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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
# examples/python/reconstruction_system/sensors/realsense_pcd_visualizer.py
# pyrealsense2 is required.
# Please see instructions in https://github.com/IntelRealSense/librealsense/tree/master/wrappers/python
import pyrealsense2 as rs
import numpy as np
from enum import IntEnum
from datetime import datetime
import open3d as o3d
from os.path import abspath
import sys
sys.path.append(abspath(__file__))
from realsense_helper import get_profiles
class Preset(IntEnum):
Custom = 0
Default = 1
Hand = 2
HighAccuracy = 3
HighDensity = 4
MediumDensity = 5
def get_intrinsic_matrix(frame):
intrinsics = frame.profile.as_video_stream_profile().intrinsics
out = o3d.camera.PinholeCameraIntrinsic(640, 480, intrinsics.fx,
intrinsics.fy, intrinsics.ppx,
intrinsics.ppy)
return out
if __name__ == "__main__":
# Create a pipeline
pipeline = rs.pipeline()
#Create a config and configure the pipeline to stream
# different resolutions of color and depth streams
config = rs.config()
color_profiles, depth_profiles = get_profiles()
print('Using the default profiles: \n color:{}, depth:{}'.format(
color_profiles[0], depth_profiles[0]))
w, h, fps, fmt = depth_profiles[0]
config.enable_stream(rs.stream.depth, w, h, fmt, fps)
w, h, fps, fmt = color_profiles[0]
config.enable_stream(rs.stream.color, w, h, fmt, fps)
# Start streaming
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
# Using preset HighAccuracy for recording
depth_sensor.set_option(rs.option.visual_preset, Preset.HighAccuracy)
# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_scale = depth_sensor.get_depth_scale()
# We will not display the background of objects more than
# clipping_distance_in_meters meters away
clipping_distance_in_meters = 3 # 3 meter
clipping_distance = clipping_distance_in_meters / depth_scale
# print(depth_scale)
# Create an align object
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames.
align_to = rs.stream.color
align = rs.align(align_to)
vis = o3d.visualization.Visualizer()
vis.create_window()
pcd = o3d.geometry.PointCloud()
flip_transform = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]
# Streaming loop
frame_count = 0
try:
while True:
dt0 = datetime.now()
# Get frameset of color and depth
frames = pipeline.wait_for_frames()
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
aligned_depth_frame = aligned_frames.get_depth_frame()
color_frame = aligned_frames.get_color_frame()
intrinsic = o3d.camera.PinholeCameraIntrinsic(
get_intrinsic_matrix(color_frame))
# Validate that both frames are valid
if not aligned_depth_frame or not color_frame:
continue
depth_image = o3d.geometry.Image(
np.array(aligned_depth_frame.get_data()))
color_temp = np.asarray(color_frame.get_data())
color_image = o3d.geometry.Image(color_temp)
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
color_image,
depth_image,
depth_scale=1.0 / depth_scale,
depth_trunc=clipping_distance_in_meters,
convert_rgb_to_intensity=False)
temp = o3d.geometry.PointCloud.create_from_rgbd_image(
rgbd_image, intrinsic)
temp.transform(flip_transform)
pcd.points = temp.points
pcd.colors = temp.colors
if frame_count == 0:
vis.add_geometry(pcd)
vis.update_geometry(pcd)
vis.poll_events()
vis.update_renderer()
process_time = datetime.now() - dt0
print("\rFPS: " + str(1 / process_time.total_seconds()), end='')
frame_count += 1
finally:
pipeline.stop()
vis.destroy_window()
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