双目摄像机D435i使用情况
项目中对于测距有要求，因此选用了双目相机进行测距！
深度相机测距：
开发环境：windows+opencv3+python3.6
测距使用：英特尔的Realsense D435i 深度摄像头；
使用深度摄像头测距的步骤：
建一个深度流管、配置流和管道、开启流、创建流对象、对齐流、开启通道后将深度框与颜色框对齐、最后在通过.get_depth_frame()方法获取深度图。
获取到深度图后，想要获得深度图上任意一点的距离，即将深度图图像转化为数组、提取点即为对应改点的深度。
代码如下：测量点是在中心。
import pyrealsense2 as rs import numpy as np import cv2 def nothing(x): pass def creatTrackbar(): # 蓝灯 # cv.createTrackbar("hmin", "color_adjust", 0, 255, nothing) # cv.createTrackbar("hmax", "color_adjust", 250, 255, nothing) # cv.createTrackbar("smin", "color_adjust", 0, 255, nothing) # cv.createTrackbar("smax", "color_adjust", 143, 255, nothing) # cv.createTrackbar("vmin", "color_adjust", 255, 255, nothing) # cv.createTrackbar("vmax", "color_adjust", 255, 255, nothing) # 红灯 cv2.createTrackbar("hmin", "color_adjust", 0, 255, nothing) cv2.createTrackbar("hmax", "color_adjust", 30, 255, nothing) cv2.createTrackbar("smin", "color_adjust", 5, 255, nothing) cv2.createTrackbar("smax", "color_adjust", 100, 255, nothing) cv2.createTrackbar("vmin", "color_adjust", 255, 255, nothing) cv2.createTrackbar("vmax", "color_adjust", 255, 255, nothing) # 形态学操作阈值调整 cv2.createTrackbar("open", "mor_adjust", 1, 30, nothing) cv2.createTrackbar("close", "mor_adjust", 15, 30, nothing) cv2.createTrackbar("erode", "mor_adjust", 1, 30, nothing) cv2.createTrackbar("dilate", "mor_adjust", 3, 30, nothing) # 摄像头调整 # cv.createTrackbar("gamma", "cap_adjust", 100, 200, nothing) cv2.createTrackbar("z", "z_adjust", 100, 360, nothing) def hsv_change(frame): hmin = cv2.getTrackbarPos('hmin', 'color_adjust') hmax = cv2.getTrackbarPos('hmax', 'color_adjust') smin = cv2.getTrackbarPos('smin', 'color_adjust') smax = cv2.getTrackbarPos('smax', 'color_adjust') vmin = cv2.getTrackbarPos('vmin', 'color_adjust') vmax = cv2.getTrackbarPos('vmax', 'color_adjust') # gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) # cv.imshow("gray", gray) hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) lower_hsv = np.array([hmin, smin, vmin]) upper_hsv = np.array([hmax, smax, vmax]) mask = cv2.inRange(hsv, lowerb=lower_hsv, upperb=upper_hsv) return mask # Declare pointcloud object, for calculating pointclouds and texture mappings 声明云对象 pc = rs.pointcloud() # We want the points object to be persistent so we can display the last cloud when a frame drops points = rs.points() pipeline = rs.pipeline() # 创建一个管道 config = rs.config() # Create a config并配置要流式传输的管道。 config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15) config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15) # 使用选定的流参数显式启用设备流 # Start streaming 开启流 pipe_profile = pipeline.start(config) # Create an align object 创建对其流对象 # 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. # (对其流) align_to = rs.stream.color align = rs.align(align_to) # 设置为其他类型的流,意思是我们允许深度流与其他流对齐 print(type(align)) cap = cv2.VideoCapture(0) def led_practice(): # creatTrackbar() while True: frames = pipeline.wait_for_frames() # 等待开启通道 # ret, frame = cap.read() # ret 读取到图片为True 未读到图片为Falst # frame = cv2.flip(frame, 1) aligned_frames = align.process(frames) # 将深度框和颜色框对齐 depth_frame = aligned_frames.get_depth_frame() # ?获得对齐后的帧数深度数据(图) color_frame = aligned_frames.get_color_frame() # ?获得对齐后的帧数颜色数据(图) img_color = np.asanyarray(color_frame.get_data()) # 把图像像素转化为数组 img_depth = np.asanyarray(depth_frame.get_data()) # 把图像像素转化为数组 # img_color2 = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY) # Intrinsics & Extrinsics depth_intrin = depth_frame.profile.as_video_stream_profile().intrinsics color_intrin = color_frame.profile.as_video_stream_profile().intrinsics depth_to_color_extrin = depth_frame.profile.get_extrinsics_to(color_frame.profile) # 获取深度传感器的深度标尺 depth_sensor = pipe_profile.get_device().first_depth_sensor() depth_scale = depth_sensor.get_depth_scale() # 由深度到颜色 depth_pixel = [240, 320] # Random pixel depth_point = rs.rs2_deproject_pixel_to_point(depth_intrin, depth_pixel, depth_scale) color_point = rs.rs2_transform_point_to_point(depth_to_color_extrin, depth_point) color_pixel = rs.rs2_project_point_to_pixel(color_intrin, color_point) print('depth: ', color_point) print('depth: ', color_pixel) pc.map_to(color_frame) points = pc.calculate(depth_frame) vtx = np.asanyarray(points.get_vertices()) # points.get_vertices() 检索点云的顶点 tex = np.asanyarray(points.get_texture_coordinates()) i = 640*200+200 print('depth: ', [np.float(vtx[i][0]), np.float(vtx[i][1]), np.float(vtx[i][2])]) cv2.circle(img_color, (300, 250), 8, [255, 0, 255], thickness=-1) # cv2.circle(img_color, (300, 250), 8, [255, 0, 255], thickness=-1) cv2.putText(img_color, "Distance/cm:"+str(img_depth[300, 250]), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.2, [255, 0, 255]) cv2.putText(img_color, "X:"+str(np.float(vtx[i][0])), (80, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255]) cv2.putText(img_color, "Y:"+str(np.float(vtx[i][1])), (80, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255]) cv2.putText(img_color, "Z:"+str(np.float(vtx[i][2])), (80, 160), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255]) # cv2.putText api解释:https://blog.csdn.net/weixin_42039090/article/details/80679935 cv2.imshow('depth_frame', img_color) cv2.imshow("dasdsadsa", img_depth) # gray = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY) # cv2.imshow("frame", frames) # mask = hsv_change(img_color) # cv2.imshow("frame", mask) # cv2.imshow('depth_frame', gray) key = cv2.waitKey(1) if key == 27: cv2.destroyAllWindows() break led_practice() cv2.waitKey(0) cv2.destroyAllWindows() pipeline.stop()
官方精度说明是在2%之下，实际测量深度距离在25—40mm时，精度在1.5%左右，远距离没做测量，精度暂时不知!
