fixed object detection code to work in the devcontainer

object_detection/buoy_detection_node.py

@@ -11,22 +11,27 @@ from std_msgs.msg import Int32
 from autoboat_msgs.msg import ObjectDetectionResultsList
 
 from cv_bridge import CvBridge
-import torch
+
 from ultralytics import YOLO
 import cv2
 import numpy as np
 from math import sqrt
 
+
+import os
+
+current_directory_path = os.path.dirname(os.path.realpath(__file__))
+
 TRAINED_IMAGE_SIZE = (640, 640)  # pixel width and height of the images that the model was trained on
 IMAGE_CONFIDENCE = 0.3
 
-SHOULD_SAVE_IMAGES = True
+SHOULD_SAVE_IMAGES = False
 
 
 class BuoyDetectionNode(Node):
     def __init__(self):
         super().__init__("buoy_detection")
-        self.model = YOLO("/home/sailbot/sailbot_vt/src/object_detection/object_detection/weights/chris_pretty_good_11l_train32.pt")
+        self.model = YOLO(f"{current_directory_path}/weights/pretty_good_11l_train32.pt")
         self.cv_bridge = CvBridge()
 
         self.current_image_rgb = None
@@ -38,12 +43,12 @@ class BuoyDetectionNode(Node):
         self.buoy_angle_pub = self.create_publisher(Float32, "/buoy_angle", 10)
         self.buoy_depth_pixel_pub = self.create_publisher(Float32, "/buoy_depth_pixel", 10)
 
-        self.depth_image_listener = self.create_subscription(
-            msg_type=Image,
-            topic="/camera/camera/aligned_depth_to_color/image_raw",
-            callback=self.depth_image_callback,
-            qos_profile=sensor_qos_profile,
-        )
+        # self.depth_image_listener = self.create_subscription(
+        #     msg_type=Image,
+        #     topic="/camera/camera/aligned_depth_to_color/image_raw",
+        #     callback=self.depth_image_callback,
+        #     qos_profile=sensor_qos_profile,
+        # )
         # self.depth_image_listener = self.create_subscription(msg_type=Image, topic="/camera/depth/image_rect_raw", callback=self.depth_image_callback, qos_profile=sensor_qos_profile)
         self.rgb_image_listener = self.create_subscription(
             msg_type=Image, topic="/camera/camera/color/image_raw", callback=self.rgb_image_callback, qos_profile=sensor_qos_profile
@@ -215,17 +220,18 @@ class BuoyDetectionNode(Node):
                 if max_value <= 20:
                     self.current_image_rgb[y_value, x_value] = [160, 32, 240]
 
-        cv2.imwrite("rgb_image.jpg", self.current_image_rgb)
+        # cv2.imwrite("rgb_image.jpg", self.current_image_rgb)
+        result.save("rgb_image.png")
 
         # For Depth Image
-        for x_value in range(self.depth_image.shape[1]):
-            for y_value in range(self.depth_image.shape[0]):
-                difference_vector = [abs(y_value - int(box_y_center + 50)), abs(x_value - int(box_x_center + 30))]
-                max_value = max(difference_vector[0], difference_vector[1])
-                if max_value <= 20:
-                    self.depth_image[y_value, x_value] = 0
-
-        cv2.imwrite("depth_image.jpg", self.depth_image)
+        # for x_value in range(self.depth_image.shape[1]):
+        #     for y_value in range(self.depth_image.shape[0]):
+        #         difference_vector = [abs(y_value - int(box_y_center + 50)), abs(x_value - int(box_x_center + 30))]
+        #         max_value = max(difference_vector[0], difference_vector[1])
+        #         if max_value <= 20:
+        #             self.depth_image[y_value, x_value] = 0
+
+        # cv2.imwrite("depth_image.jpg", self.depth_image)
 
         if SHOULD_SAVE_IMAGES:
             print("GOT HERE")

requirements.txt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1f5b5984b32d24e707a15689e0a5f8118744936c3dbedca3ed115caae286c54a
-size 26
+oid sha256:84043c7caa3615309a0bbb48fcf899ea15d1e49ea6c461204b49dd6c710d8591
+size 40