import os
import glob
import numpy as np
import read_binary_blob
import cPickle
feat_dir = '/media/researchshare/linjie/data/snapchat/features/c3d_resize/'
fds = os.listdir(feat_dir)
feat_dim = 4096
for fd in fds:
feat_names = glob.glob(feat_dir + fd + '/*.fc7-1')
agg_feats = np.zeros((1, feat_dim))
for path in feat_names:
feats = read_binary_blob.read(path)
feats = np.asarray(feats, dtype=np.float32)
agg_feats = np.maximum(feats, agg_feats)
print 'start to save feature for %s' % fd
#print agg_feats.shape
with open(feat_dir + fd + '/agg_feats', 'wb') as f:
cPickle.dump(agg_feats, f, protocol=cPickle.HIGHEST_PROTOCOL)
fds = os.listdir(feat_dir)
pool_type='mean'
feat_dim = 4096
stage=sys.argv[1]#'train'
list_path='/home/a-linjieyang/work/video_caption/ucfTrainTestlist/%slist01.txt' % stage
with open(list_path,'r') as f:
pooled_feats=[]
for line in f:
content = line.split()
vid_fd = content[0][:-4]
feat_names = os.listdir(feat_dir+vid_fd)
feat_n = len(feat_names)
feats_seq = np.zeros((feat_n,feat_dim))
for i,path in enumerate(feat_names):
feats = read_binary_blob.read(feat_dir+vid_fd+'/'+path)
feats_seq[i,:] = np.asarray(feats, dtype=np.float32)
#agg_feats = np.maximum(feats, agg_feats)
if pool_type=='mean':
agg_feats = np.mean(feats_seq, axis=0)
else:
agg_feats = np.amax(feats_seq, axis=0)
pooled_feats.append(agg_feats)
pooled_feats = np.vstack(pooled_feats)
with open('%sc3d_pooled_%s' % (sav_dir,stage),'wb') as fout:
cPickle.dump(pooled_feats,fout, protocol=cPickle.HIGHEST_PROTOCOL)
#print 'start to save feature for %s' % fd
#print agg_feats.shape
#with open(feat_dir+fd+'/agg_feats','wb') as f:
#cPickle.dump(agg_feats,f, protocol=cPickle.HIGHEST_PROTOCOL)
import os import glob import numpy as np import read_binary_blob import cPickle feat_dir = '/media/researchshare/linjie/data/snapchat/features/c3d_resize/' fds = os.listdir(feat_dir) feat_dim = 4096 for fd in fds: feat_names = glob.glob(feat_dir+fd+'/*.fc7-1') agg_feats = np.zeros((1,feat_dim)) for path in feat_names: feats = read_binary_blob.read(path) feats = np.asarray(feats, dtype=np.float32) agg_feats = np.maximum(feats, agg_feats) print 'start to save feature for %s' % fd #print agg_feats.shape with open(feat_dir+fd+'/agg_feats','wb') as f: cPickle.dump(agg_feats,f, protocol=cPickle.HIGHEST_PROTOCOL)
fds = os.listdir(feat_dir)
pool_type = 'mean'
feat_dim = 4096
stage = sys.argv[1] #'train'
list_path = '/home/a-linjieyang/work/video_caption/ucfTrainTestlist/%slist01.txt' % stage
with open(list_path, 'r') as f:
pooled_feats = []
for line in f:
content = line.split()
vid_fd = content[0][:-4]
feat_names = os.listdir(feat_dir + vid_fd)
feat_n = len(feat_names)
feats_seq = np.zeros((feat_n, feat_dim))
for i, path in enumerate(feat_names):
feats = read_binary_blob.read(feat_dir + vid_fd + '/' + path)
feats_seq[i, :] = np.asarray(feats, dtype=np.float32)
#agg_feats = np.maximum(feats, agg_feats)
if pool_type == 'mean':
agg_feats = np.mean(feats_seq, axis=0)
else:
agg_feats = np.amax(feats_seq, axis=0)
pooled_feats.append(agg_feats)
pooled_feats = np.vstack(pooled_feats)
with open('%sc3d_pooled_%s' % (sav_dir, stage), 'wb') as fout:
cPickle.dump(pooled_feats, fout, protocol=cPickle.HIGHEST_PROTOCOL)
#print 'start to save feature for %s' % fd
#print agg_feats.shape
#with open(feat_dir+fd+'/agg_feats','wb') as f:
#cPickle.dump(agg_feats,f, protocol=cPickle.HIGHEST_PROTOCOL)