# MediaPipe graph that build feature descriptors index for specific target.

# max_queue_size limits the number of packets enqueued on any input stream
# by throttling inputs to the graph. This makes the graph only process one
# frame per time.
max_queue_size: 1

# Decodes an input video file into images and a video header.
node {
  calculator: "LocalFilePatternContentsCalculator"
  input_side_packet: "FILE_DIRECTORY:file_directory"
  input_side_packet: "FILE_SUFFIX:file_suffix"
  output_stream: "CONTENTS:encoded_image"
}

node {
  calculator: "OpenCvEncodedImageToImageFrameCalculator"
  input_stream: "encoded_image"
  output_stream: "image_frame"
}

node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE:image_frame"
  output_stream: "IMAGE:scaled_image_frame"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 320
      output_height: 320
      scale_mode: FILL_AND_CROP
    }
  }
}

node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE:scaled_image_frame"
  output_stream: "SIZE:input_video_size"
}

node {
  calculator: "FeatureDetectorCalculator"
  input_stream: "IMAGE:scaled_image_frame"
  output_stream: "FEATURES:features"
  output_stream: "LANDMARKS:landmarks"
  output_stream: "PATCHES:patches"
  node_options: {
    [type.googleapis.com/mediapipe.FeatureDetectorCalculatorOptions] {
      max_features: 400
    }
  }
}

# input tensors: 200*32*32*1 float
# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
# rest is padded by zero.
node {
  calculator: "TfLiteInferenceCalculator"
  input_stream: "TENSORS:patches"
  output_stream: "TENSORS:knift_feature_tensors"
  input_stream_handler {
    input_stream_handler: "DefaultInputStreamHandler"
  }
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "mediapipe/models/knift_float_400.tflite"
    }
  }
}

node {
  calculator: "TfLiteTensorsToFloatsCalculator"
  input_stream: "TENSORS:knift_feature_tensors"
  output_stream: "FLOATS:knift_feature_floats"
}

node {
  calculator: "BoxDetectorCalculator"
  input_side_packet: "OUTPUT_INDEX_FILENAME:output_index_filename"
  input_stream: "FEATURES:features"
  input_stream: "IMAGE_SIZE:input_video_size"
  input_stream: "DESCRIPTORS:knift_feature_floats"

  node_options: {
    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
      detector_options {
        index_type: OPENCV_BF
        detect_every_n_frame: 1
      }
    }
  }
}