Function weighted_boxes_fusion

ensemble_boxes/ensemble_boxes_wbf.py:90–152

:param boxes_list: list of boxes predictions from each model, each box is 4 numbers. It has 3 dimensions (models_number, model_preds, 4) Order of boxes: x1, y1, x2, y2. We expect float normalized coordinates [0; 1] :param scores_list: list of scores for each model :param label

(boxes_list, scores_list, labels_list, weights=None, iou_thr=0.55, skip_box_thr=0.0, conf_type='avg', allows_overflow=False)

def weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=None, iou_thr=0.55, skip_box_thr=0.0, conf_type='avg', allows_overflow=False):
    '''
    :param boxes_list: list of boxes predictions from each model, each box is 4 numbers. 
    It has 3 dimensions (models_number, model_preds, 4)
    Order of boxes: x1, y1, x2, y2. We expect float normalized coordinates [0; 1]
    :param scores_list: list of scores for each model 
    :param labels_list: list of labels for each model
    :param weights: list of weights for each model. Default: None, which means weight == 1 for each model
    :param intersection_thr: IoU value for boxes to be a match
    :param skip_box_thr: exclude boxes with score lower than this variable  
    :param conf_type: how to calculate confidence in weighted boxes. 'avg': average value, 'max': maximum value
    :param allows_overflow: false if we want confidence score not exceed 1.0 
    
    :return: boxes: boxes coordinates (Order of boxes: x1, y1, x2, y2). 
    :return: scores: confidence scores
    :return: labels: boxes labels
    '''

    if weights is None:
        weights = np.ones(len(boxes_list))
    if len(weights) != len(boxes_list):
        print('Warning: incorrect number of weights {}. Must be: {}. Set weights equal to 1.'.format(len(weights), len(boxes_list)))
        weights = np.ones(len(boxes_list))
    weights = np.array(weights)

    if conf_type not in ['avg', 'max']:
        print('Unknown conf_type: {}. Must be "avg" or "max"'.format(conf_type))
        exit()

    filtered_boxes = prefilter_boxes(boxes_list, scores_list, labels_list, weights, skip_box_thr)
    if len(filtered_boxes) == 0:
        return np.zeros((0, 4)), np.zeros((0,)), np.zeros((0,))

    overall_boxes = []
    for label in filtered_boxes:
        boxes = filtered_boxes[label]
        new_boxes = []
        weighted_boxes = []

        # Clusterize boxes
        for j in range(0, len(boxes)):
            index, best_iou = find_matching_box(weighted_boxes, boxes[j], iou_thr)
            if index != -1:
                new_boxes[index].append(boxes[j])
                weighted_boxes[index] = get_weighted_box(new_boxes[index], conf_type)
            else:
                new_boxes.append([boxes[j].copy()])
                weighted_boxes.append(boxes[j].copy())

        # Rescale confidence based on number of models and boxes
        for i in range(len(new_boxes)):
            if not allows_overflow:
                weighted_boxes[i][1] = weighted_boxes[i][1] * min(weights.sum(), len(new_boxes[i])) / weights.sum()
            else:
                weighted_boxes[i][1] = weighted_boxes[i][1] * len(new_boxes[i]) / weights.sum()
        overall_boxes.append(np.array(weighted_boxes))

    overall_boxes = np.concatenate(overall_boxes, axis=0)