Savant/samples/file_processing/module.yml at develop · ikay13/Savant · GitHub

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# module name, required
name: ${oc.env:MODULE_NAME, 'file_processing'}

# base module parameters
parameters:
  # pipeline processing frame parameters
  output_frame:
    codec: ${oc.env:CODEC, 'raw-rgba'}
  # PyFunc for drawing on frames (default implementation)
  draw_func:
    module: savant.deepstream.drawfunc
    class_name: NvDsDrawFunc
    rendered_objects:
      detector:
        person:
          bbox:
            border_color: 'FFFFFFFF'  # Green
            background_color: '00000077'  # semi-transparent black
            thickness: 2
          label:
            format: [ '{label}' ]
            font_color: 'FFFFFFFF' # White
            background_color: '000000AA' # semi-transparent black
            border_width: 2
            border_color: 'FFFFFFFF' # white
            padding: [10, 0, 10, 0] # left, top, right, bottom
            position:
              position: TopLeftOutside
              margin_x: 0
              margin_y: -10

  detected_object:
    id: 0
    label: person

  default_yolo_converter:
    module: savant.converter.yolo
    class_name: TensorToBBoxConverter
    kwargs:
      confidence_threshold: 0.25
      nms_iou_threshold: 0.45
      top_k: 300

  default_yolo_selector:
    module: savant.selector.detector
    class_name: MinMaxSizeBBoxSelector
    kwargs:
      min_width: 30
      min_height: 30

  batch_size: 1

# pipeline definition
pipeline:
  # source definition is skipped, zeromq source is used by default to connect with source adapters

  # define pipeline's main elements
  elements:
    # primary detector element, inference is provided by the nvinfer Deepstream element
    # model type is detector (other available types are: classifier, custom)
    - element: nvinfer@detector
      name: detector
      model:
        remote:
          url: s3://savant-data/models/yolo11n/yolo11n.zip
          checksum_url: s3://savant-data/models/yolo11n/yolo11n.md5
          parameters:
            endpoint: https://eu-central-1.linodeobjects.com
        format: onnx
        model_file: yolo11n.onnx
        batch_size: ${parameters.batch_size}
        workspace_size: 6144
        input:
          shape: [3, 640, 640]
          scale_factor: 0.0039215697906911373
          maintain_aspect_ratio: true
          symmetric_padding: true
        output:
          layer_names: [output0]
          num_detected_classes: 80  # required for YOLOv11
          converter: ${parameters.default_yolo_converter}
          objects:
            - class_id: ${parameters.detected_object.id}
              label: ${parameters.detected_object.label}
              selector: ${parameters.default_yolo_selector}