Cleaned load model method, restart to use downloaded model.

common/vision/lasr_vision_bodypix/src/lasr_vision_bodypix/bodypix.py

@@ -13,7 +13,6 @@
 from lasr_vision_msgs.srv import BodyPixDetectionRequest, BodyPixDetectionResponse
 
 import rospkg
-from os import path
 
 # model cache
 loaded_models = {}
@@ -23,27 +22,20 @@ def load_model_cached(dataset: str) -> None:
     '''
     Load a model into cache
     '''
-
     model = None
     if dataset in loaded_models:
         model = loaded_models[dataset]
     else:
         if dataset == 'resnet50':
-#             name = download_model(BodyPixModelPaths.RESNET50_FLOAT_STRIDE_16)
-#             rospy.logwarn(name)  /home/bentengma/keras_model/tf-bodypix/tfjs-models-savedmodel-bodypix-resnet50-float-model-stride16
-#             model = load_model(name)
-            model = load_model(path.join(r.get_path("lasr_vision_bodypix"), "models", "keras_model", "tf-bodypix", "tfjs-models-savedmodel-bodypix-resnet50-float-model-stride16"))
-            # model = load_model(download_model(BodyPixModelPaths.RESNET50_FLOAT_STRIDE_16))
+            name = download_model(BodyPixModelPaths.RESNET50_FLOAT_STRIDE_16)
+            model = load_model(name)
         elif dataset == 'mobilenet50':
             name = download_model(BodyPixModelPaths.MOBILENET_FLOAT_50_STRIDE_16)
-            rospy.logwarn(name)
             model = load_model(name)
         else:
             model = load_model(dataset)
-
         rospy.loginfo(f'Loaded {dataset} model')
         loaded_models[dataset] = model
-
     return model
 
 def detect(request: BodyPixDetectionRequest, debug_publisher: rospy.Publisher | None) -> BodyPixDetectionResponse:
@@ -54,8 +46,6 @@ def detect(request: BodyPixDetectionRequest, debug_publisher: rospy.Publisher |
     # decode the image
     rospy.loginfo('Decoding')
     img = cv2_img.msg_to_cv2_img(request.image_raw)
-    # rospy.logwarn(str(type(img)))
-    img_height, img_width, _ = img.shape  # Get image dimensions
 
     # load model
     rospy.loginfo('Loading model')
@@ -92,27 +82,16 @@ def detect(request: BodyPixDetectionRequest, debug_publisher: rospy.Publisher |
             right_shoulder = right_shoulder_keypoint.position
             neck_x = (left_shoulder.x + right_shoulder.x) / 2
             neck_y = (left_shoulder.y + right_shoulder.y) / 2
-            neck_score = (left_shoulder_keypoint.score + right_shoulder_keypoint.score) / 2  # Optional: average score
         elif left_shoulder_keypoint:
             # Only left shoulder detected, use it as neck coordinate
             left_shoulder = left_shoulder_keypoint.position
             neck_x = left_shoulder.x
             neck_y = left_shoulder.y
-            neck_score = left_shoulder_keypoint.score
         elif right_shoulder_keypoint:
             # Only right shoulder detected, use it as neck coordinate
             right_shoulder = right_shoulder_keypoint.position
             neck_x = right_shoulder.x
             neck_y = right_shoulder.y
-            neck_score = right_shoulder_keypoint.score
-
-        # # Convert neck coordinates to relative positions (0-1)
-        # rel_neck_x = neck_x / img_width
-        # rel_neck_y = neck_y / img_height
-
-        # pose = BodyPixPose()
-        # pose.coord = np.array([rel_neck_x, rel_neck_y]).astype(np.float32)
-        # neck_coordinates.append(pose)
 
         pose = BodyPixPose()
         pose.coord = np.array([neck_x, neck_y]).astype(np.int32)
@@ -130,7 +109,6 @@ def detect(request: BodyPixDetectionRequest, debug_publisher: rospy.Publisher |
             keypoints_color=(255, 100, 100),
             skeleton_color=(100, 100, 255),
         )
-
         debug_publisher.publish(cv2_img.cv2_img_to_msg(coloured_mask))
 
     response = BodyPixDetectionResponse()