test

benchmarks/yolov8.py

@@ -35,9 +35,40 @@ def __init__(self,
         self.iou_thres = 0.45
         super().__init__()
 
-    def predict(self, source):
+    def predict(self, image: Image.Image) -> Optional[BoundingBox]:
+        """
+        Runs the model on the supplied images
 
-
+        Returns a list with dictionary items for each prediction
+        """
+        im0 = np.array(image)
+
+        # Resize and transform im0 into one the model can read
+        im, ratio, (dw, dh) = letterbox(im0,
+                                        self.imgsz,
+                                        stride=self.model.stride,
+                                        auto=False,
+                                        scaleup=False)  # padded resize
+        im = im.transpose((2, 0, 1))  # HWC to CHW
+        im = np.ascontiguousarray(im)  # contiguous
+
+        im = torch.from_numpy(im).to(self.model.device)
+        im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
+        im /= 255  # 0 - 255 to 0.0 - 1.0
+        if len(im.shape) == 3:
+            im = im[None]  # expand for batch dim       
+
+
+
+
+        results = self.model.predict(im,
+                                     conf=self.conf_thres,
+                                     iou=self.iou_thres,
+                                     imgsz=self.imgsz,
+                                     half=self.half,
+                                     device=self.device,
+                                     max_det=self.max_det,
+                                     )
         print(results)
 
 if __name__ == "__main__":
@@ -46,7 +77,8 @@ def predict(self, source):
 
     detector = Yolov8Detector()
     bundles = MultiBundleLoader(glob.glob("tmp/labelled-bundles/*"))
-    print(bundles.count)
+    benchmark(detector, bundles)
+