Update

Signed-off-by: shiyu22 <shiyu.chen@zilliz.com>

README.md

@@ -23,12 +23,12 @@ __init__(self, size: int)
   - supported types: `int`
 
 ```python
-int__call__(self, img: Union[Image.Image, torch.Tensor, str])
+int__call__(self, img_path: Union[Image.Image, torch.Tensor, str])
 ```
 
 **Args:**
 
-- img:
+- img_path:
   - the input image to be processed
   - supported type:  `PIL.image`, `torch.Tensor` or `str` (path of the image)
 
@@ -46,8 +46,6 @@ You can get the required python package by [requirements.txt](https://zilliverse
 
 ## **How it works**
 
-The `towhee/transform-image-operator-template` Operator is used for image transformation and is an important part of data preprocessing. It can be added to the pipeline and is usually used as the first custom operator of the pipeline. For example, it's the first Operator named processing within [image-embedding-pipeline-template](https://hub.towhee.io/towhee/image-embedding-pipeline-template) pipeline, and it is the red box in the picture below.
-
-![img](./readme_res/operator.png)
+The `towhee/transform-image-operator-template` Operator is used for image transformation and is an important part of data preprocessing. It can be added to the pipeline and is usually used as the first custom operator of the pipeline. For example, it's the first Operator named processing within [image-embedding-pipeline-template](https://hub.towhee.io/towhee/image-embedding-pipeline-template) pipeline.
 
 ## **Reference**

test_transform_image_operator_template.py

@@ -6,7 +6,6 @@ from config import TEST_IMG, SIZE
 
 
 class TestTransformImageOperatorTemplate(unittest.TestCase):
-    test_img = Image.open(TEST_IMG)
     tfms = transforms.Compose(
         [
             transforms.Resize(SIZE),
@@ -19,7 +18,7 @@ class TestTransformImageOperatorTemplate(unittest.TestCase):
 
     def test_transform_image(self):
         op = TransformImageOperatorTemplate(SIZE)
-        outputs = op(self.test_img)
+        outputs = op(TEST_IMG)
         print("The output tyep of operator:", type(outputs.img_transformed))
         c = (self.img1.numpy() == outputs.img_transformed.numpy())
         self.assertEqual(c.all(), True)

transform_image_operator_template.py

@@ -29,7 +29,7 @@ class TransformImageOperatorTemplate(Operator):
         super().__init__()
         #user defined initialization
 
-    def __call__(self, img: Union[Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
+    def __call__(self, img_path: Union[Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
         results =  #user defined function: get the transformed image
         Outputs = NamedTuple('Outputs', [('img_transformed', torch.Tensor)])
         return Outputs(results)

transform_image_operator_template.yaml

@@ -8,6 +8,6 @@ init:
   size: int
 call:
   input:
-    img: Union[Image.Image, torch.Tensor, str]
+    img_path: Union[Image.Image, torch.Tensor, str]
   output:
     img_transformed: torch.Tensor