mediapipe-rs/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt

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2022-06-11 21:25:48 +02:00
# MediaPipe graph performing common processing to detect faces using
# face_detection_full_range_sparse.tflite model, currently consisting of tensor
# post processing.
#
# EXAMPLE:
# node {
# calculator: "FaceDetectionFullRangeCommon"
# input_stream: "TENSORS:detection_tensors"
# input_stream: "MATRIX:transform_matrix"
# output_stream: "DETECTIONS:detections"
# }
type: "FaceDetectionShortRangeCommon"
# Detection tensors. (std::vector<Tensor>)
input_stream: "TENSORS:detection_tensors"
# A 4x4 row-major-order matrix that maps a point represented in the detection
# tensors to a desired coordinate system, e.g., in the original input image
# before scaling/cropping. (std::array<float, 16>)
input_stream: "MATRIX:transform_matrix"
# Detected faces. (std::vector<Detection>)
# NOTE: there will not be an output packet in the DETECTIONS stream for this
# particular timestamp if none of faces detected. However, the MediaPipe
# framework will internally inform the downstream calculators of the absence of
# this packet so that they don't wait for it unnecessarily.
output_stream: "DETECTIONS:detections"
# 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"
options: {
[mediapipe.SsdAnchorsCalculatorOptions.ext] {
num_layers: 1
min_scale: 0.1484375
max_scale: 0.75
input_size_height: 192
input_size_width: 192
anchor_offset_x: 0.5
anchor_offset_y: 0.5
strides: 4
aspect_ratios: 1.0
fixed_anchor_size: true
interpolated_scale_aspect_ratio: 0.0
}
}
}
# 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: "TensorsToDetectionsCalculator"
input_stream: "TENSORS:detection_tensors"
input_side_packet: "ANCHORS:anchors"
output_stream: "DETECTIONS:unfiltered_detections"
options: {
[mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
num_classes: 1
num_boxes: 2304
num_coords: 16
box_coord_offset: 0
keypoint_coord_offset: 4
num_keypoints: 6
num_values_per_keypoint: 2
sigmoid_score: true
score_clipping_thresh: 100.0
reverse_output_order: true
x_scale: 192.0
y_scale: 192.0
h_scale: 192.0
w_scale: 192.0
min_score_thresh: 0.6
}
}
}
# Performs non-max suppression to remove excessive detections.
node {
calculator: "NonMaxSuppressionCalculator"
input_stream: "unfiltered_detections"
output_stream: "filtered_detections"
options: {
[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
min_suppression_threshold: 0.3
overlap_type: INTERSECTION_OVER_UNION
algorithm: WEIGHTED
}
}
}
# Projects the detections from input tensor to the corresponding locations on
# the original image (input to the graph).
node {
calculator: "DetectionProjectionCalculator"
input_stream: "DETECTIONS:filtered_detections"
input_stream: "PROJECTION_MATRIX:transform_matrix"
output_stream: "DETECTIONS:detections"
}