python Intel Realsense udp协议 局域网传输实时视频流并通过窗口显示 （opencv压缩解码）
2024-05-11 22:36:14
文章目录
- 发送端
- 接收端
- 执行结果
- 发送端
- 接收端
发送端
# -*- coding: utf-8 -*-
"""
@File : 200103_obstacle_detection_发送数据_测试udp传输上限.py
@Time : 2020/1/3 14:28
@Author : Dontla
@Email : sxana@qq.com
@Software: PyCharm
"""import socket
import struct
import time
import numpy as np
import pyrealsense2 as rs
import cv2
import sys
from numba import jitdef udp_send_image(img, pack_size, socket, ip_port):_, img_encode = cv2.imencode('.jpg', img)data = img_encode.tobytes()# print(len(data)) # 有很多超过65535的# 【定义文件头、数据】（打包名为l？不是，l表示长整型，占四个字节）fhead = struct.pack('i', len(data))# 【发送文件头、数据】socket.sendto(fhead, ip_port)# 每次发送x字节，计算所需发送次数send_times = len(data) // pack_size + 1for count in range(send_times):# time.sleep(0.01)if count < send_times - 1:socket.sendto(data[pack_size * count:pack_size * (count + 1)], ip_port)else:socket.sendto(data[pack_size * count:], ip_port)# @jit
# 貌似开不了jit，不知啥原因，开了也没明显看到加速
def filter_alpha(depth_image, filter_alpha):if filter_alpha > 1:# 获取depth_image宽高h, w = depth_image.shape[0], depth_image.shape[1] # 360,640# 创建上下alpha（不同方法都能创建）# filter_upper = np.array([1] * int(h / 2))filter_upper = np.full(int(h / 2), 1)filter_lower = np.linspace(1, filter_alpha, h / 2)# 将filter_upper和filter_lower连在一起filter = np.r_[filter_upper, filter_lower]# print(filter)# print(filter.shape) # (360,)# print(filter_alpha_upper)# print(filter_alpha_upper.shape) # (180,)# print(filter_alpha_lower)# print(filter_alpha_lower.shape) # (180,)return (depth_image.T * filter).Telse:return depth_image# 如果要防止下面棉花过近被误探测,可用两层for循环设置梯度过滤
# 不过貌似还得中间对半分,下面直接舍弃掉,只用上面作为判断,因为就算下面用了梯度...(还是得用梯度...)
@jit
def traversing_pixels(depth_image, threshold_dangerous_distance):num_dangerous = 0num_all_pixels = 0depth_image_ravel = depth_image.ravel()# depth_image_segmentation为分割后的图像（红蓝两色）depth_image_segmentation_ravel = []for pixel in depth_image_ravel:num_all_pixels += 1# 第一种效果要好一些if pixel < threshold_dangerous_distance and pixel != 0:# if pixel < threshold_dangerous_distance:num_dangerous += 1depth_image_segmentation_ravel.append(0)else:depth_image_segmentation_ravel.append(6000)depth_image_segmentation = np.array(depth_image_segmentation_ravel).reshape(depth_image.shape)return num_all_pixels, num_dangerous, depth_image_segmentationclass ObstacleDetection(object):def __init__(self):# self.cam_serials = ['838212073161', '827312071726']# self.cam_serials = ['838212073161', '827312071726', '838212073249', '827312070790', '836612072369',# '826212070395']self.cam_serials = ['838212073161']self.cam_width, self.cam_height = 640, 360# 【危险距离：单位mm】self.threshold_dangerous_distance = 3000# 【摄像头到棉花平面垂直距离（单位mm）】self.distance_cam_vertical_to_cotton_top = 260# 【危险距离补偿系数】用于让最下面深度远离临界值，避免造成误检测self.factor_compensation_dangerous_distance = 1.5# 【危险距离像素占比】self.threshold_dangerous_scale = 0.05# 【摄像头视场角（单位°）】self.FOV_width = 69.4self.FOV_height = 42.5self.FOV_scale = self.FOV_height / self.FOV_width # 0.6123919308357348# 【实际变换后height视场角】if self.cam_height / self.cam_width < self.FOV_scale:self.FOV_height_actual = self.FOV_width * self.cam_height / self.cam_widthelse:self.FOV_height_actual = self.FOV_height# 【计算过滤α值（distance_min为图像最下方的深度，看到最近棉花的距离）】# 当摄像头到棉花顶垂直距离为800，最小距离为2256，当危险距离为2000时，alpha滤值为0.88# 当摄像头到棉花顶垂直距离为800，最小距离为2256，当危险距离为3000时，alpha滤值为1.32# 所以，后面进行滤值时需判断self.filter_alpha的值是否大于1（已添加进filter_alpha()函数中）self.distance_min = self.distance_cam_vertical_to_cotton_top / (np.tan(self.FOV_height_actual / 2 * np.pi / 180))self.filter_alpha = self.threshold_dangerous_distance / self.distance_min * self.factor_compensation_dangerous_distance# 【UDP信号发送模块】# 远程主机ip地址及端口self.ip_port = ('192.168.1.49', 9000)self.udp_server_client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)# self.bytes_udp_pack = 1024self.bytes_udp_pack = 65507def obstacle_detection(self):# print(self.distance_min) # 2256.7829632201597# print(self.filter_alpha) # 0.8862172537611853# 摄像头个数（在这里设置所需使用摄像头的总个数）cam_num = 6ctx = rs.context()'''连续验证机制'''# D·C 1911202：创建最大验证次数max_veri_times；创建连续稳定值continuous_stable_value，用于判断设备重置后是否处于稳定状态max_veri_times = 100continuous_stable_value = 5print('\n', end='')print('开始连续验证，连续验证稳定值：{}，最大验证次数：{}：'.format(continuous_stable_value, max_veri_times))continuous_value = 0veri_times = 0while True:devices = ctx.query_devices()connected_cam_num = len(devices)print('摄像头个数：{}'.format(connected_cam_num))if connected_cam_num == cam_num:continuous_value += 1if continuous_value == continuous_stable_value:breakelse:continuous_value = 0veri_times += 1if veri_times == max_veri_times:print("检测超时，请检查摄像头连接！")sys.exit()'''循环reset摄像头'''# hardware_reset()后是不是应该延迟一段时间？不延迟就会报错print('\n', end='')print('开始初始化摄像头：')for dev in ctx.query_devices():# 先将设备的序列号放进一个变量里，免得在下面for循环里访问设备的信息过多（虽然不知道它会不会每次都重新访问）dev_serial = dev.get_info(rs.camera_info.serial_number)# 匹配序列号，重置我们需重置的特定摄像头（注意两个for循环顺序，哪个在外哪个在内很重要，不然会导致刚重置的摄像头又被访问导致报错）for serial in self.cam_serials:if serial == dev_serial:dev.hardware_reset()# 像下面这条语句居然不会报错，不是刚刚才重置了dev吗？莫非区别在于没有通过for循环ctx.query_devices()去访问？# 是不是刚重置后可以通过ctx.query_devices()去查看有这个设备，但是却没有存储设备地址？如果是这样，# 也就能够解释为啥能够通过len(ctx.query_devices())函数获取设备数量，但访问序列号等信息就会报错的原因了print('摄像头{}初始化成功'.format(dev.get_info(rs.camera_info.serial_number)))'''连续验证机制'''# D·C 1911202：创建最大验证次数max_veri_times；创建连续稳定值continuous_stable_value，用于判断设备重置后是否处于稳定状态print('\n', end='')print('开始连续验证，连续验证稳定值：{}，最大验证次数：{}：'.format(continuous_stable_value, max_veri_times))continuous_value = 0veri_times = 0while True:devices = ctx.query_devices()connected_cam_num = len(devices)print('摄像头个数：{}'.format(connected_cam_num))if connected_cam_num == cam_num:continuous_value += 1if continuous_value == continuous_stable_value:breakelse:continuous_value = 0veri_times += 1if veri_times == max_veri_times:print("检测超时，请检查摄像头连接！")sys.exit()'''配置各个摄像头的基本对象'''for i in range(len(self.cam_serials)):locals()['pipeline' + str(i)] = rs.pipeline(ctx)locals()['config' + str(i)] = rs.config()locals()['config' + str(i)].enable_device(self.cam_serials[i])# 为啥我设置成1280×720就报错呢？明明Intel Realsense的usb接口已经显示为3.0了# locals()['config' + str(i)].enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)# locals()['config' + str(i)].enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)locals()['config' + str(i)].enable_stream(rs.stream.depth, self.cam_width, self.cam_height, rs.format.z16,30)locals()['config' + str(i)].enable_stream(rs.stream.color, self.cam_width, self.cam_height, rs.format.bgr8,30)locals()['pipeline' + str(i)].start(locals()['config' + str(i)])# 创建对齐对象（深度对齐颜色）locals()['align' + str(i)] = rs.align(rs.stream.color)'''运行摄像头'''try:while True:start_time = time.time()for i in range(len(self.cam_serials)):locals()['frames' + str(i)] = locals()['pipeline' + str(i)].wait_for_frames()# 获取对齐帧集locals()['aligned_frames' + str(i)] = locals()['align' + str(i)].process(locals()['frames' + str(i)])# 获取对齐后的深度帧和彩色帧locals()['aligned_depth_frame' + str(i)] = locals()['aligned_frames' + str(i)].get_depth_frame()locals()['color_frame' + str(i)] = locals()['aligned_frames' + str(i)].get_color_frame()if not locals()['aligned_depth_frame' + str(i)] or not locals()['color_frame' + str(i)]:continue# 获取颜色帧内参locals()['color_profile' + str(i)] = locals()['color_frame' + str(i)].get_profile()locals()['cvsprofile' + str(i)] = rs.video_stream_profile(locals()['color_profile' + str(i)])locals()['color_intrin' + str(i)] = locals()['cvsprofile' + str(i)].get_intrinsics()locals()['color_intrin_part' + str(i)] = [locals()['color_intrin' + str(i)].ppx,locals()['color_intrin' + str(i)].ppy,locals()['color_intrin' + str(i)].fx,locals()['color_intrin' + str(i)].fy]locals()['color_image' + str(i)] = np.asanyarray(locals()['color_frame' + str(i)].get_data())locals()['depth_image' + str(i)] = np.asanyarray(locals()['aligned_depth_frame' + str(i)].get_data())# 【阿尔法过滤】locals()['depth_image_alpha_filter' + str(i)] = filter_alpha(locals()['depth_image' + str(i)],self.filter_alpha)# 【遍历深度图像素值,如存在小于危险值范围比例超过阈值,则告警】locals()['num_all_pixels' + str(i)], locals()['num_dangerous' + str(i)], locals()['depth_image_segmentation' + str(i)] = traversing_pixels(locals()['depth_image_alpha_filter' + str(i)], self.threshold_dangerous_distance)print('num_all_pixels:{}'.format(locals()['num_all_pixels' + str(i)]))print('num_dangerous:{}'.format(locals()['num_dangerous' + str(i)]))locals()['dangerous_scale' + str(i)] = locals()['num_dangerous' + str(i)] / locals()['num_all_pixels' + str(i)]print('危险比例:{}'.format(locals()['dangerous_scale' + str(i)]))locals()['depth_colormap' + str(i)] = cv2.applyColorMap(cv2.convertScaleAbs(locals()['depth_image_segmentation' + str(i)], alpha=0.0425),cv2.COLORMAP_JET)locals()['image' + str(i)] = np.hstack((locals()['color_image' + str(i)], locals()['depth_colormap' + str(i)]))# 注意: 窗口名不要用中文字符, 小心乱码cv2.imshow('win{}:{}'.format(i, self.cam_serials[i]), locals()['image' + str(i)])# cv2.imshow('colorWin{}: {}'.format(i, self.cam_serials[i]), locals()['color_image' + str(i)])# cv2.imshow('depthWin{}: {}'.format(i, self.cam_serials[i]), locals()['depth_colormap' + str(i)])cv2.waitKey(1)# 【向远端发送告警信号及图片：】if locals()['dangerous_scale' + str(i)] > self.threshold_dangerous_scale:print("距离警告，向远端发送告警信息!")# self.udp_server_client.sendto('摄像头{}告警'.format(i).encode('utf-8'), self.ip_port)# print(locals()['image' + str(i)].shape) # (360, 1280, 3)udp_send_image(locals()['image' + str(i)], self.bytes_udp_pack, self.udp_server_client,self.ip_port)end_time = time.time()# print('单帧运行时间：{}'.format(end_time - start_time))# 遇到异常再次启动检测函数，如有需要可以将连续监测和摄像头重置全放进去# except:# print('\n出现异常，请重新检查摄像头连接！\n')# for i in range(len(self.cam_serials)):# cv2.destroyAllWindows()# locals()['pipeline' + str(i)].stop()# ObstacleDetection().obstacle_detection()finally:for i in range(len(self.cam_serials)):locals()['pipeline' + str(i)].stop()if __name__ == '__main__':ObstacleDetection().obstacle_detection()接收端
# -*- encoding: utf-8 -*-
"""
@File : 201003_避障程序信号及图像接收端_测试udp传输上限.py
@Time : 2020/1/3 14:32
@Author : Dontla
@Email : sxana@qq.com
@Software: PyCharm
"""import socket
import struct
import timeimport cv2
import numpy as npip_port = ('192.168.1.49', 9000)
BUFSIZE = 65507udp_server_client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)udp_server_client.bind(ip_port)def receive():while True:try:while True:# 计算fmt字节长度（貌似没必要我先注释掉了）# fhead_size = struct.calcsize('i')# print('fhead_size大小：{}'.format(fhead_size)) # 4# 获取数据头信息，第一个参数为信息，第二个参数是发送方ip地址buffer, _ = udp_server_client.recvfrom(BUFSIZE)# print(buffer)# print(len(buffer)) # 可能为4或60000+if len(buffer) == 4:# print(buffer) # b';\xfb\x00\x00' # 每次都不一样的# 解包，看看有多大（unpack返回的是只有一个元素的元组，如(64282,)，元素个数貌似取决于fmt）data_size = struct.unpack('i', buffer)[0]# data_size = struct.unpack('i', buf)[0]# print(data_size) # 64315else:print('不是struct头，继续下次循环！')# cv2.destroyAllWindows()continue# 重写接收程序recv_times = data_size // BUFSIZE + 1# print(recv_times) # 按目前的BUFSIZE，为1或2，大部分为2data_total = b''recvd_size = 0for count in range(recv_times):data, _ = udp_server_client.recvfrom(BUFSIZE)recvd_size += len(data)data_total += data# 判断data_total长度是否等于图片长度，不是就继续下次循环# print(len(data_total))if len(data_total) != data_size:print('一定又是哪接收出错了，导致没接收上，继续下轮循环！')continue# recvd_size = 0# data_total = b''# while recvd_size < data_size:# if data_size - recvd_size >= BUFSIZE:# data, _ = udp_server_client.recvfrom(BUFSIZE)# recvd_size += len(data)# else:# data, _ = udp_server_client.recvfrom(data_size - recvd_size)# recvd_size += len(data)# data_total += dataprint('received!')# print(data_total)# print(type(data_total))# <class 'bytes'>nparr = np.fromstring(data_total, np.uint8)# print(nparr) # [255 216 255 ... 15 255 217] # 每次不一样的img_decode = cv2.imdecode(nparr, cv2.IMREAD_COLOR)cv2.imshow('win', img_decode)# 保存截图# cv2.imwrite('{}.jpg'.format(time.clock()), img_decode)cv2.waitKey(1)# data, addr = udp_server_client.recvfrom(BUFSIZE)# print(data.decode('utf-8'), addr)# print(data, addr)# nparr = np.fromstring(data, np.uint8)# img_decode = cv2.imdecode(nparr, cv2.IMREAD_COLOR)# cv2.imshow('result', img_decode)# cv2.waitKey()except:print('出现异常，继续调用receive()函数！')# receive()finally:# cv2.destroyAllWindows()pass# breakif __name__ == '__main__':receive()执行结果
发送端
接收端
参考文章1：python通过udp传输图片
参考文章2：python 网络编程 问题记录
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