Visual object detection: support non maximum suppression method

[SamVanhoutte]

When performing object detection in images, multiple shaped boxes can be found for the same object (overlapping). This method should take the best matching shapes and return that.
This method is use non maximum suppression method

Sample code:

def nms(boxes, overlapThresh):
    # if there are no boxes, return an empty list
    if len(boxes) == 0:
        return []
    
    # initialize the list of picked indexes
    pick = []
    
    # grab the coordinates of the bounding boxes
    x1 = boxes[:,0]
    y1 = boxes[:,1]
    x2 = boxes[:,2]
    y2 = boxes[:,3]
    prob = boxes[:,4]
    
    # compute the area of the bounding boxes and sort the bounding
    # boxes by the bottom-right y-coordinate of the bounding box
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = np.argsort(prob)
    # keep looping while some indexes still remain in the indexes
    # list
    while len(idxs) > 0:
        # grab the last index in the indexes list, add the index
        # value to the list of picked indexes, then initialize
        # the suppression list (i.e. indexes that will be deleted)
        # using the last index
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
        suppress = [last]
        # loop over all indexes in the indexes list
        for pos in range(0, last):
            # grab the current index
            j = idxs[pos]
            # find the largest (x, y) coordinates for the start of
            # the bounding box and the smallest (x, y) coordinates
            # for the end of the bounding box
            xx1 = max(x1[i], x1[j])
            yy1 = max(y1[i], y1[j])
            xx2 = min(x2[i], x2[j])
            yy2 = min(y2[i], y2[j])
            # compute the width and height of the bounding box
            w = max(0, xx2 - xx1 + 1)
            h = max(0, yy2 - yy1 + 1)
            # compute the ratio of overlap between the computed
            # bounding box and the bounding box in the area list
            overlap = float(w * h) / area[j]
            # if there is sufficient overlap, suppress the
            # current bounding box
            if overlap > overlapThresh:
                suppress.append(pos)
        # delete all indexes from the index list that are in the
        # suppression list
        idxs = np.delete(idxs, suppress)
    # return only the bounding boxes that were picked
    return boxes[pick]

[SamVanhoutte]

An example where this is needed and used for, is object detection. Consider the following image, where you want to detect the location of the Diamond on the playing card. That image can be detected multiple times, but this NMS (Non Maximum Suppression) method will pick the best rectangle that fits the diamond and avoid the overlapping rectangles.

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