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Who doesn't want to cast their own spells and feeling magical? In this project, we are building a digital "magic wand", and it consist of wave the wand in one of the three gestures (wing, ring, and slope). If you’re successful casting the spells, you will see the corresponding gesture showing up on the screen and the red LED on the Arduino board should light.
This project uses deep learning model to train and evaluate on human input gestures collected using the build-in multi-dimensional sensor on the Arduino board. Specifically, it uses TensorFlow Lite to run a 20 kilobyte convolutional neural network model to recognize gestures with an accelerometer. With this fun project, you can see how deep learning can transform strange numerical data into meaningful information, to magical effect.
About our Model:Our model takes in 128 sets of X, Y, and Z values at once, which at a rate of 25 Hz adds up to a little over five seconds’ worth of data. Each value is a 32 bit floating point number that indicates the amount of acceleration in that direction. Once data has been captured and inference has been run, our application will determine if a valid gesture was detected, print some output to the terminal, and light an LED. Here we use a pre-trained model that was performed by numerous people on the following gestures.
Our gesture-detecting model is consist of the following structures (as shown in the schematics at the end):
- main loop: continuous loop, no preprocessing required, thus running multiple per seconds
- accelerometer handler: input data from sensor and writes to model's input tensor
- TF Lite interpreter and model: 128 sets of X, Y, and Z values data array, and run by the interpreter. It’s nice and small, weighing in at only 19.5 KB.
- gesture predictor: takes the model's output and decides if a gesture has been performed
- output handler : lights the LED and prints output to the screen if correct gesture was performed
This project is tested and build in the following OS system:
- Ubuntu 16.04 LTS (64-bit)
- Memory 31.3 GiB
- Processor Intel® Core™ i7-6700 CPU @ 3.40GHz × 8
Follow the link here (new link as the old one on the chapter was not working) and run the following command:
make -f tensorflow/lite/experimental/micro/tools/make/Makefile test_magic_wand_test
make -f tensorflow/lite/experimental/micro/tools/make/Makefile test_gesture_accelerometer_handler_test
make -f tensorflow/lite/experimental/micro/tools/make/Makefile test_gesture_predictor_test
make -f tensorflow/lite/experimental/micro/tools/make/Makefile test_gesture_output_handler_test
make -f tensorflow/lite/experimental/micro/tools/make/Makefile magic_wand
You should confirm that the program builds and runs. Then it is ready for next step.
Deploy to Arduino:- Install and open Arduino IDE
- Install Arduino_TensorFlowLite: open the Arduino library manager in Tools -> Manage Libraries... and search for Arduino_TensorFlowLite
- Install Arduino_LSM9DS1: Tools -> Manage Libraries... and search for Arduino_LSM9DS1. Install version 1.0.0 of the driver to ensure the following instructions work. Note that you may not need to "patch the driver" anymore (as mentioned in the link) since I believe that they have fixed it already and I didn't need to do it
- Load and run magic wand:
- Open the Arduino Serial Monitor (Tools -> Serial Monitor). And start using your magic wand! Hold the Arduino with its components facing upwards and the USB cable to your left. Perform the gestures "WING", "RING"(clockwise), and "SLOPE", and you should see the corresponding output:
- Note: it does takes a few trial and errors to figure out the correct gesture to wave. I found that for me the "slope" gestures comes out much easier than the "wing" gesture. The board is sensitive to tilt angles or different velocities when drawing in the air. Try few more times and watch yourself cast a spell and just have fun!!!
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
#define kChannelNumber 3
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
extern int begin_index;
extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
float* input, int length, bool reset_buffer);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 "accelerometer_handler.h"
#include <Arduino.h>
#include <Arduino_LSM9DS1.h>
#include "constants.h"
// A buffer holding the last 200 sets of 3-channel values
float save_data[600] = {0.0};
// Most recent position in the save_data buffer
int begin_index = 0;
// True if there is not yet enough data to run inference
bool pending_initial_data = true;
// How often we should save a measurement during downsampling
int sample_every_n;
// The number of measurements since we last saved one
int sample_skip_counter = 1;
TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter) {
// Wait until we know the serial port is ready
while (!Serial) {
}
// Switch on the IMU
if (!IMU.begin()) {
error_reporter->Report("Failed to initialize IMU");
return kTfLiteError;
}
// Determine how many measurements to keep in order to
// meet kTargetHz
float sample_rate = IMU.accelerationSampleRate();
sample_every_n = static_cast<int>(roundf(sample_rate / kTargetHz));
error_reporter->Report("Magic starts!");
return kTfLiteOk;
}
bool ReadAccelerometer(tflite::ErrorReporter* error_reporter, float* input,
int length, bool reset_buffer) {
// Clear the buffer if required, e.g. after a successful prediction
if (reset_buffer) {
memset(save_data, 0, 600 * sizeof(float));
begin_index = 0;
pending_initial_data = true;
}
// Keep track of whether we stored any new data
bool new_data = false;
// Loop through new samples and add to buffer
while (IMU.accelerationAvailable()) {
float x, y, z;
// Read each sample, removing it from the device's FIFO buffer
if (!IMU.readAcceleration(x, y, z)) {
error_reporter->Report("Failed to read data");
break;
}
// Throw away this sample unless it's the nth
if (sample_skip_counter != sample_every_n) {
sample_skip_counter += 1;
continue;
}
// Write samples to our buffer, converting to milli-Gs
// and flipping y and x order for compatibility with
// model (sensor orientation is different on Arduino
// Nano BLE Sense compared with SparkFun Edge)
save_data[begin_index++] = y * 1000;
save_data[begin_index++] = x * 1000;
save_data[begin_index++] = z * 1000;
// Since we took a sample, reset the skip counter
sample_skip_counter = 1;
// If we reached the end of the circle buffer, reset
if (begin_index >= 600) {
begin_index = 0;
}
new_data = true;
}
// Skip this round if data is not ready yet
if (!new_data) {
return false;
}
// Check if we are ready for prediction or still pending more initial data
if (pending_initial_data && begin_index >= 200) {
pending_initial_data = false;
}
// Return if we don't have enough data
if (pending_initial_data) {
return false;
}
// Copy the requested number of bytes to the provided input tensor
for (int i = 0; i < length; ++i) {
int ring_array_index = begin_index + i - length;
if (ring_array_index < 0) {
ring_array_index += 600;
}
input[i] = save_data[ring_array_index];
}
return true;
}
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 "constants.h"
// The number of expected consecutive inferences for each gesture type.
// Established with the Arduino Nano 33 BLE Sense.
const int kConsecutiveInferenceThresholds[3] = {8, 5, 4};
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 "main_functions.h"
// Arduino automatically calls the setup() and loop() functions in a sketch, so
// where other systems need their own main routine in this file, it can be left
// empty.
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 <TensorFlowLite.h>
#include "main_functions.h"
#include "accelerometer_handler.h"
#include "gesture_predictor.h"
#include "magic_wand_model_data.h"
#include "output_handler.h"
#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* model_input = nullptr;
int input_length;
// Create an area of memory to use for input, output, and intermediate arrays.
// The size of this will depend on the model you're using, and may need to be
// determined by experimentation.
constexpr int kTensorArenaSize = 60 * 1024;
uint8_t tensor_arena[kTensorArenaSize];
// Whether we should clear the buffer next time we fetch data
bool should_clear_buffer = false;
} // namespace
// The name of this function is important for Arduino compatibility.
void setup() {
// Set up logging. Google style is to avoid globals or statics because of
// lifetime uncertainty, but since this has a trivial destructor it's okay.
static tflite::MicroErrorReporter micro_error_reporter; // NOLINT
error_reporter = µ_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
model = tflite::GetModel(g_magic_wand_model_data);
if (model->version() != TFLITE_SCHEMA_VERSION) {
error_reporter->Report(
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return;
}
// Pull in only the operation implementations we need.
// This relies on a complete list of all the ops needed by this graph.
// An easier approach is to just use the AllOpsResolver, but this will
// incur some penalty in code space for op implementations that are not
// needed by this graph.
static tflite::MicroMutableOpResolver micro_mutable_op_resolver; // NOLINT
micro_mutable_op_resolver.AddBuiltin(
tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
micro_mutable_op_resolver.AddBuiltin(
tflite::BuiltinOperator_MAX_POOL_2D,
tflite::ops::micro::Register_MAX_POOL_2D());
micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_CONV_2D,
tflite::ops::micro::Register_CONV_2D());
micro_mutable_op_resolver.AddBuiltin(
tflite::BuiltinOperator_FULLY_CONNECTED,
tflite::ops::micro::Register_FULLY_CONNECTED());
micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
tflite::ops::micro::Register_SOFTMAX());
// Build an interpreter to run the model with
static tflite::MicroInterpreter static_interpreter(
model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
error_reporter);
interpreter = &static_interpreter;
// Allocate memory from the tensor_arena for the model's tensors
interpreter->AllocateTensors();
// Obtain pointer to the model's input tensor
model_input = interpreter->input(0);
if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
(model_input->dims->data[1] != 128) ||
(model_input->dims->data[2] != kChannelNumber) ||
(model_input->type != kTfLiteFloat32)) {
error_reporter->Report("Bad input tensor parameters in model");
return;
}
input_length = model_input->bytes / sizeof(float);
TfLiteStatus setup_status = SetupAccelerometer(error_reporter);
if (setup_status != kTfLiteOk) {
error_reporter->Report("Set up failed\n");
}
}
void loop() {
// Attempt to read new data from the accelerometer
bool got_data = ReadAccelerometer(error_reporter, model_input->data.f,
input_length, should_clear_buffer);
// Don't try to clear the buffer again
should_clear_buffer = false;
// If there was no new data, wait until next time
if (!got_data) return;
// Run inference, and report any error
TfLiteStatus invoke_status = interpreter->Invoke();
if (invoke_status != kTfLiteOk) {
error_reporter->Report("Invoke failed on index: %d\n", begin_index);
return;
}
// Analyze the results to obtain a prediction
int gesture_index = PredictGesture(interpreter->output(0)->data.f);
// Clear the buffer next time we read data
should_clear_buffer = gesture_index < 3;
// Produce an output
HandleOutput(error_reporter, gesture_index);
}
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
// Automatically created from a TensorFlow Lite flatbuffer using the command:
// xxd -i magic_wand_model.tflite > magic_wand_model_data.cc
// See the README for a full description of the creation process.
#include "magic_wand_model_data.h"
// We need to keep the data array aligned on some architectures.
#ifdef __has_attribute
#define HAVE_ATTRIBUTE(x) __has_attribute(x)
#else
#define HAVE_ATTRIBUTE(x) 0
#endif
#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
#else
#define DATA_ALIGN_ATTRIBUTE
#endif
const unsigned char g_magic_wand_model_data[] DATA_ALIGN_ATTRIBUTE = {
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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
// This is a standard TensorFlow Lite model file that has been converted into a
// C data array, so it can be easily compiled into a binary for devices that
// don't have a file system. It was created using the command:
// xxd -i magic_wand_model.tflite > magic_wand_model_data.cc
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
extern const unsigned char g_magic_wand_model_data[];
extern const int g_magic_wand_model_data_len;
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
// Initializes all data needed for the example. The name is important, and needs
// to be setup() for Arduino compatibility.
void setup();
// Runs one iteration of data gathering and inference. This should be called
// repeatedly from the application code. The name needs to be loop() for Arduino
// compatibility.
void loop();
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
void HandleOutput(tflite::ErrorReporter* error_reporter, int kind);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 "output_handler.h"
#include "Arduino.h"
void HandleOutput(tflite::ErrorReporter* error_reporter, int kind) {
// The first time this method runs, set up our LED
static bool is_initialized = false;
if (!is_initialized) {
pinMode(LED_BUILTIN, OUTPUT);
is_initialized = true;
}
// Toggle the LED every time an inference is performed
static int count = 0;
++count;
if (count & 1) {
digitalWrite(LED_BUILTIN, HIGH);
} else {
digitalWrite(LED_BUILTIN, LOW);
}
// Print some ASCII art for each gesture
if (kind == 0) {
error_reporter->Report(
"WING:\n\r* * *\n\r * * * "
"*\n\r * * * *\n\r * * * *\n\r * * "
"* *\n\r * *\n\r");
} else if (kind == 1) {
error_reporter->Report(
"RING:\n\r *\n\r * *\n\r * *\n\r "
" * *\n\r * *\n\r * *\n\r "
" *\n\r");
} else if (kind == 2) {
error_reporter->Report(
"SLOPE:\n\r *\n\r *\n\r *\n\r *\n\r "
"*\n\r *\n\r *\n\r * * * * * * * *\n\r");
}
}
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
// The expected accelerometer data sample frequency
const float kTargetHz = 25;
// The number of expected consecutive inferences for each gesture type
extern const int kConsecutiveInferenceThresholds[3];
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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 "gesture_predictor.h"
#include "constants.h"
// How many times the most recent gesture has been matched in a row
int continuous_count = 0;
// The result of the last prediction
int last_predict = -1;
// Return the result of the last prediction
// 0: wing("W"), 1: ring("O"), 2: slope("angle"), 3: unknown
int PredictGesture(float* output) {
// Find whichever output has a probability > 0.8 (they sum to 1)
int this_predict = -1;
for (int i = 0; i < 3; i++) {
if (output[i] > 0.8) this_predict = i;
}
// No gesture was detected above the threshold
if (this_predict == -1) {
continuous_count = 0;
last_predict = 3;
return 3;
}
if (last_predict == this_predict) {
continuous_count += 1;
} else {
continuous_count = 0;
}
last_predict = this_predict;
// If we haven't yet had enough consecutive matches for this gesture,
// report a negative result
if (continuous_count < kConsecutiveInferenceThresholds[this_predict]) {
return 3;
}
// Otherwise, we've seen a positive result, so clear all our variables
// and report it
continuous_count = 0;
last_predict = -1;
return this_predict;
}
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
extern int PredictGesture(float* output);
#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
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