Some small updates.

extras/ros/docker-compose.yml

@@ -67,4 +67,4 @@ services:
             - DISPLAY
             - QT_X11_NO_MITSHM=1
             - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'sleep 2 && export ROS_DOMAIN_ID=18 && ros2 run image_view image_view --ros-args --remap image:=/camera01/img_with_pose -p autosize:=True -p window_name:=MyImage'
+        command: /bin/bash -i -c 'sleep 2 && export ROS_DOMAIN_ID=18 && ros2 run image_view image_view --ros-args --remap image:=/camera01/img_with_pose -p autosize:=True -p window_name:=MyPoseImage'

extras/ros/pose2d_visualizer/pose2d_visualizer/pose2d_visualizer.py

@@ -1,5 +1,4 @@
 import json
-import os
 import sys
 import threading
 import time
@@ -13,10 +12,6 @@ from rclpy.qos import QoSHistoryPolicy, QoSProfile, QoSReliabilityPolicy
 from sensor_msgs.msg import Image
 from std_msgs.msg import String
 
-filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
-sys.path.append(filepath + "../../../../scripts/")
-import utils_pipeline
-
 from skelda import utils_view
 
 # ==================================================================================================
@@ -36,13 +31,21 @@ lock = threading.Lock()
 # ==================================================================================================
 
 
+def bayer2rgb(bayer):
+    img = cv2.cvtColor(bayer, cv2.COLOR_BayerBG2RGB)
+    return img
+
+
+# ==================================================================================================
+
+
 def callback_images(image_data):
     global last_input_image, lock
 
     # Convert into cv images from image string
     if image_data.encoding == "bayer_rggb8":
         bayer_image = bridge.imgmsg_to_cv2(image_data, "bayer_rggb8")
-        color_image = utils_pipeline.bayer2rgb(bayer_image)
+        color_image = bayer2rgb(bayer_image)
     elif image_data.encoding == "mono8":
         gray_image = bridge.imgmsg_to_cv2(image_data, "mono8")
         color_image = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2RGB)

rpt/triangulator.cpp

@@ -689,7 +689,7 @@ std::tuple<std::vector<cv::Mat>, std::vector<cv::Mat>> TriangulatorInternal::pro
         const cv::Mat &body3D = bodies3D[i];
 
         // Extract coordinates
-        const cv::Mat points3d = body3D.colRange(0, 3);
+        const cv::Mat &points3d = body3D.colRange(0, 3);
 
         // Project from world to camera coordinate system
         cv::Mat xyz = (points3d - T_repeated) * R_transposed;

scripts/test_skelda_dataset.cpp

@@ -60,7 +60,7 @@ std::vector<cv::Mat> load_images(json &item)
     }
 
     // Convert image format to Bayer encoding to simulate real camera input
-    // This also resulted in notably better MPJPE results in most cases, presumbly since the
+    // This also resulted in notably better MPJPE results in most cases, presumably since the
     // demosaicing algorithm from OpenCV is better than the default one from the cameras
     for (size_t i = 0; i < images.size(); i++)
     {