cp update shitu whl
zengshao0622
zengshao0622
parent
8eba0f0b24
commit
10376757ce
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# PP-ShiTu Whl 使用说明
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PaddleClas 支持 Python Whl 包方式进行预测。
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---
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## 目录
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<a name="1"></a>
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## 1. 安装 paddleclas
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* **[推荐]** 直接 pip 安装：
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```bash
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pip3 install paddleclas
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```
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* 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：
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```bash
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python3 setup.py install
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```
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<a name="2"></a>
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## 2. 快速开始
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<a name="2.1"></a>
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### 2.1 构建索引库
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下载demo数据集，命令如下：
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```shell
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# 下载 demo 数据并解压
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
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```
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解压完毕后，`drink_dataset_v2.0/` 文件夹下应有如下文件结构：
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```log
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├── drink_dataset_v2.0/
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│ ├── gallery/
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│ ├── index/
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│ ├── index_all/
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│ └── test_images/
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├── ...
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```
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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。
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**在Python代码中构建索引库**
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```python
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from paddleclas import PaddleClas
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build = PaddleClas(
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build_gallery=True,
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gallery_image_root='./drink_dataset_v2.0/gallery/',
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gallery_data_file='./drink_dataset_v2.0/gallery/drink_label.txt',
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index_dir='./drink_dataset_v2.0/index')
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```
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参数说明：
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- build_gallery：是否使用索引库构建模式，默认为`False`。
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- gallery_image_root：构建索引库使用的`gallery`图像地址。
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- gallery_data_file：构建索引库图像的真值文件。
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- index_dir：索引库存放地址。
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**在命令行中构建索引库**
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```shell
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paddleclas --build_gallery=True --model_name="PP-ShiTuV2" \
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-o IndexProcess.image_root=./drink_dataset_v2.0/gallery/ \
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-o IndexProcess.index_dir=./drink_dataset_v2.0/index \
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-o IndexProcess.data_file=./drink_dataset_v2.0/gallery/drink_label.txt
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```
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其中参数`build_gallery(bool)`控制是否使用索引库构建模式，默认为`False`。
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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：
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- IndexProcess.image_root(str): 构建索引库使用的`gallery`图像地址。
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- IndexProcess.index_dir(str): 索引库存放地址。
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- IndexProcess.data_file(str): 构建索引库图像的真值文件。
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<a name="2.2"></a>
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### 2.2 瓶装饮料识别
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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。
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待检索图像如下：
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**在Python代码中进行识别和检索**
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='PP-ShiTuV2',
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index_dir='./drink_dataset_v2.0/index')
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infer_imgs='./drink_dataset_v2.0/test_images/001.jpeg'
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result=clas.predict(infer_imgs, predict_type='shitu')
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print(next(result))
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```
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参数说明：
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- model_name(str):用于检索和识别的模型。
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- index_dir(str):用于检索的索引库地址。
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最终输出结果如下：
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```
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[{'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}]
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```
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**在命令行中进行识别和检索**
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```shell
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paddleclas --model_name=PP-ShiTuV2 --predict_type=shitu \
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-o Global.infer_imgs='./drink_dataset_v2.0/test_images/001.jpeg' \
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-o IndexProcess.index_dir='./drink_dataset_v2.0/index'
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```
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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。
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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：
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- Global.infer_imgs(str)：待检索图像地址。
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- IndexProcess.index_dir(str): 索引库存放地址。
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最终输出结果如下：
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```
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[{'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
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```
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111
paddleclas.py
111
paddleclas.py
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@ -33,6 +33,7 @@ from .ppcls.utils import logger
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from .deploy.python.predict_cls import ClsPredictor
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from .deploy.python.predict_system import SystemPredictor
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from .deploy.python.build_gallery import GalleryBuilder
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from .deploy.utils.get_image_list import get_image_list
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from .deploy.utils import config
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@ -196,7 +197,8 @@ PULC_MODEL_BASE_DOWNLOAD_URL = "https://paddleclas.bj.bcebos.com/models/PULC/inf
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PULC_MODELS = [
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"car_exists", "language_classification", "person_attribute",
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"person_exists", "safety_helmet", "text_image_orientation",
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"textline_orientation", "traffic_sign", "vehicle_attribute"
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"textline_orientation", "traffic_sign", "vehicle_attribute",
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"table_attribute"
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]
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SHITU_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/{}_infer.tar"
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@ -226,7 +228,9 @@ class InputModelError(Exception):
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def init_config(model_type, model_name, inference_model_dir, **kwargs):
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if model_type == "pulc":
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if kwargs.get("build_gallery", False):
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cfg_path = "deploy/configs/inference_general.yaml"
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elif model_type == "pulc":
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cfg_path = f"deploy/configs/PULC/{model_name}/inference_{model_name}.yaml"
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elif model_type == "shitu":
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cfg_path = "deploy/configs/inference_general.yaml"
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@ -235,7 +239,8 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
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__dir__ = os.path.dirname(__file__)
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cfg_path = os.path.join(__dir__, cfg_path)
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cfg = config.get_config(cfg_path, show=False)
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cfg = config.get_config(
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cfg_path, overrides=kwargs.get("override", None), show=False)
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if cfg.Global.get("inference_model_dir"):
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cfg.Global.inference_model_dir = inference_model_dir
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else:
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@ -282,6 +287,7 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
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if "thresh" in kwargs and kwargs[
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"thresh"] and "ThreshOutput" in cfg.PostProcess:
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cfg.PostProcess.ThreshOutput.thresh = kwargs["thresh"]
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if cfg.get("PostProcess"):
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if "Topk" in cfg.PostProcess:
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if "topk" in kwargs and kwargs["topk"]:
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@ -301,7 +307,26 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
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if "type_threshold" in kwargs and kwargs["type_threshold"]:
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cfg.PostProcess.VehicleAttribute.type_threshold = kwargs[
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"type_threshold"]
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if "TableAttribute" in cfg.PostProcess:
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if "source_threshold" in kwargs and kwargs["source_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"source_threshold"]
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if "number_threshold" in kwargs and kwargs["number_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"number_threshold"]
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if "color_threshold" in kwargs and kwargs["color_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"color_threshold"]
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if "clarity_threshold" in kwargs and kwargs["clarity_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"clarity_threshold"]
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if "obstruction_threshold" in kwargs and kwargs[
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"obstruction_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"obstruction_threshold"]
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if "angle_threshold" in kwargs and kwargs["angle_threshold"]:
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cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
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"angle_threshold"]
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if "save_dir" in kwargs and kwargs["save_dir"]:
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cfg.PostProcess.SavePreLabel.save_dir = kwargs["save_dir"]
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@ -316,10 +341,15 @@ def args_cfg():
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parser.add_argument(
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"--infer_imgs",
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type=str,
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required=True,
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required=False,
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help="The image(s) to be predicted.")
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parser.add_argument(
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"--model_name", type=str, help="The model name to be used.")
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parser.add_argument(
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"--predict_type",
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type=str,
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default="cls",
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help="The predict type to be selected.")
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parser.add_argument(
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"--inference_model_dir",
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type=str,
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@ -374,7 +404,17 @@ def args_cfg():
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parser.add_argument(
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"--resize_short", type=int, help="Resize according to short size.")
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parser.add_argument("--crop_size", type=int, help="Centor crop size.")
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parser.add_argument(
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"--build_gallery",
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type=str2bool,
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default=False,
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help="Whether build gallery.")
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parser.add_argument(
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'-o',
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'--override',
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action='append',
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default=[],
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help='config options to be overridden')
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args = parser.parse_args()
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return vars(args)
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@ -514,6 +554,10 @@ class PaddleClas(object):
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"""
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def __init__(self,
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build_gallery: bool=False,
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gallery_image_root: str=None,
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gallery_data_file: str=None,
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index_dir: str=None,
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model_name: str=None,
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inference_model_dir: str=None,
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**kwargs):
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@ -528,14 +572,35 @@ class PaddleClas(object):
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"""
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super().__init__()
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self.model_type, inference_model_dir = self._check_input_model(
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model_name, inference_model_dir)
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self._config = init_config(self.model_type, model_name,
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inference_model_dir, **kwargs)
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if self.model_type == "shitu":
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self.predictor = SystemPredictor(self._config)
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if build_gallery:
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self.model_type, inference_model_dir = self._check_input_model(
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model_name
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if model_name else "PP-ShiTuV2", inference_model_dir)
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self._config = init_config(self.model_type, model_name
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if model_name else "PP-ShiTuV2",
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inference_model_dir, **kwargs)
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if gallery_image_root:
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self._config.IndexProcess.image_root = gallery_image_root
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if gallery_data_file:
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self._config.IndexProcess.data_file = gallery_data_file
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if index_dir:
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self._config.IndexProcess.index_dir = index_dir
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logger.info("Building Gallery...")
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GalleryBuilder(self._config)
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else:
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self.predictor = ClsPredictor(self._config)
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self.model_type, inference_model_dir = self._check_input_model(
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model_name, inference_model_dir)
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self._config = init_config(self.model_type, model_name,
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inference_model_dir, **kwargs)
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if self.model_type == "shitu":
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if index_dir:
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self._config.IndexProcess.index_dir = index_dir
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self.predictor = SystemPredictor(self._config)
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else:
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self.predictor = ClsPredictor(self._config)
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def get_config(self):
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"""Get the config.
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@ -679,6 +744,9 @@ class PaddleClas(object):
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prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names".
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The format of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
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"""
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if input_data == None and self._config.Global.infer_imgs:
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input_data = self._config.Global.infer_imgs
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if isinstance(input_data, np.ndarray):
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yield self.predictor.predict(input_data)
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elif isinstance(input_data, str):
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@ -721,6 +789,8 @@ class PaddleClas(object):
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input_data: Union[str, np.array],
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print_pred: bool=False,
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predict_type="cls"):
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assert predict_type in ["cls", "shitu"
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], "Predict type should be 'cls' or 'shitu'."
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if predict_type == "cls":
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return self.predict_cls(input_data, print_pred)
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elif predict_type == "shitu":
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@ -739,13 +809,14 @@ def main():
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print_info()
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cfg = args_cfg()
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clas_engine = PaddleClas(**cfg)
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res = clas_engine.predict(
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cfg["infer_imgs"],
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print_pred=True,
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predict_type="cls" if "PP-ShiTu" not in cfg["model_name"] else "shitu")
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for _ in res:
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pass
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logger.info("Predict complete!")
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if cfg["build_gallery"] == False:
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res = clas_engine.predict(
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cfg["infer_imgs"],
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print_pred=True,
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predict_type=cfg["predict_type"])
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for _ in res:
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pass
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logger.info("Predict complete!")
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return
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