Hello,
I trained a model for detecting an object. I used YOLOv7 for training. Then I transformed the weights file(best.pt) to blob file by using this tool: https://tools.luxonis.com/. As an initial step, I tried to use this blob file in one of the example codes which is: depthai-python/examples/SpatialDetection/spatial_mobilenet.py. I just changed the model file, Labels, and preview size. Then the code became like this:
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time
'''
Spatial detection network demo.
Performs inference on RGB camera and retrieves spatial location coordinates: x,y,z relative to the center of depth map.
'''
# Get argument first
nnBlobPath = str((Path(file).parent / Path('/home/apakgrup/depthai-python/examples/spatial/best_openvino_2022.1_6shave.blob')).resolve().absolute())
if len(sys.argv) > 1:
nnBlobPath = sys.argv[1]
if not Path(nnBlobPath).exists():
import sys
raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
# MobilenetSSD label texts
labelMap = ["fire", "smoke"]
syncNN = True
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutNN = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")
xoutNN.setStreamName("detections")
xoutDepth.setStreamName("depth")
# Properties
camRgb.setPreviewSize(640, 640)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setCamera("left")
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setCamera("right")
# Setting node configs
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# Align depth map to the perspective of RGB camera, on which inference is done
stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A)
stereo.setSubpixel(True)
stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
spatialDetectionNetwork.setBlobPath(nnBlobPath)
spatialDetectionNetwork.setConfidenceThreshold(0.5)
spatialDetectionNetwork.input.setBlocking(False)
spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
spatialDetectionNetwork.setDepthLowerThreshold(100)
spatialDetectionNetwork.setDepthUpperThreshold(5000)
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
camRgb.preview.link(spatialDetectionNetwork.input)
if syncNN:
spatialDetectionNetwork.passthrough.link(xoutRgb.input)
else:
camRgb.preview.link(xoutRgb.input)
spatialDetectionNetwork.out.link(xoutNN.input)
stereo.depth.link(spatialDetectionNetwork.inputDepth)
spatialDetectionNetwork.passthroughDepth.link(xoutDepth.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
previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)
depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
startTime = time.monotonic()
counter = 0
fps = 0
color = (255, 255, 255)
while True:
inPreview = previewQueue.get()
inDet = detectionNNQueue.get()
depth = depthQueue.get()
counter+=1
current_time = time.monotonic()
if (current_time - startTime) > 1 :
fps = counter / (current_time - startTime)
counter = 0
startTime = current_time
frame = inPreview.getCvFrame()
depthFrame = depth.getFrame() # depthFrame values are in millimeters
depth_downscaled = depthFrame[::4]
min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
max_depth = np.percentile(depth_downscaled, 99)
depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
detections = inDet.detections
# If the frame is available, draw bounding boxes on it and show the frame
height = frame.shape[0]
width = frame.shape[1]
for detection in detections:
roiData = detection.boundingBoxMapping
roi = roiData.roi
roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
topLeft = roi.topLeft()
bottomRight = roi.bottomRight()
xmin = int(topLeft.x)
ymin = int(topLeft.y)
xmax = int(bottomRight.x)
ymax = int(bottomRight.y)
cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)
# Denormalize bounding box
x1 = int(detection.xmin \* width)
x2 = int(detection.xmax \* width)
y1 = int(detection.ymin \* height)
y2 = int(detection.ymax \* height)
try:
label = labelMap[detection.label]
except:
label = detection.label
cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, "{:.2f}".format(detection.confidence\*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)
cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, (255,255,255))
cv2.imshow("depth", depthFrameColor)
cv2.imshow("preview", frame)
if cv2.waitKey(1) == ord('q'):
break
At the beginning, the code runs normally, then after few seconds it gives me this error:RuntimeError: Communication exception - possible device error/misconfiguration. Original message 'Couldn't read data from stream: 'detections' (X_LINK_ERROR)'
the example code run normally, this error happened after making these modifications. Can someone help me? thank you
