@@ -31,7 +31,7 @@ For the detailed compilation directions of different development environments, p
...
@@ -31,7 +31,7 @@ For the detailed compilation directions of different development environments, p
**NOTE**:
**NOTE**:
1. If you download the inference library from [Paddle-Lite official document](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc), please choose `with_extra=ON` , `with_cv=ON` .
1. If you download the inference library from [Paddle-Lite official document](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc), please choose `with_extra=ON` , `with_cv=ON` .
2. It is recommended to build inference library using [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) develop branch if you want to deploy the [quantitative](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md) model to mobile phones. Please refer to the [link](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2) for more detailed information about compiling.
2. It is recommended to build inference library using [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) develop branch if you want to deploy the [quantitative](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md) model to mobile phones. Please refer to the [link](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2) for more detailed information about compiling.
* model_name(str): model's name. If not assigning `model_file`and`params_file`, you can assign this param. If using inference model based on ImageNet1k provided by Paddle, set as default='ResNet50'.
* model_name(str): model's name. If not assigning `model_file`and`params_file`, you can assign this param. If using inference model based on ImageNet1k provided by Paddle, set as default='ResNet50'.
* image_file(str): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray.
* image_file(str or numpy.ndarray): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray, the channel order is [B, G, R].
* use_gpu(bool): Whether to use GPU or not, defalut=False。
* use_gpu(bool): Whether to use GPU or not, defalut=False。
* use_tensorrt(bool): whether to open tensorrt or not. Using it can greatly promote predict preformance, default=False.
* use_tensorrt(bool): whether to open tensorrt or not. Using it can greatly promote predict preformance, default=False.
* is_preprocessed(bool): Assign the image data has been preprocessed or not when the image_file is numpy.ndarray.
* resize_short(int): resize the minima between height and width into resize_short(int), default=256
* resize_short(int): resize the minima between height and width into resize_short(int), default=256
* resize(int): resize image into resize(int), default=224.
* resize(int): resize image into resize(int), default=224.
* normalize(bool): whether normalize image or not, default=True.
* normalize(bool): whether normalize image or not, default=True.
...
@@ -84,80 +87,75 @@ paddleclas -h
...
@@ -84,80 +87,75 @@ paddleclas -h
###### python
###### python
```python
```python
frompaddleclasimportPaddleClas
frompaddleclasimportPaddleClas
clas=PaddleClas(model_file='user-specified model path',
clas=PaddleClas(model_file='the path of model file',
paddleclas --model_file='user-specified model path'--params_file='parmas path'--image_file='image path'
paddleclas --model_file='user-specified model path'--params_file='parmas path'--image_file='docs/images/whl/demo.jpg'
```
```
* Use inference model which PaddlePaddle provides to predict, you need to choose one of model when initializing PaddleClas to assign `model_name`. You may not assign `model_file` , and the model you chosen will be download in `BASE_INFERENCE_MODEL_DIR` ,which will be saved in folder named by `model_name`,avoiding overlay different inference model.
* Use inference model which PaddlePaddle provides to predict, you need to choose one of model proviede by PaddleClas to assign `model_name`. So there's no need to assign `model_file`. And the model you chosen will be download in `~/.paddleclas/`, which will be saved in folder named by `model_name`.
* You can assign input as format`np.ndarray` which has been preprocessed `--image_file=np.ndarray`.
* You can assign input as format`numpy.ndarray` which has been preprocessed `image_file=np.ndarray`. Note that the image data must be three channel. If need To preprocess the image, the image channels order must be [B, G, R].
* You can assign `--pre_label_image=True`, `--pre_label_out_idr= './output_pre_label/'`.Then images will be copied into folder named by top-1 class_id.
* You can assign `--pre_label_image=True`, `--pre_label_out_idr= './output_pre_label/'`.Then images will be copied into folder named by top-1 class_id.
* If you use inference model that Paddle provides, you do not need assign `label_name_path`. Program will take `ppcls/utils/imagenet1k_label_list.txt` as defaults. If you hope using your own training model, you can provide `label_name_path` outputing 'label_name' and scores, otherwise no 'label_name' in output information.
* If you use inference model that PaddleClas provides, you do not need assign `label_name_path`. Program will take `ppcls/utils/imagenet1k_label_list.txt` as defaults. If you hope using your own training model, you can provide `label_name_path` outputing 'label_name' and scores, otherwise no 'label_name' in output information.
paddleclas --model_file='the path of model file'--params_file='the path of params file'--image_file='docs/images/whl/demo.jpg'--label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
paddleclas --model_file='the path of model file'--params_file='the path of params file'--image_file='docs/images/whl/demo.jpg'--label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
img: input image for clas, support single image , internet url, folder path containing series of images
input_data(string, NumPy.ndarray): image to be classified, support:
string: local path of image file, internet URL, directory containing series of images;
NumPy.ndarray: preprocessed image data that has 3 channels and accords with [C, H, W], or raw image data that has 3 channels and accords with [H, W, C]
Returns:
Returns:
dict：{image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty.
dict: {image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty.
