coco-froc-analysis 0.0.22

A small package that evaluates COCO detection results from OpenMMLab and Detectron(2).

COCO FROC analysis

FROC analysis for COCO annotations and Detectron(2) results. The COCO annotation style is defined here.

Example

A single annotation record in the ground-truth file might look like this:

{
  "area": 2120,
  "iscrowd": 0,
  "bbox": [111, 24, 53, 40],
  "category_id": 3,
  "ignore": 0,
  "segmentation": [],
  "image_id": 407,
  "id": 945
}

While the prediction (here for bounding box) given by the region detection framework is such:

{
  "image_id": 407,
  "category_id": 3,
  "score": 0.9990422129631042,
  "bbox": [
    110.72555541992188,
    13.9161834716797,
    49.4566650390625,
    36.65155029296875
  ]
}

The FROC analysis counts the number of images, number of lesions in the ground truth file for all categories and then counts the lesion localization predictions and the non-lesion localization predictions. A lesion is localized by default if its center is inside any ground truth box and the categories match or if you wish to use IoU you should provide threshold upon which you can define the 'close enough' relation.

Usage

No dependencies.

python froc_analysis.py --gt_ann <path_to_ground_truth_annotation_in_COCO_format>\
                        --pred_ann <path_to_prediction_annotation_in_COCO_format>\
                        --use_iou <flag_parameter_if_used_then_it_is_automatically_set_to_true>\
                        --iou_thres <will_be_used_with_the_above_optional_flag>\
                        --plot_title <custumize_the_title_of_the_plot>\
                        --plot_output_path <costumize_the_plot_output_path>
# arguments that are required: --gt_ann, --pred_ann

By default centroid closeness is used, if the --use_iou flag is set, --iou_thres defaults to .75 while the --score_thres score defaults to .5. The code outputs the FROC curve on the given detection results and GT dataset.