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Tools Guide

This guide maps the utilities that currently exist under tools/.

Tool Inventory

Tool Main files Purpose
quality comparator tools/quality-comparator/comparator.py, visualizer.py compare SR outputs against HR references
video analyzer tools/video-analyzer/analyzer.py, metrics.py, main.py analyze video quality and temporal behavior
benchmark runner tools/benchmark-runner/runner.py, profiler.py run TFLite enhancement on desktop and collect timings
reporting tools/reporting/report_generator.py, visualizer.py build reports and charts from benchmark CSVs
docx creator tools/docx-creator/converter.py convert supported text sources into DOCX outputs

Quality Comparator

See the full README:

../../tools/quality-comparator/README.md

Typical use:

python tools/quality-comparator/comparator.py --lr_dir inputs --sr_dir outputs --hr_dir references --output_dir outputs/comparison
python tools/quality-comparator/visualizer.py --lr input.png --sr span.png eduscale.png --hr reference.png --output outputs/comparison.png

Video Analyzer

See the full README:

../../tools/video-analyzer/README.md

Typical use:

python tools/video-analyzer/analyzer.py --input video.mp4 --model models/span/original/span_x2_ch48.pth --output_dir outputs/video-analysis

Benchmark Runner

This is the desktop TFLite runner used by the benchmark scripts.

python tools/benchmark-runner/runner.py --input datasets/benchmark/lr --output_dir outputs/enhanced --preset balanced

The runner can handle both image directories and individual videos. It also writes inference-times.csv into the output directory unless you override it.

Reporting

Generate summary docs from benchmarks/results/:

python tools/reporting/report_generator.py --results_dir benchmarks/results --output_format markdown --output benchmarks/results/report.md

Use tools/reporting/visualizer.py when you want plots rather than text reports.

DOCX Creator

The tools/docx-creator/ folder includes a converter and its own README. Use it when you need Word-formatted deliverables built from repo content or related artifacts.

How these tools fit together

  1. generate or collect datasets
  2. train and export a checkpoint
  3. run desktop enhancement with the benchmark runner
  4. compare outputs with the quality comparator
  5. summarize results with reporting scripts
  6. explore trends in notebooks under experiments/

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