在之前的文档中我们有介绍
简单来说，$WBF$ 算法首先将所有边界框按照置信度分数的递减顺序进行排序；然后生成另一个可能的框“融合”(组合)列表，并尝试检查这些融合是否与原始框匹配；最后使用一个公式来调整坐标和框列表中所有框的置信度分数。下面我们就来详细看一下
如下图
1、建立两个链表
2、建立空链表
3、遍历循环
4、在步骤
5、在步骤
6、匹配到框后，使用
7、当
具体实现代码如下：
# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
import numpy as np
def bb_intersection_over_union(A, B):
xA = max(A[0], B[0])
yA = max(A[1], B[1])
xB = min(A[2], B[2])
yB = min(A[3], B[3])
# compute the area of intersection rectangle
interArea = max(0, xB - xA) * max(0, yB - yA)
if interArea == 0:
return 0.0
# compute the area of both the prediction and ground-truth rectangles
boxAArea = (A[2] - A[0]) * (A[3] - A[1])
boxBArea = (B[2] - B[0]) * (B[3] - B[1])
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def prefilter_boxes(boxes, scores, labels, weights, thr):
# Create dict with boxes stored by its label
new_boxes = dict()
for t in range(len(boxes)):
for j in range(len(boxes[t])):
score = scores[t][j]
if score < thr:
continue
label = int(labels[t][j])
box_part = boxes[t][j]
b = [int(label), float(score) * weights[t], float(box_part[0]), float(box_part[1]), float(box_part[2]), float(box_part[3])]
if label not in new_boxes:
new_boxes[label] = []
new_boxes[label].append(b)
# Sort each list in dict by score and transform it to numpy array
for k in new_boxes:
current_boxes = np.array(new_boxes[k])
new_boxes[k] = current_boxes[current_boxes[:, 1].argsort()[::-1]]
return new_boxes
def get_weighted_box(boxes, conf_type='avg'):
"""
Create weighted box for set of boxes
:param boxes: set of boxes to fuse
:param conf_type: type of confidence one of 'avg' or 'max'
:return: weighted box
"""
box = np.zeros(6, dtype=np.float32)
conf = 0
conf_list = []
for b in boxes:
box[2:] += (b[1] * b[2:])
conf += b[1]
conf_list.append(b[1])
box[0] = boxes[0][0]
if conf_type == 'avg':
box[1] = conf / len(boxes)
elif conf_type == 'max':
box[1] = np.array(conf_list).max()
box[2:] /= conf
return box
def find_matching_box(boxes_list, new_box, match_iou):
best_iou = match_iou
best_index = -1
for i in range(len(boxes_list)):
box = boxes_list[i]
if box[0] != new_box[0]:
continue
iou = bb_intersection_over_union(box[2:], new_box[2:])
if iou > best_iou:
best_index = i
best_iou = iou
return best_index, best_iou
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 iou_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)
overall_boxes = overall_boxes[overall_boxes[:, 1].argsort()[::-1]]
boxes = overall_boxes[:, 2:]
scores = overall_boxes[:, 1]
labels = overall_boxes[:, 0]
return boxes, scores, labels原论文中作者也给出了多个模型使用
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