让spark机器FOLLOW
这段代码是一个ROS（Robot Operating System）节点，主要用于检测图像中的物体，并结合机器人跟随功能。以下是代码的主要部分和功能：1. 导入所需的库，包括TensorFlow、OpenCV、ROS消息类型等。2. 设置模型和标签地图的路径。这里使用了SSD（Single Shot MultiBox Detector）MobileNet模型，针对COCO（Common Objects in Context）数据集训练。3. 定义一个`detector`类，该类包含一个图像回调函数`image_cb`，用于处理订阅的图像消息。在回调函数中，使用TensorFlow模型对图像进行物体检测，并将检测结果发布出去。4. 在`image_cb`函数中，首先将ROS图像消息转换为OpenCV图像，然后使用TensorFlow模型进行物体检测，最后将检测结果发布出去。5. `object_predict`函数用于根据TensorFlow模型的输出预测物体的类别和位置。6. `main`函数初始化ROS节点，并创建一个`detector`对象来处理图像消息。7. 最后，定义了一个ROS launch文件，用于启动该节点和其他相关节点。此外，代码还包括了机器人跟随功能的部分参考代码，如`cal_center`函数用于计算物体中心的坐标，`check_inbox`函数用于检查物体是否在特定的区域内，`start_following`函数用于根据检测到的物体位置控制机器人的运动。这个节点可以用于机器人视觉导航、物体识别和跟随等应用。它订阅了一个图像话题，使用TensorFlow模型进行物体检测，并将检测结果发布到多个话题。物体检测结果包括物体的类别、位置和置信度分数。
#!/usr/bin/env python## Author: Rohit## Date: July, 25, 2017# Purpose: Ros node to detect objects using tensorflow # 加载必要的功能函数，包括tensorflow import osimport sysimport cv2import numpy as nptry: import tensorflow as tfexcept ImportError: print("unable to import TensorFlow. Is it installed?") print("sudo apt install python-pip") print("sudo pip install tensorflow") sys.exit(1) # 加载ros相关message文件import rospyfrom std_msgs.msg import String , Headerfrom sensor_msgs.msg import Imagefrom cv_bridge import CvBridge, CvBridgeErrorfrom vision_msgs.msg import Detection2D, Detection2DArray, ObjectHypothesisWithPosefrom dl_msgs.msg import DetectionArray, ObjectInfo # 加载物体识别的功能包import object_detectionfrom object_detection.utils import label_map_utilfrom object_detection.utils import visualization_utils as vis_util # 设置GPU占用率如果利用GPU进行学习GPU_FRACTION = 0.4 ######### 设置学习的模型 ############MODEL_NAME ='ssd_mobilenet_v1_coco_11_06_2017'# By default models are stored in data/models/MODEL_PATH = os.path.join(os.path.dirname(sys.path),'data','models' , MODEL_NAME)# Path to frozen detection graph. This is the actual model that is used for the object detection.PATH_TO_CKPT = MODEL_PATH + '/frozen_inference_graph.pb' ######### 设置物体标签地图 ###########LABEL_NAME = 'mscoco_label_map.pbtxt'# By default label maps are stored in data/labels/PATH_TO_LABELS = os.path.join(os.path.dirname(sys.path),'data','labels', LABEL_NAME)######### Set the number of classes here #########NUM_CLASSES = 90 detection_graph = tf.Graph()with detection_graph.as_default():od_graph_def = tf.GraphDef()with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') ## 加载标签地图# 标签地图通过序号索引到物体名称，所以当卷积网络预测到数字5时，我们知道是对应着’airplane’label_map = label_map_util.load_labelmap(PATH_TO_LABELS)categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)category_index = label_map_util.create_category_index(categories) # 设置 GPU 选项来 使用我们之前设置的GPU占用率config = tf.ConfigProto()config.gpu_options.per_process_gpu_memory_fraction = GPU_FRACTION # 开始识别with detection_graph.as_default():with tf.Session(graph=detection_graph,config=config) as sess: class detector: def __init__(self): self.image_pub = rospy.Publisher("debug_image",Image, queue_size=1) self.object_pub = rospy.Publisher("objects", Detection2DArray, queue_size=1) self.obj_info_pub = rospy.Publisher("objects_info", ObjectInfo, queue_size=1) self.objectinfo_list_pub = rospy.Publisher("object_info_list", DetectionArray, queue_size=1) self.bridge = CvBridge() self.image_sub = rospy.Subscriber("image", Image, self.image_cb, queue_size=1, buff_size=2**24) #图像回调 def image_cb(self, data): objArray = Detection2DArray() objDetection = DetectionArray() obj_info = ObjectInfo() try: cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8") except CvBridgeError as e: print(e) image=cv2.cvtColor(cv_image,cv2.COLOR_BGR2RGB) # 基于数组的图像会在后面被用作带有方框和标签的图像结果 image_np = np.asarray(image) # 把维度展开因为模型认为图像有格式 image_np_expanded = np.expand_dims(image_np, axis=0) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # 每个方框boxes都代表着被识别到的物体的范围 boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # 每个分数score都代表着对识别到的物体的精确度，分数是与方框和名字一起呈现在屏幕上的. scores = detection_graph.get_tensor_by_name('detection_scores:0') classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') (boxes, scores, classes, num_detections) = sess.run(, feed_dict={image_tensor: image_np_expanded}) objects=vis_util.visualize_boxes_and_labels_on_image_array( image, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=2) # 定义识别到的物体信息 objArray.detections =[] obj_info = ObjectInfo() objDetection.objectinfo = [] objArray.header=data.header object_count=1 for i in range(len(objects)): object_count+=1 obj, obj_info = self.object_predict(objects,data.header,image_np,cv_image) objArray.detections.append(obj) objDetection.objectinfo.append(obj_info) self.obj_info_pub.publish(obj_info) self.objectinfo_list_pub.publish(objDetection) self.object_pub.publish(objArray) img=cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) image_out = Image() try: image_out = self.bridge.cv2_to_imgmsg(img,"bgr8") except CvBridgeError as e: print(e) image_out.header = data.header self.image_pub.publish(image_out) def object_predict(self,object_data, header, image_np,image): image_height,image_width,channels = image.shape obj=Detection2D() obj_info=ObjectInfo() obj_hypothesis= ObjectHypothesisWithPose() object_id=object_data object_score=object_data dimensions=object_data object_name=category_index[‘name’] obj.header=header obj_hypothesis.id = object_id obj_hypothesis.score = object_score obj.results.append(obj_hypothesis) obj.bbox.size_y = int((dimensions-dimensions)*image_height) obj.bbox.size_x = int((dimensions-dimensions)*image_width) obj.bbox.center.x = int((dimensions + dimensions)*image_height/2) obj.bbox.center.y = int((dimensions + dimensions)*image_width/2) def main(args):rospy.init_node('detector_node')obj=detector()try: rospy.spin()except KeyboardInterrupt: print("ShutDown")cv2.destroyAllWindows() if __name__=='__main__':main(sys.argv)
def cal_center(self, box): xc = int((box + box) / 2) yc = int((box + box) / 2) return xc, yc # 检查是否在原位 def check_inbox(self): global xc, yc, xc_prev, yc_prev inbox = False if abs(xc_prev - xc) < 40 and abs(yc_prev - yc) < 40: xc_prev, yc_prev = xc, yc inbox = True return inbox # 跟随代码 def start_following(self, pointcloud, x_range, y_range, xc): cmd_pub = Twist() x = 0 z = 0 n = 0 p = [] # get the range of detected box for i in x_range: for j in y_range: pc = point_cloud2.read_points(pointcloud, field_names=("x","y","z"), skip_nans=True, uvs=[]) for p in pc: if p <= 2.5: x += -p z += p n += 1 if not p or n == 0: x_linear = 0 z_angular = 0 elif abs(xc - IMAGE_WIDTH / 2) < 20: z /= n x_linear = z - GOAL_DEPTH z_angular = 0 else: x /= n z /= n rospy.loginfo("n: " + str(n) + ",x: " + str(x) + ",z: " + str(z)) dist = math.sqrt(x * x + z * z) x_linear = z - GOAL_DEPTH z_angular = math.asin(x / dist) if abs(x_linear) < DEPTH_THRESHOLD: x_linear = 0 elif x_linear > 1.2: x_linear = 1 if z_angular > TURN_THRESHOLD: z_angular = TURN_THRESHOLD elif z_angular < -TURN_THRESHOLD: z_angular = -TURN_THRESHOLD cmd_pub.linear.x = x_linear cmd_pub.angular.z = z_angular rospy.loginfo(cmd_pub) self.pub_cmd.publish(cmd_pub) rospy.sleep(5) #发布消息 def publish(self, detections, image_outgoing): """ - publish detection information and drawn image """ self.pub_det.publish(detections) self.pub_det_rgb.publish(image_outgoing)
设置启动文件!-- 启动文件 第三课：人脸识别主人跟随 --> <launch> <arg name="camera_types" default="astrapro"/> <!--spark 驱动，机器人描述，相机，底盘--> <include file="$(find spark_bringup)/launch/driver_bringup.launch"> <arg name="camera_type_tel" value="$(arg camera_types)" /> </include> <node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros_DLunit3.py"output="screen"> <remap from="image" to="/camera/color/image_raw" if="$(eval arg('camera_types')=='d435')"/> <remap from="image" to="/camera/rgb/image_raw" if="$(eval arg('camera_types')=='astrapro')"/> </node> <arg name="master_name" value=”any_name”/> <node pkg="unit3_follow" name="face_recognition_node" type="face_recognizer.py" output="screen"> <param name="master_name"value="$(arg master_name)"/> <remap from="image" to="/camera/rgb/image_raw" /> </node> <!-- rviz --> <arg name ="rviz" default="true" /> <arg name ="rviz_file" default="$(find unit3_follow)/rviz/display.rviz"/> <node pkg ="rviz" type="rviz" name="rviz" output="screen" args= "-d $(arg rviz_file)" if="$(arg rviz)"/> </launch>
仅供参考，spark机器人跟随的部分参考代码
牛牛牛，很有创新;P ;P加油很有参考意义 刘子康 发表于 2024-7-6 17:42
牛牛牛，很有创新
谢谢 陈厚树 发表于 2024-7-6 17:47
加油很有参考意义
谢谢 很新颖的观点 继续加油，很有帮助 这个功能很新颖 这个功能很有趣 很有创新和有趣的功能
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