Realsense D435i深度测距和普通摄像头单目测距的区别(附带可用实测代码)

深度摄像头测距

环境：windows+opencv3+python3.64
我采用的是: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。官方资料仓库

import pyrealsense2 as rs
import numpy as np
import cv2def nothing(x):passdef 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 & Extrinsicsdepth_intrin = depth_frame.profile.as_video_stream_profile().intrinsicscolor_intrin = color_frame.profile.as_video_stream_profile().intrinsicsdepth_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 pixeldepth_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+200print('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/80679935cv2.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()breakled_practice()
cv2.waitKey(0)
cv2.destroyAllWindows()
pipeline.stop()

这里附上我实现的代码，测量中心点的距离。代码注释的都比较详细

单目测距

效果图：
在这里插入图片描述

代码：

import numpy as np
import cv2def nothing(x):pass# 形态学开操作
def open_binary(binary, x, y):kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (x, y))  # 获取图像结构化元素dst = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)  # 开操作return dst# 形态学闭操作
def close_binary(binary, x, y):kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (x, y))  # 获取图像结构化元素dst = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)  # 开操作return dst# 形态学腐蚀操作
def erode_binary(binary, x, y):kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (x, y))  # 获取图像结构化元素dst = cv2.erode(binary, kernel)  # 腐蚀return dst# 形态学膨胀操作
def dilate_binary(binary, x, y):kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (x, y))  # 获取图像结构化元素dst = cv2.dilate(binary, kernel)  # 膨胀返回return dst# 找到目标函数
def find_marker(image):img1, contours, heriachy1 = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 获取轮廓点集(坐标)cv2.drawContours(frame, contours, -1, (0, 0, 255), 2)return contours# 距离计算函数
def distance_to_camera(knownWidth, focalLength, perWidth):# compute and return the distance from the maker to the cameraif knownWidth * focalLength == 0 or perWidth == 0:return 0else:return (knownWidth * focalLength) / perWidthdef color_detetc(frame):hmin1 = cv2.getTrackbarPos('hmin1', 'color_adjust1')hmax1 = cv2.getTrackbarPos('hmax1', 'color_adjust1')smin1 = cv2.getTrackbarPos('smin1', 'color_adjust1')smax1 = cv2.getTrackbarPos('smax1', 'color_adjust1')vmin1 = cv2.getTrackbarPos('vmin1', 'color_adjust1')vmax1 = cv2.getTrackbarPos('vmax1', 'color_adjust1')close = cv2.getTrackbarPos('close', 'mor_adjust')erode = cv2.getTrackbarPos('erode', 'mor_adjust')dilate = cv2.getTrackbarPos('dilate', 'mor_adjust')hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)  # hsv 色彩空间 分割肤色lower_hsv1 = np.array([hmin1, smin1, vmin1])upper_hsv1 = np.array([hmax1, smax1, vmax1])mask1 = cv2.inRange(hsv, lowerb=lower_hsv1, upperb=upper_hsv1)  # hsv 掩码ret, thresh1 = cv2.threshold(mask1, 40, 255, cv2.THRESH_BINARY)  # 二值化处理dst_close = close_binary(thresh1, close, close)dst_erode = erode_binary(dst_close, erode, erode)dst_dilate = dilate_binary(dst_erode, dilate, dilate)cv2.imshow('dst_close:', dst_close)cv2.imshow('dst_erode:', dst_erode)cv2.imshow('dst_dilate:', dst_dilate)return dst_dilateif __name__ == "__main__":KNOWN_DISTANCE = 24.0# A4纸的长和宽(单位:inches)KNOWN_WIDTH = 2.69KNOWN_HEIGHT = 2.27# 通过摄像头标定获取的像素焦距focalLength = 811.82print('focalLength = ', focalLength)# 打开摄像头camera = cv2.VideoCapture(0)cv2.namedWindow("color_adjust1")cv2.namedWindow("mor_adjust")cv2.createTrackbar("hmin1", "color_adjust1", 16, 255, nothing)cv2.createTrackbar("hmax1", "color_adjust1", 31, 255, nothing)cv2.createTrackbar("smin1", "color_adjust1", 119, 255, nothing)cv2.createTrackbar("smax1", "color_adjust1", 255, 255, nothing)cv2.createTrackbar("vmin1", "color_adjust1", 0, 255, nothing)cv2.createTrackbar("vmax1", "color_adjust1", 255, 255, nothing)cv2.createTrackbar("close", "mor_adjust", 5, 30, nothing)cv2.createTrackbar("erode", "mor_adjust", 10, 30, nothing)cv2.createTrackbar("dilate", "mor_adjust", 10, 30, nothing)while camera.isOpened():# get a frameret, frame = camera.read()dst_dilate = color_detetc(frame)contours = find_marker(dst_dilate)if contours == 0:continue# compute the bounding box of the of the paper region and return it# cv2.minAreaRect() c代表点集，返回rect[0]是最小外接矩形中心点坐标，# rect[1][0]是width，rect[1][1]是height，rect[2]是角度for i, contour in enumerate(contours):area1 = cv2.contourArea(contour)if area1 > 100:(x1, y1), radius1 = cv2.minEnclosingCircle(contours[i])x1 = int(x1)y1 = int(y1)center1 = (int(x1), int(y1))radius1 = int(radius1)cv2.circle(frame, center1, 3, (0, 0, 255), -1)  # 画出重心print("黄色坐标:", (x1, y1))cv2.putText(frame, "yellow:", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX,0.5, [255, 255, 255])marker = cv2.minAreaRect(contours[0])inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])# draw a bounding box around the image and display it# inches 转换为 cmcv2.putText(frame, "%.2fcm" % (inches * 30.48 / 12),(frame.shape[1] - 300, frame.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,2.0, (0, 255, 0), 3)# show a framecv2.imshow("capture", frame)if cv2.waitKey(1) & 0xFF == 27:breakcamera.release()cv2.destroyAllWindows()

**说明：**代码为通过hsv识别一个黄色的小球，然后对小球单目测距。摄像头的焦距，是我简单测试的，起个演示作用，最好是通过相机标定测试摄像头的内部参数，然后来实现测距。也可以用几个A4纸测试一下，算出相机焦距。

深度与单目的区别

深度测距比较准确，并且不需要所谓目标的实际尺寸，可以随意检测目标距离，而不是特定目标的距离。相机不需要标定知道相机内部的参数。相机标定在出厂时已经标定完了。
单目测距精度方面不如深度准确，并且还需要知道相机内部参数，需要相机标定，还得知道测距目标的实际尺寸，只能针对特定目标进行测距。但是优点也是显著的，简单，对相机没有要求。对测距精度不高的环境合适。
对比分析：像素和测距的对比。当然用深度的目的远不止用于测距的作用，realsenseD435i的像素不算很高，即想看的清，又想测得准的话，realsenseD435i是不太实用的。单目虽然测得不算很准，但是相比之下可以选择像素高的单目相机，同样还是可以测距。但是realsenseD435i测距准确,但是像素固定无法提高了。看适用场合选取合适的摄像头。