Project import generated by Copybara.

PiperOrigin-RevId: 254856010

.bazelrc

@@ -12,6 +12,8 @@ build --copt='-Wno-comment'
 build --copt='-Wno-return-type'
 build --copt='-Wno-unused-local-typedefs'
 build --copt='-Wno-ignored-attributes'
+# Temporarily set the incompatiblity flag for Bazel 0.27.0 and above
+build --incompatible_disable_deprecated_attr_params=false
 
 # Sets the default Apple platform to macOS.
 build --apple_platform_type=macos

README.md

@@ -1,9 +1,7 @@
 ![MediaPipe](mediapipe/docs/images/mediapipe_small.png?raw=true "MediaPipe logo")
 =======================================================================
 
-#### We will be [presenting at CVPR 2019](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe) on June 17~20 in Long Beach, CA. Come join us!
+[MediaPipe](http://mediapipe.dev) is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions. http://mediapipe.dev
-
-[MediaPipe](http://g.co/mediapipe) is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions.
 
 ![Real-time Face Detection](mediapipe/docs/images/realtime_face_detection.gif)
 
@@ -14,13 +12,24 @@ Follow these [instructions](mediapipe/docs/install.md).
 See mobile and desktop [examples](mediapipe/docs/examples.md).
 
 ## Documentation
-[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).
+[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/) or [docs.mediapipe.dev](https://docs.mediapipe.dev)
+
+Check out the [Examples page] for tutorials on how to use MediaPipe. [Concepts page](https://mediapipe.readthedocs.io/en/latest/concepts.html) for basic definitions
 
 ## Visualizing MediaPipe graphs
-A web-based visualizer is hosted on [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/). Please also see instructions [here](mediapipe/docs/visualizer.md).
+A web-based visualizer is hosted on [viz.mediapipe.dev](https://viz.mediapipe.dev/). Please also see instructions [here](mediapipe/docs/visualizer.md).
+
+## Community forum
+*  [discuss](https://groups.google.com/forum/#!forum/mediapipe) - General community discussion around MediaPipe
 
 ## Publications
-* [MediaPipe: A Framework for Building Perception Pipelines](https://tiny.cc/mediapipe_paper) (draft)
+* [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/abs/1906.08172)
+
+## Events
+[Open sourced at CVPR 2019](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe) on June 17~20 in Long Beach, CA
+
+## Alpha Disclaimer
+MediaPipe is currently in alpha for v0.5. We are still making breaking API changes and expect to get to stable API by v1.0.
 
 ## Contributing
 We welcome contributions. Please follow these [guidelines](./CONTRIBUTING.md).

mediapipe/calculators/image/BUILD

@@ -246,6 +246,23 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "image_cropping_calculator",
+    srcs = ["image_cropping_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_frame_opencv",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
+
 cc_library(
     name = "luminance_calculator",
     srcs = ["luminance_calculator.cc"],

mediapipe/calculators/image/image_cropping_calculator.cc

@@ -0,0 +1,150 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// 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 "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/image_frame.h"
+#include "mediapipe/framework/formats/image_frame_opencv.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+
+// Crops the input texture to the given rectangle region. The rectangle can
+// be at arbitrary location on the image with rotation. If there's rotation, the
+// output texture will have the size of the input rectangle. The rotation should
+// be in radian, see rect.proto for detail.
+// Currently it only works for CPU.
+//
+// Input:
+//   IMAGE: ImageFrame representing the input image.
+//   One of the following two tags:
+//   RECT - A Rect proto specifying the width/height and location of the
+//          cropping rectangle.
+//   NORM_RECT - A NormalizedRect proto specifying the width/height and location
+//          of the cropping rectangle in normalized coordinates.
+//
+// Output:
+//   IMAGE - Cropped frames.
+class ImageCroppingCalculator : public CalculatorBase {
+ public:
+  ImageCroppingCalculator() = default;
+  ~ImageCroppingCalculator() override = default;
+
+  static ::mediapipe::Status GetContract(CalculatorContract* cc);
+  ::mediapipe::Status Process(CalculatorContext* cc) override;
+
+ private:
+  ::mediapipe::Status RenderCpu(CalculatorContext* cc);
+  ::mediapipe::Status RenderGpu(CalculatorContext* cc);
+
+  // TODO: Merge with GlCroppingCalculator to have GPU support.
+  bool use_gpu_{};
+};
+REGISTER_CALCULATOR(ImageCroppingCalculator);
+
+::mediapipe::Status ImageCroppingCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(cc->Inputs().HasTag("IMAGE"));
+  RET_CHECK(cc->Outputs().HasTag("IMAGE"));
+
+  cc->Inputs().Tag("IMAGE").Set<ImageFrame>();
+
+  if (cc->Inputs().HasTag("RECT")) {
+    cc->Inputs().Tag("RECT").Set<Rect>();
+  }
+  if (cc->Inputs().HasTag("NORM_RECT")) {
+    cc->Inputs().Tag("NORM_RECT").Set<NormalizedRect>();
+  }
+
+  cc->Outputs().Tag("IMAGE").Set<ImageFrame>();
+
+  return ::mediapipe::OkStatus();
+}
+
+::mediapipe::Status ImageCroppingCalculator::Process(CalculatorContext* cc) {
+  if (use_gpu_) {
+    RETURN_IF_ERROR(RenderGpu(cc));
+  } else {
+    RETURN_IF_ERROR(RenderCpu(cc));
+  }
+  return ::mediapipe::OkStatus();
+}
+
+::mediapipe::Status ImageCroppingCalculator::RenderCpu(CalculatorContext* cc) {
+  const auto& input_img = cc->Inputs().Tag("IMAGE").Get<ImageFrame>();
+  cv::Mat input_mat = formats::MatView(&input_img);
+
+  float rect_center_x = input_img.Width() / 2.0f;
+  float rect_center_y = input_img.Height() / 2.0f;
+  float rotation = 0.0f;
+  int target_width = input_img.Width();
+  int target_height = input_img.Height();
+  if (cc->Inputs().HasTag("RECT")) {
+    const auto& rect = cc->Inputs().Tag("RECT").Get<Rect>();
+    if (rect.width() > 0 && rect.height() > 0 && rect.x_center() >= 0 &&
+        rect.y_center() >= 0) {
+      rotation = rect.rotation();
+      rect_center_x = rect.x_center();
+      rect_center_y = rect.y_center();
+      target_width = rect.width();
+      target_height = rect.height();
+    }
+  } else if (cc->Inputs().HasTag("NORM_RECT")) {
+    const auto& rect = cc->Inputs().Tag("NORM_RECT").Get<NormalizedRect>();
+    if (rect.width() > 0.0 && rect.height() > 0.0 && rect.x_center() >= 0.0 &&
+        rect.y_center() >= 0.0) {
+      rotation = rect.rotation();
+      rect_center_x = std::round(rect.x_center() * input_img.Width());
+      rect_center_y = std::round(rect.y_center() * input_img.Height());
+      target_width = std::round(rect.width() * input_img.Width());
+      target_height = std::round(rect.height() * input_img.Height());
+    }
+  }
+
+  cv::Mat rotated_mat;
+  if (std::abs(rotation) > 1e-5) {
+    // TODO: Use open source common math library.
+    const float pi = 3.1415926f;
+    rotation = rotation * 180.0 / pi;
+
+    // First rotation the image.
+    cv::Point2f src_center(rect_center_x, rect_center_y);
+    cv::Mat rotation_mat = cv::getRotationMatrix2D(src_center, rotation, 1.0);
+    cv::warpAffine(input_mat, rotated_mat, rotation_mat, input_mat.size());
+  } else {
+    input_mat.copyTo(rotated_mat);
+  }
+
+  // Then crop the requested area.
+  const cv::Rect cropping_rect(rect_center_x - target_width / 2,
+                               rect_center_y - target_height / 2, target_width,
+                               target_height);
+  cv::Mat cropped_image = cv::Mat(rotated_mat, cropping_rect);
+
+  std::unique_ptr<ImageFrame> output_frame(new ImageFrame(
+      input_img.Format(), cropped_image.cols, cropped_image.rows));
+  cv::Mat output_mat = formats::MatView(output_frame.get());
+  cropped_image.copyTo(output_mat);
+  cc->Outputs().Tag("IMAGE").Add(output_frame.release(), cc->InputTimestamp());
+  return ::mediapipe::OkStatus();
+}
+
+::mediapipe::Status ImageCroppingCalculator::RenderGpu(CalculatorContext* cc) {
+  return ::mediapipe::UnimplementedError("GPU support is not implemented yet.");
+}
+
+}  // namespace mediapipe

mediapipe/calculators/image/image_transformation_calculator.cc

@@ -300,6 +300,37 @@ REGISTER_CALCULATOR(ImageTransformationCalculator);
   int input_width = cc->Inputs().Tag("IMAGE").Get<ImageFrame>().Width();
   int input_height = cc->Inputs().Tag("IMAGE").Get<ImageFrame>().Height();
 
+  const auto& input_img = cc->Inputs().Tag("IMAGE").Get<ImageFrame>();
+  cv::Mat input_mat = formats::MatView(&input_img);
+  cv::Mat scaled_mat;
+
+  if (scale_mode_ == mediapipe::ScaleMode_Mode_STRETCH) {
+    cv::resize(input_mat, scaled_mat, cv::Size(output_width_, output_height_));
+  } else {
+    const float scale =
+        std::min(static_cast<float>(output_width_) / input_width,
+                 static_cast<float>(output_height_) / input_height);
+    const int target_width = std::round(input_width * scale);
+    const int target_height = std::round(input_height * scale);
+
+    if (scale_mode_ == mediapipe::ScaleMode_Mode_FIT) {
+      cv::Mat intermediate_mat;
+      cv::resize(input_mat, intermediate_mat,
+                 cv::Size(target_width, target_height));
+      const int top = (output_height_ - target_height) / 2;
+      const int bottom = output_height_ - target_height - top;
+      const int left = (output_width_ - target_width) / 2;
+      const int right = output_width_ - target_width - left;
+      cv::copyMakeBorder(intermediate_mat, scaled_mat, top, bottom, left, right,
+                         options_.constant_padding() ? cv::BORDER_CONSTANT
+                                                     : cv::BORDER_REPLICATE);
+    } else {
+      cv::resize(input_mat, scaled_mat, cv::Size(target_width, target_height));
+      output_width_ = target_width;
+      output_height_ = target_height;
+    }
+  }
+
   int output_width;
   int output_height;
   ComputeOutputDimensions(input_width, input_height, &output_width,
@@ -318,26 +349,15 @@ REGISTER_CALCULATOR(ImageTransformationCalculator);
         cc->InputSidePackets().Tag("ROTATION_DEGREES").Get<int>());
   }
 
-  const auto& input_img = cc->Inputs().Tag("IMAGE").Get<ImageFrame>();
-  std::unique_ptr<ImageFrame> output_frame(
-      new ImageFrame(input_img.Format(), output_width, output_height));
-  cv::Mat input_mat = formats::MatView(&input_img);
-  cv::Mat output_mat = formats::MatView(output_frame.get());
-
-  cv::Mat scaled_mat;
-  if (scale_mode_ != mediapipe::ScaleMode_Mode_STRETCH) {
-    // TODO finish CPU version features.
-    return ::mediapipe::UnimplementedError(
-        "Only STRETCH scale mode currently supported.");
-  }
-  cv::resize(input_mat, scaled_mat, cv::Size(output_width_, output_height_));
-
   cv::Mat rotated_mat;
   const int angle = RotationModeToDegrees(rotation_);
   cv::Point2f src_center(scaled_mat.cols / 2.0, scaled_mat.rows / 2.0);
   cv::Mat rotation_mat = cv::getRotationMatrix2D(src_center, angle, 1.0);
   cv::warpAffine(scaled_mat, rotated_mat, rotation_mat, scaled_mat.size());
 
+  std::unique_ptr<ImageFrame> output_frame(
+      new ImageFrame(input_img.Format(), output_width, output_height));
+  cv::Mat output_mat = formats::MatView(output_frame.get());
   rotated_mat.copyTo(output_mat);
   cc->Outputs().Tag("IMAGE").Add(output_frame.release(), cc->InputTimestamp());
 

mediapipe/calculators/image/image_transformation_calculator.proto

@@ -46,4 +46,8 @@ message ImageTransformationCalculatorOptions {
   optional bool flip_horizontally = 5 [default = false];
   // Scale mode.
   optional ScaleMode.Mode scale_mode = 6;
+  // Padding type. This option is only used when the scale mode is FIT.
+  // Default is to use BORDER_CONSTANT. If set to false, it will use
+  // BORDER_REPLICATE instead.
+  optional bool constant_padding = 7 [default = true];
 }

mediapipe/calculators/util/detection_letterbox_removal_calculator.cc

@@ -84,6 +84,11 @@ class DetectionLetterboxRemovalCalculator : public CalculatorBase {
   }
 
   ::mediapipe::Status Process(CalculatorContext* cc) override {
+    // Only process if there's input detections.
+    if (cc->Inputs().Tag(kDetectionsTag).IsEmpty()) {
+      return ::mediapipe::OkStatus();
+    }
+
     const auto& input_detections =
         cc->Inputs().Tag(kDetectionsTag).Get<std::vector<Detection>>();
     const auto& letterbox_padding =

mediapipe/calculators/util/non_max_suppression_calculator.cc

@@ -323,6 +323,9 @@ class NonMaxSuppressionCalculator : public CalculatorBase {
       }
       auto weighted_detection = detection;
       if (!candidates.empty()) {
+        const int num_keypoints =
+            detection.location_data().relative_keypoints_size();
+        std::vector<float> keypoints(num_keypoints * 2);
         float w_xmin = 0.0f;
         float w_ymin = 0.0f;
         float w_xmax = 0.0f;
@@ -330,13 +333,20 @@ class NonMaxSuppressionCalculator : public CalculatorBase {
         float total_score = 0.0f;
         for (const auto& candidate : candidates) {
           total_score += candidate.second;
-          const auto& bbox = detections[candidate.first]
+          const auto& location_data =
-                                 .location_data()
+              detections[candidate.first].location_data();
-                                 .relative_bounding_box();
+          const auto& bbox = location_data.relative_bounding_box();
           w_xmin += bbox.xmin() * candidate.second;
           w_ymin += bbox.ymin() * candidate.second;
           w_xmax += (bbox.xmin() + bbox.width()) * candidate.second;
           w_ymax += (bbox.ymin() + bbox.height()) * candidate.second;
+
+          for (int i = 0; i < num_keypoints; ++i) {
+            keypoints[i * 2] +=
+                location_data.relative_keypoints(i).x() * candidate.second;
+            keypoints[i * 2 + 1] +=
+                location_data.relative_keypoints(i).y() * candidate.second;
+          }
         }
         auto* weighted_location = weighted_detection.mutable_location_data()
                                       ->mutable_relative_bounding_box();
@@ -346,6 +356,12 @@ class NonMaxSuppressionCalculator : public CalculatorBase {
                                      weighted_location->xmin());
         weighted_location->set_height((w_ymax / total_score) -
                                       weighted_location->ymin());
+        for (int i = 0; i < num_keypoints; ++i) {
+          auto* keypoint = weighted_detection.mutable_location_data()
+                               ->mutable_relative_keypoints(i);
+          keypoint->set_x(keypoints[i * 2] / total_score);
+          keypoint->set_y(keypoints[i * 2 + 1] / total_score);
+        }
       }
       remained_indexed_scores = std::move(remained);
       output_detections->push_back(weighted_detection);

mediapipe/docs/face_detection_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![face_detection_android_gpu_graph](images/mobile/face_detection_android_gpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on GPU.

mediapipe/docs/hair_segmentation_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![hair_segmentation_android_gpu_graph](images/mobile/hair_segmentation_android_gpu.png){width="600"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.

mediapipe/docs/hello_world_desktop.md

@@ -49,7 +49,7 @@
     ```
 
     You can visualize this graph using
-    [MediaPipe Visualizer](https://mediapipe-viz.appspot.com) by pasting the
+    [MediaPipe Visualizer](https://viz.mediapipe.dev) by pasting the
     CalculatorGraphConfig content below into the visualizer. See
     [here](./visualizer.md) for help on the visualizer.
 

mediapipe/docs/index.rst

@@ -31,6 +31,10 @@ APIs for MediaPipe
     * Graph Execution API in Java (Android)
     * (Coming Soon) Graph Execution API in Objective-C (iOS)
 
+Alpha Disclaimer
+==================
+MediaPipe is currently in alpha for v0.5. We are still making breaking API changes and expect to get to stable API by v1.0. We recommend that you target a specific version of MediaPipe, and periodically bump to the latest release. That way you have control over when a breaking change affects you.
+
 User Documentation
 ==================
 

mediapipe/docs/install.md

@@ -23,7 +23,7 @@ Required libraries
 
 *   Android SDK release 28.0.3 and above
 
-*   Android NDK r18b and above
+*   Android NDK r17c and above
 
 ### Installing on Debian and Ubuntu
 

mediapipe/docs/object_detection_android_cpu.md

@@ -37,7 +37,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![object_detection_android_cpu_graph](images/mobile/object_detection_android_cpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on CPU.

mediapipe/docs/object_detection_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![object_detection_android_gpu_graph](images/mobile/object_detection_android_gpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on GPU.

mediapipe/docs/object_detection_desktop.md

@@ -8,7 +8,14 @@ interested in running the same TensorfFlow Lite model on Android, please see the
 [Object Detection on GPU on Android](object_detection_android_gpu.md) and
 [Object Detection on CPU on Android](object_detection_android_cpu.md) examples.
 
-### TensorFlow Model
+We show the object detection demo with both TensorFlow model and TensorFlow Lite model:
+
+-  [TensorFlow Object Detection Demo](#tensorflow-object-detection-demo)
+-  [TensorFlow Lite Object Detection Demo](#tensorflow-lite-object-detection-demo)
+
+Note: If MediaPipe depends on OpenCV 2, please see the [known issues with OpenCV 2](#known-issues-with-opencv-2) section.
+
+### TensorFlow Object Detection Demo
 
 To build and run the TensorFlow example on desktop, run:
 
@@ -40,7 +47,7 @@ $ bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tensorf
 
 To visualize the graph as shown above, copy the text specification of the graph
 below and paste it into
-[MediaPipe Visualizer](https://mediapipe-viz.appspot.com).
+[MediaPipe Visualizer](https://viz.mediapipe.dev).
 
 ```bash
 # MediaPipe graph that performs object detection on desktop with TensorFlow
@@ -176,7 +183,7 @@ node {
 }
 ```
 
-### TensorFlow Lite Model
+### TensorFlow Lite Object Detection Demo
 
 To build and run the TensorFlow Lite example on desktop, run:
 
@@ -204,7 +211,7 @@ $ bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite
 
 To visualize the graph as shown above, copy the text specification of the graph
 below and paste it into
-[MediaPipe Visualizer](https://mediapipe-viz.appspot.com).
+[MediaPipe Visualizer](https://viz.mediapipe.dev).
 
 ```bash
 # MediaPipe graph that performs object detection on desktop with TensorFlow Lite

mediapipe/docs/visualizer.md

@@ -5,7 +5,7 @@
 
 To help users understand the structure of their calculator graphs and to
 understand the overall behavior of their machine learning inference pipelines,
-we have built the [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/) that is available online.
+we have built the [MediaPipe Visualizer](https://viz.mediapipe.dev/) that is available online.
 
 *   A graph view allows users to see a connected calculator graph as expressed
     through a graph configuration that is pasted into the graph editor or

mediapipe/framework/encode_binary_proto.bzl

@@ -33,7 +33,7 @@ def _canonicalize_proto_path_oss(all_protos, genfile_path):
     """
     proto_paths = []
     proto_file_names = []
-    for s in all_protos:
+    for s in all_protos.to_list():
         if s.path.startswith(genfile_path):
             repo_name, _, file_name = s.path[len(genfile_path + "/external/"):].partition("/")
             proto_paths.append(genfile_path + "/external/" + repo_name)
@@ -60,7 +60,7 @@ def _encode_binary_proto_impl(ctx):
     # order of gendir before ., is needed for the proto compiler to resolve
     # import statements that reference proto files produced by a genrule.
     ctx.actions.run_shell(
-        inputs = list(all_protos) + [textpb, ctx.executable._proto_compiler],
+        tools = all_protos.to_list() + [textpb, ctx.executable._proto_compiler],
         outputs = [binarypb],
         command = " ".join(
             [
@@ -110,7 +110,7 @@ def _generate_proto_descriptor_set_impl(ctx):
     # order of gendir before ., is needed for the proto compiler to resolve
     # import statements that reference proto files produced by a genrule.
     ctx.actions.run(
-        inputs = list(all_protos) + [ctx.executable._proto_compiler],
+        inputs = all_protos.to_list() + [ctx.executable._proto_compiler],
         outputs = [descriptor],
         executable = ctx.executable._proto_compiler,
         arguments = [
@@ -119,7 +119,7 @@ def _generate_proto_descriptor_set_impl(ctx):
                         "--proto_path=" + ctx.genfiles_dir.path,
                         "--proto_path=.",
                     ] +
-                    [s.path for s in all_protos],
+                    [s.path for s in all_protos.to_list()],
         mnemonic = "GenerateProtoDescriptor",
     )
 

mediapipe/framework/testdata/BUILD

@@ -24,7 +24,6 @@ load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library"
 proto_library(
     name = "sky_light_calculator_proto",
     srcs = ["sky_light_calculator.proto"],
-    visibility = ["//mediapipe:__subpackages__"],
     deps = ["//mediapipe/framework:calculator_proto"],
 )
 
@@ -39,7 +38,6 @@ mediapipe_cc_proto_library(
 proto_library(
     name = "night_light_calculator_proto",
     srcs = ["night_light_calculator.proto"],
-    visibility = ["//mediapipe:__subpackages__"],
     deps = ["//mediapipe/framework:calculator_proto"],
 )
 
@@ -54,7 +52,6 @@ mediapipe_cc_proto_library(
 proto_library(
     name = "zoo_mutator_proto",
     srcs = ["zoo_mutator.proto"],
-    visibility = ["//mediapipe:__subpackages__"],
     deps = ["@protobuf_archive//:any_proto"],
 )
 

mediapipe/framework/transitive_protos.bzl

@@ -94,7 +94,7 @@ def _transitive_protos_aspect_impl(target, ctx):
 
     proto_libs = []
     if ctx.rule.kind == "proto_library":
-        proto_libs = [f for f in target.files if f.extension == "a"]
+        proto_libs = [f for f in target.files.to_list() if f.extension == "a"]
 
     # Searching through the hdrs attribute is necessary because of
     # portable_proto_library. In portable mode, that macro

mediapipe/gpu/gl_texture_buffer.cc

@@ -77,7 +77,8 @@ bool GlTextureBuffer::CreateInternal(const void* data) {
       // TODO: maybe we do not actually have to wait for the
       // consumer sync here. Check docs.
       sync_token->WaitOnGpu();
-      if (glIsTexture(name_to_delete)) glDeleteTextures(1, &name_to_delete);
+      DCHECK(glIsTexture(name_to_delete));
+      glDeleteTextures(1, &name_to_delete);
     });
   };