Full Code of google-coral/tflite for AI

Repository: google-coral/tflite
Branch: master
Commit: eced31ac01e9
Files: 17
Total size: 51.0 KB

Directory structure:
gitextract_s01zy75z/

├── LICENSE
├── README.md
├── cpp/
│   └── examples/
│       ├── classification/
│       │   ├── Makefile
│       │   └── classify.cc
│       └── lstpu/
│           ├── BUILD
│           ├── Makefile
│           ├── README.md
│           ├── WORKSPACE
│           └── lstpu.cc
└── python/
    └── examples/
        ├── classification/
        │   ├── README.md
        │   ├── classify.py
        │   ├── classify_image.py
        │   └── install_requirements.sh
        └── detection/
            ├── README.md
            ├── detect.py
            ├── detect_image.py
            └── install_requirements.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Coral examples using TensorFlow Lite API

This repo contains example code for running inference on [Coral
devices](https://coral.withgoogle.com/products) using the [TensorFlow Lite
API](https://www.tensorflow.org/lite). Each example executes a different type of
model, such as an image classification or object detection model.

For instructions to set up and run the code, see the README inside each example.


================================================
FILE: cpp/examples/classification/Makefile
================================================
# This is a Makefile to cross-compile classify.cc example.
# 1. Download latest Edge TPU runtime archive from https://coral.ai/software/
#    and extract next to the Makefile:
#    $ wget https://dl.google.com/coral/edgetpu_api/edgetpu_runtime_20200710.zip
#    $ unzip edgetpu_runtime_20200710.zip
# 2. Download TensorFlow to the Linux machine:
#    $ git clone https://github.com/tensorflow/tensorflow.git
# 3. Download external dependencies for TensorFlow Lite:
#    $ tensorflow/tensorflow/lite/tools/make/download_dependencies.sh
# 4. Cross-compile TensorFlow Lite for aarch64:
#    $ tensorflow/tensorflow/lite/tools/make/build_aarch64_lib.sh
# 5. Cross-compile classify.cc example for aarch64:
#    $ TENSORFLOW_DIR=<location> make
# 6. Copy the following files to Coral Dev board:
#      * Generated 'classify' binary
#      * Model file 'mobilenet_v1_1.0_224_quant_edgetpu.tflite' from 'test_data'
#      * Label file 'imagenet_labels.txt ' from 'test_data'
#      * Image file 'resized_cat.bmp' from 'test_data'
#    and finally run 'classify' binary on the board:
#    $ classify mobilenet_v1_1.0_224_quant_edgetpu.tflite \
#               imagenet_labels.txt \
#               resized_cat.bmp \
#               0.0001
#    INFO: Initialized TensorFlow Lite runtime.
#    INFO: Replacing 1 node(s) with delegate (EdgeTpuDelegateForCustomOp) node, yielding 1 partitions.
#    0.81641 286  Egyptian cat
#    0.10938 283  tiger cat
#    0.03516 282  tabby, tabby cat
#    0.01172 812  space heater
#    0.00781 754  radiator
#    0.00391 540  doormat, welcome mat
#    0.00391 285  Siamese cat, Siamese
MAKEFILE_DIR := $(realpath $(dir $(lastword $(MAKEFILE_LIST))))
TENSORFLOW_DIR ?=

classify: classify.cc
	aarch64-linux-gnu-g++ -std=c++11 -o classify classify.cc \
	-I$(MAKEFILE_DIR)/edgetpu_runtime/libedgetpu/ \
	-I$(TENSORFLOW_DIR) \
	-I$(TENSORFLOW_DIR)/tensorflow/lite/tools/make/downloads/flatbuffers/include \
	-L$(TENSORFLOW_DIR)/tensorflow/lite/tools/make/gen/linux_aarch64/lib \
	-L$(MAKEFILE_DIR)/edgetpu_runtime/libedgetpu/direct/aarch64/ \
	-ltensorflow-lite -l:libedgetpu.so.1.0 -lpthread -lm -ldl

clean:
	rm -f classify


================================================
FILE: cpp/examples/classification/classify.cc
================================================
#include <algorithm>
#include <cassert>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <string>
#include <utility>
#include <vector>

#include "edgetpu_c.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/model.h"

namespace {
constexpr size_t kBmpFileHeaderSize = 14;
constexpr size_t kBmpInfoHeaderSize = 40;
constexpr size_t kBmpHeaderSize = kBmpFileHeaderSize + kBmpInfoHeaderSize;

int32_t ToInt32(const char p[4]) {
  return (p[3] << 24) | (p[2] << 16) | (p[1] << 8) | p[0];
}

std::vector<uint8_t> ReadBmpImage(const char* filename,
                                  int* out_width = nullptr,
                                  int* out_height = nullptr,
                                  int* out_channels = nullptr) {
  assert(filename);

  std::ifstream file(filename, std::ios::binary);
  if (!file) return {};  // Open failed.

  char header[kBmpHeaderSize];
  if (!file.read(header, sizeof(header))) return {};  // Read failed.

  const char* file_header = header;
  const char* info_header = header + kBmpFileHeaderSize;

  if (file_header[0] != 'B' || file_header[1] != 'M')
    return {};  // Invalid file type.

  const int channels = info_header[14] / 8;
  if (channels != 1 && channels != 3) return {};  // Unsupported bits per pixel.

  if (ToInt32(&info_header[16]) != 0) return {};  // Unsupported compression.

  const uint32_t offset = ToInt32(&file_header[10]);
  if (offset > kBmpHeaderSize &&
      !file.seekg(offset - kBmpHeaderSize, std::ios::cur))
    return {};  // Seek failed.

  int width = ToInt32(&info_header[4]);
  if (width < 0) return {};  // Invalid width.

  int height = ToInt32(&info_header[8]);
  const bool top_down = height < 0;
  if (top_down) height = -height;

  const int line_bytes = width * channels;
  const int line_padding_bytes =
      4 * ((8 * channels * width + 31) / 32) - line_bytes;
  std::vector<uint8_t> image(line_bytes * height);
  for (int i = 0; i < height; ++i) {
    uint8_t* line = &image[(top_down ? i : (height - 1 - i)) * line_bytes];
    if (!file.read(reinterpret_cast<char*>(line), line_bytes))
      return {};  // Read failed.
    if (!file.seekg(line_padding_bytes, std::ios::cur))
      return {};  // Seek failed.
    if (channels == 3) {
      for (int j = 0; j < width; ++j) std::swap(line[3 * j], line[3 * j + 2]);
    }
  }

  if (out_width) *out_width = width;
  if (out_height) *out_height = height;
  if (out_channels) *out_channels = channels;
  return image;
}

std::vector<std::string> ReadLabels(const std::string& filename) {
  std::ifstream file(filename);
  if (!file) return {};  // Open failed.

  std::vector<std::string> lines;
  for (std::string line; std::getline(file, line);) lines.emplace_back(line);
  return lines;
}

std::string GetLabel(const std::vector<std::string>& labels, int label) {
  if (label >= 0 && label < labels.size()) return labels[label];
  return std::to_string(label);
}

std::vector<float> Dequantize(const TfLiteTensor& tensor) {
  const auto* data = reinterpret_cast<const uint8_t*>(tensor.data.data);
  std::vector<float> result(tensor.bytes);
  for (int i = 0; i < tensor.bytes; ++i)
    result[i] = tensor.params.scale * (data[i] - tensor.params.zero_point);
  return result;
}

std::vector<std::pair<int, float>> Sort(const std::vector<float>& scores,
                                        float threshold) {
  std::vector<const float*> ptrs(scores.size());
  std::iota(ptrs.begin(), ptrs.end(), scores.data());
  auto end = std::partition(ptrs.begin(), ptrs.end(),
                            [=](const float* v) { return *v >= threshold; });
  std::sort(ptrs.begin(), end,
            [](const float* a, const float* b) { return *a > *b; });

  std::vector<std::pair<int, float>> result;
  result.reserve(end - ptrs.begin());
  for (auto it = ptrs.begin(); it != end; ++it)
    result.emplace_back(*it - scores.data(), **it);
  return result;
}
}  // namespace

int main(int argc, char* argv[]) {
  if (argc != 5) {
    std::cerr << argv[0]
              << " <model_file> <label_file> <image_file> <threshold>"
              << std::endl;
    return 1;
  }

  const std::string model_file = argv[1];
  const std::string label_file = argv[2];
  const std::string image_file = argv[3];
  const float threshold = std::stof(argv[4]);

  // Find TPU device.
  size_t num_devices;
  std::unique_ptr<edgetpu_device, decltype(&edgetpu_free_devices)> devices(
      edgetpu_list_devices(&num_devices), &edgetpu_free_devices);

  if (num_devices == 0) {
    std::cerr << "No connected TPU found" << std::endl;
    return 1;
  }
  const auto& device = devices.get()[0];

  // Load labels.
  auto labels = ReadLabels(label_file);
  if (labels.empty()) {
    std::cerr << "Cannot read labels from " << label_file << std::endl;
    return 1;
  }

  // Load image.
  int image_bpp, image_width, image_height;
  auto image =
      ReadBmpImage(image_file.c_str(), &image_width, &image_height, &image_bpp);
  if (image.empty()) {
    std::cerr << "Cannot read image from " << image_file << std::endl;
    return 1;
  }

  // Load model.
  auto model = tflite::FlatBufferModel::BuildFromFile(model_file.c_str());
  if (!model) {
    std::cerr << "Cannot read model from " << model_file << std::endl;
    return 1;
  }

  // Create interpreter.
  tflite::ops::builtin::BuiltinOpResolver resolver;
  std::unique_ptr<tflite::Interpreter> interpreter;
  if (tflite::InterpreterBuilder(*model, resolver)(&interpreter) != kTfLiteOk) {
    std::cerr << "Cannot create interpreter" << std::endl;
    return 1;
  }

  auto* delegate =
      edgetpu_create_delegate(device.type, device.path, nullptr, 0);
  interpreter->ModifyGraphWithDelegate({delegate, edgetpu_free_delegate});

  // Allocate tensors.
  if (interpreter->AllocateTensors() != kTfLiteOk) {
    std::cerr << "Cannot allocate interpreter tensors" << std::endl;
    return 1;
  }

  // Set interpreter input.
  const auto* input_tensor = interpreter->input_tensor(0);
  if (input_tensor->type != kTfLiteUInt8 ||           //
      input_tensor->dims->data[0] != 1 ||             //
      input_tensor->dims->data[1] != image_height ||  //
      input_tensor->dims->data[2] != image_width ||   //
      input_tensor->dims->data[3] != image_bpp) {
    std::cerr << "Input tensor shape does not match input image" << std::endl;
    return 1;
  }

  std::copy(image.begin(), image.end(),
            interpreter->typed_input_tensor<uint8_t>(0));

  // Run inference.
  if (interpreter->Invoke() != kTfLiteOk) {
    std::cerr << "Cannot invoke interpreter" << std::endl;
    return 1;
  }

  // Get interpreter output.
  auto results = Sort(Dequantize(*interpreter->output_tensor(0)), threshold);
  for (auto& result : results)
    std::cout << std::setw(7) << std::fixed << std::setprecision(5)
              << result.second << GetLabel(labels, result.first) << std::endl;

  return 0;
}


================================================
FILE: cpp/examples/lstpu/BUILD
================================================
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
cc_binary(
    name = "lstpu",
    srcs = ["lstpu.cc"],
    deps = [
        "@edgetpu//libedgetpu:header",
    ],
    copts = ["-Iexternal/edgetpu/libedgetpu"],
)


================================================
FILE: cpp/examples/lstpu/Makefile
================================================
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
SHELL := /bin/bash
MAKEFILE_DIR := $(realpath $(dir $(lastword $(MAKEFILE_LIST))))
# Allowed CPU values: k8, armv7a, aarch64
CPU ?= k8
# Allowed COMPILATION_MODE values: opt, dbg
COMPILATION_MODE ?= opt

BAZEL_OUT_DIR :=  $(MAKEFILE_DIR)/bazel-out/$(CPU)-$(COMPILATION_MODE)/bin
BAZEL_BUILD_FLAGS := --crosstool_top=@crosstool//:toolchains \
                     --compilation_mode=$(COMPILATION_MODE) \
                     --compiler=gcc \
                     --cpu=$(CPU) \
                     --linkopt=-L$(shell bazel info output_base)/external/edgetpu/libedgetpu/direct/$(CPU) \
                     --linkopt=-l:libedgetpu.so.1

lstpu:
	bazel build $(BAZEL_BUILD_FLAGS) //:lstpu

clean:
	bazel clean


================================================
FILE: cpp/examples/lstpu/README.md
================================================
# Simple C++ code example

This example shows how to build a simple C++ program that uses the Edge TPU
runtime library to lists available Edge TPU devices. (It does not perform an
inference.)

## Requirements

You need to install [Bazel](https://bazel.build/) in order to build the binary.
Follow the [Bazel install
instructions](https://docs.bazel.build/versions/master/install.html)

The example is configured to use the cross-compilation toolchain definition from
[crosstool](https://github.com/google-coral/crosstool), but you don't need
to download that repo.

## Compile the example

For the native compilation you need to install at least `build-essential`
package:

```
sudo apt-get install -y build-essential
```

Then run `make` command.

For cross-compilation you need to install `build-essential` packages for
the corresponding architectures:

```
sudo apt-get install -y crossbuild-essential-armhf \
                        crossbuild-essential-arm64
```

Then run `make CPU=armv7a` or `make CPU=aarch64`.

Find the output binary file inside `blaze-out` directory.


================================================
FILE: cpp/examples/lstpu/WORKSPACE
================================================
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
workspace(name = "lstpu")

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

http_archive(
    name = "io_bazel_rules_closure",
    sha256 = "5b00383d08dd71f28503736db0500b6fb4dda47489ff5fc6bed42557c07c6ba9",
    strip_prefix = "rules_closure-308b05b2419edb5c8ee0471b67a40403df940149",
    urls = [
        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/rules_closure/archive/308b05b2419edb5c8ee0471b67a40403df940149.tar.gz",
        "https://github.com/bazelbuild/rules_closure/archive/308b05b2419edb5c8ee0471b67a40403df940149.tar.gz",  # 2019-06-13
    ],
)

http_archive(
    name = "bazel_skylib",
    sha256 = "1dde365491125a3db70731e25658dfdd3bc5dbdfd11b840b3e987ecf043c7ca0",
    urls = ["https://github.com/bazelbuild/bazel-skylib/releases/download/0.9.0/bazel_skylib-0.9.0.tar.gz"],
)

TENSORFLOW_COMMIT = "d855adfc5a0195788bf5f92c3c7352e638aa1109";
TENSORFLOW_SHA256 = "b8a691dbea2bb028fa8f7ce407b70ad236dae0a8705c8010dc7bad8af7e93bac"
http_archive(
    name = "org_tensorflow",
    sha256 = TENSORFLOW_SHA256,
    strip_prefix = "tensorflow-" + TENSORFLOW_COMMIT,
    urls = [
        "https://github.com/tensorflow/tensorflow/archive/" + TENSORFLOW_COMMIT + ".tar.gz",
    ],
)

load("@org_tensorflow//tensorflow:workspace.bzl", "tf_workspace")
tf_workspace(tf_repo_name = "org_tensorflow")

http_archive(
    name = "edgetpu",
    sha256 = "dc5eb443fa1b4132f6828fc0796169e0595643d415b585351839d3c4f796e6a8",
    strip_prefix = "edgetpu-14237f65ba07b7b1d8287e9f60dd20c88562871a",
    urls = [
        "https://github.com/google-coral/edgetpu/archive/14237f65ba07b7b1d8287e9f60dd20c88562871a.tar.gz",
    ]
)

http_archive(
    name = "coral_crosstool",
    sha256 = "cb31b1417ccdcf7dd9fca5ec63e1571672372c30427730255997a547569d2feb",
    strip_prefix = "crosstool-9e00d5be43bf001f883b5700f5d04882fea00229",
    urls = [
        "https://github.com/google-coral/crosstool/archive/9e00d5be43bf001f883b5700f5d04882fea00229.tar.gz",
    ],
)
load("@coral_crosstool//:configure.bzl", "cc_crosstool")
cc_crosstool(name = "crosstool")


================================================
FILE: cpp/examples/lstpu/lstpu.cc
================================================
// Copyright 2019 Google LLC
//
// 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 <iostream>
#include <memory>

#include "edgetpu_c.h"

std::string ToString(edgetpu_device_type type) {
  switch (type) {
    case EDGETPU_APEX_PCI:
      return "PCI";
    case EDGETPU_APEX_USB:
      return "USB";
  }
  return "Unknown";
}

int main(int argc, char* argv[]) {
  size_t num_devices;
  std::unique_ptr<edgetpu_device, decltype(&edgetpu_free_devices)> devices(
      edgetpu_list_devices(&num_devices), &edgetpu_free_devices);

  for (size_t i = 0; i < num_devices; ++i) {
    const auto& device = devices.get()[i];
    std::cout << i << " " << ToString(device.type) << " " << device.path
              << std::endl;
  }

  return 0;
}


================================================
FILE: python/examples/classification/README.md
================================================
# Image classification example on Coral with TensorFlow Lite

This example uses [TensorFlow Lite](https://tensorflow.org/lite) with Python
to run an image classification model with acceleration on the Edge TPU, using a
Coral device such as the
[USB Accelerator](https://coral.withgoogle.com/products/accelerator) or
[Dev Board](https://coral.withgoogle.com/products/dev-board).

The Python script takes arguments for the model, labels file, and image
you want to process. It then prints the model's prediction for what the
image is to the terminal screen.

## Set up your device

1.  First, be sure you have completed the [setup instructions for your Coral
    device](https://coral.withgoogle.com/docs/accelerator/get-started/).

    Importantly, you should have the latest TensorFlow Lite runtime installed,
    as per the [Python quickstart](
    https://www.tensorflow.org/lite/guide/python).

2.  Clone this Git repo onto your computer:

    ```
    mkdir google-coral && cd google-coral

    git clone https://github.com/google-coral/tflite --depth 1
    ```

3.  Install this example's dependencies:

    ```
    cd tflite/python/examples/classification

    ./install_requirements.sh
    ```

## Run the code

Use this command to run image classification with the model and photo
downloaded by the above script (photo shown in figure 1):

```
python3 classify_image.py \
  --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
  --labels models/inat_bird_labels.txt \
  --input images/parrot.jpg
```

<img width="200"
     src="https://github.com/google-coral/edgetpu/raw/master/test_data/parrot.jpg" />
<br><b>Figure 1.</b> parrot.jpg

You should see results like this:

```.language-bash
Initializing TF Lite interpreter...
INFO: Initialized TensorFlow Lite runtime.
----INFERENCE TIME----
Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.
11.8ms
3.0ms
2.8ms
2.9ms
2.9ms
-------RESULTS--------
Ara macao (Scarlet Macaw): 0.76562
```

To demonstrate varying inference speeds, the example repeats the same inference
five times. Your inference speeds might be different based on your host platform
and whether you're using the USB Accelerator with a USB 2.0 or 3.0 connection.

To compare the performance when not using the Edge TPU, try
running it again with the model that's *not* compiled for the Edge TPU:

```
python3 classify_image.py \
  --model models/mobilenet_v2_1.0_224_inat_bird_quant.tflite \
  --labels models/inat_bird_labels.txt \
  --input images/parrot.jpg
```



================================================
FILE: python/examples/classification/classify.py
================================================
# Lint as: python3
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
"""Functions to work with classification models."""

import collections
import operator
import numpy as np

Class = collections.namedtuple('Class', ['id', 'score'])


def input_details(interpreter, key):
  """Returns input details by specified key."""
  return interpreter.get_input_details()[0][key]


def input_size(interpreter):
  """Returns input image size as (width, height) tuple."""
  _, height, width, _ = input_details(interpreter, 'shape')
  return width, height


def input_tensor(interpreter):
  """Returns input tensor view as numpy array of shape (height, width, 3)."""
  tensor_index = input_details(interpreter, 'index')
  return interpreter.tensor(tensor_index)()[0]


def output_tensor(interpreter, dequantize=True):
  """Returns output tensor of classification model.

  Integer output tensor is dequantized by default.

  Args:
    interpreter: tflite.Interpreter;
    dequantize: bool; whether to dequantize integer output tensor.

  Returns:
    Output tensor as numpy array.
  """
  output_details = interpreter.get_output_details()[0]
  output_data = np.squeeze(interpreter.tensor(output_details['index'])())

  if dequantize and np.issubdtype(output_details['dtype'], np.integer):
    scale, zero_point = output_details['quantization']
    return scale * (output_data - zero_point)

  return output_data


def set_input(interpreter, data):
  """Copies data to input tensor."""
  input_tensor(interpreter)[:, :] = data


def get_output(interpreter, top_k=1, score_threshold=0.0):
  """Returns no more than top_k classes with score >= score_threshold."""
  scores = output_tensor(interpreter)
  classes = [
      Class(i, scores[i])
      for i in np.argpartition(scores, -top_k)[-top_k:]
      if scores[i] >= score_threshold
  ]
  return sorted(classes, key=operator.itemgetter(1), reverse=True)


================================================
FILE: python/examples/classification/classify_image.py
================================================
# Lint as: python3
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
r"""Example using TF Lite to classify a given image using an Edge TPU.

   To run this code, you must attach an Edge TPU attached to the host and
   install the Edge TPU runtime (`libedgetpu.so`) and `tflite_runtime`. For
   device setup instructions, see g.co/coral/setup.

   Example usage (use `install_requirements.sh` to get these files):
   ```
   python3 classify_image.py \
     --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite  \
     --labels models/inat_bird_labels.txt \
     --input images/parrot.jpg
   ```
"""

import argparse
import time

from PIL import Image

import classify
import tflite_runtime.interpreter as tflite
import platform

EDGETPU_SHARED_LIB = {
  'Linux': 'libedgetpu.so.1',
  'Darwin': 'libedgetpu.1.dylib',
  'Windows': 'edgetpu.dll'
}[platform.system()]


def load_labels(path, encoding='utf-8'):
  """Loads labels from file (with or without index numbers).

  Args:
    path: path to label file.
    encoding: label file encoding.
  Returns:
    Dictionary mapping indices to labels.
  """
  with open(path, 'r', encoding=encoding) as f:
    lines = f.readlines()
    if not lines:
      return {}

    if lines[0].split(' ', maxsplit=1)[0].isdigit():
      pairs = [line.split(' ', maxsplit=1) for line in lines]
      return {int(index): label.strip() for index, label in pairs}
    else:
      return {index: line.strip() for index, line in enumerate(lines)}


def make_interpreter(model_file):
  model_file, *device = model_file.split('@')
  return tflite.Interpreter(
      model_path=model_file,
      experimental_delegates=[
          tflite.load_delegate(EDGETPU_SHARED_LIB,
                               {'device': device[0]} if device else {})
      ])


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument(
      '-m', '--model', required=True, help='File path of .tflite file.')
  parser.add_argument(
      '-i', '--input', required=True, help='Image to be classified.')
  parser.add_argument(
      '-l', '--labels', help='File path of labels file.')
  parser.add_argument(
      '-k', '--top_k', type=int, default=1,
      help='Max number of classification results')
  parser.add_argument(
      '-t', '--threshold', type=float, default=0.0,
      help='Classification score threshold')
  parser.add_argument(
      '-c', '--count', type=int, default=5,
      help='Number of times to run inference')
  args = parser.parse_args()

  labels = load_labels(args.labels) if args.labels else {}

  interpreter = make_interpreter(args.model)
  interpreter.allocate_tensors()

  size = classify.input_size(interpreter)
  image = Image.open(args.input).convert('RGB').resize(size, Image.ANTIALIAS)
  classify.set_input(interpreter, image)

  print('----INFERENCE TIME----')
  print('Note: The first inference on Edge TPU is slow because it includes',
        'loading the model into Edge TPU memory.')
  for _ in range(args.count):
    start = time.perf_counter()
    interpreter.invoke()
    inference_time = time.perf_counter() - start
    classes = classify.get_output(interpreter, args.top_k, args.threshold)
    print('%.1fms' % (inference_time * 1000))

  print('-------RESULTS--------')
  for klass in classes:
    print('%s: %.5f' % (labels.get(klass.id, klass.id), klass.score))


if __name__ == '__main__':
  main()


================================================
FILE: python/examples/classification/install_requirements.sh
================================================
#!/bin/bash
#
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
readonly SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
readonly TEST_DATA_URL=https://github.com/google-coral/edgetpu/raw/master/test_data

# Install required Python packages,
# but not on Mendel (Dev Board)—it has these already and shouldn't use pip
if [[ ! -f /etc/mendel_version ]]; then
  if ! python3 -m pip --version > /dev/null; then
    echo "Install pip first by following https://pip.pypa.io/en/stable/installing/ guide."
    exit 1
  fi
  python3 -m pip install numpy Pillow
fi

# Get TF Lite model and labels
MODEL_DIR="${SCRIPT_DIR}/models"
mkdir -p "${MODEL_DIR}"

(cd "${MODEL_DIR}" && \
curl -OL "${TEST_DATA_URL}/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite" \
     -OL "${TEST_DATA_URL}/mobilenet_v2_1.0_224_inat_bird_quant.tflite" \
     -OL "${TEST_DATA_URL}/inat_bird_labels.txt")

# Get example image
IMAGE_DIR="${SCRIPT_DIR}/images"
mkdir -p "${IMAGE_DIR}"

(cd "${IMAGE_DIR}" && \
curl -OL "${TEST_DATA_URL}/parrot.jpg")


================================================
FILE: python/examples/detection/README.md
================================================
# Object detection example on Coral with TensorFlow Lite

This example uses [TensorFlow Lite](https://tensorflow.org/lite) with Python
to run an object detection model with acceleration on the Edge TPU, using a
Coral device such as the
[USB Accelerator](https://coral.withgoogle.com/products/accelerator) or
[Dev Board](https://coral.withgoogle.com/products/dev-board).

The Python script takes arguments for the model, labels file, and image
you want to process. It then prints each detected object and the location
coordinates, and saves/displays the original image with bounding boxes and
labels drawn on top.

## Set up your device

1.  First, be sure you have completed the [setup instructions for your Coral
    device](https://coral.withgoogle.com/docs/accelerator/get-started/).

    Importantly, you should have the latest TensorFlow Lite runtime installed,
    as per the [Python quickstart](
    https://www.tensorflow.org/lite/guide/python).

2.  Clone this Git repo onto your computer:

    ```
    mkdir google-coral && cd google-coral

    git clone https://github.com/google-coral/tflite --depth 1
    ```

3.  Install this example's dependencies:

    ```
    cd tflite/python/examples/detection

    ./install_requirements.sh
    ```

## Run the code

Use this command to run object detection with the model and photo
downloaded by the above script (photo shown in figure 1):

```
python3 detect_image.py \
  --model models/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite \
  --labels models/coco_labels.txt \
  --input images/grace_hopper.bmp \
  --output images/grace_hopper_processed.bmp
```

<img width="200"
     src="https://github.com/google-coral/edgetpu/raw/master/test_data/grace_hopper.bmp" />
<br><b>Figure 1.</b> grace_hopper.bmp

You should see results like this:

```
INFO: Initialized TensorFlow Lite runtime.
----INFERENCE TIME----
Note: The first inference is slow because it includes loading the model into Edge TPU memory.
33.92 ms
19.71 ms
19.91 ms
19.91 ms
19.90 ms
-------RESULTS--------
tie
  id:     31
  score:  0.83984375
  bbox:   BBox(xmin=228, ymin=421, xmax=293, ymax=545)
person
  id:     0
  score:  0.83984375
  bbox:   BBox(xmin=2, ymin=5, xmax=513, ymax=596)
```

To demonstrate varying inference speeds, the example repeats the same inference
five times. Your inference speeds might be different based on your host platform
and whether you're using the USB Accelerator with a USB 2.0 or 3.0 connection.

To compare the performance when not using the Edge TPU, try
running it again with the model that's *not* compiled for the Edge TPU:

```
python3 detect_image.py \
  --model models/ssd_mobilenet_v2_coco_quant_postprocess.tflite \
  --labels models/coco_labels.txt \
  --input images/grace_hopper.bmp
```


================================================
FILE: python/examples/detection/detect.py
================================================
# Lint as: python3
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
"""Functions to work with detection models."""

import collections
import numpy as np

Object = collections.namedtuple('Object', ['id', 'score', 'bbox'])


class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
  """Bounding box.

  Represents a rectangle which sides are either vertical or horizontal, parallel
  to the x or y axis.
  """
  __slots__ = ()

  @property
  def width(self):
    """Returns bounding box width."""
    return self.xmax - self.xmin

  @property
  def height(self):
    """Returns bounding box height."""
    return self.ymax - self.ymin

  @property
  def area(self):
    """Returns bound box area."""
    return self.width * self.height

  @property
  def valid(self):
    """Returns whether bounding box is valid or not.

    Valid bounding box has xmin <= xmax and ymin <= ymax which is equivalent to
    width >= 0 and height >= 0.
    """
    return self.width >= 0 and self.height >= 0

  def scale(self, sx, sy):
    """Returns scaled bounding box."""
    return BBox(xmin=sx * self.xmin,
                ymin=sy * self.ymin,
                xmax=sx * self.xmax,
                ymax=sy * self.ymax)

  def translate(self, dx, dy):
    """Returns translated bounding box."""
    return BBox(xmin=dx + self.xmin,
                ymin=dy + self.ymin,
                xmax=dx + self.xmax,
                ymax=dy + self.ymax)

  def map(self, f):
    """Returns bounding box modified by applying f for each coordinate."""
    return BBox(xmin=f(self.xmin),
                ymin=f(self.ymin),
                xmax=f(self.xmax),
                ymax=f(self.ymax))

  @staticmethod
  def intersect(a, b):
    """Returns the intersection of two bounding boxes (may be invalid)."""
    return BBox(xmin=max(a.xmin, b.xmin),
                ymin=max(a.ymin, b.ymin),
                xmax=min(a.xmax, b.xmax),
                ymax=min(a.ymax, b.ymax))

  @staticmethod
  def union(a, b):
    """Returns the union of two bounding boxes (always valid)."""
    return BBox(xmin=min(a.xmin, b.xmin),
                ymin=min(a.ymin, b.ymin),
                xmax=max(a.xmax, b.xmax),
                ymax=max(a.ymax, b.ymax))

  @staticmethod
  def iou(a, b):
    """Returns intersection-over-union value."""
    intersection = BBox.intersect(a, b)
    if not intersection.valid:
      return 0.0
    area = intersection.area
    return area / (a.area + b.area - area)


def input_size(interpreter):
  """Returns input image size as (width, height) tuple."""
  _, height, width, _ = interpreter.get_input_details()[0]['shape']
  return width, height


def input_tensor(interpreter):
  """Returns input tensor view as numpy array of shape (height, width, 3)."""
  tensor_index = interpreter.get_input_details()[0]['index']
  return interpreter.tensor(tensor_index)()[0]


def set_input(interpreter, size, resize):
  """Copies a resized and properly zero-padded image to the input tensor.

  Args:
    interpreter: Interpreter object.
    size: original image size as (width, height) tuple.
    resize: a function that takes a (width, height) tuple, and returns an RGB
      image resized to those dimensions.
  Returns:
    Actual resize ratio, which should be passed to `get_output` function.
  """
  width, height = input_size(interpreter)
  w, h = size
  scale = min(width / w, height / h)
  w, h = int(w * scale), int(h * scale)
  tensor = input_tensor(interpreter)
  tensor.fill(0)  # padding
  _, _, channel = tensor.shape
  tensor[:h, :w] = np.reshape(resize((w, h)), (h, w, channel))
  return scale, scale


def output_tensor(interpreter, i):
  """Returns output tensor view."""
  tensor = interpreter.tensor(interpreter.get_output_details()[i]['index'])()
  return np.squeeze(tensor)


def get_output(interpreter, score_threshold, image_scale=(1.0, 1.0)):
  """Returns list of detected objects."""
  boxes = output_tensor(interpreter, 0)
  class_ids = output_tensor(interpreter, 1)
  scores = output_tensor(interpreter, 2)
  count = int(output_tensor(interpreter, 3))

  width, height = input_size(interpreter)
  image_scale_x, image_scale_y = image_scale
  sx, sy = width / image_scale_x, height / image_scale_y

  def make(i):
    ymin, xmin, ymax, xmax = boxes[i]
    return Object(
        id=int(class_ids[i]),
        score=float(scores[i]),
        bbox=BBox(xmin=xmin,
                  ymin=ymin,
                  xmax=xmax,
                  ymax=ymax).scale(sx, sy).map(int))

  return [make(i) for i in range(count) if scores[i] >= score_threshold]


================================================
FILE: python/examples/detection/detect_image.py
================================================
# Lint as: python3
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
"""Example using TF Lite to detect objects in a given image."""

import argparse
import time

from PIL import Image
from PIL import ImageDraw

import detect
import tflite_runtime.interpreter as tflite
import platform

EDGETPU_SHARED_LIB = {
  'Linux': 'libedgetpu.so.1',
  'Darwin': 'libedgetpu.1.dylib',
  'Windows': 'edgetpu.dll'
}[platform.system()]


def load_labels(path, encoding='utf-8'):
  """Loads labels from file (with or without index numbers).

  Args:
    path: path to label file.
    encoding: label file encoding.
  Returns:
    Dictionary mapping indices to labels.
  """
  with open(path, 'r', encoding=encoding) as f:
    lines = f.readlines()
    if not lines:
      return {}

    if lines[0].split(' ', maxsplit=1)[0].isdigit():
      pairs = [line.split(' ', maxsplit=1) for line in lines]
      return {int(index): label.strip() for index, label in pairs}
    else:
      return {index: line.strip() for index, line in enumerate(lines)}


def make_interpreter(model_file):
  model_file, *device = model_file.split('@')
  return tflite.Interpreter(
      model_path=model_file,
      experimental_delegates=[
          tflite.load_delegate(EDGETPU_SHARED_LIB,
                               {'device': device[0]} if device else {})
      ])


def draw_objects(draw, objs, labels):
  """Draws the bounding box and label for each object."""
  for obj in objs:
    bbox = obj.bbox
    draw.rectangle([(bbox.xmin, bbox.ymin), (bbox.xmax, bbox.ymax)],
                   outline='red')
    draw.text((bbox.xmin + 10, bbox.ymin + 10),
              '%s\n%.2f' % (labels.get(obj.id, obj.id), obj.score),
              fill='red')


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('-m', '--model', required=True,
                      help='File path of .tflite file.')
  parser.add_argument('-i', '--input', required=True,
                      help='File path of image to process.')
  parser.add_argument('-l', '--labels',
                      help='File path of labels file.')
  parser.add_argument('-t', '--threshold', type=float, default=0.4,
                      help='Score threshold for detected objects.')
  parser.add_argument('-o', '--output',
                      help='File path for the result image with annotations')
  parser.add_argument('-c', '--count', type=int, default=5,
                      help='Number of times to run inference')
  args = parser.parse_args()

  labels = load_labels(args.labels) if args.labels else {}
  interpreter = make_interpreter(args.model)
  interpreter.allocate_tensors()

  image = Image.open(args.input)
  scale = detect.set_input(interpreter, image.size,
                           lambda size: image.resize(size, Image.ANTIALIAS))

  print('----INFERENCE TIME----')
  print('Note: The first inference is slow because it includes',
        'loading the model into Edge TPU memory.')
  for _ in range(args.count):
    start = time.perf_counter()
    interpreter.invoke()
    inference_time = time.perf_counter() - start
    objs = detect.get_output(interpreter, args.threshold, scale)
    print('%.2f ms' % (inference_time * 1000))

  print('-------RESULTS--------')
  if not objs:
    print('No objects detected')

  for obj in objs:
    print(labels.get(obj.id, obj.id))
    print('  id:    ', obj.id)
    print('  score: ', obj.score)
    print('  bbox:  ', obj.bbox)

  if args.output:
    image = image.convert('RGB')
    draw_objects(ImageDraw.Draw(image), objs, labels)
    image.save(args.output)
    image.show()


if __name__ == '__main__':
  main()


================================================
FILE: python/examples/detection/install_requirements.sh
================================================
#!/bin/bash
#
# Copyright 2019 Google LLC
#
# 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
#
#     https://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.
readonly SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
readonly TEST_DATA_URL=https://github.com/google-coral/edgetpu/raw/master/test_data

# Install required Python packages,
# but not on Mendel (Dev Board)—it has these already and shouldn't use pip
if [[ ! -f /etc/mendel_version ]]; then
  if ! python3 -m pip --version > /dev/null; then
    echo "Install pip first by following https://pip.pypa.io/en/stable/installing/ guide."
    exit 1
  fi
  python3 -m pip install numpy Pillow
fi

# If running Raspberry Pi, also install 'imagemagick' to display images
MODEL=$(tr -d '\0' < /proc/device-tree/model)
if [[ "${MODEL}" == "Raspberry Pi"* ]]; then
  sudo apt-get install imagemagick
fi

# Get TF Lite model and labels
MODEL_DIR="${SCRIPT_DIR}/models"
mkdir -p "${MODEL_DIR}"
(cd "${MODEL_DIR}"
curl -OL "${TEST_DATA_URL}/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite" \
     -OL "${TEST_DATA_URL}/ssd_mobilenet_v2_coco_quant_postprocess.tflite" \
     -OL "${TEST_DATA_URL}/coco_labels.txt")

# Get example image
IMAGE_DIR="${SCRIPT_DIR}/images"
mkdir -p "${IMAGE_DIR}"
(cd "${IMAGE_DIR}"
curl -OL "${TEST_DATA_URL}/grace_hopper.bmp")