chore: import upstream snapshot with attribution

tensorflow/lite/examples/label_image/README.md

@@ -0,0 +1,241 @@
+# TensorFlow Lite C++ image classification demo
+
+This example shows how you can load a pre-trained and converted
+TensorFlow Lite model and use it to recognize objects in images.
+
+Before you begin,
+make sure you [have TensorFlow installed](https://www.tensorflow.org/install).
+
+You also need to
+[install Bazel](https://docs.bazel.build/versions/master/install.html) in order
+to build this example code. And be sure you have the Python `future` module
+installed:
+
+```
+pip install future --user
+```
+
+## Build the example
+
+First run `$TENSORFLOW_ROOT/configure`. To build for Android, set
+Android NDK or configure NDK setting in
+`$TENSORFLOW_ROOT/WORKSPACE` first.
+
+Build it for desktop machines (tested on Ubuntu and OS X):
+
+```
+bazel build -c opt //tensorflow/lite/examples/label_image:label_image
+```
+
+Build it for Android ARMv8:
+
+```
+bazel build -c opt --config=android_arm64 \
+  //tensorflow/lite/examples/label_image:label_image
+```
+
+Build it for Android arm-v7a:
+
+```
+bazel build -c opt --config=android_arm \
+  //tensorflow/lite/examples/label_image:label_image
+```
+
+## Download sample model and image
+
+You can use any compatible model, but the following MobileNet v1 model offers
+a good demonstration of a model trained to recognize 1,000 different objects.
+
+```
+# Get model
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp
+
+# Get labels
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz  | tar xzv -C /tmp  mobilenet_v1_1.0_224/labels.txt
+
+mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/
+```
+
+## Run the sample on a desktop
+
+```
+bazel-bin/tensorflow/lite/examples/label_image/label_image \
+  --tflite_model /tmp/mobilenet_v1_1.0_224.tflite \
+  --labels /tmp/labels.txt \
+  --image tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp
+```
+
+You should see results like this:
+
+```
+Loaded model /tmp/mobilenet_v1_1.0_224.tflite
+resolved reporter
+invoked
+average time: 68.12 ms
+0.860174: 653 653:military uniform
+0.0481017: 907 907:Windsor tie
+0.00786704: 466 466:bulletproof vest
+0.00644932: 514 514:cornet, horn, trumpet, trump
+0.00608029: 543 543:drumstick
+```
+
+## Run the sample on an Android device
+
+Prepare data on devices, e.g.,
+
+```
+adb push bazel-bin/tensorflow/lite/examples/label_image/label_image  /data/local/tmp
+adb push /tmp/mobilenet_v1_1.0_224.tflite  /data/local/tmp
+adb push tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp  /data/local/tmp
+adb push /tmp/labels.txt /data/local/tmp
+```
+
+Run it,
+
+```
+adb shell "/data/local/tmp/label_image \
+    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
+    -i /data/local/tmp/grace_hopper.bmp \
+    -l /data/local/tmp/labels.txt"
+```
+
+then you should see something like the following:
+
+```
+Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
+resolved reporter
+INFO: Initialized TensorFlow Lite runtime.
+invoked
+average time: 25.03 ms
+0.907071: 653 military uniform
+0.0372416: 907 Windsor tie
+0.00733753: 466 bulletproof vest
+0.00592852: 458 bow tie
+0.00414091: 514 cornet
+```
+
+Run the model with NNAPI delegate (`-a 1`),
+
+```
+adb shell "/data/local/tmp/label_image \
+    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
+    -i /data/local/tmp/grace_hopper.bmp \
+    -l /data/local/tmp/labels.txt -a 1 -f 1"
+```
+
+then you should see something like the following:
+
+```
+Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
+resolved reporter
+INFO: Initialized TensorFlow Lite runtime.
+INFO: Created TensorFlow Lite delegate for NNAPI. Applied NNAPI delegate.
+invoked
+average time:10.348 ms
+0.905401: 653 military uniform
+0.0379589: 907 Windsor tie
+0.00735866: 466 bulletproof vest
+0.00605307: 458 bow tie
+0.00422573: 514 cornet
+```
+
+To run a model with the Hexagon Delegate, assuming we have followed the
+[Hexagon Delegate Guide](https://www.tensorflow.org/lite/android/delegates/hexagon)
+and installed Hexagon libraries in `/data/local/tmp`. Run it with (`-j 1`)
+
+```
+adb shell \
+    "/data/local/tmp/label_image \
+    -m /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite \
+    -i /data/local/tmp/grace_hopper.bmp \
+    -l /data/local/tmp/labels.txt -j 1"
+```
+
+then you should see something like the followings:
+
+```
+Loaded model /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite
+resolved reporter
+INFO: Initialized TensorFlow Lite runtime.
+loaded libcdsprpc.so
+INFO: Created TensorFlow Lite delegate for Hexagon.
+INFO: Hexagon delegate: 31 nodes delegated out of 31 nodes with 1 partitions.
+Applied Hexagon delegate.
+invoked
+average time: 4.231 ms
+0.639216: 458 bow tie
+0.329412: 653 military uniform
+0.00784314: 835 suit
+0.00784314: 611 jersey
+0.00392157: 514 cornet
+```
+
+Run the model with the XNNPACK delegate (`-x 1`),
+
+```shell
+adb shell \
+    "/data/local/tmp/label_image \
+    -m /data/local/tmp/mobilenet_v1_1.0_224.tflite \
+    -i /data/local/tmp/grace_hopper.bmp \
+    -l /data/local/tmp/labels.txt -x 1"
+```
+
+then you should see something like the following:
+
+```
+Loaded model /data/local/tmp/mobilenet_v1_1.0_224.tflite
+resolved reporter
+INFO: Initialized TensorFlow Lite runtime. Applied XNNPACK delegate.
+invoked
+average time: 17.33 ms
+0.90707: 653 military uniform
+0.0372418: 907 Windsor tie
+0.0073376: 466 bulletproof vest
+0.00592857: 458 bow tie
+0.00414093: 514 cornet
+```
+
+With `-h` or any other unsupported flags, `label_image` will list supported
+options:
+
+```shell
+sargo:/data/local/tmp $ ./label_image -h
+./label_image: invalid option -- h
+label_image
+--accelerated, -a: [0|1], use Android NNAPI or not
+--old_accelerated, -d: [0|1], use old Android NNAPI delegate or not
+--allow_fp16, -f: [0|1], allow running fp32 models with fp16 or not
+--count, -c: loop interpreter->Invoke() for certain times
+--gl_backend, -g: [0|1]: use GPU Delegate on Android
+--hexagon_delegate, -j: [0|1]: use Hexagon Delegate on Android
+--input_mean, -b: input mean
+--input_std, -s: input standard deviation
+--image, -i: image_name.bmp
+--labels, -l: labels for the model
+--tflite_model, -m: model_name.tflite
+--profiling, -p: [0|1], profiling or not
+--num_results, -r: number of results to show
+--threads, -t: number of threads
+--verbose, -v: [0|1] print more information
+--warmup_runs, -w: number of warmup runs
+--xnnpack_delegate, -x [0:1]: xnnpack delegate`
+```
+
+See the `label_image.cc` source code for other command line options.
+
+Note that this binary also supports more runtime/delegate arguments introduced
+by the [delegate registrar](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/delegates).
+If there is any conflict, the arguments mentioned earlier are given precedence.
+For example, you can run the binary with additional command line options
+such as `--use_nnapi=true --nnapi_accelerator_name=google-edgetpu` to utilize
+the EdgeTPU in a 4th-gen Pixel phone. Please be aware that the "=" in the option
+should not be omitted.
+
+```
+adb shell \
+    "/data/local/tmp/label_image \
+    -m /data/local/tmp/mobilenet_v1_1.0_224_quant.tflite \
+    -i /data/local/tmp/grace_hopper.bmp \
+    -l /data/local/tmp/labels.txt -j 1 \
+    --use_nnapi=true --nnapi_accelerator_name=google-edgetpu"
+```