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MediaPipe Team Project import generated by Copybara. 2946872 Aug 17, 2019
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# 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
}
}
}
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