Add SFace for face recognition (#3)

* SFace and demo impl

* benchmark support and results for SFace

Author: fengyuentau

README.md

@@ -17,7 +17,7 @@ Hardware Setup:
 
 ***Important Notes***:
 - The time data that shown on the following tables presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
-- The time data that shown on the following tables is averaged from a 100-time run.
+- The time data that shown on the following tables is the median of benchmark runs.
 - View [benchmark/config](./benchmark/config) for more details on benchmarking different models.
 
 <!--
@@ -29,9 +29,10 @@ Hardware Setup:
 -->
 | Model | Input Size | CPU x86_64 (ms) | CPU ARM (ms) |
 |-------|------------|-----------------|--------------|
-| [YuNet](./models/face_detection_yunet) | 160x120 | 2.17   | 8.87    |
-| [DB](./models/text_detection_db)       | 640x480 | 148.65 | 2759.88 |
-| [CRNN](./models/text_recognition_crnn) | 100x32  | 23.23  | 235.87  |
+| [YuNet](./models/face_detection_yunet)   | 160x120 | 2.35   | 8.72    |
+| [DB](./models/text_detection_db)         | 640x480 | 137.38 | 2780.78 |
+| [CRNN](./models/text_recognition_crnn)   | 100x32  | 50.21  | 234.32  |
+| [SFace](./models/face_recognition_sface) | 112x112 | 8.69 | 96.79 |
 
 
 ## License

benchmark/benchmark.py

@@ -78,7 +78,7 @@ def __init__(self, **kwargs):
     def _load_label(self):
         labels = dict.fromkeys(self._files, None)
         for filename in self._files:
-            labels[filename] = np.loadtxt(os.path.join(self._path, '{}.txt'.format(filename[:-4])))
+            labels[filename] = np.loadtxt(os.path.join(self._path, '{}.txt'.format(filename[:-4])), ndmin=2)
         return labels
 
     def __getitem__(self, idx):

benchmark/config/face_detection_yunet.yaml

@@ -1,7 +1,7 @@
 Benchmark:
   name: "Face Detection Benchmark"
   data:
-    path: "benchmark/data/face"
+    path: "benchmark/data/face/detection"
     files: ["group.jpg", "concerts.jpg", "dance.jpg"]
   metric:
     sizes: # [[w1, h1], ...], Omit to run at original scale

benchmark/config/face_recognition_sface.yaml

@@ -0,0 +1,17 @@
+Benchmark:
+  name: "Face Recognition Benchmark"
+  data:
+    path: "benchmark/data/face/recognition"
+    files: ["Aaron_Tippin_0001.jpg", "Alvaro_Uribe_0028.jpg", "Alvaro_Uribe_0029.jpg", "Jose_Luis_Rodriguez_Zapatero_0001.jpg"]
+    useLabel: True
+  metric: # 'sizes' is omitted since this model requires input of fixed size
+    warmup: 3
+    repeat: 10
+    batchSize: 1
+    reduction: 'median'
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "SFace"
+  modelPath: "models/face_recognition_sface/face_recognition_sface.onnx"

models/__init__.py

@@ -1,6 +1,7 @@
 from .face_detection_yunet.yunet import YuNet
 from .text_detection_db.db import DB
 from .text_recognition_crnn.crnn import CRNN
+from .face_recognition_sface.sface import SFace
 
 class Registery:
     def __init__(self, name):
@@ -16,4 +17,5 @@ def register(self, item):
 MODELS = Registery('Models')
 MODELS.register(YuNet)
 MODELS.register(DB)
-MODELS.register(CRNN)
+MODELS.register(CRNN)
+MODELS.register(SFace)

models/face_detection_yunet/demo.py

@@ -77,8 +77,8 @@ def visualize(image, results, box_color=(0, 255, 0), text_color=(0, 0, 255), fps
         # Print results
         print('{} faces detected.'.format(results.shape[0]))
         for idx, det in enumerate(results):
-            print('{}: [{:.0f}, {:.0f}] [{:.0f}, {:.0f}], {:.2f}'.format(
-                idx, det[0], det[1], det[2], det[3], det[-1])
+            print('{}: {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f}'.format(
+                idx, *det[:-1])
             )
 
         # Draw results on the input image

models/face_recognition_sface/LICENSE

@@ -0,0 +1,202 @@
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models/face_recognition_sface/README.md

@@ -0,0 +1,27 @@
+# SFace
+
+SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
+
+SFace is contributed by [Yaoyao Zhong](https://github.com/zhongyy/SFace). [face_recognition_sface.onnx](./face_recognition_sface.onnx) is converted from the model from https://github.com/zhongyy/SFace thanks to [Chengrui Wang](https://github.com/crywang).
+
+Note:
+- There is [a PR for OpenCV adding this model](https://github.com/opencv/opencv/pull/20422) to work with OpenCV DNN in C++ implementation.
+- Support 5-landmark warp for now.
+- `demo.py` requires [../face_detection_yunet](../face_detection_yunet) to run.
+
+## Demo
+
+Run the following command to try the demo:
+```shell
+# recognize on images
+python demo.py --input1 /path/to/image1 --input2 /path/to/image2
+```
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+## Reference
+
+- https://ieeexplore.ieee.org/document/9318547
+- https://github.com/zhongyy/SFace

models/face_recognition_sface/demo.py

@@ -0,0 +1,71 @@
+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+import sys
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from sface import SFace
+
+sys.path.append('../face_detection_yunet')
+from yunet import YuNet
+
+def str2bool(v):
+    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
+        return True
+    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
+        return False
+    else:
+        raise NotImplementedError
+
+parser = argparse.ArgumentParser(
+    description="SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition (https://ieeexplore.ieee.org/document/9318547)")
+parser.add_argument('--input1', '-i1', type=str, help='Path to the input image 1.')
+parser.add_argument('--input2', '-i2', type=str, help='Path to the input image 2.')
+parser.add_argument('--model', '-m', type=str, default='face_recognition_sface.onnx', help='Path to the model.')
+parser.add_argument('--dis_type', type=int, choices=[0, 1], default=0, help='Distance type. \'0\': cosine, \'1\': norm_l1.')
+parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
+parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    # Instantiate SFace for face recognition
+    recognizer = SFace(modelPath=args.model)
+    # Instantiate YuNet for face detection
+    detector = YuNet(modelPath='../face_detection_yunet/face_detection_yunet.onnx',
+                     inputSize=[320, 320],
+                     confThreshold=0.9,
+                     nmsThreshold=0.3,
+                     topK=5000,
+                     keepTopK=750)
+
+    img1 = cv.imread(args.input1)
+    img2 = cv.imread(args.input2)
+
+    # Detect faces
+    detector.setInputSize([img1.shape[1], img1.shape[0]])
+    face1 = detector.infer(img1)
+    assert face1.shape[0] > 0, 'Cannot find a face in {}'.format(args.input1)
+    detector.setInputSize([img2.shape[1], img2.shape[0]])
+    face2 = detector.infer(img2)
+    assert face2.shape[0] > 0, 'Cannot find a face in {}'.format(args.input2)
+
+    # Match
+    distance = recognizer.match(img1, face1[0][:-1], img2, face2[0][:-1], args.dis_type)
+    print(distance)
+    if args.dis_type == 0:
+        dis_type = 'Cosine'
+        threshold = 0.363
+        result = 'same identity' if distance >= threshold else 'different identity'
+    elif args.dis_type == 1:
+        dis_type = 'Norm-L2'
+        threshold = 1.128
+        result = 'same identity' if distance <= threshold else 'different identity'
+    else:
+        raise NotImplementedError()
+    print('Using {} distance, threshold {}: {}.'.format(dis_type, threshold, result))

models/face_recognition_sface/face_recognition_sface.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9279be00242f4240ba4de7b6b93837fdd5a17b98d2f278f63711d55b65b4312
+size 38696080