made face detection static

facedetection.py

@@ -3,40 +3,37 @@ import cv2
 
 
 
-class FaceDetector():
-    def __init__(self, detection_threshold=0.6):
 
-        self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
-        self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
-        self.detection_threshold = detection_threshold
-        self.targetimageSize = [224, 224]
-        self.image = None
 
-    def detect_faces(self, frame, model):
-            self.resizeImage(frame)
+def detect_faces(frame, model, detection_threshold ):
 
-            outputs, _ = model.faceDetection.run([], {'input': self.image})
-            outputs = outputs[0]
-            faceLocation = np.argmax(outputs[0].flatten())
-            x = faceLocation % 56
-            y = faceLocation // 56
+        image = resizeImage(frame)
 
-            if outputs[0, y, x] < self.detection_threshold:
-                return None
+        outputs, _ = model.faceDetection.run([], {'input': image})
+        outputs = outputs[0]
+        faceLocation = np.argmax(outputs[0].flatten())
+        x = faceLocation % 56
+        y = faceLocation // 56
 
-            r = outputs[1, y, x] * 112.
-            results= (((x * 4) - r, (y * 4) - r, r*2,r*2))
-            results = np.array(results).astype(np.float32)
-            results[[0,2]] *= frame.shape[1] / 224.
-            results[[1,3]] *= frame.shape[0] / 224.
+        if outputs[0, y, x] < detection_threshold:
+            return None
 
-            return results
+        r = outputs[1, y, x] * 112.
+        results= (((x * 4) - r, (y * 4) - r, r*2,r*2))
+        results = np.array(results).astype(np.float32)
+        results[[0,2]] *= frame.shape[1] / 224.
+        results[[1,3]] *= frame.shape[0] / 224.
 
-    def resizeImage(self, frame):
-        image = cv2.resize(frame, self.targetimageSize, interpolation=cv2.INTER_LINEAR) * self.std + self.mean
-        image = np.expand_dims(image, 0)
-        image = np.transpose(image, (0,3,1,2))
-        self.image = image
+        return results
+
+def resizeImage(frame):
+    mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
+    std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
+    targetimageSize = [224, 224]
+    image = cv2.resize(frame, targetimageSize, interpolation=cv2.INTER_LINEAR) * std + mean
+    image = np.expand_dims(image, 0)
+    image = np.transpose(image, (0,3,1,2))
+    return image
 
 
 

tracker.py

@@ -52,7 +52,7 @@ class Models():
 class Tracker():
     def __init__(self, width, height, messageQueue, featureType, model_type=3, detection_threshold=0.6, threshold=0.6, silent=False):
 
-        self.FaceDetector = facedetection.FaceDetector(detection_threshold = detection_threshold)
+        self.detection_threshold = detection_threshold
         self.EyeTracker = eyes.EyeTracker()
         self.Landmarks = landmarks.Landmarks(width, height, threshold)
         self.model = Models(model_type = model_type)
@@ -119,7 +119,7 @@ class Tracker():
 
         if self.face is None:
             start_fd = time.perf_counter()
-            self.face = self.FaceDetector.detect_faces(frame, self.model)
+            self.face = facedetection.detect_faces(frame, self.model, self.detection_threshold)
             duration_fd = 1000 * (time.perf_counter() - start_fd)
             if self.face is None:
                 return self.early_exit("No faces found", start)