Example yolov8n (#1078)

* Added yolov8n model download and example script for 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