Add AP and MAP to polygon metrics (#200)

* Add map to polygon metrics

* fix

* bump version

* fix docstring

* bump patch version

Author: phil-scale

nucleus/metrics/__init__.py

@@ -1,6 +1,8 @@
 from .base import Metric, MetricResult
 from .polygon_metrics import (
+    PolygonAveragePrecision,
     PolygonIOU,
+    PolygonMAP,
     PolygonMetric,
     PolygonPrecision,
     PolygonRecall,

nucleus/metrics/filters.py

@@ -3,14 +3,14 @@
 from nucleus.prediction import PredictionList
 
 from .polygon_utils import (
-    BoxOrPolygonAnnotation,
+    BoxOrPolygonAnnoOrPred,
     polygon_annotation_to_geometry,
 )
 
 
 def polygon_area_filter(
-    polygons: List[BoxOrPolygonAnnotation], min_area: float, max_area: float
-) -> List[BoxOrPolygonAnnotation]:
+    polygons: List[BoxOrPolygonAnnoOrPred], min_area: float, max_area: float
+) -> List[BoxOrPolygonAnnoOrPred]:
     filter_fn = (
         lambda polygon: min_area
         <= polygon_annotation_to_geometry(polygon).signed_area
@@ -32,3 +32,9 @@ def confidence_filter(
             filter(filter_fn, predictions.__dict__[attr])
         )
     return predictions_copy
+
+
+def polygon_label_filter(
+    polygons: List[BoxOrPolygonAnnoOrPred], label: str
+) -> List[BoxOrPolygonAnnoOrPred]:
+    return list(filter(lambda polygon: polygon.label == label, polygons))

nucleus/metrics/metric_utils.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+
+def compute_average_precision(recall, precision):
+    """Compute the average precision, given the recall and precision curves.
+    Code originally from https://github.com/rbgirshick/py-faster-rcnn.
+    # Arguments
+        recall:    The recall curve (list).
+        precision: The precision curve (list).
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+    # correct AP calculation
+    # first append sentinel values at the end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([0.0], precision, [0.0]))
+
+    # compute the precision envelope
+    for i in range(mpre.size - 1, 0, -1):
+        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+    # to calculate area under PR curve, look for points
+    # where X axis (recall) changes value
+    i = np.where(mrec[1:] != mrec[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap