Kinect2.0-Python调用-PyKinect2
Kinect2.0-Python调用-PyKinect2
一.环境
1.win10
2.python3.8.5
3.Kinect2.0SDK
4.opencv4.5.1
5.vscode
二.调试步骤
1.安装Kinect2.0SDK
正常安装即可
2.安装python版本为3.8.5的anaconda
3.安装OpenCV
下载opencv_python-4.5.1.48-cp38-cp38-win_amd64.whl和opencv_contrib_python-4.5.1.48-cp38-cp38-win_amd64.whl到anaconda的base路径下
在命令行中执行pip install opencv_python-4.5.1.48-cp38-cp38-win_amd64.whl和pip install opencv_contrib_python-4.5.1.48-cp38-cp38-win_amd64.whl。
4.在vscode中执行代码
# -*- coding：utf-8 -*-
"""
time:2019/5/1 19:34
author:Lance
organization: HIT
contact: QQ:261983626 , wechat:yuan261983626
——————————————————————————————
description：
$ 自己基于Pykinect2 写的一个Kinect的类。
主要包括：
彩色图像、深度图像、红外图像的获取
彩色图像和深度图像的坐标空间互换
——————————————————————————————
note：
$
"""
from pykinect2 import PyKinectV2
from pykinect2.PyKinectV2 import *
from pykinect2 import PyKinectRuntime
import numpy as np
import ctypes
import math
import cv2 as cv
import time
import copy
class Kinect(object):
def __init__(self):
self._kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Depth | PyKinectV2.FrameSourceTypes_Infrared)
self.depth_ori = None
self.infrared_frame = None
self.color_frame = None
self.w_color = 1920
self.h_color = 1080
self.w_depth = 512
self.h_depth = 424
"""————————————————(2019/5/10)——————————————————"""
self.csp_type = _ColorSpacePoint * np.int(1920 * 1080)
self.csp = ctypes.cast(self.csp_type(), ctypes.POINTER(_DepthSpacePoint))
"""————————————————(2019/9/4)——————————————————"""
self.color = None
self.depth = None
self.depth_draw = None
self.color_draw = None
self.infrared = None
self.first_time = True
"""————————————————(2019/5/1)——————————————————"""
"""获取最新的图像数据"""
def get_the_last_color(self):
"""
Time :2019/5/1
FunC:获取最新的图像数据
Input:无
Return:无
"""
if self._kinect.has_new_color_frame():
# 获得的图像数据是二维的，需要转换为需要的格式
frame = self._kinect.get_last_color_frame()
# 返回的是4通道，还有一通道是没有注册的
gbra = frame.reshape([self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4])
# 取出彩色图像数据
self.color_frame = gbra[:, :, 0:3]
return self.color_frame
"""————————————————(2019/5/1)——————————————————"""
"""获取最新的深度数据"""
def get_the_last_depth(self):
"""
Time :2019/5/1
FunC:获取最新的图像数据
Input:无
Return:无
"""
if self._kinect.has_new_depth_frame():
# 获得深度图数据
frame = self._kinect.get_last_depth_frame()
# 转换为图像排列
image_depth_all = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
self.depth_ori = image_depth_all
return self.depth_ori
"""————————————————(2019/5/1)——————————————————"""
"""获取最新的红外数据"""
def get_the_last_infrared(self):
"""
Time :2019/5/1
FunC:获取最新的图像数据
Input:无
Return:无
"""
if self._kinect.has_new_infrared_frame():
# 获得深度图数据
frame = self._kinect.get_last_infrared_frame()
# 转换为图像排列
image_infrared_all = frame.reshape([self._kinect.infrared_frame_desc.Height,
self._kinect.infrared_frame_desc.Width])
self.infrared_frame = image_infrared_all
return self.infrared_frame
"""————————————————(2019/5/1)——————————————————"""
"""将深度像素点匹配到彩色图像中"""
def map_depth_point_to_color_point(self, depth_point):
"""
Time :2019/5/1
FunC: 将深度图像坐标映射到彩色坐标中，
Input: depth_points:深度像素点，列表、数组格式,图像坐标，且为人眼视角
Return: color_points:对应的彩色像素点，列表格式
均采用图像坐标的形式
"""
depth_point_to_color = copy.deepcopy(depth_point)
n = 0
while 1:
self.get_the_last_depth()
self.get_the_last_color()
if self.depth_ori is None:
continue
color_point = self._kinect._mapper.MapDepthPointToColorSpace(
_DepthSpacePoint(511-depth_point_to_color[1], depth_point_to_color[0]), self.depth_ori[depth_point_to_color[0], 511-depth_point_to_color[1]])
"""————————————————(2019/6/22)——————————————————"""
# cobot 第二次培训更改
# color_point = self._kinect._mapper.MapDepthPointToColorSpace(
# _DepthSpacePoint(depth_point[0], depth_point[1]), self.depth[depth_point[1], depth_point[0]])
"""————————————————(2019/6/22)——————————————————"""
if math.isinf(float(color_point.y)):
n += 1
if n >= 50000:
print('深度映射彩色，无效点')
color_point = [0, 0]
break
else:
color_point = [np.int0(color_point.y), 1920-np.int0(color_point.x)] # 图像坐标，人眼视角
break
return color_point
"""————————————————(2019/5/10)——————————————————"""
"""将彩色像素数组映射到深度图像中"""
def map_color_points_to_depth_points(self, color_points):
"""
Time :2019/5/1
FunC: 将深度图像坐标映射到彩色坐标中，输入的为图像坐标
Input: depth_points:彩色像素点
Return: color_points:对应的深度像素点，图像坐标
"""
self.get_the_last_depth()
self.get_the_last_color()
self._kinect._mapper.MapColorFrameToDepthSpace(
ctypes.c_uint(512 * 424), self._kinect._depth_frame_data, ctypes.c_uint(1920 * 1080), self.csp)
depth_points = [self.map_color_point_to_depth_point(x, True) for x in color_points]
return depth_points
"""————————————————(2019/5/1)——————————————————"""
"""将彩色像素点映射到深度图像中"""
def map_color_point_to_depth_point(self, color_point, if_call_flg=False):
"""
Time :2019/5/1
FunC: 将深度图像坐标映射到彩色坐标中，输入的为图像坐标
Input: depth_points:彩色像素点
Return: color_points:对应的深度像素点，图像坐标
"""
n = 0
color_point_to_depth = copy.deepcopy(color_point)
color_point_to_depth[1] = 1920 - color_point_to_depth[1]
while 1:
self.get_the_last_depth()
self.get_the_last_color()
# self.depth = cv.medianBlur(image_depth_all, 5)
if not if_call_flg:
self._kinect._mapper.MapColorFrameToDepthSpace(
ctypes.c_uint(512 * 424), self._kinect._depth_frame_data, ctypes.c_uint(1920 * 1080), self.csp)
if math.isinf(float(self.csp[color_point_to_depth[0]*1920+color_point_to_depth[1]-1].y)) or np.isnan(self.csp[color_point_to_depth[0]*1920+color_point_to_depth[1]-1].y):
n += 1
if n >= 50000:
print('彩色映射深度，无效的点')
depth_point = [0, 0]
break
else:
self.cor = self.csp[color_point_to_depth[0]*1920+color_point_to_depth[1]-1].y
try:
depth_point = [np.int0(self.csp[color_point_to_depth[0]*1920+color_point_to_depth[1]-1].y),
np.int0(self.csp[color_point_to_depth[0]*1920+color_point_to_depth[1]-1].x)]
except OverflowError as e:
print('彩色映射深度，无效的点')
depth_point = [0, 0]
break
depth_point[1] = 512-depth_point[1]
return depth_point
# depth_points = [self._kinect._mapper.MapColorPointToDepthSpace(_ColorSpacePoint(color_point[0],color_point[1]),self.color_frame[depth_point]))
# for depth_point in depth_points]
# return color_points
"""————————————————(2019/4/26)——————————————————"""
"""获得最新的彩色和深度图像以及红外图像"""
def get_the_data_of_color_depth_infrared_image(self, Infrared_threshold = 16000):
"""
# ————————查看是否有新的一帧————————
:return:
"""
# 访问新的RGB帧
time_s = time.time()
if self.first_time:
while 1:
n = 0
if self._kinect.has_new_color_frame():
# # 获得的图像数据是二维的，需要转换为需要的格式
frame = self._kinect.get_last_color_frame()
# 返回的是4通道，还有一通道是没有注册的
gbra = frame.reshape([self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4])
# 取出彩色图像数据
# self.color = gbra[:, :, 0:3]
self.color = gbra[:, :, 0:3][:,::-1,:]
# 这是因为在python中直接复制该图像的效率不如直接再从C++中获取一帧来的快
frame = self._kinect.get_last_color_frame()
# 返回的是4通道，还有一通道是没有注册的
gbra = frame.reshape([self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4])
# 取出彩色图像数据
# self.color_draw = gbra[:, :, 0:3][:,::-1,:]
self.color_draw = gbra[:, :, 0:3][:,::-1,:]
n += 1
# 访问新的Depth帧
if self._kinect.has_new_depth_frame():
# 获得深度图数据
frame = self._kinect.get_last_depth_frame()
# 转换为图像排列
image_depth_all = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
image_depth_all = image_depth_all.reshape(
[self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width, 1])
self.depth_ori = np.squeeze(image_depth_all)
self.depth = np.squeeze(image_depth_all)[:,::-1]
"""————————————————(2019/5/11)——————————————————"""
# 获得深度图数据
frame = self._kinect.get_last_depth_frame()
# 转换为图像排列
depth_all_draw = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
depth_all_draw = depth_all_draw.reshape(
[self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width, 1])
depth_all_draw[depth_all_draw >= 1500] = 0
depth_all_draw[depth_all_draw <= 500] = 0
depth_all_draw = np.uint8(depth_all_draw / 1501 * 255)
self.depth_draw = depth_all_draw[:,::-1,:]
n += 1
# 获取红外数据
if self._kinect.has_new_infrared_frame():
# 获得深度图数据
frame = self._kinect.get_last_infrared_frame()
# 转换为图像排列
image_infrared_all = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
image_infrared_all[image_infrared_all > Infrared_threshold] = 0
image_infrared_all = image_infrared_all / Infrared_threshold * 255
self.infrared = image_infrared_all[:,::-1]
n += 1
t = time.time() - time_s
if n == 3:
self.first_time = False
break
elif t > 5:
print('未获取图像数据，请检查Kinect2连接是否正常')
break
else:
if self._kinect.has_new_color_frame():
# # 获得的图像数据是二维的，需要转换为需要的格式
frame = self._kinect.get_last_color_frame()
# 返回的是4通道，还有一通道是没有注册的
gbra = frame.reshape([self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4])
# 取出彩色图像数据
# self.color = gbra[:, :, 0:3]
self.color = gbra[:, :, 0:3][:, ::-1, :]
# 这是因为在python中直接复制该图像的效率不如直接再从C++中获取一帧来的快
frame = self._kinect.get_last_color_frame()
# 返回的是4通道，还有一通道是没有注册的
gbra = frame.reshape([self._kinect.color_frame_desc.Height, self._kinect.color_frame_desc.Width, 4])
# 取出彩色图像数据
# self.color_draw = gbra[:, :, 0:3][:,::-1,:]
self.color_draw = gbra[:, :, 0:3][:, ::-1, :]
# 访问新的Depth帧
if self._kinect.has_new_depth_frame():
# 获得深度图数据
frame = self._kinect.get_last_depth_frame()
# 转换为图像排列
image_depth_all = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
image_depth_all = image_depth_all.reshape(
[self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width, 1])
self.depth_ori = np.squeeze(image_depth_all)
self.depth = np.squeeze(image_depth_all)[:, ::-1]
"""————————————————(2019/5/11)——————————————————"""
# 获得深度图数据
frame = self._kinect.get_last_depth_frame()
# 转换为图像排列
depth_all_draw = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
depth_all_draw = depth_all_draw.reshape(
[self._kinect.depth_frame_desc.Height, self._kinect.depth_frame_desc.Width, 1])
depth_all_draw[depth_all_draw >= 1500] = 0
depth_all_draw[depth_all_draw <= 500] = 0
depth_all_draw = np.uint8(depth_all_draw / 1501 * 255)
self.depth_draw = depth_all_draw[:, ::-1, :]
# 获取红外数据
if self._kinect.has_new_infrared_frame():
# 获得深度图数据
frame = self._kinect.get_last_infrared_frame()
# 转换为图像排列
image_infrared_all = frame.reshape([self._kinect.depth_frame_desc.Height,
self._kinect.depth_frame_desc.Width])
# 转换为（n，m，1） 形式
image_infrared_all[image_infrared_all > Infrared_threshold] = 0
image_infrared_all = image_infrared_all / Infrared_threshold * 255
self.infrared = image_infrared_all[:, ::-1]
return self.color, self.color_draw, self.depth, self.depth_draw, self.infrared
"""————————————————(2019/9/3)——————————————————"""
"""显示各种图像的视频流"""
def Kinect_imshow(self,type_im='rgb'):
"""
Time :2019/9/3
FunC:
Input: color_data
Return: color_data
"""
if type_im =='all':
pass
elif type_im =='rgb':
pass
elif type_im =='depth':
pass
elif type_im =='grared':
pass
if __name__ == '__main__':
a = Kinect()
while 1:
t = time.time()
color_data = a.get_the_data_of_color_depth_infrared_image()
cv.imshow('a',color_data[0])
cv.waitKey(1)