Intersection.py

def get_union(boxA, boxB):
    # determine the (x, y)-coordinates of the intersection rectangle
    """
    boxA  : xmin ymin xmax ymax

    boxB  : x, y, x+w, y+h

    #examples of good and bad Intersection over Union scores.
              
                Poor <0.5
                Good >0.7
                Excelent >0.9

                More Read :https://mc.ai/distance-iou-loss-an-improvement-of-iou-based-loss-for-object-detection-bounding-box-regression/

               



    """
    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 = abs(max((xB - xA, 0)) * max((yB - yA), 0))
    if interArea == 0:
        return 0
    # compute the area of both the prediction and ground-truth
    # rectangles
    boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1]))
    boxBArea = abs((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