# RGB & YOLOv8 Nano This example demonstrates how to run Yolov8 Nano on the RGB input frame and how to display both the RGB preview and the metadata results from the Yolov8 model on the preview. Decoding is done on the [RVC](https://docs.luxonis.com/hardware/platform/rvc/rvc2.md#rvc2), instead of on the host computer. Yolov8 is anchorless, meaning it does not use predefined anchor boxes to predict bounding boxes, simplifying the object detection process. Configurable, network dependent parameters are required for correct decoding: * setNumClasses - number of classes * setCoordinateSize - size of coordinate * setIouThreshold - intersection over union threshold * setConfidenceThreshold - confidence threshold above which objects are detected This model is part of the [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) project, licensed under the [Ultralytics License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). ## Demo ## 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 """ 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 yolo v8n model blob file path nnPath = str((Path(__file__).parent / Path('../models/yolov8n_coco_640x352.blob')).resolve().absolute()) if not Path(nnPath).exists(): import sys raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"') # yolo v8 abel 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(640, 352) 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.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 ```