# Thermal People Detection This example uses a thermal sensor, found on [OAK Thermal camera](https://shop.luxonis.com/products/oak-t), to detect people using a neural network. The detected people are then displayed on the thermal camera frames. > **New model** > We're currently (Dec 2024) also training a new version of the thermal NN model which should be more accurate and faster. Stay tuned! ## Setup Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download all required dependencies. Please note that this script must be ran from git context, so you have to download the [depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md). ## Source code #### Python ```python #!/usr/bin/env python3 import depthai as dai import cv2 from pathlib import Path import numpy as np import sys nnPath = str((Path(__file__).parent / Path('../models/yolov6n_thermal_people_256x192_openvino_2022.1_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"') labels = ["person"] device = dai.Device() pipeline = dai.Pipeline() nnet = pipeline.create(dai.node.YoloDetectionNetwork) nnet.setBlobPath(nnPath) nnet.setConfidenceThreshold(0.5) nnet.setNumClasses(1) nnet.setCoordinateSize(4) nnet.setIouThreshold(0.4) thermalCam = pipeline.create(dai.node.Camera) thermalCam.setBoardSocket(dai.CameraBoardSocket.CAM_E) thermalCam.setPreviewSize(256, 192) thermalCam.raw.link(nnet.input) rawOut = pipeline.createXLinkOut() rawOut.setStreamName("preview") thermalCam.preview.link(rawOut.input) xoutNn = pipeline.createXLinkOut() xoutNn.setStreamName("nn") nnet.out.link(xoutNn.input) xoutPass = pipeline.createXLinkOut() xoutPass.setStreamName("pass") nnet.passthrough.link(xoutPass.input) device.startPipeline(pipeline) qNn = device.getOutputQueue(name="nn", maxSize=2, blocking=False) qPass = device.getOutputQueue(name="pass", maxSize=2, blocking=False) qPreview = device.getOutputQueue(name="preview", maxSize=2, blocking=False) cv2.namedWindow("nnet", cv2.WINDOW_NORMAL) cv2.namedWindow("raw", cv2.WINDOW_NORMAL) cv2.resizeWindow("nnet", 640, 480) cv2.resizeWindow("raw", 640, 480) while True: inNn = qNn.get() inPass = qPass.tryGet() inPreview = qPreview.get() if inNn and inPass: frame = inPass.getCvFrame().astype(np.float32) min_, max_ = frame.min(), frame.max() colormappedFrame = cv2.normalize(frame, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U) colormappedFrame = cv2.applyColorMap(colormappedFrame, cv2.COLORMAP_MAGMA) detections = inNn.detections for detection in detections: xmin = max(0.0, detection.xmin) ymin = max(0.0, detection.ymin) xmax = min(1.0, detection.xmax) ymax = min(1.0, detection.ymax) pt1 = int(xmin * 256), int(ymin * 192) pt2 = int(xmax * 256), int(ymax * 192) cv2.rectangle(colormappedFrame, pt1, pt2, (0, 255, 0)) cv2.putText(colormappedFrame, labels[detection.label], pt1, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA) cv2.imshow("nnet", colormappedFrame) if inPreview: cv2.imshow("raw", inPreview.getCvFrame()) if cv2.waitKey(1) == ord("q"): break ``` #### C++ WIP ## 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