def __init__(self, params_file, data_path):
self.model = Model(params_file, batch_size=1)
self.data_path = data_path
self.batch_size = 1000
self.data = []
self.loadData()
def __init__(self, params_file, data_path):
self.model = Model(params_file, batch_size=1)
self.data_path = data_path
self.batch_size = 1000
self.data = []
self.loadData()
class DataBase:
def __init__(self, params_file, data_path):
self.model = Model(params_file, batch_size=1)
self.data_path = data_path
self.batch_size = 1000
self.data = []
self.loadData()
def loadData(self):
file_names = get_files(self.data_path)
data = []
for fn in file_names:
with open(fn, 'r') as f:
data += pickle.load(f)
self.data = data
def __save2database(self, imgIDs, deepIDfeas):
"""
save data to database(pickle files)
save style: [(id_1, fea_1), .... , (id_batch_size, fea_batch_size)]
"""
data_folder = self.data_path
batch_size = self.batch_size
def save(count, imgIDs, deepIDfeas):
fn = data_folder + count.__str__() + '.pkl'
if os.path.isfile(fn):
with open(fn, 'r') as f:
orgnl_data = pickle.load(f)
left_space = batch_size - len(orgnl_data)
with open(fn, 'w') as f:
if left_space >= len(imgIDs):
pickle.dump(orgnl_data + zip(imgIDs, deepIDfeas), f)
else:
pickle.dump(
orgnl_data + zip(imgIDs[0:left_space],
deepIDfeas[0:left_space]), f)
save(count + 1, imgIDs[left_space::],
deepIDfeas[left_space::])
else:
with open(fn, 'w') as f:
if batch_size >= len(imgIDs):
pickle.dump(zip(imgIDs, deepIDfeas), f)
else:
pickle.dump(
zip(imgIDs[0:batch_size],
deepIDfeas[0:batch_size]), f)
save(count + 1, imgIDs[batch_size::],
deepIDfeas[batch_size::])
file_names = get_files(data_folder)
n_files = len(file_names)
count = n_files if n_files != 0 else 1
save(count, imgIDs, deepIDfeas)
def add_faces(self, imgIDs, img_paths):
"""
model: retrieval_face.Model
add faces to database,
return invalid image ids
"""
inval_ids = []
imgs = []
for path, id in zip(img_paths, imgIDs):
aligned_path = send2align(path)
aligned_arr = path2arr(aligned_path)
if aligned_arr is None:
inval_ids.append(id)
imgIDs.remove(id)
else:
imgs.append(aligned_arr)
if len(imgs) != 0:
deepIDfeas = self.model.getID(imgs)
self.__save2database(imgIDs, deepIDfeas)
return inval_ids
def search(self, fn, top_n=10, sim_thresh=None):
"""
retrieval face from database,
return top_n similar faces' imgIDs, return None if failed
"""
if top_n > len(self.data):
top_n = len(self.data)
aligned_fn = send2align(fn)
aligned_arr = path2arr(aligned_fn)
if aligned_arr is None:
print "align none."
return None
deepIDfea = self.model.getID([aligned_arr])[0]
sims = [
cosine_similarity(deepIDfea, item[1])[0][0] for item in self.data
]
#print len(self.data), len(sims)
for i in range(len(sims)):
print sims[i], self.data[i][0]
sort_index = np.argsort(-np.array(sims))
result = []
if sim_thresh is None:
for index in np.nditer(sort_index):
cur_id = self.data[index][0].split('-')[0]
if cur_id not in result and len(result) < top_n:
result.append(cur_id)
return result
else:
for index in np.nditer(sort_index):
if sims[index] < sim_thresh:
break
cur_id = self.data[index][0].split('-')[0]
if cur_id not in result:
result.append(cur_id)
return result
class DataBase:
def __init__(self, params_file, data_path):
self.model = Model(params_file, batch_size=1)
self.data_path = data_path
self.batch_size = 1000
self.data = []
self.loadData()
def loadData(self):
file_names = get_files(self.data_path)
data = []
for fn in file_names:
with open(fn, "r") as f:
data += pickle.load(f)
self.data = data
def __save2database(self, imgIDs, deepIDfeas):
"""
save data to database(pickle files)
save style: [(id_1, fea_1), .... , (id_batch_size, fea_batch_size)]
"""
data_folder = self.data_path
batch_size = self.batch_size
def save(count, imgIDs, deepIDfeas):
fn = data_folder + count.__str__() + ".pkl"
if os.path.isfile(fn):
with open(fn, "r") as f:
orgnl_data = pickle.load(f)
left_space = batch_size - len(orgnl_data)
with open(fn, "w") as f:
if left_space >= len(imgIDs):
pickle.dump(orgnl_data + zip(imgIDs, deepIDfeas), f)
else:
pickle.dump(orgnl_data + zip(imgIDs[0:left_space], deepIDfeas[0:left_space]), f)
save(count + 1, imgIDs[left_space::], deepIDfeas[left_space::])
else:
with open(fn, "w") as f:
if batch_size >= len(imgIDs):
pickle.dump(zip(imgIDs, deepIDfeas), f)
else:
pickle.dump(zip(imgIDs[0:batch_size], deepIDfeas[0:batch_size]), f)
save(count + 1, imgIDs[batch_size::], deepIDfeas[batch_size::])
file_names = get_files(data_folder)
n_files = len(file_names)
count = n_files if n_files != 0 else 1
save(count, imgIDs, deepIDfeas)
def add_faces(self, imgIDs, img_paths):
"""
model: retrieval_face.Model
add faces to database,
return invalid image ids
"""
inval_ids = []
imgs = []
for path, id in zip(img_paths, imgIDs):
aligned_path = send2align(path)
aligned_arr = path2arr(aligned_path)
if aligned_arr is None:
inval_ids.append(id)
imgIDs.remove(id)
else:
imgs.append(aligned_arr)
if len(imgs) != 0:
deepIDfeas = self.model.getID(imgs)
self.__save2database(imgIDs, deepIDfeas)
return inval_ids
def search(self, fn, top_n=10, sim_thresh=None):
"""
retrieval face from database,
return top_n similar faces' imgIDs, return None if failed
"""
if top_n > len(self.data):
top_n = len(self.data)
aligned_fn = send2align(fn)
aligned_arr = path2arr(aligned_fn)
if aligned_arr is None:
print "align none."
return None
deepIDfea = self.model.getID([aligned_arr])[0]
sims = [cosine_similarity(deepIDfea, item[1])[0][0] for item in self.data]
# print len(self.data), len(sims)
for i in range(len(sims)):
print sims[i], self.data[i][0]
sort_index = np.argsort(-np.array(sims))
result = []
if sim_thresh is None:
for index in np.nditer(sort_index):
cur_id = self.data[index][0].split("-")[0]
if cur_id not in result and len(result) < top_n:
result.append(cur_id)
return result
else:
for index in np.nditer(sort_index):
if sims[index] < sim_thresh:
break
cur_id = self.data[index][0].split("-")[0]
if cur_id not in result:
result.append(cur_id)
return result
