138 lines
3.7 KiB
Plaintext
138 lines
3.7 KiB
Plaintext
# MediaPipe graph that performs template matching with TensorFlow Lite on CPU.
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# Used in the examples in
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# mediapipe/examples/android/src/java/com/mediapipe/apps/templatematchingcpu
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# Images on GPU coming into and out of the graph.
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input_stream: "input_video"
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output_stream: "output_video"
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# Throttles the images flowing downstream for flow control.
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "input_video"
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input_stream: "FINISHED:detections"
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input_stream_info: {
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tag_index: "FINISHED"
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back_edge: true
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}
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output_stream: "throttled_input_video"
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}
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# Transfers the input image from GPU to CPU memory.
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node: {
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calculator: "GpuBufferToImageFrameCalculator"
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input_stream: "throttled_input_video"
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output_stream: "input_video_cpu"
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}
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# Scale the image's longer side to 640, keeping aspect ratio.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:input_video_cpu"
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output_stream: "IMAGE:transformed_input_video_cpu"
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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:transformed_input_video_cpu"
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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:transformed_input_video_cpu"
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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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delegate { xnnpack {} }
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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_GPU:throttled_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_GPU:output_video"
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}
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