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输入节点名字:“images”;数据:float32[1,3,640,640]。
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输出节点1的名字:“output0”;数据:float32[1,116,8400]。其中116的前84个字段跟 YOLOv8目标检测模型输出定义完全一致,即cx,cy,w,h和80类的分数;后32个字段用于计算掩膜数据。
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输出节点2的名字:“output1”;数据:float32[1,32,160,160]。output0后32个字段与output1的数据做矩阵乘法后得到的结果,即为对应目标的掩膜数据。
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# Initialize the VideoCapture
cap =cv2.VideoCapture("store-aisle-detection.mp4")
# Initialize YOLOv5 Instance Segmentator
model_path ="yolov8n-seg.xml"
device_name ="GPU"
yoloseg =YOLOSeg(model_path, device_name, conf_thres=0.3, iou_thres=0.3)
whilecap.isOpened():
# Read frame from the video
ret, frame =cap.read()
ifnotret:
break
# Update object localizer
start =time.time()
boxes, scores, class_ids, masks =yoloseg(frame)
# postprocess and draw masks
combined_img =yoloseg.draw_masks(frame)
end =time.time()
# show FPS
fps =(1/(end -start))
fps_label ="Throughput: %.2fFPS"%fps
cv2.putText(combined_img, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# show ALL
cv2.imshow("YOLOv8 Segmentation OpenVINO inference Demo", combined_img)
# Press Any key stop
ifcv2.waitKey(1) >-1:
print("finished by user")
break
运行结果,如下图所示:
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原文标题:在AI爱克斯开发板上用OpenVINO加速YOLOv8-seg实例分割模型
文章出处:【微信号:SDNLAB,微信公众号:SDNLAB】欢迎添加关注!文章转载请注明出处。
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