Hello,
I've been getting this error :
[DetectionNetwork(4)] [error] Input tensor 'images' (0) exceeds available data range. Data size (153600B), tensor offset (0), size (307200B) - skipping inference
The only thing I know, is that the size is double the data size.
Using this code, what causes the error? :
import depthai as dai
import cv2
# Model paths
plank_model_path = 'plank.blob'
label_model_path = 'label.blob'
pipeline = dai.Pipeline()
# Define a source - color camera
cam = pipeline.create(dai.node.ColorCamera)
cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam.setInterleaved(False)
cam.setBoardSocket(dai.CameraBoardSocket.RGB)
# Create outputs
xout_rgb = pipeline.create(dai.node.XLinkOut)
xout_rgb.setStreamName("rgb")
# Create ImageManip node for cropping
manip = pipeline.create(dai.node.ImageManip)
manip.initialConfig.setResizeThumbnail(320, 320)
manip.initialConfig.setKeepAspectRatio(False)
manip.setMaxOutputFrameSize(320*320 * 3)
# Camera control / input
controlIn = pipeline.create(dai.node.XLinkIn)
ctrl = dai.CameraControl()
controlIn.setStreamName('control')
plankDet = pipeline.create(dai.node.YoloDetectionNetwork)
plankDet.setBlobPath(plank_model_path)
plankDet.setConfidenceThreshold(0.5)
plankDet.input.setBlocking(False)
labelDet = pipeline.create(dai.node.YoloDetectionNetwork)
labelDet.setBlobPath(label_model_path)
cam.video.link(manip.inputImage)
manip.out.link(xout_rgb.input)
controlIn.out.link(cam.inputControl)
manip.out.link(plankDet.input)
with dai.Device(pipeline) as device:
# Output queues will be used to get the rgb frames and NN data from the outputs defined above
q_rgb = device.getOutputQueue(xout_rgb.getStreamName(), maxSize=4, blocking=False)
q_ctrl = device.getInputQueue(controlIn.getStreamName(), maxSize=4, blocking=False)
ctrl = dai.CameraControl()
ctrl.setManualExposure(2500, 1300)
ctrl.setManualFocus(103)
q_ctrl.send(ctrl)
while True:
in_rgb = q_rgb.get()
# If 'q' is pressed on the keyboard, exit this loop
if cv2.waitKey(1) == ord('q'):
break
cv2.imshow("RGB", in_rgb.getCvFrame())
# Clean up
cv2.destroyAllWindows()
