RGB & Tiny YOLO¶

This example shows how to run YOLO on the RGB input frame, and how to display both the RGB preview and the metadata results from the YOLO model on the preview. Decoding is done on the RVC instead on the host computer.

Configurable, network dependent parameters are required for correct decoding:

  • setNumClasses - number of YOLO classes

  • setCoordinateSize - size of coordinate

  • setAnchors - yolo anchors

  • setAnchorMasks - anchorMasks26, anchorMasks13 (anchorMasks52 - additionally for full YOLOv4)

  • setIouThreshold - intersection over union threshold

  • setConfidenceThreshold - confidence threshold above which objects are detected

By default, Tiny YOLOv4 is used. You can add yolo3 as a CMD argument to use Tiny YOLOv3.

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 script requires external file(s) to run. If you are using:

  • depthai-python, run python3 examples/install_requirements.py to download required file(s)

  • dephtai-core, required file(s) will get downloaded automatically when building the example

Source code¶

Python

C++

Also available on GitHub

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#!/usr/bin/env python3

"""
The code is the same as for Tiny Yolo V3 and V4, the only difference is the blob file
- Tiny YOLOv3: https://github.com/david8862/keras-YOLOv3-model-set
- Tiny YOLOv4: https://github.com/TNTWEN/OpenVINO-YOLOV4
"""

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

# Get argument first
nnPath = str((Path(__file__).parent / Path('../models/yolo-v4-tiny-tf_openvino_2021.4_6shave.blob')).resolve().absolute())
if 1 < len(sys.argv):
    arg = sys.argv[1]
    if arg == "yolo3":
        nnPath = str((Path(__file__).parent / Path('../models/yolo-v3-tiny-tf_openvino_2021.4_6shave.blob')).resolve().absolute())
    elif arg == "yolo4":
        nnPath = str((Path(__file__).parent / Path('../models/yolo-v4-tiny-tf_openvino_2021.4_6shave.blob')).resolve().absolute())
    else:
        nnPath = arg
else:
    print("Using Tiny YoloV4 model. If you wish to use Tiny YOLOv3, call 'tiny_yolo.py yolo3'")

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

# tiny yolo v4 label texts
labelMap = [
    "person",         "bicycle",    "car",           "motorbike",     "aeroplane",   "bus",           "train",
    "truck",          "boat",       "traffic light", "fire hydrant",  "stop sign",   "parking meter", "bench",
    "bird",           "cat",        "dog",           "horse",         "sheep",       "cow",           "elephant",
    "bear",           "zebra",      "giraffe",       "backpack",      "umbrella",    "handbag",       "tie",
    "suitcase",       "frisbee",    "skis",          "snowboard",     "sports ball", "kite",          "baseball bat",
    "baseball glove", "skateboard", "surfboard",     "tennis racket", "bottle",      "wine glass",    "cup",
    "fork",           "knife",      "spoon",         "bowl",          "banana",      "apple",         "sandwich",
    "orange",         "broccoli",   "carrot",        "hot dog",       "pizza",       "donut",         "cake",
    "chair",          "sofa",       "pottedplant",   "bed",           "diningtable", "toilet",        "tvmonitor",
    "laptop",         "mouse",      "remote",        "keyboard",      "cell phone",  "microwave",     "oven",
    "toaster",        "sink",       "refrigerator",  "book",          "clock",       "vase",          "scissors",
    "teddy bear",     "hair drier", "toothbrush"
]

syncNN = True

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
xoutRgb = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")
nnOut.setStreamName("nn")

# Properties
camRgb.setPreviewSize(416, 416)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
camRgb.setFps(40)

# Network specific settings
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.setNumClasses(80)
detectionNetwork.setCoordinateSize(4)
detectionNetwork.setAnchors([10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319])
detectionNetwork.setAnchorMasks({"side26": [1, 2, 3], "side13": [3, 4, 5]})
detectionNetwork.setIouThreshold(0.5)
detectionNetwork.setBlobPath(nnPath)
detectionNetwork.setNumInferenceThreads(2)
detectionNetwork.input.setBlocking(False)

# Linking
camRgb.preview.link(detectionNetwork.input)
if syncNN:
    detectionNetwork.passthrough.link(xoutRgb.input)
else:
    camRgb.preview.link(xoutRgb.input)

detectionNetwork.out.link(nnOut.input)

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

    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

    frame = None
    detections = []
    startTime = time.monotonic()
    counter = 0
    color2 = (255, 255, 255)

    # 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 displayFrame(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, 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]), color, 2)
        # Show the frame
        cv2.imshow(name, frame)

    while True:
        if syncNN:
            inRgb = qRgb.get()
            inDet = qDet.get()
        else:
            inRgb = qRgb.tryGet()
            inDet = qDet.tryGet()

        if inRgb is not None:
            frame = inRgb.getCvFrame()
            cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
                        (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)

        if inDet is not None:
            detections = inDet.detections
            counter += 1

        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>

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

/*
The code is the same as for Tiny-yolo-V3, the only difference is the blob file.
The blob was compiled following this tutorial: https://github.com/TNTWEN/OpenVINO-YOLOV4
*/

static const std::vector<std::string> labelMap = {
    "person",        "bicycle",      "car",           "motorbike",     "aeroplane",   "bus",         "train",       "truck",        "boat",
    "traffic light", "fire hydrant", "stop sign",     "parking meter", "bench",       "bird",        "cat",         "dog",          "horse",
    "sheep",         "cow",          "elephant",      "bear",          "zebra",       "giraffe",     "backpack",    "umbrella",     "handbag",
    "tie",           "suitcase",     "frisbee",       "skis",          "snowboard",   "sports ball", "kite",        "baseball bat", "baseball glove",
    "skateboard",    "surfboard",    "tennis racket", "bottle",        "wine glass",  "cup",         "fork",        "knife",        "spoon",
    "bowl",          "banana",       "apple",         "sandwich",      "orange",      "broccoli",    "carrot",      "hot dog",      "pizza",
    "donut",         "cake",         "chair",         "sofa",          "pottedplant", "bed",         "diningtable", "toilet",       "tvmonitor",
    "laptop",        "mouse",        "remote",        "keyboard",      "cell phone",  "microwave",   "oven",        "toaster",      "sink",
    "refrigerator",  "book",         "clock",         "vase",          "scissors",    "teddy bear",  "hair drier",  "toothbrush"};

static std::atomic<bool> syncNN{true};

int main(int argc, char** argv) {
    using namespace std;
    using namespace std::chrono;
    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 camRgb = pipeline.create<dai::node::ColorCamera>();
    auto detectionNetwork = pipeline.create<dai::node::YoloDetectionNetwork>();
    auto xoutRgb = pipeline.create<dai::node::XLinkOut>();
    auto nnOut = pipeline.create<dai::node::XLinkOut>();

    xoutRgb->setStreamName("rgb");
    nnOut->setStreamName("detections");

    // Properties
    camRgb->setPreviewSize(416, 416);
    camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P);
    camRgb->setInterleaved(false);
    camRgb->setColorOrder(dai::ColorCameraProperties::ColorOrder::BGR);
    camRgb->setFps(40);

    // Network specific settings
    detectionNetwork->setConfidenceThreshold(0.5f);
    detectionNetwork->setNumClasses(80);
    detectionNetwork->setCoordinateSize(4);
    detectionNetwork->setAnchors({10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319});
    detectionNetwork->setAnchorMasks({{"side26", {1, 2, 3}}, {"side13", {3, 4, 5}}});
    detectionNetwork->setIouThreshold(0.5f);
    detectionNetwork->setBlobPath(nnPath);
    detectionNetwork->setNumInferenceThreads(2);
    detectionNetwork->input.setBlocking(false);

    // Linking
    camRgb->preview.link(detectionNetwork->input);
    if(syncNN) {
        detectionNetwork->passthrough.link(xoutRgb->input);
    } else {
        camRgb->preview.link(xoutRgb->input);
    }

    detectionNetwork->out.link(nnOut->input);

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

    // Output queues will be used to get the rgb frames and nn data from the outputs defined above
    auto qRgb = device.getOutputQueue("rgb", 4, false);
    auto qDet = device.getOutputQueue("detections", 4, false);

    cv::Mat frame;
    std::vector<dai::ImgDetection> detections;
    auto startTime = steady_clock::now();
    int counter = 0;
    float fps = 0;
    auto color2 = cv::Scalar(255, 255, 255);

    // 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;

            uint32_t 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, 255);
            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, 255);
            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);
    };

    while(true) {
        std::shared_ptr<dai::ImgFrame> inRgb;
        std::shared_ptr<dai::ImgDetections> inDet;

        if(syncNN) {
            inRgb = qRgb->get<dai::ImgFrame>();
            inDet = qDet->get<dai::ImgDetections>();
        } else {
            inRgb = qRgb->tryGet<dai::ImgFrame>();
            inDet = qDet->tryGet<dai::ImgDetections>();
        }

        counter++;
        auto currentTime = steady_clock::now();
        auto elapsed = duration_cast<duration<float>>(currentTime - startTime);
        if(elapsed > seconds(1)) {
            fps = counter / elapsed.count();
            counter = 0;
            startTime = currentTime;
        }

        if(inRgb) {
            frame = inRgb->getCvFrame();
            std::stringstream fpsStr;
            fpsStr << "NN fps: " << std::fixed << std::setprecision(2) << fps;
            cv::putText(frame, fpsStr.str(), cv::Point(2, inRgb->getHeight() - 4), cv::FONT_HERSHEY_TRIPLEX, 0.4, color2);
        }

        if(inDet) {
            detections = inDet->detections;
        }

        if(!frame.empty()) {
            displayFrame("rgb", frame, detections);
        }

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

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