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167 lines
7.3 KiB
167 lines
7.3 KiB
#!/usr/bin/env python3
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import sys
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import cv2
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import numpy as np
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import math
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import rospy
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from std_msgs.msg import Header
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from sensor_msgs.msg import Image
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from sensor_msgs.msg import CameraInfo
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from sensor_msgs import point_cloud2
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from sensor_msgs.msg import PointCloud2, PointField
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from visualization_msgs.msg import Marker
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from visualization_msgs.msg import MarkerArray
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from geometry_msgs.msg import Point
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from scipy.spatial.transform import Rotation
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import ros_numpy
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import message_filters
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import struct
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import body_landmark_detector_python3 as bl
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import detection_ros_markers_python3 as dr
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import detection_2d_to_3d_python3 as d2
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class DetectionNode:
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def __init__(self, detector, default_marker_name, node_name,
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topic_base_name, fit_plane, min_box_side_m=None,
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max_box_side_m=None, modify_3d_detections=None):
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self.rgb_image = None
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self.rgb_image_timestamp = None
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self.depth_image = None
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self.depth_image_timestamp = None
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self.camera_info = None
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self.all_points = []
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self.publish_marker_point_clouds = True
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self.detector = detector
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self.marker_collection = dr.DetectionBoxMarkerCollection(default_marker_name)
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self.landmark_color_dict = self.detector.get_landmark_color_dict()
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self.topic_base_name = topic_base_name
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self.node_name = node_name
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self.fit_plane = fit_plane
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self.min_box_side_m = min_box_side_m
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self.max_box_side_m = max_box_side_m
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self.modify_3d_detections = modify_3d_detections
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self.image_count = 0
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def image_callback(self, ros_rgb_image, ros_depth_image, rgb_camera_info):
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self.rgb_image = ros_numpy.numpify(ros_rgb_image)
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self.rgb_image_timestamp = ros_rgb_image.header.stamp
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self.depth_image = ros_numpy.numpify(ros_depth_image)
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self.depth_image_timestamp = ros_depth_image.header.stamp
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self.camera_info = rgb_camera_info
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self.image_count = self.image_count + 1
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# OpenCV expects bgr images, but numpify by default returns rgb images.
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self.rgb_image = cv2.cvtColor(self.rgb_image, cv2.COLOR_RGB2BGR)
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# Copy the depth image to avoid a change to the depth image
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# during the update.
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time_diff = self.rgb_image_timestamp - self.depth_image_timestamp
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time_diff = abs(time_diff.to_sec())
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if time_diff > 0.0001:
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print('WARNING: The rgb image and the depth image were not taken at the same time.')
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print(' The time difference between their timestamps =', closest_time_diff, 's')
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# Rotate the image by 90deg to account for camera
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# orientation. In the future, this may be performed at the
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# image source.
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detection_box_image = cv2.rotate(self.rgb_image, cv2.ROTATE_90_CLOCKWISE)
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debug_input = False
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if debug_input:
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print('DetectionNode.image_callback: received an image!')
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print('DetectionNode.image_callback: detection_box_image.shape =', detection_box_image.shape)
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cv2.imwrite('./output_images/deep_learning_input_' + str(self.image_count).zfill(4) + '.png', detection_box_image)
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debug_output = False
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detections_2d, output_image = self.detector.apply_to_image(detection_box_image, draw_output=debug_output)
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if debug_output:
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print('DetectionNode.image_callback: processed image with deep network!')
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print('DetectionNode.image_callback: output_image.shape =', output_image.shape)
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cv2.imwrite('./output_images/deep_learning_output_' + str(self.image_count).zfill(4) + '.png', output_image)
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detections_3d = d2.detections_2d_to_3d(detections_2d, self.rgb_image, self.camera_info, self.depth_image, fit_plane=self.fit_plane, min_box_side_m=self.min_box_side_m, max_box_side_m=self.max_box_side_m)
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if self.modify_3d_detections is not None:
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detections_3d = self.modify_3d_detections(detections_3d)
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self.marker_collection.update(detections_3d, self.rgb_image_timestamp)
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marker_array = self.marker_collection.get_ros_marker_array(self.landmark_color_dict)
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include_axes = True
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include_z_axes = False
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axes_array = None
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if include_axes or include_z_axes:
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axes_array = self.marker_collection.get_ros_axes_array(include_z_axes, include_axes)
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if self.publish_marker_point_clouds:
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for marker in self.marker_collection:
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marker_points = marker.get_marker_point_cloud()
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self.add_point_array_to_point_cloud(marker_points)
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publish_plane_points = False
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if publish_plane_points:
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plane_points = marker.get_plane_fit_point_cloud()
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self.add_point_array_to_point_cloud(plane_points)
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self.publish_point_cloud()
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self.visualize_markers_pub.publish(marker_array)
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if axes_array is not None:
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self.visualize_axes_pub.publish(axes_array)
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def add_to_point_cloud(self, x_mat, y_mat, z_mat, mask):
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points = [[x, y, z] for x, y, z, m in zip(x_mat.flatten(), y_mat.flatten(), z_mat.flatten(), mask.flatten()) if m > 0]
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self.all_points.extend(points)
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def add_point_array_to_point_cloud(self, point_array):
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if point_array is not None:
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self.all_points.extend(list(point_array))
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def publish_point_cloud(self):
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header = Header()
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header.frame_id = '/camera_color_optical_frame'
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header.stamp = rospy.Time.now()
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fields = [PointField('x', 0, PointField.FLOAT32, 1),
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PointField('y', 4, PointField.FLOAT32, 1),
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PointField('z', 8, PointField.FLOAT32, 1),
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PointField('rgba', 12, PointField.UINT32, 1)]
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r = 255
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g = 0
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b = 0
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a = 128
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rgba = struct.unpack('I', struct.pack('BBBB', b, g, r, a))[0]
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points = [[x, y, z, rgba] for x, y, z in self.all_points]
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point_cloud = point_cloud2.create_cloud(header, fields, points)
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self.visualize_point_cloud_pub.publish(point_cloud)
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self.all_points = []
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def main(self):
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rospy.init_node(self.node_name)
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name = rospy.get_name()
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rospy.loginfo("{0} started".format(name))
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self.rgb_topic_name = '/camera/color/image_raw' #'/camera/infra1/image_rect_raw'
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self.rgb_image_subscriber = message_filters.Subscriber(self.rgb_topic_name, Image)
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self.depth_topic_name = '/camera/aligned_depth_to_color/image_raw'
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self.depth_image_subscriber = message_filters.Subscriber(self.depth_topic_name, Image)
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self.camera_info_subscriber = message_filters.Subscriber('/camera/color/camera_info', CameraInfo)
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self.synchronizer = message_filters.TimeSynchronizer([self.rgb_image_subscriber, self.depth_image_subscriber, self.camera_info_subscriber], 10)
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self.synchronizer.registerCallback(self.image_callback)
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self.visualize_markers_pub = rospy.Publisher('/' + self.topic_base_name + '/marker_array', MarkerArray, queue_size=1)
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self.visualize_axes_pub = rospy.Publisher('/' + self.topic_base_name + '/axes', MarkerArray, queue_size=1)
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self.visualize_point_cloud_pub = rospy.Publisher('/' + self.topic_base_name + '/point_cloud2', PointCloud2, queue_size=1)
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