Esempio n. 1
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def batch_generate_pymeta(data_root_folder, force_to_generate=False):
    """
    This function will convert batches into pymeta style which can be loaded by convnet
    """
    allfolder = iu.getfolderlist(data_root_folder)
    print "Get %d folders" % len(allfolder)
    l = []
    import sys

    for fn in allfolder:
        a = DHMLPE()
        fp = iu.fullfile(data_root_folder, fn, "matlab_meta.mat")
        if iu.exists(fp, "file"):
            save_fp = iu.fullfile(data_root_folder, fn, "batches.meta")
            print "-----------------------------"
            print "Processing ", fp
            if iu.exists(save_fp, "file") and not force_to_generate:
                print "Ha ha, it exists!"
            else:
                meta = a.get_convnet_meta(fp)
                mio.pickle(save_fp, meta)
            print "Saved %s" % save_fp
        else:
            l = l + [fp]
    print "=============\n"
    print "Here is what I cannot find (%d in total)" % len(l)
    print l
Esempio n. 2
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def batch_generate_pymeta(data_root_folder, force_to_generate=False):
    """
    This function will convert batches into pymeta style which can be loaded by convnet
    """
    allfolder = iu.getfolderlist(data_root_folder)
    print 'Get %d folders' % len(allfolder)
    l = []
    import sys
    for fn in allfolder:
        a = DHMLPE()
        fp = iu.fullfile(data_root_folder, fn, 'matlab_meta.mat')
        if iu.exists(fp, 'file'):
            save_fp = iu.fullfile(data_root_folder, fn, 'batches.meta')
            print '-----------------------------'
            print 'Processing ', fp
            if iu.exists(save_fp, 'file') and not force_to_generate:
                print 'Ha ha, it exists!'
            else:
                meta = a.get_convnet_meta(fp)
                mio.pickle(save_fp, meta)
            print 'Saved %s' % save_fp
        else:
            l = l + [fp]
    print '=============\n'
    print 'Here is what I cannot find (%d in total)' % len(l)
    print l
Esempio n. 3
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def show_bm_cmp(ndata, gt_target, mv_target, bm_targets, mv_score, gt_score,
                bm_score, solver=None, save_path=None):
    mv_margin = MMLSSolver.calc_margin(gt_target - mv_target).flatten() * 1200
    bm_margin = MMLSSolver.calc_margin(gt_target - bm_targets).flatten() * 1200
    save_path = '/public/sijinli2/ibuffer/2015-02-04/bm_cmp_SP_t004_act_14_graph_0010'
    save_path = '/public/sijinli2/ibuffer/2015-02-14/bm_cmp_FCJ0_act_14_graph_0020_test_'

    d = {'gt_target':gt_target, 'mv_target':mv_target, 'bm_target':bm_targets,
         'mv_score':mv_score, 'gt_score':gt_score, 'bm_score':bm_score
    }
    if save_path:
        mio.pickle(save_path, d)
    ndata = mv_margin.size
    pt_size = 15
    pl.subplot(4,1,1)
    avg = np.mean(bm_margin)
    pl.scatter(range(ndata), bm_margin, s=pt_size,c='r',label='mpjpe between max_score and best match (avg={:.3f})'.format(avg))
    pl.legend()
    pl.subplot(4,1,2)
    pl.scatter(range(ndata), mv_score - bm_score, s=pt_size,c='b',label='max score - best_match_score')
    pl.legend()
    pl.subplot(4,1,3)
    pl.scatter(range(ndata), (mv_score - bm_score)/np.abs(mv_score), c='b',label='(max_score - best_match_score)/abs(max_score)')
    pl.legend()
    pl.subplot(4,1,4)
    pl.scatter(range(ndata), mv_score, s=pt_size, c='r',label='max score')
    pl.scatter(range(ndata), bm_score, s=pt_size,c='b',label='best match score')
    pl.legend()
    pl.show()
Esempio n. 4
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def ReadDataToHMLPEDic(imgdir,example_path, data_category, max_per_batch,save_dir):
    """
    Read all data in 'data_category'
    into HMLPE dictionary
    There is no need to generating training data, since they can be generated in
    hmlpe.py 
    """
    import scipy.io as sio
    import iutils as iu
    import iread.myio as mio
    import iread.hmlpe as hmlpe
    import imgproc
    from PIL import Image
    if data_category != 'istest':
        print 'Warn: The correctness of data type %s is not guaranteed' % data_category
    all_example = sio.loadmat(example_path)['examples']
    examples = ExtractSubExample(all_example, data_category)
    ndata = examples.shape[-1]
    iu.ensure_dir(save_dir)
    buf_size = min(ndata, max_per_batch)
    dimdic = {'data':(112,112,3), 'part_indmap':(8,8), 'joint_indmap':(8,8)} 
    nparts  = 7
    njoints = 8
    d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
    d['oridet'] = np.zeros((4,buf_size), dtype=np.int)
    d['coords'] = np.ndarray((2,29, buf_size), dtype=np.float32)
    tdsize = dimdic['data'][0]
    dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
    d['data'] = d['data'].reshape((dsize, -1),order='F')
    d['is_positive'][:] = True
    d['is_mirror'][:] = False
    bid = 1
    j = 0
    for i in range(ndata):
        if j == max_per_batch:
           mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
           bid = bid + 1
           if ndata - i < max_per_batch:
               d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
        fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
        img = Image.open(fp)
        tbox = examples[i]['torsobox'][0].reshape((4))
        d['filenames'][j] = fp
        d['coords'][...,j] = examples[i]['coords']
        d['oribbox'][...,j] = bbox = ExtendBndbox(tbox, img.size) 
        orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape((8,2),order='C') - 1 # to python stype 0-idx
        d['joints8'][...,j] = TransformPoints(orijoints8, bbox, dimdic['data']).reshape((8,2),order='C')
        imgarr = imgproc.ensure_rgb(np.asarray(img))
        sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2],:])
        data_img = np.asarray(sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape((dsize),order='F') 
        d['data'][...,j] = data_img
        d['indmap'][...,j] = hmlpe.HMLPE.create_part_indicatormap(d['joints8'][...,j], hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0,  12.0)
        d['joint_indmap'][...,j] = hmlpe.HMLPE.create_joint_indicatormap(d['joints8'][...,j], dimdic['joint_indmap'], 30.0, 12.0)
        d['jointmasks'][...,j] = hmlpe.HMLPE.makejointmask(dimdic['data'], d['joints8'][...,j])
        j = j + 1
    mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
Esempio n. 5
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def show_what_is_best_all(train_dp, test_dp, solver):
    """
    In this funciton, I will show what is the upper bound for the KNN search like methods
    """
    candidates = train_dp.data_dic['feature_list'][0][..., train_dp.data_range]
    eval_data = train_dp.data_dic['feature_list'][0][..., test_dp.data_range]
    print 'Begin to calc the best mpjpe candidate shape {} eval shape'.format(candidates.shape, eval_data.shape)
    res = what_is_the_best_match(candidates, eval_data, solver)
    save_path = '/public/sijinli2/ibuffer/2015-01-16/best_match_act_14'
    mio.pickle(save_path, {'best_mpjpe':res})
Esempio n. 6
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def show_what_is_best_all(train_dp, test_dp, solver):
    """
    In this funciton, I will show what is the upper bound for the KNN search like methods
    """
    candidates = train_dp.data_dic["feature_list"][0][..., train_dp.data_range]
    eval_data = train_dp.data_dic["feature_list"][0][..., test_dp.data_range]
    print "Begin to calc the best mpjpe candidate shape {} eval shape".format(candidates.shape, eval_data.shape)
    res = what_is_the_best_match(candidates, eval_data, solver)
    save_path = "/public/sijinli2/ibuffer/2015-01-16/best_match_act_14"
    mio.pickle(save_path, {"best_mpjpe": res})
Esempio n. 7
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def ReadCropImageToHMLPEDic(dataset_dir, save_dir, istrain = False, isOC = True):
    """
    This function will be used for generating testing data
    Because training and testing data has different format in oribbox

    For generating trainign samples,
    please use
    create_lsp_regression_data.m
    (dataset_dir, type=3, opt)
      opt.OC = ?
      
    and hmlpe.py
    """
    import iutils as iu
    import iread.hmlpe as hmlpe
    import iread.myio as mio
    import scipy.io as sio
    from PIL import Image
    ndata = 1000
    if istrain:
        s_idx = 0
    else:
        s_idx = 1000
    imgdir = iu.fullfile(dataset_dir, 'images-crop')
    if isOC:
        dmat = sio.loadmat(iu.fullfile(dataset_dir, 'jointsOC.mat'))
    else:
        dmat = sio.loadmat(iu.fullfile(dataset_dir, 'joints-crop.mat'))
    lsp_jt = dmat['joints']
    dimdic = {'data':(112,112,3), 'part_indmap':(8,8), 'joint_indmap': (8,8)}
    nparts = 7
    njoints = 8
    d = hmlpe.HMLPE.prepare_savebuffer(dimdic, ndata, nparts, njoints)
    d['data'] = d['data'].reshape((-1,ndata),order='F')
    d['is_mirror'][:] = False
    d['is_positive'][:] = True
    for idx in range(s_idx, s_idx + ndata):
        imgpath = iu.fullfile(imgdir, 'im%04d.jpg' % (idx + 1))
        img = Image.open(imgpath)
        i = idx - s_idx
        orijoints8, isvisible = GetJoints8(lsp_jt[...,idx]) 
        bbox = GetUpperBodyBox(img.size)
        img_arr = np.asarray(img)[bbox[1]:bbox[3], bbox[0]:bbox[2],:]
        s = np.asarray([(dimdic['data'][1]-1.0)/(bbox[2] - bbox[0]),(dimdic['data'][0]-1.0)/(bbox[3]-bbox[1])]).reshape((1,2))
        tjoints = (orijoints8 - bbox[0:2,:].reshape((1,2)))*s
        masks = hmlpe.HMLPE.makejointmask(dimdic['data'], tjoints)
        d['data'][...,i] = np.asarray(Image.fromarray(img_arr).resize((dimdic['data'][0], dimdic['data'][1]))).reshape((-1,1),order='F').flatten()
        d['joints8'][...,i] =  tjoints
        d['jointmasks'][...,i] = np.logical_and(masks, isvisible)
        d['filenames'][i] = imgpath
        d['oribbox'][...,i] = bbox.flatten()
        d['indmap'][...,i] = hmlpe.HMLPE.create_part_indicatormap(tjoints, hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
        d['joint_indmap'][...,i] = hmlpe.HMLPE.create_joint_indicatormap(tjoints, dimdic['joint_indmap'], 30.0, 12.0)
    mio.pickle(iu.fullfile(save_dir, 'data_batch_1'), d)
Esempio n. 8
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def save_again():
    """
    change net_list objects to saved dict
    """
    d = Solver.get_saved_model("/public/sijinli2/ibuffer/2015-01-13/itheano_test")
    save_path = "/public/sijinli2/ibuffer/2015-01-13/saved_haha"
    for net_name in d["net_list"]:
        net = d["net_list"][net_name]
        res_dic = dict()
        for e in net["layers"]:
            l = net["layers"][e]
            res_dic[e] = [l[0], l[1], l[2].save_to_dic()]
        d["net_list"][net_name]["layers"] = res_dic
    print d["net_list"]["eval_net"].keys()
    mio.pickle(save_path, d)
Esempio n. 9
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def save_again():
    """
    change net_list objects to saved dict
    """
    d = Solver.get_saved_model(
        '/public/sijinli2/ibuffer/2015-01-13/itheano_test')
    save_path = '/public/sijinli2/ibuffer/2015-01-13/saved_haha'
    for net_name in d['net_list']:
        net = d['net_list'][net_name]
        res_dic = dict()
        for e in net['layers']:
            l = net['layers'][e]
            res_dic[e] = [l[0], l[1], l[2].save_to_dic()]
        d['net_list'][net_name]['layers'] = res_dic
    print d['net_list']['eval_net'].keys()
    mio.pickle(save_path, d)
Esempio n. 10
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def show_bm_cmp(ndata, gt_target, mv_target, bm_targets, mv_score, gt_score, bm_score, solver=None, save_path=None):
    mv_margin = MMLSSolver.calc_margin(gt_target - mv_target).flatten() * 1200
    bm_margin = MMLSSolver.calc_margin(gt_target - bm_targets).flatten() * 1200
    save_path = "/public/sijinli2/ibuffer/2015-01-22/saved_batch_data_test_net4_K_rbf_correct_test"

    d = {
        "gt_target": gt_target,
        "mv_target": mv_target,
        "bm_target": bm_targets,
        "mv_score": mv_score,
        "gt_score": gt_score,
        "bm_score": bm_score,
    }
    if save_path:
        mio.pickle(save_path, d)
    ndata = mv_margin.size
    pt_size = 15
    pl.subplot(4, 1, 1)
    avg = np.mean(bm_margin)
    pl.scatter(
        range(ndata),
        bm_margin,
        s=pt_size,
        c="r",
        label="mpjpe between max_score and best match (avg={:.3f})".format(avg),
    )
    pl.legend()
    pl.subplot(4, 1, 2)
    pl.scatter(range(ndata), mv_score - bm_score, s=pt_size, c="b", label="max score - best_match_score")
    pl.legend()
    pl.subplot(4, 1, 3)
    pl.scatter(
        range(ndata),
        (mv_score - bm_score) / np.abs(mv_score),
        c="b",
        label="(max_score - best_match_score)/abs(max_score)",
    )
    pl.legend()
    pl.subplot(4, 1, 4)
    pl.scatter(range(ndata), mv_score, s=pt_size, c="r", label="max score")
    pl.scatter(range(ndata), bm_score, s=pt_size, c="b", label="best match score")
    pl.legend()
    pl.show()
Esempio n. 11
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 def eval_mpjpe(self, op):
     """
     This function will simple evaluate mpjpe
     """
     train = False
     net = self.feature_net
     output_layer_name = self.prediction_layer_name
     pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
     pred_func = theano.function(inputs=net.inputs,
                                 outputs=pred_outputs,
                                 on_unused_input='ignore')
     itm = iu.itimer()
     itm.restart()
     cur_data = self.get_next_batch(train)
     pred_mpjpe_l = []
     pred_target_l = []
     save_folder = op.get_value('save_res_path')
     assert (save_folder is not None)
     iu.ensure_dir(save_folder)
     save_path = iu.fullfile(save_folder, 'pose_prediction')
     net.set_train_mode(
         False)  # added in Oct 27, 2015 After ICCV submission
     while True:
         self.print_iteration()
         ndata = cur_data[2][0].shape[-1]
         input_data = self.prepare_data(cur_data[2])
         gt_target = input_data[1].T
         print 'gt_target.shape  = {}'.format(gt_target.shape)
         preds = pred_func(*[input_data[0]])[0].T
         print 'Prediction.shape = {}'.format(preds.shape)
         pred_mpjpe = dutils.calc_mpjpe_from_residual(
             preds - gt_target, 17) * 1200
         print 'pred_mpjpe = {}'.format(pred_mpjpe.mean())
         self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
         pred_mpjpe_l.append(pred_mpjpe)
         pred_target_l.append(preds)
         if self.epoch == 1:
             break
         cur_data = self.get_next_batch(train)
     mio.pickle(save_path, {'preds': pred_target_l, 'mpjpe': pred_mpjpe_l})
     preds = np.concatenate(pred_target_l, axis=1)
     mpjpe = np.concatenate(pred_mpjpe_l, axis=1)
     mio.pickle(save_path, {'preds': pred_target_l, 'mpjpe': pred_mpjpe_l})
Esempio n. 12
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def cvt1(source_exp_name, target_exp_name):
    print '''
    SP_t004_act_14:
    source meta [rel_gt,  img_feature_accv_fc_j0,  relskel_feature_t004]
    Raw_SP_t004_act_14:
    target meta [rel_gt,  img_feature_accv_fc_j0,  rel_gt]
    '''
    base_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/'
    source_meta = mio.unpickle(
        iu.fullfile(base_path, 'folder_%s' % source_exp_name, 'batches.meta'))
    target_meta_folder = iu.fullfile(base_path, 'folder_%s' % target_exp_name)
    target_meta_path = iu.fullfile(target_meta_folder, 'batches.meta')
    d = source_meta.copy()
    print d.keys()
    d['feature_list'] = [source_meta['feature_list'][k] for k in [0, 1, 0]]
    d['feature_dim'] = [source_meta['feature_dim'][k] for k in [0, 1, 0]]
    # print d['info']
    print 'folder :{}\n path {}'.format(target_meta_folder, target_meta_path)
    iu.ensure_dir(target_meta_folder)
    mio.pickle(target_meta_path, d)
Esempio n. 13
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def cvt1(source_exp_name, target_exp_name):
    print '''
    SP_t004_act_14:
    source meta [rel_gt,  img_feature_accv_fc_j0,  relskel_feature_t004]
    Raw_SP_t004_act_14:
    target meta [rel_gt,  img_feature_accv_fc_j0,  rel_gt]
    '''
    base_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/'
    source_meta = mio.unpickle(iu.fullfile(base_path, 'folder_%s' % source_exp_name,
                                           'batches.meta'))
    target_meta_folder = iu.fullfile(base_path, 'folder_%s' % target_exp_name) 
    target_meta_path =  iu.fullfile(target_meta_folder, 'batches.meta') 
    d = source_meta.copy()
    print d.keys()
    d['feature_list'] = [source_meta['feature_list'][k] for k in [0, 1, 0]]
    d['feature_dim'] = [source_meta['feature_dim'][k] for k in [0, 1, 0]]
    # print d['info']
    print 'folder :{}\n path {}'.format(target_meta_folder, target_meta_path)
    iu.ensure_dir(target_meta_folder)
    mio.pickle(target_meta_path, d)
Esempio n. 14
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 def eval_mpjpe(self, op):
     """
     This function will simple evaluate mpjpe
     """
     train=False
     net = self.feature_net
     output_layer_name = self.prediction_layer_name
     pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
     pred_func = theano.function(inputs=net.inputs, outputs=pred_outputs,on_unused_input='ignore')
     itm = iu.itimer()
     itm.restart()
     cur_data = self.get_next_batch(train)
     pred_mpjpe_l = []
     pred_target_l = []
     save_folder = op.get_value('save_res_path')
     assert(save_folder is not None)
     iu.ensure_dir(save_folder)
     save_path = iu.fullfile(save_folder, 'pose_prediction')
     net.set_train_mode(False) # added in Oct 27, 2015 After ICCV submission
     while True:
         self.print_iteration()
         ndata = cur_data[2][0].shape[-1]
         input_data = self.prepare_data(cur_data[2])
         gt_target = input_data[1].T
         print 'gt_target.shape  = {}'.format(gt_target.shape) 
         preds = pred_func(*[input_data[0]])[0].T
         print 'Prediction.shape = {}'.format(preds.shape)
         pred_mpjpe = dutils.calc_mpjpe_from_residual(preds - gt_target, 17) * 1200
         print 'pred_mpjpe = {}'.format(pred_mpjpe.mean())
         self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
         pred_mpjpe_l.append(pred_mpjpe)
         pred_target_l.append(preds)
         if self.epoch == 1:
             break
         cur_data = self.get_next_batch(train)
     mio.pickle(save_path, {'preds':pred_target_l, 'mpjpe':pred_mpjpe_l})
     preds = np.concatenate(pred_target_l, axis=1)
     mpjpe = np.concatenate(pred_mpjpe_l, axis=1)
     mio.pickle(save_path, {'preds':pred_target_l, 'mpjpe':pred_mpjpe_l})
Esempio n. 15
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def ReadDataToHMLPEDic(imgdir, example_path, data_category, max_per_batch,
                       save_dir):
    """
    Read all data in 'data_category'
    into HMLPE dictionary
    There is no need to generating training data, since they can be generated in
    hmlpe.py 
    """
    import scipy.io as sio
    import iutils as iu
    import iread.myio as mio
    import iread.hmlpe as hmlpe
    import imgproc
    from PIL import Image
    if data_category != 'istest':
        print 'Warn: The correctness of data type %s is not guaranteed' % data_category
    all_example = sio.loadmat(example_path)['examples']
    examples = ExtractSubExample(all_example, data_category)
    ndata = examples.shape[-1]
    iu.ensure_dir(save_dir)
    buf_size = min(ndata, max_per_batch)
    dimdic = {
        'data': (112, 112, 3),
        'part_indmap': (8, 8),
        'joint_indmap': (8, 8)
    }
    nparts = 7
    njoints = 8
    d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
    d['oridet'] = np.zeros((4, buf_size), dtype=np.int)
    d['coords'] = np.ndarray((2, 29, buf_size), dtype=np.float32)
    tdsize = dimdic['data'][0]
    dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
    d['data'] = d['data'].reshape((dsize, -1), order='F')
    d['is_positive'][:] = True
    d['is_mirror'][:] = False
    bid = 1
    j = 0
    for i in range(ndata):
        if j == max_per_batch:
            mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
            bid = bid + 1
            if ndata - i < max_per_batch:
                d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts,
                                                   njoints)
        fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
        img = Image.open(fp)
        tbox = examples[i]['torsobox'][0].reshape((4))
        d['filenames'][j] = fp
        d['coords'][..., j] = examples[i]['coords']
        d['oribbox'][..., j] = bbox = ExtendBndbox(tbox, img.size)
        orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape(
            (8, 2), order='C') - 1  # to python stype 0-idx
        d['joints8'][..., j] = TransformPoints(orijoints8, bbox,
                                               dimdic['data']).reshape(
                                                   (8, 2), order='C')
        imgarr = imgproc.ensure_rgb(np.asarray(img))
        sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2], :])
        data_img = np.asarray(
            sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape(
                (dsize), order='F')
        d['data'][..., j] = data_img
        d['indmap'][..., j] = hmlpe.HMLPE.create_part_indicatormap(
            d['joints8'][..., j], hmlpe.part_idx, dimdic['part_indmap'], 0.3,
            30.0, 12.0)
        d['joint_indmap'][..., j] = hmlpe.HMLPE.create_joint_indicatormap(
            d['joints8'][..., j], dimdic['joint_indmap'], 30.0, 12.0)
        d['jointmasks'][...,
                        j] = hmlpe.HMLPE.makejointmask(dimdic['data'],
                                                       d['joints8'][..., j])
        j = j + 1
    mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
Esempio n. 16
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def ReadTestDataToHMLPEDic(imgdir, annot_dir, tech_report_path, save_path=None, iou_thresh=0.5):
    """
    This function will use detection window provided by the author
     Also, erro detection window are not included
     For image without detection, all the joint points will be zero
     
    """
    import Stickmen
    import scipy.io as sio
    import PIL
    import iutils as iu
    from PIL import Image
    import matplotlib.pyplot as plt
    import iread.hmlpe as hp
    import iread.myio as mio
    import ipyml.geometry as igeo
    rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
    d_annot = ExtractAnnotation(annot_dir)
    ndata = rp.shape[1]
    data = []
    tsize = 112 # the size of test image
    part_ind_dim = (8,8)
    joint_ind_dim = (8,8)
    filter_size = 32.0
    stride = 12.0
    indrate = 0.3
    joint_filter_size = 32.0
    joint_stride = 12.0
    njoints = 8
    nparts = 7
    ndet = ndata # One test point for one image
    newdim = (tsize,tsize,3)
    data = hp.HMLPE.prepare_savebuffer({'data':newdim,\
                                        'part_indmap': part_ind_dim,\
                                        'joint_indmap':joint_ind_dim },\
                                        ndet, nparts, njoints)
    # add Buffy specific field
    add_buffy_specific_field(data, ndet, imgdir)
    idx = 0
    f_calc_area = lambda rec: (rec[1][1]-rec[0][1]) * (rec[1][0]-rec[0][0]) if len(rec)==2 else 0
    f_mk_rec = lambda det: ((det[0],det[1]), (det[2], det[3]))
    for i in range(ndata):
        ep = rp[0,i][1][0][0]
        fr = rp[0,i][2][0][0]
        imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
                               ('%06d' % fr) + '.jpg')
        img = Image.open(imgpath)
        data['ep'][...,i] = ep
        data['fr'][...,i] = fr
        data['filenames'][i] = imgpath
        data['is_positive'][...,i] = True
        data['annotation'][...,i] = Stickmen.ReorderStickmen(d_annot[ep][fr])
        data['gt_det'][...,i] = np.asarray(Stickmen.EstDetFromStickmen( data['annotation'][...,i]))
        gt_rec = f_mk_rec(data['gt_det'][...,i]) #
        gt_area = f_calc_area(gt_rec) 
        if rp[0,i][0].size == 0: # No detection are found in this image
            # imgdata will also be all zero
            # oridet will be all zero
            # oribbox will be all zero
            # joints8 will be all zero
            # jointmasks wiil be all zero
            # indmap will be all zero
            # joint_indmap will be all zero
            # nothing need to be done, since they are default value
            pass
        else:
            m = -1
            for j in range(rp[0,i][0].size):
                det = rp[0,i][0][0,j]['det'][0] # det = (x,y,x1,y1)
                cur_rec = f_mk_rec(det)
                int_area = f_calc_area(igeo.RectIntersectRect(cur_rec, gt_rec))
                if int_area > ( gt_area - int_area ) * iou_thresh:
                  m = j
                  break
            if m != -1:
                oribbox = ExtendBndbox(det, img.size)
                arr_img = np.asarray(img)[oribbox[1]:oribbox[3]+1, \
                                          oribbox[0]:oribbox[2]+1,:]
                res_img = Image.fromarray(arr_img).resize((tsize,tsize))
                data['data'][...,i] =np.asarray(res_img)
                tmppoints = convert_AHEcoor_to_joints8(data['annotation'][...,i])
                data['joints8'][...,i] = TransformPoints(tmppoints, oribbox, np.asarray(res_img.size) -1)
                data['jointmasks'][...,i] = hp.HMLPE.makejointmask(newdim, data['joints8'][...,i])
                data['indmap'][...,i] = hp.HMLPE.create_part_indicatormap(data['joints8'][...,i], hp.part_idx, part_ind_dim, indrate, filter_size, stride)
                data['joint_indmap'][...,i] = hp.HMLPE.create_joint_indicatormap(data['joints8'][...,i], joint_ind_dim, joint_filter_size, joint_stride)
                data['oribbox'][...,i] = oribbox
    data['data'] = data['data'].reshape((-1,ndet),order='F')
    if save_path is not None:
        mio.pickle(save_path, data)
    return data
Esempio n. 17
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def ReadCropImageToHMLPEDic(dataset_dir, save_dir, istrain=False, isOC=True):
    """
    This function will be used for generating testing data
    Because training and testing data has different format in oribbox

    For generating trainign samples,
    please use
    create_lsp_regression_data.m
    (dataset_dir, type=3, opt)
      opt.OC = ?
      
    and hmlpe.py
    """
    import iutils as iu
    import iread.hmlpe as hmlpe
    import iread.myio as mio
    import scipy.io as sio
    from PIL import Image
    ndata = 1000
    if istrain:
        s_idx = 0
    else:
        s_idx = 1000
    imgdir = iu.fullfile(dataset_dir, 'images-crop')
    if isOC:
        dmat = sio.loadmat(iu.fullfile(dataset_dir, 'jointsOC.mat'))
    else:
        dmat = sio.loadmat(iu.fullfile(dataset_dir, 'joints-crop.mat'))
    lsp_jt = dmat['joints']
    dimdic = {
        'data': (112, 112, 3),
        'part_indmap': (8, 8),
        'joint_indmap': (8, 8)
    }
    nparts = 7
    njoints = 8
    d = hmlpe.HMLPE.prepare_savebuffer(dimdic, ndata, nparts, njoints)
    d['data'] = d['data'].reshape((-1, ndata), order='F')
    d['is_mirror'][:] = False
    d['is_positive'][:] = True
    for idx in range(s_idx, s_idx + ndata):
        imgpath = iu.fullfile(imgdir, 'im%04d.jpg' % (idx + 1))
        img = Image.open(imgpath)
        i = idx - s_idx
        orijoints8, isvisible = GetJoints8(lsp_jt[..., idx])
        bbox = GetUpperBodyBox(img.size)
        img_arr = np.asarray(img)[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
        s = np.asarray([(dimdic['data'][1] - 1.0) / (bbox[2] - bbox[0]),
                        (dimdic['data'][0] - 1.0) / (bbox[3] - bbox[1])
                        ]).reshape((1, 2))
        tjoints = (orijoints8 - bbox[0:2, :].reshape((1, 2))) * s
        masks = hmlpe.HMLPE.makejointmask(dimdic['data'], tjoints)
        d['data'][..., i] = np.asarray(
            Image.fromarray(img_arr).resize(
                (dimdic['data'][0], dimdic['data'][1]))).reshape(
                    (-1, 1), order='F').flatten()
        d['joints8'][..., i] = tjoints
        d['jointmasks'][..., i] = np.logical_and(masks, isvisible)
        d['filenames'][i] = imgpath
        d['oribbox'][..., i] = bbox.flatten()
        d['indmap'][..., i] = hmlpe.HMLPE.create_part_indicatormap(
            tjoints, hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
        d['joint_indmap'][..., i] = hmlpe.HMLPE.create_joint_indicatormap(
            tjoints, dimdic['joint_indmap'], 30.0, 12.0)
    mio.pickle(iu.fullfile(save_dir, 'data_batch_1'), d)
Esempio n. 18
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def ReadTestDataToHMLPEDic(imgdir,
                           annot_dir,
                           tech_report_path,
                           save_path=None,
                           iou_thresh=0.5):
    """
    This function will use detection window provided by the author
     Also, erro detection window are not included
     For image without detection, all the joint points will be zero
     
    """
    import Stickmen
    import scipy.io as sio
    import PIL
    import iutils as iu
    from PIL import Image
    import matplotlib.pyplot as plt
    import iread.hmlpe as hp
    import iread.myio as mio
    import ipyml.geometry as igeo
    rp = sio.loadmat(tech_report_path)['techrep2010_buffy']
    d_annot = ExtractAnnotation(annot_dir)
    ndata = rp.shape[1]
    data = []
    tsize = 112  # the size of test image
    part_ind_dim = (8, 8)
    joint_ind_dim = (8, 8)
    filter_size = 32.0
    stride = 12.0
    indrate = 0.3
    joint_filter_size = 32.0
    joint_stride = 12.0
    njoints = 8
    nparts = 7
    ndet = ndata  # One test point for one image
    newdim = (tsize, tsize, 3)
    data = hp.HMLPE.prepare_savebuffer({'data':newdim,\
                                        'part_indmap': part_ind_dim,\
                                        'joint_indmap':joint_ind_dim },\
                                        ndet, nparts, njoints)
    # add Buffy specific field
    add_buffy_specific_field(data, ndet, imgdir)
    idx = 0
    f_calc_area = lambda rec: (rec[1][1] - rec[0][1]) * (rec[1][0] - rec[0][
        0]) if len(rec) == 2 else 0
    f_mk_rec = lambda det: ((det[0], det[1]), (det[2], det[3]))
    for i in range(ndata):
        ep = rp[0, i][1][0][0]
        fr = rp[0, i][2][0][0]
        imgpath = iu.fullfile(imgdir, 'buffy_s5e'+str(ep)+'_original', \
                               ('%06d' % fr) + '.jpg')
        img = Image.open(imgpath)
        data['ep'][..., i] = ep
        data['fr'][..., i] = fr
        data['filenames'][i] = imgpath
        data['is_positive'][..., i] = True
        data['annotation'][..., i] = Stickmen.ReorderStickmen(d_annot[ep][fr])
        data['gt_det'][..., i] = np.asarray(
            Stickmen.EstDetFromStickmen(data['annotation'][..., i]))
        gt_rec = f_mk_rec(data['gt_det'][..., i])  #
        gt_area = f_calc_area(gt_rec)
        if rp[0, i][0].size == 0:  # No detection are found in this image
            # imgdata will also be all zero
            # oridet will be all zero
            # oribbox will be all zero
            # joints8 will be all zero
            # jointmasks wiil be all zero
            # indmap will be all zero
            # joint_indmap will be all zero
            # nothing need to be done, since they are default value
            pass
        else:
            m = -1
            for j in range(rp[0, i][0].size):
                det = rp[0, i][0][0, j]['det'][0]  # det = (x,y,x1,y1)
                cur_rec = f_mk_rec(det)
                int_area = f_calc_area(igeo.RectIntersectRect(cur_rec, gt_rec))
                if int_area > (gt_area - int_area) * iou_thresh:
                    m = j
                    break
            if m != -1:
                oribbox = ExtendBndbox(det, img.size)
                arr_img = np.asarray(img)[oribbox[1]:oribbox[3]+1, \
                                          oribbox[0]:oribbox[2]+1,:]
                res_img = Image.fromarray(arr_img).resize((tsize, tsize))
                data['data'][..., i] = np.asarray(res_img)
                tmppoints = convert_AHEcoor_to_joints8(data['annotation'][...,
                                                                          i])
                data['joints8'][..., i] = TransformPoints(
                    tmppoints, oribbox,
                    np.asarray(res_img.size) - 1)
                data['jointmasks'][..., i] = hp.HMLPE.makejointmask(
                    newdim, data['joints8'][..., i])
                data['indmap'][..., i] = hp.HMLPE.create_part_indicatormap(
                    data['joints8'][..., i], hp.part_idx, part_ind_dim,
                    indrate, filter_size, stride)
                data['joint_indmap'][...,
                                     i] = hp.HMLPE.create_joint_indicatormap(
                                         data['joints8'][...,
                                                         i], joint_ind_dim,
                                         joint_filter_size, joint_stride)
                data['oribbox'][..., i] = oribbox
    data['data'] = data['data'].reshape((-1, ndet), order='F')
    if save_path is not None:
        mio.pickle(save_path, data)
    return data