#!/usr/bin/env python3

Trying to run nightvision with the monocamera but will not work I keep getting

No blob found at yolov4_tiny_coco_416x416. Looking into DepthAI model zoo.

[18443010812B990F00] [2.6] [0.932] [system] [critical] Fatal error. Please report to developers. Log: 'OsDrvOV9282' '1269'

Traceback (most recent call last):

File "c:\Users\gusta\Desktop\depthai-experiments\gen2-yolo\device-decoding\api.py", line 127, in <module>

inRgb = qRgb.get()

        ^^^^^^^^^^

RuntimeError: Communication exception - possible device error/misconfiguration. Original message 'Couldn't read data from stream: 'rgb' (X_LINK_ERROR)'

If I use the colorcamera this works

Any help would be highly appreciated here is my code working with and OAKDPRO FIXED FOCUS

"""

The code is edited from docs (https://docs.luxonis.com/projects/api/en/latest/samples/Yolo/tiny_yolo/)

We add parsing from JSON files that contain configuration

"""

from pathlib import Pathimport sysimport cv2import depthai as daiimport numpy as npimport timeimport argparseimport jsonimport blobconverter
# parse argumentsparser = argparse.ArgumentParser()parser.add_argument("-m", "--model", help="Provide model name or model path for inference", default='yolov4_tiny_coco_416x416', type=str)parser.add_argument("-c", "--config", help="Provide config path for inference", default='json/yolov4-tiny.json', type=str)args = parser.parse_args()
# parse configconfigPath = Path(args.config)if not configPath.exists(): raise ValueError("Path {} does not exist!".format(configPath))
with configPath.open() as f: config = json.load(f)nnConfig = config.get("nn_config", {})
# parse input shapeif "input_size" in nnConfig: W, H = tuple(map(int, nnConfig.get("input_size").split('x')))
# extract metadatametadata = nnConfig.get("NN_specific_metadata", {})classes = metadata.get("classes", {})coordinates = metadata.get("coordinates", {})anchors = metadata.get("anchors", {})anchorMasks = metadata.get("anchor_masks", {})iouThreshold = metadata.get("iou_threshold", {})confidenceThreshold = metadata.get("confidence_threshold", {})
print(metadata)
# parse labelsnnMappings = config.get("mappings", {})labels = nnMappings.get("labels", {})
# get model pathnnPath = args.modelif not Path(nnPath).exists(): print("No blob found at {}. Looking into DepthAI model zoo.".format(nnPath)) nnPath = str(blobconverter.from_zoo(args.model, shaves = 6, zoo_type = "depthai", use_cache=True))# sync outputssyncNN = True
# Create pipelinepipeline = dai.Pipeline()
# Define sources and outputscamRgb = pipeline.create(dai.node.MonoCamera)camRgb.setBoardSocket(dai.CameraBoardSocket.CAM_B)
detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)xoutRgb = pipeline.create(dai.node.XLinkOut)nnOut = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")nnOut.setStreamName("nn")
xoutRgb.input.setBlocking(False)nnOut.input.setBlocking(False)
camRgb.setResolution(dai.MonoCameraProperties.SensorResolution.THE_1200_P)camRgb.setFps(40)
# Network specific settingsdetectionNetwork.setConfidenceThreshold(confidenceThreshold)detectionNetwork.setNumClasses(classes)detectionNetwork.setCoordinateSize(coordinates)detectionNetwork.setAnchors(anchors)detectionNetwork.setAnchorMasks(anchorMasks)detectionNetwork.setIouThreshold(iouThreshold)detectionNetwork.setBlobPath(nnPath)detectionNetwork.setNumInferenceThreads(2)detectionNetwork.input.setBlocking(False)
# LinkingcamRgb.out.link(detectionNetwork.input)detectionNetwork.passthrough.link(xoutRgb.input)detectionNetwork.out.link(nnOut.input)
# Connect to device and start pipelinewith 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, detections): color = (255, 0, 0) for detection in detections: bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) cv2.putText(frame, labels[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: inRgb = qRgb.get() inDet = qDet.get()
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, detections)
if cv2.waitKey(1) == ord('q'): break