| // Copyright (c) Facebook, Inc. and its affiliates. | |
| namespace py = pybind11; | |
| namespace detectron2 { | |
| namespace COCOeval { | |
| // Annotation data for a single object instance in an image | |
| struct InstanceAnnotation { | |
| InstanceAnnotation( | |
| uint64_t id, | |
| double score, | |
| double area, | |
| bool is_crowd, | |
| bool ignore) | |
| : id{id}, score{score}, area{area}, is_crowd{is_crowd}, ignore{ignore} {} | |
| uint64_t id; | |
| double score = 0.; | |
| double area = 0.; | |
| bool is_crowd = false; | |
| bool ignore = false; | |
| }; | |
| // Stores intermediate results for evaluating detection results for a single | |
| // image that has D detected instances and G ground truth instances. This stores | |
| // matches between detected and ground truth instances | |
| struct ImageEvaluation { | |
| // For each of the D detected instances, the id of the matched ground truth | |
| // instance, or 0 if unmatched | |
| std::vector<uint64_t> detection_matches; | |
| // The detection score of each of the D detected instances | |
| std::vector<double> detection_scores; | |
| // Marks whether or not each of G instances was ignored from evaluation (e.g., | |
| // because it's outside area_range) | |
| std::vector<bool> ground_truth_ignores; | |
| // Marks whether or not each of D instances was ignored from evaluation (e.g., | |
| // because it's outside aRng) | |
| std::vector<bool> detection_ignores; | |
| }; | |
| template <class T> | |
| using ImageCategoryInstances = std::vector<std::vector<std::vector<T>>>; | |
| // C++ implementation of COCO API cocoeval.py::COCOeval.evaluateImg(). For each | |
| // combination of image, category, area range settings, and IOU thresholds to | |
| // evaluate, it matches detected instances to ground truth instances and stores | |
| // the results into a vector of ImageEvaluation results, which will be | |
| // interpreted by the COCOeval::Accumulate() function to produce precion-recall | |
| // curves. The parameters of nested vectors have the following semantics: | |
| // image_category_ious[i][c][d][g] is the intersection over union of the d'th | |
| // detected instance and g'th ground truth instance of | |
| // category category_ids[c] in image image_ids[i] | |
| // image_category_ground_truth_instances[i][c] is a vector of ground truth | |
| // instances in image image_ids[i] of category category_ids[c] | |
| // image_category_detection_instances[i][c] is a vector of detected | |
| // instances in image image_ids[i] of category category_ids[c] | |
| std::vector<ImageEvaluation> EvaluateImages( | |
| const std::vector<std::array<double, 2>>& area_ranges, // vector of 2-tuples | |
| int max_detections, | |
| const std::vector<double>& iou_thresholds, | |
| const ImageCategoryInstances<std::vector<double>>& image_category_ious, | |
| const ImageCategoryInstances<InstanceAnnotation>& | |
| image_category_ground_truth_instances, | |
| const ImageCategoryInstances<InstanceAnnotation>& | |
| image_category_detection_instances); | |
| // C++ implementation of COCOeval.accumulate(), which generates precision | |
| // recall curves for each set of category, IOU threshold, detection area range, | |
| // and max number of detections parameters. It is assumed that the parameter | |
| // evaluations is the return value of the functon COCOeval::EvaluateImages(), | |
| // which was called with the same parameter settings params | |
| py::dict Accumulate( | |
| const py::object& params, | |
| const std::vector<ImageEvaluation>& evalutations); | |
| } // namespace COCOeval | |
| } // namespace detectron2 | |