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-# inceptionresnetv1
+# Inception-ResNet v1 Face Embedding Operator
+
+*author: David Wang*
+
+
+
+## Desription
+
+This operator extracts embedding vector from facial image using [Inception-ResNet](https://arxiv.org/pdf/1602.07261.pdf). The implementation is an adaptation from [timesler/facenet-pytorch](https://github.com/timesler/facenet-pytorch). 
+
+## Code Example
+
+Extract face image embedding from './img.png'. 
+
+ *Write the pipeline in simplified style*:
+
+```python
+import towhee
+
+towhee.glob('./img.png') \
+  .image_decode.cv2() \
+  .face_embedding.inceptionresnetv1() \
+  .tolist()
+```
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee
+
+towhee.glob['path']('./img.png') \
+  .image_decode.cv2['path', 'img']() \
+  .face_embedding.inceptionresnetv1['img', 'vec']() \
+  .select('img','vec') \
+  .show()
+```
+<img src="https://towhee.io/face-embedding/inceptionresnetv1/raw/branch/main/result.png" alt="result" style="height:60px;"/>
+
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***face_embedding.inceptionresnetv1(image_size = 160)***
+
+**Parameters:**
+
+***image_size***: *int*
+
+Scaled input image size to extract embedding. The higher resolution would generate the more discriminateive feature but cost more time to calculate.  
+
+supported types: `int`, default is 160.
+
+
+## Interface
+
+A face embedding operator takes a face image as input. It extracts the embedding back to ndarray.
+
+**Parameters:**
+
+​	***img***: *towhee.types.Image (a sub-class of numpy.ndarray)*
+
+​	The input image.
+
+**Returns**: *numpy.ndarray*
+
+​	The extracted image embedding.
 
diff --git a/__init__.py b/__init__.py
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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# import os
+
+# # For requirements.
+# try:
+#     import efficientnet_pytorch
+# except ModuleNotFoundError:
+#     os.system('pip install efficientnet_pytorch')
+from .inceptionresnetv1 import Inceptionresnetv1
+
+def inceptionresnetv1(image_size = 160 ):
+    return Inceptionresnetv1(image_size)
+
diff --git a/inceptionresnetv1.py b/inceptionresnetv1.py
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+++ b/inceptionresnetv1.py
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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import NamedTuple
+
+import numpy as np
+import torch
+import cv2
+from facenet_pytorch import InceptionResnetV1
+
+from towhee import register
+from towhee.operator import NNOperator
+from towhee.types.image_utils import to_pil
+from towhee._types import Image
+from towhee.types import arg, to_image_color
+
+@register(output_schema=['vec'])
+class Inceptionresnetv1(NNOperator):
+    """
+    comment placeholder
+    """
+    def __init__(self, image_size = 160):
+        self.image_size = image_size
+        self._model = InceptionResnetV1(pretrained='vggface2')
+        self._model.eval()
+
+    @arg(1, to_image_color('RGB') )
+    def __call__(self, img: Image) -> np.ndarray:
+        img = self.preprocess(img)
+        embs = self._model(torch.FloatTensor(img).permute(0,3,1,2)).detach().numpy()
+        return embs
+
+    def preprocess(self, img: Image):
+        #img shape expected to be [n, h, w, c=3] or [h, w, c=3].
+        if len(img.shape) == 3:
+            img = cv2.resize(img, (self.image_size, self.image_size))
+            img = np.expand_dims(img, 0)
+        elif len(img.shape) == 4:
+            pass
+        else:
+            raise ValueError('unknown tensor shape, need to be [n, h, w, c=3] or [h, w, c=3].')
+        img = self._fixed_image_standardization(img)
+        return img
+
+    def _fixed_image_standardization(self, image_tensor):
+        processed_tensor = (image_tensor - 127.5) / 128.0
+        return processed_tensor
+
+    def train(self):
+        """
+        For training model
+        """
+        pass
diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+opencv-python>=4.1.2
+torch>=1.7.0
+torchvision>=0.8.1
+numpy
+facenet-pytorch>=2.5.2
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