Here's my code for your reference
from depthai_sdk import OakCamera
import depthai as dai
from depthai_sdk.classes import DetectionPacket
def cb(packet: DetectionPacket):
print(packet.img_detections)
with OakCamera() as oak:
color = oak.create_camera('color')
# List of models that are supported out-of-the-box by the SDK:
# https://docs.luxonis.com/projects/sdk/en/latest/features/ai_models/#sdk-supported-models
nn = oak.create_nn('yolov8n_coco_640x352', color, tracker=True, spatial=True)
nn.config_nn(resize_mode='stretch')
nn.config_tracker(
tracker_type=dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM,
track_labels=[0], # Track only 1st object from the object map. If unspecified, track all object types
# track_labels=['person'] # Track only people (for coco datasets, person is 1st object in the map)
assignment_policy=dai.TrackerIdAssignmentPolicy.SMALLEST_ID,
max_obj=1, # Max objects to track, which can improve performance
threshold=0.1 # Tracker threshold
)
nn.config_spatial(
bb_scale_factor=0.3, # Scaling bounding box before averaging the depth in that ROI
lower_threshold=500, # Discard depth points below 30cm
upper_threshold=8000, # Discard depth pints above 10m
# Average depth points before calculating X and Y spatial coordinates:
calc_algo=dai.SpatialLocationCalculatorAlgorithm.AVERAGE
)
oak.visualize([nn.out.tracker], fps=True)
# oak.callback(nn.out.tracker, callback=cb)
oak.visualize([nn.out.image_manip], fps=True)
oak.visualize([nn.out.spatials], fps=True)
oak.visualize(nn.out.passthrough)
# oak.start(blocking=True)
oak.start(blocking=True)
