Project import generated by Copybara.

GitOrigin-RevId: 50714fe28298d7b707eff7304547d89d6ec34a54

mediapipe/calculators/image/scale_image_calculator.proto

@@ -36,7 +36,8 @@ message ScaleImageCalculatorOptions {
 
   // If ratio is positive, crop the image to this minimum and maximum
   // aspect ratio (preserving the center of the frame).  This is done
-  // before scaling.
+  // before scaling.  The string must contain "/", so to disable cropping,
+  // set both to "0/1".
   //   For example, for a min_aspect_ratio of "9/16" and max of "16/9" the
   //   following cropping will occur:
   //       1920x1080 (which is 16:9) is not cropped

mediapipe/calculators/tensorflow/tfrecord_reader_calculator.cc

@@ -85,7 +85,7 @@ class TFRecordReaderCalculator : public CalculatorBase {
   tensorflow::io::RecordReader reader(file.get(),
                                       tensorflow::io::RecordReaderOptions());
   tensorflow::uint64 offset = 0;
-  std::string example_str;
+  tensorflow::tstring example_str;
   const int target_idx =
       cc->InputSidePackets().HasTag(kRecordIndex)
           ? cc->InputSidePackets().Tag(kRecordIndex).Get<int>()
@@ -98,7 +98,7 @@ class TFRecordReaderCalculator : public CalculatorBase {
     if (current_idx == target_idx) {
       if (cc->OutputSidePackets().HasTag(kExampleTag)) {
         tensorflow::Example tf_example;
-        tf_example.ParseFromString(example_str);
+        tf_example.ParseFromArray(example_str.data(), example_str.size());
         cc->OutputSidePackets()
             .Tag(kExampleTag)
             .Set(MakePacket<tensorflow::Example>(std::move(tf_example)));

mediapipe/calculators/tflite/tflite_inference_calculator.cc

@@ -64,6 +64,28 @@ typedef id<MTLBuffer> GpuTensor;
 size_t RoundUp(size_t n, size_t m) { return ((n + m - 1) / m) * m; }  // NOLINT
 }  // namespace
 
+#if defined(MEDIAPIPE_EDGE_TPU)
+#include "edgetpu.h"
+
+// Creates and returns an Edge TPU interpreter to run the given edgetpu model.
+std::unique_ptr<tflite::Interpreter> BuildEdgeTpuInterpreter(
+    const tflite::FlatBufferModel& model,
+    tflite::ops::builtin::BuiltinOpResolver* resolver,
+    edgetpu::EdgeTpuContext* edgetpu_context) {
+  resolver->AddCustom(edgetpu::kCustomOp, edgetpu::RegisterCustomOp());
+  std::unique_ptr<tflite::Interpreter> interpreter;
+  if (tflite::InterpreterBuilder(model, *resolver)(&interpreter) != kTfLiteOk) {
+    std::cerr << "Failed to build edge TPU interpreter." << std::endl;
+  }
+  interpreter->SetExternalContext(kTfLiteEdgeTpuContext, edgetpu_context);
+  interpreter->SetNumThreads(1);
+  if (interpreter->AllocateTensors() != kTfLiteOk) {
+    std::cerr << "Failed to allocate edge TPU tensors." << std::endl;
+  }
+  return interpreter;
+}
+#endif  // MEDIAPIPE_EDGE_TPU
+
 // TfLiteInferenceCalculator File Layout:
 //  * Header
 //  * Core
@@ -162,6 +184,11 @@ class TfLiteInferenceCalculator : public CalculatorBase {
   TFLBufferConvert* converter_from_BPHWC4_ = nil;
 #endif
 
+#if defined(MEDIAPIPE_EDGE_TPU)
+  std::shared_ptr<edgetpu::EdgeTpuContext> edgetpu_context_ =
+      edgetpu::EdgeTpuManager::GetSingleton()->OpenDevice();
+#endif
+
   std::string model_path_ = "";
   bool gpu_inference_ = false;
   bool gpu_input_ = false;
@@ -425,6 +452,9 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
 #endif
     delegate_ = nullptr;
   }
+#if defined(MEDIAPIPE_EDGE_TPU)
+  edgetpu_context_.reset();
+#endif
   return ::mediapipe::OkStatus();
 }
 
@@ -458,16 +488,18 @@ REGISTER_CALCULATOR(TfLiteInferenceCalculator);
   model_ = tflite::FlatBufferModel::BuildFromFile(model_path_.c_str());
   RET_CHECK(model_);
 
+  tflite::ops::builtin::BuiltinOpResolver op_resolver;
   if (cc->InputSidePackets().HasTag("CUSTOM_OP_RESOLVER")) {
-    const auto& op_resolver =
-        cc->InputSidePackets()
-            .Tag("CUSTOM_OP_RESOLVER")
-            .Get<tflite::ops::builtin::BuiltinOpResolver>();
-    tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
-  } else {
-    const tflite::ops::builtin::BuiltinOpResolver op_resolver;
-    tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
+    op_resolver = cc->InputSidePackets()
+                      .Tag("CUSTOM_OP_RESOLVER")
+                      .Get<tflite::ops::builtin::BuiltinOpResolver>();
   }
+#if defined(MEDIAPIPE_EDGE_TPU)
+  interpreter_ =
+      BuildEdgeTpuInterpreter(*model_, &op_resolver, edgetpu_context_.get());
+#else
+  tflite::InterpreterBuilder(*model_, op_resolver)(&interpreter_);
+#endif  // MEDIAPIPE_EDGE_TPU
 
   RET_CHECK(interpreter_);
 

mediapipe/calculators/util/labels_to_render_data_calculator.cc

@@ -93,6 +93,7 @@ REGISTER_CALCULATOR(LabelsToRenderDataCalculator);
 }
 
 ::mediapipe::Status LabelsToRenderDataCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
   options_ = cc->Options<LabelsToRenderDataCalculatorOptions>();
   num_colors_ = options_.color_size();
   label_height_px_ = std::ceil(options_.font_height_px() * kFontHeightScale);