本文整理汇总了Python中PoseTools.classify_movie方法的典型用法代码示例。如果您正苦于以下问题:Python PoseTools.classify_movie方法的具体用法?Python PoseTools.classify_movie怎么用?Python PoseTools.classify_movie使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类PoseTools
的用法示例。
在下文中一共展示了PoseTools.classify_movie方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: classifyfold
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import classify_movie [as 别名]
def classifyfold(conffile,curfold,curgpu,batch_size,
redo,outdir,confname='conf',outtype = 2,
extra_str=None):
imp_mod = importlib.import_module(conffile)
conf = imp_mod.__dict__[confname]
if batch_size>0:
conf.batch_size = batch_size
ext = '_fold_{}'.format(curfold)
conf.valdatafilename = conf.valdatafilename + ext
conf.trainfilename = conf.trainfilename + ext
conf.valfilename = conf.valfilename + ext
conf.fulltrainfilename += ext
conf.baseoutname = conf.baseoutname + ext
conf.mrfoutname += ext
conf.fineoutname += ext
conf.baseckptname += ext
conf.mrfckptname += ext
conf.fineckptname += ext
conf.basedataname += ext
conf.finedataname += ext
conf.mrfdataname += ext
isval,localdirs,seldirs = multiResData.load_val_data(conf)
for ndx,curl in enumerate(localdirs):
if not os.path.exists(curl):
print(curl + ' {} doesnt exist!!!!'.format(ndx))
return
os.environ['CUDA_VISIBLE_DEVICES'] = curgpu
max_chunk_size = 1000
self = PoseTools.create_network(conf, outtype)
sess = tf.Session()
PoseTools.init_network(self, sess, outtype)
for ndx in range(len(localdirs)):
if not isval.count(ndx):
continue
mname,_ = os.path.splitext(os.path.basename(localdirs[ndx]))
oname = re.sub('!','__',conf.getexpname(localdirs[ndx]))
if extra_str is not None:
oname += '_'+extra_str
print(oname)
pname = os.path.join(outdir , oname)
# detect
if redo or not (os.path.isfile(pname + '.mat') or os.path.isfile(pname + '.h5')):
cap = cv2.VideoCapture(localdirs[ndx])
height = int(cap.get(cvc.FRAME_HEIGHT))
width = int(cap.get(cvc.FRAME_WIDTH))
orig_crop_loc = conf.cropLoc[(height,width)]
nframes = int(cap.get(cvc.FRAME_COUNT))
nblocks = int(math.ceil(float(nframes)/max_chunk_size))
cap.release()
out_sz = [0,0]
out_sz[0] = height//conf.pool_scale//conf.rescale
out_sz[1] = width//conf.pool_scale//conf.rescale
with h5py.File(pname+'.h5','w') as f:
f.create_dataset('expname', data=localdirs[ndx])
for cur_b in range(nblocks):
startat = cur_b*max_chunk_size
stopat = min((cur_b+1)*max_chunk_size,nframes)
frames_read = stopat-startat
predList = PoseTools.classify_movie(conf, localdirs[ndx], outtype,
self, sess, max_frames=frames_read,
start_at=startat)
scale_fac = conf.pool_scale*conf.rescale
crop_loc = [old_div(x,scale_fac) for x in orig_crop_loc]
end_pad = [old_div(height,scale_fac)-crop_loc[0]-old_div(conf.imsz[0],scale_fac),
old_div(width,scale_fac)-crop_loc[1]-old_div(conf.imsz[1],scale_fac)]
pp = [(0,0),(crop_loc[0],end_pad[0]),(crop_loc[1],end_pad[1]),(0,0),(0,0)]
predScores = np.pad(predList[1],pp,mode='constant',constant_values=-1.)
if cur_b == 0:
locs_o = f.create_dataset('locs', (nframes, conf.n_classes, 2, 2))
scores_o = f.create_dataset('scores', (nframes, predScores.shape[1],
predScores.shape[2], conf.n_classes, 2))
predLocs = predList[0]
predLocs[:,:,:,0] += orig_crop_loc[1]
predLocs[:,:,:,1] += orig_crop_loc[0]
locs_o[startat:stopat,...] = predLocs
scores_o[startat:stopat,...] = predScores
# io.savemat(pname + '.mat',{'locs':predLocs,'scores':predScores[...,0],'expname':localdirs[ndx]})
print('Detecting:%s'%oname)
示例2: range
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import classify_movie [as 别名]
PoseTools.init_network(self, sess, outtype)
scale = conf.rescale*conf.pool_scale
_,valmovies = multiResData.get_movie_lists(conf)
for ndx in range(len(valmovies)):
mname,_ = os.path.splitext(os.path.basename(valmovies[ndx]))00
oname = re.sub('!','__',conf.getexpname(valmovies[ndx]))
pname = os.path.join(localSetup.bdir, 'data', 'out', oname + extrastr)
if os.path.isfile(pname + '.mat') and not redo:
continue
if not os.path.isfile(valmovies[ndx]):
continue
predList = PoseTools.classify_movie(conf, valmovies[ndx], outtype, self, sess)
# PoseTools.createPredMovie(conf,predList,valmovies[ndx],pname + '.avi',outtype)
cap = cv2.VideoCapture(valmovies[ndx])
height = int(cap.get(cvc.FRAME_HEIGHT))
width = int(cap.get(cvc.FRAME_WIDTH))
orig_crop_loc = conf.cropLoc[(height,width)]
crop_loc = [x/scale for x in orig_crop_loc]
end_pad = [height/scale-crop_loc[0]-conf.imsz[0]/scale,width/scale-crop_loc[1]-conf.imsz[1]/scale]
pp = [(0,0),(crop_loc[0],end_pad[0]),(crop_loc[1],end_pad[1]),(0,0),(0,0)]
predScores = np.pad(predList[1],pp,mode='constant',constant_values=-1.)
predLocs = predList[0]
predLocs[:,:,:,0] += orig_crop_loc[1]
predLocs[:,:,:,1] += orig_crop_loc[0]
示例3: main
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import classify_movie [as 别名]
#.........这里部分代码省略.........
conf.fineoutname += ext
conf.baseckptname += ext
conf.mrfckptname += ext
conf.fineckptname += ext
conf.basedataname += ext
conf.finedataname += ext
conf.mrfdataname += ext
# conf.batch_size = 1
if args.detect:
self = PoseTools.create_network(conf, outtype)
sess = tf.Session()
PoseTools.init_network(self, sess, outtype)
for ndx in range(len(valmovies)):
mname,_ = os.path.splitext(os.path.basename(valmovies[ndx]))
oname = re.sub('!','__',conf.getexpname(valmovies[ndx]))
pname = os.path.join(args.outdir , oname + extrastr)
print(oname)
flynum = conf.getflynum(smovies[ndx])
# print "Parsed fly number as %d"%flynum
if flynum not in dltdict:
print('No dlt file, skipping')
continue
# detect
if args.detect and os.path.isfile(valmovies[ndx]) and \
(args.redo or not os.path.isfile(pname + '.mat')):
predList = PoseTools.classify_movie(conf, valmovies[ndx], outtype, self, sess)
if args.makemovie:
PoseTools.create_pred_movie(conf, predList, valmovies[ndx], pname + '.avi', outtype)
cap = cv2.VideoCapture(valmovies[ndx])
height = int(cap.get(cvc.FRAME_HEIGHT))
width = int(cap.get(cvc.FRAME_WIDTH))
orig_crop_loc = conf.cropLoc[(height,width)]
crop_loc = [old_div(x,4) for x in orig_crop_loc]
end_pad = [old_div(height,4)-crop_loc[0]-old_div(conf.imsz[0],4),old_div(width,4)-crop_loc[1]-old_div(conf.imsz[1],4)]
pp = [(0,0),(crop_loc[0],end_pad[0]),(crop_loc[1],end_pad[1]),(0,0),(0,0)]
predScores = np.pad(predList[1],pp,mode='constant',constant_values=-1.)
predLocs = predList[0]
predLocs[:,:,:,0] += orig_crop_loc[1]
predLocs[:,:,:,1] += orig_crop_loc[0]
io.savemat(pname + '.mat',{'locs':predLocs,'scores':predScores[...,0],'expname':valmovies[ndx]})
print('Detecting:%s'%oname)
# track
if args.track and view == 1:
oname_side = re.sub('!','__',conf.getexpname(smovies[ndx]))
oname_front = re.sub('!','__',conf.getexpname(fmovies[ndx]))
pname_side = os.path.join(args.outdir , oname_side + '_side.mat')
pname_front = os.path.join(args.outdir , oname_front + '_front.mat')
# 3d trajectories
basename_front,_ = os.path.splitext(fmovies[ndx])
basename_side,_ = os.path.splitext(smovies[ndx])
savefile = basename_side+'_3Dres.mat'
#savefile = os.path.join(args.outdir , oname_side + '_3Dres.mat')