Modify the TensorToImageFrameCalculator to support normalized outputs.

PiperOrigin-RevId: 540104988

mediapipe/calculators/tensorflow/BUILD

@@ -1077,6 +1077,7 @@ cc_test(
     linkstatic = 1,
     deps = [
         ":tensor_to_image_frame_calculator",
+        ":tensor_to_image_frame_calculator_cc_proto",
         "//mediapipe/framework:calculator_framework",
         "//mediapipe/framework:calculator_runner",
         "//mediapipe/framework/formats:image_frame",

mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator.cc

@@ -65,6 +65,7 @@ class TensorToImageFrameCalculator : public CalculatorBase {
 
  private:
   float scale_factor_;
+  bool scale_per_frame_min_max_;
 };
 
 REGISTER_CALCULATOR(TensorToImageFrameCalculator);
@@ -88,6 +89,8 @@ absl::Status TensorToImageFrameCalculator::GetContract(CalculatorContract* cc) {
 absl::Status TensorToImageFrameCalculator::Open(CalculatorContext* cc) {
   scale_factor_ =
       cc->Options<TensorToImageFrameCalculatorOptions>().scale_factor();
+  scale_per_frame_min_max_ = cc->Options<TensorToImageFrameCalculatorOptions>()
+                                 .scale_per_frame_min_max();
   cc->SetOffset(TimestampDiff(0));
   return absl::OkStatus();
 }
@@ -109,16 +112,38 @@ absl::Status TensorToImageFrameCalculator::Process(CalculatorContext* cc) {
   auto format = (depth == 3 ? ImageFormat::SRGB : ImageFormat::GRAY8);
   const int32_t total_size = height * width * depth;
 
+  if (scale_per_frame_min_max_) {
+    RET_CHECK_EQ(input_tensor.dtype(), tensorflow::DT_FLOAT)
+        << "Setting scale_per_frame_min_max requires FLOAT input tensors.";
+  }
   ::std::unique_ptr<const ImageFrame> output;
   if (input_tensor.dtype() == tensorflow::DT_FLOAT) {
     // Allocate buffer with alignments.
     std::unique_ptr<uint8_t[]> buffer(
         new (std::align_val_t(EIGEN_MAX_ALIGN_BYTES)) uint8_t[total_size]);
     auto data = input_tensor.flat<float>().data();
+    float min = 1e23;
+    float max = -1e23;
+    if (scale_per_frame_min_max_) {
       for (int i = 0; i < total_size; ++i) {
         float d = scale_factor_ * data[i];
+        if (d < min) {
+          min = d;
+        }
+        if (d > max) {
+          max = d;
+        }
+      }
+    }
+    for (int i = 0; i < total_size; ++i) {
+      float d = data[i];
+      if (scale_per_frame_min_max_) {
+        d = 255 * (d - min) / (max - min + 1e-9);
+      } else {
+        d = scale_factor_ * d;
         if (d < 0) d = 0;
         if (d > 255) d = 255;
+      }
       buffer[i] = d;
     }
     output = ::absl::make_unique<ImageFrame>(

mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator.proto

@@ -26,4 +26,8 @@ message TensorToImageFrameCalculatorOptions {
   // Multiples floating point tensor outputs by this value before converting to
   // uint8. This is useful for converting from range [0, 1] to [0, 255]
   optional float scale_factor = 1 [default = 1.0];
+
+  // If true, scales any FLOAT tensor input of [min, max] to be between [0, 255]
+  // per frame. This overrides any explicit scale_factor.
+  optional bool scale_per_frame_min_max = 2 [default = false];
 }

mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator_test.cc

@@ -11,7 +11,9 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
+#include <type_traits>
 
+#include "mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/calculator_runner.h"
 #include "mediapipe/framework/formats/image_frame.h"
@@ -32,11 +34,14 @@ constexpr char kImage[] = "IMAGE";
 template <class TypeParam>
 class TensorToImageFrameCalculatorTest : public ::testing::Test {
  protected:
-  void SetUpRunner() {
+  void SetUpRunner(bool scale_per_frame_min_max = false) {
     CalculatorGraphConfig::Node config;
     config.set_calculator("TensorToImageFrameCalculator");
     config.add_input_stream("TENSOR:input_tensor");
     config.add_output_stream("IMAGE:output_image");
+    config.mutable_options()
+        ->MutableExtension(mediapipe::TensorToImageFrameCalculatorOptions::ext)
+        ->set_scale_per_frame_min_max(scale_per_frame_min_max);
     runner_ = absl::make_unique<CalculatorRunner>(config);
   }
 
@@ -157,4 +162,47 @@ TYPED_TEST(TensorToImageFrameCalculatorTest,
   }
 }
 
+TYPED_TEST(TensorToImageFrameCalculatorTest,
+           Converts3DTensorToImageFrame2DGrayWithScaling) {
+  this->SetUpRunner(true);
+  auto& runner = this->runner_;
+  constexpr int kWidth = 16;
+  constexpr int kHeight = 8;
+  const tf::TensorShape tensor_shape{kHeight, kWidth};
+  auto tensor = absl::make_unique<tf::Tensor>(
+      tf::DataTypeToEnum<TypeParam>::v(), tensor_shape);
+  auto tensor_vec = tensor->template flat<TypeParam>().data();
+
+  // Writing sequence of integers as floats which we want normalized.
+  tensor_vec[0] = 255;
+  for (int i = 1; i < kWidth * kHeight; ++i) {
+    tensor_vec[i] = 200;
+  }
+
+  const int64_t time = 1234;
+  runner->MutableInputs()->Tag(kTensor).packets.push_back(
+      Adopt(tensor.release()).At(Timestamp(time)));
+
+  if (!std::is_same<TypeParam, float>::value) {
+    EXPECT_FALSE(runner->Run().ok());
+    return;  // Short circuit because does not apply to other types.
+  } else {
+    EXPECT_TRUE(runner->Run().ok());
+    const std::vector<Packet>& output_packets =
+        runner->Outputs().Tag(kImage).packets;
+    EXPECT_EQ(1, output_packets.size());
+    EXPECT_EQ(time, output_packets[0].Timestamp().Value());
+    const ImageFrame& output_image = output_packets[0].Get<ImageFrame>();
+    EXPECT_EQ(ImageFormat::GRAY8, output_image.Format());
+    EXPECT_EQ(kWidth, output_image.Width());
+    EXPECT_EQ(kHeight, output_image.Height());
+
+    EXPECT_EQ(255, output_image.PixelData()[0]);
+    for (int i = 1; i < kWidth * kHeight; ++i) {
+      const uint8_t pixel_value = output_image.PixelData()[i];
+      ASSERT_EQ(0, pixel_value);
+    }
+  }
+}
+
 }  // namespace mediapipe