RGB & MobileNetSSD @ 4K¶
This example shows how to run MobileNetv2SSD on the RGB input frame, and how to display both the RGB preview and the metadata results from the MobileNetv2SSD on the preview. The preview size is set to 4K resolution.
It’s a variation of RGB & MobilenetSSD.
Similar 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 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¶
Also available on GitHub
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | #!/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_5shave.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 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 camRgb = pipeline.create(dai.node.ColorCamera) nn = pipeline.create(dai.node.MobileNetDetectionNetwork) xoutVideo = pipeline.create(dai.node.XLinkOut) xoutPreview = pipeline.create(dai.node.XLinkOut) nnOut = pipeline.create(dai.node.XLinkOut) xoutVideo.setStreamName("video") xoutPreview.setStreamName("preview") nnOut.setStreamName("nn") # Properties camRgb.setPreviewSize(300, 300) # NN input camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_4_K) camRgb.setInterleaved(False) camRgb.setPreviewKeepAspectRatio(False) # 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 camRgb.video.link(xoutVideo.input) camRgb.preview.link(xoutPreview.input) camRgb.preview.link(nn.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 frames and nn data from the outputs defined above qVideo = device.getOutputQueue(name="video", maxSize=4, blocking=False) qPreview = device.getOutputQueue(name="preview", maxSize=4, blocking=False) qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False) previewFrame = None videoFrame = 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 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, 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) cv2.namedWindow("video", cv2.WINDOW_NORMAL) cv2.resizeWindow("video", 1280, 720) print("Resize video window with mouse drag!") while True: # Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise inVideo = qVideo.tryGet() inPreview = qPreview.tryGet() inDet = qDet.tryGet() if inVideo is not None: videoFrame = inVideo.getCvFrame() if inPreview is not None: previewFrame = inPreview.getCvFrame() if inDet is not None: detections = inDet.detections if videoFrame is not None: displayFrame("video", videoFrame) if previewFrame is not None: displayFrame("preview", previewFrame) if cv2.waitKey(1) == ord('q'): break |
Also available on GitHub
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | #include <iostream> // Includes 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 camRgb = pipeline.create<dai::node::ColorCamera>(); auto nn = pipeline.create<dai::node::MobileNetDetectionNetwork>(); auto xoutVideo = pipeline.create<dai::node::XLinkOut>(); auto xoutPreview = pipeline.create<dai::node::XLinkOut>(); auto nnOut = pipeline.create<dai::node::XLinkOut>(); xoutVideo->setStreamName("video"); xoutPreview->setStreamName("preview"); nnOut->setStreamName("nn"); // Properties camRgb->setPreviewSize(300, 300); // NN input camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_4_K); camRgb->setInterleaved(false); camRgb->setPreviewKeepAspectRatio(false); // 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 camRgb->video.link(xoutVideo->input); camRgb->preview.link(xoutPreview->input); camRgb->preview.link(nn->input); nn->out.link(nnOut->input); // Connect to device and start pipeline dai::Device device(pipeline); // Output queues will be used to get the frames and nn data from the outputs defined above auto qVideo = device.getOutputQueue("video", 4, false); auto qPreview = device.getOutputQueue("preview", 4, false); auto qDet = device.getOutputQueue("nn", 4, false); cv::Mat previewFrame; cv::Mat videoFrame; std::vector<dai::ImgDetection> detections; // 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, 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::namedWindow("video", cv::WINDOW_NORMAL); cv::resizeWindow("video", 1280, 720); cout << "Resize video window with mouse drag!" << endl; while(true) { // Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise auto inVideo = qVideo->tryGet<dai::ImgFrame>(); auto inPreview = qPreview->tryGet<dai::ImgFrame>(); auto inDet = qDet->tryGet<dai::ImgDetections>(); if(inVideo) { videoFrame = inVideo->getCvFrame(); } if(inPreview) { previewFrame = inPreview->getCvFrame(); } if(inDet) { detections = inDet->detections; } if(!videoFrame.empty()) { displayFrame("video", videoFrame, detections); } if(!previewFrame.empty()) { displayFrame("preview", previewFrame, detections); } int key = cv::waitKey(1); if(key == 'q' || key == 'Q') { return 0; } } return 0; } |
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