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Example yolov8n (#1078)
* Added yolov8n model download and example script for yolov8n
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‎examples/Yolo/yolov8_nano.py

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#!/usr/bin/env python3
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"""
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The code is the same as for Tiny Yolo V3 and V4, the only difference is the blob file
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- Tiny YOLOv3: https://github.com/david8862/keras-YOLOv3-model-set
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- Tiny YOLOv4: https://github.com/TNTWEN/OpenVINO-YOLOV4
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"""
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from pathlib import Path
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import sys
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import cv2
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import depthai as dai
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import numpy as np
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import time
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# Get yolo v8n model blob file path
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nnPath = str((Path(__file__).parent / Path('../models/yolov8n_coco_640x352.blob')).resolve().absolute())
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if not Path(nnPath).exists():
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import sys
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raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
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# yolo v8 abel texts
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labelMap = [
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"person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train",
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"truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
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"bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
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"bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie",
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"suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
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"baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
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"fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
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"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
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"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor",
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"laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
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"toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
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"teddy bear", "hair drier", "toothbrush"
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]
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syncNN = True
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# Create pipeline
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pipeline = dai.Pipeline()
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# Define sources and outputs
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camRgb = pipeline.create(dai.node.ColorCamera)
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detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
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xoutRgb = pipeline.create(dai.node.XLinkOut)
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nnOut = pipeline.create(dai.node.XLinkOut)
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xoutRgb.setStreamName("rgb")
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nnOut.setStreamName("nn")
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# Properties
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camRgb.setPreviewSize(640, 352)
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camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
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camRgb.setInterleaved(False)
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camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
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camRgb.setFps(40)
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# Network specific settings
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detectionNetwork.setConfidenceThreshold(0.5)
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detectionNetwork.setNumClasses(80)
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detectionNetwork.setCoordinateSize(4)
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detectionNetwork.setIouThreshold(0.5)
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detectionNetwork.setBlobPath(nnPath)
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detectionNetwork.setNumInferenceThreads(2)
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detectionNetwork.input.setBlocking(False)
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# Linking
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camRgb.preview.link(detectionNetwork.input)
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if syncNN:
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detectionNetwork.passthrough.link(xoutRgb.input)
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else:
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camRgb.preview.link(xoutRgb.input)
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detectionNetwork.out.link(nnOut.input)
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# Connect to device and start pipeline
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with dai.Device(pipeline) as device:
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# Output queues will be used to get the rgb frames and nn data from the outputs defined above
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qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
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qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
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frame = None
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detections = []
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startTime = time.monotonic()
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counter = 0
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color2 = (255, 255, 255)
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# nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
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def frameNorm(frame, bbox):
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normVals = np.full(len(bbox), frame.shape[0])
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normVals[::2] = frame.shape[1]
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return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
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def displayFrame(name, frame):
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color = (255, 0, 0)
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for detection in detections:
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bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
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cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
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cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
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cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
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# Show the frame
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cv2.imshow(name, frame)
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while True:
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if syncNN:
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inRgb = qRgb.get()
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inDet = qDet.get()
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else:
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inRgb = qRgb.tryGet()
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inDet = qDet.tryGet()
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if inRgb is not None:
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frame = inRgb.getCvFrame()
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cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
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(2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)
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if inDet is not None:
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detections = inDet.detections
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counter += 1
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if frame is not None:
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displayFrame("rgb", frame)
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if cv2.waitKey(1) == ord('q'):
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break

‎examples/install_requirements.py

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"zoo_type": "depthai",
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"default_ov_version": "2022.1"
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},
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"yolov8n_coco_640x352": {
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"shaves": [6],
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"compile_params": ["-ip U8"],
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"zoo_type": "depthai",
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"default_ov_version": "2021.4"
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},
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}
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blobconverter_cmds = [

‎examples/models/v8n-yolo/model.yml

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# Copyright (c) 2021 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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description: >-
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v8n-yolo
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task_type: object_attributes
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files:
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- name: yolov8n_coco_640x352.blob
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size: 6376024
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sha256: 6c2b9a63b8324cd80a4d413cffb3e0f4471e6731faa90513faa1e7fed83f6348
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source: https://artifacts.luxonis.com/artifactory/luxonis-depthai-data-local/network/yolov8n_coco_640x352.blob
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framework: dldt
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license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE

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