246 lines
8.3 KiB
Markdown
246 lines
8.3 KiB
Markdown
# Object Detection (CPU)
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This doc focuses on the
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[example graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt)
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that performs object detection with TensorFlow Lite on CPU.
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This is very similar to the
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[Object Detection on GPU on Android](object_detection_android_gpu.md) example
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except that at the beginning and the end of the graph it performs GPU-to-CPU and
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CPU-to-GPU image transfer respectively. As a result, the rest of graph, which
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shares the same configuration as the
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[GPU graph](images/mobile/object_detection_mobile_gpu.png), runs entirely on
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CPU.
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## Android
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[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu)
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To build and install the app:
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```bash
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bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu
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adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu/objectdetectioncpu.apk
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```
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## iOS
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[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handdetectiongpu).
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See the general [instructions](./mediapipe_ios_setup.md) for building iOS
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examples and generating an Xcode project. This will be the ObjectDetectionCpuApp
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target.
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To build on the command line:
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```bash
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bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/objectdetectioncpu:ObjectDetectionCpuApp
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```
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## Graph
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To visualize the graph as shown above, copy the text specification of the graph
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below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
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[Source pbtxt file](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt)
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```bash
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# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
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# Used in the examples in
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# mediapipie/examples/android/src/java/com/mediapipe/apps/objectdetectioncpu and
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# mediapipie/examples/ios/objectdetectioncpu.
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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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# Transfers the input image from GPU to CPU memory for the purpose of
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# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
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# origin defined at the bottom-left corner (OpenGL convention). As a result,
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# the transferred image on CPU also shares the same representation.
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node: {
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calculator: "GpuBufferToImageFrameCalculator"
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input_stream: "input_video"
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output_stream: "input_video_cpu"
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}
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# Throttles the images flowing downstream for flow control. It passes through
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# the very first incoming image unaltered, and waits for
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# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
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# generating the corresponding detections before it passes through another
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# image. All images that come in while waiting are dropped, limiting the number
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# of in-flight images between this calculator and
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# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
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# from queuing up incoming images and data excessively, which leads to increased
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# latency and memory usage, unwanted in real-time mobile applications. It also
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# eliminates unnecessarily computation, e.g., a transformed image produced by
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# ImageTransformationCalculator may get dropped downstream if the subsequent
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# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
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# processing previous inputs.
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "input_video_cpu"
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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_cpu"
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}
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# Transforms the input image on CPU to a 320x320 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:throttled_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: 320
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output_height: 320
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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 stored as a
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# TfLiteTensor.
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE:transformed_input_video_cpu"
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output_stream: "TENSORS:image_tensor"
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}
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# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
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# vector of tensors representing, for instance, detection boxes/keypoints and
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# scores.
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node {
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calculator: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:image_tensor"
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output_stream: "TENSORS:detection_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "ssdlite_object_detection.tflite"
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}
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}
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}
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# Generates a single side packet containing a vector of SSD anchors based on
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# the specification in the options.
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node {
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calculator: "SsdAnchorsCalculator"
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output_side_packet: "anchors"
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node_options: {
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[type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
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num_layers: 6
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min_scale: 0.2
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max_scale: 0.95
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input_size_height: 320
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input_size_width: 320
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anchor_offset_x: 0.5
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anchor_offset_y: 0.5
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strides: 16
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strides: 32
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strides: 64
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strides: 128
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strides: 256
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strides: 512
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aspect_ratios: 1.0
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aspect_ratios: 2.0
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aspect_ratios: 0.5
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aspect_ratios: 3.0
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aspect_ratios: 0.3333
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reduce_boxes_in_lowest_layer: true
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}
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}
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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 SSD anchors and the specification in the options, into a vector of
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# detections. Each detection 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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input_side_packet: "ANCHORS:anchors"
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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: 91
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num_boxes: 2034
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num_coords: 4
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ignore_classes: 0
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sigmoid_score: true
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apply_exponential_on_box_size: true
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x_scale: 10.0
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y_scale: 10.0
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h_scale: 5.0
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w_scale: 5.0
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min_score_thresh: 0.6
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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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max_num_detections: 3
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overlap_type: INTERSECTION_OVER_UNION
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return_empty_detections: true
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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: "ssdlite_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: 4.0
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color { r: 255 g: 0 b: 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: "INPUT_FRAME:throttled_input_video_cpu"
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input_stream: "render_data"
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output_stream: "OUTPUT_FRAME:output_video_cpu"
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}
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# Transfers the annotated image from CPU back to GPU memory, to be sent out of
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# the graph.
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node: {
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calculator: "ImageFrameToGpuBufferCalculator"
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input_stream: "output_video_cpu"
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output_stream: "output_video"
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}
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```
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