cp update shitu whl

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

+# PP-ShiTu Whl 使用说明
+
+PaddleClas 支持 Python Whl 包方式进行预测。
+
+---
+
+## 目录
+
+
+<a name="1"></a>
+
+## 1. 安装 paddleclas
+
+* **[推荐]** 直接 pip 安装：
+
+```bash
+pip3 install paddleclas
+```
+
+* 如需使用 PaddleClas develop 分支体验最新功能，或是需要基于 PaddleClas 进行二次开发，请本地构建安装：
+
+```bash
+python3 setup.py install
+```
+
+<a name="2"></a>
+
+## 2. 快速开始
+
+<a name="2.1"></a>
+
+### 2.1 构建索引库
+
+下载demo数据集，命令如下：
+```shell
+# 下载 demo 数据并解压
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
+```
+
+解压完毕后，`drink_dataset_v2.0/` 文件夹下应有如下文件结构：
+
+```log
+├── drink_dataset_v2.0/
+│   ├── gallery/
+│   ├── index/
+│   ├── index_all/
+│   └── test_images/
+├── ...
+```
+
+其中 `gallery` 文件夹中存放的是用于构建索引库的原始图像，`index` 表示基于原始图像构建得到的索引库信息，`test_images` 文件夹中存放的是用于测试识别效果的图像列表。
+
+
+
+
+**在Python代码中构建索引库**
+```python
+from paddleclas import PaddleClas
+build = PaddleClas(
+    build_gallery=True,
+    gallery_image_root='./drink_dataset_v2.0/gallery/',
+    gallery_data_file='./drink_dataset_v2.0/gallery/drink_label.txt',
+    index_dir='./drink_dataset_v2.0/index')
+```
+参数说明：
+- build_gallery：是否使用索引库构建模式，默认为`False`。
+- gallery_image_root：构建索引库使用的`gallery`图像地址。
+- gallery_data_file：构建索引库图像的真值文件。
+- index_dir：索引库存放地址。
+
+
+**在命令行中构建索引库**
+```shell
+paddleclas --build_gallery=True --model_name="PP-ShiTuV2" \
+-o IndexProcess.image_root=./drink_dataset_v2.0/gallery/ \
+-o IndexProcess.index_dir=./drink_dataset_v2.0/index \
+-o IndexProcess.data_file=./drink_dataset_v2.0/gallery/drink_label.txt
+```
+其中参数`build_gallery(bool)`控制是否使用索引库构建模式，默认为`False`。
+
+同时可以通过`-o`指令更改构建索引库使用的配置，字段说明如下：
+
+- IndexProcess.image_root(str): 构建索引库使用的`gallery`图像地址。
+- IndexProcess.index_dir(str): 索引库存放地址。
+- IndexProcess.data_file(str): 构建索引库图像的真值文件。
+
+
+<a name="2.2"></a>
+
+### 2.2 瓶装饮料识别
+
+体验瓶装饮料识别，对图像`./drink_dataset_v2.0/test_images/001.jpeg`进行识别与检索。
+
+待检索图像如下：
+![](../../../images/recognition/drink_data_demo/test_images/100.jpeg)
+
+**在Python代码中进行识别和检索**
+```python
+from paddleclas import PaddleClas
+clas = PaddleClas(model_name='PP-ShiTuV2',
+                  index_dir='./drink_dataset_v2.0/index')
+infer_imgs='./drink_dataset_v2.0/test_images/001.jpeg'
+result=clas.predict(infer_imgs, predict_type='shitu')
+print(next(result))
+```
+参数说明：
+- model_name(str):用于检索和识别的模型。
+- index_dir(str):用于检索的索引库地址。
+
+最终输出结果如下：
+```
+[{'bbox': [437, 71, 660, 728], 'rec_docs': '元气森林', 'rec_scores': 0.7740249}, {'bbox': [221, 72, 449, 701], 'rec_docs': '元气森林', 'rec_scores': 0.6950992}, {'bbox': [794, 104, 979, 652], 'rec_docs': '元气森林', 'rec_scores': 0.6305153}]
+```
+
+**在命令行中进行识别和检索**
+```shell
+paddleclas --model_name=PP-ShiTuV2 --predict_type=shitu \
+-o Global.infer_imgs='./drink_dataset_v2.0/test_images/001.jpeg' \
+-o IndexProcess.index_dir='./drink_dataset_v2.0/index'
+```
+其中参数`model_name`为用于检索和识别的模型、`predict_type`设置为'shitu'模式。
+
+同时可以通过`-o`指令更改检索图像以及索引库，字段说明如下：
+- Global.infer_imgs(str)：待检索图像地址。
+- IndexProcess.index_dir(str): 索引库存放地址。
+
+最终输出结果如下：
+```
+[{'bbox': [437, 71, 660, 728], 'rec_docs': '元气森林', 'rec_scores': 0.7740249}, {'bbox': [221, 72, 449, 701], 'rec_docs': '元气森林', 'rec_scores': 0.6950992}, {'bbox': [794, 104, 979, 652], 'rec_docs': '元气森林', 'rec_scores': 0.6305153}], filename: ./drink_dataset_v2.0/test_images/100.jpeg
+```

paddleclas.py

@@ -33,6 +33,7 @@ from .ppcls.utils import logger
 
 from .deploy.python.predict_cls import ClsPredictor
 from .deploy.python.predict_system import SystemPredictor
+from .deploy.python.build_gallery import GalleryBuilder
 from .deploy.utils.get_image_list import get_image_list
 from .deploy.utils import config
 
@@ -196,7 +197,8 @@ PULC_MODEL_BASE_DOWNLOAD_URL = "https://paddleclas.bj.bcebos.com/models/PULC/inf
 PULC_MODELS = [
     "car_exists", "language_classification", "person_attribute",
     "person_exists", "safety_helmet", "text_image_orientation",
-    "textline_orientation", "traffic_sign", "vehicle_attribute"
+    "textline_orientation", "traffic_sign", "vehicle_attribute",
+    "table_attribute"
 ]
 
 SHITU_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/{}_infer.tar"
@@ -226,7 +228,9 @@ class InputModelError(Exception):
 
 def init_config(model_type, model_name, inference_model_dir, **kwargs):
 
-    if model_type == "pulc":
+    if kwargs.get("build_gallery", False):
+        cfg_path = "deploy/configs/inference_general.yaml"
+    elif model_type == "pulc":
         cfg_path = f"deploy/configs/PULC/{model_name}/inference_{model_name}.yaml"
     elif model_type == "shitu":
         cfg_path = "deploy/configs/inference_general.yaml"
@@ -235,7 +239,8 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
 
     __dir__ = os.path.dirname(__file__)
     cfg_path = os.path.join(__dir__, cfg_path)
-    cfg = config.get_config(cfg_path, show=False)
+    cfg = config.get_config(
+        cfg_path, overrides=kwargs.get("override", None), show=False)
     if cfg.Global.get("inference_model_dir"):
         cfg.Global.inference_model_dir = inference_model_dir
     else:
@@ -282,6 +287,7 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
     if "thresh" in kwargs and kwargs[
             "thresh"] and "ThreshOutput" in cfg.PostProcess:
         cfg.PostProcess.ThreshOutput.thresh = kwargs["thresh"]
+
     if cfg.get("PostProcess"):
         if "Topk" in cfg.PostProcess:
             if "topk" in kwargs and kwargs["topk"]:
@@ -301,7 +307,26 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
             if "type_threshold" in kwargs and kwargs["type_threshold"]:
                 cfg.PostProcess.VehicleAttribute.type_threshold = kwargs[
                     "type_threshold"]
-
+        if "TableAttribute" in cfg.PostProcess:
+            if "source_threshold" in kwargs and kwargs["source_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "source_threshold"]
+            if "number_threshold" in kwargs and kwargs["number_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "number_threshold"]
+            if "color_threshold" in kwargs and kwargs["color_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "color_threshold"]
+            if "clarity_threshold" in kwargs and kwargs["clarity_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "clarity_threshold"]
+            if "obstruction_threshold" in kwargs and kwargs[
+                    "obstruction_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "obstruction_threshold"]
+            if "angle_threshold" in kwargs and kwargs["angle_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "angle_threshold"]
     if "save_dir" in kwargs and kwargs["save_dir"]:
         cfg.PostProcess.SavePreLabel.save_dir = kwargs["save_dir"]
 
@@ -316,10 +341,15 @@ def args_cfg():
     parser.add_argument(
         "--infer_imgs",
         type=str,
-        required=True,
+        required=False,
         help="The image(s) to be predicted.")
     parser.add_argument(
         "--model_name", type=str, help="The model name to be used.")
+    parser.add_argument(
+        "--predict_type",
+        type=str,
+        default="cls",
+        help="The predict type to be selected.")
     parser.add_argument(
         "--inference_model_dir",
         type=str,
@@ -374,7 +404,17 @@ def args_cfg():
     parser.add_argument(
         "--resize_short", type=int, help="Resize according to short size.")
     parser.add_argument("--crop_size", type=int, help="Centor crop size.")
-
+    parser.add_argument(
+        "--build_gallery",
+        type=str2bool,
+        default=False,
+        help="Whether build gallery.")
+    parser.add_argument(
+        '-o',
+        '--override',
+        action='append',
+        default=[],
+        help='config options to be overridden')
     args = parser.parse_args()
     return vars(args)
 
@@ -514,6 +554,10 @@ class PaddleClas(object):
     """
 
     def __init__(self,
+                 build_gallery: bool=False,
+                 gallery_image_root: str=None,
+                 gallery_data_file: str=None,
+                 index_dir: str=None,
                  model_name: str=None,
                  inference_model_dir: str=None,
                  **kwargs):
@@ -528,14 +572,35 @@ class PaddleClas(object):
         """
         super().__init__()
 
-        self.model_type, inference_model_dir = self._check_input_model(
-            model_name, inference_model_dir)
-        self._config = init_config(self.model_type, model_name,
-                                   inference_model_dir, **kwargs)
-        if self.model_type == "shitu":
-            self.predictor = SystemPredictor(self._config)
+        if build_gallery:
+            self.model_type, inference_model_dir = self._check_input_model(
+                model_name
+                if model_name else "PP-ShiTuV2", inference_model_dir)
+            self._config = init_config(self.model_type, model_name
+                                       if model_name else "PP-ShiTuV2",
+                                       inference_model_dir, **kwargs)
+            if gallery_image_root:
+                self._config.IndexProcess.image_root = gallery_image_root
+            if gallery_data_file:
+                self._config.IndexProcess.data_file = gallery_data_file
+            if index_dir:
+                self._config.IndexProcess.index_dir = index_dir
+
+            logger.info("Building Gallery...")
+            GalleryBuilder(self._config)
+
         else:
-            self.predictor = ClsPredictor(self._config)
+            self.model_type, inference_model_dir = self._check_input_model(
+                model_name, inference_model_dir)
+            self._config = init_config(self.model_type, model_name,
+                                       inference_model_dir, **kwargs)
+
+            if self.model_type == "shitu":
+                if index_dir:
+                    self._config.IndexProcess.index_dir = index_dir
+                self.predictor = SystemPredictor(self._config)
+            else:
+                self.predictor = ClsPredictor(self._config)
 
     def get_config(self):
         """Get the config.
@@ -679,6 +744,9 @@ class PaddleClas(object):
                 prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names".
                 The format of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
         """
+        if input_data == None and self._config.Global.infer_imgs:
+            input_data = self._config.Global.infer_imgs
+
         if isinstance(input_data, np.ndarray):
             yield self.predictor.predict(input_data)
         elif isinstance(input_data, str):
@@ -721,6 +789,8 @@ class PaddleClas(object):
                 input_data: Union[str, np.array],
                 print_pred: bool=False,
                 predict_type="cls"):
+        assert predict_type in ["cls", "shitu"
+                                ], "Predict type should be 'cls' or 'shitu'."
         if predict_type == "cls":
             return self.predict_cls(input_data, print_pred)
         elif predict_type == "shitu":
@@ -739,13 +809,14 @@ def main():
     print_info()
     cfg = args_cfg()
     clas_engine = PaddleClas(**cfg)
-    res = clas_engine.predict(
-        cfg["infer_imgs"],
-        print_pred=True,
-        predict_type="cls" if "PP-ShiTu" not in cfg["model_name"] else "shitu")
-    for _ in res:
-        pass
-    logger.info("Predict complete!")
+    if cfg["build_gallery"] == False:
+        res = clas_engine.predict(
+            cfg["infer_imgs"],
+            print_pred=True,
+            predict_type=cfg["predict_type"])
+        for _ in res:
+            pass
+        logger.info("Predict complete!")
     return