renamed directory + pytorch image

Containers/PyTorch.Dockerfile

@@ -0,0 +1,8 @@
+FROM nvcr.io/nvidia/pytorch:24.08-py3
+
+COPY ../conversion_tools /conversion_tools
+
+# ORT
+RUN pip3 install -U --no-cache-dir --verbose jetson-stats numpy onnx pycuda ultralytics
+
+ENTRYPOINT [ "/bin/bash" ]

ros2_ws/src/python_workspace/python_workspace/camera_node.py

@@ -152,12 +152,12 @@ def publish_zed_frames(self):
                 gpu_image.upload(cv_image)
 
                 # Transform to BGR8 format and resize using CUDA 
-                cv2.cuda.cvtColor(gpu_image, cv2.COLOR_BGR2RGB)
+                cv2.cuda.cvtColor(gpu_image, cv2.COLOR_BGRA2RGB)
                 cv2.cuda.resize(gpu_image, self.dimensions)
 
                 # Convert the image to float32
-                image_gpu = image_gpu.transpose((2, 0, 1)).astype(np.float32)
-                image_gpu = np.expand_dims(image_gpu, axis=0)
+                # gpu_image = gpu_image.transpose((2, 0, 1)).astype(np.float32)
+                # gpu_image = np.expand_dims(gpu_image, axis=0)
 
                 # # Transpose the image
                 # image_gpu = cv2.cuda.transpose(image_gpu)
@@ -194,15 +194,15 @@ def publish_image(self, image):
             image = gpu_image.download() # does this update outside scope?
 
         try:
-            image_msg = self.bridge.cv2_to_imgmsg(image, encoding='rgb8')
+            image_msg = self.bridge.cv2_to_imgmsg(image, encoding='8UC4') # rgb8
         except CvBridgeError as e:
             print(e)
 
         image_msg.header = header
         image_msg.is_bigendian = 0 
         image_msg.step = image_msg.width * 3
 
-        self.model_publisher.publish(image_msg)
+        self.publisher.publish(image_msg)
         size = sys.getsizeof(image_msg)
         self.get_logger().info(f'Published image frame: {self.index} with message size {size} bytes')
         time.sleep(1.0 / self.frame_rate) # delay to control framerate

ros2_ws/src/python_workspace/python_workspace/jetson_node.py

@@ -18,8 +18,9 @@ class JetsonNode(Node):
     def __init__(self):
         super().__init__('jetson_node')
         self.declare_parameter('engine_path', '.../assets/model.trt')
+        self.engine_path = self.get_parameter('engine_path').get_parameter_value().string_value
 
-        self.camera_subscriber = self.create_subscription(Image, 'image_data', self.callback, 10)
+        self.camera_subscriber = self.create_subscription(Image, 'image_data', self.image_callback, 10)
         self.bbox_publisher = self.create_publisher(String, 'bounding_boxes', 10)
         self.bridge = CvBridge()
         self.engine = self.load_engine(self.engine_path)
@@ -57,7 +58,7 @@ def allocate_buffers(self):
         # Create a CUDA stream for async execution
         self.stream = cuda.Stream()
 
-    def listener_callback(self, msg):
+    def image_callback(self, msg):
         now = self.get_clock().now()
         self.get_logger().info(f"Received: {msg.header.frame_id}")
         latency = now - Time.from_msg(msg.header.stamp)