GitHub Open-Source Code for Hand Gesture Recognition ? Sign Language Translation

This doc focuses on the
example graph
that performs hand tracking with TensorFlow Lite on GPU. It is related to the
hand detection example, and we recommend users
to review the hand detection example first.

For overall context on hand detection and hand tracking, please read this
Google AI Blog post.

In the visualization above, the red dots represent the localized hand landmarks,
and the green lines are simply connections between selected landmark pairs for
visualization of the hand skeleton. The red box represents a hand rectangle that
covers the entire hand, derived either from hand detection (see
hand detection example) or from the pervious
round of hand landmark localization using an ML model (see also
model card). Hand landmark localization is
performed only within the hand rectangle for computational efficiency and
accuracy, and hand detection is only invoked when landmark localization could
not identify hand presence in the previous iteration.

The example can also run in a mode that localizes hand landmarks in 3D (i.e.,
estimating an extra z coordinate):

In the visualization above, the localized hand landmarks are represented by dots
in different shades, with the brighter ones denoting landmarks closer to the
camera.

Android

Source

An arm64 APK can be
downloaded here,
and a version running the 3D mode can be
downloaded here.

To build the app yourself, run:

bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu

To build for the 3D mode, run:

bazel build -c opt --config=android_arm64 --define 3D=true mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu

Once the app is built, install it on Android device with:

adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/handtrackinggpu.apk

iOS

Source.

See the general instructions for building iOS
examples and generating an Xcode project. This will be the HandDetectionGpuApp
target.

To build on the command line:

bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp

To build for the 3D mode, run:

bazel build -c opt --config=ios_arm64 --define 3D=true mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp

Graph

The hand tracking main graph internally utilizes a
hand detection subgraph, a
hand landmark subgraph and a
renderer subgraph.

The subgraphs show up in the main graph visualization as nodes colored in
purple, and the subgraph itself can also be visualized just like a regular
graph. For more information on how to visualize a graph that includes subgraphs,
see the Visualizing Subgraphs section in the
visualizer documentation.

Main Graph

Source pbtxt file

# MediaPipe graph that performs hand tracking with TensorFlow Lite on GPU.
# Used in the examples in
# mediapipie/examples/android/src/java/com/mediapipe/apps/handtrackinggpu and
# mediapipie/examples/ios/handtrackinggpu.

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

# Throttles the images flowing downstream for flow control. It passes through
# the very first incoming image unaltered, and waits for downstream nodes
# (calculators and subgraphs) in the graph to finish their tasks before it
# passes through another image. All images that come in while waiting are
# dropped, limiting the number of in-flight images in most part of the graph to
# 1. This prevents the downstream nodes from queuing up incoming images and data
# excessively, which leads to increased latency and memory usage, unwanted in
# real-time mobile applications. It also eliminates unnecessarily computation,
# e.g., the output produced by a node may get dropped downstream if the
# subsequent nodes are still busy processing previous inputs.
node {
  calculator: "FlowLimiterCalculator"
  input_stream: "input_video"
  input_stream: "FINISHED:hand_rect"
  input_stream_info: {
    tag_index: "FINISHED"
    back_edge: true
  }
  output_stream: "throttled_input_video"
}

# Caches a hand-presence decision fed back from HandLandmarkSubgraph, and upon
# the arrival of the next input image sends out the cached decision with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand-presence decision. Note that upon the arrival
# of the very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:throttled_input_video"
  input_stream: "LOOP:hand_presence"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_hand_presence"
}

# Drops the incoming image if HandLandmarkSubgraph was able to identify hand
# presence in the previous image. Otherwise, passes the incoming image through
# to trigger a new round of hand detection in HandDetectionSubgraph.
node {
  calculator: "GateCalculator"
  input_stream: "throttled_input_video"
  input_stream: "DISALLOW:prev_hand_presence"
  output_stream: "hand_detection_input_video"

  node_options: {
    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
      empty_packets_as_allow: true
    }
  }
}

# Subgraph that detections hands (see hand_detection_gpu.pbtxt).
node {
  calculator: "HandDetectionSubgraph"
  input_stream: "hand_detection_input_video"
  output_stream: "DETECTIONS:palm_detections"
  output_stream: "NORM_RECT:hand_rect_from_palm_detections"
}

# Subgraph that localizes hand landmarks (see hand_landmark_gpu.pbtxt).
node {
  calculator: "HandLandmarkSubgraph"
  input_stream: "IMAGE:throttled_input_video"
  input_stream: "NORM_RECT:hand_rect"
  output_stream: "LANDMARKS:hand_landmarks"
  output_stream: "NORM_RECT:hand_rect_from_landmarks"
  output_stream: "PRESENCE:hand_presence"
}

# Caches a hand rectangle fed back from HandLandmarkSubgraph, and upon the
# arrival of the next input image sends out the cached rectangle with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand rectangle. Note that upon the arrival of the
# very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:throttled_input_video"
  input_stream: "LOOP:hand_rect_from_landmarks"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_hand_rect_from_landmarks"
}

# Merges a stream of hand rectangles generated by HandDetectionSubgraph and that
# generated by HandLandmarkSubgraph into a single output stream by selecting
# between one of the two streams. The formal is selected if the incoming packet
# is not empty, i.e., hand detection is performed on the current image by
# HandDetectionSubgraph (because HandLandmarkSubgraph could not identify hand
# presence in the previous image). Otherwise, the latter is selected, which is
# never empty because HandLandmarkSubgraphs processes all images (that went
# through FlowLimiterCaculator).
node {
  calculator: "MergeCalculator"
  input_stream: "hand_rect_from_palm_detections"
  input_stream: "prev_hand_rect_from_landmarks"
  output_stream: "hand_rect"
}

# Subgraph that renders annotations and overlays them on top of the input
# images (see renderer_gpu.pbtxt).
node {
  calculator: "RendererSubgraph"
  input_stream: "IMAGE:throttled_input_video"
  input_stream: "LANDMARKS:hand_landmarks"
  input_stream: "NORM_RECT:hand_rect"
  input_stream: "DETECTIONS:palm_detections"
  output_stream: "IMAGE:output_video"
}

Hand Detection Subgraph

Source pbtxt file

# MediaPipe hand detection subgraph.

type: "HandDetectionSubgraph"

input_stream: "input_video"
output_stream: "DETECTIONS:palm_detections"
output_stream: "NORM_RECT:hand_rect_from_palm_detections"

# Transforms the input image on GPU to a 256x256 image. To scale the input
# image, the scale_mode option is set to FIT to preserve the aspect ratio,
# resulting in potential letterboxing in the transformed image.
node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE_GPU:input_video"
  output_stream: "IMAGE_GPU:transformed_input_video"
  output_stream: "LETTERBOX_PADDING:letterbox_padding"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 256
      output_height: 256
      scale_mode: FIT
    }
  }
}

# Generates a single side packet containing a TensorFlow Lite op resolver that
# supports custom ops needed by the model used in this graph.
node {
  calculator: "TfLiteCustomOpResolverCalculator"
  output_side_packet: "opresolver"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
      use_gpu: true
    }
  }
}

# Converts the transformed input image on GPU into an image tensor stored as a
# TfLiteTensor.
node {
  calculator: "TfLiteConverterCalculator"
  input_stream: "IMAGE_GPU:transformed_input_video"
  output_stream: "TENSORS_GPU:image_tensor"
}

# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
  calculator: "TfLiteInferenceCalculator"
  input_stream: "TENSORS_GPU:image_tensor"
  output_stream: "TENSORS:detection_tensors"
  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "palm_detection.tflite"
      use_gpu: true
    }
  }
}

# Generates a single side packet containing a vector of SSD anchors based on
# the specification in the options.
node {
  calculator: "SsdAnchorsCalculator"
  output_side_packet: "anchors"
  node_options: {
    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
      num_layers: 5
      min_scale: 0.1171875
      max_scale: 0.75
      input_size_height: 256
      input_size_width: 256
      anchor_offset_x: 0.5
      anchor_offset_y: 0.5
      strides: 8
      strides: 16
      strides: 32
      strides: 32
      strides: 32
      aspect_ratios: 1.0
      fixed_anchor_size: true
    }
  }
}

# Decodes the detection tensors generated by the TensorFlow Lite model, based on
# the SSD anchors and the specification in the options, into a vector of
# detections. Each detection describes a detected object.
node {
  calculator: "TfLiteTensorsToDetectionsCalculator"
  input_stream: "TENSORS:detection_tensors"
  input_side_packet: "ANCHORS:anchors"
  output_stream: "DETECTIONS:detections"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
      num_classes: 1
      num_boxes: 2944
      num_coords: 18
      box_coord_offset: 0
      keypoint_coord_offset: 4
      num_keypoints: 7
      num_values_per_keypoint: 2
      sigmoid_score: true
      score_clipping_thresh: 100.0
      reverse_output_order: true

      x_scale: 256.0
      y_scale: 256.0
      h_scale: 256.0
      w_scale: 256.0
      min_score_thresh: 0.7
    }
  }
}

# Performs non-max suppression to remove excessive detections.
node {
  calculator: "NonMaxSuppressionCalculator"
  input_stream: "detections"
  output_stream: "filtered_detections"
  node_options: {
    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
      min_suppression_threshold: 0.3
      overlap_type: INTERSECTION_OVER_UNION
      algorithm: WEIGHTED
      return_empty_detections: true
    }
  }
}

# Maps detection label IDs to the corresponding label text ("Palm"). The label
# map is provided in the label_map_path option.
node {
  calculator: "DetectionLabelIdToTextCalculator"
  input_stream: "filtered_detections"
  output_stream: "labeled_detections"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
      label_map_path: "palm_detection_labelmap.txt"
    }
  }
}

# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
# letterboxed image (after image transformation with the FIT scale mode) to the
# corresponding locations on the same image with the letterbox removed (the
# input image to the graph before image transformation).
node {
  calculator: "DetectionLetterboxRemovalCalculator"
  input_stream: "DETECTIONS:labeled_detections"
  input_stream: "LETTERBOX_PADDING:letterbox_padding"
  output_stream: "DETECTIONS:palm_detections"
}

# Extracts image size from the input images.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_GPU:input_video"
  output_stream: "SIZE:image_size"
}

# Converts results of palm detection into a rectangle (normalized by image size)
# that encloses the palm and is rotated such that the line connecting center of
# the wrist and MCP of the middle finger is aligned with the Y-axis of the
# rectangle.
node {
  calculator: "DetectionsToRectsCalculator"
  input_stream: "DETECTIONS:palm_detections"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "NORM_RECT:palm_rect"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionsToRectsCalculatorOptions] {
      rotation_vector_start_keypoint_index: 0  # Center of wrist.
      rotation_vector_end_keypoint_index: 2  # MCP of middle finger.
      rotation_vector_target_angle_degrees: 90
      output_zero_rect_for_empty_detections: true
    }
  }
}

# Expands and shifts the rectangle that contains the palm so that it's likely
# to cover the entire hand.
node {
  calculator: "RectTransformationCalculator"
  input_stream: "NORM_RECT:palm_rect"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "hand_rect_from_palm_detections"
  node_options: {
    [type.googleapis.com/mediapipe.RectTransformationCalculatorOptions] {
      scale_x: 2.6
      scale_y: 2.6
      shift_y: -0.5
      square_long: true
    }
  }
}

Hand Landmark Subgraph

Source pbtxt file

# MediaPipe hand landmark localization subgraph.

type: "HandLandmarkSubgraph"

input_stream: "IMAGE:input_video"
input_stream: "NORM_RECT:hand_rect"
output_stream: "LANDMARKS:hand_landmarks"
output_stream: "NORM_RECT:hand_rect_for_next_frame"
output_stream: "PRESENCE:hand_presence"

# Crops the rectangle that contains a hand from the input image.
node {
  calculator: "ImageCroppingCalculator"
  input_stream: "IMAGE_GPU:input_video"
  input_stream: "NORM_RECT:hand_rect"
  output_stream: "IMAGE_GPU:hand_image"
}

# Transforms the input image on GPU to a 256x256 image. To scale the input
# image, the scale_mode option is set to FIT to preserve the aspect ratio,
# resulting in potential letterboxing in the transformed image.
node: {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE_GPU:hand_image"
  output_stream: "IMAGE_GPU:transformed_hand_image"
  output_stream: "LETTERBOX_PADDING:letterbox_padding"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 256
      output_height: 256
      scale_mode: FIT
    }
  }
}

# Converts the transformed input image on GPU into an image tensor stored as a
# TfLiteTensor.
node {
  calculator: "TfLiteConverterCalculator"
  input_stream: "IMAGE_GPU:transformed_hand_image"
  output_stream: "TENSORS_GPU:image_tensor"
}

# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
  calculator: "TfLiteInferenceCalculator"
  input_stream: "TENSORS_GPU:image_tensor"
  output_stream: "TENSORS:output_tensors"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
      model_path: "hand_landmark.tflite"
      use_gpu: true
    }
  }
}

# Splits a vector of tensors into multiple vectors.
node {
  calculator: "SplitTfLiteTensorVectorCalculator"
  input_stream: "output_tensors"
  output_stream: "landmark_tensors"
  output_stream: "hand_flag_tensor"
  node_options: {
    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
      ranges: { begin: 0 end: 1 }
      ranges: { begin: 1 end: 2 }
    }
  }
}

# Converts the hand-flag tensor into a float that represents the confidence
# score of hand presence.
node {
  calculator: "TfLiteTensorsToFloatsCalculator"
  input_stream: "TENSORS:hand_flag_tensor"
  output_stream: "FLOAT:hand_presence_score"
}

# Applies a threshold to the confidence score to determine whether a hand is
# present.
node {
  calculator: "ThresholdingCalculator"
  input_stream: "FLOAT:hand_presence_score"
  output_stream: "FLAG:hand_presence"
  node_options: {
    [type.googleapis.com/mediapipe.ThresholdingCalculatorOptions] {
      threshold: 0.1
    }
  }
}

# Decodes the landmark tensors into a vector of lanmarks, where the landmark
# coordinates are normalized by the size of the input image to the model.
node {
  calculator: "TfLiteTensorsToLandmarksCalculator"
  input_stream: "TENSORS:landmark_tensors"
  output_stream: "NORM_LANDMARKS:landmarks"
  node_options: {
    [type.googleapis.com/mediapipe.TfLiteTensorsToLandmarksCalculatorOptions] {
      num_landmarks: 21
      input_image_width: 256
      input_image_height: 256
    }
  }
}

# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
# image (after image transformation with the FIT scale mode) to the
# corresponding locations on the same image with the letterbox removed (hand
# image before image transformation).
node {
  calculator: "LandmarkLetterboxRemovalCalculator"
  input_stream: "LANDMARKS:landmarks"
  input_stream: "LETTERBOX_PADDING:letterbox_padding"
  output_stream: "LANDMARKS:scaled_landmarks"
}

# Projects the landmarks from the cropped hand image to the corresponding
# locations on the full image before cropping (input to the graph).
node {
  calculator: "LandmarkProjectionCalculator"
  input_stream: "NORM_LANDMARKS:scaled_landmarks"
  input_stream: "NORM_RECT:hand_rect"
  output_stream: "NORM_LANDMARKS:hand_landmarks"
}

# Extracts image size from the input images.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_GPU:input_video"
  output_stream: "SIZE:image_size"
}

# Converts hand landmarks to a detection that tightly encloses all landmarks.
node {
  calculator: "LandmarksToDetectionCalculator"
  input_stream: "NORM_LANDMARKS:hand_landmarks"
  output_stream: "DETECTION:hand_detection"
}

# Converts the hand detection into a rectangle (normalized by image size)
# that encloses the hand and is rotated such that the line connecting center of
# the wrist and MCP of the middle finger is aligned with the Y-axis of the
# rectangle.
node {
  calculator: "DetectionsToRectsCalculator"
  input_stream: "DETECTION:hand_detection"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "NORM_RECT:hand_rect_from_landmarks"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionsToRectsCalculatorOptions] {
      rotation_vector_start_keypoint_index: 0  # Center of wrist.
      rotation_vector_end_keypoint_index: 9  # MCP of middle finger.
      rotation_vector_target_angle_degrees: 90
    }
  }
}

# Expands the hand rectangle so that in the next video frame it's likely to
# still contain the hand even with some motion.
node {
  calculator: "RectTransformationCalculator"
  input_stream: "NORM_RECT:hand_rect_from_landmarks"
  input_stream: "IMAGE_SIZE:image_size"
  output_stream: "hand_rect_for_next_frame"
  node_options: {
    [type.googleapis.com/mediapipe.RectTransformationCalculatorOptions] {
      scale_x: 1.6
      scale_y: 1.6
      square_long: true
    }
  }
}

Renderer Subgraph

Source pbtxt file

# MediaPipe hand tracking rendering subgraph.

type: "RendererSubgraph"

input_stream: "IMAGE:input_image"
input_stream: "DETECTIONS:detections"
input_stream: "LANDMARKS:landmarks"
input_stream: "NORM_RECT:rect"
output_stream: "IMAGE:output_image"

# Converts detections to drawing primitives for annotation overlay.
node {
  calculator: "DetectionsToRenderDataCalculator"
  input_stream: "DETECTIONS:detections"
  output_stream: "RENDER_DATA:detection_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
      thickness: 4.0
      color { r: 0 g: 255 b: 0 }
    }
  }
}

# Converts landmarks to drawing primitives for annotation overlay.
node {
  calculator: "LandmarksToRenderDataCalculator"
  input_stream: "NORM_LANDMARKS:landmarks"
  output_stream: "RENDER_DATA:landmark_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
      landmark_connections: 0
      landmark_connections: 1
      landmark_connections: 1
      landmark_connections: 2
      landmark_connections: 2
      landmark_connections: 3
      landmark_connections: 3
      landmark_connections: 4
      landmark_connections: 0
      landmark_connections: 5
      landmark_connections: 5
      landmark_connections: 6
      landmark_connections: 6
      landmark_connections: 7
      landmark_connections: 7
      landmark_connections: 8
      landmark_connections: 5
      landmark_connections: 9
      landmark_connections: 9
      landmark_connections: 10
      landmark_connections: 10
      landmark_connections: 11
      landmark_connections: 11
      landmark_connections: 12
      landmark_connections: 9
      landmark_connections: 13
      landmark_connections: 13
      landmark_connections: 14
      landmark_connections: 14
      landmark_connections: 15
      landmark_connections: 15
      landmark_connections: 16
      landmark_connections: 13
      landmark_connections: 17
      landmark_connections: 0
      landmark_connections: 17
      landmark_connections: 17
      landmark_connections: 18
      landmark_connections: 18
      landmark_connections: 19
      landmark_connections: 19
      landmark_connections: 20
      landmark_color { r: 255 g: 0 b: 0 }
      connection_color { r: 0 g: 255 b: 0 }
      thickness: 4.0
    }
  }
}

# Converts normalized rects to drawing primitives for annotation overlay.
node {
  calculator: "RectToRenderDataCalculator"
  input_stream: "NORM_RECT:rect"
  output_stream: "RENDER_DATA:rect_render_data"
  node_options: {
    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
      filled: false
      color { r: 255 g: 0 b: 0 }
      thickness: 4.0
    }
  }
}

# Draws annotations and overlays them on top of the input images.
node {
  calculator: "AnnotationOverlayCalculator"
  input_stream: "INPUT_FRAME_GPU:input_image"
  input_stream: "detection_render_data"
  input_stream: "landmark_render_data"
  input_stream: "rect_render_data"
  output_stream: "OUTPUT_FRAME_GPU:output_image"
}

GitHub Open-Source Code for Hand Gesture Recognition ? Sign Language Translation

Android

iOS

Graph

Main Graph

Hand Detection Subgraph

Hand Landmark Subgraph

Renderer Subgraph

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