Merge branch 'master' of http://git.wxchen.site/wxchen/maintain into da

modified:   .gitignore
	modified:   .vscode/tasks.json
	modified:   src/maintain/scripts/test.py
	modified:   src/vision_opencv/cv_bridge/python/cv_bridge/__pycache__/core.cpython-38.pyc

.gitignore

@@ -1,6 +1,5 @@
 .catkin_tools
 .vscode
-.vscode/
 .vscode/*
 /build
 /devel

.vscode/c_cpp_properties.json

@@ -0,0 +1,20 @@
+{
+  "configurations": [
+    {
+      "browse": {
+        "databaseFilename": "${default}",
+        "limitSymbolsToIncludedHeaders": false
+      },
+      "includePath": [
+        "/opt/ros/melodic/include/**",
+        "/usr/include/**"
+      ],
+      "name": "ROS",
+      "intelliSenseMode": "gcc-x64",
+      "compilerPath": "/usr/bin/gcc",
+      "cStandard": "gnu11",
+      "cppStandard": "c++14"
+    }
+  ],
+  "version": 4
+}

.vscode/settings.json

@@ -0,0 +1,8 @@
+{
+    "python.autoComplete.extraPaths": [
+        "/opt/ros/melodic/lib/python2.7/dist-packages"
+    ],
+    "python.analysis.extraPaths": [
+        "/opt/ros/melodic/lib/python2.7/dist-packages"
+    ]
+}

.vscode/tasks.json

@@ -0,0 +1,18 @@
+{
+    "tasks": [
+        {
+            "type": "shell",
+            "command": "catkin",
+            "args": [
+                "build",
+                "-DPYTHON_EXECUTABLE=/home/da/miniconda3/envs/gsmini/bin/python",
+                //"-DPYTHON_EXECUTABLE=${HOME}/.conda/envs/gsmini/bin/python"
+            ],
+            "problemMatcher": [
+                "$catkin-gcc"
+            ],
+            "group": "build",
+            "label": "catkin: build"
+        }
+    ]
+}

src/maintain/scripts/maintain.py

@@ -1,136 +0,0 @@
-#! /home/wxchen/.conda/envs/gsmini/bin/python
-
-import rospy
-import numpy as np
-import open3d as o3d
-from sensor_msgs.msg import Image , CameraInfo, PointCloud2
-from detection_msgs.msg import BoundingBox, BoundingBoxes
-import sensor_msgs.point_cloud2 as pc2
-import cv_bridge
-from cv_bridge import CvBridge, CvBridgeError
-import cv2
-import tf2_ros
-import tf
-from geometry_msgs.msg import PoseStamped, TransformStamped
-
-bridge = CvBridge()
-color_intrinsics = None
-cloud = None
-box = None
-d_width = 100
-
-def camera_info_callback(msg):
-    global color_intrinsics
-    color_intrinsics = msg
-
-def depth_image_callback(msg):
-    global depth_image
-    depth_image = bridge.imgmsg_to_cv2(msg, '16UC1')
-
-def point_cloud_callback(msg):
-    global cloud
-    cloud = pc2.read_points(msg, field_names=("x", "y", "z"), skip_nans=True)
-
-def bounding_boxes_callback(msg):
-    global box
-    for bounding_box in msg.bounding_boxes:
-        # Assuming there's only one box, you can add a condition to filter the boxes if needed
-        box = [bounding_box.xmin - d_width, bounding_box.ymin - d_width, bounding_box.xmax + d_width, bounding_box.ymax + d_width]
-
-def main():
-    rospy.init_node("plane_fitting_node")
-
-    rospy.Subscriber("/camera/color/camera_info", CameraInfo, camera_info_callback)
-    rospy.Subscriber("/camera/aligned_depth_to_color/image_raw", Image, depth_image_callback)
-    rospy.Subscriber("/camera/depth/color/points", PointCloud2, point_cloud_callback)
-    rospy.Subscriber("/yolov5/detections", BoundingBoxes, bounding_boxes_callback)
-    tf_broadcaster = tf2_ros.TransformBroadcaster()
-
-    plane_pub = rospy.Publisher("/plane_pose", PoseStamped, queue_size=10)
-
-    rate = rospy.Rate(10) # 10 Hz
-
-    while not rospy.is_shutdown():
-        if color_intrinsics is not None and cloud is not None and box is not None:
-            # Get the 3D points corresponding to the box
-            fx, fy = color_intrinsics.K[0], color_intrinsics.K[4]
-            cx, cy = color_intrinsics.K[2], color_intrinsics.K[5]
-            points = []
-
-            center_x = (box[0] + box[2]) / 2
-            center_y = (box[1] + box[3]) / 2
-
-            depth_array = np.array(depth_image, dtype=np.float32)
-            pz = depth_array[int(center_y), int(center_x)] / 1000.0
-            px = (center_x - color_intrinsics.K[2]) * pz / color_intrinsics.K[0]
-            py = (center_y - color_intrinsics.K[5]) * pz / color_intrinsics.K[4]
-
-            rospy.loginfo("Center point: {}".format([px, py, pz]))
-
-            screw_point = None
-            for x, y, z in cloud:
-                if z != 0:
-                    u = int(np.round((x * fx) / z + cx))
-                    v = int(np.round((y * fy) / z + cy))
-                    if u == center_x and v == center_y:
-                        screw_point = [x, y, z]
-                    if u >= box[0] and u <= box[2] and v >= box[1] and v <= box[3]:
-                        points.append([x, y, z])
-            points = np.array(points)
-            
-            if px != 0 and py != 0 and pz != 0:
-
-                # rospy.loginfo("Screw point: {}".format(screw_point))
-
-                # Fit a plane to the points
-                pcd = o3d.geometry.PointCloud()
-                pcd.points = o3d.utility.Vector3dVector(points)
-                plane_model, inliers = pcd.segment_plane(distance_threshold=0.02, ransac_n=3, num_iterations=100)
-                [a, b, c, d] = plane_model
-
-                # Calculate the rotation between the plane normal and the Z axis
-                normal = np.array([a, b, c])
-                z_axis = np.array([0, 0, 1])
-                cos_theta = np.dot(normal, z_axis) / (np.linalg.norm(normal) * np.linalg.norm(z_axis))
-                theta = np.arccos(cos_theta)
-                rotation_axis = np.cross(z_axis, normal)
-                rotation_axis = rotation_axis / np.linalg.norm(rotation_axis)
-                quaternion = np.hstack((rotation_axis * np.sin(theta / 2), [np.cos(theta / 2)]))
-                
-
-                # Publish the plane pose
-                # plane_pose = PoseStamped()
-                # plane_pose.header.stamp = rospy.Time.now()
-                # plane_pose.header.frame_id = "camera_color_optical_frame"
-                # plane_pose.pose.position.x = screw_point[0]
-                # plane_pose.pose.position.y = screw_point[1]
-                # plane_pose.pose.position.z = -d / np.linalg.norm(normal)
-                # plane_pose.pose.orientation.x = quaternion[0]
-                # plane_pose.pose.orientation.y = quaternion[1]
-                # plane_pose.pose.orientation.z = quaternion[2]
-                # plane_pose.pose.orientation.w = quaternion[3]
-                # plane_pub.publish(plane_pose)
-
-                # publish screw tf
-                screw_tf = TransformStamped()
-                screw_tf.header.stamp = rospy.Time.now()
-                screw_tf.header.frame_id = "camera_color_optical_frame"
-                screw_tf.child_frame_id = "screw"
-                screw_tf.transform.translation.x = px
-                screw_tf.transform.translation.y = py
-                screw_tf.transform.translation.z = pz
-                screw_tf.transform.rotation.x = quaternion[0]
-                screw_tf.transform.rotation.y = quaternion[1]
-                screw_tf.transform.rotation.z = quaternion[2]
-                screw_tf.transform.rotation.w = quaternion[3]
-
-                tf_broadcaster.sendTransform(screw_tf)
-
-
-        rate.sleep()
-
-if __name__ == "__main__":
-    try:
-        main()
-    except rospy.ROSInterruptException:
-        pass

src/maintain/scripts/test copy.py

@@ -0,0 +1,93 @@
+#! /home/wxchen/.conda/envs/gsmini/bin/python
+
+import numpy as np
+import cv2 as cv
+from matplotlib import pyplot as plt
+import rospy
+from sensor_msgs.msg import Image
+import message_filters
+from cv_bridge import CvBridge, CvBridgeError
+import rospkg
+
+MIN_MATCH_COUNT = 10
+pkg_path = rospkg.RosPack().get_path('maintain')
+rospy.loginfo(pkg_path)
+img_template = cv.imread(pkg_path + '/scripts/tt.png',0)
+
+def callback(rgb, depth):
+    rospy.loginfo("callback")
+    bridge = CvBridge()
+    # rospy.loginfo(rgb.header.stamp)
+    # rospy.loginfo(depth.header.stamp)
+    try:
+        rgb_image = bridge.imgmsg_to_cv2(rgb, 'bgr8')
+        depth_image = bridge.imgmsg_to_cv2(depth, '16UC1')
+
+        img_matcher = matcher(rgb_image)
+        cv.imshow("img_matcher", img_matcher)
+        cv.waitKey(1000)
+    
+    except CvBridgeError as e:
+        print(e)
+
+def matcher(img):
+
+    try:
+        # Initiate SIFT detector
+        sift = cv.SIFT_create()
+
+        # find the keypoints and descriptors with SIFT
+        kp1, des1 = sift.detectAndCompute(img_template,None)
+        kp2, des2 = sift.detectAndCompute(img,None)
+
+        FLANN_INDEX_KDTREE = 1
+        index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
+        search_params = dict(checks = 50)
+
+        flann = cv.FlannBasedMatcher(index_params, search_params)
+        matches = flann.knnMatch(des1,des2,k=2)
+
+        # store all the good matches as per Lowe's ratio test.
+        good = []
+        for m,n in matches:
+            if m.distance < 0.7*n.distance:
+                good.append(m)
+
+        if len(good)>MIN_MATCH_COUNT:
+            src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
+            dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
+
+            M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
+            matchesMask = mask.ravel().tolist()
+
+            h,w = img_template.shape
+            pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
+            dst = cv.perspectiveTransform(pts,M)
+
+            roi = img[np.int32(dst)[0][0][1]:np.int32(dst)[2][0][1], np.int32(dst)[0][0][0]:np.int32(dst)[2][0][0]]
+            # roi = detect_black(roi)
+            
+            # img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
+        else:
+            print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
+
+        return roi
+    except Exception as e:
+        print(e)
+
+
+
+
+if __name__ == "__main__":
+
+    rospy.init_node("maintain")  
+    rospy.loginfo("maintain task start ......") 
+
+    rgb_sub = message_filters.Subscriber("/camera/color/image_raw", Image)
+    depth_sub = message_filters.Subscriber("/camera/aligned_depth_to_color/image_raw", Image)
+
+    ts = message_filters.TimeSynchronizer([rgb_sub, depth_sub], 1)
+    ts.registerCallback(callback)
+
+
+    rospy.spin()

src/maintain/scripts/test.py

@@ -1,166 +1,28 @@
-#! /home/wxchen/.conda/envs/gsmini/bin/python
+#! /home/da/miniconda3/envs/gsmini/bin/python
 
 import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 import rospy
 import tf2_ros
-import tf
+import tf2_ros
-from sensor_msgs.msg import Image , CameraInfo, PointCloud2
+from sensor_msgs.msg import Image , CameraInfo
-from geometry_msgs.msg import PoseStamped, TransformStamped, Quaternion
+from geometry_msgs.msg import PoseStamped, TransformStamped
+from geometry_msgs.msg import PoseStamped, TransformStamped
 import message_filters
 from cv_bridge import CvBridge, CvBridgeError
 import rospkg
-# import open3d as o3d
-# from open3d_ros_helper import open3d_ros_helper as orh
 
-import math
+import os
+import sys
 from rostopic import get_topic_type
 from detection_msgs.msg import BoundingBox, BoundingBoxes
 
 bridge = CvBridge()
-annulus_width = 10
-
-# 2d to 3d
-def computer_2d_3d(x, y, depth_roi, color_intrinsics):
-    pz = depth_roi[int(y), int(x)] / 1000.0
-    px = (x - color_intrinsics[2]) * pz / color_intrinsics[0]
-    py = (y - color_intrinsics[5]) * pz / color_intrinsics[4]
-    return px, py, pz
-
-def compute_plane_normal(box, depth, color_intrinsics):
-    # 计算相机内参
-    fx = color_intrinsics[0]
-    fy = color_intrinsics[4]
-    cx = color_intrinsics[2]
-    cy = color_intrinsics[5]
-    # 计算矩形中心点坐标
-    x_center = (box[0] + box[2]) / 2
-    y_center = (box[1] + box[3]) / 2
-    z = depth[int(y_center), int(x_center)]
-    x = (x_center - cx) * z / fx
-    y = (y_center - cy) * z / fy
-    # 计算四个顶点坐标
-    x1 = (box[0] - cx) * z / fx
-    y1 = (box[1] - cy) * z / fy
-    x2 = (box[2] - cx) * z / fx
-    y2 = (box[1] - cy) * z / fy
-    x3 = (box[2] - cx) * z / fx
-    y3 = (box[3] - cy) * z / fy
-    x4 = (box[0] - cx) * z / fx
-    y4 = (box[3] - cy) * z / fy
-    # 计算矩形边缘向量
-    v1 = np.array([x2 - x1, y2 - y1, depth[int(box[1]), int(box[0])] - z])
-    v2 = np.array([x3 - x2, y3 - y2, depth[int(box[1]), int(box[2])] - z])
-    v3 = np.array([x4 - x3, y4 - y3, depth[int(box[3]), int(box[2])] - z])
-    v4 = np.array([x1 - x4, y1 - y4, depth[int(box[3]), int(box[0])] - z])
-    # 计算平面法向量
-    normal = np.cross(v1, v2)
-    normal += np.cross(v2, v3)
-    normal += np.cross(v3, v4)
-    normal += np.cross(v4, v1)
-    normal /= np.linalg.norm(normal)
-    # 计算法向量相对于参考向量的旋转角度和旋转轴
-    ref_vector = np.array([0, 0, 1])
-    normal_vector = normal
-    angle = math.acos(np.dot(ref_vector, normal_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(normal_vector)))
-    axis = np.cross(ref_vector, normal_vector)
-    axis = axis / np.linalg.norm(axis)
-
-    # 将旋转角度和旋转轴转换为四元数
-    qx, qy, qz, qw = tf.transformations.quaternion_about_axis(angle, axis)
-    quaternion = [qx, qy, qz, qw]
-    return quaternion  
-
-def calculate_image_edge_plane_normal(depth_roi):
-    # Get the shape of the depth_roi
-    height, width = depth_roi.shape
-    
-    # Get the edges of the ROI
-    left_edge = [(0, y) for y in range(height)]
-    right_edge = [(width-1, y) for y in range(height)]
-    top_edge = [(x, 0) for x in range(width)]
-    bottom_edge = [(x, height-1) for x in range(width)]
-    edges = left_edge + right_edge + top_edge + bottom_edge
-
-    # Create a 2D grid of X and Y coordinates
-    X, Y = np.meshgrid(np.arange(width), np.arange(height))
-
-    # Reshape the X, Y, and depth_roi arrays into one-dimensional arrays
-    X = X.reshape(-1)
-    Y = Y.reshape(-1)
-    Z = depth_roi.reshape(-1)
-
-    # Stack the X, Y, and depth_roi arrays vertically to create a 3D array of points in the form of [X, Y, Z]
-    points = np.vstack([X, Y, Z]).T
-
-    # Compute the mean depth value of the edges
-    edge_depths = []
-    for edge_point in edges:
-        edge_depths.append(depth_roi[edge_point[1], edge_point[0]])
-    mean_depth = np.mean(edge_depths)
-
-    # Create a mask to extract the points on the edges
-    mask = np.zeros_like(depth_roi, dtype=np.uint8)
-    for edge_point in edges:
-        mask[edge_point[1], edge_point[0]] = 1
-    masked_depth_roi = depth_roi * mask
-
-    # Extract the 3D coordinates of the points on the edges
-    edge_points = []
-    for edge_point in edges:
-        edge_points.append([edge_point[0], edge_point[1], masked_depth_roi[edge_point[1], edge_point[0]]])
-
-    # Convert the list of edge points to a numpy array
-    edge_points = np.array(edge_points)
-
-    # Shift the edge points so that the mean depth value is at the origin
-    edge_points = edge_points - np.array([width/2, height/2, mean_depth])
-
-    # Compute the singular value decomposition (SVD) of the edge points
-    U, S, V = np.linalg.svd(edge_points)
-
-    # Extract the normal vector of the plane that best fits the edge points from the right-singular vector corresponding to the smallest singular value
-    normal = V[2]
-
-    return normal
-
-# def compute_normal_vector(p1, p2, p3, p4):
-#     # Compute two vectors in the plane
-#     v1 = np.array(p2) - np.array(p1)
-#     v2 = np.array(p3) - np.array(p1)
-#     # Compute the cross product of the two vectors to get the normal vector
-#     n = np.cross(v1, v2)
-#     # Compute the fourth point in the plane
-#     p4 = np.array(p4)
-#     # Check if the fourth point is on the same side of the plane as the origin
-#     if np.dot(n, p4 - np.array(p1)) < 0:
-#         n = -n
-#     # Normalize the normal vector to obtain a unit vector
-#     n = n / np.linalg.norm(n)
-#     theta = math.acos(n[2])
-#     sin_theta_2 = math.sin(theta/2)
-#     quaternion = [math.cos(theta/2), sin_theta_2 * n[0], sin_theta_2 * n[1], sin_theta_2 * n[2]]
-#     return quaternion
-
-def filter_quaternion(quat, quat_prev, alpha):
-    if quat_prev is None:
-        quat_prev = quat
-    # Apply low-pass filter to quaternion
-    quat_filtered = np.zeros(4)
-    for i in range(4):
-        quat_filtered[i] = alpha * quat[i] + (1-alpha) * quat_prev[i]
-    # Normalize the quaternion
-    quat_filtered = quat_filtered / np.linalg.norm(quat_filtered)
-    return quat_filtered  
-
 def box_callback(box, depth, color_info):
     try:
         color_intrinsics = color_info.K
         depth_image = bridge.imgmsg_to_cv2(depth, '16UC1')
-        # pc = orh.rospc_to_o3dpc(pc_msg)
-
-        if box is not None and len(box.bounding_boxes) > 0:
         # get the center of screw 
         boundingBox = box.bounding_boxes[0]
         screw_x = (boundingBox.xmax + boundingBox.xmin) / 2
@@ -168,46 +30,38 @@ def box_callback(box, depth, color_info):
         # print(screw_x,screw_y)
 
         depth_array = np.array(depth_image, dtype=np.float32)
-            # depth_roi = depth_array[boundingBox.ymin:boundingBox.ymax, boundingBox.xmin:boundingBox.xmax]
+        depth_roi = depth_array[boundingBox.ymin:boundingBox.ymax, boundingBox.xmin:boundingBox.xmax]
 
-            x, y, z = computer_2d_3d(screw_x, screw_y, depth_array, color_intrinsics)
+        z = np.mean(depth_roi) * 0.001
-            # rospy.loginfo("screw pose: x: %f, y: %f, z: %f", x, y, z)
+        x = (screw_x - color_intrinsics[2]) * z / color_intrinsics[0]
+        y = (screw_y - color_intrinsics[5]) * z / color_intrinsics[4]
+        rospy.loginfo("screw pose: x: %f, y: %f, z: %f", x, y, z)
         # calculate normal direction of screw area
-            box = [boundingBox.ymin - annulus_width, boundingBox.xmin - annulus_width, boundingBox.ymax + annulus_width, boundingBox.xmax + annulus_width]
-            # p1x, p1y, p1z = computer_2d_3d(boundingBox.xmin-annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
-            # p2x, p2y, p2z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
-            # p3x, p3y, p3z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymax+annulus_width, depth_array, color_intrinsics)
-            # p4x, p4y, p4z = computer_2d_3d(boundingBox.xmin-annulus_width, boundingBox.ymax+annulus_width, depth_array, color_intrinsics)
-            # p1 = [p1x, p1y, p1z]
-            # p2 = [p2x, p2y, p2z]
-            # p3 = [p3x, p3y, p3z]
-            # p4 = [p4x, p4y, p4z]
-            # normal_q = compute_normal_vector(p1, p2, p3, p4)
-            normal_q = compute_plane_normal(box, depth_array, color_intrinsics)
 
-            # annulus_roi = depth_array[boundingBox.ymin-annulus_width:boundingBox.ymax+annulus_width, boundingBox.xmin-annulus_width:boundingBox.xmax+annulus_width]
+        X,Y = np.meshgrid(np.arange(depth_roi.shape[1]), np.arange(depth_roi.shape[0]))
-            # normal = calculate_image_edge_plane_normal(annulus_roi)
+        X = X.reshape(-1)
+        Y = Y.reshape(-1)
+        Z = depth_roi.reshape(-1)
+        points = np.vstack([X, Y, Z]).T
+        center = np.mean(points, axis=0)
+        points = points - center
+        U, S, V = np.linalg.svd(points)
+        normal = V[2]
         # print(normal)
 
+        # publish screw pose
+        # screw_pose = PoseStamped()
+        # screw_pose.header.stamp = rospy.Time.now()
+        # screw_pose.header.frame_id = "camera_color_optical_frame"
+        # screw_pose.pose.position.x = x
+        # screw_pose.pose.position.y = y
+        # screw_pose.pose.position.z = z
+        # screw_pose.pose.orientation.x = 0
+        # screw_pose.pose.orientation.y = 0
+        # screw_pose.pose.orientation.z = 0
+        # screw_pose.pose.orientation.w = 1
 
-            # normal vector to quaternion
+        # pose_pub.publish(screw_pose)
-            screw_quat = tf.transformations.quaternion_from_euler(0, 0, 0)
-            screw_quat[0] = normal_q[0]
-            screw_quat[1] = normal_q[1]
-            screw_quat[2] = normal_q[2]
-            screw_quat[3] = normal_q[3]
-
-            # quaternion to euler
-            screw_euler = tf.transformations.euler_from_quaternion(screw_quat)
-            screw_quat_zero_z = tf.transformations.quaternion_from_euler(screw_euler[0], screw_euler[1], 0)
-
-
-            # Apply low-pass filter to screw quaternion
-            alpha = 0.4
-            global screw_quat_prev
-            screw_quat_filtered = filter_quaternion(screw_quat, screw_quat_prev, alpha)
-            screw_quat_prev = screw_quat_filtered
-
 
         # publish screw tf
         screw_tf = TransformStamped()
@@ -217,22 +71,21 @@ def box_callback(box, depth, color_info):
         screw_tf.transform.translation.x = x
         screw_tf.transform.translation.y = y
         screw_tf.transform.translation.z = z
-            screw_tf.transform.rotation.x = screw_quat_filtered[0]
+        screw_tf.transform.rotation.x = normal[0]
-            screw_tf.transform.rotation.y = screw_quat_filtered[1]
+        screw_tf.transform.rotation.y = normal[1]
-            screw_tf.transform.rotation.z = screw_quat_filtered[2]
+        screw_tf.transform.rotation.z = normal[2]
-            screw_tf.transform.rotation.w = screw_quat_filtered[3]
+        screw_tf.transform.rotation.w = 0
 
         tf_broadcaster.sendTransform(screw_tf)
 
 
+
     except Exception as e:
         print(e)
 
 
 
 if __name__ == "__main__":
-    global screw_quat_prev
-    screw_quat_prev = None
 
     rospy.init_node("maintain")  
     rospy.loginfo("maintain task start ......") 
@@ -240,7 +93,6 @@ if __name__ == "__main__":
     box_sub = message_filters.Subscriber("/yolov5/detections", BoundingBoxes)
     depth_sub = message_filters.Subscriber("/camera/aligned_depth_to_color/image_raw", Image)
     color_info = message_filters.Subscriber("/camera/color/camera_info", CameraInfo)
-    # pc_sub = message_filters.Subscriber("/camera/depth/color/points", PointCloud2)
 
     tf_broadcaster = tf2_ros.TransformBroadcaster()
 

src/yolov5_ros/launch/yolov5.launch

@@ -50,8 +50,7 @@
         <param name="publish_image" value="$(arg publish_image)"/>
         <param name="output_image_topic" value="$(arg output_image_topic)"/>
     </node>
-    <include file="$(find realsense2_camera)/launch/my_camera.launch" >
+    <!-- <include file="$(find camera_launch)/launch/d435.launch"/> -->
-    </include>
 
 
 </launch>

src/yolov5_ros/launch/yolov5_d435.launch

@@ -0,0 +1,56 @@
+<launch>
+    <!-- Detection configuration -->
+    <arg name="weights" default="$(find yolov5_ros)/best.pt"/>
+    <arg name="data" default="$(find yolov5_ros)/src/yolov5/data/coco128.yaml"/>
+    <arg name="confidence_threshold" default="0.5"/>
+    <arg name="iou_threshold" default="0.45"/>
+    <arg name="maximum_detections" default="1000"/>
+    <arg name="device" default="0"/>
+    <arg name="agnostic_nms" default="true"/>
+    <arg name="line_thickness" default="3"/>
+    <arg name="dnn" default="true"/>
+    <arg name="half" default="false"/>
+
+    <!-- replace imgsz -->
+    <arg name="inference_size_h" default="640"/>
+    <arg name="inference_size_w" default="640"/>
+
+    <!-- Visualize using OpenCV window -->
+    <arg name="view_image" default="true"/>
+
+    <!-- ROS topics -->
+    <arg name="input_image_topic" default="/clover0/main_camera/image_raw"/>
+    <arg name="output_topic" default="/yolov5/detections"/>
+
+    <!-- Optional topic (publishing annotated image) -->
+    <arg name="publish_image" default="false"/>
+    <arg name="output_image_topic" default="/yolov5/image_out"/>
+
+
+    <node pkg="yolov5_ros" name="detect" type="detect.py" output="screen">
+        <param name="weights" value="$(arg weights)"/>
+        <param name="data" value="$(arg data)"/>
+        <param name="confidence_threshold" value="$(arg confidence_threshold)"/>
+        <param name="iou_threshold" value="$(arg iou_threshold)" />
+        <param name="maximum_detections" value="$(arg maximum_detections)"/>
+        <param name="device" value="$(arg device)" />
+        <param name="agnostic_nms" value="$(arg agnostic_nms)" />
+        <param name="line_thickness" value="$(arg line_thickness)"/>
+        <param name="dnn" value="$(arg dnn)"/>
+        <param name="half" value="$(arg half)"/>
+
+        <param name="inference_size_h" value="$(arg inference_size_h)"/>
+        <param name="inference_size_w" value="$(arg inference_size_w)"/>
+
+        <param name="input_image_topic" value="$(arg input_image_topic)"/>
+        <param name="output_topic" value="$(arg output_topic)"/>
+
+        <param name="view_image" value="$(arg view_image)"/>
+
+        <param name="publish_image" value="$(arg publish_image)"/>
+        <param name="output_image_topic" value="$(arg output_image_topic)"/>
+    </node>
+    <include file="$(find camera_launch)/launch/d435.launch"/>
+
+
+</launch>