Some small updates.

scripts/test_triangulate.py

@@ -283,7 +283,7 @@ def main():
     if any((whole_body[k] for k in whole_body)):
         kpt_model = utils_2d_pose.load_wb_model()
     else:
-        kpt_model = utils_2d_pose.load_model()
+        kpt_model = utils_2d_pose.load_model(min_bbox_score=0.3)
 
     # Manually set matplotlib backend
     matplotlib.use("TkAgg")
@@ -340,7 +340,7 @@ def main():
         else:
             cameras = rpt.convert_cameras(camparams)
             roomp = [roomparams["room_size"], roomparams["room_center"]]
-            triangulator = rpt.Triangulator(min_match_score=0.95)
+            triangulator = rpt.Triangulator(min_match_score=0.94)
 
             stime = time.time()
             poses_3d = triangulator.triangulate_poses(

scripts/utils_2d_pose.py

@@ -46,7 +46,7 @@ def load_wb_model():
 def get_2d_pose(model, imgs, num_joints=17):
     """See: https://mmpose.readthedocs.io/en/latest/user_guides/inference.html#basic-usage"""
 
-    result_generator = model(imgs, show=False)
+    result_generator = model(imgs, show=False, bbox_thr=0.3, nms_thr=0.3)
     new_poses = []
     for _ in range(len(imgs)):
         result = next(result_generator)
@@ -56,8 +56,8 @@ def get_2d_pose(model, imgs, num_joints=17):
             kpts = result["predictions"][0][i]["keypoints"]
             scores = result["predictions"][0][i]["keypoint_scores"]
 
-            kpts = np.array(kpts)
-            scores = np.array(scores).reshape(-1, 1)
+            kpts = np.asarray(kpts)
+            scores = np.asarray(scores).reshape(-1, 1)
             scores = np.clip(scores, 0, 1)
             pose = np.concatenate((kpts, scores), axis=-1)
             poses.append(pose)

scripts/utils_2d_pose_ort.py

@@ -302,6 +302,7 @@ class RTMDet(BaseModel):
         self,
         model_path: str,
         conf_threshold: float,
+        min_area_fraction: float,
         warmup: int = 30,
     ):
         super(RTMDet, self).__init__(model_path, warmup)
@@ -309,9 +310,8 @@ class RTMDet(BaseModel):
         self.conf_threshold = conf_threshold
         self.letterbox = LetterBox(self.target_size, fill_value=114)
 
-        min_area_scale = 0.025 * 0.025
         img_area = self.target_size[0] * self.target_size[1]
-        self.min_area = img_area * min_area_scale
+        self.min_area = img_area * min_area_fraction
 
     def preprocess(self, image: np.ndarray):
         image = self.letterbox.resize_image(image)
@@ -330,11 +330,18 @@ class RTMDet(BaseModel):
         keep = boxes[:, 4] > self.conf_threshold
         boxes = boxes[keep]
 
+        if len(boxes) == 0:
+            return np.array([])
+
         # Drop boxes with too small area
+        boxes = boxes.astype(np.float32)
         areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
         keep = areas >= self.min_area
         boxes = boxes[keep]
 
+        if len(boxes) == 0:
+            return np.array([])
+
         paddings, scale, _ = self.letterbox.calc_params(image.shape)
 
         boxes[:, 0] -= paddings[0]
@@ -403,17 +410,20 @@ class RTMPose(BaseModel):
 class TopDown:
     def __init__(
         self,
-        det_model_path,
-        pose_model_path,
-        box_conf_threshold=0.6,
-        warmup=30,
+        det_model_path: str,
+        pose_model_path: str,
+        box_conf_threshold: float,
+        box_min_area: float,
+        warmup: int = 30,
     ):
         if (not det_model_path.endswith(".onnx")) or (
             not pose_model_path.endswith(".onnx")
         ):
             raise ValueError("Only ONNX models are supported.")
 
-        self.det_model = RTMDet(det_model_path, box_conf_threshold, warmup)
+        self.det_model = RTMDet(
+            det_model_path, box_conf_threshold, box_min_area, warmup
+        )
         self.pose_model = RTMPose(pose_model_path, warmup)
 
     def predict(self, image):
@@ -428,15 +438,14 @@ class TopDown:
 # ==================================================================================================
 
 
-def load_model():
+def load_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1):
     print("Loading onnx model ...")
 
     model = TopDown(
-        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_extra-steps.onnx",
         "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
-        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_extra-steps.onnx",
         "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
-        box_conf_threshold=0.3,
+        box_conf_threshold=min_bbox_score,
+        box_min_area=min_bbox_area,
         warmup=30,
     )