def main(args):
imgs_dir = data_dir + 'fig'
gt_dir = data_dir + 'mat'
paths_imgs = fs.gen_paths(imgs_dir, fs.filter_is_img)
paths_gt = fs.gen_paths(gt_dir)
paths_pairs = fs.fname_pairs(paths_imgs, paths_gt)
paths_imgs, paths_gt = map(list, zip(*paths_pairs))
lm_img_dst = lmdb_dst + phase + '_img_lmdb'
lm_gt_dst = lmdb_dst + phase + '_gt_lmdb'
if not os.path.exists(lm_img_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_img_dst)
if not os.path.exists(lm_gt_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_gt_dst)
size1 = imgs_to_lmdb(paths_imgs, lm_img_dst, CAFFE_ROOT = CAFFE_ROOT)
size2 = matfiles_to_lmdb(paths_gt, lm_gt_dst, 'gt',CAFFE_ROOT = CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
if(dif != 0):
print 'ERROR: img-gt size not match! diff:'+str(diff)
return 1
return 0
def main(args):
dir_imgs = CAFFE_ROOT + "data/fcn_label_full/" + phase + "_jpg"
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
dir_segm_labels = CAFFE_ROOT + "data/fcn_label_full/" + phase + "_maps"
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
if not os.path.exists(lmimgDst):
print "lmdb dir not exists,make it"
os.makedirs(lmimgDst)
if not os.path.exists(lmlabelDst):
print "lmdb dir not exists,make it"
os.makedirs(lmlabelDst)
# for a, b in paths_pairs:
# print a,b
size1 = to_lmdb.imgs_to_lmdb(paths_imgs, lmimgDst, CAFFE_ROOT=CAFFE_ROOT)
size2 = to_lmdb.matfiles_to_lmdb(paths_segm_labels, lmlabelDst, "gt", CAFFE_ROOT=CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
scipy.io.savemat("./size1", dict({"sz": size1}), appendmat=True)
scipy.io.savemat("./size2", dict({"sz": size2}), appendmat=True)
print "size dif:" + str(dif)
return 0
def main(args):
dir_imgs = CAFFE_ROOT+'data/fcn_label_full/' + phase + '_jpg'
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
dir_segm_labels = CAFFE_ROOT + 'data/fcn_label_full/' + phase + '_maps'
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
if not os.path.exists(lmimgDst):
print 'lmdb dir not exists,make it'
os.makedirs(lmimgDst)
if not os.path.exists(lmlabelDst):
print 'lmdb dir not exists,make it'
os.makedirs(lmlabelDst)
#for a, b in paths_pairs:
# print a,b
size1 = to_lmdb.imgs_to_lmdb(paths_imgs, lmimgDst, CAFFE_ROOT = CAFFE_ROOT)
size2 = to_lmdb.matfiles_to_lmdb(paths_segm_labels, lmlabelDst, 'gt',CAFFE_ROOT = CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
scipy.io.savemat('./size1',dict({'sz':size1}),appendmat=True)
scipy.io.savemat('./size2',dict({'sz':size2}),appendmat=True)
print 'size dif:'+str(dif)
return 0
def main(args):
imgs_dir = data_dir + 'fig'
gt_dir = data_dir + 'mat'
paths_imgs = fs.gen_paths(imgs_dir, fs.filter_is_img)
paths_gt = fs.gen_paths(gt_dir)
paths_pairs = fs.fname_pairs(paths_imgs, paths_gt)
paths_imgs, paths_gt = map(list, zip(*paths_pairs))
lm_img_dst = lmdb_dst + phase + '_img_lmdb'
lm_gt_dst = lmdb_dst + phase + '_gt_lmdb'
if not os.path.exists(lm_img_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_img_dst)
if not os.path.exists(lm_gt_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_gt_dst)
size1 = imgs_to_lmdb(paths_imgs, lm_img_dst, CAFFE_ROOT = CAFFE_ROOT)
size2 = matfiles_to_lmdb(paths_gt, lm_gt_dst, 'gt',CAFFE_ROOT = CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
if(dif != 0):
print 'ERROR: img-gt size not match! diff:'+str(diff)
return 1
return 0
def main(args):
paths_imgs = fs.gen_paths(imgs_dir, fs.filter_is_img)
paths_gt = fs.gen_paths(gt_dir)
paths_pairs = fs.fname_pairs(paths_imgs, paths_gt)
paths_imgs, paths_gt = map(list, zip(*paths_pairs))
lm_img_dst = imgs_dir + '_lmdb'
lm_gt_dst = gt_dir + '_lmdb'
if not os.path.exists(lm_img_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_img_dst)
if not os.path.exists(lm_gt_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_gt_dst)
size1 = imgs_to_lmdb(paths_imgs, lm_img_dst, CAFFE_ROOT=CAFFE_ROOT)
size2 = matfiles_to_lmdb(paths_gt, lm_gt_dst, 'gt', CAFFE_ROOT=CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
#scipy.io.savemat('./size1',dict({'sz':size1}),appendmat=True)
#scipy.io.savemat('./size2',dict({'sz':size2}),appendmat=True)
if (dif != 0):
print 'ERROR: img-gt size not match! diff:' + str(diff)
return 1
return 0
def main(args):
paths_imgs = fs.gen_paths(imgs_dir, fs.filter_is_img)
paths_gt = fs.gen_paths(gt_dir)
paths_pairs = fs.fname_pairs(paths_imgs, paths_gt)
paths_imgs, paths_gt = map(list, zip(*paths_pairs))
lm_img_dst = imgs_dir + '_lmdb'
lm_gt_dst = gt_dir + '_lmdb'
if not os.path.exists(lm_img_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_img_dst)
if not os.path.exists(lm_gt_dst):
print 'lmdb dir not exists,make it'
os.makedirs(lm_gt_dst)
size1 = imgs_to_lmdb(paths_imgs, lm_img_dst, CAFFE_ROOT = CAFFE_ROOT)
size2 = matfiles_to_lmdb(paths_gt, lm_gt_dst, 'gt',CAFFE_ROOT = CAFFE_ROOT)
dif = size1 - size2
dif = dif.sum()
#scipy.io.savemat('./size1',dict({'sz':size1}),appendmat=True)
#scipy.io.savemat('./size2',dict({'sz':size2}),appendmat=True)
if(dif != 0):
print 'ERROR: img-gt size not match! diff:'+str(diff)
return 1
return 0
def pascal_context_to_lmdb(dir_imgs,
dir_segm_labels,
fpath_labels_list,
fpath_labels_list_subset,
dst_prefix,
dir_dst,
CAFFE_ROOT=None,
val_list=None):
'''
Fine intersection of filename in both directories and create
one lmdb directory for each
val_list - list of entities to exclude from train (validation subset e.g. ['2008_000002', '2010_000433'])
'''
if dst_prefix is None:
dst_prefix = ''
labels_list = get_labels_list(fpath_labels_list)
labels_59_list = get_labels_list(fpath_labels_list_subset)
#print labels_list
#print labels_59_list
labels_lut = get_labels_lut(labels_list, labels_59_list)
def apply_labels_lut(m):
return labels_lut[m]
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
#for a, b in paths_pairs:
# print a,b
if val_list is not None:
# do train/val split
train_idx, val_idx = get_train_val_split(paths_imgs, val_list)
# images
paths_imgs_train = [paths_imgs[i] for i in train_idx]
fpath_lmdb_imgs_train = os.path.join(dir_dst,
'%scontext_imgs_train_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_train,
fpath_lmdb_imgs_train,
CAFFE_ROOT=CAFFE_ROOT)
paths_imgs_val = [paths_imgs[i] for i in val_idx]
fpath_lmdb_imgs_val = os.path.join(dir_dst,
'%scontext_imgs_val_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_val,
fpath_lmdb_imgs_val,
CAFFE_ROOT=CAFFE_ROOT)
# ground truth
paths_segm_labels_train = [paths_segm_labels[i] for i in train_idx]
fpath_lmdb_segm_labels_train = os.path.join(dir_dst,
'%scontext_labels_train_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_train,
fpath_lmdb_segm_labels_train,
'LabelMap',
CAFFE_ROOT=CAFFE_ROOT, lut=apply_labels_lut)
paths_segm_labels_val = [paths_segm_labels[i] for i in val_idx]
fpath_lmdb_segm_labels_val = os.path.join(dir_dst,
'%scontext_labels_val_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_val,
fpath_lmdb_segm_labels_val,
'LabelMap',
CAFFE_ROOT=CAFFE_ROOT, lut=apply_labels_lut)
return len(paths_imgs_train), len(paths_imgs_val),\
fpath_lmdb_imgs_train, fpath_lmdb_segm_labels_train, fpath_lmdb_imgs_val, fpath_lmdb_segm_labels_val
else:
fpath_lmdb_imgs = os.path.join(dir_dst,
'%scontext_imgs_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs,
fpath_lmdb_imgs,
CAFFE_ROOT=CAFFE_ROOT)
fpath_lmdb_segm_labels = os.path.join(dir_dst,
'%scontext_labels_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels,
fpath_lmdb_segm_labels,
'LabelMap',
CAFFE_ROOT=CAFFE_ROOT, lut=apply_labels_lut)
return len(paths_imgs), fpath_lmdb_imgs, fpath_lmdb_segm_labels
def pascal_context_to_lmdb(dir_imgs,
dir_segm_labels,
fpath_labels_list,
fpath_labels_list_subset,
dst_prefix,
dir_dst,
val_list=None):
'''
Fine intersection of filename in both directories and create
one lmdb directory for each
val_list - list of entities to exclude from train (validation subset e.g. ['2008_000002', '2010_000433'])
'''
if dst_prefix is None:
dst_prefix = ''
labels_list = get_labels_list(fpath_labels_list)
labels_59_list = get_labels_list(fpath_labels_list_subset)
#print labels_list
#print labels_59_list
labels_lut = du.get_labels_lut(labels_list, labels_59_list)
def apply_labels_lut(m):
return labels_lut[m]
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
#for a, b in paths_pairs:
# print a,b
if val_list is not None:
# do train/val split
train_idx, val_idx = du.get_train_val_split_from_names(paths_imgs, val_list)
# images
paths_imgs_train = [paths_imgs[i] for i in train_idx]
fpath_lmdb_imgs_train = os.path.join(dir_dst,
'%scontext_imgs_train_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_train,
fpath_lmdb_imgs_train)
paths_imgs_val = [paths_imgs[i] for i in val_idx]
fpath_lmdb_imgs_val = os.path.join(dir_dst,
'%scontext_imgs_val_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_val,
fpath_lmdb_imgs_val)
# ground truth
paths_segm_labels_train = [paths_segm_labels[i] for i in train_idx]
fpath_lmdb_segm_labels_train = os.path.join(dir_dst,
'%scontext_labels_train_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_train,
fpath_lmdb_segm_labels_train,
'LabelMap',
lut=apply_labels_lut)
paths_segm_labels_val = [paths_segm_labels[i] for i in val_idx]
fpath_lmdb_segm_labels_val = os.path.join(dir_dst,
'%scontext_labels_val_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_val,
fpath_lmdb_segm_labels_val,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs_train), len(paths_imgs_val),\
fpath_lmdb_imgs_train, fpath_lmdb_segm_labels_train, fpath_lmdb_imgs_val, fpath_lmdb_segm_labels_val
else:
fpath_lmdb_imgs = os.path.join(dir_dst,
'%scontext_imgs_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs,
fpath_lmdb_imgs)
fpath_lmdb_segm_labels = os.path.join(dir_dst,
'%scontext_labels_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels,
fpath_lmdb_segm_labels,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs), fpath_lmdb_imgs, fpath_lmdb_segm_labels
