122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
"""
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算法具有Sim2Real的泛化能力, 如果有条件可用雷达+深度相机收集数据, 合并至仿真数据集中一同训练, 以进一步保证实飞的可靠性
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# (1) 运行雷达里程计以记录无人机状态和地图真值. 注意保证地图和里程计处于同一坐标系，请在一次运行中同时记录图像与里程计的rosbag + 保存地图
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# (可选) 由于里程计漂移，可用utils/pointcloud_clip对地图进行微调和降噪，本文件需对无人机位置translation_no和姿态R_no(yaw, pitch, roll)做相同的变换
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# (2) 播包rosbag, 运行本文件: 记录位置、姿态、图像，保存至save_dir
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"""
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import cv2
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import numpy as np
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import time, os, sys
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from cv_bridge import CvBridge, CvBridgeError
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import rospy
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from sensor_msgs.msg import Image
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from nav_msgs.msg import Odometry
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from scipy.spatial.transform import Rotation
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# IMPORTANT PARAM
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save_dir = os.environ["FLIGHTMARE_PATH"] + "/run/yopo_realworld"
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label_path = save_dir + "/label.npz"
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if not os.path.exists(save_dir):
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os.mkdir(save_dir)
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# Due to odometry drift, the map is adjusted, and the drone's position is also adjusted accordingly.
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R_no = Rotation.from_euler('ZYX', [0.0, 0.0, 0.0], degrees=True) # yaw, pitch, roll
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translation_no = np.array([0.0, 0.0, 0.0])
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img_height = 270
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img_width = 480
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# VARIABLES
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depth_img = None
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pos = None
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quat = None
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positions = []
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quaternions = []
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frame_id = 0
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new_depth = False
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new_odom = False
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first_frame = True
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last_time = time.time()
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def callback_odometry(data):
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# NWU
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global pos, quat, new_odom, R_no, translation_no
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p_ob = np.array([[data.pose.pose.position.x],
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[data.pose.pose.position.y],
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[data.pose.pose.position.z]])
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q_ob = np.array([data.pose.pose.orientation.x,
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data.pose.pose.orientation.y,
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data.pose.pose.orientation.z,
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data.pose.pose.orientation.w])
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R_ob = Rotation.from_quat(q_ob) # old->body (xyzw)
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quat_xyzw = (R_no * R_ob).as_quat() # new->body (xyzw)
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quat = np.array([quat_xyzw[3], quat_xyzw[0], quat_xyzw[1], quat_xyzw[2]])
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pos = np.squeeze(np.dot(R_no.as_matrix(), p_ob)) + translation_no
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new_odom = True
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def callback_depth(data):
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global depth_img, new_depth, img_height, img_width
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max_dis = 20.0
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min_dis = 0.03
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height = img_height
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width = img_width
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scale = 0.001
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bridge = CvBridge()
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try:
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depth_ = bridge.imgmsg_to_cv2(data, "32FC1")
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except:
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print("CV_bridge ERROR: Possible solutions may be found at https://github.com/TJU-Aerial-Robotics/YOPO/issues/2")
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if depth_.shape[0] != height or depth_.shape[1] != width:
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depth_ = cv2.resize(depth_, (width, height), interpolation=cv2.INTER_NEAREST)
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depth_ = np.minimum(depth_ * scale, max_dis) / max_dis
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try:
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nan_mask = np.isnan(depth_) | (depth_ < min_dis)
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depth_ = cv2.inpaint(np.uint8(depth_ * 255), np.uint8(nan_mask), 3, cv2.INPAINT_NS)
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depth_ = depth_.astype(np.float32) / 255.0
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except:
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print("Interpolation failed")
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# Not necessary, but encountered some inexplicable errors previously, so temporarily kept.
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if np.sum(np.isnan(depth_)) > 0:
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depth_[np.isnan(depth_)] = 0
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print("WARN: Have NAN values in depth image")
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depth_img = depth_.copy()
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new_depth = True
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def save_data(_timer):
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global pos, quat, new_odom, depth_img, new_depth, last_time, first_frame
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global save_dir, label_path, frame_id, positions, quaternions
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if not (new_odom and new_depth):
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if not first_frame and time.time() - last_time > 1:
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np.savez(
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label_path,
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positions=np.asarray(positions),
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quaternions=np.asarray(quaternions),
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)
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print("Record Done!")
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sys.exit()
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return
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new_odom, new_depth = False, False
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image_path = save_dir + "/img_" + str(frame_id) + ".tif"
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cv2.imwrite(image_path, depth_img)
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positions.append(pos)
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quaternions.append(quat)
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last_time = time.time()
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first_frame = False
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frame_id = frame_id + 1
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if __name__ == "__main__":
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rospy.init_node('data_collect', anonymous=False)
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odom_ref_sub = rospy.Subscriber("/odometry/imu", Odometry, callback_odometry, queue_size=1)
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depth_sub = rospy.Subscriber("/camera/depth/image_rect_raw", Image, callback_depth, queue_size=1)
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timer = rospy.Timer(rospy.Duration(0.033), save_data)
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print("Data Collection Node Ready!")
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rospy.spin()
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