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Python PoseTools.classify_movie方法代码示例

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)
开发者ID:mkabra,项目名称:poseTF,代码行数:96,代码来源:singleViewCV.py

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]
开发者ID:mkabra,项目名称:poseTF,代码行数:33,代码来源:cellCodeBody.py

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')
开发者ID:mkabra,项目名称:poseTF,代码行数:70,代码来源:trackStephenHead_KB_CV.py


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