Changed things to better suit my single face way of operating

facedetection.py

@@ -2,17 +2,19 @@ import os
 import numpy as np
 import cv2
 import onnxruntime
+import time
 
 
 #kinda split on how to handle this one
 #It's the cause of all of my slowest frames
 #but it's not run often
 #and I don't know how to make it faster
-class FaceDetector():
-    def __init__(self, detection_threshold=0.6):
 
+class FaceDetectionModel():
+    def __init__(self):
         model_base_path = os.path.join(os.path.dirname(__file__), os.path.join("models"))
         providersList = onnxruntime.capi._pybind_state.get_available_providers()
+
         options = onnxruntime.SessionOptions()
         options.inter_op_num_threads = 1
         options.intra_op_num_threads = 1
@@ -21,32 +23,45 @@ class FaceDetector():
         options.log_severity_level = 3
         self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options, providers=providersList)
 
+    def run(self, image):
+        outputs, maxpool = self.detection.run([], {'input': image})
+        return outputs[0]
+
+
+
+
+class FaceDetector():
+    def __init__(self, detection_threshold=0.6):
+        self.model = FaceDetectionModel()
+
         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):
+            self.resizeImage(frame)
 
-            im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR) * self.std + self.mean
-            im = np.expand_dims(im, 0)
-            im = np.transpose(im, (0,3,1,2))
+            outputs = self.model.run(self.image)
+            faceLocation = np.argmax(outputs[0].flatten())
+            x = faceLocation % 56
+            y = faceLocation // 56
+            if outputs[0, y, x] < self.detection_threshold:
+                return []
 
-            outputs, maxpool = self.detection.run([], {'input': im})
-
-            outputs[0, 0, outputs[0, 0] != maxpool[0, 0]] = 0
-            detections = np.flip(np.argsort(outputs[0,0].flatten()))
-            results = []
-            if len(detections) == 0:
-                return results
-
-            x = detections[0] % 56
-            y = detections[0] // 56
-            if outputs[0, 0, y, x] < self.detection_threshold:
-                return results
-
-            r = outputs[0, 1, y, x] * 112.
-            results.append(((x * 4) - r, (y * 4) - r, r*2,r*2))
+            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.
             return results
+
+    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
+
+
+