?這份代碼展示了如何使用 sort.py。注意，此處，我將文件名改為 my_sort.py。

你并不能直接 copy 使用，因為環境，包，還有模型。

此處使用 SSD-MobileNetv2 進行物體檢測，將結果傳入以 np 數組的形式傳入sort 模塊，經過處理，以 np.empty((0, 5))的格式傳出，在繪畫模塊，提取信息，標識矩形框和物體ID。

#!/home/ncut/miniconda3/envs/tf/bin/python
# -*- coding: utf-8 -*-
import rospy
import tensorflow as tf
import cv2
import numpy as np
import time
from sensor_msgs.msg import Image
from sensor_msgs.msg import CompressedImage
from cv_bridge import CvBridge, CvBridgeError
from my_sort import Sort  # 確保 my_sort 模塊在 Python 路徑下# --------------------- 模型推理模塊 ---------------------
def load_model(model_dir):"""加載 TensorFlow SavedModel（例如 ssd-mobilenet-v2 或 efficientdet）返回推理函數。"""model = tf.saved_model.load(model_dir)infer = model.signatures["serving_default"]return inferdef preprocess_frame(frame):"""預處理輸入圖像：- 將 BGR 轉換為 RGB（模型輸入要求）- 將圖像 resize 為 320x320，并擴展 batch 維度返回：input_tensor: 模型輸入 tensorwidth: 原圖寬度height: 原圖高度"""img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)resized_frame = cv2.resize(frame, (320, 320), interpolation=cv2.INTER_AREA)input_tensor = tf.convert_to_tensor(resized_frame, dtype=tf.uint8)input_tensor = tf.expand_dims(input_tensor, 0)  # 增加 batch 維度height, width = frame.shape[:2]return input_tensor, width, heightdef run_inference(infer, input_tensor):"""利用推理函數執行模型預測，返回檢測框和置信度。"""detections = infer(input_tensor)num_detections = int(detections['num_detections'].numpy()[0])boxes = detections['detection_boxes'].numpy()[0][:num_detections]scores = detections['detection_scores'].numpy()[0][:num_detections]return boxes, scoresdef convert_detections_to_sort(boxes, scores, width, height, threshold=0.5):"""將檢測結果（歸一化坐標）轉換為 SORT 跟蹤器所需格式：[x1, y1, x2, y2, score]"""sort_inputs = []for i in range(len(scores)):if scores[i] < threshold:continueymin, xmin, ymax, xmax = boxes[i]x1 = int(xmin * width)x2 = int(xmax * width)y1 = int(ymin * height)y2 = int(ymax * height)sort_inputs.append([x1, y1, x2, y2, scores[i]])return np.array(sort_inputs)# --------------------- 跟蹤與可視化模塊 ---------------------
def draw_tracks(frame, tracks):"""在圖像上繪制跟蹤結果（邊框和跟蹤ID）。"""for track in tracks:x1, y1, x2, y2, track_id = track.astype(int)cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)cv2.putText(frame, f"ID: {track_id}", (x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)return frame# --------------------- ROS 圖像訂閱與處理模塊 ---------------------
# 全局變量：模型推理函數、SORT 跟蹤器、CvBridge 實例
infer = None
tracker = None
bridge = CvBridge()def image_callback(msg):"""ROS 圖像回調函數：- 將 ROS 圖像消息轉換為 OpenCV 格式- 進行模型推理和 SORT 跟蹤- 顯示帶跟蹤結果的圖像"""global infer, tracker, bridgetry:cv_image = bridge.imgmsg_to_cv2(msg, "bgr8") # raw#cv_image = bridge.compressed_imgmsg_to_cv2(msg,"bgr8") #compressed compressed_imgmsg_to_cv2except CvBridgeError as e:rospy.logerr("CvBridge 轉換錯誤: %s", e)return# 圖像預處理和模型推理input_tensor, width, height = preprocess_frame(cv_image)boxes, scores = run_inference(infer, input_tensor)detections = convert_detections_to_sort(boxes, scores, width, height, threshold=0.5)# 更新 SORT 跟蹤器并繪制跟蹤結果tracks = tracker.update(detections)tracked_frame = draw_tracks(cv_image.copy(), tracks)# 顯示帶跟蹤結果的圖像cv2.imshow("Tracking", tracked_frame)cv2.waitKey(1)def main():global infer, tracker# 初始化 ROS 節點rospy.init_node("tracking_inference_node", anonymous=True)# 加載模型model_dir = "/home/ncut/models/ssd-mobilenet-v2"  # 根據需要更新模型路徑time_before_load = time.time()infer = load_model(model_dir)time_after_load = time.time()rospy.loginfo("模型加載耗時：%.2f 秒", time_after_load - time_before_load)# 初始化 SORT 跟蹤器tracker = Sort(max_age=1, min_hits=3, iou_threshold=0.3)# 訂閱圖像話題 below is a description of the car launch file astra    /camera/rgb/image_raw/compressed# to original photo, topic is /camera/rgb/image_raw                 average rate is 2Hz     4.02MB/s# to compressed photo, topic is /camera/rgb/image_raw/compressed    average rate is 30Hz    1.25MB/srospy.Subscriber("/camera/rgb/image_raw", Image, image_callback)    # Image for raw, CompressedImage for compressedrospy.loginfo("Tracking Inference Node 已啟動，訂閱話題：/camera/rgb/image_raw")# ROS 循環等待消息rospy.spin()cv2.destroyAllWindows()if __name__ == '__main__':try:main()except rospy.ROSInterruptException:pass