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15 Commits
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18
.vscode/tasks.json
vendored
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18
.vscode/tasks.json
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@@ -0,0 +1,18 @@
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{
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"tasks": [
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{
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"type": "shell",
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"command": "catkin",
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"args": [
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"build",
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"-DPYTHON_EXECUTABLE=/home/da/miniconda3/envs/gsmini/bin/python",
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//"-DPYTHON_EXECUTABLE=${HOME}/.conda/envs/gsmini/bin/python"
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],
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"problemMatcher": [
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"$catkin-gcc"
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],
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"group": "build",
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"label": "catkin: build"
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}
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]
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}
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@@ -1,4 +1,4 @@
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<launch>
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<launch>
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<node pkg="maintain" type="test.py" name="maintain" output="screen">
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<node pkg="maintain" type="maintain.py" name="maintain" output="screen">
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</node>
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</node>
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</launch>
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</launch>
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@@ -1,4 +1,4 @@
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#! /home/wxchen/.conda/envs/gsmini/bin/python
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#! /home/da/miniconda3/envs/gsmini/bin/python
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import rospy
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import rospy
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import numpy as np
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import numpy as np
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@@ -78,9 +78,10 @@ def main():
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points.append([x, y, z])
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points.append([x, y, z])
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points = np.array(points)
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points = np.array(points)
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if px != 0 and py != 0 and pz != 0:
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if points.shape[0] > 200 and px != 0 and py != 0 and pz != 0:
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# rospy.loginfo("Screw point: {}".format(screw_point))
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# rospy.loginfo("Screw point: {}".format(screw_point))
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# rospy.loginfo(points.shape)
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# Fit a plane to the points
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# Fit a plane to the points
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pcd = o3d.geometry.PointCloud()
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pcd = o3d.geometry.PointCloud()
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@@ -118,12 +119,14 @@ def main():
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screw_tf.child_frame_id = "screw"
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screw_tf.child_frame_id = "screw"
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screw_tf.transform.translation.x = px
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screw_tf.transform.translation.x = px
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screw_tf.transform.translation.y = py
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screw_tf.transform.translation.y = py
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screw_tf.transform.translation.z = pz
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screw_tf.transform.translation.z = -d / np.linalg.norm(normal)
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screw_tf.transform.rotation.x = quaternion[0]
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screw_tf.transform.rotation.x = quaternion[0]
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screw_tf.transform.rotation.y = quaternion[1]
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screw_tf.transform.rotation.y = quaternion[1]
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screw_tf.transform.rotation.z = quaternion[2]
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screw_tf.transform.rotation.z = quaternion[2]
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screw_tf.transform.rotation.w = quaternion[3]
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screw_tf.transform.rotation.w = quaternion[3]
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rospy.loginfo("tf_broadcaster")
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tf_broadcaster.sendTransform(screw_tf)
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tf_broadcaster.sendTransform(screw_tf)
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@@ -1,4 +1,4 @@
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#! /home/wxchen/.conda/envs/gsmini/bin/python
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#! /home/da/miniconda3/envs/gsmini/bin/python
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import numpy as np
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import numpy as np
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import cv2 as cv
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import cv2 as cv
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@@ -19,7 +19,7 @@ from rostopic import get_topic_type
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from detection_msgs.msg import BoundingBox, BoundingBoxes
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from detection_msgs.msg import BoundingBox, BoundingBoxes
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bridge = CvBridge()
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bridge = CvBridge()
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annulus_width = 10
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annulus_width = 20
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# 2d to 3d
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# 2d to 3d
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def computer_2d_3d(x, y, depth_roi, color_intrinsics):
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def computer_2d_3d(x, y, depth_roi, color_intrinsics):
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@@ -37,7 +37,7 @@ def compute_plane_normal(box, depth, color_intrinsics):
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# 计算矩形中心点坐标
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# 计算矩形中心点坐标
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x_center = (box[0] + box[2]) / 2
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x_center = (box[0] + box[2]) / 2
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y_center = (box[1] + box[3]) / 2
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y_center = (box[1] + box[3]) / 2
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z = depth[int(y_center), int(x_center)]
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z = depth[int(y_center), int(x_center)] / 1000
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x = (x_center - cx) * z / fx
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x = (x_center - cx) * z / fx
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y = (y_center - cy) * z / fy
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y = (y_center - cy) * z / fy
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# 计算四个顶点坐标
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# 计算四个顶点坐标
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@@ -50,10 +50,10 @@ def compute_plane_normal(box, depth, color_intrinsics):
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x4 = (box[0] - cx) * z / fx
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x4 = (box[0] - cx) * z / fx
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y4 = (box[3] - cy) * z / fy
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y4 = (box[3] - cy) * z / fy
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# 计算矩形边缘向量
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# 计算矩形边缘向量
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v1 = np.array([x2 - x1, y2 - y1, depth[int(box[1]), int(box[0])] - z])
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v1 = np.array([x2 - x1, y2 - y1, depth[int(box[1]), int(box[0])] / 1000 - z])
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v2 = np.array([x3 - x2, y3 - y2, depth[int(box[1]), int(box[2])] - z])
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v2 = np.array([x3 - x2, y3 - y2, depth[int(box[1]), int(box[2])] / 1000 - z])
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v3 = np.array([x4 - x3, y4 - y3, depth[int(box[3]), int(box[2])] - z])
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v3 = np.array([x4 - x3, y4 - y3, depth[int(box[3]), int(box[2])] / 1000 - z])
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v4 = np.array([x1 - x4, y1 - y4, depth[int(box[3]), int(box[0])] - z])
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v4 = np.array([x1 - x4, y1 - y4, depth[int(box[3]), int(box[0])] / 1000 - z])
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# 计算平面法向量
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# 计算平面法向量
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normal = np.cross(v1, v2)
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normal = np.cross(v1, v2)
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normal += np.cross(v2, v3)
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normal += np.cross(v2, v3)
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@@ -72,6 +72,18 @@ def compute_plane_normal(box, depth, color_intrinsics):
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quaternion = [qx, qy, qz, qw]
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quaternion = [qx, qy, qz, qw]
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return quaternion
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return quaternion
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# 计算法向量相对于参考向量的旋转角度和旋转轴
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ref_vector = np.array([0, 0, 1])
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normal_vector = normal
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angle = math.acos(np.dot(ref_vector, normal_vector) / (np.linalg.norm(ref_vector) * np.linalg.norm(normal_vector)))
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axis = np.cross(ref_vector, normal_vector)
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axis = axis / np.linalg.norm(axis)
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# 将旋转角度和旋转轴转换为四元数
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qx, qy, qz, qw = tf.transformations.quaternion_about_axis(angle, axis)
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quaternion = [qx, qy, qz, qw]
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return quaternion
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def calculate_image_edge_plane_normal(depth_roi):
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def calculate_image_edge_plane_normal(depth_roi):
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# Get the shape of the depth_roi
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# Get the shape of the depth_roi
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height, width = depth_roi.shape
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height, width = depth_roi.shape
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@@ -173,7 +185,7 @@ def box_callback(box, depth, color_info):
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x, y, z = computer_2d_3d(screw_x, screw_y, depth_array, color_intrinsics)
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x, y, z = computer_2d_3d(screw_x, screw_y, depth_array, color_intrinsics)
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# rospy.loginfo("screw pose: x: %f, y: %f, z: %f", x, y, z)
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# rospy.loginfo("screw pose: x: %f, y: %f, z: %f", x, y, z)
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# calculate normal direction of screw area
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# calculate normal direction of screw area
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box = [boundingBox.ymin - annulus_width, boundingBox.xmin - annulus_width, boundingBox.ymax + annulus_width, boundingBox.xmax + annulus_width]
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box = [boundingBox.xmin - annulus_width, boundingBox.ymin - annulus_width, boundingBox.xmax + annulus_width, boundingBox.ymax + annulus_width]
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# p1x, p1y, p1z = computer_2d_3d(boundingBox.xmin-annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
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# p1x, p1y, p1z = computer_2d_3d(boundingBox.xmin-annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
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# p2x, p2y, p2z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
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# p2x, p2y, p2z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymin-annulus_width, depth_array, color_intrinsics)
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# p3x, p3y, p3z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymax+annulus_width, depth_array, color_intrinsics)
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# p3x, p3y, p3z = computer_2d_3d(boundingBox.xmax+annulus_width, boundingBox.ymax+annulus_width, depth_array, color_intrinsics)
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@@ -189,7 +201,6 @@ def box_callback(box, depth, color_info):
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# normal = calculate_image_edge_plane_normal(annulus_roi)
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# normal = calculate_image_edge_plane_normal(annulus_roi)
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# print(normal)
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# print(normal)
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# normal vector to quaternion
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# normal vector to quaternion
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screw_quat = tf.transformations.quaternion_from_euler(0, 0, 0)
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screw_quat = tf.transformations.quaternion_from_euler(0, 0, 0)
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screw_quat[0] = normal_q[0]
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screw_quat[0] = normal_q[0]
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@@ -201,6 +212,7 @@ def box_callback(box, depth, color_info):
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screw_euler = tf.transformations.euler_from_quaternion(screw_quat)
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screw_euler = tf.transformations.euler_from_quaternion(screw_quat)
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screw_quat_zero_z = tf.transformations.quaternion_from_euler(screw_euler[0], screw_euler[1], 0)
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screw_quat_zero_z = tf.transformations.quaternion_from_euler(screw_euler[0], screw_euler[1], 0)
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print(screw_euler)
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# Apply low-pass filter to screw quaternion
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# Apply low-pass filter to screw quaternion
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alpha = 0.4
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alpha = 0.4
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@@ -2,7 +2,7 @@
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<!-- Detection configuration -->
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<!-- Detection configuration -->
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<arg name="weights" default="$(find yolov5_ros)/src/yolov5/best.pt"/>
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<arg name="weights" default="$(find yolov5_ros)/src/yolov5/best.pt"/>
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<arg name="data" default="$(find yolov5_ros)/src/yolov5/data/mydata.yaml"/>
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<arg name="data" default="$(find yolov5_ros)/src/yolov5/data/mydata.yaml"/>
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<arg name="confidence_threshold" default="0.75"/>
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<arg name="confidence_threshold" default="0.70"/>
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<arg name="iou_threshold" default="0.45"/>
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<arg name="iou_threshold" default="0.45"/>
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<arg name="maximum_detections" default="1000"/>
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<arg name="maximum_detections" default="1000"/>
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<arg name="device" default="0"/>
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<arg name="device" default="0"/>
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@@ -23,7 +23,7 @@
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<arg name="output_topic" default="/yolov5/detections"/>
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<arg name="output_topic" default="/yolov5/detections"/>
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<!-- Optional topic (publishing annotated image) -->
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<!-- Optional topic (publishing annotated image) -->
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<arg name="publish_image" default="false"/>
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<arg name="publish_image" default="true"/>
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<arg name="output_image_topic" default="/yolov5/image_out"/>
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<arg name="output_image_topic" default="/yolov5/image_out"/>
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@@ -1,4 +1,4 @@
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#!/usr/bin/env python3
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#! /home/da/miniconda3/envs/gsmini/bin/python
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import rospy
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import rospy
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import cv2
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import cv2
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