def _calculate_complex_metrics(values):
tp = values[TRUE_POSITIVE]
tn = values[TRUE_NEGATIVE]
fp = values[FALSE_POSITIVE]
fn = values[FALSE_NEGATIVE]
values[ACCURACY] = safe_ratio(tp + tn, tp + tn + fp + fn)
values[PRECISION] = safe_ratio(tp, tp + fp)
values[RECALL] = safe_ratio(tp, tp + fn)
values[F1_MEASURE] = safe_ratio(2.0 * tp, 2.0 * tp + fp + fn)
def get_metrics(self):
return {
cls_gt: {
INTERSECTION:
safe_ratio(class_counters[INTERSECTION],
class_counters[TOTAL_GROUND_TRUTH]),
UNION:
safe_ratio(class_counters[UNION],
class_counters[TOTAL_GROUND_TRUTH]),
IOU:
safe_ratio(class_counters[INTERSECTION], class_counters[UNION])
}
for cls_gt, class_counters in self._counters.items()
}
def _iou(first_rect, second_rect):
intersection_rects = first_rect.crop(second_rect)
if len(intersection_rects) == 0:
return 0
intersection_rect = intersection_rects[0]
return safe_ratio(
intersection_rect.area,
first_rect.area + second_rect.area - intersection_rect.area)
def get_labels_iou(label_1: Label, label_2: Label, img_size):
if isinstance(label_1.geometry, Rectangle):
return get_iou_rect(label_1.geometry, label_2.geometry)
elif isinstance(label_2.geometry, Rectangle):
return get_iou_rect(label_2.geometry, label_1.geometry)
else:
mask_1 = np.full(img_size, False)
label_1.geometry.draw(mask_1, color=True)
mask_2 = np.full(img_size, False)
label_2.geometry.draw(mask_2, color=True)
return safe_ratio((mask_1 & mask_2).sum(), (mask_1 | mask_2).sum())
def get_geometries_iou(geometry_1: Geometry, geometry_2: Geometry):
if isinstance(geometry_1, Rectangle):
return get_iou_rect(geometry_1, geometry_2)
elif isinstance(geometry_2, Rectangle):
return get_iou_rect(geometry_2, geometry_1)
else:
common_bbox = Rectangle.from_geometries_list((geometry_1, geometry_2))
g1 = geometry_1.relative_crop(common_bbox)[0]
g2 = geometry_2.relative_crop(common_bbox)[0]
mask_1 = np.full(common_bbox.to_size(), False)
g1.draw(mask_1, color=True)
mask_2 = np.full(common_bbox.to_size(), False)
g2.draw(mask_2, color=True)
return safe_ratio((mask_1 & mask_2).sum(), (mask_1 | mask_2).sum())
def get_total_metrics(self):
result = sum_counters(self._counters.values(), (INTERSECTION, UNION))
result[IOU] = safe_ratio(result[INTERSECTION], result[UNION])
return result
def get_iou(mask_1, mask_2):
return safe_ratio(get_intersection(mask_1, mask_2), get_union(mask_1, mask_2))
def _metrics_from_counters(counters):
return {PRECISION: safe_ratio(counters[TRUE_POSITIVE], counters[TRUE_POSITIVE] + counters[FALSE_POSITIVE]),
RECALL: safe_ratio(counters[TRUE_POSITIVE], counters[TRUE_POSITIVE] + counters[FALSE_NEGATIVE]),
ACCURACY: safe_ratio(counters[TRUE_POSITIVE] + counters[TRUE_NEGATIVE], counters[TOTAL])}
def _compute_composite_metrics(metrics_dict):
metrics_dict[PRECISION] = safe_ratio(metrics_dict[TRUE_POSITIVE],
metrics_dict[TOTAL_PREDICTIONS])
metrics_dict[RECALL] = safe_ratio(metrics_dict[TRUE_POSITIVE],
metrics_dict[TOTAL_GROUND_TRUTH])
def get_labels_iou(label_1: Label, label_2: Label, img_size):
mask_1 = np.full(img_size, 0, dtype=np.uint8)
label_1.geometry.draw(mask_1, color=1)
mask_2 = np.full(img_size, 0, dtype=np.uint8)
label_2.geometry.draw(mask_2, color=1)
return safe_ratio((mask_1 & mask_2).sum(), (mask_1 | mask_2).sum())
def get_labels_iou(label_1: Label, label_2: Label, img_size):
mask_1 = np.full(img_size, False)
label_1.geometry.draw(mask_1, color=True)
mask_2 = np.full(img_size, False)
label_2.geometry.draw(mask_2, color=True)
return safe_ratio((mask_1 & mask_2).sum(), (mask_1 | mask_2).sum())
