continuityguard-cli - npm Package Compare versions

Comparing version 0.1.4 to 0.1.5

dist/cli.js

@@ -15,5 +15,5 @@ #!/usr/bin/env node
 import { Command } from 'commander';
-import { resolve } from 'node:path';
-import { realpathSync } from 'node:fs';
-import { pathToFileURL } from 'node:url';
+import { dirname, resolve } from 'node:path';
+import { readFileSync, realpathSync } from 'node:fs';
+import { fileURLToPath, pathToFileURL } from 'node:url';
 import { checkFfmpegAvailable, buildMissingFfmpegMessage, extractFramesFromDirectory, cleanupFrames, listClips, } from './ingest/ffmpeg.js';
@@ -24,3 +24,11 @@ import { scoreConsistency, CONSISTENCY_SIMILARITY_THRESHOLD, } from './score/consistency.js';
 import { renderTerminalReport } from './report/terminal.js';
-const TOOL_VERSION = '0.1.0';
+// Read the version straight from package.json instead of duplicating it in a
+// hardcoded string literal, which drifted out of sync with the real
+// published version (this file said '0.1.0' while npm had 0.1.4 live).
+// package.json sits one directory up from this file both in src/ (run via
+// tsx) and in dist/ (the built, published layout) -- npm always includes
+// package.json in the published tarball regardless of the "files" field, so
+// this path resolves correctly in a real installed package too.
+const packageJsonPath = resolve(dirname(fileURLToPath(import.meta.url)), '..', 'package.json');
+const TOOL_VERSION = JSON.parse(readFileSync(packageJsonPath, 'utf8')).version;
 export function createProgram() {
@@ -27,0 +35,0 @@ const program = new Command();

dist/cli.js.map

@@ -1,1 +0,1 @@
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package.json

 {
   "name": "continuityguard-cli",
-  "version": "0.1.4",
-  "description": "Free, local-first CLI that scores already-generated AI short-drama clips/frames for character-consistency and physics-plausibility problems. Zero network calls, everything runs on your own machine.",
+  "version": "0.1.5",
+  "description": "Free, local-first CLI that scores already-generated AI short-drama clips/frames for character-consistency and physics-plausibility problems before you waste render credits re-shooting a broken shot. Zero network calls, everything runs on your own machine.",
   "keywords": [
@@ -14,8 +14,3 @@ "cli",
     "local-first",
-    "offline",
-    "computer-vision",
-    "video-processing",
-    "zero-network",
-    "typescript",
-    "onnx"
+    "offline"
   ],
@@ -22,0 +17,0 @@ "homepage": "https://github.com/RudrenduPaul/ContinuityGuard#readme",

README.md

@@ -169,12 +169,12 @@ # ContinuityGuard
 
-CG02 infers which character a clip belongs to from its filename, using a `<character>_<shot-id>.<ext>` convention (for example `mei_shot01.mp4`, `mei_shot02.mp4`). Clips sharing a character prefix are compared against that character's first-seen shot. There is no industry-standard character-tagging metadata format across AI short-drama pipelines, so v0.1 reads it from the filename instead of requiring a separate manifest. Clips that don't match the convention are still decoded and scored by CG03, just not compared for character consistency.
+CG02 infers which character a clip belongs to from its filename, using a `<character>_<shot-id>.<ext>` convention (for example `mei_shot01.mp4`, `mei_shot02.mp4`). Clips sharing a character prefix are compared against that character's first-seen shot. There is no industry-standard character-tagging metadata format across AI short-drama pipelines, so ContinuityGuard currently reads it from the filename instead of requiring a separate manifest. Clips that don't match the convention are still decoded and scored by CG03, just not compared for character consistency.
 
 ## Known limitations (read before trusting a flag)
 
-- **Character-consistency scoring is best-validated on photorealistic content.** v0.1 uses a generic ImageNet-pretrained visual-similarity embedding (MobileNetV2, see `src/score/models/NOTICE.md` for the reasoning behind that choice). Its accuracy on stylized or anime-adjacent character designs, which describes most short-drama content, is genuinely unverified going into v0.1. Treat a flag on stylized footage as a prompt to look closer before you trust it.
+- **Character-consistency scoring is best-validated on photorealistic content.** ContinuityGuard currently uses a generic ImageNet-pretrained visual-similarity embedding (MobileNetV2, see `src/score/models/NOTICE.md` for the reasoning behind that choice). Its accuracy on stylized or anime-adjacent character designs, which describes most short-drama content, is genuinely unverified. Treat a flag on stylized footage as a prompt to look closer before you trust it.
 - **The consistency embedding measures general visual similarity: color, texture, and coarse shape between crops.** That's real signal on photorealistic faces, though weaker than a dedicated face-recognition embedding would give you. On stylized designs it's weaker still, and unvalidated.
 - **Physics-plausibility scoring is a frame-to-frame diff heuristic.** It compares motion between consecutive frames against a shot's own local baseline and flags outliers for human review; it makes no attempt to simulate real-world physics. Expect both false positives (legitimate fast motion, stylized jump-cuts) and false negatives (subtly implausible motion that stays under the threshold).
-- **Thresholds are calibrated on a small, fully synthetic fixture set** (solid-color clips, no real faces or recorded motion). See `CHANGELOG.md` for the exact numbers and the command that produced them. This is a real, reproducible starting point for v0.1, calibrated from two synthetic pairs rather than a large labeled dataset. Expect the numbers to move as real-world reports come in.
-- **The `<character>_<shot-id>` filename convention is a v0.1 simplification**, built for a category that has no standard character-tagging metadata format yet. A clip that doesn't follow it still gets scored for physics; its consistency comparison is simply skipped.
-- **Requires a system `ffmpeg` install.** ContinuityGuard checks for it at startup and prints the exact install command for your OS if it's missing (for example `brew install ffmpeg` on macOS, `apt install ffmpeg` on Debian/Ubuntu). It does not bundle a static ffmpeg binary in v0.1. A bundled per-platform build would be materially larger than this project's own dependency footprint and would inherit ffmpeg's own shifting LGPL/GPL licensing terms depending on which codecs are compiled in. Depending on a system install keeps this package small and its licensing surface simple.
+- **Thresholds are calibrated on a small, fully synthetic fixture set** (solid-color clips, no real faces or recorded motion). See `CHANGELOG.md` for the exact numbers and the command that produced them. This is a real, reproducible starting point, calibrated from two synthetic pairs rather than a large labeled dataset. Expect the numbers to move as real-world reports come in.
+- **The `<character>_<shot-id>` filename convention is a current simplification**, built for a category that has no standard character-tagging metadata format yet. A clip that doesn't follow it still gets scored for physics; its consistency comparison is simply skipped.
+- **Requires a system `ffmpeg` install.** ContinuityGuard checks for it at startup and prints the exact install command for your OS if it's missing (for example `brew install ffmpeg` on macOS, `apt install ffmpeg` on Debian/Ubuntu). It does not currently bundle a static ffmpeg binary. A bundled per-platform build would be materially larger than this project's own dependency footprint and would inherit ffmpeg's own shifting LGPL/GPL licensing terms depending on which codecs are compiled in. Depending on a system install keeps this package small and its licensing surface simple.
 
@@ -187,6 +187,6 @@ ## How it compares
 |---|---|---|---|---|---|
-| [Vchitect/VBench](https://github.com/Vchitect/VBench) | 1,718 | Mar 2026 | Apache-2.0 | CVPR 2024 academic benchmark suite that scores video-generation models across 16 dimensions, including a DINOv2-based "subject consistency" metric and a motion-smoothness metric, aggregated across many generated clips | The closest mechanism match: it also scores subject consistency and motion quality on already-generated video. It's a GPU-dependent research benchmark for evaluating a generation model in aggregate, not a lightweight local CLI built to give one creator a per-clip QA report on their own footage. |
-| [serengil/deepface](https://github.com/serengil/deepface) | 23,204 | Jun 2026 | MIT | Widely used, actively maintained Python library for face verification, embeddings, and facial attribute analysis | A component library for face-embedding similarity, not a video-native or short-drama-specific tool. Useful as a building block, but ships no physics check, no clip decoding, and no packaged report format of its own. |
-| [deepinsight/insightface](https://github.com/deepinsight/insightface) | 29,406 | Jul 2026 | No single machine-readable LICENSE in the repo; project docs describe MIT-licensed code with non-commercial restrictions on some pretrained models | State-of-the-art 2D/3D face analysis toolbox (detection, recognition, alignment) | Same category as DeepFace: a component library, not an end-to-end QA CLI. No physics-plausibility check, no report layer. |
-| [evalcrafter/EvalCrafter](https://github.com/evalcrafter/EvalCrafter) | 195 | Oct 2024 | None listed | CVPR 2024 research framework benchmarking video-generation models on visual quality, motion quality, and text-video alignment across 700 prompts | Adjacent research tool with no commits in roughly two years. Built to benchmark generation models with a heavy multi-metric research stack, not to give a fast local QA pass on a folder of already-generated clips. |
+| [Vchitect/VBench](https://github.com/Vchitect/VBench) | 1,697 | Mar 2026 | Apache-2.0 | CVPR 2024 academic benchmark suite that scores video-generation models across 16 dimensions, including a DINOv2-based "subject consistency" metric and a motion-smoothness metric, aggregated across many generated clips | The closest mechanism match: it also scores subject consistency and motion quality on already-generated video. It's a GPU-dependent research benchmark for evaluating a generation model in aggregate, not a lightweight local CLI built to give one creator a per-clip QA report on their own footage. |
+| [serengil/deepface](https://github.com/serengil/deepface) | 23,113 | Jun 2026 | MIT | Widely used, actively maintained Python library for face verification, embeddings, and facial attribute analysis | A component library for face-embedding similarity, not a video-native or short-drama-specific tool. Useful as a building block, but ships no physics check, no clip decoding, and no packaged report format of its own. |
+| [deepinsight/insightface](https://github.com/deepinsight/insightface) | 29,263 | May 2026 | No single machine-readable LICENSE in the repo; project docs describe MIT-licensed code with non-commercial restrictions on some pretrained models | State-of-the-art 2D/3D face analysis toolbox (detection, recognition, alignment) | Same category as DeepFace: a component library, not an end-to-end QA CLI. No physics-plausibility check, no report layer. |
+| [evalcrafter/EvalCrafter](https://github.com/evalcrafter/EvalCrafter) | 193 | Oct 2024 | None listed | CVPR 2024 research framework benchmarking video-generation models on visual quality, motion quality, and text-video alignment across 700 prompts | Adjacent research tool with no commits in roughly two years. Built to benchmark generation models with a heavy multi-metric research stack, not to give a fast local QA pass on a folder of already-generated clips. |
 
@@ -222,3 +222,3 @@ Star counts and last-activity dates above were pulled live from the GitHub API while writing this table.
 **Why do I need to install ffmpeg separately instead of it being bundled?**
-A bundled static ffmpeg binary would add tens of megabytes per platform to this package and would inherit ffmpeg's own licensing terms, which shift between LGPL and GPL depending on which codecs are compiled in. ffmpeg is close to ubiquitous on developer machines already, so v0.1 depends on a system install and checks for it at startup with a clear, OS-specific error if it's missing.
+A bundled static ffmpeg binary would add tens of megabytes per platform to this package and would inherit ffmpeg's own licensing terms, which shift between LGPL and GPL depending on which codecs are compiled in. ffmpeg is close to ubiquitous on developer machines already, so ContinuityGuard depends on a system install and checks for it at startup with a clear, OS-specific error if it's missing.
 
@@ -238,3 +238,3 @@ **Is this on npm yet?**
 **How does this compare to a face-embedding library like deepface instead of a generic ImageNet model?**
-Directly, they solve different layers of the same problem. `serengil/deepface` (MIT-licensed, actively maintained) is a dedicated face-verification and embedding library; wiring it into a scan pipeline yourself would likely give more accurate character-consistency scoring than ContinuityGuard's current generic MobileNetV2 embedding, especially on stylized content. ContinuityGuard's role isn't to out-perform a dedicated face-embedding library on embeddings alone: it's the packaged CLI on top, doing ffmpeg decoding, the physics-plausibility pass, and structured report output in one zero-network command. Swapping in a stronger embedding model later is an open, tracked improvement, not a claim already delivered in v0.1. See "How it compares" above for the fuller table.
+Directly, they solve different layers of the same problem. `serengil/deepface` (MIT-licensed, actively maintained) is a dedicated face-verification and embedding library; wiring it into a scan pipeline yourself would likely give more accurate character-consistency scoring than ContinuityGuard's current generic MobileNetV2 embedding, especially on stylized content. ContinuityGuard's role isn't to out-perform a dedicated face-embedding library on embeddings alone: it's the packaged CLI on top, doing ffmpeg decoding, the physics-plausibility pass, and structured report output in one zero-network command. Swapping in a stronger embedding model later is an open, tracked improvement, not a claim already delivered. See "How it compares" above for the fuller table.
 
@@ -257,2 +257,2 @@ **Is this free to use commercially?**
 
-Everything in this repo: Apache 2.0. Free forever, no paid tier in v0.1.
+Everything in this repo: Apache 2.0. Free forever, no paid tier.

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