D435的基本使用

Author: CGC

相机基础使用

配置RealSense深度相机

# 配置RealSense深度相机
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)

# 启动深度和RGB流
pipeline.start(config)

获取RGB图像和深度图像

# 等待帧
frames = pipeline.wait_for_frames()
depth_frame = frames.get_depth_frame()
color_frame = frames.get_color_frame()

# 将深度图像和RGB图像转换为numpy数组
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
# 转化图像空间
color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)

输出图像

# 输出图像
cv2.imshow("color_image", color_image)
cv2.imshow("depth_image", depth_image)
cv2.waitKey(1)

D435+Yolov5

import cv2
from loguru import logger
import numpy as np
from pyzbar import pyzbar
import pyrealsense2 as rs
import pytesseract
import torch
import sys 
import json
import math

def detect_obj_yolov5(depth_image, color_image, detect_target: str = None, model = None, show = 0):
    if model is not None:
        results = model(color_image)
        # 将结果转为json数据
        json_data = results.pandas().xyxy[0].to_json(orient="records")
        # 解析 JSON 数据
        target_x = 0
        target_y = 0
        depth = 0
        try:
            data = json.loads(json_data)
            for d in data:
                # 匹配目标
                if d.get('name') == detect_target and all(key in d for key in ('xmin', 'xmax', 'ymin', 'ymax')):
                    target_x = int((d['xmin'] + d['xmax']) / 2)
                    target_y = int((d['ymin'] + d['ymax']) / 2)
                    color_image = results.render()[0]
                    depth = depth_image[target_y, target_x]
                    break
                else:
                    target_x = 0
                    target_y = 0
                    depth = 0
        except:
            target_x = 0
            target_y = 0
            depth = 0
        logger.info('{}, {}, depth: {}'.format(int(target_x), int(target_y), depth))

    if show:
        cv2.imshow("detect_obj_yolov5", color_image)
        cv2.waitKey(1)

if __name__ == "__main__":
    path1 = '/home/c/Library/Cv_for_Orinnano/detection_module'
    path2 = '/home/c/Library/Cv_for_Orinnano/detection_module/models/yolov5n.pt'
    # model_land = torch.hub.load(path1, 'custom', path2_land, source='local', device = 0, force_reload = True) # 不用的话注释掉提高启动效率
    model = torch.hub.load(path1, 'custom', path2, source='local', device = 0, force_reload = True) # 不用的话注释掉提高启动效率

    # 配置RealSense深度相机
    pipeline = rs.pipeline()
    config = rs.config()
    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
    config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)

    # 启动深度和RGB流
    pipeline.start(config)

    while True:
        # 等待帧
        frames = pipeline.wait_for_frames()
        depth_frame = frames.get_depth_frame()
        color_frame = frames.get_color_frame()

        # 将深度图像和RGB图像转换为numpy数组
        depth_image = np.asanyarray(depth_frame.get_data())
        color_image = np.asanyarray(color_frame.get_data())
        # 转化图像空间
        color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)

        detect_obj_yolov5(depth_image, color_image, detect_target = 'person', model = model, show = 1)