feat: command for running VMAF on arbitrary files using configured pi…

…peline

Signed-off-by: Gustav Grusell <[email protected]>

README.md

@@ -181,6 +181,8 @@ Commands:
                                     videofile source                   [default]
   autovmaf suggest-ladder <folder>  Suggest bitrate ladder given vmaf results
   autovmaf export-csv <folder>      Export Vmaf results as csv
+  autovmaf vmaf                     Use pipeline to run VMAF for the given
+                                    distorted and reference files
 
 Positionals:
   source  SOURCEFILE                                                    [string]
@@ -307,6 +309,23 @@ Options:
 If your job uses variables ([See above](#using-variables-in-the-job-definition)), the variables that should
 be included in the csv data should be specified with the `--variables` option.
 
+### Run VMAF only
+This command allows using a configured pipeline to run VMAF measurements on any files without an related autovmaf job.
+In case of an aws pipeline, output will be written to the `outputBucket` configured in the pipeline.
+```
+autovmaf vmaf
+
+Use pipeline to run VMAF for the given distorted and reference files
+
+Options:
+  --version    Show version number                                     [boolean]
+  --help       Show help                                               [boolean]
+  --pipeline   Path to pipeline file                         [string] [required]
+  --reference  Uri or path to reference file                 [string] [required]
+  --distorted  Uri or path to distorted file                 [string] [required]
+  --model      VMAF model to use                        [string] [default: "HD"]
+```
+
 ## Development
 
 ### Run tests

src/cli.ts

@@ -12,6 +12,11 @@ import suggestLadder from './suggest-ladder';
 import ObjectsToCsv from 'objects-to-csv';
 import { pairVmafWithResolutionAndBitrate } from './pairVmaf';
 import { VmafBitratePair } from './models/vmaf-bitrate-pair';
+import { loadPipeline } from './load-pipeline';
+import {
+  QualityAnalysisModel,
+  stringToQualityAnalysisModel
+} from './models/quality-analysis-model';
 
 class ValidationError extends Error {
   constructor(message) {
@@ -133,10 +138,57 @@ async function run() {
       },
       exportWmafResultToCsv
     )
+    .command(
+      'vmaf',
+      'Use pipeline to run VMAF for the given distorted and reference files',
+      (yargs) => {
+        return yargs.options({
+          pipeline: {
+            type: 'string',
+            description: 'Path to pipeline file',
+            required: true
+          },
+          reference: {
+            type: 'string',
+            description: 'Uri or path to reference file',
+            required: true
+          },
+          distorted: {
+            type: 'string',
+            description: 'Uri or path to distorted file',
+            required: true
+          },
+          model: {
+            type: 'string',
+            description: 'VMAF model to use',
+            default: 'HD'
+          }
+        });
+      },
+      runVmaf
+    )
     .help()
     .parse();
 }
 
+async function runVmaf(argv) {
+  const reference = argv.reference;
+  const distorted = argv.distorted;
+  const pipeline = await loadPipeline(argv.pipeline);
+  const qualityFile = argv.distorted.replace('.mp4', '_vmaf.json');
+  const model: QualityAnalysisModel = stringToQualityAnalysisModel(argv.model);
+  const jobDescription = `${model},${reference},${distorted},${qualityFile}`;
+  pipeline
+    .analyzeQuality(reference, distorted, qualityFile, model)
+    .then(() => {
+      logger.info(`Finished VMAF measurement for ${jobDescription}`);
+    })
+    .catch((err) => {
+      logger.error(`Error running VMAF for ${jobDescription}: ${err}`);
+    });
+  logger.info(`Started VMAF measurement for ${jobDescription}`);
+}
+
 async function runSuggestLadder(argv) {
   const { ladder, pairs } = await suggestLadder(argv.folder);
   logger.info(`ladder: ${ladder}`);