Internal changes only

tensorflow_lite_support/examples/task/vision/desktop/BUILD

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+package(
+    default_visibility = [
+        "//tensorflow_lite_support:users",
+    ],
+    licenses = ["notice"],  # Apache 2.0
+)
+
+cc_binary(
+    name = "image_classifier_demo",
+    srcs = ["image_classifier_demo.cc"],
+    deps = [
+        "//tensorflow_lite_support/cc/port:statusor",
+        "//tensorflow_lite_support/cc/task/core:external_file_handler",
+        "//tensorflow_lite_support/cc/task/core/proto:external_file_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision:image_classifier",
+        "//tensorflow_lite_support/cc/task/vision/proto:class_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:classifications_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:image_classifier_options_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/utils:frame_buffer_common_utils",
+        "//tensorflow_lite_support/examples/task/vision/desktop/utils:image_utils",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/flags:parse",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings:str_format",
+        "@org_tensorflow//tensorflow/core:lib",
+    ],
+)
+
+cc_binary(
+    name = "object_detector_demo",
+    srcs = ["object_detector_demo.cc"],
+    deps = [
+        "//tensorflow_lite_support/cc/port:statusor",
+        "//tensorflow_lite_support/cc/task/core:external_file_handler",
+        "//tensorflow_lite_support/cc/task/core/proto:external_file_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision:object_detector",
+        "//tensorflow_lite_support/cc/task/vision/proto:bounding_box_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:class_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:detections_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:object_detector_options_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/utils:frame_buffer_common_utils",
+        "//tensorflow_lite_support/examples/task/vision/desktop/utils:image_utils",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/flags:parse",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/strings:str_format",
+        "@org_tensorflow//tensorflow/core:lib",
+    ],
+)
+
+cc_binary(
+    name = "image_segmenter_demo",
+    srcs = ["image_segmenter_demo.cc"],
+    deps = [
+        "//tensorflow_lite_support/cc/port:statusor",
+        "//tensorflow_lite_support/cc/task/core:external_file_handler",
+        "//tensorflow_lite_support/cc/task/core/proto:external_file_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision:image_segmenter",
+        "//tensorflow_lite_support/cc/task/vision/proto:image_segmenter_options_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/proto:segmentations_proto_inc",
+        "//tensorflow_lite_support/cc/task/vision/utils:frame_buffer_common_utils",
+        "//tensorflow_lite_support/examples/task/vision/desktop/utils:image_utils",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/flags:parse",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/strings:str_format",
+        "@org_tensorflow//tensorflow/core:lib",
+    ],
+)

tensorflow_lite_support/examples/task/vision/desktop/README.md

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+# CLI Demos for C++ Vision Task APIs
+
+This folder contains simple command-line tools for easily trying out the C++
+Vision Task APIs.
+
+## Image Classifier
+
+#### Prerequisites
+
+You will need:
+
+* a TFLite image classification model (e.g. [aiy/vision/classifier/birds_V1]
+(https://tfhub.dev/google/lite-model/aiy/vision/classifier/birds_V1/2),
+a bird classification model available on TensorFlow Hub),
+* a PNG, JPEG or GIF image to run classification on, e.g.:
+
+![sparrow](g3doc/sparrow.jpg)
+
+#### Usage
+
+In the console, run:
+
+```bash
+# Download the model:
+curl \
+  -L https://tfhub.dev/google/lite-model/aiy/vision/classifier/birds_V1/2?lite-format=tflite \
+  -o /tmp/aiy_vision_classifier_birds_V1_2.tflite
+
+# Run the classification tool:
+bazel run -c opt examples/task/vision/desktop:image_classifier_demo -- \
+  --model_path=/tmp/aiy_vision_classifier_birds_V1_2.tflite \
+  --image_path=$(pwd)/examples/task/vision/desktop/g3doc/sparrow.jpg \
+  --max_results=3
+```
+
+#### Results
+
+In the console, you should get:
+
+```
+Results:
+  Rank #0:
+   index       : 671
+   score       : 0.91797
+   class name  : /m/01bwbt
+   display name: Passer montanus
+  Rank #1:
+   index       : 670
+   score       : 0.00391
+   class name  : /m/0193xp
+   display name: Troglodytes hiemalis
+  Rank #2:
+   index       : 495
+   score       : 0.00391
+   class name  : /m/05sjn7
+   display name: Mimus gilvus
+```
+
+## Object Detector
+
+#### Prerequisites
+
+TODO(b/161960089): the model used in this example has an off-by-one error in its
+label map, which will cause the model to return "cat" instead of "dog" in the
+following example. It will soon be updated on tfhub.dev with a fix.
+
+You will need:
+
+* a TFLite object detection model (e.g. [ssd_mobilenet_v1]
+(https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1),
+a generic object detection model available on TensorFlow Hub),
+* a PNG, JPEG or GIF image to run detection on, e.g.:
+
+![dogs](g3doc/dogs.jpg)
+
+#### Usage
+
+In the console, run:
+
+```bash
+# Download the model:
+curl \
+  -L https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1?lite-format=tflite \
+  -o /tmp/ssd_mobilenet_v1_1_metadata_1.tflite
+
+# Run the detection tool:
+bazel run -c opt examples/task/vision/desktop:object_detector_demo -- \
+  --model_path=/tmp/ssd_mobilenet_v1_1_metadata_1.tflite \
+  --image_path=$(pwd)/examples/task/vision/desktop/g3doc/dogs.jpg \
+  --output_png=/tmp/detection-output.png \
+  --max_results=2
+```
+
+#### Results
+
+In the console, you should get:
+
+```
+Results saved to: /tmp/detection-output.png
+Results:
+ Detection #0 (red):
+  Box: (x: 355, y: 133, w: 190, h: 206)
+  Top-1 class:
+   index       : 17
+   score       : 0.73828
+   class name  : dog
+ Detection #1 (green):
+  Box: (x: 103, y: 15, w: 138, h: 369)
+  Top-1 class:
+   index       : 17
+   score       : 0.73047
+   class name  : dog
+```
+
+And `/tmp/detection-output.jpg` should contain:
+
+![detection-output](g3doc/detection-output.png)
+
+## Image Segmenter
+
+#### Prerequisites
+
+TODO(b/161957922): the model used in this example doesn't include a label map,
+which will cause the console output to be less complete than in the example
+below. It will soon be updated on tfhub.dev with a fix.
+
+You will need:
+
+* a TFLite image segmentation model (e.g. [deeplab_v3]
+(https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/1),
+a generic segmentation model available on TensorFlow Hub),
+* a PNG, JPEG or GIF image to run segmentation on, e.g.:
+
+![plane](g3doc/plane.jpg)
+
+#### Usage
+
+In the console, run:
+
+```bash
+# Download the model:
+curl \
+  -L https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/1?lite-format=tflite \
+  -o /tmp/deeplabv3_1_metadata_1.tflite
+
+# Run the segmentation tool:
+bazel run -c opt examples/task/vision/desktop:image_segmenter_demo -- \
+  --model_path=/tmp/deeplabv3_1_metadata_1.tflite \
+  --image_path=$(pwd)/examples/task/vision/desktop/g3doc/plane.jpg \
+  --output_mask_png=/tmp/segmentation-output.png
+```
+
+#### Results
+
+In the console, you should get:
+
+```
+Category mask saved to: /tmp/segmentation-output.png
+Color Legend:
+ (r: 000, g: 000, b: 000):
+  index       : 0
+  class name  : background
+ (r: 128, g: 000, b: 000):
+  index       : 1
+  class name  : aeroplane
+
+# (omitting multiple lines for conciseness) ...
+
+ (r: 128, g: 192, b: 000):
+  index       : 19
+  class name  : train
+ (r: 000, g: 064, b: 128):
+  index       : 20
+  class name  : tv
+Tip: use a color picker on the output PNG file to inspect the output mask with
+this legend.
+```
+
+And `/tmp/segmentation-output.jpg` should contain the segmentation mask:
+
+![segmentation-output](g3doc/segmentation-output.png)