def find_objects_ensemble_stacking(bboxInfo_list,
MODEL_WEIGHT_LIST,
MODEL_CONF_LIST,
Z_THRESHOLD,
OBJ_THRESH=0.5,
SAME_BOX_THRESHOLD=np.array([1.6, 1.6]),
SCORE_THRESHOLD=0.6,
OBJECT_CLS_WEIGHTS={},
SAME_CLS_BOOST=2.,
CLS_PRIORITY_DICT={
"mass": 6,
"calcific nodule": 5,
"solid nodule": 4,
"GGN": 3,
"0-3nodule": 2,
"nodule": 1,
"pleural nodule": 3
}):
"""将boundingbox转换为结节。
:param bboxInfo_List: 一个列表，每个元素对应多模型中一个模型预测出的Bounding Box表格（pandas.DataFrame类型）的列表,index必须为自然顺序。
每一行代表一个预测出来的bounding box，包含下面的列：
* 'instanceNumber'列：表示当前bounding box所在的层面的InstanceNumber，编号从1开始。
* 'xmin'列：表示当前bounding box的左上角x坐标（高度[H]方向）。
* 'ymin'列：表示当前bounding box的左上角y坐标（左右[W]方向）。
* 'xmax'列：表示当前bounding box的右下角x坐标（高度[H]方向）。
* 'ymax'列：表示当前bounding box的右下角y坐标（左右[W]方向）。
* 'class'列：表示当前bounding box的预测类别（如'object', 'mass'等）。
* 'prob'里：表示当前bounding box的预测概率。
:param Z_THRESHOLD： 每个新层面向前做贪心匹配时往前找的最大层面数
:param MODEL_WEIGHT_LIST: 存储各模型权重的列表，用于模型结果的加权
:param MODEL_CONF_LIST: 存储各模型置信度概率的列表，用于估计各模型各检出框为真阳的概率
:param OBJECT_FREQ_THRESH: 匹配时判断是否保留各等价类框的频率阈值
:param SAME_BOX_THRESHOLD: 判断同一层面两个框是否为等价类的中心点偏移阈值
:param SCORE_THRESHOLD: 判断不同层面两个框是否匹配的sim_metric_3d阈值
:param object_cls_weights: 检出框的类别的权重
:param same_cls_boost: 当不同层面两个框匹配时，如果类别相同的奖励系数
:return objectInfo: 在bnd上附加一列'object'，取值为-1, 1..n。
-1代表当前Bounding box不属于任何一个object;
1..n代表当前bounding box所属的结节编号。
我们不允许一个结节在同一个层面内存在多个重合的bounding box(一个等价类中没有匹配上的框设为-1)。
":return objects: 结节信息的列表，每个元素为一个字典
"""
# compute normalized model weights
norm_weight_list = norm_weight(MODEL_WEIGHT_LIST)
print 'bboxInfo_list:'
print bboxInfo_list
bboxInfo = pd.DataFrame()
for i, bboxInfo_df in enumerate(bboxInfo_list):
bboxInfo_i = bboxInfo_df.copy()
bboxInfo_i['model_idx'] = i
#将每个检出框的概率根据其检出模型的权重修正
bboxInfo_i['prob'] = bboxInfo_i['prob'] * norm_weight_list[i]
bboxInfo = bboxInfo.append(bboxInfo_i, ignore_index=True)
assert len(bboxInfo_list) == len(MODEL_WEIGHT_LIST), 'bounding box list and model weight list must contain the same' \
'number of models'
# 初始化匹配anchor的度规类AnchorMetric:
anchor_metric = AnchorMetric(dim=2)
# 初始化并查集
unionFindSet = bboxInfo.index.tolist()
objectSlices = {}
print 'bboxInfo:'
print bboxInfo
for i in bboxInfo["instanceNumber"].unique():
lst = bboxInfo.query("instanceNumber == @i")
if len(lst) > 1:
for j1 in range(len(lst.index)):
for j2 in range(j1 + 1, len(lst.index)):
# 如果两个box中心点相对位移小于SAME_BOX_THRESHOLD，那么认为这两个box表示同一个结节
iou = anchor_metric.iou(
get_bounding_box_nparray(lst.iloc[j1]),
get_bounding_box_nparray(lst.iloc[j2]))
if iou > 0 and np.all(
anchor_metric.center_deviation_iou(
get_bounding_box_nparray(lst.iloc[j1]),
get_bounding_box_nparray(lst.iloc[j2])) <
SAME_BOX_THRESHOLD):
# 将两个box插入一颗等价类树
union(lst.index[j1], lst.index[j2], unionFindSet)
objectSlices[i] = lst.index.tolist()
for i in range(len(unionFindSet)):
unionFindSet[i] = find_parent(unionFindSet[i], unionFindSet)
# unionFindSet保存了当前bounding box与本层面内其他bounding box的归属关系
# 对于bounding box i，如果bboxInfo.loc[i]["unionFindSet"] != i，
# 那么表明它与bboxInfo.loc[i]["unionFindSet"]表示同一个结节
bboxInfo["unionFindSet"] = unionFindSet
# 在不同层面间对结节进行匹配，并且获得结节列表
objects = []
BOXID_VALUE = 10000
for curZ in sorted(objectSlices.keys()):
# 枚举之前的所有结节，检查最后一个层面，如果与当前层面的instanceNumber差值在Z_THRESHOLD之内，那么作为备选结节加入lastBoxes
lastBoxes = [{"objectID": k["id"],
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k["objectList"][-1]]),
"class": bboxInfo.loc[k["objectList"][-1]]["class"],
"prob": bboxInfo.loc[k["objectList"][-1]]["prob"]}
for k in objects if 0 < curZ - bboxInfo.loc[k["objectList"][-1]]["instanceNumber"] <= \
Z_THRESHOLD[bboxInfo.loc[k["objectList"][-1]]["class"]]]
# 枚举本层面所有bounding box，每个框的评分为类别评分*置信度概率
curBoxes = [
{
"matched": False,
"boxID": k,
"model_idx": bboxInfo.loc[k]["model_idx"],
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k]),
"class": bboxInfo.loc[k]["class"],
"prob": bboxInfo.loc[k]["prob"],
}
#"score": OBJECT_CLS_WEIGHTS[bboxInfo.loc[k]["class"]] * bboxInfo.loc[k]["prob"]}
for k in objectSlices[curZ]
]
# 对于有多个box表示一个结节的，只选择其中一个
# （选择bboxInfo.loc[i]["unionFindSet"] == i的那个，即等价类树的根节点）
curBoxes_root = \
[{"boxID": k,
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k]),
"class": bboxInfo.loc[k]["class"],
"prob": bboxInfo.loc[k]["prob"]}
for k in objectSlices[curZ] if bboxInfo.loc[k]["unionFindSet"] == k]
# 首先找到每个等价类中包括的框，并根据AnchorMetric.nms方法合并框．如果合并之后框的置信度>=OBJ_THRESH, 则保留该框否则视为假阳不保留．
curBoxes_union = []
for i in curBoxes_root:
Boxes_union = [
j for j in curBoxes
if bboxInfo.loc[j["boxID"]]["unionFindSet"] == i["boxID"]
]
Boxes_union_model_idx = [j['model_idx'] for j in Boxes_union]
# merged all boxes detected by different models into one
# count the occurence (frequency) of boxes predicted by each model in the specified equivalent class
idx_count_dict = {
model_idx: Boxes_union_model_idx.count(model_idx)
for model_idx in range(len(bboxInfo_list))
}
bndbox_list = [j["bndbox"] for j in Boxes_union]
class_list = [j["class"] for j in Boxes_union]
weight_list = [
j["prob"] / idx_count_dict[j["model_idx"]] for j in Boxes_union
]
merged_bndbox, merged_weight, merged_cls = anchor_metric.nms(
bndbox_list=bndbox_list,
weight_list=weight_list,
cls_list=class_list,
cls_priority_dict=CLS_PRIORITY_DICT)
if merged_weight >= OBJ_THRESH:
curBoxes_union.append({
"matched":
False,
# add the id
"boxID":
i["boxID"],
"model_idx":
-1, # -1 stands for merged
"bndbox":
merged_bndbox,
"class":
merged_cls,
"prob":
merged_weight,
"score":
merged_weight * OBJECT_CLS_WEIGHTS[merged_cls]
})
# # 根据保留后的等价类枚举本层面所有bounding box，每个框的评分为类别评分*置信度概率
# curBoxes_kept = []
# for i in curBoxes_union:
# curBoxes_kept += [{"matched": False,
# "boxID": k,
# "bndbox": get_bounding_box_nparray(bboxInfo.loc[k]),
# "class": bboxInfo.loc[k]["class"],
# "prob": bboxInfo.loc[k]["prob"],
# "score": object_cls_weights[bboxInfo.loc[k]["class"]] * bboxInfo.loc[k]["prob"]}
# for k in objectSlices[curZ] if bboxInfo.loc[k]["unionFindSet"] == bboxInfo.loc[i["boxID"]]["unionFindSet"]]
# 如果在之前层面（Z_THRESHOLD以内）没有结节，将新的等价类插入作为新结节的开始
if len(lastBoxes) == 0:
for k in curBoxes_union:
add_object(objects, k["boxID"])
continue
# 建立二分图
g = nx.Graph()
g.add_nodes_from([i["boxID"] + BOXID_VALUE for i in curBoxes_union])
g.add_nodes_from([i["objectID"] for i in lastBoxes])
for i in lastBoxes:
for j in curBoxes_union:
# 定义不同层面的3D中心点相对偏移（sim_metric），因为结节在３D上移动很少，只对中心点相对偏移在一定阈值内的两个结节做匹配
evalScore = anchor_metric.center_deviation_sqrt(
i["bndbox"], j["bndbox"])
if evalScore < SCORE_THRESHOLD:
if i["class"] not in OBJECT_CLS_WEIGHTS or j[
"class"] not in OBJECT_CLS_WEIGHTS:
print "object class not found in OBJECT_CLS_WEIGHTS"
raise KeyError
object_weight = OBJECT_CLS_WEIGHTS[i["class"]] * i["prob"]
box_weight = OBJECT_CLS_WEIGHTS[j["class"]] * j["prob"]
# we suppress 3d matching for misaligned boxes, only valid for matching lung objects
misalign_suppress = np.exp(-evalScore)
# 定义不同层面两个bounding box的分数作为边权
if i["class"] == j["class"]:
matchingScore = SAME_CLS_BOOST * object_weight * box_weight * misalign_suppress
else:
matchingScore = object_weight * box_weight * misalign_suppress
g.add_weighted_edges_from([[
i["objectID"], j["boxID"] + BOXID_VALUE, matchingScore
]])
# 求出最大权匹配,networkx2.0之后nx.algorithms.max_weight_matching返回set,之前版本都是字典（本代码默认用较低版本运行）
matchRes = nx.algorithms.max_weight_matching(g)
matched_object_list = []
reduced_matched_object_list = []
for i in matchRes.keys():
if i < BOXID_VALUE:
matched_object_list.append(i)
reduced_matched_object_list.append(i)
if len(matched_object_list) == 0:
for i in curBoxes_union:
# 对于没有匹配上的bounding box，认为是一个新结节的开始
if i["matched"] == False:
add_object(objects, i["boxID"])
continue
# elif len(matched_object_list) == 1:
# box_union = bboxInfo.loc[matchRes[matched_object_list[0]] - BOXID_VALUE]["unionFindSet"]
# box_union_index = [i for i in range(len(curBoxes_union)) if
# bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"] == box_union]
# if len(box_union_index) != 1:
# print ('there should be one and only one box for the same equivalent class in curBoxes_union')
# raise IndexError
# curBoxes_union[box_union_index[0]]["matched"] = True
# # 检查最大权匹配出的框有没有在一个等价类中，如果有，舍弃边权较低的那条边
# else:
# for j in range(len(matched_object_list)):
# if j < len(matched_object_list) - 1:
# for k in range(len(matched_object_list)-j-1):
# # 两个匹配了的框的等价类ID
# box_union1 = bboxInfo.loc[matchRes[matched_object_list[j]]-BOXID_VALUE]["unionFindSet"]
# box_union2 = bboxInfo.loc[matchRes[matched_object_list[j+k+1]]-BOXID_VALUE]["unionFindSet"]
#
# box_union_index1 = [i for i in range(len(curBoxes_union)) if bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"]== box_union1]
# box_union_index2 = [i for i in range(len(curBoxes_union)) if bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"]== box_union2]
#
# if len(box_union_index1) != 1 or len(box_union_index2) != 1:
# print ('there should be one and only one box for the same equivalent class in curBoxes_union')
# raise IndexError
# curBoxes_union[box_union_index1[0]]["matched"] = True
# curBoxes_union[box_union_index2[0]]["matched"] = True
# if box_union1 == box_union2:
# if g[matched_object_list[j]][matchRes[matched_object_list[j]]]['weight'] <= \
# g[matched_object_list[j+k+1]][matchRes[matched_object_list[j+k+1]]]['weight']:
# if matched_object_list[j] in reduced_matched_object_list:
# reduced_matched_object_list.remove(matched_object_list[j])
# elif matched_object_list[j+k+1] in reduced_matched_object_list:
# reduced_matched_object_list.remove(matched_object_list[j+k+1])
# 对于已经匹配上的bounding box，加入对应的objectList中
for i in range(len(matched_object_list)):
box_union = bboxInfo.loc[matchRes[matched_object_list[i]] -
BOXID_VALUE]["unionFindSet"]
box_union_index = [j for j in curBoxes_union if \
bboxInfo.loc[j["boxID"]]["unionFindSet"] == box_union]
if len(box_union_index) != 1:
print(
'there should be one and only one box for the same equivalent class in curBoxes_union'
)
raise IndexError
box_union_index[0]["matched"] = True
objects[matched_object_list[i] -
1]["objectList"].append(matchRes[matched_object_list[i]] -
BOXID_VALUE)
for i in curBoxes_union:
# 对于没有匹配上的bounding box，认为是一个新结节的开始
if i["matched"] == False:
add_object(objects, i["boxID"])
object_result = [-1] * len(bboxInfo)
for i in objects:
for j in i["objectList"]:
object_result[j] = i["id"]
bboxInfo["object"] = object_result
return bboxInfo[[
"instanceNumber", "xmin", "ymin", "xmax", "ymax", "class", "prob",
"object"
]], objects
def find_objects(bboxInfo, Z_THRESHOLD, SAME_BOX_THRESHOLD=np.array([1.6, 1.6]), SCORE_THRESHOLD=0.6,
object_cls_weights={}, same_cls_boost = 2.):
"""将boundingbox转换为结节。
:param bboxInfo: Bounding Box表格（pandas.DataFrame类型）,index必须为自然顺序。
每一行代表一个预测出来的bounding box，包含下面的列：
* 'instanceNumber'列：表示当前bounding box所在的层面的InstanceNumber，编号从1开始。
* 'xmin'列：表示当前bounding box的左上角x坐标（高度[H]方向）。
* 'ymin'列：表示当前bounding box的左上角y坐标（左右[W]方向）。
* 'xmax'列：表示当前bounding box的右下角x坐标（高度[H]方向）。
* 'ymax'列：表示当前bounding box的右下角y坐标（左右[W]方向）。
* 'class'列：表示当前bounding box的预测类别（如'object', 'mass'等）。
* 'prob'里：表示当前bounding box的预测概率。
:param Z_THRESHOLD： 每个新层面向前做贪心匹配时往前找的最大层面数
:param SAME_BOX_THRESHOLD: 判断同一层面两个框是否为等价类的中心点偏移阈值
:param SCORE_THRESHOLD: 判断不同层面两个框是否匹配的sim_metric_3d阈值
:param object_cls_weights: 检出框的类别的权重
:param same_cls_boost: 当不同层面两个框匹配时，如果类别相同的奖励系数
:return objectInfo: 在bnd上附加一列'object'，取值为-1, 1..n。
-1代表当前Bounding box不属于任何一个object;
1..n代表当前bounding box所属的结节编号。
我们不允许一个结节在同一个层面内存在多个重合的bounding box(一个等价类中没有匹配上的框设为-1)。
":return objects: 结节信息的列表，每个元素为一个字典
"""
# 初始化匹配anchor的度规类AnchorMetric:
anchor_metric = AnchorMetric(dim=2)
# 首先计算同一层面内dice coefficient比较高的， 并且认为这些bounding box标记了同一个结节
bboxInfo = bboxInfo.copy()
# 初始化并查集
unionFindSet = bboxInfo.index.tolist()
objectSlices = {}
for i in bboxInfo["instanceNumber"].unique():
lst = bboxInfo.query("instanceNumber == @i")
if len(lst) > 1:
for j1 in range(len(lst.index)):
for j2 in range(j1 + 1, len(lst.index)):
# 如果两个box中心点相对位移小于SAME_BOX_THRESHOLD，那么认为这两个box表示同一个结节
iou = anchor_metric.iou(get_bounding_box_nparray(lst.iloc[j1]), get_bounding_box_nparray(lst.iloc[j2]))
if iou > 0 and np.all(anchor_metric.center_deviation_iou(get_bounding_box_nparray(lst.iloc[j1]),
get_bounding_box_nparray(lst.iloc[j2])) < SAME_BOX_THRESHOLD):
# 将两个box插入一颗等价类树
union(lst.index[j1], lst.index[j2], unionFindSet)
objectSlices[i] = lst.index.tolist()
for i in range(len(unionFindSet)):
unionFindSet[i] = find_parent(unionFindSet[i], unionFindSet)
# unionFindSet保存了当前bounding box与本层面内其他bounding box的归属关系
# 对于bounding box i，如果bboxInfo.loc[i]["unionFindSet"] != i，
# 那么表明它与bboxInfo.loc[i]["unionFindSet"]表示同一个结节
bboxInfo["unionFindSet"] = unionFindSet
# 在不同层面间对结节进行匹配，并且获得结节列表
objects = []
BOXID_VALUE = 10000
for curZ in sorted(objectSlices.keys()):
# 枚举之前的所有结节，检查最后一个层面，如果与当前层面的instanceNumber差值在Z_THRESHOLD之内，那么作为备选结节加入lastBoxes
lastBoxes = [{"objectID": k["id"],
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k["objectList"][-1]]),
"class": bboxInfo.loc[k["objectList"][-1]]["class"],
"prob": bboxInfo.loc[k["objectList"][-1]]["prob"]}
for k in objects if 0 < curZ - bboxInfo.loc[k["objectList"][-1]]["instanceNumber"] <= \
Z_THRESHOLD[bboxInfo.loc[k["objectList"][-1]]["class"]]]
# 枚举本层面所有bounding box，每个框的评分为类别评分*置信度概率
curBoxes = [{"matched": False,
"boxID": k,
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k]),
"class": bboxInfo.loc[k]["class"],
"prob": bboxInfo.loc[k]["prob"],
"score": object_cls_weights[bboxInfo.loc[k]["class"]] * bboxInfo.loc[k]["prob"]}
for k in objectSlices[curZ]]
# 对于有多个box表示一个结节的，只选择其中一个
# （选择bboxInfo.loc[i]["unionFindSet"] == i的那个，即等价类树的根节点）
curBoxes_root = \
[{"boxID": k,
"bndbox": get_bounding_box_nparray(bboxInfo.loc[k]),
"class": bboxInfo.loc[k]["class"],
"prob": bboxInfo.loc[k]["prob"]}
for k in objectSlices[curZ] if bboxInfo.loc[k]["unionFindSet"] == k]
# 选取每个等价类中评分最高的框，"matched"记录该框是否与前层结节相匹配（最大权匹配），若没有则视为新结节插入。
curBoxes_union = []
for i in curBoxes_root:
Boxes_union = [j for j in curBoxes if bboxInfo.loc[j["boxID"]]["unionFindSet"] == i["boxID"]]
Boxes_union = sorted(Boxes_union, key=lambda k: k["score"])
curBoxes_union.append(Boxes_union[-1])
# 如果在之前层面（Z_THRESHOLD以内）没有结节，将新的等价类插入作为新结节的开始
if len(lastBoxes) == 0:
for k in curBoxes_union:
add_object(objects, k["boxID"])
continue
# 建立二分图
g = nx.Graph()
g.add_nodes_from([i["boxID"] + BOXID_VALUE for i in curBoxes])
g.add_nodes_from([i["objectID"] for i in lastBoxes])
for i in lastBoxes:
for j in curBoxes:
# 定义不同层面的3D中心点相对偏移（sim_metric），因为结节在３D上移动很少，只对中心点相对偏移在一定阈值内的两个结节做匹配
evalScore = anchor_metric.center_deviation_sqrt(i["bndbox"], j["bndbox"])
if evalScore < SCORE_THRESHOLD:
if i["class"] not in object_cls_weights or j["class"] not in object_cls_weights:
print "object class not found in objects_cls_weights"
raise KeyError
object_weight = object_cls_weights[i["class"]] * i["prob"]
box_weight = object_cls_weights[j["class"]] * j["prob"]
# we suppress 3d matching for misaligned boxes, only valid for matching lung objects
misalign_suppress = np.exp(-evalScore)
# 定义不同层面两个bounding box的分数作为边权
if i["class"] == j["class"]:
matchingScore = same_cls_boost * object_weight * box_weight * misalign_suppress
else:
matchingScore = object_weight * box_weight * misalign_suppress
g.add_weighted_edges_from([[i["objectID"], j["boxID"] + BOXID_VALUE, matchingScore]])
# 求出最大权匹配,networkx2.0之后nx.algorithms.max_weight_matching返回set,之前版本都是字典（本代码默认用较低版本运行）
matchRes = nx.algorithms.max_weight_matching(g)
matched_object_list = []
reduced_matched_object_list = []
for i in matchRes.keys():
if i < BOXID_VALUE:
matched_object_list.append(i)
reduced_matched_object_list.append(i)
if len(matched_object_list) == 0:
for i in curBoxes_union:
# 对于没有匹配上的bounding box，认为是一个新结节的开始
if i["matched"] == False:
add_object(objects, i["boxID"])
continue
elif len(matched_object_list) == 1:
box_union = bboxInfo.loc[matchRes[matched_object_list[0]] - BOXID_VALUE]["unionFindSet"]
box_union_index = [i for i in range(len(curBoxes_union)) if
bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"] == box_union]
if len(box_union_index) != 1:
print ('there should be one and only one box for the same equivalent class in curBoxes_union')
raise IndexError
curBoxes_union[box_union_index[0]]["matched"] = True
# 检查最大权匹配出的框有没有在一个等价类中，如果有，舍弃边权较低的那条边
else:
for j in range(len(matched_object_list)):
if j < len(matched_object_list) - 1:
for k in range(len(matched_object_list)-j-1):
# 两个匹配了的框的等价类ID
box_union1 = bboxInfo.loc[matchRes[matched_object_list[j]]-BOXID_VALUE]["unionFindSet"]
box_union2 = bboxInfo.loc[matchRes[matched_object_list[j+k+1]]-BOXID_VALUE]["unionFindSet"]
box_union_index1 = [i for i in range(len(curBoxes_union)) if bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"]== box_union1]
box_union_index2 = [i for i in range(len(curBoxes_union)) if bboxInfo.loc[curBoxes_union[i]["boxID"]]["unionFindSet"]== box_union2]
if len(box_union_index1) != 1 or len(box_union_index2) != 1:
print ('there should be one and only one box for the same equivalent class in curBoxes_union')
raise IndexError
curBoxes_union[box_union_index1[0]]["matched"] = True
curBoxes_union[box_union_index2[0]]["matched"] = True
if box_union1 == box_union2:
if g[matched_object_list[j]][matchRes[matched_object_list[j]]]['weight'] <= \
g[matched_object_list[j+k+1]][matchRes[matched_object_list[j+k+1]]]['weight']:
if matched_object_list[j] in reduced_matched_object_list:
reduced_matched_object_list.remove(matched_object_list[j])
elif matched_object_list[j+k+1] in reduced_matched_object_list:
reduced_matched_object_list.remove(matched_object_list[j+k+1])
# 对于已经匹配上的bounding box，加入对应的objectList中
for i in reduced_matched_object_list:
objects[i - 1]["objectList"].append(matchRes[i] - BOXID_VALUE)
for i in curBoxes_union:
# 对于没有匹配上的bounding box，认为是一个新结节的开始
if i["matched"] == False:
add_object(objects, i["boxID"])
object_result = [-1] * len(bboxInfo)
for i in objects:
for j in i["objectList"]:
object_result[j] = i["id"]
bboxInfo["object"] = object_result
return bboxInfo[["instanceNumber", "xmin", "ymin", "xmax", "ymax", "class", "prob", "object"]], objects
def cal_same_bbox_iou(bbox_gt, bbox_pt, thresh, dim):
anchor_metric = AnchorMetric(dim=dim)
return anchor_metric.iou(bbox_gt, bbox_pt) > thresh