def query(image): # image or image_path
# 读取提取好的图片特征集
h5f = h5py.File('CNN_extracted_image_feature_many.h5', 'r')
img_feats_set = h5f['dataset_1'][:]
img_names_set = h5f['dataset_2'][:]
img_names_decode = [bytes(img_name).decode('utf-8', 'ignore') for img_name in img_names_set]
h5f.close()
# TODO: 在加载模型之前清除后台session，避免报错 - TypeError: Cannot interpret feed_dict key as Tensor:
# Tensor Tensor("Placeholder_8:0", shape=(3, 3, 128, 256), dtype=float32) is not an element of this graph.
keras.backend.clear_session()
# init VGGNet16 model
model = VGGNet()
# TODO: 随便生成一个向量让 model 执行一次 predict 函数
# TODO: 避免报错 - ValueError: Tensor Tensor("dense_2/Softmax:0", shape=(?, 8), dtype=float32) is not an element of this graph.
model.model.predict(np.zeros((1, 224, 224, 3)))
# extract query image's feature,then compute similarity score and sort
query_img_feat = model.extract_feature(image)
# dot()函数计算并返回两个numpy数组的内积
# 即**检索图片与图片库内各图片的相似度的数组**
simil_scores = np.dot(query_img_feat, img_feats_set.T)
# argsort()函数返回将数组元素从小到大排列后所对应的原索引号组成的数组
# 列表切片操作[::-1]则将该索引数组的内容翻转输出
rank_index = np.argsort(simil_scores)[::-1]
rank_scores = simil_scores[rank_index]
# # output similar images by required number
# max_ret = 3
# ret_img_list = [img_names_decode[index] for i, index in enumerate(rank_index[0:max_ret])]
# output similar images by similarity scores
# return similar images' path list
rank_scores_index = [index for index, element in enumerate(rank_scores) if element >= 0.5]
rank_index_ok = [rank_index[index] for index in rank_scores_index]
# TODO: 此处待优化，否则函数可重用性将受限
ret_img_nams = ['../static/database-many/'+img_names_decode[index] for index in rank_index_ok]
print("retrieve images's index: ", rank_index_ok)
print('retrieved images in order are: ', ret_img_nams)
# return similar images' PIL.Image.Image obj list
# Image.open() method returns An :py:class:`~PIL.Image.Image` object.
# similar_images = [Image.open("database-less/"+name) for name in ret_img_nams]
return ret_img_nams
print(" searching starts ")
print("--------------------------------------------------")
# read and show query image
queryDir = args["query"]
queryImg = mpimg.imread(queryDir)
plt.title("Query Image")
plt.imshow(queryImg)
plt.show()
plt.close()
# init VGGNet16 model
model = VGGNet()
# extract query image's feature,compute similarity score and sort
query_img_feat = model.extract_feature(queryDir)
# dot()函数计算并返回两个numpy数组的内积
# 即**查询图片与图片库内各图片的相似度数组**
simil_scores = np.dot(query_img_feat, img_feats_set.T)
print("about shape:", query_img_feat.shape, img_feats_set[0].shape,
img_feats_set.shape)
print("dot(query_img_feat, img_feats_set[1]): ",
np.dot(query_img_feat, img_feats_set[1]))
# argsort函数返回将数组元素从小到大排列后所对应的原索引号组成的数组
# 列表切片操作[::-1]则将该索引数组的内容翻转输出
rank_index = np.argsort(simil_scores)[::-1]
rank_scores = simil_scores[rank_index]
# TODO
print(type(simil_scores), type(rank_index), type(rank_scores))
print("similarity score array: ", simil_scores)
print("img_index_array: ", rank_index)
if __name__ == "__main__":
db = args["database"]
imgs_path_list = get_imlist(db)
print(imgs_path_list)
print("--------------------------------------------------")
print(" feature extraction starts ")
print("--------------------------------------------------")
feats = []
names = []
model = VGGNet()
for i, img_path in enumerate(imgs_path_list):
img_feat = model.extract_feature(img_path)
# os.path.split(PATH)函数以PATH参数的最后一个'\'作为分隔符，
# 返回目录名和文件名组成的元组,索引0为目录名，索引1则为文件名
img_name = os.path.split(img_path)[1]
feats.append(img_feat)
names.append(img_name)
print(
"extracting feature from image No. %d , %d images in total -> " %
((i + 1), len(imgs_path_list)), img_name)
print("--------------------------------------------------")
print(" writing feature extraction results ")
print("--------------------------------------------------")
# file of storing extracted features
# 创建一个h5py文件(对象)