Update to OpenCV APIs (YuNet -> FaceDetectorYN, SFace -> FaceRecognizerSF) (#6)

* update YuNet and SFace impl with opencv-python api

README.md

@@ -29,10 +29,10 @@ Hardware Setup:
 -->
 | Model | Input Size | CPU x86_64 (ms) | CPU ARM (ms) |
 |-------|------------|-----------------|--------------|
-| [YuNet](./models/face_detection_yunet)   | 160x120 | 2.35   | 8.72    |
+| [YuNet](./models/face_detection_yunet)   | 160x120 | 1.45   | 6.22    |
 | [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 |
+| [SFace](./models/face_recognition_sface) | 112x112 | 8.65 | 99.20 |
 | [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58
 | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 |
 

benchmark/config/face_detection_yunet.yaml

@@ -19,5 +19,4 @@ Model:
   modelPath: "models/face_detection_yunet/face_detection_yunet.onnx"
   confThreshold: 0.6
   nmsThreshold: 0.3
-  topK: 5000
-  keepTopK: 750
\ No newline at end of file
+  topK: 5000
\ No newline at end of file

benchmark/requirements.txt

 numpy==1.21.2
-opencv-python==4.5.3.56
+opencv-python==4.5.4.58
 tqdm
 pyyaml
 requests
\ No newline at end of file

models/face_detection_yunet/demo.py

@@ -25,7 +25,6 @@ parser.add_argument('--model', '-m', type=str, default='face_detection_yunet.onn
 parser.add_argument('--conf_threshold', type=float, default=0.9, help='Filter out faces of confidence < conf_threshold.')
 parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
 parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
-parser.add_argument('--keep_top_k', type=int, default=750, help='Keep keep_top_k bounding boxes after NMS.')
 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()
@@ -62,8 +61,7 @@ if __name__ == '__main__':
                   inputSize=[320, 320],
                   confThreshold=args.conf_threshold,
                   nmsThreshold=args.nms_threshold,
-                  topK=args.top_k,
-                  keepTopK=args.keep_top_k)
+                  topK=args.top_k)
 
     # If input is an image
     if args.input is not None:

models/face_detection_yunet/yunet.py

@@ -10,140 +10,57 @@ import numpy as np
 import cv2 as cv
 
 class YuNet:
-    def __init__(self, modelPath, inputSize=[320, 320], confThreshold=0.6, nmsThreshold=0.3, topK=5000, keepTopK=750):
+    def __init__(self, modelPath, inputSize=[320, 320], confThreshold=0.6, nmsThreshold=0.3, topK=5000, backendId=0, targetId=0):
         self._modelPath = modelPath
-        self._model = cv.dnn.readNet(self._modelPath)
-
-        self._inputNames = ''
-        self._outputNames = ['loc', 'conf', 'iou']
-        self._inputSize = inputSize # [w, h]
+        self._inputSize = tuple(inputSize) # [w, h]
         self._confThreshold = confThreshold
         self._nmsThreshold = nmsThreshold
         self._topK = topK
-        self._keepTopK = keepTopK
-
-        self._min_sizes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]
-        self._steps = [8, 16, 32, 64]
-        self._variance = [0.1, 0.2]
+        self._backendId = backendId
+        self._targetId = targetId
 
-        # Generate priors
-        self._priorGen()
+        self._model = cv.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId)
 
     @property
     def name(self):
         return self.__class__.__name__
 
-    def setBackend(self, backend):
-        self._model.setPreferableBackend(backend)
-
-    def setTarget(self, target):
-        self._model.setPreferableTarget(target)
+    def setBackend(self, backendId):
+        self._backendId = backendId
+        self._model = cv.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+    def setTarget(self, targetId):
+        self._targetId = targetId
+        self._model = cv.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId)
 
     def setInputSize(self, input_size):
-        self._inputSize = input_size # [w, h]
-
-        # Regenerate priors
-        self._priorGen()
-
-    def _preprocess(self, image):
-        return cv.dnn.blobFromImage(image)
+        self._model.setInputSize(tuple(input_size))
 
     def infer(self, image):
-        assert image.shape[0] == self._inputSize[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self._inputSize[1])
-        assert image.shape[1] == self._inputSize[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self._inputSize[0])
-
-        # Preprocess
-        inputBlob = self._preprocess(image)
-
         # Forward
-        self._model.setInput(inputBlob, self._inputNames)
-        outputBlob = self._model.forward(self._outputNames)
-
-        # Postprocess
-        results = self._postprocess(outputBlob)
-
-        return results
-
-    def _postprocess(self, outputBlob):
-        # Decode
-        dets = self._decode(outputBlob)
-
-        # NMS
-        keepIdx = cv.dnn.NMSBoxes(
-            bboxes=dets[:, 0:4].tolist(),
-            scores=dets[:, -1].tolist(),
-            score_threshold=self._confThreshold,
-            nms_threshold=self._nmsThreshold,
-            top_k=self._topK
-        ) # box_num x class_num
-        if len(keepIdx) > 0:
-            dets = dets[keepIdx]
-            dets = np.squeeze(dets, axis=1)
-            return dets[:self._keepTopK]
-        else:
-            return np.empty(shape=(0, 15))
-
-    def _priorGen(self):
-        w, h = self._inputSize
-        feature_map_2th = [int(int((h + 1) / 2) / 2),
-                           int(int((w + 1) / 2) / 2)]
-        feature_map_3th = [int(feature_map_2th[0] / 2),
-                           int(feature_map_2th[1] / 2)]
-        feature_map_4th = [int(feature_map_3th[0] / 2),
-                           int(feature_map_3th[1] / 2)]
-        feature_map_5th = [int(feature_map_4th[0] / 2),
-                           int(feature_map_4th[1] / 2)]
-        feature_map_6th = [int(feature_map_5th[0] / 2),
-                           int(feature_map_5th[1] / 2)]
-
-        feature_maps = [feature_map_3th, feature_map_4th,
-                        feature_map_5th, feature_map_6th]
-
-        priors = []
-        for k, f in enumerate(feature_maps):
-            min_sizes = self._min_sizes[k]
-            for i, j in product(range(f[0]), range(f[1])): # i->h, j->w
-                for min_size in min_sizes:
-                    s_kx = min_size / w
-                    s_ky = min_size / h
-
-                    cx = (j + 0.5) * self._steps[k] / w
-                    cy = (i + 0.5) * self._steps[k] / h
-
-                    priors.append([cx, cy, s_kx, s_ky])
-        self.priors = np.array(priors, dtype=np.float32)
-
-    def _decode(self, outputBlob):
-        loc, conf, iou = outputBlob
-        # get score
-        cls_scores = conf[:, 1]
-        iou_scores = iou[:, 0]
-        # clamp
-        _idx = np.where(iou_scores < 0.)
-        iou_scores[_idx] = 0.
-        _idx = np.where(iou_scores > 1.)
-        iou_scores[_idx] = 1.
-        scores = np.sqrt(cls_scores * iou_scores)
-        scores = scores[:, np.newaxis]
-
-        scale = np.array(self._inputSize)
-
-        # get bboxes
-        bboxes = np.hstack((
-            (self.priors[:, 0:2] + loc[:, 0:2] * self._variance[0] * self.priors[:, 2:4]) * scale,
-            (self.priors[:, 2:4] * np.exp(loc[:, 2:4] * self._variance)) * scale
-        ))
-        # (x_c, y_c, w, h) -> (x1, y1, w, h)
-        bboxes[:, 0:2] -= bboxes[:, 2:4] / 2
-
-        # get landmarks
-        landmarks = np.hstack((
-            (self.priors[:, 0:2] + loc[:,  4: 6] * self._variance[0] * self.priors[:, 2:4]) * scale,
-            (self.priors[:, 0:2] + loc[:,  6: 8] * self._variance[0] * self.priors[:, 2:4]) * scale,
-            (self.priors[:, 0:2] + loc[:,  8:10] * self._variance[0] * self.priors[:, 2:4]) * scale,
-            (self.priors[:, 0:2] + loc[:, 10:12] * self._variance[0] * self.priors[:, 2:4]) * scale,
-            (self.priors[:, 0:2] + loc[:, 12:14] * self._variance[0] * self.priors[:, 2:4]) * scale
-        ))
-
-        dets = np.hstack((bboxes, landmarks, scores))
-        return dets
\ No newline at end of file
+        faces = self._model.detect(image)
+        return faces[1]
\ No newline at end of file

models/face_recognition_sface/demo.py

@@ -35,14 +35,13 @@ args = parser.parse_args()
 
 if __name__ == '__main__':
     # Instantiate SFace for face recognition
-    recognizer = SFace(modelPath=args.model)
+    recognizer = SFace(modelPath=args.model, disType=args.dis_type)
     # 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)
+                     topK=5000)
 
     img1 = cv.imread(args.input1)
     img2 = cv.imread(args.input2)
@@ -56,16 +55,5 @@ if __name__ == '__main__':
     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))
\ No newline at end of file
+    result = recognizer.match(img1, face1[0][:-1], img2, face2[0][:-1])
+    print('Result: {}.'.format('same identity' if result else 'different identities'))
\ No newline at end of file

models/face_recognition_sface/sface.py

@@ -10,156 +10,60 @@ import cv2 as cv
 from _testcapi import FLT_MIN
 
 class SFace:
-    def __init__(self, modelPath):
-        self._model = cv.dnn.readNet(modelPath)
-        self._input_size = [112, 112]
-        self._dst = np.array([
-            [38.2946, 51.6963],
-            [73.5318, 51.5014],
-            [56.0252, 71.7366],
-            [41.5493, 92.3655],
-            [70.7299, 92.2041]
-        ], dtype=np.float32)
-        self._dst_mean = np.array([56.0262, 71.9008], dtype=np.float32)
+    def __init__(self, modelPath, disType=0, backendId=0, targetId=0):
+        self._modelPath = modelPath
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model = cv.FaceRecognizerSF.create(
+            model=self._modelPath,
+            config="",
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+        self._disType = disType # 0: cosine similarity, 1: Norm-L2 distance
+        assert self._disType in [0, 1], "0: Cosine similarity, 1: norm-L2 distance, others: invalid"
+
+        self._threshold_cosine = 0.363
+        self._threshold_norml2 = 1.128
 
     @property
     def name(self):
         return self.__class__.__name__
 
-    def setBackend(self, backend_id):
-        self._model.setPreferableBackend(backend_id)
-
-    def setTarget(self, target_id):
-        self._model.setPreferableTarget(target_id)
+    def setBackend(self, backendId):
+        self._backendId = backendId
+        self._model = cv.FaceRecognizerSF.create(
+            model=self._modelPath,
+            config="",
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+    def setTarget(self, targetId):
+        self._targetId = targetId
+        self._model = cv.FaceRecognizerSF.create(
+            model=self._modelPath,
+            config="",
+            backend_id=self._backendId,
+            target_id=self._targetId)
 
     def _preprocess(self, image, bbox):
-        aligned_image = self._alignCrop(image, bbox)
-        return cv.dnn.blobFromImage(aligned_image)
+        return self._model.alignCrop(image, bbox)
 
     def infer(self, image, bbox):
         # Preprocess
         inputBlob = self._preprocess(image, bbox)
 
         # Forward
-        self._model.setInput(inputBlob)
-        outputBlob = self._model.forward()
-
-        # Postprocess
-        results = self._postprocess(outputBlob)
-
-        return results
+        features = self._model.feature(inputBlob)
+        return features
 
-    def _postprocess(self, outputBlob):
-        return outputBlob / cv.norm(outputBlob)
-
-    def match(self, image1, face1, image2, face2, dis_type=0):
+    def match(self, image1, face1, image2, face2):
         feature1 = self.infer(image1, face1)
         feature2 = self.infer(image2, face2)
 
-        if dis_type == 0: # COSINE
-            return np.sum(feature1 * feature2)
-        elif dis_type == 1: # NORM_L2
-            return cv.norm(feature1, feature2)
-        else:
-            raise NotImplementedError()
-
-    def _alignCrop(self, image, face):
-        # Retrieve landmarks
-        if face.shape[-1] == (4 + 5 * 2):
-            landmarks = face[4:].reshape(5, 2)
-        else:
-            raise NotImplementedError()
-        warp_mat = self._getSimilarityTransformMatrix(landmarks)
-        aligned_image = cv.warpAffine(image, warp_mat, self._input_size, flags=cv.INTER_LINEAR)
-        return aligned_image
-
-    def _getSimilarityTransformMatrix(self, src):
-        # compute the mean of src and dst
-        src_mean = np.array([np.mean(src[:, 0]), np.mean(src[:, 1])], dtype=np.float32)
-        dst_mean = np.array([56.0262, 71.9008], dtype=np.float32)
-        # subtract the means from src and dst
-        src_demean = src.copy()
-        src_demean[:, 0] = src_demean[:, 0] - src_mean[0]
-        src_demean[:, 1] = src_demean[:, 1] - src_mean[1]
-        dst_demean = self._dst.copy()
-        dst_demean[:, 0] = dst_demean[:, 0] - dst_mean[0]
-        dst_demean[:, 1] = dst_demean[:, 1] - dst_mean[1]
-
-        A = np.array([[0., 0.], [0., 0.]], dtype=np.float64)
-        for i in range(5):
-            A[0][0] += dst_demean[i][0] * src_demean[i][0]
-            A[0][1] += dst_demean[i][0] * src_demean[i][1]
-            A[1][0] += dst_demean[i][1] * src_demean[i][0]
-            A[1][1] += dst_demean[i][1] * src_demean[i][1]
-        A = A / 5
-
-        d = np.array([1.0, 1.0], dtype=np.float64)
-        if A[0][0] * A[1][1] - A[0][1] * A[1][0] < 0:
-            d[1] = -1
-
-        T = np.array([
-            [1.0, 0.0, 0.0],
-            [0.0, 1.0, 0.0],
-            [0.0, 0.0, 1.0]
-        ], dtype=np.float64)
-
-        s, u, vt = cv.SVDecomp(A)
-        smax = s[0][0] if s[0][0] > s[1][0] else s[1][0]
-        tol = smax * 2 * FLT_MIN
-        rank = int(0)
-        if s[0][0] > tol:
-            rank += 1
-        if s[1][0] > tol:
-            rank += 1
-        det_u = u[0][0] * u[1][1] - u[0][1] * u[1][0]
-        det_vt = vt[0][0] * vt[1][1] - vt[0][1] * vt[1][0]
-        if rank == 1:
-            if det_u * det_vt > 0:
-                uvt = np.matmul(u, vt)
-                T[0][0] = uvt[0][0]
-                T[0][1] = uvt[0][1]
-                T[1][0] = uvt[1][0]
-                T[1][1] = uvt[1][1]
-            else:
-                temp = d[1]
-                d[1] = -1
-                D = np.array([[d[0], 0.0], [0.0, d[1]]], dtype=np.float64)
-                Dvt = np.matmul(D, vt)
-                uDvt = np.matmul(u, Dvt)
-                T[0][0] = uDvt[0][0]
-                T[0][1] = uDvt[0][1]
-                T[1][0] = uDvt[1][0]
-                T[1][1] = uDvt[1][1]
-                d[1] = temp
-        else:
-            D = np.array([[d[0], 0.0], [0.0, d[1]]], dtype=np.float64)
-            Dvt = np.matmul(D, vt)
-            uDvt = np.matmul(u, Dvt)
-            T[0][0] = uDvt[0][0]
-            T[0][1] = uDvt[0][1]
-            T[1][0] = uDvt[1][0]
-            T[1][1] = uDvt[1][1]
-
-        var1 = 0.0
-        var2 = 0.0
-        for i in range(5):
-            var1 += src_demean[i][0] * src_demean[i][0]
-            var2 += src_demean[i][1] * src_demean[i][1]
-        var1 /= 5
-        var2 /= 5
-
-        scale = 1.0 / (var1 + var2) * (s[0][0] * d[0] + s[1][0] * d[1])
-        TS = [
-            T[0][0] * src_mean[0] + T[0][1] * src_mean[1],
-            T[1][0] * src_mean[0] + T[1][1] * src_mean[1]
-        ]
-        T[0][2] = dst_mean[0] - scale * TS[0]
-        T[1][2] = dst_mean[1] - scale * TS[1]
-        T[0][0] *= scale
-        T[0][1] *= scale
-        T[1][0] *= scale
-        T[1][1] *= scale
-        return np.array([
-            [T[0][0], T[0][1], T[0][2]],
-            [T[1][0], T[1][1], T[1][2]]
-        ], dtype=np.float64)
\ No newline at end of file
+        if self._disType == 0: # COSINE
+            cosine_score = self._model.match(feature1, feature2, self._disType)
+            return 1 if cosine_score >= self._threshold_cosine else 0
+        else: # NORM_L2
+            norml2_distance = self._model.match(feature1, feature2, self._disType)
+            return 1 if norml2_distance <= self._threshold_norml2 else 0
\ No newline at end of file