print('model: {}, caffemodel: {}, prototxt: {}'.format(
modelname, caffemodel, prototxt))
net = model(modelname, caffemodel, prototxt, layername, gpu_id)
dataset = Dataset(dataset)
image_iters = dataset.get_image_list()
#image_iters = image_iters[0:100]
for idx, image_iter in enumerate(image_iters):
image_name = image_iter.split('.')[0]
image_id = dataset.get_image_id(image_name)
if image_id:
image_path = os.path.join(dataset.data_path, image_name + '.jpg')
image = cv2.imread(image_path)
net_time = Timer()
net_time.tic()
feature = copy.deepcopy(net.forward(image, layername))
net_time.toc()
dataset.add_feature(image_name, feature, image_id)
view_bar(idx, len(image_iters))
#if idx % 5000 == 0:
def main():
image_root = os.path.join(cfg.DATA_DIR, cfg.DATASET, 'image')
datasetname = cfg.DATASET
dataset = Dataset(datasetname)
query_lists, _ = dataset.get_retrieval_list()
feature_file = open(os.path.join(cfg.FEATURE_DIR, cfg.DATASET, \
cfg.MODEL, cfg.DISTANCE_METRIC, \
cfg.SAVE_NAME), 'rb')
feature_dict = cPickle.load(feature_file)
save_root = os.path.join(cfg.RESULT_DIR, cfg.DATASET, cfg.MODEL,
cfg.DISTANCE_METRIC)
if not os.path.exists(save_root):
os.makedirs(save_root)
net = model(cfg.MODEL, cfg.CAFFEMODEL, cfg.PROTOTXT, cfg.FEATURE_NAME[0],
cfg.GPU_ID)
for idx, query_list in enumerate(query_lists):
query_name, query_id = query_list.strip().split(' ')
if query_name in feature_dict.keys():
q_feature = copy.deepcopy(feature_dict[query_name]['feature'])
q_id = copy.deepcopy(feature_dict[query_name]['id'])
else:
image_path = os.path.join(image_root, query_name + '.jpg')
image = cv2.imread(image_path)
q_feature = copy.deepcopy(net.forward(image, cfg.FEATURE_NAME[0]))
q_id = dataset.get_image_id(query_name)
retrieval_results = []
for key in feature_dict.keys():
p_feature = feature_dict[key]['feature']
p_id = feature_dict[key]['id']
if cfg.DISTANCE_METRIC == 'L2':
tmp = norm_L2_distance(q_feature, p_feature)
elif cfg.DISTANCE_METRIC == 'cosin':
tmp = norm_cosin_distance(q_feature, p_feature)
retrieval_results.append({
'filename': key,
'distance': tmp,
'id': p_id
})
retrieval_results.sort(lambda x, y: cmp(y['distance'], x['distance']))
#pdb.set_trace()
f = open(os.path.join(save_root, query_name + '.txt'), 'w')
# query image is also store in dataset
for i in range(0, cfg.TOP_K + 1):
filename = retrieval_results[i]['filename']
distance = retrieval_results[i]['distance']
f.writelines(
str(query_name) + '_' + str(filename) + ' ' + str(distance) +
'\n')
f.close()
view_bar(idx, len(query_lists))
