BenchmarkIn

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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，
7# 并选择是否将报告记录为警告。
8benchmarkIn.sendReportEveryNMessages(100)
9benchmarkIn.logReportsAsWarnings(False)
10
11# 例如，将 NeuralNetwork 的输出链接到 BenchmarkIn：
12neuralNetwork = pipeline.create(dai.node.NeuralNetwork)
13neuralNetwork.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 以每 100 条消息发送一次报告，
7// 并决定是否将报告记录为警告。
8benchmarkIn->sendReportEveryNMessages(100);
9benchmarkIn->logReportsAsWarnings(false);
10
11// 例如，将 NeuralNetwork 的输出链接到 BenchmarkIn：
12auto neuralNetwork = pipeline.create<dai::node::NeuralNetwork>();
13neuralNetwork->out.link(benchmarkIn->input);

1pipeline = dai.Pipeline()
2
3# 创建一个 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# 创建一个输出队列以接收基准测试报告
13outputQueue = benchmarkIn.report.createOutputQueue()
14
15while pipeline.isRunning():
16    benchmarkReport = outputQueue.get()
17    print(f"FPS is {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 is " << benchmarkReport.fps << std::endl;
13}