Merge branch 'feat/subscriber-benchmark' into v2/develop

# Conflicts:
#	orbbec_camera/src/ob_camera_node.cpp
#	orbbec_camera/tools/multi_save_rgbir.cpp

orbbec_camera/scripts/common_benchmark_node.py

@@ -4,11 +4,14 @@
 Support: ROS2
 name: common_benchmark_node.py
 function: A ROS2 node to monitor and log the performance of an Orbbec camera node:
-          frame rates, delays, CPU and RAM usage, packet/frame loss statistics.
+          subscriber-side frame rates, end-to-end delays, CPU and RAM usage,
+          and estimated frame loss statistics.
 usage:
     ros2 run orbbec_camera common_benchmark_node.py --run_time 20 --csv_file /tmp/cam_log.csv
                     You can also pass an ideal frame rate for drop detection: --ideal_fps 30
                     Monitor multiple cameras: --camera_names camera,camera01
+                    Select topics explicitly: --topics color,depth
+                    Compressed images and point clouds require full topic names.
 """
 
 import argparse
@@ -21,11 +24,22 @@ import csv
 import os
 from collections import defaultdict
 from orbbec_camera_msgs.msg import DeviceStatus
-from sensor_msgs.msg import Image
+from sensor_msgs.msg import CompressedImage, Image, PointCloud2
 
 from tabulate import tabulate
 
 CAMERA_NODE_NAMES = ["component_container", "orbbec_camera_node", "nodelet"]
+MONITORED_STREAMS = (
+    "color",
+    "depth",
+    "ir",
+    "left_ir",
+    "right_ir",
+    "left_color",
+    "right_color",
+)
+DISCOVERY_INTERVAL_SECONDS = 0.1
+DISCOVERY_DURATION_SECONDS = 1.0
 DOCUMENTATION_URL = (
     "https://orbbec.github.io/OrbbecSDK_ROS2/en/source/camera_devices/"
     "6_benchmark/benchmark_tools.html"
@@ -99,24 +113,62 @@ def estimate_dropped_frames(dt, expected_interval):
 # ----------------------------------------------
 
 class TopicTracker:
-    def __init__(self, logger=None):
+    def __init__(self, logger=None, sample_start_time=None):
         self.received = 0
-
-        self.last_time = None
+        self.sample_start_time = sample_start_time or time.monotonic()
+        self.last_sample_time = self.sample_start_time
+        self.last_sample_received = 0
+        self.last_header_stamp = None
+        self.estimated_interval = None
         self.drop_frames = 0
 
         self.logger = logger
 
-    def on_msg(self, header, avg_fps):
-        stamp = header.stamp.sec + header.stamp.nanosec * 1e-9
+    def on_msg(self, header, ros_receive_time, ideal_fps):
+        """Record one received message and return its age in milliseconds."""
+        header_stamp = header.stamp.sec + header.stamp.nanosec * 1e-9
         self.received += 1
+        delay_ms = None
 
-        if self.last_time is not None and avg_fps > 0:
-            dt = stamp - self.last_time
-            expected_interval = 1.0 / avg_fps
-            self.drop_frames += estimate_dropped_frames(dt, expected_interval)
+        if header_stamp > 0:
+            delay_ms = (ros_receive_time - header_stamp) * 1000.0
+            if self.last_header_stamp is not None:
+                header_dt = header_stamp - self.last_header_stamp
+                if header_dt > 0:
+                    expected_interval = (
+                        1.0 / ideal_fps
+                        if ideal_fps and ideal_fps > 0.0
+                        else self.estimated_interval
+                    )
+                    if expected_interval is not None:
+                        self.drop_frames += estimate_dropped_frames(
+                            header_dt, expected_interval
+                        )
+                    self.update_estimated_interval(header_dt)
+            self.last_header_stamp = header_stamp
 
-        self.last_time = stamp
+        return delay_ms
+
+    def sample_fps(self, sample_time):
+        """Calculate current and average subscriber throughput."""
+        window_elapsed = sample_time - self.last_sample_time
+        total_elapsed = sample_time - self.sample_start_time
+        if window_elapsed <= 0.0 or total_elapsed <= 0.0:
+            return None
+
+        window_received = self.received - self.last_sample_received
+        current_fps = window_received / window_elapsed
+        average_fps = self.received / total_elapsed
+        self.last_sample_time = sample_time
+        self.last_sample_received = self.received
+        return current_fps, average_fps
+
+    def update_estimated_interval(self, interval):
+        """Learn the nominal source interval while excluding likely frame gaps."""
+        if self.estimated_interval is None or interval < 0.75 * self.estimated_interval:
+            self.estimated_interval = interval
+        elif interval <= 1.5 * self.estimated_interval:
+            self.estimated_interval = 0.9 * self.estimated_interval + 0.1 * interval
 
     def frames_loss_rate(self):
         total = self.received + self.drop_frames
@@ -129,17 +181,27 @@ class TopicTracker:
 
 
 class CameraMonitorNode(Node):
-    def __init__(self, run_time, csv_file="camera_monitor_log.csv", ideal_fps: float = 0.0, camera_names=None):
+    def __init__(
+        self,
+        run_time,
+        csv_file="camera_monitor_log.csv",
+        ideal_fps: float = 0.0,
+        camera_names=None,
+        topics=None,
+    ):
         super().__init__("camera_monitor_node")
 
         self.run_time = run_time
-        self.start_time = time.time()
+        self.discovery_start_time = time.time()
+        self.discovery_start_monotonic = None
+        self.start_time = None
         self.process = psutil.Process(os.getpid())
         self.first_data_collected = False
         self.camera_names = parse_camera_names(camera_names)
         self.node_names = {camera_name: "Not Found" for camera_name in self.camera_names}
         self.total_node_name = "Not Found"
-        # If > 0, use this ideal fps value for drop-frame detection instead of the reported average
+        # If > 0, use this ideal FPS for drop detection instead of learning the
+        # nominal interval from received image timestamps.
         self.ideal_fps = float(ideal_fps) if ideal_fps is not None else 0.0
         self.finished = False
 
@@ -153,22 +215,26 @@ class CameraMonitorNode(Node):
                 "stats": defaultdict(make_stat),
                 "cpu_stats": make_stat(),
                 "ram_stats": make_stat(),
-                "trackers": {
-                    "color": TopicTracker(logger=self.get_logger()),
-                    "depth": TopicTracker(logger=self.get_logger())
-                }
+                "trackers": {},
             }
 
         self.total_cpu_stats = make_stat()
         self.total_ram_stats = make_stat()
+        self.topic_subscriptions = {}
+        self.topic_configs = {}
+        self.discovered_streams = {}
+        self.discovery_complete = False
+        self.discovery_timer = None
+        self.timer = None
+        self.requested_streams = self.parse_requested_topics(topics)
 
         # CSV
         self.csv_file = csv_file
         self.csv_fh = open(self.csv_file, "w", newline="")
         self.csv_writer = csv.writer(self.csv_fh)
-        self.csv_writer.writerow(self.build_csv_header())
 
-        # subscriptions
+        # Device status subscriptions are always present. Data subscriptions
+        # are created once after automatic discovery or explicit selection.
         for camera_name in self.camera_names:
             ns = self.camera_namespace(camera_name)
             self.create_subscription(
@@ -177,29 +243,26 @@ class CameraMonitorNode(Node):
                 lambda msg, name=camera_name: self.status_callback(msg, name),
                 5
             )
-            self.create_subscription(
-                Image,
-                f"{ns}/color/image_raw",
-                lambda msg, name=camera_name: self.image_callback(msg, name, "color"),
-                5
-            )
-            self.create_subscription(
-                Image,
-                f"{ns}/depth/image_raw",
-                lambda msg, name=camera_name: self.image_callback(msg, name, "depth"),
-                5
-            )
 
-        # timer runs every 1s to update system stats, log csv and print status
-        self.timer = self.create_timer(1.0, self.timer_callback)
+        if self.requested_streams:
+            self.finish_topic_discovery(self.requested_streams)
+        else:
+            self.discovery_start_monotonic = time.monotonic()
+            self.discovery_timer = self.create_timer(
+                DISCOVERY_INTERVAL_SECONDS, self.update_topic_discovery
+            )
 
     def timer_callback(self):
+        if not self.discovery_complete:
+            return
+
         elapsed = time.time() - self.start_time
         if elapsed > self.run_time:
             self.finish()
             rclpy.shutdown()
             return
 
+        self.update_topic_fps_stats()
         camera_sys_stats, total_cpu, total_ram, self.total_node_name = self.get_camera_stats()
         for camera_name in self.camera_names:
             camera = self.cameras[camera_name]
@@ -220,7 +283,8 @@ class CameraMonitorNode(Node):
             return
         self.finished = True
 
-        elapsed = time.time() - self.start_time
+        timer_start = self.start_time or self.discovery_start_time
+        elapsed = time.time() - timer_start
         try:
             self.csv_fh.close()
         except Exception:
@@ -231,6 +295,150 @@ class CameraMonitorNode(Node):
     def camera_namespace(self, camera_name):
         return "/" + camera_name.strip("/")
 
+    def image_topic_name(self, camera_name, stream):
+        return f"{self.camera_namespace(camera_name)}/{stream}/image_raw"
+
+    def make_raw_image_config(self, camera_name, stream):
+        return {
+            "camera_name": camera_name,
+            "topic_id": stream,
+            "topic_name": self.image_topic_name(camera_name, stream),
+            "msg_type": Image,
+        }
+
+    def parse_full_topic(self, topic_name):
+        normalized_topic = "/" + topic_name.strip("/")
+        for camera_name in self.camera_names:
+            namespace_prefix = self.camera_namespace(camera_name) + "/"
+            if not normalized_topic.startswith(namespace_prefix):
+                continue
+
+            relative_name = normalized_topic[len(namespace_prefix):]
+            for stream in MONITORED_STREAMS:
+                raw_name = f"{stream}/image_raw"
+                if relative_name == raw_name:
+                    return self.make_raw_image_config(camera_name, stream)
+                if relative_name == f"{raw_name}/compressed":
+                    return {
+                        "camera_name": camera_name,
+                        "topic_id": f"{stream}_compressed",
+                        "topic_name": normalized_topic,
+                        "msg_type": CompressedImage,
+                    }
+                if relative_name == f"{raw_name}/compressedDepth":
+                    return {
+                        "camera_name": camera_name,
+                        "topic_id": f"{stream}_compressed_depth",
+                        "topic_name": normalized_topic,
+                        "msg_type": CompressedImage,
+                    }
+
+            point_cloud_ids = {
+                "depth/points": "depth_points",
+                "depth_registered/points": "depth_registered_points",
+            }
+            if relative_name in point_cloud_ids:
+                return {
+                    "camera_name": camera_name,
+                    "topic_id": point_cloud_ids[relative_name],
+                    "topic_name": normalized_topic,
+                    "msg_type": PointCloud2,
+                }
+        return None
+
+    def parse_requested_topics(self, topics):
+        if not topics:
+            return {}
+
+        requested = str(topics).replace(";", ",").split(",")
+        selected = {}
+        for value in requested:
+            value = value.strip()
+            if not value:
+                continue
+            if value in MONITORED_STREAMS:
+                for camera_name in self.camera_names:
+                    config = self.make_raw_image_config(camera_name, value)
+                    selected[(camera_name, config["topic_id"])] = config
+                continue
+
+            config = self.parse_full_topic(value)
+            if config is None:
+                raise ValueError(
+                    f"Unsupported topic '{value}'. Specify a raw or compressed image topic, "
+                    "or a depth/points or depth_registered/points topic under a configured "
+                    "camera namespace."
+                )
+            selected[(config["camera_name"], config["topic_id"])] = config
+        return selected
+
+    def find_published_image_streams(self):
+        published_streams = {}
+        for camera_name in self.camera_names:
+            for stream in MONITORED_STREAMS:
+                config = self.make_raw_image_config(camera_name, stream)
+                key = (camera_name, config["topic_id"])
+                if self.count_publishers(config["topic_name"]) > 0:
+                    published_streams[key] = config
+        return published_streams
+
+    def update_topic_discovery(self):
+        if self.discovery_complete:
+            return
+
+        self.discovered_streams.update(self.find_published_image_streams())
+        elapsed = time.monotonic() - self.discovery_start_monotonic
+        if elapsed >= DISCOVERY_DURATION_SECONDS:
+            self.finish_topic_discovery(self.discovered_streams)
+
+    def finish_topic_discovery(self, selected_streams):
+        self.topic_configs = dict(selected_streams)
+        sample_start_time = time.monotonic()
+        for key, config in self.topic_configs.items():
+            camera_name = config["camera_name"]
+            topic_id = config["topic_id"]
+            self.cameras[camera_name]["trackers"][topic_id] = TopicTracker(
+                logger=self.get_logger(), sample_start_time=sample_start_time
+            )
+            self.topic_subscriptions[key] = self.create_subscription(
+                config["msg_type"],
+                config["topic_name"],
+                lambda msg, name=camera_name, selected_topic_id=topic_id: self.topic_callback(
+                    msg, name, selected_topic_id
+                ),
+                5,
+            )
+
+        self.csv_writer.writerow(self.build_csv_header())
+        self.csv_fh.flush()
+        self.start_time = time.time()
+        self.discovery_complete = True
+        self.timer = self.create_timer(1.0, self.timer_callback)
+        if self.discovery_timer is not None:
+            self.discovery_timer.cancel()
+
+    def topics_for_camera(self, camera_name):
+        return [
+            config
+            for config in self.topic_configs.values()
+            if config["camera_name"] == camera_name
+        ]
+
+    def update_topic_fps_stats(self):
+        sample_time = time.monotonic()
+        for config in self.topic_configs.values():
+            camera = self.cameras[config["camera_name"]]
+            topic_id = config["topic_id"]
+            fps_sample = camera["trackers"][topic_id].sample_fps(sample_time)
+            if fps_sample is None:
+                continue
+            current_fps, average_fps = fps_sample
+            self.update_sample_stat(
+                camera["stats"][f"{topic_id}_fps"],
+                current_fps,
+                average=average_fps,
+            )
+
     def cmdline_has_camera_namespace(self, cmdline_args, camera_name):
         ns = self.camera_namespace(camera_name)
         candidates = [
@@ -311,32 +519,30 @@ class CameraMonitorNode(Node):
 
         camera["prev_online"] = msg.device_online
 
-        # update stats from DeviceStatus message fields
-        self.update_stats(camera["stats"], "color_fps", msg.color_frame_rate_cur, msg.color_frame_rate_min, msg.color_frame_rate_max, msg.color_frame_rate_avg)
-        self.update_stats(camera["stats"], "color_delay", msg.color_delay_ms_cur, msg.color_delay_ms_min, msg.color_delay_ms_max, msg.color_delay_ms_avg)
-        self.update_stats(camera["stats"], "depth_fps", msg.depth_frame_rate_cur, msg.depth_frame_rate_min, msg.depth_frame_rate_max, msg.depth_frame_rate_avg)
-        self.update_stats(camera["stats"], "depth_delay", msg.depth_delay_ms_cur, msg.depth_delay_ms_min, msg.depth_delay_ms_max, msg.depth_delay_ms_avg)
-
-    def image_callback(self, msg: Image, camera_name: str, stream: str):
-        if stream not in ("color", "depth"):
-            return
-        header = msg.header
+    def topic_callback(self, msg, camera_name: str, topic_id: str):
+        self.first_data_collected = True
         camera = self.cameras[camera_name]
-        tracker = camera["trackers"][stream]
-        # Prefer a user-specified ideal fps for drop detection when provided.
-        fps_to_use = self.ideal_fps if (self.ideal_fps and self.ideal_fps > 0.0) else camera["stats"][f"{stream}_fps"]["avg"]
-        tracker.on_msg(header, fps_to_use)
+        camera["data_collected"] = True
+        tracker = camera["trackers"][topic_id]
+        ros_receive_time = self.get_clock().now().nanoseconds * 1e-9
+        delay_ms = tracker.on_msg(
+            msg.header,
+            ros_receive_time,
+            self.ideal_fps,
+        )
 
-    def update_stats(self, stats, key, cur, min_val, max_val, avg_val):
-        if min_val <= 1e-3 or avg_val < 0:  # ignore invalid data
-            return
-        s = stats[key]
-        s["cur"] = (cur)
-        s["count"] += 1
-        s["sum"] += avg_val
-        s["avg"] = s["sum"] / s["count"] if s["count"] > 0 else 0.0
-        s["min"] = min(s["min"], min_val)
-        s["max"] = max(s["max"], max_val)
+        if delay_ms is not None:
+            self.update_sample_stat(
+                camera["stats"][f"{topic_id}_delay"], delay_ms
+            )
+
+    def update_sample_stat(self, stat, value, average=None):
+        stat["cur"] = value
+        stat["count"] += 1
+        stat["sum"] += value
+        stat["avg"] = average if average is not None else stat["sum"] / stat["count"]
+        stat["min"] = min(stat["min"], value)
+        stat["max"] = max(stat["max"], value)
 
     def update_sys_stat(self, stat_dict, value, online=True):
         stat_dict["cur"] = value
@@ -353,7 +559,7 @@ class CameraMonitorNode(Node):
         row = [round(elapsed, 2)]
         for camera_name in self.camera_names:
             camera = self.cameras[camera_name]
-            row.extend(self.build_camera_csv_values(camera))
+            row.extend(self.build_camera_csv_values(camera_name, camera))
 
         row.extend([
             round(self.total_cpu_stats["cur"], 2), round(self.total_cpu_stats["avg"], 2),
@@ -365,19 +571,42 @@ class CameraMonitorNode(Node):
 
     def build_csv_header(self):
         header = ["time(s)"]
-        camera_fields = [
-            "connection_type", "status_online", "disconnects",
-            "color_fps_cur", "color_fps_avg", "color_fps_min", "color_fps_max",
-            "color_delay_cur", "color_delay_avg", "color_delay_min", "color_delay_max",
-            "depth_fps_cur", "depth_fps_avg", "depth_fps_min", "depth_fps_max",
-            "depth_delay_cur", "depth_delay_avg", "depth_delay_min", "depth_delay_max",
-            "cpu_cur", "cpu_avg", "cpu_min", "cpu_max",
-            "ram_cur", "ram_avg", "ram_min", "ram_max",
-            "color_frames_loss", "color_frames_loss_rate(%)",
-            "depth_frames_loss", "depth_frames_loss_rate(%)"
-        ]
         for camera_name in self.camera_names:
-            header.extend([f"{camera_name}_{field}" for field in camera_fields])
+            header.extend(
+                [
+                    f"{camera_name}_connection_type",
+                    f"{camera_name}_status_online",
+                    f"{camera_name}_disconnects",
+                ]
+            )
+            for config in self.topics_for_camera(camera_name):
+                topic_id = config["topic_id"]
+                header.extend(
+                    [
+                        f"{camera_name}_{topic_id}_fps_cur",
+                        f"{camera_name}_{topic_id}_fps_avg",
+                        f"{camera_name}_{topic_id}_fps_min",
+                        f"{camera_name}_{topic_id}_fps_max",
+                        f"{camera_name}_{topic_id}_delay_cur",
+                        f"{camera_name}_{topic_id}_delay_avg",
+                        f"{camera_name}_{topic_id}_delay_min",
+                        f"{camera_name}_{topic_id}_delay_max",
+                        f"{camera_name}_{topic_id}_sub_lost_count",
+                        f"{camera_name}_{topic_id}_sub_lost_rate(%)",
+                    ]
+                )
+            header.extend(
+                [
+                    f"{camera_name}_cpu_cur",
+                    f"{camera_name}_cpu_avg",
+                    f"{camera_name}_cpu_min",
+                    f"{camera_name}_cpu_max",
+                    f"{camera_name}_ram_cur",
+                    f"{camera_name}_ram_avg",
+                    f"{camera_name}_ram_min",
+                    f"{camera_name}_ram_max",
+                ]
+            )
 
         header.extend([
             "total_cpu_cur", "total_cpu_avg", "total_cpu_min", "total_cpu_max",
@@ -385,10 +614,7 @@ class CameraMonitorNode(Node):
         ])
         return header
 
-    def build_camera_csv_values(self, camera):
-        color_tracker = camera["trackers"]["color"]
-        depth_tracker = camera["trackers"]["depth"]
-
+    def build_camera_csv_values(self, camera_name, camera):
         def safe(k):
             v = camera["stats"].get(k, {})
             return (
@@ -398,29 +624,47 @@ class CameraMonitorNode(Node):
                 self.format_csv_number(v.get("max", 0.0)),
             )
 
-        if not camera["prev_online"]:
-            return [
-                camera["connection_type"], camera["prev_online"], camera["disconnect_count"],
-                *["N/A"] * 16,
-                round(camera["cpu_stats"]["cur"], 2), "N/A", "N/A", "N/A",
-                round(camera["ram_stats"]["cur"], 2), "N/A", "N/A", "N/A",
-                color_tracker.drop_frames, round(color_tracker.frames_loss_rate() * 100.0, 3),
-                depth_tracker.drop_frames, round(depth_tracker.frames_loss_rate() * 100.0, 3)
-            ]
-
-        return [
-            camera["connection_type"], camera["prev_online"], camera["disconnect_count"],
-            *safe("color_fps"),
-            *safe("color_delay"),
-            *safe("depth_fps"),
-            *safe("depth_delay"),
-            round(camera["cpu_stats"]["cur"], 2), round(camera["cpu_stats"]["avg"], 2),
-            self.format_csv_number(camera["cpu_stats"]["min"]), self.format_csv_number(camera["cpu_stats"]["max"]),
-            round(camera["ram_stats"]["cur"], 2), round(camera["ram_stats"]["avg"], 2),
-            self.format_csv_number(camera["ram_stats"]["min"]), self.format_csv_number(camera["ram_stats"]["max"]),
-            color_tracker.drop_frames, round(color_tracker.frames_loss_rate() * 100.0, 3),
-            depth_tracker.drop_frames, round(depth_tracker.frames_loss_rate() * 100.0, 3)
+        values = [
+            camera["connection_type"],
+            camera["prev_online"],
+            camera["disconnect_count"],
         ]
+        for config in self.topics_for_camera(camera_name):
+            topic_id = config["topic_id"]
+            tracker = camera["trackers"][topic_id]
+            if camera["prev_online"]:
+                values.extend(safe(f"{topic_id}_fps"))
+                values.extend(safe(f"{topic_id}_delay"))
+            else:
+                values.extend(["N/A"] * 8)
+            values.extend(
+                [
+                    tracker.drop_frames,
+                    round(tracker.frames_loss_rate() * 100.0, 3),
+                ]
+            )
+
+        if camera["prev_online"]:
+            values.extend(
+                [
+                    round(camera["cpu_stats"]["cur"], 2),
+                    round(camera["cpu_stats"]["avg"], 2),
+                    self.format_csv_number(camera["cpu_stats"]["min"]),
+                    self.format_csv_number(camera["cpu_stats"]["max"]),
+                    round(camera["ram_stats"]["cur"], 2),
+                    round(camera["ram_stats"]["avg"], 2),
+                    self.format_csv_number(camera["ram_stats"]["min"]),
+                    self.format_csv_number(camera["ram_stats"]["max"]),
+                ]
+            )
+        else:
+            values.extend(
+                [
+                    round(camera["cpu_stats"]["cur"], 2), "N/A", "N/A", "N/A",
+                    round(camera["ram_stats"]["cur"], 2), "N/A", "N/A", "N/A",
+                ]
+            )
+        return values
 
     def format_csv_number(self, value):
         if value == float("inf") or value == float("-inf"):
@@ -436,25 +680,30 @@ class CameraMonitorNode(Node):
         rows = []
         for camera_name in self.camera_names:
             camera = self.cameras[camera_name]
-            for stream in ["color", "depth"]:
-                fps_key = f"{stream}_fps"
-                delay_key = f"{stream}_delay"
-                topic_name = f"/{camera_name}/{stream}/image_raw"
+            for config in self.topics_for_camera(camera_name):
+                topic_id = config["topic_id"]
+                fps_key = f"{topic_id}_fps"
+                delay_key = f"{topic_id}_delay"
+                topic_name = config["topic_name"]
                 if not camera["prev_online"]:
                     rows.append([camera_name, topic_name, *["N/A"] * 10])
                 else:
                     fps_vals = format_stats(camera["stats"][fps_key])
                     delay_vals = format_stats(camera["stats"][delay_key])
-                    tracker = camera["trackers"][stream]
+                    tracker = camera["trackers"][topic_id]
 
                     frames_loss = tracker.drop_frames
                     frames_loss_rate = round(tracker.frames_loss_rate() * 100.0, 3)
                     rows.append([camera_name, topic_name, *fps_vals, *delay_vals, frames_loss, frames_loss_rate])
 
-        header_bottom = ["Camera", "Topic", "fps_cur", "fps_avg", "fps_min", "fps_max", "delay_cur(ms)", "delay_avg(ms)", "delay_min(ms)", "delay_max(ms)", "Pub_lost_count", "Pub_lost_rate(%)"]
+        header_bottom = [
+            "Camera", "Topic", "fps_cur", "fps_avg", "fps_min", "fps_max",
+            "delay_cur(ms)", "delay_avg(ms)", "delay_min(ms)", "delay_max(ms)",
+            "Sub_lost_count", "Sub_lost_rate(%)",
+        ]
 
         os.system("clear")
-        print("Orbbec Camera Benchmark\n")
+        print("Orbbec Camera Subscriber Benchmark\n")
         print(tabulate([header_bottom] + rows, tablefmt="fancy_grid"))
 
         sys_rows = []
@@ -491,13 +740,36 @@ def main(argv=None):
     )
     parser.add_argument("--run_time", type=str, default="10s", help="Total run time for monitoring, e.g., 10s, 5m, 1h.")
     parser.add_argument("--csv_file", type=str, default="camera_monitor_log.csv")
-    parser.add_argument("--ideal_fps", type=float, default=0.0, help="Optional ideal frame rate to use for drop detection (overrides reported avg).")
+    parser.add_argument(
+        "--ideal_fps",
+        type=float,
+        default=0.0,
+        help=(
+            "Optional ideal frame rate for subscriber-side drop detection; "
+            "otherwise it is learned from image timestamps."
+        ),
+    )
     parser.add_argument("--camera_names", type=str, default="camera", help="Comma-separated camera namespaces, e.g., camera,camera01,camera02.")
+    parser.add_argument(
+        "--topics",
+        type=str,
+        default="",
+        help=(
+            "Comma-separated raw stream names or full raw/compressed image and point cloud "
+            "topics. Automatic discovery only selects raw image topics."
+        ),
+    )
     cli_args, _ = parser.parse_known_args(argv)
 
     rclpy.init(args=argv)
     run_time = parse_duration(cli_args.run_time)
-    node = CameraMonitorNode(run_time, cli_args.csv_file, ideal_fps=cli_args.ideal_fps, camera_names=cli_args.camera_names)
+    node = CameraMonitorNode(
+        run_time,
+        cli_args.csv_file,
+        ideal_fps=cli_args.ideal_fps,
+        camera_names=cli_args.camera_names,
+        topics=cli_args.topics,
+    )
 
     try:
         rclpy.spin(node)