update the readme.

Signed-off-by: wxywb <xy.wang@zilliz.com>

README.md

@@ -1,47 +1,58 @@
-# Retinaface Face Detection (Pytorch)
+# RetinaFace Face Detection 
 
 *Authors: David Wang*
 
+
+
 ## Desription
 
-This opertator detects faces in the images by using RetinaFace Detector[1]. It will returns the bounding box positions and the confidence scores of detected faces. This repo is a adopataion from [2].
+This operator detects faces in the images by using RetinaFace Detector[1]. It will return the bounding box positions and the confidence scores of detected faces. This repo is a adapataion from [2].
 
+## Code Example
+
+Load an image from path './dog.jpg'
+and use the pretrained RetinaFace to generate face bounding boxes. 
+
+ *Write the pipeline in simplified style*:
 
 ```python
 from towhee import ops
 
-model = ops.face_detection.retinaface()
-embedding = model(img)
+bboxes = dc.glob('nmb46.jpg') \
+  .image_decode.cv2() \
+  .face_detection.retinaface() \
+  .to_list() 
 ```
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import dc
 
+dc.glob['path']('./dog.jpg') \
+  .image_decode.cv2['path', 'img']() \
+  .image_embedding.timm['img', 'vec'](model_name='resnet50') \
+  .face_detection.retinaface() \
+  .to_list()
+```
 ## Factory Constructor
 
-Create the operator via the following factory method
+Create the operator via the following factory method.
 
 ***ops.face_detection.retinaface()***
 
-
 ## Interface
 
-A face detection operator takes an image as input. it generates the bounding box position and confidence score back to ndarray.
-
-**Args:**
-
-​	***framework***
-
-​  the framework of the model
-
-​  supported types: `str`, default is 'pytorch'
+A face detection operator takes an image as input. it generates the bounding box positions and confidence scores back to ndarray.
 
 **Parameters:**
 
-​	***image***
+​	***image***: *numpy.ndarray.*
 
 ​	the image to detect faces.
 
 ​	supported types: numpy.ndarray
 
-**Returns:**: 
+**Returns:**
 
 *numpy.ndarray*
 
@@ -51,33 +62,6 @@ A face detection operator takes an image as input. it generates the bounding box
 
 ​	The detected face bounding boxes confident scores.
 
-## Code Example
-
-get detected face bounding boxes from './img1.jpg'. 
-
- *Write the pipeline in simplified style*:
-
-```python
-import towhee.DataCollection as dc
-
-dc.glob('./img1.jpg')
-  .image_decode.cv2()
-  .face_detection.retinaface()
-  .to_list()
-```
-
-*Write a same pipeline with explicit inputs/outputs name specifications:*
-
-```python
-import towhee.DataCollection as dc
-
-dc.glob['path']('./img1.jpg')
-  .image_decode.cv2['path', 'img']()
-  .face_detection.retinaface()
-  .to_list()
-```
-
-
 ## Reference
 
 [1]. https://arxiv.org/abs/1905.00641  

__init__.py

@@ -14,6 +14,6 @@
 
 from .retinaface import Retinaface
 
-def retinaface():
-    return Retinaface()
+def retinaface(framework: str = 'pytorch'):
+    return Retinaface(framework)
 

retinaface.py

@@ -25,25 +25,23 @@ import numpy
 from towhee import register
 from towhee.operator import Operator
 from towhee.types.image_utils import to_pil
-from towhee._types import Image
-from towhee.types import arg, to_image_color
+from towhee._types import Image 
+from towhee.types.arg import arg, to_image_color
 
-from timm.data import resolve_data_config
-from timm.data.transforms_factory import create_transform
 
-@register(output_schema=['bboxes', 'scores'])
+@register(output_schema=['bbox', 'score'])
 class Retinaface(Operator):
     """
     Retinaface
     """
-    def __init__(self, framework: str = 'pytorch') -> None:
+    def __init__(self) -> None:
         super().__init__()
         sys.path.append(str(Path(__file__).parent))
         from retinaface_impl import Model
         self.model = Model()
 
     @arg(1, to_image_color('RGB') )
-    def __call__(self, image: 'towhee._types.Image'):
+    def __call__(self, image: Image):
         img = torch.FloatTensor(numpy.asarray(to_pil(image)))
         bboxes, keypoints = self.model(img)
         bboxes = bboxes.cpu().detach().numpy()