Video & MobilenetSSD

This example shows how to MobileNetv2SSD on the RGB input frame, which is read from the specified file, and not from the RGB camera, and how to display both the RGB frame and the metadata results from the MobileNetv2SSD on the frame. DepthAI is used here only as a processing unit

Similiar samples:

Demo

Setup

Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script

git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py

For additional information, please follow installation guide

This example also requires MobilenetSDD blob (mobilenet-ssd_openvino_2021.2_8shave.blob file) and prerecorded video (construction_vest.mp4 file) to work - you can download them here: mobilenet-ssd_openvino_2021.2_8shave.blob and construction_vest.mp4

Source code

Also available on GitHub

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#!/usr/bin/env python3

from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
from time import monotonic

# Get argument first
parentDir = Path(__file__).parent
nnPath = str((parentDir / Path('models/mobilenet-ssd_openvino_2021.4_8shave.blob')).resolve().absolute())
videoPath = str((parentDir / Path('models/construction_vest.mp4')).resolve().absolute())
if len(sys.argv) > 2:
    nnPath = sys.argv[1]
    videoPath = sys.argv[2]

if not Path(nnPath).exists() or not Path(videoPath).exists():
    import sys
    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')

# MobilenetSSD label texts
labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
nn = pipeline.createMobileNetDetectionNetwork()

xinFrame = pipeline.createXLinkIn()
nnOut = pipeline.createXLinkOut()

xinFrame.setStreamName("inFrame")
nnOut.setStreamName("nn")

# Properties
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Linking
xinFrame.out.link(nn.input)
nn.out.link(nnOut.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    # Input queue will be used to send video frames to the device.
    qIn = device.getInputQueue(name="inFrame")
    # Output queue will be used to get nn data from the video frames.
    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

    frame = None
    detections = []

    # nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
    def frameNorm(frame, bbox):
        normVals = np.full(len(bbox), frame.shape[0])
        normVals[::2] = frame.shape[1]
        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)

    def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
        return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()

    def displayFrame(name, frame):
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
        # Show the frame
        cv2.imshow(name, frame)

    cap = cv2.VideoCapture(videoPath)
    while cap.isOpened():
        read_correctly, frame = cap.read()
        if not read_correctly:
            break

        img = dai.ImgFrame()
        img.setData(to_planar(frame, (300, 300)))
        img.setTimestamp(monotonic())
        img.setWidth(300)
        img.setHeight(300)
        qIn.send(img)

        inDet = qDet.tryGet()

        if inDet is not None:
            detections = inDet.detections

        if frame is not None:
            displayFrame("rgb", frame)

        if cv2.waitKey(1) == ord('q'):
            break

Also available on GitHub

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#include <chrono>
#include <iostream>

#include "utility.hpp"

// Inludes common necessary includes for development using depthai library
#include "depthai/depthai.hpp"

// MobilenetSSD label texts
static const std::vector<std::string> labelMap = {"background", "aeroplane", "bicycle",     "bird",  "boat",        "bottle", "bus",
                                                  "car",        "cat",       "chair",       "cow",   "diningtable", "dog",    "horse",
                                                  "motorbike",  "person",    "pottedplant", "sheep", "sofa",        "train",  "tvmonitor"};

int main(int argc, char** argv) {
    using namespace std;
    using namespace std::chrono;
    // Default blob path provided by Hunter private data download
    // Applicable for easier example usage only
    std::string nnPath(BLOB_PATH);
    std::string videoPath(VIDEO_PATH);

    // If path to blob specified, use that
    if(argc > 2) {
        nnPath = std::string(argv[1]);
        videoPath = std::string(argv[2]);
    }

    // Print which blob we are using
    printf("Using blob at path: %s\n", nnPath.c_str());
    printf("Using video at path: %s\n", videoPath.c_str());

    // Create pipeline
    dai::Pipeline pipeline;

    // Define source and outputs
    auto nn = pipeline.create<dai::node::MobileNetDetectionNetwork>();

    auto xinFrame = pipeline.create<dai::node::XLinkIn>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();

    xinFrame->setStreamName("inFrame");
    nnOut->setStreamName("nn");

    // Properties
    nn->setConfidenceThreshold(0.5);
    nn->setBlobPath(nnPath);
    nn->setNumInferenceThreads(2);
    nn->input.setBlocking(false);

    // Linking
    xinFrame->out.link(nn->input);
    nn->out.link(nnOut->input);

    // Connect to device and start pipeline
    dai::Device device(pipeline);

    // Input queue will be used to send video frames to the device.
    auto qIn = device.getInputQueue("inFrame");
    // Output queue will be used to get nn data from the video frames.
    auto qDet = device.getOutputQueue("nn", 4, false);

    // Add bounding boxes and text to the frame and show it to the user
    auto displayFrame = [](std::string name, cv::Mat frame, std::vector<dai::ImgDetection>& detections) {
        auto color = cv::Scalar(255, 0, 0);
        // nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
        for(auto& detection : detections) {
            int x1 = detection.xmin * frame.cols;
            int y1 = detection.ymin * frame.rows;
            int x2 = detection.xmax * frame.cols;
            int y2 = detection.ymax * frame.rows;

            int labelIndex = detection.label;
            std::string labelStr = to_string(labelIndex);
            if(labelIndex < labelMap.size()) {
                labelStr = labelMap[labelIndex];
            }
            cv::putText(frame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
            std::stringstream confStr;
            confStr << std::fixed << std::setprecision(2) << detection.confidence * 100;
            cv::putText(frame, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color);
            cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX);
        }
        // Show the frame
        cv::imshow(name, frame);
    };

    cv::Mat frame;
    cv::VideoCapture cap(videoPath);

    cv::namedWindow("inFrame", cv::WINDOW_NORMAL);
    cv::resizeWindow("inFrame", 1280, 720);
    std::cout << "Resize video window with mouse drag!" << std::endl;

    while(cap.isOpened()) {
        // Read frame from video
        cap >> frame;
        if(frame.empty()) break;

        auto img = std::make_shared<dai::ImgFrame>();
        frame = resizeKeepAspectRatio(frame, cv::Size(300, 300), cv::Scalar(0));
        toPlanar(frame, img->getData());
        img->setTimestamp(steady_clock::now());
        img->setWidth(300);
        img->setHeight(300);
        qIn->send(img);

        auto inDet = qDet->get<dai::ImgDetections>();
        auto detections = inDet->detections;

        displayFrame("inFrame", frame, detections);

        int key = cv::waitKey(1);
        if(key == 'q' || key == 'Q') return 0;
    }
    return 0;
}

Got questions?

We’re always happy to help with code or other questions you might have.