mediapipe-rs/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt
Victor Dudochkin 5578aa50e8 code fill
2022-03-01 19:04:01 +07:00

135 lines
3.7 KiB
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# MediaPipe Objectron object detection CPU subgraph.
type: "ObjectDetectionOidV4Subgraph"
input_stream: "IMAGE:input_video"
input_side_packet: "LABELS_CSV:allowed_labels"
output_stream: "DETECTIONS:detections"
# Crops, resizes, and converts the input video into tensor.
# Preserves aspect ratio of the images.
node {
calculator: "ImageToTensorCalculator"
input_stream: "IMAGE:input_video"
output_stream: "TENSORS:image_tensor"
output_stream: "LETTERBOX_PADDING:letterbox_padding"
options {
[mediapipe.ImageToTensorCalculatorOptions.ext] {
output_tensor_width: 300
output_tensor_height: 300
keep_aspect_ratio: false
output_tensor_float_range {
min: -1.0
max: 1.0
}
}
}
}
# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
calculator: "InferenceCalculator"
input_stream: "TENSORS:image_tensor"
output_stream: "TENSORS:detection_tensors"
options: {
[mediapipe.InferenceCalculatorOptions.ext] {
model_path: "mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
delegate { xnnpack {} }
}
}
}
# 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: 6
min_scale: 0.2
max_scale: 0.95
input_size_height: 300
input_size_width: 300
anchor_offset_x: 0.5
anchor_offset_y: 0.5
strides: 16
strides: 32
strides: 64
strides: 128
strides: 256
strides: 512
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
reduce_boxes_in_lowest_layer: 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: "TensorsToDetectionsCalculator"
input_stream: "TENSORS:detection_tensors"
input_side_packet: "ANCHORS:anchors"
output_stream: "DETECTIONS:all_detections"
options: {
[mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
num_classes: 24
num_boxes: 1917
num_coords: 4
ignore_classes: 0
sigmoid_score: true
apply_exponential_on_box_size: true
x_scale: 10.0
y_scale: 10.0
h_scale: 5.0
w_scale: 5.0
min_score_thresh: 0.5
}
}
}
# Maps detection label IDs to the corresponding label text. The label map is
# provided in the label_map_path option.
node {
calculator: "DetectionLabelIdToTextCalculator"
input_stream: "all_detections"
output_stream: "labeled_detections"
options: {
[mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
label_map_path: "mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt"
}
}
}
# Filters the detections to only those with valid scores
# for the specified allowed labels.
node {
calculator: "FilterDetectionCalculator"
input_stream: "DETECTIONS:labeled_detections"
output_stream: "DETECTIONS:filtered_detections"
input_side_packet: "LABELS_CSV:allowed_labels"
}
# Performs non-max suppression to remove excessive detections.
node {
calculator: "NonMaxSuppressionCalculator"
input_stream: "filtered_detections"
output_stream: "detections"
options: {
[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
min_suppression_threshold: 0.5
max_num_detections: 100
overlap_type: INTERSECTION_OVER_UNION
return_empty_detections: true
}
}
}