ENV:
  min_subgraph_size: 3
  trt_calib_mode: False
  cpu_threads: 1
  trt_use_static: False
  save_img: True
  return_res: True
  print_res: True

MODEL:
  - DetectionOp:
      name: det
      param_path: paddlecv://models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdiparams
      model_path: paddlecv://models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdmodel
      batch_size: 2
      image_shape: [3, 640, 640]
      PreProcess:
        - Resize:
            interp: 2
            keep_ratio: false
            target_size: [640, 640]
        - NormalizeImage:
            is_scale: true
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
        - Permute:
      PostProcess:
        - ParserDetResults:
            label_list:
              - foreground
            threshold: 0.2
            max_det_results: 5
      Inputs:
        - input.image

  - BboxCropOp:
      name: crop
      Inputs:
        - input.image
        - det.dt_bboxes

  - FeatureExtractionOp:
      name: feature
      param_path: paddlecv://models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdiparams
      model_path: paddlecv://models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdmodel
      batch_size: 2
      PreProcess:
        - ResizeImage:
            size: [224, 224]
            return_numpy: False
            interpolation: bilinear
            backend: cv2
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: hwc
        - ToCHWImage:
        - ExpandDim:
            axis: 0
      PostProcess:
        - NormalizeFeature:
        - Index:
            dist_type: "IP"
            vector_path: "paddlecv://dict/pp-shitu/drink_dataset_v1.0_vector.index"
            id_map_path: "paddlecv://dict/pp-shitu/drink_dataset_v1.0_id_map.pkl"
            score_thres: 0.5
        - NMS4Rec:
            thresh: 0.05
      Inputs:
        - input.image
        - crop.crop_image
        - det.dt_bboxes

  - FeatureOutput:
      name: print
      Inputs:
        - input.fn
        - feature.dt_bboxes
        - feature.rec_score
        - feature.rec_doc