142 lines
3.9 KiB
Plaintext
142 lines
3.9 KiB
Plaintext
# MediaPipe graph that performs object detection on desktop with TensorFlow Lite
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# on CPU.
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# Used in the example in
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# mediapipe/examples/desktop/template_matching:template_matching_tflite
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# max_queue_size limits the number of packets enqueued on any input stream
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# by throttling inputs to the graph. This makes the graph only process one
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# frame per time.
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max_queue_size: 1
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# Decodes an input video file into images and a video header.
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node {
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calculator: "OpenCvVideoDecoderCalculator"
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input_side_packet: "INPUT_FILE_PATH:input_video_path"
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output_stream: "VIDEO:input_video"
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output_stream: "VIDEO_PRESTREAM:input_video_header"
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}
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:input_video"
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output_stream: "IMAGE:scaled_input_video"
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node_options: {
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[type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
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output_width: 640
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output_height: 640
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scale_mode: FILL_AND_CROP
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}
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}
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}
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node {
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calculator: "ImagePropertiesCalculator"
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input_stream: "IMAGE:scaled_input_video"
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output_stream: "SIZE:input_video_size"
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}
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node {
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calculator: "FeatureDetectorCalculator"
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input_stream: "IMAGE:scaled_input_video"
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output_stream: "FEATURES:features"
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output_stream: "LANDMARKS:landmarks"
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output_stream: "PATCHES:patches"
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}
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# input tensors: 200*32*32*1 float
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# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
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# rest is padded by zero.
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node {
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calculator: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:patches"
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output_stream: "TENSORS:knift_feature_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "mediapipe/models/knift_float.tflite"
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}
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}
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}
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node {
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calculator: "TfLiteTensorsToFloatsCalculator"
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input_stream: "TENSORS:knift_feature_tensors"
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output_stream: "FLOATS:knift_feature_floats"
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}
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node {
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calculator: "BoxDetectorCalculator"
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input_stream: "FEATURES:features"
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input_stream: "IMAGE_SIZE:input_video_size"
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input_stream: "DESCRIPTORS:knift_feature_floats"
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output_stream: "BOXES:detections"
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node_options: {
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[type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
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detector_options {
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index_type: OPENCV_BF
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detect_every_n_frame: 1
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}
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index_proto_filename: "mediapipe/models/knift_index.pb"
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}
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}
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}
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node {
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calculator: "TimedBoxListIdToLabelCalculator"
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input_stream: "detections"
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output_stream: "labeled_detections"
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node_options: {
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[type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
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label_map_path: "mediapipe/models/knift_labelmap.txt"
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}
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}
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}
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node {
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calculator: "TimedBoxListToRenderDataCalculator"
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input_stream: "BOX_LIST:labeled_detections"
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output_stream: "RENDER_DATA:box_render_data"
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node_options: {
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[type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
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box_color { r: 255 g: 0 b: 0 }
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thickness: 5.0
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}
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}
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}
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node {
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calculator: "LandmarksToRenderDataCalculator"
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input_stream: "NORM_LANDMARKS:landmarks"
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output_stream: "RENDER_DATA:landmarks_render_data"
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node_options: {
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[type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
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landmark_color { r: 0 g: 255 b: 0 }
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thickness: 2.0
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}
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}
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}
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# Draws annotations and overlays them on top of the input images.
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node {
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calculator: "AnnotationOverlayCalculator"
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input_stream: "IMAGE:input_video"
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input_stream: "box_render_data"
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input_stream: "landmarks_render_data"
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output_stream: "IMAGE:output_video"
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}
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# Encodes the annotated images into a video file, adopting properties specified
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# in the input video header, e.g., video framerate.
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node {
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calculator: "OpenCvVideoEncoderCalculator"
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input_stream: "VIDEO:output_video"
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input_stream: "VIDEO_PRESTREAM:input_video_header"
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input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
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node_options: {
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[type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
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codec: "avc1"
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video_format: "mp4"
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}
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}
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}
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