I was reading through the paper : Ferrari et al. in the "Affinity Measures" section. I understood that Ferrari et al. tries to obtain affinity by :
However, I have 2 main problems:
Any suggestions or solutions to my problems described above? Thank you and your help is very much appreciated.
Try intersection over Union
Intersection over Union is an evaluation metric used to measure the accuracy of an object detector on a particular dataset.
More formally, in order to apply Intersection over Union to evaluate an (arbitrary) object detector we need:
Below I have included a visual example of a ground-truth bounding box versus a predicted bounding box:
The predicted bounding box is drawn in red while the ground-truth (i.e., hand labeled) bounding box is drawn in green.
In the figure above we can see that our object detector has detected the presence of a stop sign in an image.
Computing Intersection over Union can therefore be determined via:
As long as we have these two sets of bounding boxes we can apply Intersection over Union.
Here is the Python code
# import the necessary packages
from collections import namedtuple
import numpy as np
import cv2
# define the `Detection` object
Detection = namedtuple("Detection", ["image_path", "gt", "pred"])
def bb_intersection_over_union(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# compute the area of intersection rectangle
interArea = (xB - xA) * (yB - yA)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
return iou
The gt
and pred
are
gt
: The ground-truth bounding box.pred
: The predicted bounding box from our model.For more information, you can click this post