Replace the old model with the new one.

common/vision/lasr_vision_msgs/CMakeLists.txt

@@ -51,6 +51,7 @@ add_message_files(
   BodyPixMaskRequest.msg
   ColourPrediction.msg
   FeatureWithColour.msg
+  # Description.msg
 )
 
 ## Generate services in the 'srv' folder
@@ -59,6 +60,7 @@ add_service_files(
   YoloDetection.srv
   BodyPixDetection.srv
   TorchFaceFeatureDetection.srv
+  TorchFaceFeatureDetectionDescription.srv
 )
 
 ## Generate actions in the 'action' folder

common/vision/lasr_vision_msgs/msg/Description.msg

@@ -0,0 +1 @@
+string discription

common/vision/lasr_vision_msgs/srv/TorchFaceFeatureDetectionDescription.srv

@@ -0,0 +1,14 @@
+# Image to run inference on
+sensor_msgs/Image image_raw
+
+uint8[] head_mask_data  # For serialized array data
+uint32[] head_mask_shape  # To store the shape of the array
+string head_mask_dtype  # Data type of the array elements
+
+uint8[] torso_mask_data
+uint32[] torso_mask_shape
+string torso_mask_dtype
+---
+
+# Detection result
+string description

common/vision/lasr_vision_torch/nodes/service

@@ -1,4 +1,4 @@
-from lasr_vision_msgs.srv import TorchFaceFeatureDetection, TorchFaceFeatureDetectionRequest, TorchFaceFeatureDetectionResponse
+from lasr_vision_msgs.srv import TorchFaceFeatureDetection, TorchFaceFeatureDetectionRequest, TorchFaceFeatureDetectionResponse, TorchFaceFeatureDetectionDescriptionRequest, TorchFaceFeatureDetectionDescriptionResponse
 from lasr_vision_msgs.msg import FeatureWithColour, ColourPrediction
 from cv2_img import msg_to_cv2_img
 from torch_module.helpers import binary_erosion_dilation, median_color_float
@@ -13,7 +13,7 @@ import lasr_vision_torch
 from os import path
 
 
-def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetectionResponse:
+def detect(request: TorchFaceFeatureDetectionDescriptionRequest) -> TorchFaceFeatureDetectionDescriptionRequest:
     # decode the image
     rospy.loginfo('Decoding')
     full_frame = msg_to_cv2_img(request.image_raw)
@@ -24,20 +24,22 @@ def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetecti
     head_frame = lasr_vision_torch.extract_mask_region(full_frame, head_mask.astype(np.uint8), expand_x=0.4, expand_y=0.5)
     torso_frame = lasr_vision_torch.extract_mask_region(full_frame, torso_mask.astype(np.uint8), expand_x=0.2, expand_y=0.0)
 
-    class_pred, colour_pred = lasr_vision_torch.predict_frame(head_frame, torso_frame, full_frame, head_mask, torso_mask, lasr_vision_torch.model, lasr_vision_torch.thresholds_mask, lasr_vision_torch.erosion_iterations, lasr_vision_torch.dilation_iterations, lasr_vision_torch.thresholds_pred)
+    # class_pred, colour_pred = lasr_vision_torch.predict_frame(head_frame, torso_frame, full_frame, head_mask, torso_mask, lasr_vision_torch.model, lasr_vision_torch.thresholds_mask, lasr_vision_torch.erosion_iterations, lasr_vision_torch.dilation_iterations, lasr_vision_torch.thresholds_pred)
+    rst_str = lasr_vision_torch.predict_frame(head_frame, torso_frame, full_frame, head_mask, torso_mask, lasr_vision_torch.model, lasr_vision_torch.thresholds_mask, lasr_vision_torch.erosion_iterations, lasr_vision_torch.dilation_iterations, lasr_vision_torch.thresholds_pred)
 
-    response = TorchFaceFeatureDetectionResponse()
+    response = TorchFaceFeatureDetectionDescriptionRequest()
+    response.description = rst_str
     # response.detected_features = str(class_pred) + str(colour_pred)
-    response.detected_features = []
-    for c in ['hair', 'hat', 'glasses', 'cloth',]:
-        # colour_pred[c] = {k: v[0] for k, v in colour_pred[c].items()}
-        sorted_list = sorted(colour_pred[c].items(), key=lambda item: item[1], reverse=True)
-        # rospy.loginfo(str(sorted_list))
-        if len(sorted_list) > 3:
-            sorted_list = sorted_list[0:3]
-        sorted_list = [k for k, v in sorted_list]
-        # rospy.loginfo(str(colour_pred[c]))
-        response.detected_features.append(FeatureWithColour(c, class_pred[c], sorted_list))
+    # response.detected_features = []
+    # for c in ['hair', 'hat', 'glasses', 'cloth',]:
+    #     # colour_pred[c] = {k: v[0] for k, v in colour_pred[c].items()}
+    #     sorted_list = sorted(colour_pred[c].items(), key=lambda item: item[1], reverse=True)
+    #     # rospy.loginfo(str(sorted_list))
+    #     if len(sorted_list) > 3:
+    #         sorted_list = sorted_list[0:3]
+    #     sorted_list = [k for k, v in sorted_list]
+    #     # rospy.loginfo(str(colour_pred[c]))
+    #     response.detected_features.append(FeatureWithColour(c, class_pred[c], sorted_list))
     return response