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GitOrigin-RevId: 852dfb05d450167899c0dd5ef7c45622a12e865b
This commit is contained in:
MediaPipe Team
2020-02-10 14:13:25 -08:00
committed by Hadon Nash
parent d144e564d8
commit de4fbc10e6
100 changed files with 1664 additions and 628 deletions
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mediapipe/examples/coral/BUILD
+2 -2
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@@ -40,7 +40,7 @@ cc_library(
# Demos
cc_binary(
name = "object_detection_cpu",
name = "object_detection_tpu",
deps = [
"//mediapipe/examples/coral:demo_run_graph_main",
"//mediapipe/graphs/object_detection:desktop_tflite_calculators",
@@ -48,7 +48,7 @@ cc_binary(
)
cc_binary(
name = "face_detection_cpu",
name = "face_detection_tpu",
deps = [
"//mediapipe/examples/coral:demo_run_graph_main",
"//mediapipe/graphs/face_detection:desktop_tflite_calculators",
mediapipe/examples/coral/README.md
+17 -17
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@@ -19,13 +19,13 @@ Docker container for building MediaPipe applications that run on Edge TPU.
* (on coral device) prepare MediaPipe
cd ~
sudo apt-get install git
sudo apt-get install -y git
git clone https://github.com/google/mediapipe.git
mkdir mediapipe/bazel-bin
* (on coral device) install opencv 3.2
sudo apt-get update && apt-get install -y libopencv-dev
sudo apt-get update && sudo apt-get install -y libopencv-dev
* (on coral device) find all opencv libs
@@ -78,7 +78,7 @@ Docker container for building MediaPipe applications that run on Edge TPU.
return NULL;
* Edit /edgetpu/libedgetpu/BUILD
* Edit /edgetpu/libedgetpu/BUILD
to add this build target
@@ -90,9 +90,9 @@ Docker container for building MediaPipe applications that run on Edge TPU.
visibility = ["//visibility:public"],
)
* Edit *tflite_inference_calculator.cc* BUILD rules:
* Edit /mediapipe/mediapipe/calculators/tflite/BUILD to change rules for *tflite_inference_calculator.cc*
sed -i 's/\":tflite_inference_calculator_cc_proto\",/\":tflite_inference_calculator_cc_proto\",\n\t\"@edgetpu\/\/:header\",\n\t\"@libedgetpu\/\/:lib\",/g' mediapipe/calculators/tflite/BUILD
sed -i 's/\":tflite_inference_calculator_cc_proto\",/\":tflite_inference_calculator_cc_proto\",\n\t\"@edgetpu\/\/:header\",\n\t\"@libedgetpu\/\/:lib\",/g' /mediapipe/mediapipe/calculators/tflite/BUILD
The above command should add
@@ -105,37 +105,37 @@ Docker container for building MediaPipe applications that run on Edge TPU.
* Object detection demo
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:object_detection_cpu
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:object_detection_tpu
Copy object_detection_cpu binary to the MediaPipe checkout on the coral device
Copy object_detection_tpu binary to the MediaPipe checkout on the coral device
# outside docker env, open new terminal on host machine #
docker ps
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/object_detection_cpu /tmp/.
mdt push /tmp/object_detection_cpu /home/mendel/mediapipe/bazel-bin/.
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/object_detection_tpu /tmp/.
mdt push /tmp/object_detection_tpu /home/mendel/mediapipe/bazel-bin/.
* Face detection demo
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:face_detection_cpu
bazel build -c opt --crosstool_top=@crosstool//:toolchains --compiler=gcc --cpu=aarch64 --define MEDIAPIPE_DISABLE_GPU=1 --copt -DMEDIAPIPE_EDGE_TPU --copt=-flax-vector-conversions mediapipe/examples/coral:face_detection_tpu
Copy face_detection_cpu binary to the MediaPipe checkout on the coral device
Copy face_detection_tpu binary to the MediaPipe checkout on the coral device
# outside docker env, open new terminal on host machine #
docker ps
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/face_detection_cpu /tmp/.
mdt push /tmp/face_detection_cpu /home/mendel/mediapipe/bazel-bin/.
docker cp <container-id>:/mediapipe/bazel-bin/mediapipe/examples/coral/face_detection_tpu /tmp/.
mdt push /tmp/face_detection_tpu /home/mendel/mediapipe/bazel-bin/.
## On the coral device (with display)
# Object detection
cd ~/mediapipe
chmod +x bazel-bin/object_detection_cpu
chmod +x bazel-bin/object_detection_tpu
export GLOG_logtostderr=1
bazel-bin/object_detection_cpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/object_detection_desktop_live.pbtxt
bazel-bin/object_detection_tpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/object_detection_desktop_live.pbtxt
# Face detection
cd ~/mediapipe
chmod +x bazel-bin/face_detection_cpu
chmod +x bazel-bin/face_detection_tpu
export GLOG_logtostderr=1
bazel-bin/face_detection_cpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/face_detection_desktop_live.pbtxt
bazel-bin/face_detection_tpu --calculator_graph_config_file=mediapipe/examples/coral/graphs/face_detection_desktop_live.pbtxt
mediapipe/examples/coral/graphs/face_detection_desktop_live.pbtxt
+5 -5
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@@ -1,6 +1,6 @@
# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
# MediaPipe graph that performs face detection with TensorFlow Lite on TPU.
# Used in the examples in
# mediapipe/examples/coral:face_detection_cpu.
# mediapipe/examples/coral:face_detection_tpu.
# Images on GPU coming into and out of the graph.
input_stream: "input_video"
@@ -36,7 +36,7 @@ node {
node: {
calculator: "ImageTransformationCalculator"
input_stream: "IMAGE:throttled_input_video"
output_stream: "IMAGE:transformed_input_video_cpu"
output_stream: "IMAGE:transformed_input_video"
output_stream: "LETTERBOX_PADDING:letterbox_padding"
options: {
[mediapipe.ImageTransformationCalculatorOptions.ext] {
@@ -51,7 +51,7 @@ node: {
# TfLiteTensor.
node {
calculator: "TfLiteConverterCalculator"
input_stream: "IMAGE:transformed_input_video_cpu"
input_stream: "IMAGE:transformed_input_video"
output_stream: "TENSORS:image_tensor"
options: {
[mediapipe.TfLiteConverterCalculatorOptions.ext] {
@@ -60,7 +60,7 @@ node {
}
}
# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# Runs a TensorFlow Lite model on TPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
mediapipe/examples/coral/graphs/object_detection_desktop_live.pbtxt
+5 -5
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@@ -1,8 +1,8 @@
# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
# MediaPipe graph that performs object detection with TensorFlow Lite on TPU.
# Used in the examples in
# mediapipie/examples/coral:object_detection_cpu.
# mediapipie/examples/coral:object_detection_tpu.
# Images on CPU coming into and out of the graph.
# Images on TPU coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"
@@ -30,7 +30,7 @@ node {
output_stream: "throttled_input_video"
}
# Transforms the input image on CPU to a 320x320 image. To scale the image, by
# Transforms the input image on CPU to a 300x300 image. To scale the image, by
# default it uses the STRETCH scale mode that maps the entire input image to the
# entire transformed image. As a result, image aspect ratio may be changed and
# objects in the image may be deformed (stretched or squeezed), but the object
@@ -60,7 +60,7 @@ node {
}
}
# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# Runs a TensorFlow Lite model on TPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
mediapipe/examples/desktop/youtube8m/generate_vggish_frozen_graph.py
+1 -1
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@@ -25,7 +25,7 @@ import sys
from absl import app
import tensorflow.compat.v1 as tf
from tensorflow.python.tools import freeze_graph
from tensorflow.compat.v1.python.tools import freeze_graph
BASE_DIR = '/tmp/mediapipe/'