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License: MIT License
dakrnet yolov3 weights runs in deepstream with python api
License: MIT License
Prerequisites: - DeepStreamSDK 5.0 - Python 3.6 - Gst-python Test Enviromen: - Jetson NX Running Prerequisites: - Put yolov3.weights to /opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-yolov3-python,weights file can download with darknet official website:https://pjreddie.com/media/files/yolov3.weights - Compile so library: - put diretory nvdsinfer_custom_impl_Yolo in /opt/nvidia/deepstream/deepstream/sources/objectDetector_Yolo,modify nvdsparsebbox_Yolo.cpp static const int NUM_CLASSES_YOLO = 80; then : export CUDA_VER=10.2 make copy libnvdsinfer_custom_impl_Yolo.so in /opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps/deepstream-yolov3-python/nvdsinfer_custom_impl_Yolo modify config_infer_primary_yoloV3.txt [property] gpu-id=0 net-scale-factor=0.0039215697906911373 #0=RGB, 1=BGR model-color-format=0 custom-network-config=yolov3.cfg model-file=yolov3.weights model-engine-file=model_b1_gpu0_fp16.engine labelfile-path=labels.txt int8-calib-file=yolov3-calibration.table.trt7.0 ## 0=FP32, 1=INT8, 2=FP16 mode network-mode=2 num-detected-classes=80 gie-unique-id=1 network-type=0 is-classifier=0 ## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering) cluster-mode=2 maintain-aspect-ratio=1 parse-bbox-func-name=NvDsInferParseCustomYoloV3 custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so engine-create-func-name=NvDsInferYoloCudaEngineGet #scaling-filter=0 #scaling-compute-hw=0 [class-attrs-all] nms-iou-threshold=0.3 threshold=0.7 To run: $ python3 deepstream_test_3.py <uri1> [uri2] ... [uriN] e.g. $ python3 deepstream_test_3.py file:///home/ubuntu/video1.mp4 file:///home/ubuntu/video2.mp4 $ python3 deepstream_test_3.py rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 more info can visit my blogs:https://blog.csdn.net/FL1623863129/article/details/111034113
请问跑多路视频的时候会卡的不行,如何进行改进呢?
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