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MediaPipe Team
jqtang
parent
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commit
86b3283b2f
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# MediaPipe graph that performs LSTM based object detection on desktop with
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# TensorFlow Lite on CPU.
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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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# Transforms the input image on CPU to a 256x256 image. To scale the image, by
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# default it uses the STRETCH scale mode that maps the entire input image to the
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# entire transformed image. As a result, image aspect ratio may be changed and
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# objects in the image may be deformed (stretched or squeezed), but the object
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# detection model used in this graph is agnostic to that deformation.
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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:transformed_input_video"
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node_options: {
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[type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
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output_width: 256
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output_height: 256
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}
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}
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}
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# Converts the transformed input image on CPU into an image tensor as a
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# TfLiteTensor. The zero_center option is set to true to normalize the
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# pixel values to [-1.f, 1.f] as opposed to [0.f, 1.f].
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE:transformed_input_video"
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output_stream: "TENSORS:image_tensor"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
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zero_center: true
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use_quantized_tensors: true
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}
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}
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}
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# Joins the input image_tensor and previous lstm state tensors into a single
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# output vector of tensors.
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node {
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calculator: "ConcatenateTfLiteTensorVectorCalculator"
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input_stream: "image_tensor"
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input_stream: "prev_lstm_tensors"
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output_stream: "concatenated_tensors"
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}
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# Runs a TensorFlow Lite model on CPU that takes an image tensor and state
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# tensors, then outputs a vector of tensors representing, for instance,
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# detection boxes/keypoints and scores.
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node {
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calculator: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:concatenated_tensors"
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output_stream: "TENSORS:inference_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "mediapipe/models/lstdlite_object_detection.tflite"
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}
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}
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}
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# Splits up the output tensors into two streams [detection_boxes,
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# detection_classes, detection_scores, num_detections], and [lstm_c, lstm_h].
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node {
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calculator: "SplitTfLiteTensorVectorCalculator"
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input_stream: "inference_tensors"
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output_stream: "detection_tensors"
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output_stream: "lstm_tensors"
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options {
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[type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
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ranges: { begin: 0 end: 4 }
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ranges: { begin: 4 end: 6 }
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}
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}
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}
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# Waits for the LSTM state output from the TfLite inferencing to feed back in as
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# an input tensor for the next frame.
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node {
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calculator: "PreviousLoopbackCalculator"
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input_stream: "MAIN:transformed_input_video"
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input_stream: "LOOP:lstm_tensors"
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input_stream_info: { tag_index: "LOOP" back_edge: true }
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output_stream: "PREV_LOOP:prev_lstm_tensors"
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}
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# Decodes the detection tensors generated by the TensorFlow Lite model, based on
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# the specification in the options, into a vector of detections. Each detection
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# describes a detected object.
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node {
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calculator: "TfLiteTensorsToDetectionsCalculator"
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input_stream: "TENSORS:detection_tensors"
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output_stream: "DETECTIONS:detections"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
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num_classes: 7
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num_boxes: 40
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num_coords: 4
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}
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}
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}
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# Performs non-max suppression to remove excessive detections.
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node {
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calculator: "NonMaxSuppressionCalculator"
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input_stream: "detections"
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output_stream: "filtered_detections"
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node_options: {
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[type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
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min_suppression_threshold: 0.4
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min_score_threshold: 0.2
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max_num_detections: 5
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overlap_type: INTERSECTION_OVER_UNION
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}
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}
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}
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# Maps detection label IDs to the corresponding label text. The label map is
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# provided in the label_map_path option.
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node {
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calculator: "DetectionLabelIdToTextCalculator"
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input_stream: "filtered_detections"
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output_stream: "output_detections"
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node_options: {
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[type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
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label_map_path: "mediapipe/models/lstdlite_object_detection_labelmap.txt"
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}
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}
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}
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# Converts the detections to drawing primitives for annotation overlay.
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node {
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calculator: "DetectionsToRenderDataCalculator"
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input_stream: "DETECTIONS:output_detections"
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output_stream: "RENDER_DATA:render_data"
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node_options: {
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[type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
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thickness: 2.0
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color { r: 255 g: 255 b: 255 }
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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 original image coming into
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# the graph.
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node {
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calculator: "AnnotationOverlayCalculator"
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input_stream: "INPUT_FRAME:input_video"
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input_stream: "render_data"
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output_stream: "OUTPUT_FRAME: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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