The method of detecting IOU for python targets

This article mainly introduces "the method of python target detection IOU". In the daily operation, I believe that many people have doubts about the method of python target detection IOU. The editor consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "python target detection IOU method". Next, please follow the editor to study!

What is IOU?

IOU is an index to evaluate the target detector.

The following picture is an example: the green box in the picture is the actual box (it doesn't seem to be very green …... The red box is the prediction box. What indicators do we need to use when we need to judge the relationship between the two boxes?

You need to use IOU at this point.

The formula for calculating IOU is:

You can see that IOU is a ratio, that is, the cross-merge ratio.

In the molecular part, the value is the overlap between the prediction box and the actual box

In the denominator part, the value is the total area occupied by the prediction box and the actual box.

The ratio of intersection region to union region is IOU.

Characteristics of IOU

Unlike the classification task, the coordinates of our prediction box need to match the coordinates of the actual box, but the complete matching of coordinates is not realistic. Therefore, we need to define an evaluation indicator to reward those prediction boxes that match well with the matching box.

All codes

This article will draw two rectangular boxes and calculate their IOU.

The effect is as follows:

Import cv2import numpy as npdef CountIOU (RecA, RecB): xA = max (RecA [0], RecB [0]) yA = max (RecA [1], RecB [1]) xB = min (RecA [2], RecB [2]) yB = min (RecA [3], RecB [3]) # calculate the intersection area interArea = max (0, xB-xA + 1) * max (0) YB-yA + 1) # calculate the area of predicted and real values RecA_Area = (RecA [2]-RecA [0] + 1) * (RecA [3]-RecA [1] + 1) RecB_Area = (RecB [2]-RecB [0] + 1) * (RecB [3]-RecB [1] + 1) # calculate IOU iou = interArea / float (RecA_Area + RecB_Area-interArea) Return iouimg = np.zeros ((512pr. 512jp3) Np.uint8) img.fill RecA = [50Power50300300] RecB = [60Power60320320] cv2.rectangle (img, (RecA [0], RecA [1]), (RecA [2], RecA [3]), (0,255,0), 5) cv2.rectangle (img, (RecB [0], RecB [1]), (RecB [2], RecB [3]), (255,0,0), 5) IOU = CountIOU (RecA,RecB) font = cv2.FONT_HERSHEY_SIMPLEXcv2.putText (img) "IOU =% .2f"% IOU, (130,190), font,0.8, (0memo), 2) cv2.imshow ("image", img) cv2.waitKey () cv2.destroyAllWindows () so far The study on "the method of python target detection IOU" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!