BenchmarkIn

1import depthai as dai
2
3pipeline = dai.Pipeline()
4benchmarkIn = pipeline.create(dai.node.BenchmarkIn)
5
6# 配置为每100条消息发送一次性能报告，并选择是否将报告作为警告记录。
7benchmarkIn.sendReportEveryNMessages(100)
8benchmarkIn.logReportsAsWarnings(False)
9
10# 例如，将 NeuralNetwork 的输出连接到 BenchmarkIn：
11neuralNetwork = pipeline.create(dai.node.NeuralNetwork)
12neuralNetwork.out.link(benchmarkIn.input)

1#include "depthai/depthai.hpp"
2
3dai::Pipeline pipeline;
4auto benchmarkIn = pipeline.create<dai::node::BenchmarkIn>();
5
6// 配置 BenchmarkIn 每100条消息发送一次报告，并决定是否将报告作为警告记录。
7benchmarkIn->sendReportEveryNMessages(100);
8benchmarkIn->logReportsAsWarnings(false);
9
10// 例如，将 NeuralNetwork 的输出连接到 BenchmarkIn：
11auto neuralNetwork = pipeline.create<dai::node::NeuralNetwork>();
12neuralNetwork->out.link(benchmarkIn->input);

1pipeline = dai.Pipeline()
2
3# 创建 BenchmarkIn 节点并进行配置：
4benchmarkIn = pipeline.create(dai.node.BenchmarkIn)
5benchmarkIn.sendReportEveryNMessages(100)
6benchmarkIn.logReportsAsWarnings(False)
7
8# 示例：将 BenchmarkIn 连接到 NeuralNetwork 节点
9neuralNetwork = pipeline.create(dai.node.NeuralNetwork)
10neuralNetwork.out.link(benchmarkIn.input)
11
12# 创建用于接收基准测试报告的输出队列
13outputQueue = benchmarkIn.report.createOutputQueue()
14
15while pipeline.isRunning():
16    benchmarkReport = outputQueue.get()
17    print(f"FPS 是 {benchmarkReport.fps}")

1dai::Pipeline pipeline;
2auto benchmarkIn = pipeline.create<dai::node::BenchmarkIn>();
3benchmarkIn->sendReportEveryNMessages(100);
4benchmarkIn->logReportsAsWarnings(false);
5
6auto neuralNetwork = pipeline.create<dai::node::NeuralNetwork>();
7neuralNetwork->out.link(benchmarkIn->input);
8
9auto outputQueue = benchmarkIn->report.createOutputQueue();
10while(pipeline.isRunning()) {
11    auto benchmarkReport = outputQueue->get<dai::BenchmarkReport>();
12    std::cout << "FPS 是 " << benchmarkReport.fps << std::endl;
13}