西安微信网站建设,聊天软件开发厂家有哪些,音乐网站制作策划书,怎么做网站的排名这篇文章将结合之前写的两篇文章 无人机实战系列#xff08;一#xff09;在局域网内传输数据 和 无人机实战系列#xff08;二#xff09;本地摄像头 Depth-Anything V2 实现了以下功能#xff1a;
本地笔记本摄像头发布图像 远程GPU实时处理#xff08;无回传#…这篇文章将结合之前写的两篇文章 无人机实战系列一在局域网内传输数据 和 无人机实战系列二本地摄像头 Depth-Anything V2 实现了以下功能
本地笔记本摄像头发布图像 远程GPU实时处理无回传【异步】本地笔记本摄像头发布图像 远程GPU实时处理回传至笔记本并展示【同步】本地笔记本摄像头发布图像 远程GPU实时处理回传至笔记本并展示
建议在运行这个demo之前先查看先前的两篇文章以熟悉 zmq 库与 Depth-Anything V2 这个模型
这里之所以提供两个在是否回传上的demo是因为你需要根据自己的实际状况进行选择尽管回传并显示深度图能够更加直观查看计算结果但你仍然需要平衡以下两个方面
深度本身体积较大回传会占用通讯带宽回传的图像本地显示会占用本地算力
【注意】这篇文章的代码需要在 无人机实战系列二本地摄像头 Depth-Anything V2 中的文件夹下运行否则会报错找不到对应的文件与模型。 本地笔记本摄像头发布图像 远程GPU实时处理无回传
这个demo实现了本地笔记本打开摄像头后将图像发布出去远程GPU服务器接受到图像后使用 Depth-Anything V2 处理图像并展示。
本地笔记本发布摄像头图像
在下面的代码中有以下几点需要注意
设置发布频率 send_fps较低的发布频率可以让减少GPU端的压力设置发布队列大小 socket.setsockopt(zmq.SNDHWM, 1)让发布队列始终仅有当前帧画面降低带宽压力设置仅保存最新消息 socket.setsockopt(zmq.CONFLATE, 1)让发布队列仅存储最新的画面降低接受端的画面延迟
import zmq
import cv2
import timecontext zmq.Context()
socket context.socket(zmq.PUB)
socket.bind(tcp://*:5555) # 本地绑定端口socket.setsockopt(zmq.SNDHWM, 1) # 发送队列大小为1
socket.setsockopt(zmq.CONFLATE, 1) # 仅保存最新消息cap cv2.VideoCapture(0) # 读取摄像头send_fps 30 # 限制传输的fps降低接受方的处理压力while True:start_time time.time()ret, frame cap.read()if not ret:continue_, buffer cv2.imencode(.jpg, frame) # 编码成JPEG格式socket.send(buffer.tobytes()) # 发送图像数据cv2.imshow(Origin image, frame)if cv2.waitKey(1) 0xFF ord(q):breaktime.sleep(max(1/send_fps - (time.time() - start_time), 0))运行
$ python camera_pub.pyGPU 服务器接受端
在下面的代码中有以下几点需要注意
绑定发布端地址 socket.connect(tcp://192.168.75.201:5555)根据你笔记本的地址进行修改仅接受最新消息 socket.setsockopt(zmq.CONFLATE, 1)清空旧数据帧 socket.setsockopt(zmq.RCVHWM, 1) socket.poll(1)
import argparse
import cv2
import numpy as np
import torch
import time
import zmqfrom depth_anything_v2.dpt import DepthAnythingV2context zmq.Context()
socket context.socket(zmq.SUB)
socket.connect(tcp://192.168.75.201:5555) # 远程发布端地址
socket.setsockopt(zmq.SUBSCRIBE, b)
socket.setsockopt(zmq.CONFLATE, 1) # 仅接受最新消息
socket.setsockopt(zmq.RCVHWM, 1) # 清空旧数据帧if __name__ __main__:parser argparse.ArgumentParser(descriptionDepth Anything V2)parser.add_argument(--input-size, typeint, default518)parser.add_argument(--encoder, typestr, defaultvits, choices[vits, vitb, vitl, vitg])parser.add_argument(--pred-only, actionstore_true, helponly display the prediction)parser.add_argument(--grayscale, actionstore_true, helpdo not apply colorful palette)args parser.parse_args()DEVICE cuda if torch.cuda.is_available() else cpumodel_configs {vits: {encoder: vits, features: 64, out_channels: [48, 96, 192, 384]},vitb: {encoder: vitb, features: 128, out_channels: [96, 192, 384, 768]},vitl: {encoder: vitl, features: 256, out_channels: [256, 512, 1024, 1024]},vitg: {encoder: vitg, features: 384, out_channels: [1536, 1536, 1536, 1536]}}depth_anything DepthAnythingV2(**model_configs[args.encoder])depth_anything.load_state_dict(torch.load(f./models/depth_anything_v2_{args.encoder}.pth, map_locationcpu))depth_anything depth_anything.to(DEVICE).eval()margin_width 50while True:start_time time.time()# **优化 1: ZMQ 数据接收**try:while socket.poll(1): # 尝试不断读取新数据丢弃旧数据msg socket.recv(zmq.NOBLOCK)zmq_time time.time()# **优化 2: OpenCV 解码**raw_frame cv2.imdecode(np.frombuffer(msg, dtypenp.uint8), 1)decode_time time.time()# **优化 3: 模型推理**with torch.no_grad():depth depth_anything.infer_image(raw_frame, args.input_size)infer_time time.time()# **优化 4: 归一化 OpenCV 伪彩色映射**depth ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8)if args.grayscale:depth np.repeat(depth[..., np.newaxis], 3, axis-1)else:depth cv2.applyColorMap(depth, cv2.COLORMAP_JET)process_time time.time()# **优化 5: 合并图像**split_region np.ones((raw_frame.shape[0], margin_width, 3), dtypenp.uint8) * 255combined_frame cv2.hconcat([raw_frame, split_region, depth])cv2.imshow(Raw Frame and Depth Prediction, combined_frame)if cv2.waitKey(1) 0xFF ord(q):breakprint(f[{args.encoder}] Frame cost time: {time.time() - start_time:.4f} s)print(f ZMQ receive: {zmq_time - start_time:.4f} s)print(f Decode: {decode_time - zmq_time:.4f} s)print(f Inference: {infer_time - decode_time:.4f} s)print(f Processing: {process_time - infer_time:.4f} s)except zmq.Again:print(No msg received, skip...)continue # 没有消息就跳过cv2.destroyAllWindows()
运行
$ python camera_recv.py【异步】本地笔记本摄像头发布图像 远程GPU实时处理回传至笔记本并展示
和上面的代码基本一致只不过在发送与接收端都增加了一个收发对象通常情况下使用异步方式处理收发因为可以避免一端服务来不及处理而导致另一端持续等待。
本地笔记本发布摄像头图像
import zmq
import cv2
import numpy as np
import timecontext zmq.Context()# 发布原始数据
pub_socket context.socket(zmq.PUB)
pub_socket.bind(tcp://*:5555) # 发布数据# 接收处理结果
pull_socket context.socket(zmq.PULL)
pull_socket.bind(tcp://*:5556) # 监听处理方返回数据send_fps 30cap cv2.VideoCapture(0)while True:start_time time.time()ret, frame cap.read()if not ret:continue# [可选] 图像降采样frame cv2.pyrDown(frame)frame cv2.pyrDown(frame)_, buffer cv2.imencode(.jpg, frame) # 压缩图像pub_socket.send(buffer.tobytes()) # 发布数据# 非阻塞接收处理结果try:processed_data pull_socket.recv(zmq.NOBLOCK)processed_frame cv2.imdecode(np.frombuffer(processed_data, dtypenp.uint8), 1)except zmq.Again:print(No image received, continue...)continuecv2.imshow(Processed Frame, processed_frame)if cv2.waitKey(1) 0xFF ord(q):breaktime.sleep(max(1/send_fps - (time.time() - start_time), 0))cv2.destroyAllWindows()运行
$ python camera_pub_async.pyGPU 服务器接受端异步
import argparse
import cv2
import numpy as np
import torch
import time
import zmqfrom depth_anything_v2.dpt import DepthAnythingV2context zmq.Context()
sub_socket context.socket(zmq.SUB)
sub_socket.connect(tcp://192.168.75.201:5555)
sub_socket.setsockopt(zmq.SUBSCRIBE, b)
sub_socket.setsockopt(zmq.CONFLATE, 1) # 仅接受最新消息
sub_socket.setsockopt(zmq.RCVHWM, 1) # 清空旧数据帧# 发送处理结果
push_socket context.socket(zmq.PUSH)
push_socket.connect(tcp://192.168.75.201:5556)if __name__ __main__:parser argparse.ArgumentParser(descriptionDepth Anything V2)parser.add_argument(--input-size, typeint, default518)parser.add_argument(--encoder, typestr, defaultvits, choices[vits, vitb, vitl, vitg])parser.add_argument(--pred-only, actionstore_true, helponly display the prediction)parser.add_argument(--grayscale, actionstore_true, helpdo not apply colorful palette)args parser.parse_args()DEVICE cuda if torch.cuda.is_available() else cpumodel_configs {vits: {encoder: vits, features: 64, out_channels: [48, 96, 192, 384]},vitb: {encoder: vitb, features: 128, out_channels: [96, 192, 384, 768]},vitl: {encoder: vitl, features: 256, out_channels: [256, 512, 1024, 1024]},vitg: {encoder: vitg, features: 384, out_channels: [1536, 1536, 1536, 1536]}}depth_anything DepthAnythingV2(**model_configs[args.encoder])depth_anything.load_state_dict(torch.load(f./models/depth_anything_v2_{args.encoder}.pth, map_locationcpu))depth_anything depth_anything.to(DEVICE).eval()margin_width 50while True:start_time time.time()# **优化 1: ZMQ 数据接收**try:while sub_socket.poll(1): # 尝试不断读取新数据丢弃旧数据msg sub_socket.recv(zmq.NOBLOCK)msg sub_socket.recv()zmq_time time.time()# **优化 2: OpenCV 解码**raw_frame cv2.imdecode(np.frombuffer(msg, dtypenp.uint8), 1)decode_time time.time()# **优化 3: 模型推理**with torch.no_grad():depth depth_anything.infer_image(raw_frame, args.input_size)infer_time time.time()# **优化 4: 归一化 OpenCV 伪彩色映射**depth ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8)if args.grayscale:depth np.repeat(depth[..., np.newaxis], 3, axis-1)else:depth cv2.applyColorMap(depth, cv2.COLORMAP_JET)process_time time.time()# **优化 5: 合并图像**split_region np.ones((raw_frame.shape[0], margin_width, 3), dtypenp.uint8) * 255combined_frame cv2.hconcat([raw_frame, split_region, depth])cv2.imshow(Raw Frame and Depth Prediction, combined_frame)if cv2.waitKey(1) 0xFF ord(q):breakprint(f[{args.encoder}] Frame cost time: {time.time() - start_time:.4f} s)print(f ZMQ receive: {zmq_time - start_time:.4f} s)print(f Decode: {decode_time - zmq_time:.4f} s)print(f Inference: {infer_time - decode_time:.4f} s)print(f Processing: {process_time - infer_time:.4f} s)_, buffer cv2.imencode(.jpg, combined_frame)push_socket.send(buffer.tobytes()) # 发送回处理结果except zmq.Again:print(No msg received, skip...)continue # 没有消息就跳过cv2.destroyAllWindows()
运行
$ python camera_recv_async.py【同步】本地笔记本摄像头发布图像 远程GPU实时处理回传至笔记本并展示
通常情况下这种视频流的传递不会考虑同步方式因为这需要发布方与接收端保持一致对网络稳定性有较高的要求。
本地笔记本发布摄像头图像
这个demo需要注意以下几点
设置发送端为请求响应模式 context.socket(zmq.REQ) 阻塞等待服务器回传数据 pub_socket.recv()
import zmq
import cv2
import numpy as np
import timecontext zmq.Context()# 发布原始数据
pub_socket context.socket(zmq.REQ) # 使用请求响应模式
pub_socket.bind(tcp://*:5555) # 发布数据send_fps 30
cap cv2.VideoCapture(0)while True:start_time time.time()ret, frame cap.read()if not ret:continue# [可选] 图像降采样frame cv2.pyrDown(frame)frame cv2.pyrDown(frame)try:_, buffer cv2.imencode(.jpg, frame) # 压缩图像pub_socket.send(buffer.tobytes()) # 发布数据print(Waitting for server processed.)processed_data pub_socket.recv()processed_frame cv2.imdecode(np.frombuffer(processed_data, dtypenp.uint8), 1)except zmq.Again:print(No image received, continue...)continuecv2.imshow(Processed Frame, processed_frame)if cv2.waitKey(1) 0xFF ord(q):breaktime.sleep(max(1/send_fps - (time.time() - start_time), 0))cv2.destroyAllWindows()运行
$ python camera_pub_sync.pyGPU 服务器接受端
这个demo需要注意以下几点
设置接受端为请求响应模式 context.socket(zmq.REP) 阻塞接受发布端数据 sub_socket.recv() 将处理好的数据进行同步回传 sub_socket.send(buffer.tobytes())
import argparse
import cv2
import numpy as np
import torch
import time
import zmqfrom depth_anything_v2.dpt import DepthAnythingV2context zmq.Context()
sub_socket context.socket(zmq.REP)
sub_socket.connect(tcp://192.168.75.201:5555)if __name__ __main__:parser argparse.ArgumentParser(descriptionDepth Anything V2)parser.add_argument(--input-size, typeint, default518)parser.add_argument(--encoder, typestr, defaultvits, choices[vits, vitb, vitl, vitg])parser.add_argument(--pred-only, actionstore_true, helponly display the prediction)parser.add_argument(--grayscale, actionstore_true, helpdo not apply colorful palette)args parser.parse_args()DEVICE cuda if torch.cuda.is_available() else cpumodel_configs {vits: {encoder: vits, features: 64, out_channels: [48, 96, 192, 384]},vitb: {encoder: vitb, features: 128, out_channels: [96, 192, 384, 768]},vitl: {encoder: vitl, features: 256, out_channels: [256, 512, 1024, 1024]},vitg: {encoder: vitg, features: 384, out_channels: [1536, 1536, 1536, 1536]}}depth_anything DepthAnythingV2(**model_configs[args.encoder])depth_anything.load_state_dict(torch.load(f./models/depth_anything_v2_{args.encoder}.pth, map_locationcpu))depth_anything depth_anything.to(DEVICE).eval()margin_width 50while True:start_time time.time()# **优化 1: ZMQ 数据接收**try:msg sub_socket.recv()zmq_time time.time()# **优化 2: OpenCV 解码**raw_frame cv2.imdecode(np.frombuffer(msg, dtypenp.uint8), 1)decode_time time.time()# **优化 3: 模型推理**with torch.no_grad():depth depth_anything.infer_image(raw_frame, args.input_size)infer_time time.time()# **优化 4: 归一化 OpenCV 伪彩色映射**depth ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8)if args.grayscale:depth np.repeat(depth[..., np.newaxis], 3, axis-1)else:depth cv2.applyColorMap(depth, cv2.COLORMAP_JET)process_time time.time()# **优化 5: 合并图像**split_region np.ones((raw_frame.shape[0], margin_width, 3), dtypenp.uint8) * 255combined_frame cv2.hconcat([raw_frame, split_region, depth])cv2.imshow(Raw Frame and Depth Prediction, combined_frame)if cv2.waitKey(1) 0xFF ord(q):breakprint(f[{args.encoder}] Frame cost time: {time.time() - start_time:.4f} s)print(f ZMQ receive: {zmq_time - start_time:.4f} s)print(f Decode: {decode_time - zmq_time:.4f} s)print(f Inference: {infer_time - decode_time:.4f} s)print(f Processing: {process_time - infer_time:.4f} s)_, buffer cv2.imencode(.jpg, combined_frame)sub_socket.send(buffer.tobytes()) # 发送回处理结果except zmq.Again:print(No msg received, skip...)continue # 没有消息就跳过cv2.destroyAllWindows()运行
$ python camera_recv_sync.py
