Detection network

1#!/usr/bin/env python3
2
3from pathlib import Path
4import cv2
5import depthai as dai
6import numpy as np
7import time
8
9# Create pipeline
10with dai.Pipeline() as pipeline:
11    cameraNode = pipeline.create(dai.node.Camera).build()
12    detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(cameraNode, dai.NNModelDescription("yolov6-nano"))
13    labelMap = detectionNetwork.getClasses()
14
15    qRgb = detectionNetwork.passthrough.createOutputQueue()
16    qDet = detectionNetwork.out.createOutputQueue()
17
18    pipeline.start()
19
20    frame = None
21    detections = []
22    startTime = time.monotonic()
23    counter = 0
24    color2 = (255, 255, 255)
25
26    # nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
27    def frameNorm(frame, bbox):
28        normVals = np.full(len(bbox), frame.shape[0])
29        normVals[::2] = frame.shape[1]
30        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
31
32    def displayFrame(name, frame):
33        color = (255, 0, 0)
34        for detection in detections:
35            bbox = frameNorm(
36                frame,
37                (detection.xmin, detection.ymin, detection.xmax, detection.ymax),
38            )
39            cv2.putText(
40                frame,
41                labelMap[detection.label],
42                (bbox[0] + 10, bbox[1] + 20),
43                cv2.FONT_HERSHEY_TRIPLEX,
44                0.5,
45                255,
46            )
47            cv2.putText(
48                frame,
49                f"{int(detection.confidence * 100)}%",
50                (bbox[0] + 10, bbox[1] + 40),
51                cv2.FONT_HERSHEY_TRIPLEX,
52                0.5,
53                255,
54            )
55            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
56        # Show the frame
57        cv2.imshow(name, frame)
58
59    while pipeline.isRunning():
60        inRgb: dai.ImgFrame = qRgb.get()
61        inDet: dai.ImgDetections = qDet.get()
62        if inRgb is not None:
63            frame = inRgb.getCvFrame()
64            cv2.putText(
65                frame,
66                "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
67                (2, frame.shape[0] - 4),
68                cv2.FONT_HERSHEY_TRIPLEX,
69                0.4,
70                color2,
71            )
72
73        if inDet is not None:
74            detections = inDet.detections
75            counter += 1
76
77        if frame is not None:
78            displayFrame("rgb", frame)
79            print("FPS: {:.2f}".format(counter / (time.monotonic() - startTime)))
80        if cv2.waitKey(1) == ord("q"):
81            pipeline.stop()
82            break

1#include <chrono>
2#include <iostream>
3#include <opencv2/opencv.hpp>
4
5#include "depthai/depthai.hpp"
6
7// Helper function to normalize frame coordinates
8cv::Rect frameNorm(const cv::Mat& frame, const dai::Point2f& topLeft, const dai::Point2f& bottomRight) {
9    float width = frame.cols, height = frame.rows;
10    return cv::Rect(cv::Point(topLeft.x * width, topLeft.y * height), cv::Point(bottomRight.x * width, bottomRight.y * height));
11}
12
13int main() {
14    // Create pipeline
15    dai::Pipeline pipeline;
16
17    // Create and configure camera node
18    auto cameraNode = pipeline.create<dai::node::Camera>();
19    cameraNode->build();
20
21    // Create and configure detection network node
22    auto detectionNetwork = pipeline.create<dai::node::DetectionNetwork>();
23
24    dai::NNModelDescription modelDescription;
25    modelDescription.model = "yolov6-nano";
26    detectionNetwork->build(cameraNode, modelDescription);
27    auto labelMap = detectionNetwork->getClasses();
28
29    // Create output queues
30    auto qRgb = detectionNetwork->passthrough.createOutputQueue();
31    auto qDet = detectionNetwork->out.createOutputQueue();
32
33    cv::Mat frame;
34    auto startTime = std::chrono::steady_clock::now();
35    int counter = 0;
36    cv::Scalar color(255, 0, 0);
37    cv::Scalar textColor(255, 255, 255);
38
39    pipeline.start();
40    while(pipeline.isRunning()) {
41        auto inRgb = qRgb->get<dai::ImgFrame>();
42        auto inDet = qDet->get<dai::ImgDetections>();
43
44        if(inRgb != nullptr) {
45            frame = inRgb->getCvFrame();
46
47            // Add FPS text
48            auto currentTime = std::chrono::steady_clock::now();
49            float fps = counter / std::chrono::duration<float>(currentTime - startTime).count();
50            cv::putText(frame, "NN fps: " + std::to_string(fps), cv::Point(2, frame.rows - 4), cv::FONT_HERSHEY_TRIPLEX, 0.4, textColor);
51        }
52
53        if(inDet != nullptr) {
54            counter++;
55        }
56
57        if(!frame.empty()) {
58            // Display detections
59            for(const auto& detection : inDet->detections) {
60                auto bbox = frameNorm(frame, dai::Point2f(detection.xmin, detection.ymin), dai::Point2f(detection.xmax, detection.ymax));
61
62                // Draw label
63                cv::putText(frame, labelMap.value()[detection.label], cv::Point(bbox.x + 10, bbox.y + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, textColor);
64
65                // Draw confidence
66                cv::putText(frame,
67                            std::to_string(static_cast<int>(detection.confidence * 100)) + "%",
68                            cv::Point(bbox.x + 10, bbox.y + 40),
69                            cv::FONT_HERSHEY_TRIPLEX,
70                            0.5,
71                            textColor);
72
73                // Draw rectangle
74                cv::rectangle(frame, bbox, color, 2);
75            }
76
77            // Show the frame
78            cv::imshow("rgb", frame);
79
80            auto currentTime = std::chrono::steady_clock::now();
81            float fps = counter / std::chrono::duration<float>(currentTime - startTime).count();
82            std::cout << "FPS: " << fps << std::endl;
83        }
84
85        if(cv::waitKey(1) == 'q') {
86            break;
87        }
88    }
89
90    return 0;
91}