Compare segmentation result to ground truth
I have some contours which represent certain objects found with cv2.findContours()
in a segmentation result. On the other hand, I also have the ground truth data in the same format (polygon points in [x, y]
). I'm now looking for the best way to measure the intersection/overlap of both polygons to measure how well the segmentation performed.
Thank you in advance!
-- Update --
To add in some context, here's for example the data from one contour found with cv2.findContours()
:
[[[1086 603]],[[1085 605]],[[1078 605]],[[1076 606]],[[1073 606]],[[1071 608]],[[1068 608]],[[1066 610]],[[1065 610]],[[1061 6,3]],[[1060 613]],[[1060 615]],[[1058 616]],[[1058 625]],[[1060 626]],[[1060 636]],[[1061 638]],[[1061 650]],[[1063 651]],[[1063 658]],[[1061 660]],[[1063 661]],[[1063 671]],[[1065 673]],[[1065 676]],[[1066 678]],[[1066 681]],[[1068 683]],[[1068 701]],[[1070 703]],[[1070 713]],[[1075 718]],[[1078 718]],[[1080 720]],[[1101 720]],[[1103 718]],[[1106 718]],[[1110 715]],[[1111 715]],[[1115 711]],[[1116 711]],[[1118 710]],[[1118 708]],[[1120 706]],[[1120 703]],[[1121 701]],[[1121 683]],[[1123 681]],[[1123 670]],[[1121 668]],[[1121 615]],[[1120 613]],[[1120 611]],[[1118 610]],[[1113 610]],[[1111 608]],[[1108 608]],[[1106 606]],[[1103 606]],[[1101 605]],[[1088 605]]]
And this is the ground truth data:
[[1054, 625], [1070, 719], [1084, 726], [1112, 724], [1125, 716], [1126, 631], [1128, 619], [1132, 610], [1127, 603], [1118, 602], [1107, 600], [1090, 600], [1074, 603], [1059, 607], [1049, 614], [1051, 620]]
(Ignore the mismatch of arrays between the contours data and ground truth, this is just an example.)
Look at the literature. There should be some defined metrics for segmentation benchmarks and probably some code available on these sites.
Off the top of my head, fill the contours (look at
drawContours
) and perform intersection over union metric (count the number of pixels that overlap over the sum of all the pixels minus the intersection).Well you need some metric like this https://www.pyimagesearch.com/2016/11...