Fit a plane to the points,

Calculate the rotation between the plane normal and the Z axis
	new file:   src/maintain/scripts/maintain.py
	deleted:    src/maintain/scripts/test copy.py
	modified:   src/maintain/scripts/test.py
	modified:   src/yolov5_ros/launch/yolov5.launch

src/maintain/scripts/maintain.py

@@ -0,0 +1,136 @@
+#! /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

@@ -1,148 +0,0 @@
-#! /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()
-
-
-# backup
-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

src/maintain/scripts/test.py

@@ -60,29 +60,88 @@ def compute_plane_normal(box, depth, color_intrinsics):
     normal += np.cross(v3, v4)
     normal += np.cross(v4, v1)
     normal /= np.linalg.norm(normal)
-    # 将法向量转换为四元数表示
+    # 计算法向量相对于参考向量的旋转角度和旋转轴
-    theta = math.acos(normal[2])
+    ref_vector = np.array([0, 0, 1])
-    sin_theta_2 = math.sin(theta/2)
+    normal_vector = normal
-    quaternion = [math.cos(theta/2), sin_theta_2 * normal[0], sin_theta_2 * normal[1], sin_theta_2 * normal[2]]
+    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 compute_normal_vector(p1, p2, p3, p4):
+def calculate_image_edge_plane_normal(depth_roi):
-    # Compute two vectors in the plane
+    # Get the shape of the depth_roi
-    v1 = np.array(p2) - np.array(p1)
+    height, width = depth_roi.shape
-    v2 = np.array(p3) - np.array(p1)
+    
-    # Compute the cross product of the two vectors to get the normal vector
+    # Get the edges of the ROI
-    n = np.cross(v1, v2)
+    left_edge = [(0, y) for y in range(height)]
-    # Compute the fourth point in the plane
+    right_edge = [(width-1, y) for y in range(height)]
-    p4 = np.array(p4)
+    top_edge = [(x, 0) for x in range(width)]
-    # Check if the fourth point is on the same side of the plane as the origin
+    bottom_edge = [(x, height-1) for x in range(width)]
-    if np.dot(n, p4 - np.array(p1)) < 0:
+    edges = left_edge + right_edge + top_edge + bottom_edge
-        n = -n
+
-    # Normalize the normal vector to obtain a unit vector
+    # Create a 2D grid of X and Y coordinates
-    n = n / np.linalg.norm(n)
+    X, Y = np.meshgrid(np.arange(width), np.arange(height))
-    theta = math.acos(n[2])
+
-    sin_theta_2 = math.sin(theta/2)
+    # Reshape the X, Y, and depth_roi arrays into one-dimensional arrays
-    quaternion = [math.cos(theta/2), sin_theta_2 * n[0], sin_theta_2 * n[1], sin_theta_2 * n[2]]
+    X = X.reshape(-1)
-    return quaternion
+    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:

src/yolov5_ros/launch/yolov5.launch

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