Example yolov8n

examples/Yolo/yolov8_nano.py

@@ -0,0 +1,127 @@
+#!/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

examples/install_requirements.py

@@ -224,6 +224,12 @@ def hasWhitespace(string):
  "zoo_type": "depthai",
  "default_ov_version": "2022.1"
  },
+ "yolov8n_coco_640x352": {
+ "shaves": [6],
+ "compile_params": ["-ip U8"],
+ "zoo_type": "depthai",
+ "default_ov_version": "2021.4"
+ },
  }
 
  blobconverter_cmds = [

examples/models/v8n-yolo/model.yml

@@ -0,0 +1,25 @@
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+description: >-
+ v8n-yolo
+task_type: object_attributes
+files:
+ - name: yolov8n_coco_640x352.blob
+ size: 6376024
+ sha256: 6c2b9a63b8324cd80a4d413cffb3e0f4471e6731faa90513faa1e7fed83f6348
+ source: https://artifacts.luxonis.com/artifactory/luxonis-depthai-data-local/network/yolov8n_coco_640x352.blob
+
+framework: dldt
+license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE