#MoonDetEval

#Current scope

///|
let detection = @moondeteval.CocoRle::new(3, 4, [1, 2, 2, 3, 4])

///|
let truth = @moondeteval.CocoRle::new(3, 4, [0, 3, 2, 1, 6])
// detection.area() == 5; detection.iou(truth) == 0.5
// detection.crowd_iou(truth) == 0.6

///|
let compressed = @moondeteval.parse_coco_rle(
  "{\"size\":[3,4],\"counts\":\"032N4\"}",
)
// compressed.to_compressed() == "032N4"; detection.iou(compressed) == 0.5

#Instance-mask evaluation (source checkout)

///|
let masks = @moondeteval.parse_coco_mask_dataset(ground_truth_json)

///|
let predictions = @moondeteval.parse_coco_mask_detections(detections_json)

///|
let report = @moondeteval.evaluate_masks(masks, predictions)

///|
let comparison = @moondeteval.compare_mask_predictions(
  masks, predictions, other_predictions,
)

moon run cli --target native -- examples/mask-ground-truth.json examples/mask-baseline.json --iou-type segm --compare examples/mask-candidate.json --diagnostics --image-ranking --pr-curves --min-ap50 1

#COCO polygon input (source checkout)

moon run cli --target native -- examples/polygon-ground-truth.json examples/polygon-detections.json --iou-type segm --compare examples/mask-candidate.json --pr-curves --image-ranking --min-ap50 1

#Related packages

#Install and call the library

///|
fn main {
  let box = @moondeteval.Box::from_xywh(0.0, 0.0, 10.0, 10.0) catch {
    error => abort(error.message())
  }
  let dataset = @moondeteval.Dataset::new(
    [@moondeteval.Image::new(1, 20, 20)],
    [@moondeteval.Category::new(7, "object")],
    [@moondeteval.GroundTruth::new(9L, 1, 7, box, area=100.0)],
  ) catch {
    error => abort(error.message())
  }
  let prediction = @moondeteval.Detection::new(0, 1, 7, box, 0.9) catch {
    error => abort(error.message())
  }
  let report = @moondeteval.evaluate(dataset, [prediction]) catch {
    error => abort(error.message())
  }
  println("AP50=\{report.summary().ap50()}")
}

#Run the source examples

python examples/batch_eval.py --ground-truth examples/compare-ground-truth.json --model baseline examples/compare-baseline.json --model candidate examples/compare-candidate.json --baseline baseline --image-ranking --pr-curves --output-dir ../moondeteval-batch-results

python examples/batch_eval.py --ground-truth examples/polygon-ground-truth.json --iou-type segm --model polygon examples/polygon-detections.json --model rle examples/mask-candidate.json --model miss examples/mask-baseline.json --baseline polygon --diagnostics --image-ranking --pr-curves --output-dir ../moondeteval-segm-batch-results

#Metric regression gates (0.2.0)

--min-metric METRIC=VALUE
--max-metric-drop METRIC=VALUE
--min-class-metric CATEGORY:METRIC=VALUE
--max-class-drop CATEGORY:METRIC=VALUE
--undefined-gate fail|skip

moon run cli --target native -- examples/compare-ground-truth.json examples/compare-baseline.json --compare examples/compare-candidate.json --max-metric-drop ap50=0.1 --min-metric ar100=0.5 --output ../regression.report.json

python examples/batch_eval.py --ground-truth examples/compare-ground-truth.json --model baseline examples/compare-baseline.json --model candidate examples/compare-candidate.json --model unchanged examples/compare-baseline.json --baseline baseline --max-metric-drop ap50=0.1 --output-dir ../gated-batch

#Reusable policy files

{"format_version":"1","undefined_policy":"fail","rules":[{"kind":"max_drop","metric":"ap50","threshold":0.1},{"kind":"minimum","metric":"ar100","threshold":0.5}]}

moon run cli --target native -- --check-gate-policy examples/regression-policy.json
moon run cli --target native -- examples/compare-ground-truth.json examples/compare-baseline.json --compare examples/compare-candidate.json --gate-policy examples/regression-policy.json
python examples/batch_eval.py --ground-truth examples/compare-ground-truth.json --model baseline examples/compare-baseline.json --model candidate examples/compare-candidate.json --model unchanged examples/compare-baseline.json --baseline baseline --gate-policy examples/regression-policy.json --output-dir ../policy-batch

moon run cli --target native -- examples/ground-truth.json examples/detections.json
moon run cli --target native -- examples/ground-truth.json examples/detections.json --diagnostics --min-ap50 0.8
moon run cli --target native -- examples/ground-truth.json examples/detections.json --output report.json
python examples/compare_demo.py
python examples/showcase_demo.py

{"schema_version":"0.1.0","summary":{"ap":1,"ap50":1,"ap75":1,"ap_small":1,"ap_medium":-1,"ap_large":-1,"ar1":0,"ar10":1,"ar100":1,"ar_small":1,"ar_medium":-1,"ar_large":-1}}

moon run cli --target native -- examples/compare-ground-truth.json examples/compare-baseline.json --compare examples/compare-candidate.json --diagnostics
moon run cli --target native -- examples/compare-ground-truth.json examples/compare-baseline.json --compare examples/compare-candidate.json --image-ranking
moon run cli --target native -- examples/compare-ground-truth.json examples/compare-baseline.json --compare examples/compare-candidate.json --pr-curves

#Multi-image YOLO predictions

{
  "category_ids": [42, 7],
  "images": [
    {"image_id": 12, "file": "labels/image-12.txt"},
    {"image_id": 9, "file": "labels/image-9.txt"}
  ]
}

moon run cli --target native -- examples/yolo/ground-truth.json examples/yolo/manifest.json --prediction-format yolo --diagnostics
moon run cli --target native -- examples/yolo/ground-truth.json examples/yolo/detections.json --compare examples/yolo/manifest.json --compare-format yolo --diagnostics
moon run cli --target native -- examples/yolo/ground-truth.json examples/yolo/manifest.json --prediction-format yolo --min-ap50 0.8 --output failed-gate.json

#Single-machine evaluation (0.2.0)

moon build cli --target native --release
# Replace the executable path with the build artifact on your platform.
_build/native/release/build/cli/cli.exe ground-truth.json predictions.json --iou-type segm --max-input-bytes 536870912 --max-total-input-bytes 1073741824 --max-records 5000000 --max-iou-cache-pairs 262144 --timings --output report.json

#Library use

///|
let dataset = @moondeteval.parse_coco_dataset(ground_truth_json)

///|
let predictions = @moondeteval.parse_coco_detections(detections_json)

///|
let report = @moondeteval.evaluate(dataset, predictions)

///|
let ap50 = report.summary().ap50()

///|
let config = @moondeteval.EvalConfig::new([0.5, 0.75], include_diagnostics=true)

///|
let custom = @moondeteval.evaluate_with_config(dataset, predictions, config)

///|
let comparison = @moondeteval.compare_predictions(
  dataset, predictions, other_predictions,
)

///|
let ap50_change = comparison.metrics()[1].delta()

///|
let yolo_predictions = @moondeteval.parse_yolo_detections(
  yolo_txt,
  dataset,
  9,
  [42, 7],
  first_detection_id=100,
)

///|
let manifest = @moondeteval.parse_yolo_manifest(manifest_json, dataset)

///|
let retained = report.counts().evaluated_detections()

///|
let warnings = report.quality_warnings()

///|
let explicit_ignore = @moondeteval.EvalConfig::new(
  [0.5],
  ignore_policy="respect_explicit",
)

#Verify a source checkout

moon fmt --check
moon check --target all --deny-warn
moon test --target all --deny-warn
python -m unittest examples/scale_test.py -v
python examples/compare_demo.py
python examples/yolo_manifest_demo.py
python -m pip install numpy==1.26.4 pycocotools==2.0.7
python reference_compare.py
python examples/mask_rle_reference.py
python examples/mask_ap_reference.py
python examples/polygon_reference.py

#AI 使用说明

#License and attribution