def WriteLmdbv1(imPath, dmapPath, lmdbPth, dataset, downscale, mirror):
"""
imPath: the folder contains images.
dmapPath: the folder contains matfiles of density map.
lmdbPth: the folder will be used to save lmdb file.
num: the count of images.
mirror: contains mirror data
"""
print('Start writing lmdb')
patches = []
dmaps = []
imLmdbName = lmdbPth + '/image_lmdb'
dmapLmdbName = lmdbPth + '/dmap_lmdb'
scale = 1
for idx in dataset:
imName = imPath + str(idx) + '.jpg'
dmapName = dmapPath + str(idx) + '.mat'
imArr = impro.ReadImage(imName, False, scale)
dmapArr = impro.ReadDmap(dmapName, False, scale)
data = impro.CropSubImage(imArr, dmapArr, downscale)
if mirror:
imArr_m = impro.ReadImage(imName, True, scale)
dmapArr_m = impro.ReadDmap(dmapName, True, scale)
data_m = impro.CropSubImage(imArr_m, dmapArr_m, downscale)
for in_idx in xrange(9):
patches.append(data[in_idx])
dmaps.append(data[in_idx + 9])
if mirror:
patches.append(data_m[in_idx])
dmaps.append(data_m[in_idx + 9])
string_ = str(idx)
sys.stdout.write("\r%s" % string_)
sys.stdout.flush()
print(len(patches))
print('\n Saving! \n')
r = np.random.permutation(len(patches))
imglmdb = lmdb.open(imLmdbName, map_size=int(1e12))
with imglmdb.begin(write=True) as in_txn:
for in_idx in xrange(len(patches)):
datum = caffe.io.array_to_datum(patches[r[in_idx]])
str_id = '{:0>10d}'.format(in_idx)
in_txn.put(str_id, datum.SerializeToString())
imglmdb.close()
dmaplmdb = lmdb.open(dmapLmdbName, map_size=int(1e12))
with dmaplmdb.begin(write=True) as in_txn:
for in_idx in xrange(len(patches)):
datum = caffe.io.array_to_datum(dmaps[r[in_idx]])
str_id = '{:0>10d}'.format(in_idx)
in_txn.put(str_id, datum.SerializeToString())
dmaplmdb.close()
"""f = h5py.File(lmdbPth, 'w')
f.create_dataset('data', data=patches, dtype=np.float32)
f.create_dataset('label',data=dmaps, dtype=np.float32)"""
print('\n Finish! \n')
def WriteLmdbTrain(imPath,dmapPath,lmdbPth,dataset,downscale,mirror):
"""
imPath: the folder contains images.
dmapPath: the folder contains matfiles of density map.
lmdbPth: the folder will be used to save lmdb file.
num: the count of images.
mirror: contains mirror data
"""
print('Start writing lmdb\n')
imLmdbName = lmdbPth + '/image_lmdb'
dmapLmdbName = lmdbPth + '/dmap_lmdb'
scales=[1]#[0.6,0.7,0.8,0.9,1.0,1.1,1.2]
num=len(dataset)*18*len(scales)
print(num)
np.random.seed(9999)
randidx = np.random.permutation(range(1,num+1))
for idx in xrange(len(dataset)):
imName = imPath+str(dataset[idx])+'.jpg'
dmapName = dmapPath + str(dataset[idx])+'.mat'
for sid in xrange(len(scales)):
imArr = impro.ReadImage(imName,False,scales[sid])
dmapArr = impro.ReadDmap(dmapName,False,scales[sid])
data = impro.CropSubImage(imArr,dmapArr,downscale)
imArr_m = impro.ReadImage(imName,True,scales[sid])
dmapArr_m = impro.ReadDmap(dmapName,True,scales[sid])
data_m = impro.CropSubImage(imArr_m,dmapArr_m,downscale)
imglmdb = lmdb.open(imLmdbName,map_size = int(1e12))
with imglmdb.begin(write=True) as in_txn:
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data[in_idx])
str_id = '{:0>8d}'.format(randidx[idx*18*len(scales)+sid*18+in_idx])#[idx*108+sid*18+in_idx]
in_txn.put(str_id,datum.SerializeToString())
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data_m[in_idx])
str_id = '{:0>8d}'.format(randidx[idx*18*len(scales)+sid*18+in_idx+9])
in_txn.put(str_id,datum.SerializeToString())
imglmdb.close()
dmaplmdb = lmdb.open(dmapLmdbName,map_size = int(1e12))
with dmaplmdb.begin(write=True) as in_txn:
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data[in_idx+9])
str_id = '{:0>8d}'.format(randidx[idx*18*len(scales)+sid*18+in_idx])
in_txn.put(str_id,datum.SerializeToString())
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data_m[in_idx+9])
str_id = '{:0>8d}'.format(randidx[idx*18*len(scales)+sid*18+in_idx+9])
in_txn.put(str_id,datum.SerializeToString())
dmaplmdb.close()
string_ = str(idx+1)+'/'+str(len(dataset))
sys.stdout.write("\r%s" % string_)
sys.stdout.flush()
print('\n Finish! \n')
def WriteLmdbTest(imPath,dmapPath,lmdbPth,dataset,downscale,mirror):
"""
imPath: the folder contains images.
dmapPath: the folder contains matfiles of density map.
lmdbPth: the folder will be used to save lmdb file.
num: the count of images.
mirror: contains mirror data
"""
print('Start writing lmdb\n')
imLmdbName = lmdbPth + '/image_lmdb'
dmapLmdbName = lmdbPth + '/dmap_lmdb'
for idx in xrange(len(dataset)):
imName = imPath+str(dataset[idx])+'.jpg'
dmapName = dmapPath + str(dataset[idx])+'.mat'
imArr = impro.ReadImage(imName)
dmapArr = impro.ReadDmap(dmapName)
data = impro.CropSubImage(imArr,dmapArr,downscale)
imglmdb = lmdb.open(imLmdbName,map_size = int(1e12))
with imglmdb.begin(write=True) as in_txn:
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data[in_idx])
str_id = '{:0>6d}'.format(idx*9+in_idx)
in_txn.put(str_id,datum.SerializeToString())
imglmdb.close()
dmaplmdb = lmdb.open(dmapLmdbName,map_size = int(1e12))
with dmaplmdb.begin(write=True) as in_txn:
for in_idx in xrange(9):
datum = caffe.io.array_to_datum(data[in_idx+9])
str_id = '{:0>6d}'.format(idx*9+in_idx)
in_txn.put(str_id,datum.SerializeToString())
dmaplmdb.close()
string_ = str(idx+1)+'/'+str(len(dataset))
sys.stdout.write("\r%s" % string_)
sys.stdout.flush()
print('\n Finish! \n')
def WriteLmdbv2(imPath, dmapPath, lmdbPth, num, downscale):
"""
imPath: the folder contains images.
dmapPath: the folder contains matfiles of density map.
lmdbPth: the folder will be used to save lmdb file.
num: the count of images.
mirror: contains mirror data
"""
print('Start writing lmdb')
randidx = np.random.permutation(range(1, num + 1))
randidx_m = np.random.permutation(range(1, num + 1))
for idx in xrange(num):
imName = imPath + '/IMG_' + str(randidx[idx]) + '.jpg'
dmapName = dmapPath + '/DMAP_' + str(randidx[idx]) + '.mat'
imLmdbName = lmdbPth + '/image_lmdb'
dmapLmdbName = lmdbPth + '/dmap_lmdb'
imArr = impro.ReadImage(imName, 'Gray')
dmapArr = impro.ReadDmap(dmapName)
imArr_m = impro.ReadImage(imName, 'Gray', True)
dmapArr_m = impro.ReadDmap(dmapName, True)
imglmdb = lmdb.open(imLmdbName, map_size=int(1e12))
with imglmdb.begin(write=True) as in_txn:
datum = caffe.io.array_to_datum(imArr)
str_id = '{:0>10d}'.format(idx * 2)
in_txn.put(str_id, datum.SerializeToString())
datum = caffe.io.array_to_datum(imArr_m)
str_id = '{:0>10d}'.format(idx * 2 + 1)
in_txn.put(str_id, datum.SerializeToString())
imglmdb.close()
dmaplmdb = lmdb.open(dmapLmdbName, map_size=int(1e12))
with dmaplmdb.begin(write=True) as in_txn:
datum = caffe.io.array_to_datum(dmapArr)
str_id = '{:0>10d}'.format(idx * 2)
in_txn.put(str_id, datum.SerializeToString())
datum = caffe.io.array_to_datum(dmapArr_m)
str_id = '{:0>10d}'.format(idx * 2 + 1)
in_txn.put(str_id, datum.SerializeToString())
dmaplmdb.close()
string_ = str(idx + 1) + '/' + str(num)
sys.stdout.write("\r%s" % string_)
sys.stdout.flush()
print('\n Finish! \n')
