Hello,
I hope this message finds you well. Currently, I am working on deploying my custom pose classification model on OAK-D Lite. My aim is to achieve comparable results for pose detection and poses landmarks to the example provided in the depthai_blazepose repository on GitHub.
To accomplish this, I am using the pose_detection_sh4.blob and pose_landmark_full_sh4.blob blob files, respectively, in order to detect and extract landmarks from the poses in my input data.
However, I am encountering some errors when attempting to run my custom pose classification model on OAK-D Lite. I have attached a screenshot of the errors below for your reference:
Here's the code for my pipeline. I've only included the relevant parts as the full code can be quite lengthy. If you would like the complete code, please let me know and I would share it with you.
import cv2
import depthai as dai
import numpy as np
##Create dai pipeline
pipeline = dai.Pipeline()
##Define RGB camera node
cam_rgb = pipeline.create(dai.node.ColorCamera)
cam_rgb.setPreviewSize(456,256)
cam_rgb.setInterleaved(False)
cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
cam_rgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam_rgb.setFps(30)
##ManIp Node
manip = pipeline.create(dai.node.ImageManip)
manip.initialConfig.setResize(224,256*3)
cam_rgb.preview.link(manip.inputImage)
##Define pose detection node
pose_detection = pipeline.createNeuralNetwork()
pose_detection.setBlob("pose_detection_sh4.blob")
pose_detection.setNumInferenceThreads(2)
pose_detection.input.setBlocking(False)
##Define pose landmark node
pose_landmark = pipeline.createNeuralNetwork()
pose_landmark.setBlob("pose_landmark_full_sh4.blob")
pose_landmark.setNumInferenceThreads(2)
pose_landmark.input.setBlocking(False)
##Pose Clasify
pose_classify = pipeline.createNeuralNetwork()
pose_classify.setBlob('yogapose_ir.blob')
pose_classify.setNumInferenceThreads(2)
pose_classify.input.setBlocking(False)
##Create XLinkOut nodes
xout_rgb = pipeline.create(dai.node.XLinkOut)
xout_rgb.setStreamName("rgb")
xout_detection = pipeline.create(dai.node.XLinkOut)
xout_detection.setStreamName("detections")
xout_landmarks = pipeline.create(dai.node.XLinkOut)
xout_landmarks.setStreamName("landmarks")
xout_classify = pipeline.create(dai.node.XLinkOut)
xout_classify.setStreamName("recognisedpose")
##Link nodes
manip.out.link(pose_detection.input)
pose_detection.out.link(pose_landmark.input)
pose_detection.out.link(xout_detection.input)
pose_landmark.out.link(xout_detection.input)
pose_detection.out.link(pose_classify.input)
pose_classify.out.link(xout_classify.input)
cam_rgb.preview.link(xout_rgb.input)
I would greatly appreciate any guidance or advice you can offer to help me resolve these errors and achieve my desired results.
Thank you for your time and assistance.