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seqnms.py
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# -*- coding: utf-8 -*-
import _init_paths
import numpy as np
from fast_rcnn.nms_wrapper import nms
import cv2
import time
import copy
import cPickle as pickle
import os
CLASSES = ('__background__',
'airplane','antelope','bear','bicycle','bird','bus',
'car','cattle','dog','domestic cat','elephant','fox',
'giant panda','hamster','horse','lion','lizard','monkey',
'motorcycle','rabbit','red panda','sheep','snake','squirrel',
'tiger','train','turtle','watercraft','whale','zebra')
CONF_THRESH = 0.5
NMS_THRESH = 0.3
IOU_THRESH = 0.6
'''
修改检测结果格式，用作后续处理
第一维：种类
第二维：帧
第三维：bbox
第四维：x1,y1,x2,y2,score
'''
def createInputs(video):
create_begin=time.time()
frames=sorted(video.keys()) #获得按序排列的帧的名称
dets=[[] for i in CLASSES[1:]] #保存最终结果
for cls_ind,cls in enumerate(CLASSES[1:]): #类
for frame_ind,frame in enumerate(frames): #帧
cls_boxes = video[frame]['boxes'][:, 4*(cls_ind+1):4*(cls_ind + 2)]
cls_scores = video[frame]['scores'][:, cls_ind+1]
cls_dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float64)
dets[cls_ind].append(cls_dets)
create_end=time.time()
print 'create inputs: {:.4f}s'.format(create_end - create_begin)
return dets
def createLinks(dets_all):
links_all=[]
#建立每相邻两帧之间的link关系
frame_num=len(dets_all[0])
cls_num=len(CLASSES)-1
#links_all=[] #保存每一类的全部link，第一维为类数，第二维为帧数-1，为该类下的links即每一帧与后一帧之间的link，第三维每帧的box数，为该帧与后一帧之间的link
for cls_ind in range(cls_num): #第一层循环，类数
links_cls=[] #保存一类下全部帧的links
link_begin=time.time()
for frame_ind in range(frame_num-1): #第二层循环，帧数-1，不循环最后一帧
dets1=dets_all[cls_ind][frame_ind]
dets2=dets_all[cls_ind][frame_ind+1]
box1_num=len(dets1)
box2_num=len(dets2)
#先计算每个box的area
if frame_ind==0:
areas1=np.empty(box1_num)
for box1_ind,box1 in enumerate(dets1):
areas1[box1_ind]=(box1[2]-box1[0]+1)*(box1[3]-box1[1]+1)
else: #当前帧的area1就是前一帧的area2，避免重复计算
areas1=areas2
areas2=np.empty(box2_num)
for box2_ind,box2 in enumerate(dets2):
areas2[box2_ind]=(box2[2]-box2[0]+1)*(box2[3]-box2[1]+1)
#计算相邻两帧同一类的link
links_frame=[] #保存相邻两帧的links
for box1_ind,box1 in enumerate(dets1):
area1=areas1[box1_ind]
x1=np.maximum(box1[0],dets2[:,0])
y1=np.maximum(box1[1],dets2[:,1])
x2=np.minimum(box1[2],dets2[:,2])
y2=np.minimum(box1[3],dets2[:,3])
w =np.maximum(0.0, x2 - x1 + 1)
h =np.maximum(0.0, y2 - y1 + 1)
inter = w * h
ovrs = inter / (area1 + areas2 - inter)
links_box=[ovr_ind for ovr_ind,ovr in enumerate(ovrs) if ovr >= IOU_THRESH] #保存第一帧的一个box对第二帧全部box的link
links_frame.append(links_box)
links_cls.append(links_frame)
link_end=time.time()
print 'link: {:.4f}s'.format(link_end - link_begin)
links_all.append(links_cls)
return links_all
def maxPath(dets_all,links_all):
for cls_ind,links_cls in enumerate(links_all):
max_begin=time.time()
dets_cls=dets_all[cls_ind]
while True:
rootindex,maxpath,maxsum=findMaxPath(links_cls,dets_cls)
if len(maxpath) <= 1:
break
rescore(dets_cls,rootindex,maxpath,maxsum)
deleteLink(dets_cls,links_cls,rootindex,maxpath,IOU_THRESH)
max_end=time.time()
print 'max path: {:.4f}s'.format(max_end - max_begin)
def NMS(dets_all):
for cls_ind,dets_cls in enumerate(dets_all):
for frame_ind,dets in enumerate(dets_cls):
keep=nms(dets, NMS_THRESH)
dets_all[cls_ind][frame_ind]=dets[keep, :]
def findMaxPath(links,dets):
maxpaths=[] #保存从每个结点到最后的最大路径与分数
roots=[] #保存所有的可作为独立路径进行最大路径比较的路径
maxpaths.append([ (box[4],[ind]) for ind,box in enumerate(dets[-1])])
for link_ind,link in enumerate(links[::-1]): #每一帧与后一帧的link，为一个list
curmaxpaths=[]
linkflags=np.zeros(len(maxpaths[0]),int)
det_ind=len(links)-link_ind-1
for ind,linkboxes in enumerate(link): #每一帧中每个box的link，为一个list
if linkboxes == []:
curmaxpaths.append((dets[det_ind][ind][4],[ind]))
continue
linkflags[linkboxes]=1
prev_ind=np.argmax([maxpaths[0][linkbox][0] for linkbox in linkboxes])
prev_score=maxpaths[0][linkboxes[prev_ind]][0]
prev_path=copy.copy(maxpaths[0][linkboxes[prev_ind]][1])
prev_path.insert(0,ind)
curmaxpaths.append((dets[det_ind][ind][4]+prev_score,prev_path))
root=[maxpaths[0][ind] for ind,flag in enumerate(linkflags) if flag == 0]
roots.insert(0,root)
maxpaths.insert(0,curmaxpaths)
roots.insert(0,maxpaths[0])
maxscore=0
maxpath=[]
for index,paths in enumerate(roots):
if paths==[]:
continue
maxindex=np.argmax([path[0] for path in paths])
if paths[maxindex][0]>maxscore:
maxscore=paths[maxindex][0]
maxpath=paths[maxindex][1]
rootindex=index
return rootindex,maxpath,maxscore
def rescore(dets, rootindex, maxpath, maxsum):
newscore=maxsum/len(maxpath)
for i,box_ind in enumerate(maxpath):
dets[rootindex+i][box_ind][4]=newscore
def deleteLink(dets,links, rootindex, maxpath,thesh):
for i,box_ind in enumerate(maxpath):
areas=[(box[2]-box[0]+1)*(box[3]-box[1]+1) for box in dets[rootindex+i]]
area1=areas[box_ind]
box1=dets[rootindex+i][box_ind]
x1=np.maximum(box1[0],dets[rootindex+i][:,0])
y1=np.maximum(box1[1],dets[rootindex+i][:,1])
x2=np.minimum(box1[2],dets[rootindex+i][:,2])
y2=np.minimum(box1[3],dets[rootindex+i][:,3])
w =np.maximum(0.0, x2 - x1 + 1)
h =np.maximum(0.0, y2 - y1 + 1)
inter = w * h
ovrs = inter / (area1 + areas - inter)
deletes=[ovr_ind for ovr_ind,ovr in enumerate(ovrs) if ovr >= IOU_THRESH] #保存待删除的box的index
if rootindex+i<len(links): #除了最后一帧，置box_ind的box的link为空
for delete_ind in deletes:
links[rootindex+i][delete_ind]=[]
if i > 0 or rootindex>0:
for priorbox in links[rootindex+i-1]: #将前一帧指向box_ind的link删除
for delete_ind in deletes:
if delete_ind in priorbox:
priorbox.remove(delete_ind)
def saveforAP(dets_all,frame_names):
#保存结果为用于计算ap的格式
for cls_ind, cls_name in enumerate(CLASSES):
if cls_name == '__background__':
continue
dirpath='/workspace/liruiguang/imagenet/seqnms-results/'
if not os.path.exists(dirpath):
os.makedirs(dirpath)
filename = '{:s}{:s}.txt'.format(dirpath,cls_name)
with open(filename, 'a') as f:
for frame_ind, frame_name in enumerate(frame_names):
dets = dets_all[cls_ind-1][frame_ind]
for k in xrange(dets.shape[0]):
f.write('{:s} {:.6f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(frame_name, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def dsnms(video):
dets=createInputs(video)
links=createLinks(dets)
maxPath(dets,links)
NMS(dets)
frame_names=sorted(video.keys())
saveforAP(dets,frame_names)
pkllistfile=open('/workspace/liruiguang/imagenet/pkllist.txt')
pkllist=pkllistfile.readlines()
pkllistfile.close()
pkllist=[pkl.strip() for pkl in pkllist]
for pkl in pkllist:
f = open('/workspace/liruiguang/imagenet/'+pkl)
video = pickle.load(f)
dsnms(video['dets'])