r"""
Uses the building detector to validate a construction event.

Given a site model:

* search for the K highest quality images at the start of the sequences.

* search for the K highest quality images at the end of the sequence.

* run the building detector on all of the chosen images.

* test if the site boundary intersects detections in the start images.

* test if the site boundary intersects detections in the end images.


.. code::

    StartTest | EndTest | Result
    ----------+---------+-------
        T     |    T    | Reject
    ----------+---------+-------
        T     |    F    | Reject
    ----------+---------+-------
        F     |    T    | Accept
    ----------+---------+-------
        F     |    F    | Reject
    ----------+---------+-------
        ?     |    T    | Accept
    ----------+---------+-------
        T     |    ?    | Reject
    ----------+---------+-------
        ?     |    F    | Reject
    ----------+---------+-------
        F     |    ?    | Accept
    ----------+---------+-------


Dataflow:

    * BAS outputs a region model with candidate site summaries

    * We should be given a kwcoco path that indexes all of the data we could
      look at. In MLOPs this will be a region cropped kwcoco path that indexes
      existant images on disk. In smartflow this will be a virtual kwcoco file
      that requires network access.
"""
#!/usr/bin/env python3
import scriptconfig as scfg
import ubelt as ub


class BuildingValidatorConfig(scfg.DataConfig):
    input_kwcoco = scfg.Value(None, help='input')

    input_region = scfg.Value(None)
    input_sites = scfg.Value(None)

    output_region_fpath = scfg.Value(None)
    output_sites_dpath = scfg.Value(None)
    output_site_manifest_fpath = scfg.Value(None)

    box_isect_threshold = scfg.Value(0.1, help='This fraction of the a detected building box must intersect the proposed polygon')
    box_score_threshold = scfg.Value(0.01, help='The detected building boxes must have a score higher than this')
    start_max_score = scfg.Value(1.0, help='The max building score needed in the start sequence')
    end_min_score = scfg.Value(0.1, help='The min building score needed in the end sequence')


IGNORE_CLASS_NAMES = ub.codeblock(
    '''
    Fixed-wing Aircraft
    Small Aircraft
    Cargo Plane
    Helicopter
    Passenger Vehicle
    Small Car
    Bus
    Pickup Truck
    Utility Truck
    Truck
    Cargo Truck
    Truck w/Box
    Truck Tractor
    Trailer
    Truck w/Flatbed
    Truck w/Liquid
    Crane Truck
    Railway Vehicle
    Passenger Car
    Cargo Car
    Flat Car
    Tank car
    Locomotive
    Maritime Vessel
    Motorboat
    Sailboat
    Tugboat
    Barge
    Fishing Vessel
    Ferry
    Yacht
    Container Ship
    Oil Tanker
    Engineering Vehicle
    Tower crane
    Container Crane
    Reach Stacker
    Straddle Carrier
    Mobile Crane
    Dump Truck
    Haul Truck
    Scraper/Tractor
    Front loader/Bulldozer
    Excavator
    Cement Mixer
    Ground Grader
    ''').split('\n')

DONT_IGNORE_CLASSNAMES = ub.codeblock(
    '''
    Aircraft Hangar
    Helipad
    Storage Tank
    Hut/Tent
    Shipping container lot
    Shipping Container
    Pylon
    Tower
    Shed
    Building
    Damaged Building
    Facility
    Construction Site
    Vehicle Lot
    ''').split('\n')


def main(cmdline=1, **kwargs):
    """
    Ignore:
        from watch.tasks.dino_detector.building_validator import *  # NOQA
        NODE_DPATH = '/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_fd298dba'
        from watch.utils.partial_format import fsubtemplate
        coco_fpath = fsubtemplate('$NODE_DPATH/pred_boxes.kwcoco.zip')
        from watch.utils.util_path import coerce_patterned_paths
        site_summary = ub.Path(fsubtemplate('$NODE_DPATH/.pred/valicrop/*/.pred/bas_poly/*/site_summaries_manifest.json'))
        site_summary = coerce_patterned_paths(site_summary)[0]

        output_region_fpath = ub.Path(fsubtemplate('$NODE_DPATH/filtered_site_summaries.geojson'))

        kwargs = {
            'input_kwcoco': coco_fpath,
            'input_region': site_summary,
            'output_region_fpath': output_region_fpath,
        }
        cmdline = 0

    Ignore:
        >>> import watch
        >>> dvc_data_dpath = watch.find_dvc_dpath(tags='phase2_data', hardware='auto')
        >>> coco_fpath = dvc_data_dpath / 'Drop6/imgonly-KR_R001.kwcoco.json'
        >>> region_fpath = dvc_data_dpath / 'annotations/drop6_hard_v1/region_models/KR_R001.geojson'

    Example:
        >>> # xdoctest: +SKIP
        >>> cmdline = 0
        >>> kwargs = dict()
        >>> main(cmdline=cmdline, **kwargs)
    """
    config = BuildingValidatorConfig.cli(cmdline=cmdline, data=kwargs, strict=True)
    import rich
    rich.print('config = ' + ub.urepr(config, nl=1))

    from watch.geoannots import geomodels
    from watch.utils import util_gis
    import kwcoco
    from watch.cli import reproject_annotations
    from watch.utils import util_time
    from kwcoco.util import util_json
    from watch.utils import process_context
    import os
    import safer
    import json

    # Args will be serailized in kwcoco, so make sure it can be coerced to json
    jsonified_config = util_json.ensure_json_serializable(config.asdict())
    walker = ub.IndexableWalker(jsonified_config)
    for problem in util_json.find_json_unserializable(jsonified_config):
        bad_data = problem['data']
        walker[problem['loc']] = str(bad_data)
    filter_output = {
        'type': 'tracking_result',
        'info': [],
        'files': [],
    }
    # Track process info
    proc_context = process_context.ProcessContext(
        name='watch.tasks.dino_detector.building_validator', type='process',
        config=jsonified_config,
        track_emissions=False,
    )
    proc_context.start()
    filter_output['info'].append(proc_context.obj)

    input_coco = kwcoco.CocoDataset(config.input_kwcoco)
    region_model = geomodels.RegionModel.coerce(config.input_region)
    output_region_fpath = ub.Path(config.output_region_fpath)

    input_site_fpaths = util_gis.coerce_geojson_paths(config.input_sites)
    # set(input_coco.annots().lookup('track_id', None))

    site_id_to_summary = {}
    for summary in region_model.site_summaries():
        assert summary.site_id not in site_id_to_summary
        site_id_to_summary[summary.site_id] = summary
    ##

    output_kwcoco = reproject_annotations.main(
        cmdline=0, src=input_coco.copy(),
        dst='return',
        region_models=config.input_region,
        status_to_catname={'system_confirmed': 'positive'},
        role='pred_poly',
        validate_checks=False,
        clear_existing=False,
    )

    site_to_accept = {}
    for video_id in ub.ProgIter(output_kwcoco.videos(), desc='validate sites'):
        video = output_kwcoco.index.videos[video_id]
        video_name = video['name']  # the vide name should be the site id
        summary = site_id_to_summary[video_name]
        site_id = summary.site_id
        video_images = output_kwcoco.images(video_id=video_id)

        start_date = summary.start_date
        end_date = summary.end_date

        # Get the starting and ending observations
        start_images = []
        end_images = []
        for coco_img in video_images.coco_images:
            img_time = util_time.coerce_datetime(coco_img['date_captured'])
            dist_start = abs(img_time - start_date)
            dist_end = abs(img_time - end_date)
            if dist_end < dist_start:
                end_images.append(coco_img)
            else:
                start_images.append(coco_img)

        start_features = []
        for coco_img in start_images:
            feat = building_in_image_features(coco_img, site_id, config)
            start_features.append(feat)

        end_features = []
        for coco_img in end_images:
            feat = building_in_image_features(coco_img, site_id, config)
            end_features.append(feat)

        if len(start_features) and len(end_features):
            max_start_score = max(f['max_score'] for f in start_features)
            max_end_score = max(f['max_score'] for f in end_features)
            accept = (
                max_start_score <= config.start_max_score and
                max_end_score >= config.end_min_score
            )
        else:
            # Unobservable case, automatically accept
            accept = True
        site_to_accept[site_id] = accept

    accept_sites = [s for s, f in site_to_accept.items() if f]
    print(f'Filter to {len(accept_sites)} / {len(site_id_to_summary)} sites')
    new_summaries = ub.udict(site_id_to_summary).subdict(accept_sites).values()

    site_to_site_fpath = ub.udict({
        p.stem: p for p in input_site_fpaths
    })
    assert set(site_to_site_fpath) ==  set(site_id_to_summary)

    # Copy the filtered site models over to the output directory
    keep_site_fpaths = site_to_site_fpath.subdict(accept_sites)
    output_sites_dpath = ub.Path(config.output_sites_dpath)
    output_sites_dpath.ensuredir()
    for old_fpath in keep_site_fpaths.values():
        new_fpath = output_sites_dpath / old_fpath.name
        old_fpath.copy(new_fpath, overwrite=True)

    new_region_model = geomodels.RegionModel.from_features(
        [region_model.header] + list(new_summaries))

    output_region_fpath.parent.ensuredir()
    with safer.open(output_region_fpath, 'w', temp_file=True) as file:
        json.dump(new_region_model, file, indent=4)

    proc_context.stop()

    if config.output_site_manifest_fpath is not None:
        filter_output['files'] = [os.fspath(output_region_fpath)]
        print(f'Write filtered site result to {config.output_site_manifest_fpath}')
        with safer.open(config.output_site_manifest_fpath, 'w', temp_file=True) as file:
            json.dump(filter_output, file, indent=4)


def building_in_image_features(coco_img, site_id, config):
    import numpy as np
    import kwimage
    import geopandas as gpd
    import warnings
    annots = coco_img.annots()
    flags = np.array([r == 'pred_poly' for r in annots.lookup('role', None)])
    box_annots = annots.compress(~flags)
    poly_annots = annots.compress(flags)
    is_main_poly = [t == site_id for t in poly_annots.lookup('track_id')]
    main_poly_annot = poly_annots.compress(is_main_poly)
    assert len(main_poly_annot) == 1

    flags = [cname not in IGNORE_CLASS_NAMES for cname in box_annots.category_names]
    box_annots = box_annots.compress(flags)

    iooa_thresh = config.box_isect_threshold
    score_thresh = config.box_score_threshold

    box_gdf = gpd.GeoDataFrame({
        'geometry': box_annots.boxes.to_shapely(),
        'class': box_annots.category_names,
        'score': box_annots.lookup('score'),
    })
    box_gdf = box_gdf[box_gdf.score > score_thresh]

    proposal_gdf = gpd.GeoDataFrame({
        'geometry': [
            kwimage.MultiPolygon.coerce(p).to_shapely() for p in main_poly_annot.lookup('segmentation')
        ],
    })
    proposal_geom = proposal_gdf.iloc[0]['geometry']

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', 'invalid value encountered in intersection')
        intersections = box_gdf.intersection(proposal_geom)
        bad_flags = (intersections.isnull() | intersections.is_empty)
        isect_flags = (~bad_flags) & (intersections.is_valid)
        feat = {
            'max_iou': 0,
            'max_iooa': 0,
            'max_score': 0,
            'num_cands': 0,
        }
        if isect_flags.any():
            cand_boxes = box_gdf[isect_flags]
            cand_isects = intersections[isect_flags]
            cand_iooa1 = cand_isects.area / cand_boxes.area
            ioaa_flags = cand_iooa1 > iooa_thresh
            if ioaa_flags.any():
                cand_boxes = cand_boxes[ioaa_flags]

                cand_unions = cand_boxes.union(proposal_geom)
                cand_ious = cand_isects.area / cand_unions.area

                feat['max_iou'] = cand_ious.max()
                feat['max_iooa'] = cand_iooa1.max()
                feat['max_score'] = cand_boxes['score'].max()
                feat['num_cands'] = len(cand_boxes)
        return feat


if __name__ == '__main__':
    """

    CommandLine:
        python ~/code/watch/watch/tasks/dino_detector/building_validator.py
        python -m watch.tasks.dino_detector.building_validator
    """
    main()

"""

Ignore:
    DVC_DATA_DPATH=$(geowatch_dvc --tags='phase2_data' --hardware=auto)
    DVC_EXPT_DPATH=$(geowatch_dvc --tags='phase2_expt' --hardware=auto)
    geowatch schedule --params="
        matrix:
            bas_pxl.package_fpath:
                - $DVC_EXPT_DPATH/models/fusion/Drop6-MeanYear10GSD/packages/Drop6_TCombo1Year_BAS_10GSD_split6_V42_cont2/Drop6_TCombo1Year_BAS_10GSD_split6_V42_cont2_epoch3_step941.pt
            bas_pxl.test_dataset:
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-KR_R002.kwcoco.zip
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-BR_R002.kwcoco.zip
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-CH_R001.kwcoco.zip
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-NZ_R001.kwcoco.zip
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-KR_R001.kwcoco.zip
                - $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-AE_R001.kwcoco.zip
            bas_pxl.chip_overlap: 0.3
            bas_pxl.chip_dims:
                - auto
            bas_pxl.time_span:
                - auto
            bas_pxl.time_sampling:
                # - auto
                # - soft5
                - soft4
            bas_poly.thresh:
                - 0.33
                #- 0.38
                #- 0.4
            bas_poly.inner_window_size:
                - 1y
                #- null
            bas_poly.inner_agg_fn:
                - mean
            bas_poly.norm_ord:
                #- 1
                - inf
            bas_poly.polygon_simplify_tolerance:
                - 1
            bas_poly.agg_fn:
                - probs
            bas_poly.resolution:
                - 10GSD
            bas_poly.moving_window_size:
                - null
                #- 1
            bas_poly.poly_merge_method:
                - 'v2'
                #- 'v1'
            bas_poly.min_area_square_meters:
                - 7200
            bas_poly.max_area_square_meters:
                - 8000000
            bas_poly.boundary_region: $DVC_DATA_DPATH/annotations/drop6/region_models
            bas_poly_eval.true_site_dpath: $DVC_DATA_DPATH/annotations/drop6/site_models
            bas_poly_eval.true_region_dpath: $DVC_DATA_DPATH/annotations/drop6/region_models
            bas_pxl.enabled: 1
            bas_pxl_eval.enabled: 1
            bas_poly.enabled: 1
            bas_poly_eval.enabled: 1
            bas_poly_viz.enabled: 0
            valicrop.enabled: 1
            valicrop.minimum_size: "256x256@2GSD"
            valicrop.num_start_frames: 3
            valicrop.num_end_frames: 3
            valicrop.context_factor: 1.5
            sv_dino_boxes.enabled: 1
            sv_dino_boxes.package_fpath: $DVC_EXPT_DPATH/models/kitware/xview_dino.pt
            sv_dino_boxes.window_dims:
                - 256
                - 512
                - 768
                - 1024
                # - 1536
            sv_dino_boxes.window_overlap:
                - "0.5"
            sv_dino_boxes.fixed_resolution:
                - "1GSD"
                - "2GSD"
                - "2.5GSD"
                - "3GSD"
                - "3.3GSD"
            sv_dino_filter.box_isect_threshold:
                - 0.1
            sv_dino_filter.box_score_threshold:
                - 0.01
            sv_dino_filter.start_max_score:
                - 1.0
                - 0.8
                # - 0.5
            sv_dino_filter.end_min_score:
                - 0.0
                # - 0.05
                - 0.1
                - 0.15
                - 0.2
                - 0.25
                - 0.3
                # - 0.4
                - 0.5
        submatrices:
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-KR_R001.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-KR_R001.kwcoco.json
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-KR_R002.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-KR_R002.kwcoco.json
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-BR_R002.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-BR_R002.kwcoco.json
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-CH_R001.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-CH_R001.kwcoco.json
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-NZ_R001.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-NZ_R001.kwcoco.json
            - bas_pxl.test_dataset: $DVC_DATA_DPATH/Drop6-MeanYear10GSD-V2/imganns-AE_R001.kwcoco.zip
              valicrop.crop_src_fpath: $DVC_DATA_DPATH/Drop6/imgonly-AE_R001.kwcoco.json
        " \
        --root_dpath="$DVC_EXPT_DPATH/_mlops_eval10_baseline" \
        --devices="0," --tmux_workers=8 \
        --backend=tmux --queue_name "_mlops_eval10_baseline" \
        --pipeline=bas_building_vali --skip_existing=1 \
        --run=1

DVC_EXPT_DPATH=$(smartwatch_dvc --tags='phase2_expt' --hardware=auto)
gwmlops aggregate \
    --pipeline=bas_building_vali \
    --target \
        "$DVC_EXPT_DPATH/_mlops_eval10_baseline" \
    --resource_report=0 \
    --rois='[KR_R002,KR_R001,BR_R002,CH_R001,NZ_R001,AE_R001]' \
    --stdout_report="
        top_k: 5
        per_group: 1
        macro_analysis: 0
        analyze: 0
        reference_region: final
        shorten: 1
    " --eval_nodes="[sv_poly_eval]" \
    --plot_params="
        enabled: True
    "

    --rois='[NZ_R001,KR_R001]' \



        print_models: True

geowatch align \
    --src "/home/joncrall/remote/toothbrush/data/dvc-repos/smart_data_dvc-ssd/Drop6/imgonly-KR_R001.kwcoco.json" \
    --dst "/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/valicrop/valicrop_id_2e8c8dc3/valicrop.kwcoco.zip" \
    --regions="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/bas_poly/bas_poly_id_dc32b2a6/site_summaries_manifest.json" \
    --site_summary=True \
    --verbose="1" \
    --workers="32" \
    --aux_workers="4" \
    --debug_valid_regions="False" \
    --visualize="False" \
    --keep="img" \
    --geo_preprop="auto"  \
    --minimum_size="256x256@2GSD" \
    --num_start_frames="3" \
    --num_end_frames="3" \
    --context_factor="1.5" \
    --include_sensors="WV" \
    --force_nodata="-9999" \
    --rpc_align_method="orthorectify" \
    --target_gsd="2" \
    --force_min_gsd="2" \
    --convexify_regions="True"

geowatch visualize /home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/valicrop/valicrop_id_2e8c8dc3/valicrop.kwcoco.zip --smart

python ~/code/watch/dev/wip/grid_sitevali_crops.py /home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/valicrop/valicrop_id_2e8c8dc3/_viz_*


python -m watch.tasks.dino_detector.predict \
    --package_fpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/models/kitware/xview_dino.pt" \
    --coco_fpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/valicrop/valicrop_id_2e8c8dc3/valicrop.kwcoco.zip" \
    --out_coco_fpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_61b8c2c7/pred_boxes.kwcoco.zip" \
    --device="0" \
    --data_workers="2" \
    --fixed_resolution="1.0GSD" \
    --window_dims="2048" \
    --batch_size="1"

geowatch visualize /home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_61b8c2c7/pred_and_truth.kwcoco.zip \
    --resolution=2GSD \
    --smart \
    --ann_score_thresh=0.3 \
    --viz_dpath /home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_61b8c2c7/_vizme

python ~/code/watch/dev/wip/grid_sitevali_crops.py --sub=_anns \
    /home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_61b8c2c7/_vizme


NODE_DPATH=/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_fd298dba/
DVC_DATA_DPATH=$(geowatch_dvc --tags='phase2_data' --hardware=auto)

geowatch reproject \
        --src "$NODE_DPATH/pred_boxes.kwcoco.zip" \
        --dst "$NODE_DPATH/pred_boxes_with_polys.kwcoco.zip" \
        --region_models "$NODE_DPATH/.pred/valicrop/*/.pred/bas_poly/*/site_summaries_manifest.json" \
        --status_to_catname="{system_confirmed: positive}" \
        --role=pred_poly \
        --validate_checks=False \
        --clear_existing=False

geowatch reproject \
        --src "$NODE_DPATH/pred_boxes_with_polys.kwcoco.zip" \
        --dst "$NODE_DPATH/pred_and_truth.kwcoco.zip" \
        --region_models="$DVC_DATA_DPATH/annotations/drop6/region_models/*.geojson" \
        --site_models="$DVC_DATA_DPATH/annotations/drop6/site_models/*.geojson" \
        --status_to_catname="{system_confirmed: positive}" \
        --role=truth \
        --clear_existing=False

gw visualize --smart 1 \
    --ann_score_thresh 0.5 \
    --draw_labels False \
    --alpha 0.5 \
    --src $NODE_DPATH/pred_and_truth.kwcoco.zip \
    --viz_dpath $NODE_DPATH/_vizme \

python ~/code/watch/dev/wip/grid_sitevali_crops.py \
    --sub=_anns \
    $NODE_DPATH/_vizme

python watch.tasks.dino_detector.building_validator \
    --input_kwcoco "$NODE_DPATH/pred_boxes.kwcoco.zip" \
    --input_region "$NODE_DPATH/.pred/valicrop/*/.pred/bas_poly/*/site_summaries_manifest.json" \
    --output_region_fpath "$NODE_DPATH/filtered_summaries.json" \
    --box_isect_threshold 0.1 \
    --box_score_threshold 0.1 \
    --start_max_score 0.0 \
    --end_min_score 0.3

python -m watch.tasks.dino_detector.building_validator \
    --input_kwcoco="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/buildings/buildings_id_663bd461/pred_boxes.kwcoco.zip" \
    --input_region="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/bas_poly/bas_poly_id_f8061df6/site_summaries_manifest.json" \
    --input_sites="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/bas_poly/bas_poly_id_f8061df6/sites_manifest.json" \
    --output_region_fpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/building_validate/building_validate_id_60a56eed/out_region.geojson" \
    --output_sites_dpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/building_validate/building_validate_id_60a56eed/out_sites" \
    --output_site_manifest_fpath="/home/joncrall/remote/toothbrush/data/dvc-repos/smart_expt_dvc/_mlops_eval10_baseline/pred/flat/building_validate/building_validate_id_60a56eed/out_site_manifest.json" \
    --box_isect_threshold="0.1" \
    --box_score_threshold="0.1" \
    --start_max_score="0.1" \
    --end_min_score="0.3"


"""
