Some further optimizations.

extras/easypose/detection.py

@@ -2,7 +2,7 @@ import numpy as np
 from typing import List
 
 from .base_model import BaseModel
-from .utils import letterbox, nms, xywh2xyxy
+from .utils import letterbox, nms_optimized, xywh2xyxy
 
 
 class RTMDet(BaseModel):
@@ -21,11 +21,10 @@ class RTMDet(BaseModel):
 
     def preprocess(self, image: np.ndarray):
         th, tw = self.input_shape[2:]
-        tensor, self.dx, self.dy, self.scale = letterbox(
+        image, self.dx, self.dy, self.scale = letterbox(
             image, (tw, th), fill_value=114
         )
-        tensor = tensor.astype(self.input_type, copy=False)
-        tensor = tensor[..., ::-1]
+        tensor = np.asarray(image).astype(self.input_type, copy=False)[..., ::-1]
         tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2))
         return tensor
 
@@ -34,7 +33,7 @@ class RTMDet(BaseModel):
         classes = np.expand_dims(np.squeeze(tensor[1], axis=0), axis=-1)
         boxes = np.concatenate([boxes, classes], axis=-1)
 
-        boxes = nms(boxes, self.iou_threshold, self.conf_threshold)
+        boxes = nms_optimized(boxes, self.iou_threshold, self.conf_threshold)
 
         if boxes.shape[0] == 0:
             return boxes