Merge pull request #2289 from RainFrost1/develop

update pp-shituv2 doc

deploy/shitu_index_manager/client.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+from PyQt5 import QtCore, QtGui, QtWidgets
+import mod.mainwindow
+"""
+完整的index库如下:
+root_path/            # 库存储目录
+|-- image_list.txt     # 图像列表，每行：image_path label。由前端生成及修改。后端只读
+|-- features.pkl       # 建库之后，保存的embedding向量，后端生成，前端无需操作
+|-- images             # 图像存储目录，由前端生成及增删查等操作。后端只读
+|   |-- md5.jpg
+|   |-- md5.jpg
+|   |-- ……
+|-- index              # 真正的生成的index库存储目录，后端生成及操作，前端无需操作。
+|   |-- vector.index   # faiss生成的索引库
+|   |-- id_map.pkl     # 索引文件
+"""
+
+
+def FrontInterface(server_ip=None, server_port=None):
+    front = QtWidgets.QApplication([])
+    main_window = mod.mainwindow.MainWindow(ip=server_ip, port=server_port)
+    main_window.showMaximized()
+    sys.exit(front.exec_())
+
+
+if __name__ == '__main__':
+    server_ip = None
+    server_port = None
+    if len(sys.argv) == 2 and len(sys.argv[1].split(' ')) == 2:
+        [server_ip, server_port] = sys.argv[1].split(' ')
+    FrontInterface(server_ip, server_port)

deploy/shitu_index_manager/index_manager.py

@@ -13,22 +13,10 @@
 # limitations under the License.
 import os
 import sys
-from PyQt5 import QtCore, QtGui, QtWidgets
-import mod.mainwindow
-
-from paddleclas.deploy.utils import config, logger
-from paddleclas.deploy.python.predict_rec import RecPredictor
-from fastapi import FastAPI
-import uvicorn
-import numpy as np
-import faiss
-from typing import List
-import pickle
-import cv2
-import socket
-import json
-import operator
-from multiprocessing import Process
+import subprocess
+import shlex
+import psutil
+import time
 """
 完整的index库如下:
 root_path/            # 库存储目录
@@ -43,307 +31,34 @@ root_path/            # 库存储目录
 |   |-- id_map.pkl     # 索引文件
 """
 
-
-class ShiTuIndexManager(object):
-
-    def __init__(self, config):
-        self.root_path = None
-        self.image_list_path = "image_list.txt"
-        self.image_dir = "images"
-        self.index_path = "index/vector.index"
-        self.id_map_path = "index/id_map.pkl"
-        self.features_path = "features.pkl"
-        self.index = None
-        self.id_map = None
-        self.features = None
-        self.config = config
-        self.predictor = RecPredictor(config)
-
-    def _load_pickle(self, path):
-        if os.path.exists(path):
-            return pickle.load(open(path, 'rb'))
-        else:
-            return None
-
-    def _save_pickle(self, path, data):
-        if not os.path.exists(os.path.dirname(path)):
-            os.makedirs(os.path.dirname(path), exist_ok=True)
-        with open(path, 'wb') as fd:
-            pickle.dump(data, fd)
-
-    def _load_index(self):
-        self.index = faiss.read_index(
-            os.path.join(self.root_path, self.index_path))
-        self.id_map = self._load_pickle(
-            os.path.join(self.root_path, self.id_map_path))
-        self.features = self._load_pickle(
-            os.path.join(self.root_path, self.features_path))
-
-    def _save_index(self, index, id_map, features):
-        faiss.write_index(index, os.path.join(self.root_path, self.index_path))
-        self._save_pickle(os.path.join(self.root_path, self.id_map_path),
-                          id_map)
-        self._save_pickle(os.path.join(self.root_path, self.features_path),
-                          features)
-
-    def _update_path(self, root_path, image_list_path=None):
-        if root_path == self.root_path:
-            pass
-        else:
-            self.root_path = root_path
-            if not os.path.exists(os.path.join(root_path, "index")):
-                os.mkdir(os.path.join(root_path, "index"))
-            if image_list_path is not None:
-                self.image_list_path = image_list_path
-
-    def _cal_featrue(self, image_list):
-        batch_images = []
-        featrures = None
-        cnt = 0
-        for idx, image_path in enumerate(image_list):
-            image = cv2.imread(image_path)
-            if image is None:
-                return "{} is broken or not exist. Stop"
-            else:
-                image = image[:, :, ::-1]
-                batch_images.append(image)
-                cnt += 1
-            if cnt % self.config["Global"]["batch_size"] == 0 or (
-                    idx + 1) == len(image_list):
-                if len(batch_images) == 0:
-                    continue
-                batch_results = self.predictor.predict(batch_images)
-                featrures = batch_results if featrures is None else np.concatenate(
-                    (featrures, batch_results), axis=0)
-                batch_images = []
-        return featrures
-
-    def _split_datafile(self, data_file, image_root):
-        '''
-        data_file: image path and info, which can be splitted by spacer
-        image_root: image path root
-        delimiter: delimiter
-        '''
-        gallery_images = []
-        gallery_docs = []
-        gallery_ids = []
-        with open(data_file, 'r', encoding='utf-8') as f:
-            lines = f.readlines()
-            for _, ori_line in enumerate(lines):
-                line = ori_line.strip().split()
-                text_num = len(line)
-                assert text_num >= 2, f"line({ori_line}) must be splitted into at least 2 parts, but got {text_num}"
-                image_file = os.path.join(image_root, line[0])
-
-                gallery_images.append(image_file)
-                gallery_docs.append(ori_line.strip())
-                gallery_ids.append(os.path.basename(line[0]).split(".")[0])
-
-        return gallery_images, gallery_docs, gallery_ids
-
-    def create_index(self,
-                     image_list: str,
-                     index_method: str = "HNSW32",
-                     image_root: str = None):
-        if not os.path.exists(image_list):
-            return "{} is not exist".format(image_list)
-        if index_method.lower() not in ['hnsw32', 'ivf', 'flat']:
-            return "The index method Only support: HNSW32, IVF, Flat"
-        self._update_path(os.path.dirname(image_list), image_list)
-
-        # get image_paths
-        image_root = image_root if image_root is not None else self.root_path
-        gallery_images, gallery_docs, image_ids = self._split_datafile(
-            image_list, image_root)
-
-        # gernerate index
-        if index_method == "IVF":
-            index_method = index_method + str(
-                min(max(int(len(gallery_images) // 32), 2), 65536)) + ",Flat"
-        index = faiss.index_factory(
-            self.config["IndexProcess"]["embedding_size"], index_method,
-            faiss.METRIC_INNER_PRODUCT)
-        self.index = faiss.IndexIDMap2(index)
-        features = self._cal_featrue(gallery_images)
-        self.index.train(features)
-        index_ids = np.arange(0, len(gallery_images)).astype(np.int64)
-        self.index.add_with_ids(features, index_ids)
-
-        self.id_map = dict()
-        for i, d in zip(list(index_ids), gallery_docs):
-            self.id_map[i] = d
-
-        self.features = {
-            "features": features,
-            "index_method": index_method,
-            "image_ids": image_ids,
-            "index_ids": index_ids.tolist()
-        }
-        self._save_index(self.index, self.id_map, self.features)
-
-    def open_index(self, root_path: str, image_list_path: str) -> str:
-        self._update_path(root_path)
-        _, _, image_ids = self._split_datafile(image_list_path, root_path)
-        if os.path.exists(os.path.join(self.root_path, self.index_path)) and \
-                os.path.exists(os.path.join(self.root_path, self.id_map_path)) and \
-                os.path.exists(os.path.join(self.root_path, self.features_path)):
-            self._update_path(root_path)
-            self._load_index()
-            if operator.eq(set(image_ids), set(self.features['image_ids'])):
-                return ""
-            else:
-                return "The image list is different from index, Please update index"
-        else:
-            return "File not exist: features.pkl, vector.index, id_map.pkl"
-
-    def update_index(self, image_list: str, image_root: str = None) -> str:
-        if self.index and self.id_map and self.features:
-            image_paths, image_docs, image_ids = self._split_datafile(
-                image_list,
-                image_root if image_root is not None else self.root_path)
-
-            # for add image
-            add_ids = list(
-                set(image_ids).difference(set(self.features["image_ids"])))
-            add_indexes = [i for i, x in enumerate(image_ids) if x in add_ids]
-            add_image_paths = [image_paths[i] for i in add_indexes]
-            add_image_docs = [image_docs[i] for i in add_indexes]
-            add_image_ids = [image_ids[i] for i in add_indexes]
-            self._add_index(add_image_paths, add_image_docs, add_image_ids)
-
-            # delete images
-            delete_ids = list(
-                set(self.features["image_ids"]).difference(set(image_ids)))
-            self._delete_index(delete_ids)
-            self._save_index(self.index, self.id_map, self.features)
-            return ""
-        else:
-            return "Failed. Please create or open index first"
-
-    def _add_index(self, image_list: List, image_docs: List, image_ids: List):
-        if len(image_ids) == 0:
-            return
-        featrures = self._cal_featrue(image_list)
-        index_ids = (np.arange(0, len(image_list)) + max(self.id_map.keys()) +
-                     1).astype(np.int64)
-        self.index.add_with_ids(featrures, index_ids)
-
-        for i, d in zip(index_ids, image_docs):
-            self.id_map[i] = d
-
-        self.features['features'] = np.concatenate(
-            [self.features['features'], featrures], axis=0)
-        self.features['image_ids'].extend(image_ids)
-        self.features['index_ids'].extend(index_ids.tolist())
-
-    def _delete_index(self, image_ids: List):
-        if len(image_ids) == 0:
-            return
-        indexes = [
-            i for i, x in enumerate(self.features['image_ids'])
-            if x in image_ids
-        ]
-        self.features["features"] = np.delete(self.features["features"],
-                                              indexes,
-                                              axis=0)
-        self.features["image_ids"] = np.delete(np.asarray(
-            self.features["image_ids"]),
-                                               indexes,
-                                               axis=0).tolist()
-        index_ids = np.delete(np.asarray(self.features["index_ids"]),
-                              indexes,
-                              axis=0).tolist()
-        id_map_values = [self.id_map[i] for i in index_ids]
-        self.index.reset()
-        ids = np.arange(0, len(id_map_values)).astype(np.int64)
-        self.index.add_with_ids(self.features['features'], ids)
-        self.id_map.clear()
-        for i, d in zip(ids, id_map_values):
-            self.id_map[i] = d
-        self.features["index_ids"] = ids
-
-
-app = FastAPI()
-
-
-@app.get("/new_index")
-def new_index(image_list_path: str,
-              index_method: str = "HNSW32",
-              index_root_path: str = None,
-              force: bool = False):
-    result = ""
-    try:
-        if index_root_path is not None:
-            image_list_path = os.path.join(index_root_path, image_list_path)
-        index_path = os.path.join(index_root_path, "index", "vector.index")
-        id_map_path = os.path.join(index_root_path, "index", "id_map.pkl")
-
-        if not (os.path.exists(index_path)
-                and os.path.exists(id_map_path)) or force:
-            manager.create_index(image_list_path, index_method, index_root_path)
-        else:
-            result = "There alrealy has index in {}".format(index_root_path)
-    except Exception as e:
-        result = e.__str__()
-    data = {"error_message": result}
-    return json.dumps(data).encode()
-
-
-@app.get("/open_index")
-def open_index(index_root_path: str, image_list_path: str):
-    result = ""
-    try:
-        image_list_path = os.path.join(index_root_path, image_list_path)
-        result = manager.open_index(index_root_path, image_list_path)
-    except Exception as e:
-        result = e.__str__()
-
-    data = {"error_message": result}
-    return json.dumps(data).encode()
-
-
-@app.get("/update_index")
-def update_index(image_list_path: str, index_root_path: str = None):
-    result = ""
-    try:
-        if index_root_path is not None:
-            image_list_path = os.path.join(index_root_path, image_list_path)
-        result = manager.update_index(image_list=image_list_path,
-                                      image_root=index_root_path)
-    except Exception as e:
-        result = e.__str__()
-    data = {"error_message": result}
-    return json.dumps(data).encode()
-
-
-def FrontInterface(server_process=None):
-    front = QtWidgets.QApplication([])
-    main_window = mod.mainwindow.MainWindow(process=server_process)
-    main_window.showMaximized()
-    sys.exit(front.exec_())
-
-
-def Server(args):
-    [app, host, port] = args
-    uvicorn.run(app, host=host, port=port)
-
-
 if __name__ == '__main__':
-    args = config.parse_args()
-    model_config = config.get_config(args.config,
-                                     overrides=args.override,
-                                     show=True)
-    manager = ShiTuIndexManager(model_config)
+    if not (len(sys.argv) == 3 or len(sys.argv) == 5):
+        print("start example:")
+        print("   python index_manager.py -c xxx.yaml")
+        print("   python index_manager.py -c xxx.yaml -p port")
+    yaml_path = sys.argv[2]
+    if len(sys.argv) == 5:
+        port = sys.argv[4]
+    else:
+        port = 8000
+    assert int(port) > 1024 and int(
+        port) < 65536, "The port should be bigger than 1024 and \
+            smaller than 65536"
+
     try:
         ip = socket.gethostbyname(socket.gethostname())
     except:
         ip = '127.0.0.1'
-    port = 8000
-    p_server = Process(target=Server, args=([app, ip, port],))
-    p_server.start()
-    #  p_client = Process(target=FrontInterface, args=())
-    #  p_client.start()
-    #  p_client.join()
-    FrontInterface(p_server)
-    p_server.terminate()
-    sys.exit(0)
+    server_cmd = "python server.py -c {} -o ip={} -o port={}".format(yaml_path,
+                                                                     ip, port)
+    server_proc = subprocess.Popen(shlex.split(server_cmd))
+    client_proc = subprocess.Popen(
+        ["python", "client.py", "{} {}".format(ip, port)])
+    try:
+        while psutil.Process(client_proc.pid).status() == "running":
+            time.sleep(0.5)
+    except:
+        pass
+
+    client_proc.terminate()
+    server_proc.terminate()

deploy/shitu_index_manager/mod/mainwindow.py

@@ -22,8 +22,6 @@ try:
     DEFAULT_HOST = socket.gethostbyname(socket.gethostname())
 except:
     DEFAULT_HOST = '127.0.0.1'
-
-#  DEFAULT_HOST = "localhost"
 DEFAULT_PORT = 8000
 PADDLECLAS_DOC_URL = "https://gitee.com/paddlepaddle/PaddleClas/docs/zh_CN/inference_deployment/shitu_gallery_manager.md"
 
@@ -35,12 +33,17 @@ class MainWindow(QtWidgets.QMainWindow):
     updateIndexMsg = QtCore.pyqtSignal(str)  # 更新索引库线程信号
     importImageCount = QtCore.pyqtSignal(int)  # 导入图像数量信号
 
-    def __init__(self, process=None):
+    def __init__(self, ip=None, port=None):
         super(MainWindow, self).__init__()
-        self.server_process = process
+        if ip is not None and port is not None:
+            self.server_ip = ip
+            self.server_port = port
+        else:
+            self.server_ip = DEFAULT_HOST
+            self.server_port = DEFAULT_PORT
+
         self.ui = ui_mainwindow.Ui_MainWindow()
         self.ui.setupUi(self)  # 初始化主窗口界面
-
         self.__imageListMgr = image_list_manager.ImageListManager()
 
         self.__appMenu = QtWidgets.QMenu()  # 应用菜单
@@ -115,8 +118,7 @@ class MainWindow(QtWidgets.QMainWindow):
         self.ui.saveImageLibraryBtn.clicked.connect(self.saveImageLibrary)
 
         self.__setToolButton(self.ui.addClassifyBtn, "添加分类",
-                             "./resource/add_classify.png",
-                             TOOL_BTN_ICON_SIZE)
+                             "./resource/add_classify.png", TOOL_BTN_ICON_SIZE)
         self.ui.addClassifyBtn.clicked.connect(
             self.__classifyUiContext.addClassify)
 
@@ -145,7 +147,10 @@ class MainWindow(QtWidgets.QMainWindow):
         self.ui.searchClassifyHistoryCmb.setToolTip("查找分类历史")
         self.ui.imageScaleSlider.setToolTip("图片缩放")
 
-    def __setToolButton(self, button, tool_tip: str, icon_path: str,
+    def __setToolButton(self,
+                        button,
+                        tool_tip: str,
+                        icon_path: str,
                         icon_size: int):
         """设置工具按钮"""
         button.setToolTip(tool_tip)
@@ -160,9 +165,9 @@ class MainWindow(QtWidgets.QMainWindow):
 
         self.__libraryAppendMenu.setTitle("导入图像")
         utils.setMenu(self.__libraryAppendMenu, "导入 image_list 图像",
-                          self.importImageListImage)
+                      self.importImageListImage)
         utils.setMenu(self.__libraryAppendMenu, "导入多文件夹图像",
-                          self.importDirsImage)
+                      self.importDirsImage)
         self.__appMenu.addMenu(self.__libraryAppendMenu)
 
         self.__appMenu.addSeparator()
@@ -179,16 +184,16 @@ class MainWindow(QtWidgets.QMainWindow):
     def __initWaitDialog(self):
         """初始化等待对话框"""
         self.__waitDialogUi.setupUi(self.__waitDialog)
-        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog
-                                         | QtCore.Qt.FramelessWindowHint)
+        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog |
+                                         QtCore.Qt.FramelessWindowHint)
 
     def __startWait(self, msg: str):
         """开始显示等待对话框"""
         self.setEnabled(False)
         self.__waitDialogUi.msgLabel.setText(msg)
-        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog
-                                         | QtCore.Qt.FramelessWindowHint
-                                         | QtCore.Qt.WindowStaysOnTopHint)
+        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog |
+                                         QtCore.Qt.FramelessWindowHint |
+                                         QtCore.Qt.WindowStaysOnTopHint)
         self.__waitDialog.show()
         self.__waitDialog.repaint()
 
@@ -196,9 +201,9 @@ class MainWindow(QtWidgets.QMainWindow):
         """停止显示等待对话框"""
         self.setEnabled(True)
         self.__waitDialogUi.msgLabel.setText("执行完毕！")
-        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog
-                                         | QtCore.Qt.FramelessWindowHint
-                                         | QtCore.Qt.CustomizeWindowHint)
+        self.__waitDialog.setWindowFlags(QtCore.Qt.Dialog |
+                                         QtCore.Qt.FramelessWindowHint |
+                                         QtCore.Qt.CustomizeWindowHint)
         self.__waitDialog.close()
 
     def __connectSignal(self):
@@ -290,8 +295,8 @@ class MainWindow(QtWidgets.QMainWindow):
 
     def __importImageListImageThread(self, from_path: str, to_path: str):
         """导入 image_list 图像 线程"""
-        count = utils.oneKeyImportFromFile(from_path=from_path,
-                                               to_path=to_path)
+        count = utils.oneKeyImportFromFile(
+            from_path=from_path, to_path=to_path)
         if count == None:
             count = -1
         self.importImageCount.emit(count)
@@ -308,9 +313,9 @@ class MainWindow(QtWidgets.QMainWindow):
             return
         from_mgr = image_list_manager.ImageListManager(from_path)
         self.__startWait("正在导入图像，请等待。。。")
-        thread = threading.Thread(target=self.__importImageListImageThread,
-                                  args=(from_mgr.filePath,
-                                        self.__imageListMgr.filePath))
+        thread = threading.Thread(
+            target=self.__importImageListImageThread,
+            args=(from_mgr.filePath, self.__imageListMgr.filePath))
         thread.start()
 
     def __importDirsImageThread(self, from_dir: str, to_image_list_path: str):
@@ -333,21 +338,25 @@ class MainWindow(QtWidgets.QMainWindow):
             QtWidgets.QMessageBox.information(self, "提示", "打开的目录不存在")
             return
         self.__startWait("正在导入图像，请等待。。。")
-        thread = threading.Thread(target=self.__importDirsImageThread,
-                                  args=(dir_path,
-                                        self.__imageListMgr.filePath))
+        thread = threading.Thread(
+            target=self.__importDirsImageThread,
+            args=(dir_path, self.__imageListMgr.filePath))
         thread.start()
 
-    def __newIndexThread(self, index_root_path: str, image_list_path: str,
-                         index_method: str, force: bool):
+    def __newIndexThread(self,
+                         index_root_path: str,
+                         image_list_path: str,
+                         index_method: str,
+                         force: bool):
         """新建重建索引库线程"""
         try:
-            client = index_http_client.IndexHttpClient(
-                DEFAULT_HOST, DEFAULT_PORT)
-            err_msg = client.new_index(image_list_path=image_list_path,
-                                       index_root_path=index_root_path,
-                                       index_method=index_method,
-                                       force=force)
+            client = index_http_client.IndexHttpClient(self.server_ip,
+                                                       self.server_port)
+            err_msg = client.new_index(
+                image_list_path=image_list_path,
+                index_root_path=index_root_path,
+                index_method=index_method,
+                force=force)
             if err_msg == None:
                 err_msg = ""
             self.newIndexMsg.emit(err_msg)
@@ -375,19 +384,20 @@ class MainWindow(QtWidgets.QMainWindow):
         force = ui.resetCheckBox.isChecked()
         if result == QtWidgets.QDialog.Accepted:
             self.__startWait("正在 新建/重建 索引库，请等待。。。")
-            thread = threading.Thread(target=self.__newIndexThread,
-                                      args=(self.__imageListMgr.dirName,
-                                            "image_list.txt", index_method,
-                                            force))
+            thread = threading.Thread(
+                target=self.__newIndexThread,
+                args=(self.__imageListMgr.dirName, "image_list.txt",
+                      index_method, force))
             thread.start()
 
     def __openIndexThread(self, index_root_path: str, image_list_path: str):
         """打开索引库线程"""
         try:
-            client = index_http_client.IndexHttpClient(
-                DEFAULT_HOST, DEFAULT_PORT)
-            err_msg = client.open_index(index_root_path=index_root_path,
-                                        image_list_path=image_list_path)
+            client = index_http_client.IndexHttpClient(self.server_ip,
+                                                       self.server_port)
+            err_msg = client.open_index(
+                index_root_path=index_root_path,
+                image_list_path=image_list_path)
             if err_msg == None:
                 err_msg = ""
             self.openIndexMsg.emit(err_msg)
@@ -408,18 +418,19 @@ class MainWindow(QtWidgets.QMainWindow):
             QtWidgets.QMessageBox.information(self, "提示", "请先打开正确的图像库")
             return
         self.__startWait("正在打开索引库，请等待。。。")
-        thread = threading.Thread(target=self.__openIndexThread,
-                                  args=(self.__imageListMgr.dirName,
-                                        "image_list.txt"))
+        thread = threading.Thread(
+            target=self.__openIndexThread,
+            args=(self.__imageListMgr.dirName, "image_list.txt"))
         thread.start()
 
     def __updateIndexThread(self, index_root_path: str, image_list_path: str):
         """更新索引库线程"""
         try:
-            client = index_http_client.IndexHttpClient(
-                DEFAULT_HOST, DEFAULT_PORT)
-            err_msg = client.update_index(image_list_path=image_list_path,
-                                          index_root_path=index_root_path)
+            client = index_http_client.IndexHttpClient(self.server_ip,
+                                                       self.server_port)
+            err_msg = client.update_index(
+                image_list_path=image_list_path,
+                index_root_path=index_root_path)
             if err_msg == None:
                 err_msg = ""
             self.updateIndexMsg.emit(err_msg)
@@ -440,9 +451,9 @@ class MainWindow(QtWidgets.QMainWindow):
             QtWidgets.QMessageBox.information(self, "提示", "请先打开正确的图像库")
             return
         self.__startWait("正在更新索引库，请等待。。。")
-        thread = threading.Thread(target=self.__updateIndexThread,
-                                  args=(self.__imageListMgr.dirName,
-                                        "image_list.txt"))
+        thread = threading.Thread(
+            target=self.__updateIndexThread,
+            args=(self.__imageListMgr.dirName, "image_list.txt"))
         thread.start()
 
     def searchClassify(self):
@@ -471,9 +482,6 @@ class MainWindow(QtWidgets.QMainWindow):
 
     def exitApp(self):
         """退出应用"""
-        if isinstance(self.server_process, Process):
-            self.server_process.terminate()
-            #  os.kill(self.server_pid)
         sys.exit(0)
 
     def __setPathBar(self, msg: str):

deploy/shitu_index_manager/server.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+from PyQt5 import QtCore, QtGui, QtWidgets
+import mod.mainwindow
+
+from paddleclas.deploy.utils import config, logger
+from paddleclas.deploy.python.predict_rec import RecPredictor
+from fastapi import FastAPI
+import uvicorn
+import numpy as np
+import faiss
+from typing import List
+import pickle
+import cv2
+import socket
+import json
+import operator
+from multiprocessing import Process
+"""
+完整的index库如下:
+root_path/            # 库存储目录
+|-- image_list.txt     # 图像列表，每行：image_path label。由前端生成及修改。后端只读
+|-- features.pkl       # 建库之后，保存的embedding向量，后端生成，前端无需操作
+|-- images             # 图像存储目录，由前端生成及增删查等操作。后端只读
+|   |-- md5.jpg
+|   |-- md5.jpg
+|   |-- ……
+|-- index              # 真正的生成的index库存储目录，后端生成及操作，前端无需操作。
+|   |-- vector.index   # faiss生成的索引库
+|   |-- id_map.pkl     # 索引文件
+"""
+
+
+class ShiTuIndexManager(object):
+    def __init__(self, config):
+        self.root_path = None
+        self.image_list_path = "image_list.txt"
+        self.image_dir = "images"
+        self.index_path = "index/vector.index"
+        self.id_map_path = "index/id_map.pkl"
+        self.features_path = "features.pkl"
+        self.index = None
+        self.id_map = None
+        self.features = None
+        self.config = config
+        self.predictor = RecPredictor(config)
+
+    def _load_pickle(self, path):
+        if os.path.exists(path):
+            return pickle.load(open(path, 'rb'))
+        else:
+            return None
+
+    def _save_pickle(self, path, data):
+        if not os.path.exists(os.path.dirname(path)):
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+        with open(path, 'wb') as fd:
+            pickle.dump(data, fd)
+
+    def _load_index(self):
+        self.index = faiss.read_index(
+            os.path.join(self.root_path, self.index_path))
+        self.id_map = self._load_pickle(
+            os.path.join(self.root_path, self.id_map_path))
+        self.features = self._load_pickle(
+            os.path.join(self.root_path, self.features_path))
+
+    def _save_index(self, index, id_map, features):
+        faiss.write_index(index, os.path.join(self.root_path, self.index_path))
+        self._save_pickle(
+            os.path.join(self.root_path, self.id_map_path), id_map)
+        self._save_pickle(
+            os.path.join(self.root_path, self.features_path), features)
+
+    def _update_path(self, root_path, image_list_path=None):
+        if root_path == self.root_path:
+            pass
+        else:
+            self.root_path = root_path
+            if not os.path.exists(os.path.join(root_path, "index")):
+                os.mkdir(os.path.join(root_path, "index"))
+            if image_list_path is not None:
+                self.image_list_path = image_list_path
+
+    def _cal_featrue(self, image_list):
+        batch_images = []
+        featrures = None
+        cnt = 0
+        for idx, image_path in enumerate(image_list):
+            image = cv2.imread(image_path)
+            if image is None:
+                return "{} is broken or not exist. Stop"
+            else:
+                image = image[:, :, ::-1]
+                batch_images.append(image)
+                cnt += 1
+            if cnt % self.config["Global"]["batch_size"] == 0 or (
+                    idx + 1) == len(image_list):
+                if len(batch_images) == 0:
+                    continue
+                batch_results = self.predictor.predict(batch_images)
+                featrures = batch_results if featrures is None else np.concatenate(
+                    (featrures, batch_results), axis=0)
+                batch_images = []
+        return featrures
+
+    def _split_datafile(self, data_file, image_root):
+        '''
+        data_file: image path and info, which can be splitted by spacer
+        image_root: image path root
+        delimiter: delimiter
+        '''
+        gallery_images = []
+        gallery_docs = []
+        gallery_ids = []
+        with open(data_file, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+            for _, ori_line in enumerate(lines):
+                line = ori_line.strip().split()
+                text_num = len(line)
+                assert text_num >= 2, f"line({ori_line}) must be splitted into at least 2 parts, but got {text_num}"
+                image_file = os.path.join(image_root, line[0])
+
+                gallery_images.append(image_file)
+                gallery_docs.append(ori_line.strip())
+                gallery_ids.append(os.path.basename(line[0]).split(".")[0])
+
+        return gallery_images, gallery_docs, gallery_ids
+
+    def create_index(self,
+                     image_list: str,
+                     index_method: str="HNSW32",
+                     image_root: str=None):
+        if not os.path.exists(image_list):
+            return "{} is not exist".format(image_list)
+        if index_method.lower() not in ['hnsw32', 'ivf', 'flat']:
+            return "The index method Only support: HNSW32, IVF, Flat"
+        self._update_path(os.path.dirname(image_list), image_list)
+
+        # get image_paths
+        image_root = image_root if image_root is not None else self.root_path
+        gallery_images, gallery_docs, image_ids = self._split_datafile(
+            image_list, image_root)
+
+        # gernerate index
+        if index_method == "IVF":
+            index_method = index_method + str(
+                min(max(int(len(gallery_images) // 32), 2), 65536)) + ",Flat"
+        index = faiss.index_factory(
+            self.config["IndexProcess"]["embedding_size"], index_method,
+            faiss.METRIC_INNER_PRODUCT)
+        self.index = faiss.IndexIDMap2(index)
+        features = self._cal_featrue(gallery_images)
+        self.index.train(features)
+        index_ids = np.arange(0, len(gallery_images)).astype(np.int64)
+        self.index.add_with_ids(features, index_ids)
+
+        self.id_map = dict()
+        for i, d in zip(list(index_ids), gallery_docs):
+            self.id_map[i] = d
+
+        self.features = {
+            "features": features,
+            "index_method": index_method,
+            "image_ids": image_ids,
+            "index_ids": index_ids.tolist()
+        }
+        self._save_index(self.index, self.id_map, self.features)
+
+    def open_index(self, root_path: str, image_list_path: str) -> str:
+        self._update_path(root_path)
+        _, _, image_ids = self._split_datafile(image_list_path, root_path)
+        if os.path.exists(os.path.join(self.root_path, self.index_path)) and \
+                os.path.exists(os.path.join(self.root_path, self.id_map_path)) and \
+                os.path.exists(os.path.join(self.root_path, self.features_path)):
+            self._update_path(root_path)
+            self._load_index()
+            if operator.eq(set(image_ids), set(self.features['image_ids'])):
+                return ""
+            else:
+                return "The image list is different from index, Please update index"
+        else:
+            return "File not exist: features.pkl, vector.index, id_map.pkl"
+
+    def update_index(self, image_list: str, image_root: str=None) -> str:
+        if self.index and self.id_map and self.features:
+            image_paths, image_docs, image_ids = self._split_datafile(
+                image_list, image_root
+                if image_root is not None else self.root_path)
+
+            # for add image
+            add_ids = list(
+                set(image_ids).difference(set(self.features["image_ids"])))
+            add_indexes = [i for i, x in enumerate(image_ids) if x in add_ids]
+            add_image_paths = [image_paths[i] for i in add_indexes]
+            add_image_docs = [image_docs[i] for i in add_indexes]
+            add_image_ids = [image_ids[i] for i in add_indexes]
+            self._add_index(add_image_paths, add_image_docs, add_image_ids)
+
+            # delete images
+            delete_ids = list(
+                set(self.features["image_ids"]).difference(set(image_ids)))
+            self._delete_index(delete_ids)
+            self._save_index(self.index, self.id_map, self.features)
+            return ""
+        else:
+            return "Failed. Please create or open index first"
+
+    def _add_index(self, image_list: List, image_docs: List, image_ids: List):
+        if len(image_ids) == 0:
+            return
+        featrures = self._cal_featrue(image_list)
+        index_ids = (
+            np.arange(0, len(image_list)) + max(self.id_map.keys()) + 1
+        ).astype(np.int64)
+        self.index.add_with_ids(featrures, index_ids)
+
+        for i, d in zip(index_ids, image_docs):
+            self.id_map[i] = d
+
+        self.features['features'] = np.concatenate(
+            [self.features['features'], featrures], axis=0)
+        self.features['image_ids'].extend(image_ids)
+        self.features['index_ids'].extend(index_ids.tolist())
+
+    def _delete_index(self, image_ids: List):
+        if len(image_ids) == 0:
+            return
+        indexes = [
+            i for i, x in enumerate(self.features['image_ids'])
+            if x in image_ids
+        ]
+        self.features["features"] = np.delete(
+            self.features["features"], indexes, axis=0)
+        self.features["image_ids"] = np.delete(
+            np.asarray(self.features["image_ids"]), indexes, axis=0).tolist()
+        index_ids = np.delete(
+            np.asarray(self.features["index_ids"]), indexes, axis=0).tolist()
+        id_map_values = [self.id_map[i] for i in index_ids]
+        self.index.reset()
+        ids = np.arange(0, len(id_map_values)).astype(np.int64)
+        self.index.add_with_ids(self.features['features'], ids)
+        self.id_map.clear()
+        for i, d in zip(ids, id_map_values):
+            self.id_map[i] = d
+        self.features["index_ids"] = ids
+
+
+app = FastAPI()
+
+
+@app.get("/new_index")
+def new_index(image_list_path: str,
+              index_method: str="HNSW32",
+              index_root_path: str=None,
+              force: bool=False):
+    result = ""
+    try:
+        if index_root_path is not None:
+            image_list_path = os.path.join(index_root_path, image_list_path)
+        index_path = os.path.join(index_root_path, "index", "vector.index")
+        id_map_path = os.path.join(index_root_path, "index", "id_map.pkl")
+
+        if not (os.path.exists(index_path) and
+                os.path.exists(id_map_path)) or force:
+            manager.create_index(image_list_path, index_method,
+                                 index_root_path)
+        else:
+            result = "There alrealy has index in {}".format(index_root_path)
+    except Exception as e:
+        result = e.__str__()
+    data = {"error_message": result}
+    return json.dumps(data).encode()
+
+
+@app.get("/open_index")
+def open_index(index_root_path: str, image_list_path: str):
+    result = ""
+    try:
+        image_list_path = os.path.join(index_root_path, image_list_path)
+        result = manager.open_index(index_root_path, image_list_path)
+    except Exception as e:
+        result = e.__str__()
+
+    data = {"error_message": result}
+    return json.dumps(data).encode()
+
+
+@app.get("/update_index")
+def update_index(image_list_path: str, index_root_path: str=None):
+    result = ""
+    try:
+        if index_root_path is not None:
+            image_list_path = os.path.join(index_root_path, image_list_path)
+        result = manager.update_index(
+            image_list=image_list_path, image_root=index_root_path)
+    except Exception as e:
+        result = e.__str__()
+    data = {"error_message": result}
+    return json.dumps(data).encode()
+
+
+def FrontInterface(server_process=None):
+    front = QtWidgets.QApplication([])
+    main_window = mod.mainwindow.MainWindow(process=server_process)
+    main_window.showMaximized()
+    sys.exit(front.exec_())
+
+
+def Server(app, host, port):
+    uvicorn.run(app, host=host, port=port)
+
+
+if __name__ == '__main__':
+    args = config.parse_args()
+    model_config = config.get_config(
+        args.config, overrides=args.override, show=True)
+    manager = ShiTuIndexManager(model_config)
+    ip = model_config.get('ip', None)
+    port = model_config.get('port', None)
+    if ip is None or port is None:
+        try:
+            ip = socket.gethostbyname(socket.gethostname())
+        except:
+            ip = '127.0.0.1'
+        port = 8000
+    Server(app, ip, port)

docs/zh_CN/deployment/PP-ShiTu/application_scenarios.md

@@ -24,30 +24,32 @@ PP-ShiTu对原数据集进行了`Gallery`库和`Query`库划分，并生成了
 
 | 场景 |示例图|场景简介|Recall@1|场景库下载地址|原数据集下载地址|
 |:---:|:---:|:---:|:---:|:---:|:---:|
-| 球类 | <img src="../../images/ppshitu_application_scenarios/Ball.jpg" height = "100"> |各种球类识别 | 0.9769 | [Balls](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Balls.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/balls-image-classification) |
-| 狗识别 | <img src="../../images/ppshitu_application_scenarios/DogBreeds.jpg" height = "100"> | 狗细分类识别，包括69种狗的图像 | 0.9606 | [DogBreeds](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/DogBreeds.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/70-dog-breedsimage-data-set) |
-| 宝石 | <img src="../../images/ppshitu_application_scenarios/Gemstones.jpg" height = "100"> | 宝石种类识别 | 0.9653 | [Gemstones](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Gemstones.tar) | [原数据下载地址](https://www.kaggle.com/datasets/lsind18/gemstones-images) |
-| 动物 | <img src="../../images/ppshitu_application_scenarios/AnimalImageDataset.jpg" height = "100"> |各种动物识别 | 0.9078 | [AnimalImageDataset](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/AnimalImageDataset.tar) | [原数据下载地址](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) |
-| 鸟类 | <img src="../../images/ppshitu_application_scenarios/Bird400.jpg" height = "100"> |鸟细分类识别，包括400种各种姿态的鸟类图像 | 0.9673 | [Bird400](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Bird400.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) |
+| 花 | <img src="../../images/ppshitu_application_scenarios/104flowers.jpeg" height = "100"> |104种花细分类识别 | 0.9788 | [104flowers](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/104flowers.tar) | [原数据下载地址](https://www.kaggle.com/datasets/msheriey/104-flowers-garden-of-eden) |
 | 交通工具 | <img src="../../images/ppshitu_application_scenarios/Vechicles.jpg" height = "100"> |车、船等交通工具粗分类识别 | 0.9307 | [Vechicles](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Vechicles.tar) | [原数据下载地址](https://www.kaggle.com/datasets/rishabkoul1/vechicle-dataset) |
-| 花 | <img src="../../images/ppshitu_application_scenarios/104flowers.jpeg" height = "100"> |104种花细分类识别 | 0.9788 | [104flowers](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/104flowrs.tar) | [原数据下载地址](https://www.kaggle.com/datasets/msheriey/104-flowers-garden-of-eden) |
-| 运动种类 | <img src="../../images/ppshitu_application_scenarios/100sports.jpg" height = "100"> |100种运动图像识别 | 0.9413 | [100sports](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/100sports.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/sports-classification) |
-| 乐器 | <img src="../../images/ppshitu_application_scenarios/MusicInstruments.jpg" height = "100"> |30种不同乐器种类识别 | 0.9467 | [MusicInstruments](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/MusicInstruments.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/musical-instruments-image-classification) |
-| 宝可梦 | <img src="../../images/ppshitu_application_scenarios/Pokemon.png" height = "100"> |宝可梦神奇宝贝识别 | 0.9236 | [Pokemon](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Pokemon.tar) | [原数据下载地址](https://www.kaggle.com/datasets/lantian773030/pokemonclassification) |
-| 船 | <img src="../../images/ppshitu_application_scenarios/Boat.jpg" height = "100"> |船种类识别 |0.9242 | [Boat](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Boat.tar) | [原数据下载地址](https://www.kaggle.com/datasets/imsparsh/dockship-boat-type-classification) |
-| 鞋子 | <img src="../../images/ppshitu_application_scenarios/Shoes.jpeg" height = "100"> |鞋子种类识别，包括靴子、拖鞋等 | 0.9000 | [Shoes](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Shoes.tar) | [原数据下载地址](https://www.kaggle.com/datasets/noobyogi0100/shoe-dataset) |
-| 巴黎建筑 | <img src="../../images/ppshitu_application_scenarios/Paris.jpg" height = "100"> |巴黎著名建筑景点识别，如：巴黎铁塔、圣母院等 | 1.000 | [Paris](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Paris.tar) | [原数据下载地址](https://www.kaggle.com/datasets/skylord/oxbuildings) |
-| 蝴蝶 | <img src="../../images/ppshitu_application_scenarios/Butterfly.jpg" height = "100"> |75种蝴蝶细分类识别 | 0.9360 | [Butterfly](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Butterfly.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/butterfly-images40-species) |
-| 野外植物 | <img src="../../images/ppshitu_application_scenarios/WildEdiblePlants.jpg" height = "100"> |野外植物识别 | 0.9758 | [WildEdiblePlants](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/WildEdiblePlants.tar) | [原数据下载地址](https://www.kaggle.com/datasets/ryanpartridge01/wild-edible-plants) |
-| 天气 | <img src="../../images/ppshitu_application_scenarios/WeatherImageRecognition.jpg" height = "100"> |各种天气场景识别，如：雨天、打雷、下雪等 | 0.9924 | [WeatherImageRecognition](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/WeatherImageRecognition.tar) | [原数据下载地址](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) |
-| 坚果 | <img src="../../images/ppshitu_application_scenarios/TreeNuts.jpg" height = "100"> |各种坚果种类识别 | 0.9412 | [TreeNuts](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/TreeNuts.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/tree-nuts-image-classification) |
+| 动物 | <img src="../../images/ppshitu_application_scenarios/AnimalImageDataset.jpg" height = "100"> |各种动物识别 | 0.9078 | [AnimalImageDataset](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/AnimalImageDataset.tar) | [原数据下载地址](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) |
 | 时装 | <img src="../../images/ppshitu_application_scenarios/FashionProductsImage.jpg" height = "100"> |首饰、挎包、化妆品等时尚商品识别 | 0.9555 | [FashionProductImageSmall](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/FashionProductImageSmall.tar) | [原数据下载地址](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small) |
-| 垃圾 | <img src="../../images/ppshitu_application_scenarios/Garbage12.jpg" height = "100"> |12种垃圾分类识别 | 0.9845 | [Garbage12](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Garbage12.tar) | [原数据下载地址](https://www.kaggle.com/datasets/mostafaabla/garbage-classification) |
-| 航拍场景 | <img src="../../images/ppshitu_application_scenarios/AID.jpg" height = "100"> |各种航拍场景识别，如机场、火车站等 | 0.9797 | [AID](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/AID.tar) | [原数据下载地址](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets) |
-| 蔬菜 | <img src="../../images/ppshitu_application_scenarios/Veg200.jpg" height = "100"> |各种蔬菜识别 | 0.8929 | [Veg200](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Veg200.tar) | [原数据下载地址](https://www.kaggle.com/datasets/zhaoyj688/vegfru) |
-| 商标 | <img src="../../images/ppshitu_application_scenarios/Logo3K.jpg" height = "100"> |两千多种logo识别 | 0.9313 | [Logo3k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Logo3k.tar) | [原数据下载地址](https://github.com/Wangjing1551/LogoDet-3K-Dataset) |
-
-
+| 球类 | <img src="../../images/ppshitu_application_scenarios/Ball.jpg" height = "100"> |各种球类识别 | 0.9769 | [Balls](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/PP-ShiTuV2_application_dataset/Balls.tar) | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/balls-image-classification) |
+| 狗识别 | <img src="../../images/ppshitu_application_scenarios/DogBreeds.jpg" height = "100"> | 狗细分类识别，包括69种狗的图像 | 0.9606 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/70-dog-breedsimage-data-set) |
+| 宝石 | <img src="../../images/ppshitu_application_scenarios/Gemstones.jpg" height = "100"> | 宝石种类识别 | 0.9653 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/lsind18/gemstones-images) |
+| 鸟类 | <img src="../../images/ppshitu_application_scenarios/Bird400.jpg" height = "100"> |鸟细分类识别，包括400种各种姿态的鸟类图像 | 0.9673 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) |
+| 运动种类 | <img src="../../images/ppshitu_application_scenarios/100sports.jpg" height = "100"> |100种运动图像识别 | 0.9413 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/sports-classification) |
+| 乐器 | <img src="../../images/ppshitu_application_scenarios/MusicInstruments.jpg" height = "100"> |30种不同乐器种类识别 | 0.9467 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/musical-instruments-image-classification) |
+| 宝可梦 | <img src="../../images/ppshitu_application_scenarios/Pokemon.png" height = "100"> |宝可梦神奇宝贝识别 | 0.9236 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/lantian773030/pokemonclassification) |
+| 船 | <img src="../../images/ppshitu_application_scenarios/Boat.jpg" height = "100"> |船种类识别 |0.9242 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/imsparsh/dockship-boat-type-classification) |
+| 鞋子 | <img src="../../images/ppshitu_application_scenarios/Shoes.jpeg" height = "100"> |鞋子种类识别，包括靴子、拖鞋等 | 0.9000 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/noobyogi0100/shoe-dataset) |
+| 巴黎建筑 | <img src="../../images/ppshitu_application_scenarios/Paris.jpg" height = "100"> |巴黎著名建筑景点识别，如：巴黎铁塔、圣母院等 | 1.000 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/skylord/oxbuildings) |
+| 蝴蝶 | <img src="../../images/ppshitu_application_scenarios/Butterfly.jpg" height = "100"> |75种蝴蝶细分类识别 | 0.9360 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/butterfly-images40-species) |
+| 野外植物 | <img src="../../images/ppshitu_application_scenarios/WildEdiblePlants.jpg" height = "100"> |野外植物识别 | 0.9758 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/ryanpartridge01/wild-edible-plants) |
+| 天气 | <img src="../../images/ppshitu_application_scenarios/WeatherImageRecognition.jpg" height = "100"> |各种天气场景识别，如：雨天、打雷、下雪等 | 0.9924 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/jehanbhathena/weather-dataset) |
+| 坚果 | <img src="../../images/ppshitu_application_scenarios/TreeNuts.jpg" height = "100"> |各种坚果种类识别 | 0.9412 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/gpiosenka/tree-nuts-image-classification) |
+| 垃圾 | <img src="../../images/ppshitu_application_scenarios/Garbage12.jpg" height = "100"> |12种垃圾分类识别 | 0.9845 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/mostafaabla/garbage-classification) |
+| 航拍场景 | <img src="../../images/ppshitu_application_scenarios/AID.jpg" height = "100"> |各种航拍场景识别，如机场、火车站等 | 0.9797 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets) |
+| 蔬菜 | <img src="../../images/ppshitu_application_scenarios/Veg200.jpg" height = "100"> |各种蔬菜识别 | 0.8929 | 添加技术交流群后即可获得 | [原数据下载地址](https://www.kaggle.com/datasets/zhaoyj688/vegfru) |
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/11568925/189877049-d17ddcea-22d2-44ab-91fe-36d12af3add8.png" width = "200" height = "200"/>
+<p>添加二维码获取相关场景库及大礼包</p>
+</div>
 
 <a name="2. 使用说明"></a>
 

docs/zh_CN/deployment/PP-ShiTu/gallery_manager.md

@@ -22,7 +22,7 @@
 - [2. 使用说明](#2)
 
   - [2.1 环境安装](#2.1)
-  - [2.2 模型准备](#2.2)
+  - [2.2 模型及数据准备](#2.2)
   - [2.3运行使用](#2.3)
 
 - [3.生成文件介绍](#3)
@@ -123,13 +123,25 @@
 pip install fastapi
 pip install uvicorn
 pip install pyqt5
+pip install psutil
 ```
 
 <a name="2.2"></a>
 
-### 2.2 模型准备
+### 2.2 模型及数据准备
 
-请按照[PP-ShiTu快速体验](../../quick_start/quick_start_recognition.md#2.2.1)中下载及准备inference model，并修改好`${PaddleClas}/deploy/configs/inference_drink.yaml`的相关参数。
+请按照[PP-ShiTu快速体验](../quick_start/quick_start_recognition.md#2.2.1)中下载及准备inference model，并修改好`${PaddleClas}/deploy/configs/inference_drink.yaml`的相关参数，同时准备好数据集。在具体使用时，请替换好自己的数据集及模型文件。
+
+```shell
+cd ${PaddleClas}/deploy/shitu_index_manager
+mkdir models
+cd models
+# 下载及解压识别模型
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
+cd ..
+# 下载及解压示例数据集
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
+```
 
 <a name="2.3"></a>
 
@@ -139,9 +151,26 @@ pip install pyqt5
 
 ```shell
 cd ${PaddleClas}/deploy/shitu_index_manager
-python index_manager.py -c ../configs/inference_drink.yaml
+cp ../configs/inference_drink.yaml .
+# 注意如果没有按照2.2中准备数据集及代码，请手动修改inference_drink.yaml，做好适配
+python index_manager.py -c inference_drink.yaml
 ```
 
+运行成功后，会自动跳转到工具界面，可以按照如下步骤，生成新的index库。
+
+1. 点击菜单栏`新建图像库`，会提示打开一个文件夹，此时请创建一个**新的文件夹**，并打开。如在`${PaddleClas}/deploy/shitu_index_manager`下新建一个`drink_index`文件夹
+2. 导入图像，或者如上面功能介绍，自己手动新增类别和相应的图像，下面介绍两种导入图像方式，操作时，二选一即可。
+   - 点击`导入图像`->`导入image_list图像`，打开`${PaddleClas}/deploy/shitu_index_manager/drink_dataset_v2.0/gallery/drink_label.txt`，此时就可以将`drink_label.txt`中的图像全部导入进来，图像类别就是`drink_label.txt`中记录的类别。
+   - 点击`导入图像`->`导入多文件夹图像`，打开`${PaddleClas}/deploy/shitu_index_manager/drink_dataset_v2.0/gallery/`文件夹，此时就将`gallery`文件夹下，所有子文件夹都导入进来，图像类别就是子文件夹的名字。
+3. 点击菜单栏中`新建/重建 索引库`，此时就会开始生成索引库。如果图片较多或者使用cpu来进行特征提取，那么耗时会比较长，请耐心等待。
+4. 生成索引库成功后，会发现在`drink_index`文件夹下生成如[3](#3) 中介绍的文件，此时`index`子文件夹下生出的文件，就是`PP-ShiTu`所使用的索引文件。
+
+**注意**：
+
+- 利用此工具生成的index库，如`drink_index`文件夹，请妥善存储。之后，可以继续使用此工具中`打开图像库`功能，打开`drink_index`文件夹，继续对index库进行增删改查操作，具体功能可以查看[功能介绍](#1)。
+- 打开一个生成好的库，在其上面进行增删改查操作后，请及时保存。保存后并及时使用菜单中`更新索引库`功能，对索引库进行更新
+- 如果要使用自己的图像库文件，图像生成格式如示例数据格式，生成`image_list.txt`或者多文件夹存储，二选一。
+
 <a name="3"></a>
 
 ## 3. 生成文件介绍

docs/zh_CN/deployment/PP-ShiTu/paddle_lite.md

@@ -124,11 +124,15 @@ PaddleClas 提供了转换并优化后的推理模型，可以直接参考下方
 ```shell
 # 进入lite_ppshitu目录
 cd $PaddleClas/deploy/lite_shitu
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/lite/ppshitu_lite_models_v1.2.tar
-tar -xf ppshitu_lite_models_v1.2.tar
-rm -f ppshitu_lite_models_v1.2.tar
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/lite/ppshituv2_lite_models_v1.0.tar
+tar -xf ppshituv2_lite_models_v1.0.tar
+rm -f ppshituv2_lite_models_v1.0.tar
 ```
 
+解压后的文件夹中包含PP-ShiTu V2的检测和特征模型的量化模型，即解压后的两个`.nb`模型。 存储大小约为14M，相对于非量化模型，模型加速一倍左右，精度降低不到一个点。如果想用非量化模型，可以参考[使用其他模型](#2.1.2)
+
+同时，文件中存在`general_PPLCNetV2_base_quant_v1.0_lite_inference_model`子文件夹，子文件夹中存储的是`general_PPLCNetV2_base_quant_v1.0_lite.nb`对应的`inference model`，在实际部署前，先需要用此`inference model`生成好对应的`index`库。实际推理时，只需要生成好的`index`库和两个量化模型来完成部署。
+
 <a name="2.1.2"></a>
 
 #### 2.1.2 使用其他模型
@@ -188,6 +192,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 
 下面介绍使用`paddle_lite_opt`完成主体检测模型和识别模型的预训练模型，转成inference模型，最终转换成Paddle-Lite的优化模型的过程。
 
+此示例是使用`fp32`的模型直接生成`.nb`模型，相对于量化模型，精度会更高，但是速度会降低。
+
 1. 转换主体检测模型
 
 ```shell
@@ -214,13 +220,13 @@ cp $code_path/PaddleDetection/inference/picodet_lcnet_x2_5_640_mainbody/mainbody
 
 ```shell
 # 识别模型下载
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar
 # 解压模型
-tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
+tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
 # 转换为Paddle-Lite模型
-paddle_lite_opt --model_file=general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdmodel --param_file=general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdiparams --optimize_out=general_PPLCNet_x2_5_lite_v1.0_infer/rec
+paddle_lite_opt --model_file=general_PPLCNetV2_base_pretrained_v1.0_infer/inference.pdmodel --param_file=general_PPLCNetV2_base_pretrained_v1.0_infer/inference.pdiparams --optimize_out=general_PPLCNetV2_base_pretrained_v1.0_infer/rec
 # 将模型文件拷贝到lite_shitu下
-cp general_PPLCNet_x2_5_lite_v1.0_infer/rec.nb deploy/lite_shitu/models/
+cp general_PPLCNetV2_base_pretrained_v1.0_infer/rec.nb deploy/lite_shitu/models/
 ```
 
 **注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
@@ -242,17 +248,12 @@ cd $PaddleClas
 python setup.py install
 cd deploy
 # 下载瓶装饮料数据集
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar && tar -xf drink_dataset_v1.0.tar
-rm -rf drink_dataset_v1.0.tar
-rm -rf drink_dataset_v1.0/index
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
+rm -rf drink_dataset_v2.0.tar
+rm -rf drink_dataset_v2.0/index
 
 # 安装1.5.3版本的faiss
 pip install faiss-cpu==1.5.3
-
-# 下载通用识别模型，可替换成自己的inference model
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
-tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
-rm -rf general_PPLCNet_x2_5_lite_v1.0_infer.tar
 ```
 
 <a name="2.2.2"></a>
@@ -262,11 +263,11 @@ rm -rf general_PPLCNet_x2_5_lite_v1.0_infer.tar
 ```shell
 # 生成新的index库，注意指定好识别模型的路径，同时将index_mothod修改成Flat，HNSW32和IVF在此版本中可能存在bug，请慎重使用。
 # 如果使用自己的识别模型，对应的修改inference model的目录
-python python/build_gallery.py -c configs/inference_drink.yaml -o Global.rec_inference_model_dir=general_PPLCNet_x2_5_lite_v1.0_infer -o IndexProcess.index_method=Flat
+python python/build_gallery.py -c configs/inference_drink.yaml -o Global.rec_inference_model_dir=lite_shitu/ppshituv2_lite_models_v1.0/general_PPLCNetV2_base_quant_v1.0_lite_inference_model -o IndexProcess.index_method=Flat
 
 # 进入到lite_shitu目录
 cd lite_shitu
-mv ../drink_dataset_v1.0 .
+mv ../drink_dataset_v2.0 .
 ```
 
 <a name="2.3"></a>
@@ -274,14 +275,17 @@ mv ../drink_dataset_v1.0 .
 ### 2.3 将yaml文件转换成json文件
 
 ```shell
+# 如果是使用自己生成的.nb模型，请修改好对应的det_model_path和rec_model_path的路径
 # 如果测试单张图像，路径使用相对路径
-python generate_json_config.py --det_model_path ppshitu_lite_models_v1.2/mainbody_PPLCNet_x2_5_640_v1.2_lite.nb  --rec_model_path ppshitu_lite_models_v1.2/general_PPLCNet_x2_5_lite_v1.2_infer.nb --img_path images/demo.jpeg
+python generate_json_config.py --det_model_path ppshituv2_lite_models_v1.0/mainbody_PPLCNet_x2_5_640_quant_v1.0_lite.nb  --rec_model_path ppshituv2_lite_models_v1.0/general_PPLCNetV2_base_quant_v1.0_lite.nb --img_path images/demo.jpeg
 # or
 # 如果测试多张图像
-python generate_json_config.py --det_model_path ppshitu_lite_models_v1.2/mainbody_PPLCNet_x2_5_640_v1.2_lite.nb  --rec_model_path ppshitu_lite_models_v1.2/general_PPLCNet_x2_5_lite_v1.2_infer.nb --img_dir images
+python generate_json_config.py --det_model_path ppshituv2_lite_models_v1.0/mainbody_PPLCNet_x2_5_640_quant_v1.0_lite.nb  --rec_model_path ppshituv2_lite_models_v1.0/general_PPLCNetV2_base_quant_v1.0_lite.nb --img_dir images
 # 执行完成后，会在lit_shitu下生成shitu_config.json配置文件
 ```
 
+**注意**：生成json文件的时候，请注意修改好模型路径，特别是使用自己生成的`.nb`模型时
+
 <a name="2.4"></a>
 
 ### 2.4 index字典转换
@@ -289,7 +293,6 @@ python generate_json_config.py --det_model_path ppshitu_lite_models_v1.2/mainbod
 由于python的检索库字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此需要进行转换
 
 ```shell
-
 # 转化id_map.pkl为id_map.txt
 python transform_id_map.py -c ../configs/inference_drink.yaml
 ```
@@ -349,8 +352,8 @@ make ARM_ABI=arm8
 ```shell
 mkdir deploy
 # 移动的模型路径要和之前生成的json文件中模型路径一致
-mv ppshitu_lite_models_v1.2 deploy/
-mv drink_dataset_v1.0 deploy/
+mv ppshituv2_lite_models_v1.0 deploy/
+mv drink_dataset_v2.0 deploy/
 mv images deploy/
 mv shitu_config.json deploy/
 cp pp_shitu deploy/
@@ -363,12 +366,13 @@ cp ../../../cxx/lib/libpaddle_light_api_shared.so deploy/
 
 ```shell
 deploy/
-|-- ppshitu_lite_models_v1.1/
-|   |--mainbody_PPLCNet_x2_5_640_quant_v1.1_lite.nb    优化后的主体检测模型文件
-|   |--general_PPLCNet_x2_5_lite_v1.1_infer.nb         优化后的识别模型文件
+|-- ppshituv2_lite_models_v1.0/
+|   |--mainbody_PPLCNet_x2_5_640_quant_v1.0_lite.nb    主体检测lite模型文件
+|   |--general_PPLCNetV2_base_quant_v1.0_lite.nb       识别lite模型文件
+|   |--general_PPLCNetV2_base_quant_v1.0_lite_inference_model 识别对应的inference model文件夹
 |-- images/
 |   |--demo.jpg                                      图片文件
-|-- drink_dataset_v1.0/                              瓶装饮料demo数据
+|-- drink_dataset_v2.0/                              瓶装饮料demo数据
 |   |--index                                         检索index目录
 |-- pp_shitu                                         生成的移动端执行文件
 |-- shitu_config.json                                执行时参数配置文件
@@ -399,8 +403,7 @@ chmod 777 pp_shitu
 运行效果如下：
 ```
 images/demo.jpeg:
-       result0: bbox[344, 98, 527, 593], score: 0.811656, label: 红牛-强化型
-       result1: bbox[0, 0, 600, 600], score: 0.729664, label: 红牛-强化型
+       result1: bbox[0, 0, 600, 600], score: 0.696782, label: 红牛-强化型
 ```
 
 <a name="FAQ"></a>