print("--------------------------------------------------")
print(" searching starts")
print("--------------------------------------------------")
# read and show query image
queryImg = mpimg.imread(query)
plt.title("Query Image")
plt.imshow(queryImg)
plt.show()
# init VGGNet16 model
model = VGGNet()
# extract query image's feature, compute simlarity score and sort
# queryVec = model.vgg_extract_feat(query) # 修改此处改变提取特征的网络
queryVec = model.resnet_extract_feat(query) # 修改此处改变提取特征的网络
# print(queryVec.shape)
# print(feats.shape)
print('--------------------------')
# print(queryVec)
# print(feats.T)
print('--------------------------')
scores = np.dot(queryVec, feats.T)
# scores = np.dot(queryVec, feats.T)/(np.linalg.norm(queryVec)*np.linalg.norm(feats.T))
rank_ID = np.argsort(scores)[::-1]
rank_score = scores[rank_ID]
# print (rank_ID)
print(rank_score)
# number of top retrieved images to show
maxres = 3 # 检索出三张相似度最高的图片
if __name__ == "__main__":
database = 'database'
index = 'models/vgg_featureCNN.resnet50.h5'
img_list = get_imlist(database)
print("--------------------------------------------------")
print(" feature extraction starts")
print("--------------------------------------------------")
feats = []
names = []
model = VGGNet()
for i, img_path in enumerate(img_list):
# norm_feat = model.vgg_extract_feat(img_path) # 修改此处改变提取特征的网络
norm_feat = model.resnet_extract_feat(img_path) # 修改此处改变提取特征的网络
img_name = os.path.split(img_path)[1]
feats.append(norm_feat)
names.append(img_name)
print("extracting feature from image No. %d , %d images in total" %
((i + 1), len(img_list)))
feats = np.array(feats)
# print(feats)
# directory for storing extracted features
# output = args["index"]
output = index
print("--------------------------------------------------")
print(" writing feature extraction results ...")
print("--------------------------------------------------")