feat(perception): add DDRNet full-video vegetation replay
This commit is contained in:
DCCONSTRUCTIONS
@@ -51,6 +51,26 @@ export interface VegetationVisualCase {
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assets: Readonly<Record<string, string>>;
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}
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export interface VegetationVideoSemanticClass {
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classId: number;
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label: string;
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colorRgb: readonly [number, number, number];
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disposition: "prediction" | "undefined";
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}
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export interface VegetationRouteVideo {
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workerResultId: string;
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m47ReferenceGraphResultId: string;
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baseM4ResultId: string;
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frameCount: 4489;
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width: 800;
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height: 600;
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centerCropXyxy: readonly [100, 0, 700, 600];
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outsideCropState: "undefined";
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taxonomy: readonly VegetationVideoSemanticClass[];
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aggregatePredictionPixels: readonly number[];
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}
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export interface VegetationShadowResult {
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resultId: string;
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createdAtUtc: string;
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@@ -59,6 +79,7 @@ export interface VegetationShadowResult {
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candidates: readonly VegetationCandidateMetrics[];
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routeCases: readonly VegetationVisualCase[];
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validationCases: readonly VegetationVisualCase[];
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routeVideo: VegetationRouteVideo | null;
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limitations: readonly string[];
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visualShadowReady: true;
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missionPolicyReadyForConfiguration: true;
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@@ -227,6 +248,98 @@ function visualCaseValue(
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};
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}
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function routeVideoValue(value: unknown): VegetationRouteVideo | null {
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if (value === null || value === undefined) return null;
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const row = objectValue(value, "vegetation.route_video");
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const workerResultId = textValue(row.worker_result_id, "vegetation.route_video.worker_result_id");
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const m47ReferenceGraphResultId = textValue(
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row.m47_reference_graph_result_id,
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"vegetation.route_video.m47_reference_graph_result_id",
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);
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const baseM4ResultId = textValue(row.base_m4_result_id, "vegetation.route_video.base_m4_result_id");
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if (
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!/^lab-v1-ravnoves-video-ddrnet-[a-f0-9]{64}$/.test(workerResultId)
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|| !/^m47-reference-graph-lab-[a-f0-9]{64}$/.test(m47ReferenceGraphResultId)
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|| !/^m4-threat-replay-[a-f0-9]{64}$/.test(baseM4ResultId)
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) {
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throw new VegetationShadowContractError("vegetation.route_video: identity invalid.");
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}
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exact(row.frame_count, 4489, "vegetation.route_video.frame_count");
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exact(row.width, 800, "vegetation.route_video.width");
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exact(row.height, 600, "vegetation.route_video.height");
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exact(row.outside_crop_state, "undefined", "vegetation.route_video.outside_crop_state");
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exact(
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row.sequence_binding,
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"sequence-0-to-masks/frame-000001.png",
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"vegetation.route_video.sequence_binding",
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);
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const crop = arrayValue(row.center_crop_xyxy, "vegetation.route_video.center_crop_xyxy")
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.map((item, index) => integerValue(item, `vegetation.route_video.crop[${index}]`));
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if (crop.join(",") !== "100,0,700,600") {
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throw new VegetationShadowContractError("vegetation.route_video: crop contract changed.");
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}
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const taxonomy = objectValue(row.taxonomy, "vegetation.route_video.taxonomy");
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exact(
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taxonomy.schema_version,
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"missioncore.lab-v1-vegetation-taxonomy/v1",
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"vegetation.route_video.taxonomy.schema",
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);
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const classes = arrayValue(taxonomy.classes, "vegetation.route_video.taxonomy.classes")
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.map((value, expectedId): VegetationVideoSemanticClass => {
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const item = objectValue(value, `vegetation.route_video.taxonomy[${expectedId}]`);
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const classId = integerValue(item.class_id, `vegetation.route_video.class_id[${expectedId}]`);
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if (classId !== expectedId) {
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throw new VegetationShadowContractError("vegetation.route_video: taxonomy order changed.");
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}
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const color = arrayValue(item.color_rgb, `vegetation.route_video.color[${expectedId}]`)
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.map((channel, index) => integerValue(channel, `vegetation.route_video.color[${expectedId}][${index}]`));
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if (color.length !== 3 || color.some((channel) => channel > 255)) {
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throw new VegetationShadowContractError("vegetation.route_video: taxonomy color invalid.");
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}
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const disposition: VegetationVideoSemanticClass["disposition"] = expectedId === 0
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? "undefined"
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: "prediction";
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if (item.disposition !== disposition) {
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throw new VegetationShadowContractError("vegetation.route_video: taxonomy disposition changed.");
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}
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return {
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classId,
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label: textValue(item.label, `vegetation.route_video.label[${expectedId}]`),
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colorRgb: color as unknown as readonly [number, number, number],
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disposition,
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};
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});
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if (classes.length !== 64) {
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throw new VegetationShadowContractError("vegetation.route_video: taxonomy must contain 64 classes.");
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}
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const aggregatePredictionPixels = arrayValue(
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row.aggregate_prediction_pixels,
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"vegetation.route_video.aggregate_prediction_pixels",
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).map((value, index) => integerValue(value, `vegetation.route_video.pixels[${index}]`));
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if (aggregatePredictionPixels.length !== 64) {
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throw new VegetationShadowContractError("vegetation.route_video: class accounting changed.");
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}
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const maskArchive = objectValue(row.mask_archive, "vegetation.route_video.mask_archive");
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exact(maskArchive.path, "video/ddrnet-semantic-masks.zip", "vegetation.route_video.mask_archive.path");
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const archiveSha256 = textValue(maskArchive.sha256, "vegetation.route_video.mask_archive.sha256");
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if (!SHA256.test(archiveSha256)) {
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throw new VegetationShadowContractError("vegetation.route_video: archive digest invalid.");
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}
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integerValue(maskArchive.byte_length, "vegetation.route_video.mask_archive.byte_length");
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return {
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workerResultId,
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m47ReferenceGraphResultId,
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baseM4ResultId,
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frameCount: 4489,
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width: 800,
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height: 600,
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centerCropXyxy: [100, 0, 700, 600],
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outsideCropState: "undefined",
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taxonomy: classes,
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aggregatePredictionPixels,
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};
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}
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function parseResult(value: unknown, resultId: string): VegetationShadowResult {
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const payload = objectValue(value, "Vegetation LAB");
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exact(payload.schema_version, "missioncore.lab-v1-vegetation-shadow/v1", "vegetation.schema");
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@@ -277,6 +390,7 @@ function parseResult(value: unknown, resultId: string): VegetationShadowResult {
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candidates: CANDIDATES.map((candidate) => candidateMetricsValue(candidates[candidate], candidate)),
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routeCases,
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validationCases,
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routeVideo: routeVideoValue(payload.route_video),
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limitations: arrayValue(payload.limitations, "vegetation.limitations")
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.map((item, index) => textValue(item, `vegetation.limitations[${index}]`)),
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visualShadowReady: true,
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@@ -290,6 +404,13 @@ function parseResult(value: unknown, resultId: string): VegetationShadowResult {
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};
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}
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export function vegetationVideoMaskUrl(resultId: string, sequence: number): string {
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if (!RESULT_ID.test(resultId) || !Number.isInteger(sequence) || sequence < 0 || sequence >= 4489) {
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throw new VegetationShadowContractError("Vegetation video mask identity недопустима.");
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}
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return `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/masks/${sequence}`;
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}
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export async function fetchVegetationShadowResult(
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resultId: string,
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{
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@@ -97,7 +97,16 @@ function SpatialState({ message: text }: { message: string }) {
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export interface M4ReplayThreatSemanticLayer {
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resultId: string;
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taxonomy: readonly E47SemanticClass[];
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spatialResultId?: string | null;
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maskUrl?: (sequence: number) => string;
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label?: string;
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maskAriaLabel?: string;
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taxonomy: readonly {
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classId: number;
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label: string;
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disposition: "labeled" | "ambiguous" | "prediction" | "undefined";
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colorRgb: readonly [number, number, number];
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}[];
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}
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export interface M4ReplayThreatReviewAnchor {
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@@ -153,6 +162,8 @@ export function M4ReplayThreatVisual({
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evidenceLabel = "M4.6",
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initialSpatialMode = null,
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classifiedSpatialLayer,
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showReferenceMediaLayers = true,
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showSpatialOverlaySummary = true,
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onActiveSequenceChange,
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}: {
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resultId: string;
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@@ -164,6 +175,8 @@ export function M4ReplayThreatVisual({
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evidenceLabel?: string;
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initialSpatialMode?: LaboratoryMetricSceneMode | null;
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classifiedSpatialLayer?: M4ReplayClassifiedSpatialLayer;
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showReferenceMediaLayers?: boolean;
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showSpatialOverlaySummary?: boolean;
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onActiveSequenceChange?: (sequence: number | null) => void;
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}) {
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const [mediaMode, setMediaMode] = useState<M4ThreatMediaMode | null>("video");
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@@ -280,16 +293,30 @@ export function M4ReplayThreatVisual({
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? lastSpatialFrameRef.current.frame
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: null;
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const cameraPointOverlay = useM4ThreatCameraPointOverlay({
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enabled: showMediaPoints,
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enabled: showReferenceMediaLayers && showMediaPoints,
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resultId,
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sequence: frame?.sequence ?? null,
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endpointRoot: timelineEndpointRoot,
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});
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const semanticSpatialResultId = semantic
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? semantic.spatialResultId === undefined ? semantic.resultId : semantic.spatialResultId
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: null;
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const spatialSemanticTaxonomy = useMemo<readonly E47SemanticClass[]>(
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() => semanticSpatialResultId && semantic
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? semantic.taxonomy.map((item) => ({
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classId: item.classId,
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label: item.label,
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disposition: item.disposition === "ambiguous" ? "ambiguous" : "labeled",
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colorRgb: item.colorRgb,
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}))
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: [],
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[semantic, semanticSpatialResultId],
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);
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const semanticTimeline = useE47SemanticTimelineFrame({
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resultId: semantic?.resultId ?? null,
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resultId: semanticSpatialResultId,
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activeSequence: frame?.sequence ?? timelineFrame.activeSequence,
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frameCount: metadata.timeline?.frameCount ?? 0,
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taxonomy: semantic?.taxonomy ?? [],
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taxonomy: spatialSemanticTaxonomy,
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});
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const displayingBufferedFrame = Boolean(
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frame
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@@ -339,7 +366,8 @@ export function M4ReplayThreatVisual({
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const staticObstacleBoxes = useMemo<readonly RecordedEvidenceBox[]>(() => {
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const timeline = metadata.timeline;
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if (
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!frame
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!showReferenceMediaLayers
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|| !frame
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|| !timeline?.cameraObstacleProjectionDelivery
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|| !showStaticObstacles
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) return [];
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@@ -348,14 +376,14 @@ export function M4ReplayThreatVisual({
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timeline.imageWidth,
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timeline.imageHeight,
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);
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}, [frame, metadata.timeline, showStaticObstacles]);
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}, [frame, metadata.timeline, showReferenceMediaLayers, showStaticObstacles]);
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const activeBoxes = useMemo(
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() => classifiedSpatialLayer ? [] : [
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() => classifiedSpatialLayer || !showReferenceMediaLayers ? [] : [
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...boxes(frame?.cameraProposals ?? []),
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...staticObstacleBoxes,
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...reviewAnchorBoxes,
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],
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[classifiedSpatialLayer, frame, reviewAnchorBoxes, staticObstacleBoxes],
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[classifiedSpatialLayer, frame, reviewAnchorBoxes, showReferenceMediaLayers, staticObstacleBoxes],
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);
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const semanticClasses = useMemo<readonly RecordedEvidenceSemanticClass[]>(
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() => semantic?.taxonomy.map((item) => ({
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@@ -367,10 +395,14 @@ export function M4ReplayThreatVisual({
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const semanticPalette = useMemo<readonly RecordedEvidenceSemanticPaletteEntry[]>(
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() => semantic?.taxonomy.map((item) => ({
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classId: item.classId,
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color: item.disposition === "ambiguous"
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color: item.disposition === "undefined"
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? { kind: "transparent" as const }
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: item.disposition === "ambiguous"
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? { kind: "token" as const, token: "--nodedc-warning-rgb" as const }
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: { kind: "diagnostic" as const, rgb: item.colorRgb },
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opacity: item.disposition === "ambiguous" ? 0.52 : 0.92,
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opacity: item.disposition === "undefined"
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? 0
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: item.disposition === "ambiguous" ? 0.52 : 0.92,
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})) ?? [],
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[semantic?.taxonomy],
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);
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@@ -580,15 +612,17 @@ export function M4ReplayThreatVisual({
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const semanticOverlay: RecordedEvidenceSemanticOverlay | undefined =
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semantic && showMediaSemantic && frame
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? {
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src: e47SemanticMaskUrl(semantic.resultId, frame.sequence),
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src: semantic.maskUrl?.(frame.sequence)
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?? e47SemanticMaskUrl(semantic.resultId, frame.sequence),
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prefetchSrcs: Array.from({ length: 12 }, (_, index) => index + 1)
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.map((offset) => frame.sequence + offset)
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.filter((sequence) => sequence < (metadata.timeline?.frameCount ?? 0))
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.map((sequence) => e47SemanticMaskUrl(semantic.resultId, sequence)),
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.map((sequence) => semantic.maskUrl?.(sequence)
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?? e47SemanticMaskUrl(semantic.resultId, sequence)),
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classes: semanticClasses,
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palette: semanticPalette,
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opacity: 0.9,
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ariaLabel: `E47 semantic mask frame ${frame.sequence + 1}`,
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ariaLabel: `${semantic.maskAriaLabel ?? "Semantic prediction"} frame ${frame.sequence + 1}`,
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}
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: undefined;
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const accumulatedCameraPoints = cameraPointOverlay.overlay?.sequence === frame?.sequence
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@@ -659,8 +693,8 @@ export function M4ReplayThreatVisual({
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);
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const mediaLayerControls = semantic
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|| metadata.timeline?.cameraPointDelivery
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|| metadata.timeline?.cameraObstacleProjectionDelivery ? (
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|| (showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery)
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|| (showReferenceMediaLayers && metadata.timeline?.cameraObstacleProjectionDelivery) ? (
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<div
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className="m4-replay-threat-visual__pane-layer-controls"
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role="group"
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@@ -677,7 +711,7 @@ export function M4ReplayThreatVisual({
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SEMANTICS
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</Button>
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) : null}
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{metadata.timeline?.cameraPointDelivery ? (
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{showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery ? (
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<Button
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size="compact"
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shape="pill"
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@@ -689,7 +723,7 @@ export function M4ReplayThreatVisual({
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POINTS
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</Button>
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) : null}
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{metadata.timeline?.cameraObstacleProjectionDelivery ? (
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{showReferenceMediaLayers && metadata.timeline?.cameraObstacleProjectionDelivery ? (
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<Button
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size="compact"
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shape="pill"
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@@ -738,7 +772,7 @@ export function M4ReplayThreatVisual({
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>
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{classifiedSpatialLayer.cellLayerLabel}
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</Button>
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{semantic ? (
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{semanticSpatialResultId ? (
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<Button
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size="compact"
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shape="pill"
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@@ -796,7 +830,7 @@ export function M4ReplayThreatVisual({
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LOW-STEP
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</Button>
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) : null}
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{semantic ? (
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{semanticSpatialResultId ? (
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<Button
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size="compact"
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shape="pill"
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@@ -901,9 +935,11 @@ export function M4ReplayThreatVisual({
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: playbackController.playback.playing ? "воспроизведение" : "пауза / seek"}
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</small>
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</div>
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<div>
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<span>Spatial evidence</span>
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<strong>{classifiedSpatialLayer
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{showSpatialOverlaySummary ? (
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<>
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<div>
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<span>Spatial evidence</span>
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<strong>{classifiedSpatialLayer
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? classifiedSpatialFrame
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? replaceClassifiedPointCloud
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? `${classifiedSpatialFrame.pointsMapGravityLocalXyzM.length.toLocaleString("ru-RU")} TGS points · ${classifiedCellCount.toLocaleString("ru-RU")} cells`
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@@ -939,15 +975,15 @@ export function M4ReplayThreatVisual({
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: showMediaPoints && cameraPointOverlay.error
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? " · накопленное camera cloud недоступно"
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: ""}
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{semantic && spatialSemanticFrame
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{semanticSpatialResultId && spatialSemanticFrame
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? ` · semantic L ${spatialSemanticFrame.counts.labeled} · A ${spatialSemanticFrame.counts.ambiguous} · U ${spatialSemanticFrame.counts.unprojected} · Ø ${spatialSemanticFrame.counts.absent}`
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: semantic ? " · semantic buffer" : ""}
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: semanticSpatialResultId ? " · semantic buffer" : ""}
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</>
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)}</small>
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</div>
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<div>
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<span>{classifiedSpatialLayer ? "TGS fail-closed" : "Virtual corridor"}</span>
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<strong>{classifiedSpatialLayer
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</div>
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<div>
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<span>{classifiedSpatialLayer ? "TGS fail-closed" : "Virtual corridor"}</span>
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<strong>{classifiedSpatialLayer
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? classifiedSpatialFrame
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? `${classifiedCellCounts.occupied} occupied · ${classifiedCellCounts.rejected} rejected · ${classifiedCellCounts.unobserved} unobserved`
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: classifiedSpatialLayer.loading || displayingBufferedFrame ? "loading" : "unavailable"
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@@ -957,7 +993,9 @@ export function M4ReplayThreatVisual({
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? `${classifiedCellCounts.ground} ground-support · visual review only · navigation authority OFF`
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: "visual review only · navigation authority OFF"
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: `${metadata.timeline.corridor.forwardLengthM} м · body ${metadata.timeline.rig.lengthM}×${metadata.timeline.rig.widthM} м · REPLAY-SIMULATED`}</small>
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</div>
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</div>
|
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</>
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) : null}
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</div>
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||||
) : undefined;
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@@ -1153,13 +1191,13 @@ export function M4ReplayThreatVisual({
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<span>{timelineFrame.error}</span>
|
||||
</div>
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) : null}
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{semantic && semanticTimeline.loading ? (
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{semanticSpatialResultId && semanticTimeline.loading ? (
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<div className="m4-replay-threat-visual__buffering" role="status">
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<span className="busy-indicator" aria-hidden="true" />
|
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<span>Догружаем semantic-point evidence E47</span>
|
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</div>
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||||
) : null}
|
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{semanticTimeline.error ? (
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{semanticSpatialResultId && semanticTimeline.error ? (
|
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<div className="m4-replay-threat-visual__buffering" role="alert">
|
||||
<Icon name="alert" size={16} />
|
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<span>{semanticTimeline.error}</span>
|
||||
@@ -1201,7 +1239,7 @@ export function M4ReplayThreatVisual({
|
||||
<div className="l3-visual-audit m4-replay-threat-visual">
|
||||
<LaboratoryEvidenceViewer
|
||||
label={semantic
|
||||
? "E47 semantic + SLAM diagnostic replay"
|
||||
? semantic.label ?? "Semantic diagnostic replay"
|
||||
: `${evidenceLabel} recorded-realtime replay`}
|
||||
className="m4-replay-threat-evidence-viewer"
|
||||
mode={mediaMode ?? "none"}
|
||||
|
||||
@@ -4,11 +4,15 @@ import {
|
||||
LaboratorySummary,
|
||||
LaboratoryWorkTemplate,
|
||||
} from "../../components/laboratory/LaboratoryPresentation";
|
||||
import type { VegetationShadowResult } from "../../core/laboratory/vegetationShadow";
|
||||
import {
|
||||
vegetationVideoMaskUrl,
|
||||
type VegetationShadowResult,
|
||||
} from "../../core/laboratory/vegetationShadow";
|
||||
import {
|
||||
M48MaskComparisonVisual,
|
||||
type M48MaskComparisonCase,
|
||||
} from "./M48FailureAtlasVisual";
|
||||
import { M4ReplayThreatVisual } from "./M4ReplayThreatVisual";
|
||||
|
||||
function decimal(value: number, digits = 1): string {
|
||||
return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
|
||||
@@ -63,20 +67,28 @@ export function VegetationShadowResultView({
|
||||
summary={(
|
||||
<LaboratorySummary
|
||||
title="LAB V1 · готовые модели растительности"
|
||||
description="Штатный M4.8-инструмент сравнивает две готовые fine-64 модели на полном GOOSE validation split и на 12 truth-backed hard cases, выбранных только по наличию нужной растительности. Sealed evidence открывается локально без Worker 006."
|
||||
status="Truth-backed model comparison · route transfer не принят"
|
||||
description={result.routeVideo
|
||||
? "M4.8 сохраняет truth-backed сравнение моделей, а штатный M4.7 viewer показывает фактический DDRNet prediction на всей записи RAVNOVES00. Все 4489 масок запечатаны локально и открываются без Worker 006."
|
||||
: "Штатный M4.8-инструмент сравнивает две готовые fine-64 модели на полном GOOSE validation split и на 12 truth-backed hard cases, выбранных только по наличию нужной растительности. Sealed evidence открывается локально без Worker 006."}
|
||||
status={result.routeVideo
|
||||
? "DDRNet full-video prediction ready · route truth отсутствует"
|
||||
: "Truth-backed model comparison · route transfer не принят"}
|
||||
statusTone="warning"
|
||||
facts={[
|
||||
{ label: "Источник", value: "GOOSE validation · 962 размеченных кадра · 12 vegetation hard cases" },
|
||||
{ label: "Сравнение", value: "DDRNet-39 vs PPLiteSeg · official fine-64 weights" },
|
||||
{ label: "Кейсы", value: "трава · куст · ствол · крона · изгородь · лес · посевы" },
|
||||
...(result.routeVideo ? [{
|
||||
label: "Видео",
|
||||
value: "RAVNOVES00 · 4489/4489 DDRNet masks · exact recorded sequence",
|
||||
}] : []),
|
||||
{ label: "Authority", value: `${rigLabel} · MODEL QUALIFICATION ONLY · commands OFF` },
|
||||
]}
|
||||
brief={{
|
||||
question: "Какие готовые веса лучше различают проезжаемую траву, кусты и стволы на размеченных off-road кадрах?",
|
||||
approach: "Обе модели последовательно прогнаны в одном изолированном CUDA-runtime на 962 кадрах. 12 визуальных кейсов выбраны детерминированно по truth-поддержке восьми растительных классов; один M4.8 viewer показывает source, truth, prediction и material-error для выбранной модели.",
|
||||
principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%. Ошибки по каждому типу теперь проверяются в одном штатном инструменте.`,
|
||||
limitation: "Это внешний GOOSE-домен, а не наш fisheye/off-road маршрут. Папоротник отдельным классом отсутствует; RAVNOVES00 не содержит truth-backed vegetation island и не используется как главное визуальное доказательство.",
|
||||
principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%. ${result.routeVideo ? "Его фактическая temporal stability теперь видна на всех 4489 кадрах штатного recorded viewer." : "Ошибки по каждому типу проверяются в одном штатном инструменте."}`,
|
||||
limitation: "GOOSE — внешний размеченный домен; RAVNOVES00 — наш fisheye, но без ручной truth-разметки. Full-video слой показывает prediction, а не доказывает правильность. Папоротник отдельным классом отсутствует.",
|
||||
}}
|
||||
method={{
|
||||
completeness: "complete",
|
||||
@@ -93,17 +105,42 @@ export function VegetationShadowResultView({
|
||||
/>
|
||||
)}
|
||||
evidence={(
|
||||
<LaboratoryEvidence
|
||||
eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
|
||||
title="ERROR: красный — пропуск · жёлтый — лишнее · фиолетовый — перепутан тип · зелёный — совпадение"
|
||||
kind="diagnostic-model"
|
||||
resizable
|
||||
>
|
||||
<M48MaskComparisonVisual
|
||||
cases={comparisonCases(result)}
|
||||
initialCandidate={result.selectedCandidate}
|
||||
/>
|
||||
</LaboratoryEvidence>
|
||||
<>
|
||||
<LaboratoryEvidence
|
||||
eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
|
||||
title="ERROR: красный — пропуск · жёлтый — лишнее · фиолетовый — перепутан тип · зелёный — совпадение"
|
||||
kind="diagnostic-model"
|
||||
resizable
|
||||
>
|
||||
<M48MaskComparisonVisual
|
||||
cases={comparisonCases(result)}
|
||||
initialCandidate={result.selectedCandidate}
|
||||
/>
|
||||
</LaboratoryEvidence>
|
||||
{result.routeVideo ? (
|
||||
<LaboratoryEvidence
|
||||
eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
|
||||
title="DDRNet PREDICTION · 4489/4489 кадров · TRUTH для этой записи отсутствует"
|
||||
kind="diagnostic-model"
|
||||
resizable
|
||||
>
|
||||
<M4ReplayThreatVisual
|
||||
resultId={result.routeVideo.baseM4ResultId}
|
||||
evidenceLabel="LAB V1 · DDRNet"
|
||||
showReferenceMediaLayers={false}
|
||||
showSpatialOverlaySummary={false}
|
||||
semantic={{
|
||||
resultId: result.routeVideo.workerResultId,
|
||||
spatialResultId: null,
|
||||
taxonomy: result.routeVideo.taxonomy,
|
||||
maskUrl: (sequence) => vegetationVideoMaskUrl(result.resultId, sequence),
|
||||
label: "DDRNet vegetation prediction · recorded video",
|
||||
maskAriaLabel: "DDRNet vegetation prediction",
|
||||
}}
|
||||
/>
|
||||
</LaboratoryEvidence>
|
||||
) : null}
|
||||
</>
|
||||
)}
|
||||
result={(
|
||||
<LaboratoryResultSummary
|
||||
@@ -146,11 +183,16 @@ export function VegetationShadowResultView({
|
||||
value: "12 truth-backed cases",
|
||||
hint: "8 vegetation strata · Worker для открытия не требуется",
|
||||
},
|
||||
...(result.routeVideo ? [{
|
||||
label: "Route video",
|
||||
value: "4489/4489 masks",
|
||||
hint: "DDRNet prediction · exact sequence · Worker-independent playback",
|
||||
}] : []),
|
||||
]}
|
||||
conclusion={{
|
||||
proved: "Обе официальные fine-64 модели воспроизводимо запускаются на Worker 006; DDRNet лучше по aggregate vegetation IoU. Truth-backed hard cases прямо показывают траву, кусты и стволы, а не случайные автомобили и здания.",
|
||||
notProved: "Не доказаны accuracy на нашем fisheye-домене, папоротник как отдельный материал, temporal stability, collision safety и physical-live поведение ровера.",
|
||||
decision: "Сохранить DDRNet как стартовый vegetation candidate. Mission-policy и автоматическое переключение пресетов подключать только после truth-backed island нашего офф-роуда; LiDAR/TGS fail-closed геометрию не ослаблять.",
|
||||
notProved: "Не доказаны accuracy на нашем fisheye-домене, папоротник как отдельный материал, collision safety и physical-live поведение ровера. Видео позволяет увидеть temporal stability, но без truth не превращает её в метрику качества.",
|
||||
decision: "Смотреть полный prediction на видео и собирать конкретные temporal/domain failure cases. DDRNet остаётся diagnostic candidate; LiDAR/TGS fail-closed геометрию не ослаблять.",
|
||||
}}
|
||||
/>
|
||||
)}
|
||||
|
||||
@@ -75,6 +75,35 @@ function visualCase(sourceKind, index) {
|
||||
};
|
||||
}
|
||||
|
||||
function routeVideo() {
|
||||
return {
|
||||
worker_result_id: `lab-v1-ravnoves-video-ddrnet-${"e".repeat(64)}`,
|
||||
m47_reference_graph_result_id: `m47-reference-graph-lab-${"f".repeat(64)}`,
|
||||
base_m4_result_id: `m4-threat-replay-${"1".repeat(64)}`,
|
||||
frame_count: 4489,
|
||||
width: 800,
|
||||
height: 600,
|
||||
center_crop_xyxy: [100, 0, 700, 600],
|
||||
outside_crop_state: "undefined",
|
||||
sequence_binding: "sequence-0-to-masks/frame-000001.png",
|
||||
taxonomy: {
|
||||
schema_version: "missioncore.lab-v1-vegetation-taxonomy/v1",
|
||||
classes: Array.from({ length: 64 }, (_, classId) => ({
|
||||
class_id: classId,
|
||||
label: classId === 0 ? "undefined" : `class-${classId}`,
|
||||
color_rgb: [classId, classId, classId],
|
||||
disposition: classId === 0 ? "undefined" : "prediction",
|
||||
})),
|
||||
},
|
||||
aggregate_prediction_pixels: Array(64).fill(0),
|
||||
mask_archive: {
|
||||
path: "video/ddrnet-semantic-masks.zip",
|
||||
sha256: "9".repeat(64),
|
||||
byte_length: 1024,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
test("vegetation LAB keeps autonomous assets and fail-closed authority", async () => {
|
||||
let requestedUrl = "";
|
||||
const result = await fetchVegetationShadowResult(resultId, {
|
||||
@@ -111,6 +140,7 @@ test("vegetation LAB keeps autonomous assets and fail-closed authority", async (
|
||||
goose: Array.from({ length: 12 }, (_, index) => visualCase("goose", index)),
|
||||
ravnoves: [],
|
||||
},
|
||||
route_video: routeVideo(),
|
||||
access: "read-only",
|
||||
}), { status: 200, headers: { "Content-Type": "application/json" } });
|
||||
},
|
||||
@@ -123,6 +153,8 @@ test("vegetation LAB keeps autonomous assets and fail-closed authority", async (
|
||||
assert.equal(result.candidates[0].vegetationMeanIouPercent, 64);
|
||||
assert.equal(result.routeCases.length, 0);
|
||||
assert.equal(result.validationCases.length, 12);
|
||||
assert.equal(result.routeVideo.frameCount, 4489);
|
||||
assert.equal(result.routeVideo.taxonomy[0].disposition, "undefined");
|
||||
assert.equal(result.validationCases[0].focus.className, "high_grass");
|
||||
assert.match(result.validationCases[0].assets.ddrnet_error, /\/assets\/visual\/goose\//);
|
||||
assert.deepEqual(result.authority, {
|
||||
@@ -133,13 +165,15 @@ test("vegetation LAB keeps autonomous assets and fail-closed authority", async (
|
||||
});
|
||||
});
|
||||
|
||||
test("vegetation LAB reuses the admitted M4.8 instrument", async () => {
|
||||
test("vegetation LAB reuses the admitted M4.8 and M4.7 instruments", async () => {
|
||||
const resultSource = await readFile(
|
||||
new URL("../src/workspaces/laboratory/VegetationShadowResult.tsx", import.meta.url),
|
||||
"utf8",
|
||||
);
|
||||
assert.match(resultSource, /M48MaskComparisonVisual/);
|
||||
assert.equal(resultSource.match(/<LaboratoryEvidence\b/g)?.length, 1);
|
||||
assert.match(resultSource, /M4ReplayThreatVisual/);
|
||||
assert.equal(resultSource.match(/<LaboratoryEvidence\b/g)?.length, 2);
|
||||
assert.match(resultSource, /showReferenceMediaLayers=\{false\}/);
|
||||
assert.doesNotMatch(resultSource, /VegetationRouteVisual|urban\/rural\/off-road presets/);
|
||||
await assert.rejects(
|
||||
access(new URL("../src/workspaces/laboratory/VegetationShadowVisual.tsx", import.meta.url)),
|
||||
|
||||
Reference in New Issue
Block a user