Mono & MobilenetSSD & Depth

This example shows how to run MobileNetv2SSD on the left grayscale camera in parallel with running the disparity depth results, displaying both the depth map and the right grayscale stream, with the bounding box from the neural network overlaid. It’s a combination of Depth Preview and Mono & MobilenetSSD.

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_6shave.blob file) to work - you can download it from here

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

# Get argument first
nnPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
if len(sys.argv) > 1:
    nnPath = sys.argv[1]

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

# MobilenetSSD label nnLabels
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
monoRight = pipeline.create(dai.node.MonoCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
manip = pipeline.create(dai.node.ImageManip)
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

nnOut = pipeline.create(dai.node.XLinkOut)
disparityOut = pipeline.create(dai.node.XLinkOut)
xoutRight = pipeline.create(dai.node.XLinkOut)

disparityOut.setStreamName("disparity")
xoutRight.setStreamName("rectifiedRight")
nnOut.setStreamName("nn")

# Properties
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)

# Produce the depth map (using disparity output as it's easier to visualize depth this way)
stereo.initialConfig.setConfidenceThreshold(255)
stereo.setRectifyEdgeFillColor(0)  # Black, to better see the cutout from rectification (black stripe on the edges)
# Convert the grayscale frame into the nn-acceptable form
manip.initialConfig.setResize(300, 300)
# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)

# Define a neural network that will make predictions based on the source frames
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Linking
monoRight.out.link(stereo.right)
monoLeft.out.link(stereo.left)
stereo.rectifiedRight.link(manip.inputImage)
stereo.disparity.link(disparityOut.input)
manip.out.link(nn.input)
manip.out.link(xoutRight.input)
nn.out.link(nnOut.input)

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

    # Output queues will be used to get the grayscale / depth frames and nn data from the outputs defined above
    qRight = device.getOutputQueue("rectifiedRight", maxSize=4, blocking=False)
    qDisparity = device.getOutputQueue("disparity", maxSize=4, blocking=False)
    qDet = device.getOutputQueue("nn", maxSize=4, blocking=False)

    rightFrame = None
    disparityFrame = 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)

    # Add bounding boxes and text to the frame and show it to the user
    def show(name, frame):
        color = (255, 0, 0)
        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, color)
            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
        # Show the frame
        cv2.imshow(name, frame)

    disparityMultiplier = 255 / stereo.initialConfig.getMaxDisparity()

    while True:
        # Instead of get (blocking), we use tryGet (nonblocking) which will return the available data or None otherwise
        inRight = qRight.tryGet()
        inDet = qDet.tryGet()
        inDisparity = qDisparity.tryGet()

        if inRight is not None:
            rightFrame = inRight.getCvFrame()

        if inDisparity is not None:
            # Frame is transformed, normalized, and color map will be applied to highlight the depth info
            disparityFrame = inDisparity.getFrame()
            disparityFrame = (disparityFrame*disparityMultiplier).astype(np.uint8)
            # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
            disparityFrame = cv2.applyColorMap(disparityFrame, cv2.COLORMAP_JET)
            show("disparity", disparityFrame)

        if rightFrame is not None:
            show("rectified right", rightFrame)

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

Also available on GitHub

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#include <iostream>

// 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;
    // Default blob path provided by Hunter private data download
    // Applicable for easier example usage only
    std::string nnPath(BLOB_PATH);

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

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

    // Create pipeline
    dai::Pipeline pipeline;

    // Define sources and outputs
    auto monoRight = pipeline.create<dai::node::MonoCamera>();
    auto monoLeft = pipeline.create<dai::node::MonoCamera>();
    auto stereo = pipeline.create<dai::node::StereoDepth>();
    auto manip = pipeline.create<dai::node::ImageManip>();
    auto nn = pipeline.create<dai::node::MobileNetDetectionNetwork>();

    auto disparityOut = pipeline.create<dai::node::XLinkOut>();
    auto xoutRight = pipeline.create<dai::node::XLinkOut>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();

    disparityOut->setStreamName("disparity");
    xoutRight->setStreamName("rectifiedRight");
    nnOut->setStreamName("nn");

    // Properties
    monoRight->setBoardSocket(dai::CameraBoardSocket::RIGHT);
    monoRight->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P);
    monoLeft->setBoardSocket(dai::CameraBoardSocket::LEFT);
    monoLeft->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P);
    // Produce the depth map (using disparity output as it's easier to visualize depth this way)
    stereo->initialConfig.setConfidenceThreshold(255);
    stereo->setRectifyEdgeFillColor(0);  // Black, to better see the cutout from rectification (black stripe on the edges)
    // Convert the grayscale frame into the nn-acceptable form
    manip->initialConfig.setResize(300, 300);
    // The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
    manip->initialConfig.setFrameType(dai::ImgFrame::Type::BGR888p);

    // Define a neural network that will make predictions based on the source frames
    nn->setConfidenceThreshold(0.5);
    nn->setBlobPath(nnPath);
    nn->setNumInferenceThreads(2);
    nn->input.setBlocking(false);

    // Linking
    monoRight->out.link(stereo->right);
    monoLeft->out.link(stereo->left);
    stereo->rectifiedRight.link(manip->inputImage);
    stereo->disparity.link(disparityOut->input);
    manip->out.link(nn->input);
    manip->out.link(xoutRight->input);
    nn->out.link(nnOut->input);

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

    // Output queues will be used to get the grayscale / depth frames and nn data from the outputs defined above
    auto qRight = device.getOutputQueue("rectifiedRight", 4, false);
    auto qDisparity = device.getOutputQueue("disparity", 4, false);
    auto qDet = device.getOutputQueue("nn", 4, false);

    cv::Mat rightFrame;
    cv::Mat disparityFrame;
    std::vector<dai::ImgDetection> detections;

    // Add bounding boxes and text to the frame and show it to the user
    auto show = [](std::string name, cv::Mat frame, std::vector<dai::ImgDetection>& detections) {
        auto color = cv::Scalar(255, 192, 203);
        // 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);
    };

    float disparityMultiplier = 255 / stereo->initialConfig.getMaxDisparity();

    while(true) {
        // Instead of get (blocking), we use tryGet (nonblocking) which will return the available data or None otherwise
        auto inRight = qRight->tryGet<dai::ImgFrame>();
        auto inDet = qDet->tryGet<dai::ImgDetections>();
        auto inDisparity = qDisparity->tryGet<dai::ImgFrame>();

        if(inDisparity) {
            // Frame is transformed, normalized, and color map will be applied to highlight the depth info
            disparityFrame = inDisparity->getFrame();
            disparityFrame.convertTo(disparityFrame, CV_8UC1, disparityMultiplier);
            // Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
            cv::applyColorMap(disparityFrame, disparityFrame, cv::COLORMAP_JET);
            show("disparity", disparityFrame, detections);
        }

        if(!rightFrame.empty()) {
            show("rectified right", rightFrame, 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.