一点修改

This commit is contained in:
GitLab 2024-03-09 11:21:54 +08:00
parent 2e77f05678
commit a491ad0f3d
15 changed files with 284 additions and 222 deletions

2
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104
abandon_code/yolo.py Normal file
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import cv2
from ultralytics import YOLO
from pyorbbecsdk import Pipeline, FrameSet
from pyorbbecsdk import Config
from pyorbbecsdk import OBSensorType, OBFormat
from pyorbbecsdk import OBError
from pyorbbecsdk import VideoStreamProfile
from img_process.orbbec_camera.utils import frame_to_bgr_image
def load_model():
# 载入 YOLOv8 模型
model = YOLO('../img_process/yolov8n.pt')
return model
# 参数说明
# --img: 输入图片的路径
# --is_save_video: 是否保存识别后的视频
def process_img(img=None):
# 暂时性配置
video_path = "output_video.mp4"
# 加载模型
model = load_model()
if img is not None:
# 对图像运行 YOLOv8 推理
results = model(img)
# 在图像上可视化结果
annotated_img = results[0].plot()
# 显示带有标注的图像
cv2.imshow("YOLOv8 推理", annotated_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
# 获取本机摄像头内容
cap = cv2.VideoCapture(0)
# 奥比中光摄像头
# ESC键的键值
ESC_KEY = 27
# 创建配置对象
config = Config()
# 创建Pipeline对象
pipeline = Pipeline()
try:
# 获取颜色传感器的流配置列表
profile_list = pipeline.get_stream_profile_list(OBSensorType.COLOR_SENSOR)
try:
# 尝试获取指定分辨率、格式和帧率的颜色配置
color_profile: VideoStreamProfile = profile_list.get_video_stream_profile(700, 0, OBFormat.RGB, 30)
except OBError as e:
# 如果出错,打印错误信息并使用默认的颜色配置
print(e)
color_profile = profile_list.get_default_video_stream_profile()
# 启用颜色流
config.enable_stream(color_profile)
except Exception as e:
# 如果出现异常,打印错误信息并返回
print(e)
return
# 启动Pipeline
pipeline.start(config)
while True:
try:
# 等待获取帧集最多等待100毫秒
frames: FrameSet = pipeline.wait_for_frames(100)
if frames is None:
continue
# 获取颜色帧
color_frame = frames.get_color_frame()
if color_frame is None:
continue
# 将帧转换为BGR格式的图像
color_image = frame_to_bgr_image(color_frame)
model.predict(source=color_image, show=True)
if color_image is None:
print("failed to convert frame to image")
continue
key = cv2.waitKey(1)
if key == ord('q') or key == ESC_KEY:
break
except KeyboardInterrupt:
# 如果捕获到键盘中断信号,则退出循环
break
# 释放视频捕获对象并关闭显示窗口
cap.release()
cv2.destroyAllWindows()
# 停止Pipeline
pipeline.stop()
if __name__ == '__main__':
process_img()

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import rospy, sys
import moveit_commander
from geometry_msgs.msg import PoseStamped, Pose
class MoveItIkDemo:
def __init__(self):
# 初始化move_group的API
moveit_commander.roscpp_initialize(sys.argv)
# 初始化ROS节点
rospy.init_node('moveit_ik_demo')
# 初始化需要使用move group控制的机械臂中的arm group
arm = moveit_commander.MoveGroupCommander('manipulator')
# 获取终端link的名称这个在setup assistant中设置过了
end_effector_link = arm.get_end_effector_link()
# 设置目标位置所使用的参考坐标系
reference_frame = 'base_link'
arm.set_pose_reference_frame(reference_frame)
# 当运动规划失败后,允许重新规划
arm.allow_replanning(True)
# 设置位置(单位:米)和姿态(单位:弧度)的允许误差
arm.set_goal_position_tolerance(0.001)
arm.set_goal_orientation_tolerance(0.01)
# 设置允许的最大速度和加速度
arm.set_max_acceleration_scaling_factor(0.5)
arm.set_max_velocity_scaling_factor(0.5)
# 控制机械臂先回到初始化位置
arm.set_named_target('home')
arm.go()
rospy.sleep(1)
# 设置机械臂工作空间中的目标位姿位置使用x、y、z坐标描述
# 姿态使用四元数描述基于base_link坐标系
target_pose = PoseStamped()
# 参考坐标系,前面设置了
target_pose.header.frame_id = reference_frame
target_pose.header.stamp = rospy.Time.now()
# 末端位置
target_pose.pose.position.x = 0.2593
target_pose.pose.position.y = 0.0636
target_pose.pose.position.z = 0.1787
# 末端姿态,四元数
target_pose.pose.orientation.x = 0.70692
target_pose.pose.orientation.y = 0.0
target_pose.pose.orientation.z = 0.0
target_pose.pose.orientation.w = 0.70729
# 设置机器臂当前的状态作为运动初始状态
arm.set_start_state_to_current_state()
# 设置机械臂终端运动的目标位姿
arm.set_pose_target(target_pose)
# arm.set_pose_target()
# 规划运动路径,返回虚影的效果
traj = arm.plan()
# 按照规划的运动路径控制机械臂运动
arm.execute(traj)
rospy.sleep(1) # 执行完成后休息1s
# 控制机械臂回到初始化位置
arm.set_named_target('home')
arm.go()
# 关闭并退出moveit
moveit_commander.roscpp_shutdown()
moveit_commander.os._exit(0)
if __name__ == "__main__":
MoveItIkDemo()

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from fairino import Robot
import time
# 与机器人控制器建立连接,连接成功返回一个机器人对象
robot = Robot.RPC('192.168.58.2')
print("与机器人控制器建立连接", robot)
ret, version = robot.GetSDKVersion() # 查询SDK版本号
if ret == 0:
print("SDK版本号为", version)
else:
print("查询失败,错误码为", ret)
ret, ip = robot.GetControllerIP() # 查询控制器IP
if ret == 0:
print("控制器IP为", ip)
else:
print("查询失败,错误码为", ret)
#
# # 机器人进入或退出拖动示教模式
# ret = robot.Mode(1) # 机器人切入手动模式
# print("机器人切入手动模式", ret)
# time.sleep(1)
# ret = robot.DragTeachSwitch(1) # 机器人切入拖动示教模式,必须在手动模式下才能切入拖动示教模式
# print("机器人切入拖动示教模式", ret)
# time.sleep(1)
# ret, state = robot.IsInDragTeach() # 查询是否处于拖动示教模式1-拖动示教模式0-非拖动示教模式
# if ret == 0:
# print("当前拖动示教模式状态:", state)
# else:
# print("查询失败,错误码为:", ret)
# time.sleep(3)
# ret = robot.DragTeachSwitch(0) # 机器人切入非拖动示教模式,必须在手动模式下才能切入非拖动示教模式
# print("机器人切入非拖动示教模式", ret)
# time.sleep(1)
# ret, state = robot.IsInDragTeach() # 查询是否处于拖动示教模式1-拖动示教模式0-非拖动示教模式
# if ret == 0:
# print("当前拖动示教模式状态:", state)
# else:
# print("查询失败,错误码为:", ret)
# time.sleep(3)
#
# # 机器人上使能或下使能
# ret = robot.RobotEnable(0.5) # 机器人下使能
# print("机器人下使能", ret)
# time.sleep(3)
# ret = robot.RobotEnable(1) # 机器人上使能,机器人上电后默认自动上使能
# print("机器人上使能", ret)
#
# # 机器人手自动模式切换
# ret = robot.Mode(0) # 机器人切入自动运行模式
# print("机器人切入自动运行模式", ret)
# time.sleep(1)
# ret = robot.Mode(1) # 机器人切入手动模式
# print("机器人切入手动模式", ret)

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@ -1,49 +1,35 @@
from ultralytics import YOLO
import cv2
from abandon_code.ColorViewer import ColorViewer
from orbbec_camera.OrbbecCamera import OrbbecCamera
class ObjectClassify:
def __init__(self):
def __init__(self, image_width=640):
self.model = YOLO('yolov8n.pt')
def image_classify(self, path=None):
res = self.model(path)
predicted_img = res[0].plot()
cv2.imshow("分类结果", predicted_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def video_classify(self):
cw = ColorViewer()
frame = cw.get_current_frame()
self.model(source=frame, show=True)
# 输入的视频的宽度
self.image_width = image_width
if __name__ == '__main__':
oc = ObjectClassify()
ob = OrbbecCamera('HW', True)
ob.run()
classify = ObjectClassify()
camera = OrbbecCamera('HW', True, image_width=classify.image_width)
camera.run()
# 处理循环
while True:
color_image = ob.get_color_image()
if color_image is None:
continue
cv2.imshow("彩色图像", color_image)
if ob.get_depth_image() is None:
continue
cv2.imshow("深度图像", ob.get_depth_image())
if ob.get_combined_image() is None:
continue
cv2.imshow("合成图像", ob.get_combined_image())
print(f"中心点的深度值: {ob.get_center_distance()}mm")
color_image = camera.get_color_image()
if color_image is not None:
oc.model.predict(source=color_image, show=True)
# conf: 置信度,小于该值的框将被过滤
# imgsz: 图像的尺寸
# half: 是否使用FP16,可有效提升速度
# vid_stride: 帧预测间隔可降低算力消耗间隔x帧进行一次预测
classify.model.predict(source=color_image,
show=True,
conf=0.5,
half=True,
imgsz=classify.image_width,
verbose=False)
key = cv2.waitKey(1)
if key == ord('q'):
ob.stop()
camera.stop()
# cw.stop()
break

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@ -12,7 +12,7 @@ class OrbbecCamera:
# 定义一个类常量作为退出键
ESC_KEY = 27
def __init__(self, mode='HW', enable_sync=True):
def __init__(self, mode='HW', enable_sync=True, image_width=640):
# 构造函数初始化Orbbec相机和配置
self.pipeline = Pipeline() # 创建Pipeline对象
self.device = self.pipeline.get_device() # 获取设备对象
@ -29,7 +29,7 @@ class OrbbecCamera:
self.thread = None # 线程对象,用于并行执行捕获循环
self.active = False # 控制捕获循环的标志
# 显示设置
self.width = 640 # 图像宽度
self.width = image_width # 图像宽度
self.fps = 30 # 帧率
# 初始化相机设置
@ -147,6 +147,24 @@ class OrbbecCamera:
def get_center_distance(self):
return self.center_distance
# 将像素坐标转换为相机坐标系下的坐标
def covert_pixel_to_carmera_coordinates(self, u, v, depth):
# u: 像素坐标x
# v: 像素坐标y
# depth: 深度值
# K[0]为相机参数
# fx = self.depth_intrinsics.K[0]
# fy = self.depth_intrinsics.K[4]
# cx = self.depth_intrinsics.K[2]
# cy = self.depth_intrinsics.K[5]
# 单位是毫米
# camera_x = (u - cx) * depth / fx
# camera_y = (v - cy) * depth / fy
# return camera_x, camera_y, depth
# 返回值: 相机坐标(x, y, z)
return None
if __name__ == "__main__":
parser = argparse.ArgumentParser()

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