calculate total latency including: + # - Buffer wait time + # - Preprocessing time (DepthAnything, etc.) - included because preprocessing + # happens between prepare_chunk() and pipeline execution + # - Pipeline processing time + # - Output queue wait time + # Use current time if no input timestamp (text-to-video mode) + if chunk_input_timestamp is None: + chunk_input_timestamp = start_time # Use chunk start time as fallback + for frame in output: try: - self.output_queue.put_nowait(frame) + # Store frame with input timestamp for latency calculation + # This preserves the original camera input timestamp through the entire + # processing pipeline (preprocessing + pipeline) for accurate E2E measurement + frame_with_latency = (frame, chunk_input_timestamp) + self.output_queue.put_nowait(frame_with_latency) except queue.Full: logger.warning("Output queue full, dropping processed frame") # Update FPS calculation based on processing time and frame count @@ -801,14 +1221,12 @@ def process_chunk(self): self.is_prepared = True - def prepare_chunk(self, chunk_size: int) -> list[torch.Tensor]: + def prepare_chunk(self, chunk_size: int) -> tuple[list[torch.Tensor], float]: """ - Sample frames uniformly from the buffer, convert them to tensors, and remove processed frames. + Take the most recent frames from the buffer, convert them to tensors, and remove processed frames. - This function implements uniform sampling across the entire buffer to ensure - temporal coverage of input frames. It samples frames at evenly distributed - indices and removes all frames up to the last sampled frame to prevent - buffer buildup. + This function always takes the most recent frames to minimize latency. It takes the last + chunk_size frames from the buffer to ensure the pipeline always processes the freshest input. Note: This function must be called with self.frame_buffer_lock held to ensure @@ -816,39 +1234,49 @@ def prepare_chunk(self, chunk_size: int) -> list[torch.Tensor]: Example: With buffer_len=8 and chunk_size=4: - - step = 8/4 = 2.0 - - indices = [0, 2, 4, 6] (uniformly distributed) - - Returns frames at positions 0, 2, 4, 6 - - Removes frames 0-6 from buffer (7 frames total) + - Takes frames at positions 4, 5, 6, 7 (most recent) + - Removes all frames 0-7 from buffer (all processed) Returns: - List of tensor frames, each (1, H, W, C) for downstream preprocess_chunk + Tuple of (list of tensor frames, earliest input timestamp) + - List of tensor frames, each (1, H, W, C) for downstream preprocess_chunk + - Earliest input timestamp for latency tracking """ - # Calculate uniform sampling step - step = len(self.frame_buffer) / chunk_size - # Generate indices for uniform sampling - indices = [round(i * step) for i in range(chunk_size)] - # Extract VideoFrames at sampled indices - video_frames = [self.frame_buffer[i] for i in indices] - - # Drop all frames up to and including the last sampled frame - last_idx = indices[-1] - for _ in range(last_idx + 1): + buffer_len = len(self.frame_buffer) + # Always take the most recent frames for minimum latency + # If buffer has fewer frames than chunk_size, take all available + start_idx = max(0, buffer_len - chunk_size) + # Extract VideoFrames from the most recent positions + video_frames = [self.frame_buffer[i] for i in range(start_idx, buffer_len)] + + # Extract earliest input timestamp for latency tracking + # This timestamp represents when the frame arrived from the camera (via put()), + # and will be preserved through preprocessing and pipeline processing for E2E latency measurement + earliest_timestamp = min( + frame.input_timestamp if isinstance(frame, _FrameWithTimestamp) else time.time() + for frame in video_frames + ) + + # Drop all processed frames from buffer (all frames up to the last one we're using) + for _ in range(buffer_len): self.frame_buffer.popleft() # Convert VideoFrames to tensors tensor_frames = [] for video_frame in video_frames: + # Unwrap if it's a _FrameWithTimestamp + actual_frame = video_frame.frame if isinstance(video_frame, _FrameWithTimestamp) else video_frame + # Convert VideoFrame into (1, H, W, C) tensor on cpu # The T=1 dimension is expected by preprocess_chunk which rearranges T H W C -> T C H W tensor = ( - torch.from_numpy(video_frame.to_ndarray(format="rgb24")) + torch.from_numpy(actual_frame.to_ndarray(format="rgb24")) .float() .unsqueeze(0) ) tensor_frames.append(tensor) - return tensor_frames + return tensor_frames, earliest_timestamp def __enter__(self): self.start() diff --git a/src/scope/server/pipeline_manager.py b/src/scope/server/pipeline_manager.py index ef8873ec7..05f75e01e 100644 --- a/src/scope/server/pipeline_manager.py +++ b/src/scope/server/pipeline_manager.py @@ -34,6 +34,13 @@ class PipelineStatus(Enum): ERROR = "error" +# List of built-in preprocessors with custom initialization +BUILTIN_PREPROCESSORS = { + "depthanything", + "passthrough", +} + + class PipelineManager: """Manager for ML pipeline lifecycle.""" @@ -44,6 +51,7 @@ def __init__(self): self._load_params = None self._error_message = None self._lock = threading.RLock() # Single reentrant lock for all access + self._async_preprocessors: dict[str, Any] = {} # Dict of preprocessor_type -> AsyncPreprocessorClient @property def status(self) -> PipelineStatus: @@ -60,6 +68,19 @@ def error_message(self) -> str | None: """Get last error message.""" return self._error_message + @property + def async_preprocessor(self): + """Get the async preprocessor client (if any). Backward compatibility - returns first preprocessor.""" + if self._async_preprocessors: + return next(iter(self._async_preprocessors.values())) + return None + + @property + def async_preprocessors(self): + """Get all async preprocessor clients.""" + return self._async_preprocessors + + def get_pipeline(self): """Get the loaded pipeline instance (thread-safe).""" with self._lock: @@ -297,6 +318,16 @@ def _unload_pipeline_unsafe(self): if self._pipeline: logger.info(f"Unloading pipeline: {self._pipeline_id}") + # Unload all async preprocessors if loaded + if self._async_preprocessors: + logger.info(f"Stopping {len(self._async_preprocessors)} async preprocessor(s)") + for preprocessor_type, client in self._async_preprocessors.items(): + try: + client.stop() + except Exception as e: + logger.error(f"Error stopping preprocessor {preprocessor_type}: {e}") + self._async_preprocessors.clear() + # Change status and pipeline atomically self._status = PipelineStatus.NOT_LOADED self._pipeline = None @@ -314,6 +345,82 @@ def _unload_pipeline_unsafe(self): except Exception as e: logger.warning(f"CUDA cleanup failed: {e}") + def _load_preprocessors(self, preprocessor_types: list[str] | None, encoder: str | None = None) -> None: + """Load preprocessors based on types. + + Args: + preprocessor_types: List of preprocessor types (e.g., ["depthanything", "passthrough"]) + encoder: For depthanything, encoder size ("vits", "vitb", or "vitl"). Defaults to "vits". + """ + from scope.core.preprocessors import PreprocessorRegistry + from scope.core.pipelines.registry import PipelineRegistry + + if preprocessor_types is None: + preprocessor_types = [] + + # Determine which preprocessors to keep, add, and remove + current_types = set(self._async_preprocessors.keys()) + new_types = set(preprocessor_types) + + # Remove preprocessors that are no longer needed + to_remove = current_types - new_types + for preprocessor_type in to_remove: + logger.info(f"Unloading preprocessor: {preprocessor_type}") + client = self._async_preprocessors.pop(preprocessor_type) + try: + client.stop() + except Exception as e: + logger.error(f"Error stopping preprocessor {preprocessor_type}: {e}") + + # Add new preprocessors + to_add = new_types - current_types + for preprocessor_type in to_add: + # Check if preprocessor is registered (built-in or plugin) + preprocessor_class = PreprocessorRegistry.get(preprocessor_type) + + # If not found in PreprocessorRegistry, check PipelineRegistry (for plugin preprocessors) + if preprocessor_class is None: + preprocessor_class = PipelineRegistry.get(preprocessor_type) + if preprocessor_class is not None: + # Plugin preprocessor registered as pipeline + logger.info(f"Loading plugin preprocessor: {preprocessor_type}") + + if preprocessor_class is None: + if preprocessor_type not in BUILTIN_PREPROCESSORS: + logger.error(f"Unknown preprocessor type: {preprocessor_type}") + continue + # Built-in preprocessor not in registry yet, continue with async client loading + + # All preprocessors run in separate processes + from scope.core.preprocessors import AsyncPreprocessorClient + + encoder_val = encoder or "vits" # Default encoder for depthanything + logger.info( + f"Loading async {preprocessor_type} preprocessor" + + (f" with encoder: {encoder_val}" if preprocessor_type == "depthanything" else "") + ) + client = AsyncPreprocessorClient( + preprocessor_type=preprocessor_type, + encoder=encoder_val if preprocessor_type == "depthanything" else None, + ) + if client.start(): + logger.info(f"Async {preprocessor_type} preprocessor started") + self._async_preprocessors[preprocessor_type] = client + else: + logger.error(f"Failed to start async {preprocessor_type} preprocessor") + + def _load_preprocessor(self, preprocessor_type: str, encoder: str | None = None) -> None: + """Load a single preprocessor (backward compatibility wrapper). + + Args: + preprocessor_type: Type of preprocessor ("depthanything", "passthrough", etc.) + encoder: For depthanything, encoder size ("vits", "vitb", or "vitl"). Defaults to "vits". + """ + if preprocessor_type is None: + self._load_preprocessors(None, encoder) + else: + self._load_preprocessors([preprocessor_type], encoder) + def _load_pipeline_implementation( self, pipeline_id: str, load_params: dict | None = None ): @@ -327,6 +434,7 @@ def _load_pipeline_implementation( BUILTIN_PIPELINES = { "streamdiffusionv2", "passthrough", + "depthanything", "longlive", "krea-realtime-video", "reward-forcing", @@ -398,6 +506,7 @@ def _load_pipeline_implementation( dtype=torch.bfloat16, ) logger.info("StreamDiffusionV2 pipeline initialized") + return pipeline elif pipeline_id == "passthrough": @@ -419,6 +528,37 @@ def _load_pipeline_implementation( logger.info("Passthrough pipeline initialized") return pipeline + elif pipeline_id == "depthanything": + from scope.core.pipelines.depthanything import DepthAnythingPipeline + + # Use load parameters for resolution and encoder, with defaults + height = 480 + width = 848 + encoder = "vits" + input_size = 392 + streaming = True + output_format = "grayscale" + if load_params: + height = load_params.get("height", height) + width = load_params.get("width", width) + encoder = load_params.get("encoder", encoder) + input_size = load_params.get("input_size", input_size) + streaming = load_params.get("streaming", streaming) + output_format = load_params.get("output_format", output_format) + + pipeline = DepthAnythingPipeline( + height=height, + width=width, + encoder=encoder, + device=get_device(), + dtype=torch.float16, + input_size=input_size, + streaming=streaming, + output_format=output_format, + ) + logger.info("DepthAnything pipeline initialized") + return pipeline + elif pipeline_id == "longlive": from scope.core.pipelines import LongLivePipeline diff --git a/src/scope/server/schema.py b/src/scope/server/schema.py index 6cb6ed28e..7bc929112 100644 --- a/src/scope/server/schema.py +++ b/src/scope/server/schema.py @@ -113,6 +113,14 @@ class Parameters(BaseModel): ge=0.0, le=2.0, ) + preprocessor_type: str | None = Field( + default=None, + description="Preprocessor type to use for video input (deprecated, use preprocessor_types). Options: 'depthanything' (extracts depth maps), 'passthrough' (passes through unchanged), None (no preprocessing).", + ) + preprocessor_types: list[str] | None = Field( + default=None, + description="List of preprocessor types to use for video input. Each preprocessor runs in a separate process. Results are concatenated along the channel dimension. Options: 'depthanything' (extracts depth maps), 'passthrough' (passes through unchanged).", + ) class SpoutConfig(BaseModel): @@ -211,6 +219,14 @@ class LoRAMergeMode(str, Enum): PERMANENT_MERGE = "permanent_merge" +class DepthPreprocessorEncoder(str, Enum): + """Depth preprocessor encoder size options.""" + + VITS = "vits" # Small - fastest, lower quality + VITB = "vitb" # Base - balanced + VITL = "vitl" # Large - best quality, slower + + class LoRAConfig(BaseModel): """Configuration for a LoRA (Low-Rank Adaptation) adapter.""" @@ -306,6 +322,10 @@ class StreamDiffusionV2LoadParams(LoRAEnabledLoadParams): default=True, description="Enable VACE (Video All-In-One Creation and Editing) support for reference image conditioning and structural guidance. When enabled, incoming video in V2V mode is routed to VACE for conditioning. When disabled, V2V uses faster regular encoding.", ) + depth_preprocessor_encoder: DepthPreprocessorEncoder | None = Field( + default=None, + description="Load Video-Depth-Anything preprocessor with specified encoder size. None = don't load. Options: vits (fast), vitb (balanced), vitl (best quality).", + ) class PassthroughLoadParams(PipelineLoadParams): @@ -345,6 +365,10 @@ class LongLiveLoadParams(LoRAEnabledLoadParams): default=True, description="Enable VACE (Video All-In-One Creation and Editing) support for reference image conditioning and structural guidance. When enabled, incoming video in V2V mode is routed to VACE for conditioning. When disabled, V2V uses faster regular encoding.", ) + depth_preprocessor_encoder: DepthPreprocessorEncoder | None = Field( + default=None, + description="Load Video-Depth-Anything preprocessor with specified encoder size. None = don't load. Options: vits (fast), vitb (balanced), vitl (best quality).", + ) class KreaRealtimeVideoLoadParams(LoRAEnabledLoadParams): @@ -411,6 +435,14 @@ class PipelineStatusResponse(BaseModel): ) +class StreamStatsResponse(BaseModel): + """Stream statistics response schema.""" + + latency: float | None = Field( + default=None, description="Average end-to-end latency in milliseconds (V2V only)" + ) + + class PipelineSchemasResponse(BaseModel): """Response containing schemas for all available pipelines. @@ -421,6 +453,12 @@ class PipelineSchemasResponse(BaseModel): pipelines: dict = Field(..., description="Pipeline schemas keyed by pipeline ID") +class PreprocessorsResponse(BaseModel): + """Response containing list of available preprocessors.""" + + preprocessors: list[str] = Field(..., description="List of preprocessor IDs") + + class AssetFileInfo(BaseModel): """Metadata for an available asset file on disk.""" diff --git a/src/scope/server/webrtc.py b/src/scope/server/webrtc.py index d0d2c458d..ddf0f7a31 100644 --- a/src/scope/server/webrtc.py +++ b/src/scope/server/webrtc.py @@ -294,6 +294,24 @@ def get_active_session_count(self) -> int: ] ) + def get_latency(self) -> float | None: + """Get latency from the first active session's frame processor. + + Returns: + Average latency in milliseconds, or None if no active session or latency unavailable. + """ + for session in self.sessions.values(): + if ( + session.pc.connectionState not in ["closed", "failed"] + and session.video_track + and hasattr(session.video_track, "frame_processor") + and session.video_track.frame_processor is not None + ): + latency = session.video_track.frame_processor.get_average_latency() + if latency > 0: + return latency + return None + async def add_ice_candidate( self, session_id: str, diff --git a/test_async_depth.py b/test_async_depth.py new file mode 100644 index 000000000..43c973eb4 --- /dev/null +++ b/test_async_depth.py @@ -0,0 +1,98 @@ +"""Simple test script for async depth preprocessor.""" + +import logging +import time + +import numpy as np +import torch + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +logger = logging.getLogger(__name__) + +def test_async_depth(): + """Test the async depth preprocessor.""" + from scope.core.preprocessors import DepthPreprocessorClient + + logger.info("=" * 60) + logger.info("Testing Async Depth Preprocessor") + logger.info("=" * 60) + + # Create client + logger.info("\n1. Creating DepthPreprocessorClient...") + client = DepthPreprocessorClient(encoder="vits") # Use vits for faster loading + + # Start client (this should wait for worker to be ready) + logger.info("\n2. Starting client (this will load the model)...") + start_time = time.time() + + if not client.start(timeout=120.0): # 2 minute timeout + logger.error("Failed to start client!") + return False + + startup_time = time.time() - start_time + logger.info(f"Client started successfully in {startup_time:.1f}s") + + # Check worker is alive + if not client.is_running(): + logger.error("Worker process is not running!") + client.stop() + return False + + logger.info("Worker process is running") + + # Create some test frames + logger.info("\n3. Creating test frames...") + frames = np.random.randint(0, 255, (12, 512, 512, 3), dtype=np.uint8) + logger.info(f"Test frames shape: {frames.shape}") + + # Submit frames + logger.info("\n4. Submitting frames...") + submit_start = time.time() + chunk_id = client.submit_frames( + frames, + target_height=512, + target_width=512, + ) + submit_time = time.time() - submit_start + logger.info(f"Frames submitted (chunk_id={chunk_id}) in {submit_time:.3f}s") + + # Wait for result + logger.info("\n5. Waiting for result...") + wait_start = time.time() + result = client.get_depth_result(wait=True, timeout=30.0) + wait_time = time.time() - wait_start + + if result is None: + logger.error(f"No result received after {wait_time:.1f}s!") + client.stop() + return False + + logger.info(f"Result received in {wait_time:.1f}s") + logger.info(f"Result chunk_id: {result.chunk_id}") + logger.info(f"Depth shape: {result.depth.shape}") + logger.info(f"Depth dtype: {result.depth.dtype}") + logger.info(f"Depth range: [{result.depth.min():.3f}, {result.depth.max():.3f}]") + + # Stop client + logger.info("\n6. Stopping client...") + client.stop() + logger.info("Client stopped") + + logger.info("\n" + "=" * 60) + logger.info("TEST PASSED!") + logger.info("=" * 60) + logger.info(f"Total time: {time.time() - start_time:.1f}s") + logger.info(f"Startup: {startup_time:.1f}s") + logger.info(f"Submit: {submit_time:.3f}s") + logger.info(f"Wait for result: {wait_time:.1f}s") + + return True + +if __name__ == "__main__": + success = test_async_depth() + exit(0 if success else 1) diff --git a/uv.lock b/uv.lock index 25e6ea742..e1500375d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,16 +1,10 @@ version = 1 revision = 3 -requires-python = ">=3.10.12" +requires-python = ">=3.10.12, <3.11" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", - "python_full_version >= '3.12' and sys_platform == 'win32'", - "python_full_version == '3.11.*' and sys_platform 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