mediapipe-rs/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt

# MediaPipe graph that performs template matching with TensorFlow Lite on CPU.
# Used in the examples in
# mediapipe/examples/android/src/java/com/mediapipe/apps/templatematchingcpu

# Images on GPU coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"

# Throttles the images flowing downstream for flow control.
node {
  calculator: "FlowLimiterCalculator"
  input_stream: "input_video"
  input_stream: "FINISHED:detections"
  input_stream_info: {
    tag_index: "FINISHED"
    back_edge: true
  }
  output_stream: "throttled_input_video"
}

# Transfers the input image from GPU to CPU memory.
node: {
  calculator: "GpuBufferToImageFrameCalculator"
  input_stream: "throttled_input_video"
  output_stream: "input_video_cpu"
}

# Scale the image's longer side to 640, keeping aspect ratio.
node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE:input_video_cpu"
  output_stream: "IMAGE:transformed_input_video_cpu"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 640
      output_height: 640
      scale_mode: FILL_AND_CROP
    }
  }
}

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

node {
  calculator: "FeatureDetectorCalculator"
  input_stream: "IMAGE:transformed_input_video_cpu"
  output_stream: "FEATURES:features"
  output_stream: "LANDMARKS:landmarks"
  output_stream: "PATCHES:patches"
}

# 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"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "mediapipe/models/knift_float.tflite"
      delegate { xnnpack {} }
    }
  }
}

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

node {
  calculator: "BoxDetectorCalculator"
  input_stream: "FEATURES:features"
  input_stream: "IMAGE_SIZE:input_video_size"
  input_stream: "DESCRIPTORS:knift_feature_floats"
  output_stream: "BOXES:detections"

  node_options: {
    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
      detector_options {
        index_type: OPENCV_BF
        detect_every_n_frame: 1
      }
      index_proto_filename: "mediapipe/models/knift_index.pb"
    }
  }
}

node {
  calculator: "TimedBoxListIdToLabelCalculator"
  input_stream: "detections"
  output_stream: "labeled_detections"
  node_options: {
    [type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
      label_map_path: "mediapipe/models/knift_labelmap.txt"
    }
  }
}

node {
  calculator: "TimedBoxListToRenderDataCalculator"
  input_stream: "BOX_LIST:labeled_detections"
  output_stream: "RENDER_DATA:box_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
      box_color { r: 255 g: 0 b: 0 }
      thickness: 5.0
    }
  }
}

node {
  calculator: "LandmarksToRenderDataCalculator"
  input_stream: "NORM_LANDMARKS:landmarks"
  output_stream: "RENDER_DATA:landmarks_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
      landmark_color { r: 0 g: 255 b: 0 }
      thickness: 2.0
    }
  }
}

# Draws annotations and overlays them on top of the input images.
node {
  calculator: "AnnotationOverlayCalculator"
  input_stream: "IMAGE_GPU:throttled_input_video"
  input_stream: "box_render_data"
  input_stream: "landmarks_render_data"
  output_stream: "IMAGE_GPU:output_video"
}